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train · 44.6k rows

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98043d1b-b9fe-4d73-a9e3-fb2b23cfb792	wide-contextual-residual-network-with-active	null	null	https://ieeexplore.ieee.org/document/8517855	https://www.researchgate.net/publication/328991664_Wide_Contextual_Residual_Network_with_Active_Learning_for_Remote_Sensing_Image_Classification	Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification	In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI) classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples. Specifically, we first design a wide contextual residual network for RSI classification. We then integrate it with AL to achieve good machine generalization with limited number of training sampling. Experimental results on the University of Pavia and Flevoland datasets demonstrate that the proposed WCRN with AL can significantly reduce the needs of samples.	['Sheng-Jie Liu', 'Jun Li', 'Zhi He', 'Ying Tu', 'Haowen Luo']	2018-07-22	null	null	null	igarss-2018-2018-ieee-international	['remote-sensing-image-classification']	['miscellaneous']	[ 5.63198209e-01 9.64411721e-02 -4.00043100e-01 -3.84969682e-01 -4.02167320e-01 -3.07532586e-03 4.49818373e-01 -1.14002936e-01 -5.61628401e-01 8.93748462e-01 -1.00753628e-01 -2.91996628e-01 -3.89592618e-01 -1.18844616e+00 -3.17212760e-01 -8.72037709e-01 -1.87752545e-01 1.53306231e-01 2.73947716e-01 1.47703802e-02 5.69659211e-02 7.43189335e-01 -1.44024372e+00 -2.07344666e-01 1.38215566e+00 1.03126216e+00 3.61233115e-01 2.19845343e-02 -1.88616291e-01 1.22443986e+00 -5.36443233e-01 3.78302693e-01 4.05144960e-01 -1.10748634e-01 -7.56005049e-01 1.33165959e-02 -7.33403713e-02 -2.22819716e-01 -1.28849193e-01 7.52519608e-01 4.71273363e-01 5.82396448e-01 4.63988125e-01 -9.61586654e-01 -3.25015336e-01 6.83036923e-01 -7.06328630e-01 2.76178658e-01 -3.80341381e-01 -2.68750459e-01 9.48523223e-01 -9.71416414e-01 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1e63668a-f63e-4a17-9e65-d5f228ef969c	d2match-leveraging-deep-learning-and	2306.06380	null	https://arxiv.org/abs/2306.06380v1	https://arxiv.org/pdf/2306.06380v1.pdf	D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching	Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature. Existing studies usually tackle it by combinatorial optimization or learning-based methods. However, they suffer from exponential computational costs or searching the matching without theoretical guarantees. In this paper, we develop D2Match by leveraging the efficiency of Deep learning and Degeneracy for subgraph matching. More specifically, we first prove that subgraph matching can degenerate to subtree matching, and subsequently is equivalent to finding a perfect matching on a bipartite graph. We can then yield an implementation of linear time complexity by the built-in tree-structured aggregation mechanism on graph neural networks. Moreover, circle structures and node attributes can be easily incorporated in D2Match to boost the matching performance. Finally, we conduct extensive experiments to show the superior performance of our D2Match and confirm that our D2Match indeed exploits the subtrees and differs from existing GNNs-based subgraph matching methods that depend on memorizing the data distribution divergence	['Haiqin Yang', 'Yujiu Yang', 'Jiaqi Sun', 'Lin Zhang', 'Xuanzhou Liu']	2023-06-10	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 0.08602829 0.22749 -0.38833073 -0.14334978 -0.76652294 -0.5917796 0.17312768 0.48626897 -0.21201973 0.34855816 -0.08528453 -0.58041906 -0.38729057 -1.3346465 -0.98197657 -0.5797725 -0.40370837 0.462345 0.4455297 -0.14192326 0.15582256 0.5104274 -1.4168619 -0.01040614 0.92013985 1.0441979 0.04631723 0.36846805 -0.20098576 0.37774488 -0.26240003 -0.6105359 0.71296924 -0.19013421 -0.83821654 -0.08427177 0.84166265 -0.16325168 -0.8665746 1.1583521 0.58687496 -0.01154076 0.13216949 -1.501606 -0.5582736 0.8536756 -0.5338307 0.1051713 0.57618725 -0.01053859 1.5064224 -0.44108766 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fcec4166-95db-4239-b9d8-cf55dd1192d8	standing-on-the-shoulders-of-predecessors	2110.14170	null	https://arxiv.org/abs/2110.14170v3	https://arxiv.org/pdf/2110.14170v3.pdf	Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding	Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering). They can be viewed as general solutions for representing KGs. However, existing KGE methods are not applicable to inductive settings, where a model trained on source KGs will be tested on target KGs with entities unseen during model training. Existing works focusing on KGs in inductive settings can only solve the inductive relation prediction task. They can not handle other out-of-KG tasks as general as KGE methods since they don't produce embeddings for entities. In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. Such meta-knowledge is modeled by entity-independent modules and learned by meta-learning. Experimental results show that our model significantly outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive settings.	['Huajun Chen', 'Changliang Xu', 'Zonggang Yuan', 'Hongting Zhou', 'Yushan Zhu', 'Wen Zhang', 'Mingyang Chen']	2021-10-27	null	null	null	null	['inductive-relation-prediction']	['graphs']	[-2.86097795e-01 8.25549424e-01 -4.92449582e-01 -3.70414674e-01 -1.86227545e-01 -3.15999627e-01 3.96050721e-01 4.53632861e-01 -1.79813996e-01 6.95035040e-01 1.32243901e-01 -3.89999211e-01 -2.66894013e-01 -1.61222112e+00 -1.06965041e+00 -2.88062304e-01 -3.16019058e-01 5.44030011e-01 3.56465667e-01 -3.72231871e-01 -2.51454473e-01 -3.74267139e-02 -1.20647538e+00 2.13722914e-01 1.15919030e+00 6.72030866e-01 -7.53264353e-02 4.34641123e-01 -4.00307238e-01 9.85528588e-01 -3.73766005e-01 -9.51355696e-01 -1.65002808e-01 -7.54381269e-02 -1.04774559e+00 -5.89895189e-01 3.41234565e-01 -1.35020077e-01 -7.16288805e-01 9.25893188e-01 3.39505225e-01 4.19375598e-02 9.69435394e-01 -1.90886569e+00 -1.63031411e+00 9.64392722e-01 -1.84531122e-01 -1.52607083e-01 4.18306202e-01 -5.32100916e-01 1.43120658e+00 -1.10489142e+00 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ec2a4364-e483-44cc-96d0-3407cb8b275f	self-supervised-mri-reconstruction-with	2306.16654	null	https://arxiv.org/abs/2306.16654v1	https://arxiv.org/pdf/2306.16654v1.pdf	Self-Supervised MRI Reconstruction with Unrolled Diffusion Models	Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promising deep learning methods have recently been proposed to reconstruct accelerated MRI scans. However, existing methods still suffer from various limitations regarding image fidelity, contextual sensitivity, and reliance on fully-sampled acquisitions for model training. To comprehensively address these limitations, we propose a novel self-supervised deep reconstruction model, named Self-Supervised Diffusion Reconstruction (SSDiffRecon). SSDiffRecon expresses a conditional diffusion process as an unrolled architecture that interleaves cross-attention transformers for reverse diffusion steps with data-consistency blocks for physics-driven processing. Unlike recent diffusion methods for MRI reconstruction, a self-supervision strategy is adopted to train SSDiffRecon using only undersampled k-space data. Comprehensive experiments on public brain MR datasets demonstrates the superiority of SSDiffRecon against state-of-the-art supervised, and self-supervised baselines in terms of reconstruction speed and quality. Implementation will be available at https://github.com/yilmazkorkmaz1/SSDiffRecon.	['Vishal Patel', 'Tolga Cukur', 'Yilmaz Korkmaz']	2023-06-29	null	null	null	null	['mri-reconstruction']	['computer-vision']	[ 1.75869748e-01 2.37702169e-02 -2.31023535e-01 -8.50855947e-01 -1.09887421e+00 -3.85187827e-02 6.82881892e-01 -2.11996928e-01 -6.10162675e-01 4.80031937e-01 6.48554504e-01 -3.57031435e-01 -2.15660661e-01 -3.33258778e-01 -6.61988378e-01 -8.20059001e-01 -1.87389776e-01 6.09898150e-01 3.23888749e-01 1.75125793e-01 1.09786130e-01 4.81456846e-01 -5.55381596e-01 3.68328869e-01 9.24178243e-01 6.97085857e-01 6.33700430e-01 5.96814513e-01 1.03497699e-01 1.15649700e+00 4.16164659e-02 -2.56748572e-02 5.79369552e-02 -4.57163602e-01 -1.18356955e+00 -1.92460880e-01 1.67497173e-01 -9.92921233e-01 -9.08376873e-01 1.04159582e+00 8.72085035e-01 8.92827064e-02 7.88282990e-01 -6.35635376e-01 -9.82829213e-01 7.51450241e-01 -6.62491918e-01 1.07744324e+00 -3.76081541e-02 5.50241530e-01 4.26373959e-01 -9.87415671e-01 8.13840449e-01 7.29926407e-01 6.76824331e-01 7.83197284e-01 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05ce9f47-749a-4db7-8479-7e0e54bb2584	automated-essay-scoring-via-pairwise	null	null	https://aclanthology.org/2022.coling-1.240	https://aclanthology.org/2022.coling-1.240.pdf	Automated Essay Scoring via Pairwise Contrastive Regression	Automated essay scoring (AES) involves the prediction of a score relating to the writing quality of an essay. Most existing works in AES utilize regression objectives or ranking objectives respectively. However, the two types of methods are highly complementary. To this end, in this paper we take inspiration from contrastive learning and propose a novel unified Neural Pairwise Contrastive Regression (NPCR) model in which both objectives are optimized simultaneously as a single loss. Specifically, we first design a neural pairwise ranking model to guarantee the global ranking order in a large list of essays, and then we further extend this pairwise ranking model to predict the relative scores between an input essay and several reference essays. Additionally, a multi-sample voting strategy is employed for inference. We use Quadratic Weighted Kappa to evaluate our model on the public Automated Student Assessment Prize (ASAP) dataset, and the experimental results demonstrate that NPCR outperforms previous methods by a large margin, achieving the state-of-the-art average performance for the AES task.	['Weiguang Qu', 'Junsheng Zhou', 'Li Kong', 'Kaiwei Cai', 'Jiayi Xie']	null	null	null	null	coling-2022-10	['automated-essay-scoring']	['natural-language-processing']	[ 1.45239830e-02 -2.62804389e-01 -2.95696169e-01 -8.86051595e-01 -1.28619778e+00 -5.77242434e-01 2.94844240e-01 3.17839086e-01 -5.78926980e-01 9.00388300e-01 5.40506504e-02 -1.57841220e-01 -5.40821612e-01 -6.05813503e-01 -3.68086189e-01 -4.99246746e-01 4.82960582e-01 2.21118212e-01 -7.41464924e-03 -2.40439653e-01 7.40986705e-01 3.44279967e-02 -1.25143194e+00 1.42573759e-01 1.33578181e+00 9.80833411e-01 -1.70124725e-01 4.77213353e-01 1.97011381e-01 1.10958219e+00 -6.64881349e-01 -1.03317475e+00 2.07009345e-01 -4.00060743e-01 -6.80590272e-01 -4.84250605e-01 7.17154801e-01 -2.51605660e-01 -3.35281670e-01 1.03207815e+00 4.41610038e-01 4.02751148e-01 8.99331748e-01 -1.20004976e+00 -9.88929868e-01 7.23140717e-01 -6.35883808e-01 -1.01119958e-01 3.21642458e-01 -1.05350718e-01 1.80163860e+00 -1.03448880e+00 -9.11078230e-03 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3c3b7876-8ce4-403a-a22a-3cdc02b4cc1d	improving-accent-identification-and-accented	2109.07349	null	https://arxiv.org/abs/2109.07349v1	https://arxiv.org/pdf/2109.07349v1.pdf	Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning	Recently, self-supervised pre-training has gained success in automatic speech recognition (ASR). However, considering the difference between speech accents in real scenarios, how to identify accents and use accent features to improve ASR is still challenging. In this paper, we employ the self-supervised pre-training method for both accent identification and accented speech recognition tasks. For the former task, a standard deviation constraint loss (SDC-loss) based end-to-end (E2E) architecture is proposed to identify accents under the same language. As for accented speech recognition task, we design an accent-dependent ASR system, which can utilize additional accent input features. Furthermore, we propose a frame-level accent feature, which is extracted based on the proposed accent identification model and can be dynamically adjusted. We pre-train our models using 960 hours unlabeled LibriSpeech dataset and fine-tune them on AESRC2020 speech dataset. The experimental results show that our proposed accent-dependent ASR system is significantly ahead of the AESRC2020 baseline and achieves $6.5\%$ relative word error rate (WER) reduction compared with our accent-independent ASR system.	['Long Ma', 'Songjun Cao', 'Keqi Deng']	2021-09-15	null	null	null	null	['accented-speech-recognition']	['speech']	[ 2.07896575e-01 -4.68937382e-02 1.69772714e-01 -8.01901937e-01 -1.08909690e+00 -7.08436191e-01 3.74011189e-01 -2.34780684e-01 -8.03043842e-01 6.82137847e-01 5.33770323e-01 -7.06443965e-01 1.66135892e-01 -2.93639123e-01 -3.32122743e-01 -6.61660731e-01 3.40870798e-01 2.00158343e-01 -1.26346022e-01 -5.42299092e-01 6.64842352e-02 3.87337983e-01 -1.14186001e+00 6.26919866e-02 8.63656282e-01 7.33639121e-01 3.87798369e-01 9.24860656e-01 -4.39542085e-02 6.31229579e-01 -1.03327346e+00 -2.82045871e-01 1.98922813e-01 -2.82224834e-01 -8.36833119e-01 1.88600510e-01 1.22942127e-01 2.55458176e-01 -4.99383688e-01 1.06340826e+00 1.01627827e+00 4.00008440e-01 7.62401521e-02 -4.76365924e-01 -4.70142871e-01 9.01555836e-01 -3.55038553e-01 4.05516356e-01 1.58711284e-01 1.20048478e-01 7.74594784e-01 -8.57065618e-01 7.75336474e-02 1.12256300e+00 1.48701563e-01 7.68247962e-01 -7.48793304e-01 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c4ac5d6e-8bbf-492d-b151-9b4a0a969d46	tuvf-learning-generalizable-texture-uv	2305.03040	null	https://arxiv.org/abs/2305.03040v2	https://arxiv.org/pdf/2305.03040v2.pdf	TUVF: Learning Generalizable Texture UV Radiance Fields	Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generation process, such that one texture code can correspond to a particular appearance style independent of any input shapes from a category. We introduce Texture UV Radiance Fields (TUVF) that generate textures in a learnable UV sphere space rather than directly on the 3D shape. This allows the texture to be disentangled from the underlying shape and transferable to other shapes that share the same UV space, i.e., from the same category. We integrate the UV sphere space with the radiance field, which provides a more efficient and accurate representation of textures than traditional texture maps. We perform our experiments on real-world object datasets where we achieve not only realistic synthesis but also substantial improvements over state-of-the-arts on texture controlling and editing. Project Page: https://www.anjiecheng.me/TUVF	['Xiaolong Wang', 'Sifei Liu', 'Xueting Li', 'An-Chieh Cheng']	2023-05-04	null	null	null	null	['texture-synthesis', '3d-shape-modeling']	['computer-vision', 'computer-vision']	[ 3.35620373e-01 1.39407068e-01 2.49485478e-01 -1.95291936e-01 -3.98919851e-01 -7.07552195e-01 7.66842842e-01 -3.69799525e-01 6.00350559e-01 4.10848409e-01 2.51874834e-01 -1.29982337e-01 1.48771837e-01 -1.19321632e+00 -9.89973545e-01 -8.95041406e-01 4.65409011e-01 4.81660932e-01 -1.47400647e-01 -2.48859569e-01 -8.09877086e-03 6.41426682e-01 -1.72754622e+00 3.80511999e-01 6.78593099e-01 1.07809615e+00 2.97218323e-01 5.85639238e-01 -2.59350687e-01 3.43304783e-01 -3.04778457e-01 -3.46031725e-01 4.78686899e-01 -2.37948477e-01 -3.03289860e-01 3.93573582e-01 7.42092669e-01 -7.10091740e-02 -3.33057642e-01 9.11547840e-01 2.72484481e-01 -2.14873888e-02 1.12008870e+00 -9.97206628e-01 -1.41478109e+00 6.95418715e-02 -5.73372245e-01 -7.28715837e-01 3.15967262e-01 1.20952673e-01 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e5b72ad2-5502-4d9d-8111-8326dc089a5f	joint-super-resolution-and-inverse-tone	2207.03367	null	https://arxiv.org/abs/2207.03367v3	https://arxiv.org/pdf/2207.03367v3.pdf	Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark	Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would largely restricts their power on versatile images. To exploit the potential power of decomposition mechanism, in this paper, we generalize it from the image domain to the broader feature domain. To this end, we propose a lightweight Feature Decomposition Aggregation Network (FDAN). In particular, we design a Feature Decomposition Block (FDB) to achieve learnable separation of detail and base feature maps, and develop a Hierarchical Feature Decomposition Group by cascading FDBs for powerful multi-level feature decomposition. Moreover, to better evaluate the comparison methods, we collect a large-scale dataset for joint SR-ITM, i.e., SRITM-4K, which provides versatile scenarios for robust model training and evaluation. Experimental results on two benchmark datasets demonstrate that our FDAN is efficient and outperforms state-of-the-art methods on joint SR-ITM. The code of our FDAN and the SRITM-4K dataset are available at https://github.com/CS-GangXu/FDAN.	['Jun Xu', 'Yu-chen Yang', 'Xian-Tong Zhen', 'Liang Wang', 'Gang Xu']	2022-07-07	null	null	null	null	['tone-mapping', 'inverse-tone-mapping']	['computer-vision', 'computer-vision']	[ 2.10514680e-01 -6.47342384e-01 -2.62558788e-01 -2.32655108e-01 -9.37892079e-01 -1.64325908e-01 2.37290412e-01 -9.99466181e-01 9.04794708e-02 4.57244456e-01 2.02296361e-01 7.70836696e-03 -2.02580199e-01 -6.78504407e-01 -6.36439681e-01 -7.85790443e-01 4.81828023e-03 -4.24684197e-01 2.59045511e-01 -3.26000720e-01 1.08635612e-03 3.97508591e-01 -1.51914465e+00 5.58636069e-01 8.30886424e-01 1.20901859e+00 6.18074179e-01 2.26443514e-01 1.19770467e-01 6.02486849e-01 -2.17863396e-01 6.48971051e-02 4.57248867e-01 -2.77829528e-01 -4.87633228e-01 1.92449912e-01 5.23492277e-01 -7.49441147e-01 -5.94091237e-01 1.02122986e+00 5.70399821e-01 2.65842438e-01 2.24712342e-01 -1.00806332e+00 -1.01036191e+00 4.88654166e-01 -9.99737799e-01 3.97857487e-01 4.80697118e-02 2.79779851e-01 9.31900382e-01 -1.31960845e+00 4.15717274e-01 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d6f686a0-6c6f-4f0a-84b5-e7078f719e14	grnet-gridding-residual-network-for-dense	2006.03761	null	https://arxiv.org/abs/2006.03761v4	https://arxiv.org/pdf/2006.03761v4.pdf	GRNet: Gridding Residual Network for Dense Point Cloud Completion	Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point clouds, which may cause the loss of details because the structural and context of point clouds are not fully considered. To solve this problem, we introduce 3D grids as intermediate representations to regularize unordered point clouds. We therefore propose a novel Gridding Residual Network (GRNet) for point cloud completion. In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information. We also present the differentiable Cubic Feature Sampling layer to extract features of neighboring points, which preserves context information. In addition, we design a new loss function, namely Gridding Loss, to calculate the L1 distance between the 3D grids of the predicted and ground truth point clouds, which is helpful to recover details. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks.	['Shangchen Zhou', 'Wenxiu Sun', 'Jiageng Mao', 'Hongxun Yao', 'Haozhe Xie', 'Shengping Zhang']	2020-06-06	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/798_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540341.pdf	eccv-2020-8	['point-cloud-completion']	['computer-vision']	[-1.92039609e-01 -2.20110998e-01 3.08554232e-01 -3.43089551e-01 -5.10089815e-01 -2.84889072e-01 5.29005468e-01 -2.02645317e-01 -3.56223971e-01 4.68706429e-01 -8.45235288e-02 -6.64889952e-03 -4.89140730e-05 -7.82699108e-01 -1.04387712e+00 -6.51901007e-01 2.49380499e-01 4.59645331e-01 1.71382964e-01 3.40267941e-02 3.22295278e-01 9.63637590e-01 -1.47227299e+00 -2.69455332e-02 1.00458360e+00 1.05495048e+00 5.68109691e-01 1.71195380e-02 -4.50590938e-01 3.36553931e-01 -1.95977762e-01 -1.51573671e-02 5.12102962e-01 4.22464818e-01 -2.90751070e-01 1.94124982e-01 5.98853230e-01 -4.01638418e-01 -4.32491034e-01 1.23491454e+00 3.00469518e-01 1.51266739e-01 5.80451727e-01 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fc4dd3c6-5dc6-4e63-a4ba-c53a571edc4e	physical-model-guided-deep-image-deraining	2003.13242	null	https://arxiv.org/abs/2003.13242v1	https://arxiv.org/pdf/2003.13242v1.pdf	Physical Model Guided Deep Image Deraining	Single image deraining is an urgent task because the degraded rainy image makes many computer vision systems fail to work, such as video surveillance and autonomous driving. So, deraining becomes important and an effective deraining algorithm is needed. In this paper, we propose a novel network based on physical model guided learning for single image deraining, which consists of three sub-networks: rain streaks network, rain-free network, and guide-learning network. The concatenation of rain streaks and rain-free image that are estimated by rain streaks network, rain-free network, respectively, is input to the guide-learning network to guide further learning and the direct sum of the two estimated images is constrained with the input rainy image based on the physical model of rainy image. Moreover, we further develop the Multi-Scale Residual Block (MSRB) to better utilize multi-scale information and it is proved to boost the deraining performance. Quantitative and qualitative experimental results demonstrate that the proposed method outperforms the state-of-the-art deraining methods. The source code will be available at \url{https://supercong94.wixsite.com/supercong94}.	['Ya-Jie Zhang', 'Zhixun Su', 'Cong Wang', 'Guohui Zhao', 'Honghe Zhu']	2020-03-30	null	null	null	null	['single-image-deraining']	['computer-vision']	[ 6.36151386e-03 -1.11852027e-01 2.66807705e-01 -4.30774629e-01 -4.61300820e-01 5.06135970e-02 5.73370941e-02 -7.61931896e-01 -3.56013119e-01 1.05495119e+00 -1.44734710e-01 -2.54348755e-01 3.50360535e-02 -6.72379553e-01 -7.22065508e-01 -1.19494808e+00 1.18976966e-01 -2.15466544e-01 3.60529572e-01 -2.54468083e-01 -7.03346133e-02 4.80237991e-01 -1.45436001e+00 -1.72214404e-01 1.50996423e+00 8.25695395e-01 7.33647346e-01 6.79894626e-01 7.05996975e-02 1.00456679e+00 -4.02787685e-01 2.89440334e-01 1.45473570e-01 -5.81822634e-01 3.72009091e-02 1.04401357e-01 7.35882342e-01 -8.59777331e-01 -6.98836029e-01 1.15758908e+00 6.07041538e-01 2.71098018e-01 2.92552322e-01 -9.73756075e-01 -5.51155865e-01 -3.98240127e-02 -5.63406527e-01 6.36700511e-01 -3.40088427e-01 2.15839475e-01 2.68297791e-01 -1.13386762e+00 3.85167539e-01 1.13856423e+00 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6735a67f-f148-4bf8-867f-1f11559c81de	eliciting-compatible-demonstrations-for-multi	2210.08073	null	https://arxiv.org/abs/2210.08073v1	https://arxiv.org/pdf/2210.08073v1.pdf	Eliciting Compatible Demonstrations for Multi-Human Imitation Learning	Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. While the ideal dataset for imitation learning is homogenous and low-variance -- reflecting a single, optimal method for performing a task -- natural human behavior has a great deal of heterogeneity, with several optimal ways to demonstrate a task. This multimodality is inconsequential to human users, with task variations manifesting as subconscious choices; for example, reaching down, then across to grasp an object, versus reaching across, then down. Yet, this mismatch presents a problem for interactive imitation learning, where sequences of users improve on a policy by iteratively collecting new, possibly conflicting demonstrations. To combat this problem of demonstrator incompatibility, this work designs an approach for 1) measuring the compatibility of a new demonstration given a base policy, and 2) actively eliciting more compatible demonstrations from new users. Across two simulation tasks requiring long-horizon, dexterous manipulation and a real-world "food plating" task with a Franka Emika Panda arm, we show that we can both identify incompatible demonstrations via post-hoc filtering, and apply our compatibility measure to actively elicit compatible demonstrations from new users, leading to improved task success rates across simulated and real environments.	['Dorsa Sadigh', 'Madeline Liao', 'Siddharth Karamcheti', 'Kanishk Gandhi']	2022-10-14	null	null	null	null	['robot-manipulation']	['robots']	[ 8.41760486e-02 -3.93791646e-02 -5.21198623e-02 -1.31990746e-01 -5.93853116e-01 -1.03315389e+00 7.11728394e-01 -1.60608575e-01 -7.67408609e-01 9.07651782e-01 4.40487005e-02 -3.95505428e-01 -3.26404333e-01 2.94472426e-02 -7.60228634e-01 -5.41981101e-01 -4.72679526e-01 5.95776856e-01 2.19613686e-01 -3.59902591e-01 4.94737893e-01 6.25775576e-01 -1.78833270e+00 1.92056194e-01 8.46118152e-01 2.75175989e-01 8.03202093e-01 8.90842319e-01 2.37592876e-01 6.73397362e-01 -6.21400237e-01 1.54450610e-01 6.34926975e-01 -6.06631637e-01 -7.80997753e-01 9.26639289e-02 1.29917204e-01 -6.28598034e-01 -2.12898925e-01 1.04255664e+00 4.07161951e-01 3.48616779e-01 9.05760050e-01 -1.81302762e+00 -3.00400496e-01 8.09226692e-01 -1.58806965e-01 -1.72821984e-01 9.24159110e-01 1.05772305e+00 8.27067852e-01 -2.07448840e-01 6.70005739e-01 1.48423171e+00 3.39425921e-01 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fb9dbcf7-c9db-4a33-b53e-f0a14df2588d	versatilegait-a-large-scale-synthetic-gait-1	2105.14421	null	https://arxiv.org/abs/2105.14421v2	https://arxiv.org/pdf/2105.14421v2.pdf	VersatileGait: A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation	Gait recognition has a rapid development in recent years. However, gait recognition in the wild is not well explored yet. An obvious reason could be ascribed to the lack of diverse training data from the perspective of intrinsic and extrinsic factors. To remedy this problem, we propose to construct a large-scale gait dataset with the help of controllable computer simulation. In detail, to diversify the intrinsic factors of gait, we generate numerous characters with diverse attributes and empower them with various types of walking styles. To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout. Finally, we design an automated generation toolkit under Unity3D for simulating the walking scenario and capturing the gait data automatically. As a result, we obtain an in-the-wild gait dataset, called VersatileGait, which has more than one million silhouette sequences of 10,000 subjects with diverse scenarios. VersatileGait possesses several nice properties, including huge dataset size, diverse pedestrian attributes, complicated camera layout, high-quality annotations, small domain gap with the real one, good scalability for new demands, and no privacy issues. Based on VersatileGait, we propose series of experiments and applications for both research exploration of gait in the wild and practical applications. Our dataset and its corresponding generation toolkit will be publicly available for further studies.	['Xi Li', 'Zequn Qin', 'Songyuan Li', 'Yuhan Zhao', 'Wenhu Zhang', 'Huanzhang Dou', 'Pengyi Zhang']	2021-05-30	null	null	null	null	['gait-recognition-in-the-wild']	['computer-vision']	[-2.82751352e-01 -6.62084162e-01 4.87443022e-02 -4.35115308e-01 -2.13603437e-01 -4.25504833e-01 1.78880215e-01 -4.45173085e-01 -2.52224714e-01 8.23754311e-01 3.02100569e-01 3.26584816e-01 1.83655500e-01 -7.78890550e-01 -4.17744011e-01 -8.64764512e-01 -1.53614767e-02 4.32127655e-01 3.78496826e-01 -3.46430004e-01 -4.03787009e-02 6.94017112e-02 -1.70634735e+00 -1.41390666e-01 9.69548464e-01 7.95405388e-01 1.02021098e-01 4.15141493e-01 3.87791097e-01 1.76671609e-01 -5.80820560e-01 -8.44931901e-01 3.55552047e-01 -4.14771914e-01 -1.93841159e-01 4.92630333e-01 1.97532460e-01 -4.56303835e-01 -4.69311625e-01 9.39219177e-01 9.86006081e-01 -3.96922603e-02 4.11689937e-01 -1.60340524e+00 -7.18005300e-01 6.70508519e-02 -6.41778469e-01 -1.48599029e-01 7.53911018e-01 7.45278180e-01 6.21634305e-01 -6.24963760e-01 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bf69a911-4d82-4415-ba1a-94d096bcf254	open-set-action-recognition-via-multi-label	2303.12698	null	https://arxiv.org/abs/2303.12698v1	https://arxiv.org/pdf/2303.12698v1.pdf	Open Set Action Recognition via Multi-Label Evidential Learning	Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel action detection methods by addressing the more general problems of single or multiple actors in the same scene, with simultaneous action(s) by any actor. Our Beta Evidential Neural Network estimates multi-action uncertainty with Beta densities based on actor-context-object relation representations. An evidence debiasing constraint is added to the objective function for optimization to reduce the static bias of video representations, which can incorrectly correlate predictions and static cues. We develop a learning algorithm based on a primal-dual average scheme update to optimize the proposed problem. Theoretical analysis of the optimization algorithm demonstrates the convergence of the primal solution sequence and bounds for both the loss function and the debiasing constraint. Uncertainty and belief-based novelty estimation mechanisms are formulated to detect novel actions. Extensive experiments on two real-world video datasets show that our proposed approach achieves promising performance in single/multi-actor, single/multi-action settings.	['Christopher Funk', 'Anthony Hoogs', 'Dawei Du', 'Chen Zhao']	2023-02-27	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.pdf	cvpr-2023-1	['open-set-action-recognition']	['computer-vision']	[ 5.54217458e-01 6.96689412e-02 -3.88988107e-01 -3.68827164e-01 -1.10248518e+00 -2.31732398e-01 4.27638501e-01 -8.72820243e-02 -3.12182724e-01 9.38116670e-01 3.96797031e-01 3.35313737e-01 -2.41529763e-01 -4.19550501e-02 -1.05764329e+00 -9.14221883e-01 -1.79095611e-01 3.99433225e-02 3.18590254e-01 3.60018998e-01 3.94085348e-01 1.26147568e-01 -1.47939503e+00 5.39244711e-01 5.40774703e-01 1.37819803e+00 -1.11622542e-01 8.09483469e-01 3.67271096e-01 1.31749237e+00 -6.70294344e-01 -3.61295581e-01 6.40725374e-01 -5.20099223e-01 -4.28911090e-01 5.76262116e-01 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2a79aceb-babe-491b-849c-449bc550eda1	complex-word-identification-in-vietnamese	null	null	https://aclanthology.org/2022.mia-1.6	https://aclanthology.org/2022.mia-1.6.pdf	Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification	Text Simplification has been an extensively researched problem in English, but has not been investigated in Vietnamese. We focus on the Vietnamese-specific Complex Word Identification task, often the first step in Lexical Simplification (Shardlow, 2013). We examine three different Vietnamese datasets constructed for other Natural Language Processing tasks and show that, like in other languages, frequency is a strong signal in determining whether a word is complex, with a mean accuracy of 86.87%. Across the datasets, we find that the 10% most frequent words in many corpus can be labelled as simple, and the rest as complex, though this is more variable for smaller corpora. We also examine how human annotators perform at this task. Given the subjective nature, there is a fair amount of variability in which words are seen as difficult, though majority results are more consistent.	['David Kauchak', 'Phuong Nguyen']	null	null	null	null	naacl-mia-2022-7	['lexical-simplification', 'vietnamese-datasets', 'complex-word-identification']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-8.94041950e-05 -4.75022942e-02 -1.88645989e-01 -2.58849591e-01 -6.98877037e-01 -8.27638149e-01 7.41706431e-01 4.65525478e-01 -1.18381584e+00 8.66191626e-01 6.52253628e-01 -3.82784277e-01 1.36628777e-01 -4.64892715e-01 -2.15672866e-01 -6.46981716e-01 5.07874429e-01 6.23518944e-01 -1.17403954e-01 -6.84312642e-01 3.96474361e-01 3.45419616e-01 -1.23713982e+00 -6.54416382e-02 9.23643053e-01 -1.14381015e-01 9.03245807e-02 4.91856694e-01 -7.88496956e-02 5.10384023e-01 -1.09179199e+00 -8.07020307e-01 2.64490515e-01 -3.01439553e-01 -8.79097700e-01 -1.60768658e-01 5.40509701e-01 1.27650261e-01 -9.72889066e-02 1.12261045e+00 6.61546230e-01 1.92153439e-01 9.11629617e-01 -6.01321220e-01 -4.34222877e-01 1.04607236e+00 -2.71774322e-01 4.85865206e-01 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0417304e-185d-4526-8acf-0a9e18572dfa	a3s-adversarial-learning-of-semantic	2302.10641	null	https://arxiv.org/abs/2302.10641v1	https://arxiv.org/pdf/2302.10641v1.pdf	A3S: Adversarial learning of semantic representations for Scene-Text Spotting	Scene-text spotting is a task that predicts a text area on natural scene images and recognizes its text characters simultaneously. It has attracted much attention in recent years due to its wide applications. Existing research has mainly focused on improving text region detection, not text recognition. Thus, while detection accuracy is improved, the end-to-end accuracy is insufficient. Texts in natural scene images tend to not be a random string of characters but a meaningful string of characters, a word. Therefore, we propose adversarial learning of semantic representations for scene text spotting (A3S) to improve end-to-end accuracy, including text recognition. A3S simultaneously predicts semantic features in the detected text area instead of only performing text recognition based on existing visual features. Experimental results on publicly available datasets show that the proposed method achieves better accuracy than other methods.	['Masato Fujitake']	2023-02-21	null	null	null	null	['text-spotting']	['computer-vision']	[ 9.36584294e-01 -5.61319292e-01 -4.58659977e-02 -2.75895476e-01 -5.25421917e-01 -4.62638050e-01 6.69857740e-01 -5.68977296e-02 -2.17719883e-01 2.10922554e-01 9.53636393e-02 -1.74513191e-01 6.59357965e-01 -7.03396440e-01 -6.84413552e-01 -6.01941466e-01 7.94824541e-01 3.52359116e-01 6.59246683e-01 1.91804171e-01 6.50797486e-01 1.76998690e-01 -1.08154571e+00 6.44225538e-01 8.95454764e-01 9.06316519e-01 4.86830682e-01 6.95517182e-01 -5.73615074e-01 9.13177729e-01 -6.93238258e-01 -4.22838330e-01 2.31968418e-01 -6.72505319e-01 -3.82423103e-01 4.73827571e-01 7.73676693e-01 -3.93085569e-01 -5.90423346e-01 1.36997974e+00 2.90801734e-01 -6.30721971e-02 7.61827409e-01 -1.06117892e+00 -6.72245443e-01 2.86263883e-01 -9.17630911e-01 8.37199297e-03 3.09404403e-01 7.97481611e-02 8.51526022e-01 -1.20259130e+00 3.10103893e-01 1.16937077e+00 6.00749731e-01 4.29316103e-01 -9.31274951e-01 -7.32717276e-01 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4cd16d94-e1f4-42e5-b2ea-5ba23185f12f	formal-ft-based-cause-consequence-reliability	2101.07174	null	https://arxiv.org/abs/2101.07174v1	https://arxiv.org/pdf/2101.07174v1.pdf	Formal FT-based Cause-Consequence Reliability Analysis using Theorem Proving	Cause-consequence Diagram (CCD) is widely used as a deductive safety analysis technique for decision-making at the critical-system design stage. This approach models the causes of subsystem failures in a highly-critical system and their potential consequences using Fault Tree (FT) and Event Tree (ET) methods, which are well-known dependability modeling techniques. Paper-and-pencil-based approaches and simulation tools, such as the Monte-Carlo approach, are commonly used to carry out CCD analysis, but lack the ability to rigorously verify essential system reliability properties. In this work, we propose to use formal techniques based on theorem proving for the formal modeling and step-analysis of CCDs to overcome the inaccuracies of the simulation-based analysis and the error-proneness of informal reasoning by mathematical proofs. In particular, we use the HOL4 theorem prover, which is a computer-based mathematical reasoning tool. To this end, we developed a formalization of CCDs in Higher-Order Logic (HOL), based on the algebraic approach, using HOL4. We demonstrate the practical effectiveness of the proposed CCD formalization by performing the formal reliability analysis of the IEEE 39-bus electrical power network. Also, we formally determine the Forced Outage Rate (FOR) of the power generation units and the network reliability index, i.e., System Average Interruption Duration Index (SAIDI). To assess the accuracy of our proposed approach, we compare our results with those obtained with MATLAB Monte-Carlo Simulation (MCS) as well as other state-of-the-art approaches for subsystem-level reliability analysis.	['Sofiene Tahar', 'Mohamed Abdelghany']	2021-01-18	null	null	null	null	['mathematical-proofs']	['miscellaneous']	[-7.05892742e-02 4.42341268e-02 4.18866009e-01 5.82759827e-03 -1.49761766e-01 -5.41318476e-01 4.03264523e-01 5.45082867e-01 1.50643572e-01 1.02226043e+00 -6.12928510e-01 -1.01556242e+00 -7.83791959e-01 -9.39201415e-01 -5.97291648e-01 -4.54759836e-01 -5.52221537e-01 2.63675749e-01 3.90568644e-01 -2.42764249e-01 9.60794017e-02 6.62228882e-01 -1.67225730e+00 -2.04860061e-01 8.69164586e-01 8.58699203e-01 -1.00036971e-01 4.75177824e-01 2.24609092e-01 9.52794075e-01 -6.95270777e-01 1.35101760e-02 -3.99876237e-01 -6.68052971e-01 -9.14087296e-01 -1.55964911e-01 -8.26523185e-01 -3.63475204e-01 -1.44068226e-01 9.47061539e-01 -2.90535957e-01 -2.57925123e-01 5.57551086e-01 -2.06669974e+00 1.02235451e-01 6.21993482e-01 -1.88040733e-01 -1.91441789e-01 5.89222372e-01 -5.76224402e-02 5.45372963e-01 -3.98132801e-01 2.65003145e-01 1.02957714e+00 3.71298611e-01 1.41099125e-01 -1.10016024e+00 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-1.31721631e-01 1.39921322e-01 -7.31215119e-01 -5.84197879e-01 -2.57520944e-01]	[5.478420734405518, 2.567106008529663]
33f2c137-7e77-423d-9bcb-357b8ad679ea	combining-machine-learning-and-agent-based	2206.01092	null	https://arxiv.org/abs/2206.01092v2	https://arxiv.org/pdf/2206.01092v2.pdf	Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems	Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their own, without imposing a priori theories of system behavior. Biological systems -- from molecules, to cells, to entire organisms -- consist of vast numbers of entities, governed by complex webs of interactions that span many spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate coupling between entities. The macroscopic properties and collective dynamics of such systems are difficult to capture via continuum modelling and mean-field formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties by enabling one to easily propose and test a set of well-defined 'rules' to be applied to the individual entities (agents) in a system. Evaluating a system and propagating its state over discrete time-steps effectively simulates the system, allowing observables to be computed and system properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, there is an opportunity to use ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, ABM calculations can generate a wealth of data, and ML can be applied there too -- e.g., to probe statistical measures that meaningfully describe a system's stochastic properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate realistic datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision various synergistic ABM$\rightleftharpoons$ML loops. This review summarizes how ABM and ML have been integrated in contexts that span spatiotemporal scales, from cellular to population-level epidemiology.	['Shayn M. Peirce', 'Cameron Mura', 'Nikita Sivakumar']	2022-06-02	null	null	null	null	['epidemiology']	['medical']	[ 1.66275144e-01 -2.26570576e-01 1.47759229e-01 2.30505720e-01 -2.35869035e-01 -6.45137787e-01 9.50463712e-01 5.76960802e-01 -1.20492592e-01 1.08136988e+00 -2.77087837e-01 -5.19482255e-01 -5.45627654e-01 -1.04357433e+00 -5.84956706e-01 -1.17559183e+00 -5.55348635e-01 5.79755902e-01 2.98985660e-01 -4.94674116e-01 5.83523400e-02 7.13672578e-01 -1.34981620e+00 -5.31667247e-02 8.40826988e-01 3.39822114e-01 1.49246722e-01 1.01079226e+00 4.93980832e-02 7.58506835e-01 -3.74889135e-01 4.16220836e-02 -1.80127248e-01 -7.73954511e-01 -4.61957395e-01 -6.21758103e-02 -3.82640421e-01 3.89387667e-01 -1.23377398e-01 6.47492409e-01 3.35714251e-01 7.85885230e-02 1.14404047e+00 -1.01511884e+00 -4.25369173e-01 2.47538574e-02 -2.30274126e-01 4.20557335e-02 2.89867759e-01 6.43583417e-01 6.38196886e-01 -5.01661837e-01 8.01060677e-01 1.38473332e+00 6.22010112e-01 4.84326303e-01 -1.96258688e+00 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-6.28643990e-01 -7.28829265e-01 2.28631407e-01 4.59134072e-01 -7.18456328e-01 -5.24142504e-01 1.53756037e-01]	[6.068594455718994, 4.296204090118408]
2db76862-71b8-418b-8179-f2a558a0c4e1	likert-scoring-with-grade-decoupling-for-long	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.pdf	Likert Scoring With Grade Decoupling for Long-Term Action Assessment	Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, longterm actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical highlights and faults may appear in the same long-term action. Hence, the final score should be determined by the comprehensive effect of different grades exhibited in the video. To explore this latent relationship, we design a novel Likert scoring paradigm inspired by the Likert scale in psychometrics, in which we quantify the grades explicitly and generate the final quality score by combining the quantitative values and the corresponding responses estimated from the video, instead of performing direct regression. Moreover, we extract gradespecific features, which will be used to estimate the responses of each grade, through a Transformer decoder architecture with diverse learnable queries. The whole model is named as Grade-decoupling Likert Transformer (GDLT), and we achieve state-of-the-art results on two long-term action assessment datasets.	['Wei-Shi Zheng', 'Ling-An Zeng', 'Angchi Xu']	2022-01-01	null	null	null	cvpr-2022-1	['action-quality-assessment', 'action-assessment']	['computer-vision', 'computer-vision']	[-1.39035910e-01 -3.08604151e-01 -9.73425433e-02 -7.66948402e-01 -1.26588726e+00 -4.58660126e-01 2.68966138e-01 6.38785884e-02 -3.46288770e-01 4.87184912e-01 8.53688478e-01 1.85821533e-01 -3.72218490e-01 -7.04505146e-01 -6.55759037e-01 -4.49099571e-01 4.25930053e-01 3.59309162e-03 2.06742793e-01 -8.65797028e-02 4.93778557e-01 -2.72903055e-01 -1.41907179e+00 6.81535959e-01 9.32441771e-01 1.40618694e+00 -1.05137065e-01 6.46169901e-01 8.67035761e-02 1.49249840e+00 -1.00764811e+00 -8.49376738e-01 -1.80017188e-01 -5.16545653e-01 -6.93355978e-01 3.77808005e-01 6.24346197e-01 -8.36441457e-01 -2.66526788e-01 1.03967500e+00 3.11214596e-01 -2.25199778e-02 6.34170055e-01 -1.02483737e+00 -8.05315733e-01 5.88051617e-01 -9.51794237e-02 6.20203912e-02 1.00659955e+00 3.81206393e-01 1.31348503e+00 -8.51010621e-01 1.50566265e-01 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f9b844ae-d060-4d64-9dcd-f3e6646de854	cross-domain-graph-anomaly-detection-via	2212.01096	null	https://arxiv.org/abs/2212.01096v1	https://arxiv.org/pdf/2212.01096v1.pdf	Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.	['Christopher Leckie', 'Wray Buntine', 'Mahsa Salehi', 'Guansong Pang', 'Qizhou Wang']	2022-12-02	null	null	null	null	['graph-anomaly-detection']	['graphs']	[ 4.29307103e-01 3.57726961e-01 9.04740617e-02 -2.71231145e-01 -3.88455451e-01 -5.93673825e-01 5.94928801e-01 5.60499847e-01 2.25104555e-01 2.41204098e-01 -2.07707420e-01 -3.46065521e-01 -2.19437405e-01 -8.39688897e-01 -3.85623038e-01 -8.83322179e-01 -4.81191128e-01 8.84293258e-01 5.63990712e-01 -4.71127123e-01 1.16161004e-01 6.47325754e-01 -1.22684526e+00 -2.92850100e-02 9.30233777e-01 8.17127407e-01 -5.78688204e-01 8.18710983e-01 -1.74372569e-01 5.43768585e-01 -7.06939161e-01 -3.39815557e-01 5.03070712e-01 -8.34297538e-01 -8.94561648e-01 4.55629677e-01 5.57418346e-01 1.14097670e-01 -4.17236239e-01 1.47450936e+00 3.58146340e-01 -1.74405170e-03 8.59527409e-01 -1.85414135e+00 -4.46700782e-01 3.36856157e-01 -1.04261708e+00 8.84868205e-01 3.14076662e-01 5.95555864e-02 1.17285824e+00 -5.59298873e-01 4.77502614e-01 1.10467613e+00 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60e3c8d1-b00b-4ab4-8f8b-920447bbd8f7	an-adaptable-task-oriented-dialog-system-for	null	null	https://aclanthology.org/P19-3009	https://aclanthology.org/P19-3009.pdf	An adaptable task-oriented dialog system for stand-alone embedded devices	This paper describes a spoken-language end-to-end task-oriented dialogue system for small embedded devices such as home appliances. While the current system implements a smart alarm clock with advanced calendar scheduling functionality, the system is designed to make it easy to port to other application domains (e.g., the dialogue component factors out domain-specific execution from domain-general actions such as requesting and updating slot values). The system does not require internet connectivity because all components, including speech recognition, natural language understanding, dialogue management, execution and text-to-speech, run locally on the embedded device (our demo uses a Raspberry Pi). This simplifies deployment, minimizes server costs and most importantly, eliminates user privacy risks. The demo video in alarm domain is here youtu.be/N3IBMGocvHU	['Guy Bashkansky', 'Yu-Heng Hong', 'Vu Cong Duy Hoang', 'Mark Johnson', 'Long Duong', 'Vladislavs Dovgalecs', 'Serge Le Huitouze', 'Jason Black', 'Andrew Bleeker', 'Tuyen Quang Pham']	2019-07-01	null	null	null	acl-2019-7	['dialogue-management']	['natural-language-processing']	[-1.23495921e-01 6.24611735e-01 -9.84478858e-04 -7.24621713e-01 -5.53332865e-01 -7.46211052e-01 4.77361798e-01 -5.44192828e-02 -9.76515487e-02 9.73348320e-01 1.27502352e-01 -8.89728606e-01 1.91660076e-01 -4.82295543e-01 2.00732365e-01 -2.74242640e-01 2.18227521e-01 7.65740335e-01 4.34717804e-01 -4.29726005e-01 3.61229405e-02 2.55190641e-01 -1.39797831e+00 1.86952241e-02 4.94448453e-01 1.04478812e+00 4.00235564e-01 9.94169354e-01 -3.21201771e-01 1.10462475e+00 -1.04221952e+00 3.24357301e-01 -1.56577811e-01 -4.08126026e-01 -9.13473606e-01 2.60722131e-01 -3.11442405e-01 -1.04387140e+00 -5.38843051e-02 5.19470990e-01 6.79195702e-01 1.01063088e-01 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f0b3dff3-2345-44e9-a28d-35ddda3db3bd	pona-pose-guided-non-local-attention-for	2012.07049	null	https://arxiv.org/abs/2012.07049v1	https://arxiv.org/pdf/2012.07049v1.pdf	PoNA: Pose-guided Non-local Attention for Human Pose Transfer	Human pose transfer, which aims at transferring the appearance of a given person to a target pose, is very challenging and important in many applications. Previous work ignores the guidance of pose features or only uses local attention mechanism, leading to implausible and blurry results. We propose a new human pose transfer method using a generative adversarial network (GAN) with simplified cascaded blocks. In each block, we propose a pose-guided non-local attention (PoNA) mechanism with a long-range dependency scheme to select more important regions of image features to transfer. We also design pre-posed image-guided pose feature update and post-posed pose-guided image feature update to better utilize the pose and image features. Our network is simple, stable, and easy to train. Quantitative and qualitative results on Market-1501 and DeepFashion datasets show the efficacy and efficiency of our model. Compared with state-of-the-art methods, our model generates sharper and more realistic images with rich details, while having fewer parameters and faster speed. Furthermore, our generated images can help to alleviate data insufficiency for person re-identification.	['Qionghai Dai', 'Yu-Kun Lai', 'Yebin Liu', 'Jinsong Zhang', 'Kun Li']	2020-12-13	null	null	null	null	['pose-transfer']	['computer-vision']	[ 6.79853978e-03 -1.61267668e-01 2.39013165e-01 -5.48259139e-01 -3.94602239e-01 -4.90541577e-01 4.75836247e-01 -5.23653865e-01 -4.71631020e-01 8.28837037e-01 3.47443014e-01 3.23281825e-01 1.82227567e-01 -8.11757207e-01 -8.78872097e-01 -5.63672304e-01 3.82571906e-01 3.45321983e-01 1.26032516e-01 -3.52501124e-01 -1.45816445e-01 4.87306535e-01 -1.23499215e+00 5.54860607e-02 8.98544729e-01 8.20559263e-01 4.35111448e-02 4.53706622e-01 3.26883048e-01 2.22308248e-01 -7.20559955e-01 -8.27125549e-01 4.65978503e-01 -6.30291104e-01 -6.40275478e-01 1.88991547e-01 8.28549325e-01 -7.17397094e-01 -6.34660959e-01 1.00335336e+00 9.22034383e-01 4.66023348e-02 4.48277384e-01 -1.24953043e+00 -9.76401150e-01 -1.21347964e-01 -7.68150210e-01 -7.24878833e-02 6.87581897e-01 4.83566344e-01 3.90329480e-01 -6.88055217e-01 3.85777146e-01 1.56357288e+00 9.32775497e-01 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e037dc0a-d5e3-4eaf-85c3-240960ab0617	toward-automatic-discourse-parsing-of-student	null	null	https://aclanthology.org/2022.bea-1.25	https://aclanthology.org/2022.bea-1.25.pdf	Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation	Providing effective automatic essay feedback is necessary for offering writing instruction at a massive scale. In particular, feedback for promoting coherent flow of ideas in essays is critical. In this paper we propose a state-of-the-art method for automated analysis of structure and flow of writing, referred to as Rhetorical Structure Theory (RST) parsing. In so doing, we lay a foundation for a generalizable approach to automated writing feedback related to structure and flow. We address challenges in automated rhetorical analysis when applied to student writing and evaluate our novel RST parser model on both a recent student writing dataset and a standard benchmark RST parsing dataset.	['Carolyn Rosé', 'David Adamson', 'Shiyan Jiang', 'James Fiacco']	null	null	null	null	naacl-bea-2022-7	['discourse-parsing']	['natural-language-processing']	[ 2.48163968e-01 4.25584733e-01 -5.46599746e-01 -2.40895927e-01 -7.62242436e-01 -9.26865101e-01 6.97317123e-01 7.57466495e-01 -9.39083919e-02 6.87467813e-01 7.63047576e-01 -1.13949466e+00 -2.37566128e-01 -7.31120825e-01 -6.01177514e-01 1.83603644e-01 9.28248107e-01 8.09893161e-02 2.23660886e-01 -5.87529778e-01 1.14578247e+00 3.79739463e-01 -1.27400684e+00 5.55435538e-01 9.94532049e-01 5.48935942e-02 2.29872108e-01 1.10488796e+00 -8.25131774e-01 1.89146578e+00 -1.14422035e+00 -7.17209458e-01 -4.14117962e-01 -8.80299985e-01 -1.42645288e+00 -1.22287445e-01 1.06620574e+00 -3.44716549e-01 -9.95585918e-02 8.25353861e-01 2.57855296e-01 1.50694668e-01 4.82089579e-01 -5.93205214e-01 -9.62830305e-01 1.22052860e+00 -3.24767411e-01 6.54876113e-01 7.09279656e-01 -2.75787681e-01 1.35827017e+00 -6.30274951e-01 1.00854063e+00 1.13243806e+00 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0c655511-741b-49a7-a179-487261fef3e2	open-set-recognition-using-vision-transformer	2203.08441	null	https://arxiv.org/abs/2203.08441v1	https://arxiv.org/pdf/2203.08441v1.pdf	Open Set Recognition using Vision Transformer with an Additional Detection Head	Deep neural networks have demonstrated prominent capacities for image classification tasks in a closed set setting, where the test data come from the same distribution as the training data. However, in a more realistic open set scenario, traditional classifiers with incomplete knowledge cannot tackle test data that are not from the training classes. Open set recognition (OSR) aims to address this problem by both identifying unknown classes and distinguishing known classes simultaneously. In this paper, we propose a novel approach to OSR that is based on the vision transformer (ViT) technique. Specifically, our approach employs two separate training stages. First, a ViT model is trained to perform closed set classification. Then, an additional detection head is attached to the embedded features extracted by the ViT, trained to force the representations of known data to class-specific clusters compactly. Test examples are identified as known or unknown based on their distance to the cluster centers. To the best of our knowledge, this is the first time to leverage ViT for the purpose of OSR, and our extensive evaluation against several OSR benchmark datasets reveals that our approach significantly outperforms other baseline methods and obtains new state-of-the-art performance.	['Xenofon Koutsoukos', 'Jie Liu', 'Zhenkai Zhang', 'Feiyang Cai']	2022-03-16	null	null	null	null	['open-set-learning']	['miscellaneous']	[ 3.65928560e-01 -5.99692389e-02 -2.59839714e-01 -1.52177900e-01 -8.74755204e-01 -6.86557353e-01 4.93157238e-01 -3.10857631e-02 -1.01450957e-01 5.52041769e-01 -3.16133559e-01 -2.63536960e-01 -1.22296125e-01 -6.12439871e-01 -8.02859843e-01 -7.35526979e-01 2.11277872e-01 7.85239458e-01 1.41270950e-01 7.99945965e-02 1.46078452e-01 4.83461499e-01 -1.86259985e+00 3.98638874e-01 7.64282227e-01 1.38696480e+00 4.55520935e-02 3.15678209e-01 1.22834608e-01 6.81297481e-01 -7.22117305e-01 -2.33297989e-01 3.84397566e-01 -6.93861097e-02 -6.14098310e-01 3.21942627e-01 5.73702991e-01 -1.63553268e-01 -6.60581529e-01 1.09217179e+00 3.10777128e-01 1.19459204e-01 9.28228080e-01 -1.21429729e+00 -1.06036806e+00 3.28887254e-01 -6.47912145e-01 1.55898869e-01 7.97318369e-02 -2.40793712e-02 1.05097938e+00 -1.13285375e+00 3.79198760e-01 8.08658957e-01 5.56702077e-01 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ddfff171-9f37-4285-95a5-5ae4da7228ad	maptr-structured-modeling-and-learning-for	2208.14437	null	https://arxiv.org/abs/2208.14437v2	https://arxiv.org/pdf/2208.14437v2.pdf	MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction	High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. We present MapTR, a structured end-to-end Transformer for efficient online vectorized HD map construction. We propose a unified permutation-equivalent modeling approach, i.e., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process. We design a hierarchical query embedding scheme to flexibly encode structured map information and perform hierarchical bipartite matching for map element learning. MapTR achieves the best performance and efficiency with only camera input among existing vectorized map construction approaches on nuScenes dataset. In particular, MapTR-nano runs at real-time inference speed ($25.1$ FPS) on RTX 3090, $8\times$ faster than the existing state-of-the-art camera-based method while achieving $5.0$ higher mAP. Even compared with the existing state-of-the-art multi-modality method, MapTR-nano achieves $0.7$ higher mAP, and MapTR-tiny achieves $13.5$ higher mAP and $3\times$ faster inference speed. Abundant qualitative results show that MapTR maintains stable and robust map construction quality in complex and various driving scenes. MapTR is of great application value in autonomous driving. Code and more demos are available at \url{https://github.com/hustvl/MapTR}.	['Chang Huang', 'Wenyu Liu', 'Qian Zhang', 'Tianheng Cheng', 'Xinggang Wang', 'Shaoyu Chen', 'Bencheng Liao']	2022-08-30	null	null	null	null	['3d-lane-detection']	['computer-vision']	[-2.39671752e-01 9.02762413e-02 -1.84315637e-01 -5.75824976e-01 -1.08627248e+00 -6.33608282e-01 3.07747900e-01 -9.13896039e-02 -4.18682963e-01 3.80513608e-01 5.13585582e-02 -4.02037442e-01 -3.30391139e-01 -1.35273468e+00 -1.24270129e+00 -5.64480186e-01 6.53483719e-02 8.51734519e-01 4.54190254e-01 -4.56967533e-01 4.68120687e-02 2.50572383e-01 -1.87494695e+00 -2.75080353e-01 7.81831264e-01 8.93090844e-01 5.13702750e-01 6.10477507e-01 1.47729516e-02 4.72694576e-01 2.10577920e-01 -3.97550374e-01 5.07038414e-01 3.44521344e-01 -3.49928081e-01 -3.63889605e-01 7.20481038e-01 -4.46224123e-01 -1.15564227e+00 1.05114746e+00 6.50261700e-01 2.89840400e-01 1.39109001e-01 -1.42239928e+00 -3.34807277e-01 4.88468170e-01 -5.61355352e-01 8.31769630e-02 4.34534773e-02 3.88237238e-01 1.10936522e+00 -1.02373576e+00 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25eda936-06d6-44cf-bfe6-1de7ecdbe6f7	st-ddpm-explore-class-clustering-for	null	null	https://openreview.net/forum?id=FuLL40HLCRn	https://openreview.net/pdf?id=FuLL40HLCRn	ST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models	Score-based generative models involve sequentially corrupting the data distribution with noise and then learns to recover the data distribution based on score matching. In this paper, for the diffusion probabilistic models, we first delve into the changes of data distribution during the forward process of the Markov chain and explore the class clustering phenomenon. Inspired by the class clustering phenomenon, we devise a novel conditional diffusion probabilistic model by explicitly modeling the class center in the forward and reverse process, and make an elegant modification to the original formulation, which enables controllable generation and gets interpretability. We also provide another direction for faster sampling and more analysis of our method. To verify the effectiveness of the formulated framework, we conduct extensive experiments on multiple tasks, and achieve competitive results compared with the state-of-the-art methods(conditional image generation on CIFAR-10 with an inception score of 9.58 and FID score of 3.05).	['Zhou Zhao', 'Zijian Zhang', 'Zhijie Lin']	2021-09-29	null	null	null	null	['conditional-image-generation']	['computer-vision']	[ 2.19312295e-01 -1.15350839e-02 1.04333244e-01 -3.74168307e-01 -9.59558249e-01 -5.91110110e-01 7.74617553e-01 -3.04919720e-01 -2.65416592e-01 7.29703665e-01 2.20389619e-01 -1.36664808e-01 -2.40701750e-01 -8.51863921e-01 -6.79580092e-01 -1.08044136e+00 8.58298317e-02 7.13249981e-01 3.94880354e-01 3.05269927e-01 1.54606983e-01 5.07797003e-02 -1.23532605e+00 3.99352610e-01 1.04506683e+00 6.48725092e-01 2.80848742e-01 8.31111968e-01 -2.81469785e-02 1.00336921e+00 -8.52266550e-01 -7.06983089e-01 -2.28723744e-04 -7.71084726e-01 -6.74020946e-01 3.18348587e-01 7.47381374e-02 -3.56149286e-01 -3.02577734e-01 1.47132623e+00 3.55998158e-01 1.12279221e-01 1.09940398e+00 -1.20919991e+00 -1.26901782e+00 9.51430023e-01 -7.64783800e-01 1.06647514e-01 1.60073027e-01 1.61740296e-02 9.74295318e-01 -9.40468013e-01 6.46633923e-01 1.23999453e+00 3.47340316e-01 5.19421279e-01 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-7.19429731e-01 5.46820760e-01 1.51930407e-01 -6.77175045e-01 -4.95664716e-01 4.39432085e-01]	[11.230016708374023, -0.10857579857110977]
bd0aa74e-9cba-49b6-9148-983b8c39ab1b	small-footprint-text-independent-speaker	2011.01709	null	https://arxiv.org/abs/2011.01709v2	https://arxiv.org/pdf/2011.01709v2.pdf	Small footprint Text-Independent Speaker Verification for Embedded Systems	Deep neural network approaches to speaker verification have proven successful, but typical computational requirements of State-Of-The-Art (SOTA) systems make them unsuited for embedded applications. In this work, we present a two-stage model architecture orders of magnitude smaller than common solutions (237.5K learning parameters, 11.5MFLOPS) reaching a competitive result of 3.31% Equal Error Rate (EER) on the well established VoxCeleb1 verification test set. We demonstrate the possibility of running our solution on small devices typical of IoT systems such as the Raspberry Pi 3B with a latency smaller than 200ms on a 5s long utterance. Additionally, we evaluate our model on the acoustically challenging VOiCES corpus. We report a limited increase in EER of 2.6 percentage points with respect to the best scoring model of the 2019 VOiCES from a Distance Challenge, against a reduction of 25.6 times in the number of learning parameters.	['Alice Coucke', 'Mathieu Poumeyrol', 'Raffaele Tavarone', 'Julien Balian']	2020-11-03	null	null	null	null	['text-independent-speaker-verification']	['speech']	[-1.39369685e-02 3.15616429e-01 3.75700325e-01 -5.19306958e-01 -1.25208616e+00 -4.16498840e-01 3.60933542e-01 -2.26357117e-01 -7.52168953e-01 3.71859014e-01 1.07965339e-03 -6.57010317e-01 1.38558391e-02 -7.84813054e-03 -5.90308964e-01 -5.50536275e-01 -4.04118672e-02 2.20064521e-01 -1.12624280e-01 -3.34761143e-02 -2.53001183e-01 3.86739522e-01 -1.63548732e+00 1.73249319e-01 2.98978537e-01 1.34674132e+00 -2.47885540e-01 1.14614403e+00 5.33447444e-01 5.60964048e-01 -1.24702287e+00 -3.82566124e-01 9.77887660e-02 8.64630342e-02 -6.89930379e-01 -4.01535809e-01 8.20786357e-01 -5.13198376e-01 -4.22169954e-01 8.38738859e-01 1.24498188e+00 1.45062447e-01 1.91543903e-02 -1.38912165e+00 -2.21983120e-01 9.67843533e-01 2.86154766e-02 2.61886597e-01 2.00226411e-01 8.52529854e-02 1.01555610e+00 -7.71575391e-01 -8.04877281e-02 1.11977398e+00 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0dab5d05-822b-4fd8-bc1f-63098fd8cfb0	recent-advances-in-neural-program-synthesis	1802.02353	null	http://arxiv.org/abs/1802.02353v1	http://arxiv.org/pdf/1802.02353v1.pdf	Recent Advances in Neural Program Synthesis	In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence. The successes in these problems has led researchers to consider the possibilities for intelligent systems to tackle a problem that humans have only recently themselves considered: program synthesis. This challenge is unlike others such as object recognition and speech translation, since its abstract nature and demand for rigor make it difficult even for human minds to attempt. While it is still far from being solved or even competitive with most existing methods, neural program synthesis is a rapidly growing discipline which holds great promise if completely realized. In this paper, we start with exploring the problem statement and challenges of program synthesis. Then, we examine the fascinating evolution of program induction models, along with how they have succeeded, failed and been reimagined since. Finally, we conclude with a contrastive look at program synthesis and future research recommendations for the field.	['Neel Kant']	2018-02-07	null	null	null	null	['program-induction']	['computer-code']	[ 4.59692746e-01 1.94811508e-01 -3.68540525e-01 -3.86744469e-01 -3.62587571e-01 -6.00849152e-01 8.39369297e-01 3.62824410e-01 -3.72317672e-01 7.10809350e-01 4.31755856e-02 -8.60040545e-01 2.35264394e-02 -8.10719073e-01 -6.64208770e-01 -4.04874206e-01 -1.05895519e-01 2.76840061e-01 -1.06295226e-02 -2.17980906e-01 5.46828747e-01 3.35060626e-01 -2.00904322e+00 2.31167883e-01 6.74917817e-01 4.76370484e-01 3.10555715e-02 5.64373434e-01 -3.25893641e-01 1.35530925e+00 -5.96579731e-01 -3.72059017e-01 -2.55661547e-01 -4.34653133e-01 -1.12618876e+00 -1.67382121e-01 3.28325212e-01 -2.97660410e-01 -2.50458181e-01 1.02482915e+00 2.00720504e-01 9.93126780e-02 2.26722822e-01 -1.25800335e+00 -7.16910541e-01 4.57162797e-01 -3.71976167e-01 1.17958657e-01 5.10639429e-01 1.21061176e-01 1.15668786e+00 -5.99808276e-01 5.74213922e-01 1.05756450e+00 6.88962042e-01 6.42526925e-01 -1.23779833e+00 -3.97011071e-01 3.01413815e-02 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9fa03403-f623-47d3-a1b4-a48b54b2f232	heads-headline-generation-as-sequence	null	null	https://aclanthology.info/papers/N15-1014/n15-1014	https://www.aclweb.org/anthology/N15-1014	HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space	null	['Marina Litvak', 'Carlos A. Colmenares', 'Fabrizio Silvestri', 'Amin Mantrach']	2015-05-01	null	null	null	hlt-2015-5	['headline-generation']	['natural-language-processing']	[-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01 -8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01 -5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01 -2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01 -7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302e-01 8.47113907e-01 6.29996777e-01 -1.62287033e+00 5.77558100e-01 6.81926727e-01 5.49597681e-01 5.66466339e-02 1.04480565e+00 6.13576770e-02 1.59847176e+00 -6.05450153e-01 -4.56729174e-01 1.43710867e-01 -1.88022718e-01 -5.35770595e-01 -3.36825520e-01 7.91536197e-02 -5.88986814e-01 -3.10633808e-01 6.07356608e-01 1.14270830e+00 -5.53557873e-02 1.07025254e+00 -1.51067197e+00 -7.97060013e-01 5.44234335e-01 2.30399013e-01 1.20632313e-01 6.10446513e-01 -1.79472014e-01 3.51212233e-01 -1.36621380e+00 6.32680655e-01 8.64870071e-01 9.47046995e-01 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0a14ebee-84fe-465d-a0ca-bc9125463901	towards-metrical-reconstruction-of-human	2204.06607	null	https://arxiv.org/abs/2204.06607v2	https://arxiv.org/pdf/2204.06607v2.pdf	Towards Metrical Reconstruction of Human Faces	Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely on a metrically correct prediction of the shape, especially, when the reconstructed subject is put into a metrical context (i.e., when there is a reference object of known size). A metrical reconstruction is also needed for any application that measures distances and dimensions of the subject (e.g., to virtually fit a glasses frame). State-of-the-art methods for face reconstruction from a single image are trained on large 2D image datasets in a self-supervised fashion. However, due to the nature of a perspective projection they are not able to reconstruct the actual face dimensions, and even predicting the average human face outperforms some of these methods in a metrical sense. To learn the actual shape of a face, we argue for a supervised training scheme. Since there exists no large-scale 3D dataset for this task, we annotated and unified small- and medium-scale databases. The resulting unified dataset is still a medium-scale dataset with more than 2k identities and training purely on it would lead to overfitting. To this end, we take advantage of a face recognition network pretrained on a large-scale 2D image dataset, which provides distinct features for different faces and is robust to expression, illumination, and camera changes. Using these features, we train our face shape estimator in a supervised fashion, inheriting the robustness and generalization of the face recognition network. Our method, which we call MICA (MetrIC fAce), outperforms the state-of-the-art reconstruction methods by a large margin, both on current non-metric benchmarks as well as on our metric benchmarks (15% and 24% lower average error on NoW, respectively).	['Justus Thies', 'Timo Bolkart', 'Wojciech Zielonka']	2022-04-13	null	null	null	null	['3d-face-reconstruction', 'face-reconstruction']	['computer-vision', 'computer-vision']	[ 1.36064693e-01 1.39497772e-01 6.09120354e-02 -5.53916395e-01 -6.27861500e-01 -4.82342392e-01 4.12741840e-01 -4.49060053e-01 -7.27933273e-02 4.63827372e-01 -8.86825696e-02 7.23840147e-02 -4.61894758e-02 -6.79542601e-01 -7.72710383e-01 -6.41371250e-01 2.17508361e-01 7.05640316e-01 -3.84054892e-02 -1.20521098e-01 1.48562191e-04 1.04011095e+00 -1.73802197e+00 -9.47916359e-02 1.66388363e-01 1.35759532e+00 -1.65826157e-01 3.96283984e-01 1.62654310e-01 2.25804135e-01 -3.14341307e-01 -7.73964524e-01 5.66754460e-01 -3.60215306e-01 -5.41924298e-01 2.80158639e-01 9.68742907e-01 -3.41253847e-01 -2.21288756e-01 8.79373372e-01 4.96343940e-01 -6.06437214e-02 6.70769572e-01 -1.11624670e+00 -5.32491326e-01 2.93030832e-02 -5.47185004e-01 -4.11110461e-01 7.05462039e-01 -1.41500458e-01 6.40510857e-01 -1.08145106e+00 8.46360981e-01 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a86f6007-24bb-4d21-96b6-f9d3d2926d32	hierarchical-memory-learning-for-fine-grained	2203.06907	null	https://arxiv.org/abs/2203.06907v4	https://arxiv.org/pdf/2203.06907v4.pdf	Hierarchical Memory Learning for Fine-Grained Scene Graph Generation	As far as Scene Graph Generation (SGG), coarse and fine predicates mix in the dataset due to the crowd-sourced labeling, and the long-tail problem is also pronounced. Given this tricky situation, many existing SGG methods treat the predicates equally and learn the model under the supervision of mixed-granularity predicates in one stage, leading to relatively coarse predictions. In order to alleviate the negative impact of the suboptimum mixed-granularity annotation and long-tail effect problems, this paper proposes a novel Hierarchical Memory Learning (HML) framework to learn the model from simple to complex, which is similar to the human beings' hierarchical memory learning process. After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates. In order to realize this hierarchical learning pattern, this paper, for the first time, formulates the HML framework using the new Concept Reconstruction (CR) and Model Reconstruction (MR) constraints. It is worth noticing that the HML framework can be taken as one general optimization strategy to improve various SGG models, and significant improvement can be achieved on the SGG benchmark (i.e., Visual Genome).	['Jiayi Ma', 'Jingdong Chen', 'Jian Wang', 'Xiang Xiang', 'Yongjun Zhang', 'Yansheng Li', 'Youming Deng']	2022-03-14	null	null	null	null	['scene-graph-generation']	['computer-vision']	[ 3.46016169e-01 4.71720487e-01 -3.65195274e-01 -2.90382385e-01 -3.81849676e-01 -1.56884640e-01 5.75342774e-01 1.47408828e-01 -7.62818381e-02 6.26501441e-01 1.04168124e-01 1.26274524e-03 -6.73458818e-03 -1.14736545e+00 -7.06726074e-01 -8.46100092e-01 3.16089422e-01 6.95322871e-01 5.28030217e-01 1.35640249e-01 1.33624524e-01 1.87769264e-01 -1.80894089e+00 4.24371988e-01 1.21253538e+00 1.03096604e+00 5.42922437e-01 2.51208961e-01 -2.98603177e-01 1.17342293e+00 -4.72178459e-01 -4.08316642e-01 9.99031663e-02 -3.07237566e-01 -9.76623774e-01 4.41978037e-01 1.95129946e-01 -1.40402569e-02 -1.66792154e-01 1.12812614e+00 4.56788510e-01 2.83332527e-01 5.69643438e-01 -1.31210959e+00 -5.03194273e-01 5.39895535e-01 -5.60202360e-01 -2.95506895e-01 2.26028979e-01 1.41850099e-01 1.21361113e+00 -9.05844331e-01 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81a95f9b-a74e-45de-92ad-4d781e007259	few-shot-partial-label-learning	2106.00984	null	https://arxiv.org/abs/2106.00984v1	https://arxiv.org/pdf/2106.00984v1.pdf	Few-Shot Partial-Label Learning	Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the meta-learner and thus lead to a compromised performance. How to enable PLL under a few-shot learning setting is an important problem, but not yet well studied. In this paper, we introduce an approach called FsPLL (Few-shot PLL). FsPLL first performs adaptive distance metric learning by an embedding network and rectifying prototypes on the tasks previously encountered. Next, it calculates the prototype of each class of a new task in the embedding network. An unseen example can then be classified via its distance to each prototype. Experimental results on widely-used few-shot datasets (Omniglot and miniImageNet) demonstrate that our FsPLL can achieve a superior performance than the state-of-the-art methods across different settings, and it needs fewer samples for quickly adapting to new tasks.	['Carlotta Domeniconi', 'Lizhen Cui', 'Zhongmin Yan', 'Lei Liu', 'Guoxian Yu', 'Yunfeng Zhao']	2021-06-02	null	null	null	null	['partial-label-learning']	['methodology']	[ 4.10704017e-01 -1.72541201e-01 -3.55881453e-01 -5.50062001e-01 -6.97501540e-01 -1.58834651e-01 4.25569683e-01 2.58782119e-01 -5.97795546e-01 7.39056468e-01 -2.61060625e-01 1.70738459e-01 -1.17933862e-01 -6.06689453e-01 -3.27447325e-01 -8.10040236e-01 2.08167747e-01 5.13405502e-01 7.15222895e-01 1.59433559e-02 7.41187856e-02 2.21638367e-01 -1.96655571e+00 3.78657967e-01 7.30908096e-01 1.11956990e+00 2.99632668e-01 2.24034354e-01 -2.88363039e-01 6.27673388e-01 -7.94315994e-01 -1.67231962e-01 2.51821071e-01 -3.70071679e-01 -5.79299629e-01 9.60908458e-02 3.58519614e-01 -6.44791275e-02 -1.10507406e-01 1.07503760e+00 7.14891434e-01 5.15739441e-01 7.58843601e-01 -1.48494256e+00 -5.28354526e-01 5.27862847e-01 -4.12265807e-01 4.69032526e-01 3.19609884e-03 1.53783500e-01 8.63183677e-01 -1.28324115e+00 6.45117044e-01 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f6dc4ab0-1b34-4171-8dd1-b8edb607eb52	leveraging-multiple-descriptive-features-for	2307.04317	null	https://arxiv.org/abs/2307.04317v1	https://arxiv.org/pdf/2307.04317v1.pdf	Leveraging Multiple Descriptive Features for Robust Few-shot Image Learning	Modern image classification is based upon directly predicting model classes via large discriminative networks, making it difficult to assess the intuitive visual ``features'' that may constitute a classification decision. At the same time, recent works in joint visual language models such as CLIP provide ways to specify natural language descriptions of image classes but typically focus on providing single descriptions for each class. In this work, we demonstrate that an alternative approach, arguably more akin to our understanding of multiple ``visual features'' per class, can also provide compelling performance in the robust few-shot learning setting. In particular, we automatically enumerate multiple visual descriptions of each class -- via a large language model (LLM) -- then use a vision-image model to translate these descriptions to a set of multiple visual features of each image; we finally use sparse logistic regression to select a relevant subset of these features to classify each image. This both provides an ``intuitive'' set of relevant features for each class, and in the few-shot learning setting, outperforms standard approaches such as linear probing. When combined with finetuning, we also show that the method is able to outperform existing state-of-the-art finetuning approaches on both in-distribution and out-of-distribution performance.	['J. Zico Kolter', 'Anna Bair', 'Zhili Feng']	2023-07-10	null	null	null	null	['image-classification', 'few-shot-learning']	['computer-vision', 'methodology']	[ 3.25211316e-01 -5.74576445e-02 -6.84871435e-01 -5.74395716e-01 -1.20344388e+00 -6.07165217e-01 8.37580383e-01 2.87453115e-01 -2.38844901e-01 4.05747294e-01 1.71095148e-01 -7.47275949e-02 -9.23494101e-02 -6.82407677e-01 -8.09539437e-01 -7.48870671e-01 7.39531964e-02 5.25654495e-01 2.28632674e-01 1.09897286e-01 2.88953960e-01 2.41852418e-01 -1.84117675e+00 4.83602315e-01 1.81126446e-01 1.22253036e+00 4.22578573e-01 7.09503829e-01 -2.73794740e-01 9.09621477e-01 -2.02685103e-01 -4.51593287e-02 1.39142677e-01 -2.52430111e-01 -6.91773891e-01 5.21171868e-01 7.39891350e-01 -2.73545831e-01 -2.41187543e-01 9.61009383e-01 1.90964729e-01 3.84631544e-01 9.69917119e-01 -1.35158587e+00 -9.57342744e-01 2.86197722e-01 -6.48415029e-01 1.26950935e-01 2.93381214e-01 4.10570949e-01 1.44126940e+00 -9.50100303e-01 7.69579530e-01 1.28633559e+00 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6e882049-9db0-4ae6-ae41-24bf406c67c1	improving-generalization-for-multimodal-fake	2305.18599	null	https://arxiv.org/abs/2305.18599v1	https://arxiv.org/pdf/2305.18599v1.pdf	Improving Generalization for Multimodal Fake News Detection	The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are not applicable to real-world data. In this paper, we propose three models that adopt and fine-tune state-of-the-art multimodal transformers for multimodal fake news detection. We conduct an in-depth analysis by manipulating the input data aimed to explore models performance in realistic use cases on social media. Our study across multiple models demonstrates that these systems suffer significant performance drops against manipulated data. To reduce the bias and improve model generalization, we suggest training data augmentation to conduct more meaningful experiments for fake news detection on social media. The proposed data augmentation techniques enable models to generalize better and yield improved state-of-the-art results.	['Eric Müller-Budack', 'Ralph Ewerth', 'Sherzod Hakimov', 'Sahar Tahmasebi']	2023-05-29	null	null	null	null	['misinformation', 'fake-news-detection']	['miscellaneous', 'natural-language-processing']	[ 8.35048407e-02 1.16898164e-01 -4.97963995e-01 -3.21691692e-01 -6.40975714e-01 -4.35283273e-01 9.28944945e-01 3.72081906e-01 -1.87508538e-01 5.16648650e-01 2.46301845e-01 -3.31580132e-01 5.00592768e-01 -5.71649730e-01 -5.88871956e-01 -8.50973725e-02 6.26336485e-02 1.90386638e-01 4.16553587e-01 -7.57871807e-01 5.82260072e-01 2.44148254e-01 -1.48413205e+00 8.89407873e-01 9.75925207e-01 4.54804331e-01 -3.44920635e-01 3.58547688e-01 -1.19103774e-01 6.33925855e-01 -1.07253313e+00 -6.84957147e-01 -5.91239035e-02 -5.24773598e-01 -5.33227861e-01 9.59805101e-02 5.27383864e-01 -3.59973699e-01 -5.59398115e-01 1.15692949e+00 3.93135071e-01 -1.57281741e-01 4.59711045e-01 -1.54646945e+00 -1.03599083e+00 6.63871348e-01 -5.63093364e-01 3.03388804e-01 4.38498199e-01 -1.64914094e-02 6.00924373e-01 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6f3d556e-d732-42c9-b915-2f8f92987fe7	perceptual-quality-assessment-of-face-video	2304.07056	null	https://arxiv.org/abs/2304.07056v2	https://arxiv.org/pdf/2304.07056v2.pdf	Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method	Recent years have witnessed an exponential increase in the demand for face video compression, and the success of artificial intelligence has expanded the boundaries beyond traditional hybrid video coding. Generative coding approaches have been identified as promising alternatives with reasonable perceptual rate-distortion trade-offs, leveraging the statistical priors of face videos. However, the great diversity of distortion types in spatial and temporal domains, ranging from the traditional hybrid coding frameworks to generative models, present grand challenges in compressed face video quality assessment (VQA). In this paper, we introduce the large-scale Compressed Face Video Quality Assessment (CFVQA) database, which is the first attempt to systematically understand the perceptual quality and diversified compression distortions in face videos. The database contains 3,240 compressed face video clips in multiple compression levels, which are derived from 135 source videos with diversified content using six representative video codecs, including two traditional methods based on hybrid coding frameworks, two end-to-end methods, and two generative methods. In addition, a FAce VideO IntegeRity (FAVOR) index for face video compression was developed to measure the perceptual quality, considering the distinct content characteristics and temporal priors of the face videos. Experimental results exhibit its superior performance on the proposed CFVQA dataset. The benchmark is now made publicly available at: https://github.com/Yixuan423/Compressed-Face-Videos-Quality-Assessment.	['Shiqi Wang', 'Meng Wang', 'Baoliang Chen', 'Bolin Chen', 'Yixuan Li']	2023-04-14	null	null	null	null	['video-quality-assessment', 'video-quality-assessment']	['computer-vision', 'time-series']	[ 1.57249376e-01 -4.06967551e-01 -1.03596203e-01 -5.48574865e-01 -8.17092061e-01 -1.38919711e-01 4.20564532e-01 -6.00224137e-01 1.78016424e-01 3.14034879e-01 4.65512514e-01 3.18609357e-01 -2.88429976e-01 -5.27077794e-01 -3.82488489e-01 -9.52578068e-01 -2.57977247e-01 1.25710338e-01 -2.72205561e-01 -1.48898363e-02 2.05843240e-01 3.06667924e-01 -1.96070361e+00 5.79217315e-01 8.22719872e-01 1.38191712e+00 1.62948012e-01 5.79221785e-01 1.04226723e-01 6.30100608e-01 -4.18522805e-01 -9.57397699e-01 2.22513676e-01 -6.60544097e-01 -3.06795329e-01 2.81793118e-01 4.30919915e-01 -6.95680559e-01 -9.56034958e-01 1.25435936e+00 7.42400825e-01 -1.07520558e-01 6.17106318e-01 -1.57763410e+00 -1.13465559e+00 2.27978781e-01 -5.73488057e-01 5.78945100e-01 5.67128420e-01 2.44934291e-01 7.53365815e-01 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1.00473809e+00 -8.93454432e-01 -4.14724976e-01 -7.93567240e-01 -6.79616213e-01 4.70941752e-01 2.07441092e-01 -9.94820178e-01 -5.12271464e-01 3.81611288e-01]	[12.99276065826416, 0.2762530744075775]
53d35099-9886-438d-a21a-70f4e462df24	deep-attention-aware-feature-learning-for	2003.00517	null	https://arxiv.org/abs/2003.00517v1	https://arxiv.org/pdf/2003.00517v1.pdf	Deep Attention Aware Feature Learning for Person Re-Identification	Visual attention has proven to be effective in improving the performance of person re-identification. Most existing methods apply visual attention heuristically by learning an additional attention map to re-weight the feature maps for person re-identification. However, this kind of methods inevitably increase the model complexity and inference time. In this paper, we propose to incorporate the attention learning as additional objectives in a person ReID network without changing the original structure, thus maintain the same inference time and model size. Two kinds of attentions have been considered to make the learned feature maps being aware of the person and related body parts respectively. Globally, a holistic attention branch (HAB) makes the feature maps obtained by backbone focus on persons so as to alleviate the influence of background. Locally, a partial attention branch (PAB) makes the extracted features be decoupled into several groups and be separately responsible for different body parts (i.e., keypoints), thus increasing the robustness to pose variation and partial occlusion. These two kinds of attentions are universal and can be incorporated into existing ReID networks. We have tested its performance on two typical networks (TriNet and Bag of Tricks) and observed significant performance improvement on five widely used datasets.	['Xiaolu Sun', 'Han Wang', 'Chu Tang', 'Yifan Chen', 'Bin Fan']	2020-03-01	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[-2.95789629e-01 8.87620822e-03 8.73069540e-02 -4.54097748e-01 3.28381434e-02 -3.82098436e-01 4.92742419e-01 2.20303372e-01 -6.21543288e-01 5.52585185e-01 3.31463158e-01 3.01257789e-01 -5.95111027e-02 -6.38086855e-01 -6.84617341e-01 -7.02402472e-01 1.43468857e-01 4.04340774e-01 2.51939714e-01 -7.31622055e-02 2.18176186e-01 4.94290888e-01 -1.56556284e+00 -3.35765690e-01 8.31432641e-01 4.83391672e-01 -1.28935091e-03 2.72994429e-01 -4.42682616e-02 3.36736828e-01 -6.51787221e-01 -8.50715220e-01 3.67647320e-01 -1.88369274e-01 -7.21368730e-01 1.18797971e-03 6.94094300e-01 -4.77753609e-01 -4.49353665e-01 1.13547611e+00 7.98648000e-01 3.02221775e-01 5.67165136e-01 -1.21216989e+00 -8.10421646e-01 4.35443342e-01 -1.10552454e+00 4.26200509e-01 2.64679790e-01 1.73218191e-01 6.34819150e-01 -6.56933844e-01 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5d18a1d4-6bd9-422b-a91c-4e3b93198db9	robust-defreg-a-robust-deformable-point-cloud	2306.04701	null	https://arxiv.org/abs/2306.04701v1	https://arxiv.org/pdf/2306.04701v1.pdf	Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks	Point cloud registration is a fundamental problem in computer vision that aims to estimate the transformation between corresponding sets of points. Non-rigid registration, in particular, involves addressing challenges including various levels of deformation, noise, outliers, and data incompleteness. This paper introduces Robust-DefReg, a robust non-rigid point cloud registration method based on graph convolutional networks (GCNNs). Robust-DefReg is a coarse-to-fine registration approach within an end-to-end pipeline, leveraging the advantages of both coarse and fine methods. The method learns global features to find correspondences between source and target point clouds, to enable appropriate initial alignment, and subsequently fine registration. The simultaneous achievement of high accuracy and robustness across all challenges is reported less frequently in existing studies, making it a key objective of the Robust-DefReg method. The proposed method achieves high accuracy in large deformations while maintaining computational efficiency. This method possesses three primary attributes: high accuracy, robustness to different challenges, and computational efficiency. The experimental results show that the proposed Robust-DefReg holds significant potential as a foundational architecture for future investigations in non-rigid point cloud registration. The source code of Robust-DefReg is available.	['Jürgen Hesser', 'Marvin Kinz', 'Sara Monji-Azad']	2023-06-07	null	null	null	null	['point-cloud-registration']	['computer-vision']	[ 2.04597097e-02 -3.59721422e-01 6.46414831e-02 -3.92157346e-01 -9.99704599e-01 -5.27475953e-01 6.07523203e-01 1.90964222e-01 -3.42189640e-01 1.29760072e-01 -4.94981892e-02 2.68277824e-01 -4.18579668e-01 -7.11911559e-01 -8.78057539e-01 -6.47747397e-01 -1.16327025e-01 7.06322134e-01 1.38493046e-01 -3.56479734e-01 2.56072611e-01 1.30459452e+00 -1.41493404e+00 -3.10270429e-01 7.73105800e-01 7.44670928e-01 -6.93898797e-02 1.56732723e-01 2.82467306e-01 -2.26271391e-01 1.11962447e-03 -2.81425625e-01 6.76590502e-01 1.26595050e-01 -7.59110808e-01 -1.27551869e-01 9.95387435e-01 -3.11784595e-02 -1.10842101e-01 1.09343219e+00 7.31218278e-01 2.57633150e-01 4.12564814e-01 -1.24400830e+00 -6.87266350e-01 7.57813379e-02 -6.03065670e-01 2.10141968e-02 3.35889488e-01 1.50238648e-01 6.59207761e-01 -1.13534558e+00 5.45689225e-01 1.06907952e+00 1.20980382e+00 2.76642710e-01 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-2.02159196e-01 7.42637932e-01 3.15980256e-01 -6.98473632e-01 -1.69235796e-01 1.20892122e-01]	[7.670616626739502, -3.010953187942505]
9a06156c-11ea-4cc5-8231-4027e74538b6	visually-grounded-compound-pcfgs	2009.12404	null	https://arxiv.org/abs/2009.12404v1	https://arxiv.org/pdf/2009.12404v1.pdf	Visually Grounded Compound PCFGs	Exploiting visual groundings for language understanding has recently been drawing much attention. In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings. Existing work on this task (Shi et al., 2019) optimizes a parser via Reinforce and derives the learning signal only from the alignment of images and sentences. While their model is relatively accurate overall, its error distribution is very uneven, with low performance on certain constituents types (e.g., 26.2% recall on verb phrases, VPs) and high on others (e.g., 79.6% recall on noun phrases, NPs). This is not surprising as the learning signal is likely insufficient for deriving all aspects of phrase-structure syntax and gradient estimates are noisy. We show that using an extension of probabilistic context-free grammar model we can do fully-differentiable end-to-end visually grounded learning. Additionally, this enables us to complement the image-text alignment loss with a language modeling objective. On the MSCOCO test captions, our model establishes a new state of the art, outperforming its non-grounded version and, thus, confirming the effectiveness of visual groundings in constituency grammar induction. It also substantially outperforms the previous grounded model, with largest improvements on more `abstract' categories (e.g., +55.1% recall on VPs).	['Ivan Titov', 'Yanpeng Zhao']	2020-09-25	null	https://aclanthology.org/2020.emnlp-main.354	https://aclanthology.org/2020.emnlp-main.354.pdf	emnlp-2020-11	['constituency-grammar-induction']	['natural-language-processing']	[ 3.73280585e-01 8.03080380e-01 -2.16589585e-01 -2.57347703e-01 -1.58485770e+00 -1.00589263e+00 6.23537719e-01 1.85454220e-01 -4.20490026e-01 6.00846529e-01 3.28986973e-01 -3.85608882e-01 3.30718458e-01 -5.76954842e-01 -1.17108679e+00 -7.78622866e-01 -1.05260201e-01 7.30655432e-01 8.95966813e-02 -2.70578533e-01 2.88633287e-01 8.54458213e-02 -1.24778819e+00 5.00306845e-01 6.53280437e-01 5.97947836e-01 5.65097153e-01 6.88382685e-01 -3.45319539e-01 8.44490051e-01 -4.25820261e-01 -5.38105130e-01 5.14145661e-03 -4.57784325e-01 -6.92572474e-01 -4.76851016e-02 1.22975755e+00 -9.39226523e-02 -6.73826262e-02 1.16275215e+00 3.52452040e-01 -2.42157161e-01 6.62267268e-01 -1.13200438e+00 -1.05049825e+00 6.25449538e-01 -7.73727894e-01 8.85605812e-02 3.42648774e-01 2.17941657e-01 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c98ab991-4cc6-4995-bfeb-a4659a5178c0	probabilistic-forecasting-of-sensory-data	1903.12549	null	http://arxiv.org/abs/1903.12549v1	http://arxiv.org/pdf/1903.12549v1.pdf	Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN	Time series forecasting is one of the challenging problems for humankind. Traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-world fluctuations. While new probabilistic methods rush to rescue, they fight with technical difficulties like quantile crossing or selecting a prior distribution. To meld the different strengths of these fields while avoiding their weaknesses as well as to push the boundary of the state-of-the-art, we introduce ForGAN - one step ahead probabilistic forecasting with generative adversarial networks. ForGAN utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts from it. We argue how to evaluate ForGAN in opposition to regression methods. To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it. This dataset will be made publicly available for comparison. Furthermore, we test ForGAN on two publicly available datasets, namely Mackey-Glass dataset and Internet traffic dataset (A5M) where the impressive performance of ForGAN demonstrate its high capability in forecasting future values.	['Andreas Dengel', 'Sheraz Ahmed', 'Alireza Koochali', 'Peter Schichtel']	2019-03-29	null	null	null	null	['probabilistic-time-series-forecasting', 'univariate-time-series-forecasting']	['time-series', 'time-series']	[-4.25089002e-02 -1.66174337e-01 2.54544646e-01 -7.20394254e-01 -1.04326308e+00 -7.37051666e-01 9.44928885e-01 -5.16703844e-01 9.11100358e-02 9.67755675e-01 2.00861216e-01 -6.14780605e-01 -2.11115137e-01 -9.83800888e-01 -6.76741242e-01 -8.96342099e-01 -2.15961114e-01 7.07047164e-01 -2.42850855e-02 -5.47479451e-01 2.19278768e-01 4.26576555e-01 -1.28891730e+00 2.16899633e-01 8.12032640e-01 1.15662456e+00 -4.89456862e-01 6.83482349e-01 -1.01844110e-01 9.14309621e-01 -9.05239582e-01 -7.17142224e-01 2.20863700e-01 -2.33889818e-01 -4.11581218e-01 -4.32434291e-01 5.84062142e-03 -2.86142677e-01 -3.61604422e-01 5.87129891e-01 4.93018955e-01 2.83332050e-01 1.06299531e+00 -1.86341655e+00 -8.29111040e-01 9.40388858e-01 -3.59190285e-01 3.58871818e-01 1.50711268e-01 1.28183782e-01 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112c9fe0-8c71-414a-bce6-8e665e95dd64	a-plug-and-play-approach-to-multiparametric	2202.05269	null	https://arxiv.org/abs/2202.05269v1	https://arxiv.org/pdf/2202.05269v1.pdf	A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers	Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neural Network (CNN), trained to remove generic white gaussian noise (not a particular subsampling artefact) from data. This CNN denoiser is then used as a data-driven shrinkage operator within the iterative reconstruction algorithm. This algorithm with the same denoiser model is then tested on two simulated acquisition processes with distinct subsampling patterns. The results show consistent de-aliasing performance against both acquisition schemes and accurate mapping of tissues' quantitative bio-properties. Software available: https://github.com/ketanfatania/QMRI-PnP-Recon-POC	['Mohammad Golbabaee', 'Peter Hall', 'Marion I. Menzel', 'Carolin M. Pirkl', 'Ketan Fatania']	2022-02-10	null	null	null	null	['de-aliasing', 'magnetic-resonance-fingerprinting']	['computer-vision', 'medical']	[ 5.04506350e-01 -1.36846930e-01 2.40083486e-01 -4.84623015e-01 -7.41319537e-01 -3.18040878e-01 3.77162844e-01 -5.60644530e-02 -5.11084676e-01 7.05190063e-01 3.06455523e-01 -1.57291502e-01 -3.53751123e-01 -4.95345145e-01 -9.72790778e-01 -9.67710257e-01 -2.70711482e-01 4.53549892e-01 1.89549237e-01 -1.74950883e-02 1.54727176e-01 7.03065455e-01 -9.67100203e-01 5.82352698e-01 4.38657075e-01 7.70962834e-01 5.28348386e-01 9.73996580e-01 3.34045947e-01 1.03856194e+00 -3.91672790e-01 2.94898540e-01 3.86155039e-01 -5.10862350e-01 -8.98117006e-01 -2.78241724e-01 2.69734174e-01 -5.48037946e-01 -6.22812867e-01 8.92521381e-01 9.93537545e-01 1.16559654e-01 3.77519429e-01 -6.05454624e-01 -3.36746275e-01 8.83767068e-01 -3.52279007e-01 9.11826968e-01 -1.14914268e-01 1.89582214e-01 -9.08053517e-02 -7.65650868e-01 7.25345254e-01 4.56445754e-01 1.15341604e+00 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7db8c616-0346-4e78-b586-1876e684b240	learn-over-past-evolve-for-future-forecasting	2306.14728	null	https://arxiv.org/abs/2306.14728v1	https://arxiv.org/pdf/2306.14728v1.pdf	Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection	Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing on future data. In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data. Specifically, we design an effective framework FTT (Forecasting Temporal Trends), which could forecast the temporal distribution patterns of news data and then guide the detector to fast adapt to future distribution. Experiments on the real-world temporally split dataset demonstrate the superiority of our proposed framework. The code is available at https://github.com/ICTMCG/FTT-ACL23.	['Zhiwei Jin', 'Zhengjia Wang', 'Danding Wang', 'Yongchun Zhu', 'Juan Cao', 'Qiang Sheng', 'Beizhe Hu']	2023-06-26	null	null	null	null	['fake-news-detection']	['natural-language-processing']	[-1.85149908e-01 -1.99842408e-01 -5.09583831e-01 -3.31643939e-01 -1.38951272e-01 -5.71542084e-01 9.32873964e-01 1.90298811e-01 7.16747425e-04 5.83818734e-01 5.04604876e-01 -2.81522781e-01 8.75812694e-02 -8.21807861e-01 -8.09887707e-01 -3.97841483e-01 -3.20205837e-01 1.88236907e-01 7.16554463e-01 -2.22946927e-01 3.77199620e-01 1.68140084e-01 -1.47997618e+00 5.33528924e-01 5.26138544e-01 9.75870967e-01 1.41792431e-01 3.45828950e-01 -6.08786941e-02 8.69804502e-01 -8.39562535e-01 -3.30136985e-01 2.03897119e-01 -4.53740895e-01 -5.04578829e-01 -4.25058231e-02 -1.15797400e-01 -2.31269240e-01 -7.50819266e-01 1.16972899e+00 -3.20926891e-03 3.13590579e-02 2.99649358e-01 -1.22778809e+00 -7.84869909e-01 8.52139056e-01 -6.56692028e-01 9.84800935e-01 1.55011982e-01 1.19892266e-02 8.62193048e-01 -6.97568953e-01 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ebe6ead3-348a-4148-98ee-beae09e18ce8	technology-pipeline-for-large-scale-cross	2211.01338	null	https://arxiv.org/abs/2211.01338v1	https://arxiv.org/pdf/2211.01338v1.pdf	Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages	Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video. This task becomes challenging when the source and target languages belong to different language families, resulting in differences in generated audio duration. This is further compounded by the original speaker's rhythm, especially for extempore speech. This paper describes the challenges in regenerating English lecture videos in Indian languages semi-automatically. A prototype is developed for dubbing lectures into 9 Indian languages. A mean-opinion-score (MOS) is obtained for two languages, Hindi and Tamil, on two different courses. The output video is compared with the original video in terms of MOS (1-5) and lip synchronisation with scores of 4.09 and 3.74, respectively. The human effort also reduces by 75%.	['Rajeev Sangal', 'S Umesh', 'Pushpak Bhattacharya', 'Hema Murthy', 'Dipti Sharma', 'Vrunda Sukhadia', 'Kada Sai Venkata Vineeth', 'Vandan Mujadia', 'Vasista Sai Lodagala', 'Sudhanshu Srivastava', 'Pruthwik Mishra', 'Nithya Ravi', 'Nihal John George', 'Navina K', 'Mudit Batra', 'Mohana N', 'Mohammad Wajahat', 'Metilda Sagaya Mary', 'Mano Ranjith Kumar M', 'K V Vikram', 'Jordan Fernandes', 'Jom Kuriakose', 'Ishika Gupta', 'Bhagyashree Mukherjee', 'Ashish Seth', 'Arun Kumar', 'Anusha Prakash']	2022-11-01	null	null	null	null	['text-to-speech-synthesis']	['speech']	[ 7.18705505e-02 9.01187398e-03 4.91057374e-02 -4.09527123e-02 -1.36436915e+00 -8.81573260e-01 1.19399101e-01 1.17674932e-01 -1.76287755e-01 9.45690513e-01 5.05195439e-01 -1.76704749e-01 2.56150275e-01 -5.15850261e-02 -6.21189117e-01 -6.86657548e-01 3.99175018e-01 5.16991019e-02 2.65153795e-01 -8.86660367e-02 2.91475832e-01 3.43035519e-01 -1.75478351e+00 6.25576973e-01 5.68123102e-01 4.74671751e-01 3.76135111e-01 1.30507672e+00 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6aa82363-b653-4f49-ad9e-c71bb8902bb5	graph-similarities-and-dual-approach-for	null	null	https://openreview.net/forum?id=CxebB5Psl1	https://openreview.net/pdf?id=CxebB5Psl1	Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval	Sequential text-to-image retrieval, a.k.a. Story-to-images task, requires semantic alignment with a given story and maintaining global coherence in drawn image sequence simultaneously. Most of the previous works have only focused on modeling how to follow the content of a given story faithfully. This kind of overfitting tendency hinders matching structural similarity between images, causing an inconsistency in global visual information such as backgrounds. To handle this imbalanced problem, we propose a novel image sequence retrieval framework that utilizes scene graph similarities of the images and a dual learning scheme. Scene graph describes high-level information of visual groundings and adjacency relations of the key entities in a visual scene. In our proposed retriever, the graph encoding head learns to maximize graph embedding similarities among sampled images, giving a strong signal that forces the retriever to also consider morphological relevance with previously sampled images. We set a video captioning as a dual learning task that reconstructs the input story from the sampled image sequence. This inverse mapping gives informative feedback for our proposed retrieval system to maintain global contextual information of a given story. We also suggest a new contextual sentence encoding architecture to embed a sentence in consideration of the surrounding context. Through extensive experiments, Our proposed framework shows better qualitative and quantitative performance with Visual Storytelling benchmark compared to conventional story-to-image models.	['Seong-Woo Kim', 'Sihyeon Jo', 'Keonwoo Kim']	2021-09-29	null	null	null	null	['visual-storytelling']	['natural-language-processing']	[ 6.31226838e-01 -2.50919402e-01 -2.05586225e-01 -3.66679072e-01 -8.18668246e-01 -4.87068415e-01 7.58610189e-01 2.00140819e-01 -1.27881840e-01 5.05271554e-01 5.19662261e-01 9.62342992e-02 -1.85067892e-01 -8.39863479e-01 -1.08941185e+00 -6.21328413e-01 3.70719731e-01 2.55112529e-01 2.96122432e-01 -2.74205536e-01 5.96694827e-01 2.37254053e-01 -1.48352540e+00 7.11254776e-01 3.91742200e-01 5.72701395e-01 1.03194475e+00 8.17704797e-01 -2.42062911e-01 1.25181460e+00 -3.73836935e-01 -2.44213939e-01 1.08623266e-01 -7.11653769e-01 -8.39669406e-01 4.67633903e-01 8.05803537e-01 -6.26391292e-01 -8.86736751e-01 1.08107591e+00 4.29698616e-01 3.14250767e-01 7.92751670e-01 -1.04549015e+00 -1.25127828e+00 4.44290191e-01 -8.11651230e-01 2.80393839e-01 8.57888937e-01 1.16475306e-01 1.17885625e+00 -8.37685645e-01 1.01706469e+00 1.26235306e+00 5.09467274e-02 3.03175956e-01 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6.81286395e-01 4.22565460e-01 -3.87122214e-01 -8.45328718e-02 4.91294473e-01]	[10.897342681884766, 0.8860433101654053]
3c756a72-d2b9-41ed-b45f-182c365ec6c0	learning-proximal-operators-using-denoising	1704.03488	null	http://arxiv.org/abs/1704.03488v2	http://arxiv.org/pdf/1704.03488v2.pdf	Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems	While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks. A remaining drawback of deep learning approaches is their requirement for an expensive retraining whenever the specific problem, the noise level, noise type, or desired measure of fidelity changes. On the contrary, variational methods have a plug-and-play nature as they usually consist of separate data fidelity and regularization terms. In this paper we study the possibility of replacing the proximal operator of the regularization used in many convex energy minimization algorithms by a denoising neural network. The latter therefore serves as an implicit natural image prior, while the data term can still be chosen independently. Using a fixed denoising neural network in exemplary problems of image deconvolution with different blur kernels and image demosaicking, we obtain state-of-the-art reconstruction results. These indicate the high generalizability of our approach and a reduction of the need for problem-specific training. Additionally, we discuss novel results on the analysis of possible optimization algorithms to incorporate the network into, as well as the choices of algorithm parameters and their relation to the noise level the neural network is trained on.	['Daniel Cremers', 'Tim Meinhardt', 'Michael Moeller', 'Caner Hazirbas']	2017-04-11	learning-proximal-operators-using-denoising-1	http://openaccess.thecvf.com/content_iccv_2017/html/Meinhardt_Learning_Proximal_Operators_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Meinhardt_Learning_Proximal_Operators_ICCV_2017_paper.pdf	iccv-2017-10	['image-deconvolution']	['computer-vision']	[ 2.96257406e-01 -1.09119490e-01 2.48113990e-01 -3.36583853e-01 -7.69537151e-01 -2.76516974e-01 5.95314980e-01 -9.74023640e-02 -7.25392401e-01 6.89411819e-01 -1.42742451e-02 -2.69051231e-02 -4.52393353e-01 -5.31487882e-01 -8.25532496e-01 -1.16803479e+00 2.08363339e-01 5.29839516e-01 -2.67367885e-02 -2.61852324e-01 1.28166348e-01 5.83839297e-01 -1.43069184e+00 -8.01623464e-02 8.02320898e-01 1.06643534e+00 3.75557959e-01 4.46403921e-01 1.52399093e-01 6.11229599e-01 -2.29300156e-01 -2.22794205e-01 3.89367849e-01 -5.02667248e-01 -5.59374869e-01 3.63713175e-01 3.80003244e-01 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a42c437b-c2b9-4339-a369-79761fa09fb6	cross-view-asymmetric-metric-learning-for	1708.08062	null	http://arxiv.org/abs/1708.08062v2	http://arxiv.org/pdf/1708.08062v2.pdf	Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification	While metric learning is important for Person re-identification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose unsupervised asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our model works much more suitable for unsupervised RE-ID compared to classical unsupervised metric learning models. We also compare with existing unsupervised RE-ID methods, and our model outperforms them with notable margins. Specifically, we report the results on large-scale unlabelled RE-ID dataset, which is important but unfortunately less concerned in literatures.	['An-Cong Wu', 'Wei-Shi Zheng', 'Hong-Xing Yu']	2017-08-27	cross-view-asymmetric-metric-learning-for-1	http://openaccess.thecvf.com/content_iccv_2017/html/Yu_Cross-View_Asymmetric_Metric_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Yu_Cross-View_Asymmetric_Metric_ICCV_2017_paper.pdf	iccv-2017-10	['unsupervised-person-re-identification']	['computer-vision']	[-9.23089013e-02 -3.15367311e-01 -1.42964572e-01 -6.45615399e-01 -6.10142469e-01 -4.28649843e-01 7.03097403e-01 -1.90253764e-01 -4.59807634e-01 4.97817397e-01 4.78547424e-01 1.59251481e-01 -1.31964728e-01 -4.99931484e-01 -3.58099669e-01 -6.49993658e-01 1.78825557e-01 6.78232849e-01 3.22217554e-01 1.21200912e-01 1.88562214e-01 4.12595719e-01 -1.75600350e+00 5.77641539e-02 6.50739014e-01 5.69351912e-01 5.29402085e-02 6.00163162e-01 3.58485103e-01 6.40757859e-01 -5.10776460e-01 -8.63958776e-01 6.18144572e-01 -4.40372318e-01 -6.84225082e-01 4.34107840e-01 8.63207281e-01 -4.08853054e-01 -6.08395457e-01 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2e5f2c95-ee50-47b0-9d29-2f56f5167ff8	brain-mri-study-for-glioma-segmentation-using	2207.07622	null	https://arxiv.org/abs/2207.07622v1	https://arxiv.org/pdf/2207.07622v1.pdf	Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand	Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of convolutional neural networks (CNNs) combined with original post-processing techniques with low computational demand was proposed. The post-processing techniques were the main responsible for the results obtained in the segmentations. The segmented regions were the whole tumor, the tumor core, and the enhancing tumor core, obtaining averaged Dice coefficients equal to 0.8934, 0.8376, and 0.8113, respectively. These results reached the state of the art in glioma segmentation determined by the winners of the challenge.	['Benito de Celis-Alonso', 'Eduardo Moreno-Barbosa', 'José Gerardo Suárez-García Javier Miguel Hernández-López']	2022-07-15	null	null	null	null	['brain-tumor-segmentation']	['medical']	[ 8.63498524e-02 2.81542659e-01 2.13835642e-01 -2.21689478e-01 -5.85806847e-01 -2.70295680e-01 5.87392151e-01 6.40591085e-01 -7.98995733e-01 5.73455572e-01 4.43169586e-02 -2.08137348e-01 -2.00007409e-01 -6.14570200e-01 -7.49586001e-02 -1.13848948e+00 -1.20053448e-01 6.71362102e-01 2.44311258e-01 -4.09865677e-02 2.95478314e-01 7.37439990e-01 -1.04698384e+00 3.04227322e-01 1.21404445e+00 1.12311685e+00 4.96378094e-01 5.08244812e-01 -3.04246008e-01 4.00209010e-01 -3.31976503e-01 -3.49020436e-02 -4.93572801e-02 -1.90460458e-01 -8.00524294e-01 4.10605557e-02 1.45761326e-01 -6.44611716e-02 -1.54212266e-01 1.22303307e+00 4.14548695e-01 -3.65030691e-02 7.25120902e-01 -8.03268492e-01 -2.76327115e-02 5.56310654e-01 -5.22743940e-01 3.20472002e-01 -4.18677986e-01 -8.09038281e-02 5.47343075e-01 -6.33858800e-01 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3e19da8b-44ab-4387-aa33-1d9f9d24d853	realfusion-360deg-reconstruction-of-any-1	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.pdf	RealFusion: 360deg Reconstruction of Any Object From a Single Image	We consider the problem of reconstructing a full 360deg photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed. We thus take an off-the-self conditional image generator based on diffusion and engineer a prompt that encourages it to "dream up" novel views of the object. Using the recent DreamFusion method, we fuse the given input view, the conditional prior, and other regularizers in a final, consistent reconstruction. We demonstrate state-of-the-art reconstruction results on benchmark images when compared to prior methods for monocular 3D reconstruction of objects. Qualitatively, our reconstructions provide a faithful match of the input view and a plausible extrapolation of its appearance and 3D shape, including to the side of the object not visible in the image.	['Andrea Vedaldi', 'Christian Rupprecht', 'Iro Laina', 'Luke Melas-Kyriazi']	2023-01-01	null	null	null	cvpr-2023-1	['3d-reconstruction']	['computer-vision']	[ 3.76114190e-01 4.34573382e-01 3.75275642e-01 -4.27123755e-01 -6.31556988e-01 -6.72389925e-01 7.55578220e-01 -7.87886739e-01 -8.25620666e-02 6.69419706e-01 4.52719837e-01 7.89137371e-03 3.49342227e-01 -5.89822412e-01 -1.08853936e+00 -8.09703887e-01 7.15007186e-01 4.99683917e-01 -1.63912356e-01 1.59005657e-01 1.78850487e-01 8.16567183e-01 -1.32022786e+00 3.80328774e-01 3.93093467e-01 7.40216076e-01 6.43970609e-01 6.40472651e-01 3.39589417e-01 8.49859118e-01 -1.20705947e-01 -3.74663025e-01 5.08385539e-01 -5.27617931e-01 -6.98180616e-01 6.51308477e-01 7.49434650e-01 -6.82321191e-01 -7.51464665e-01 1.19064271e+00 7.70620406e-02 1.57655105e-01 7.53615975e-01 -7.00033367e-01 -8.43733966e-01 -4.69045620e-03 -7.11866677e-01 -8.46233498e-03 6.40174389e-01 4.29570377e-01 5.73689520e-01 -9.89927471e-01 1.25263095e+00 1.06940401e+00 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93b594a5-2008-4f11-9fc1-daf4653dd522	detecting-photoshopped-faces-by-scripting	1906.05856	null	https://arxiv.org/abs/1906.05856v2	https://arxiv.org/pdf/1906.05856v2.pdf	Detecting Photoshopped Faces by Scripting Photoshop	Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. We present a method for detecting one very popular Photoshop manipulation -- image warping applied to human faces -- using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to "undo" a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to real, artist-created image manipulations.	['Andrew Owens', 'Sheng-Yu Wang', 'Alexei A. Efros', 'Richard Zhang', 'Oliver Wang']	2019-06-13	detecting-photoshopped-faces-by-scripting-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Detecting_Photoshopped_Faces_by_Scripting_Photoshop_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Detecting_Photoshopped_Faces_by_Scripting_Photoshop_ICCV_2019_paper.pdf	iccv-2019-10	['image-manipulation-detection']	['computer-vision']	[ 8.22806954e-01 2.07136303e-01 1.69192031e-01 -5.15091598e-01 -3.05369794e-01 -8.30746233e-01 5.60652137e-01 -3.97172421e-01 -2.74254441e-01 3.23745668e-01 -3.51714849e-01 -1.26678020e-01 6.30910218e-01 -4.18577313e-01 -9.94719267e-01 -2.39554271e-01 -4.07853276e-02 1.45349100e-01 1.26300544e-01 -2.57099539e-01 7.85813630e-01 6.96337581e-01 -1.49946237e+00 4.48326528e-01 9.12961662e-02 4.58276570e-01 -4.01371121e-01 9.49694216e-01 1.68365642e-01 7.83403933e-01 -8.90199661e-01 -1.02310479e+00 6.90539718e-01 -3.75502199e-01 -5.37061274e-01 4.69642490e-01 1.00336277e+00 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b9120446-66e7-4ce6-89ef-069e1c368b47	cycle-consistency-driven-object-discovery	2306.02204	null	https://arxiv.org/abs/2306.02204v1	https://arxiv.org/pdf/2306.02204v1.pdf	Cycle Consistency Driven Object Discovery	Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. Existing approaches have explored slot-based methods utilizing architectural priors or auxiliary information such as depth maps or flow maps to facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. However, reliance on architectural priors introduces unreliability and requires meticulous engineering to identify the correct objects. Likewise, methods relying on auxiliary information are suboptimal as such information is often unavailable for most natural scenes. To address these limitations, we propose a method that explicitly optimizes the constraint that each object in a scene should be mapped to a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. We refer to them as the \textit{cycle-consistency} objectives. By applying these consistency objectives to various existing slot-based object-centric methods, we demonstrate significant enhancements in object-discovery performance. These improvements are consistent across both synthetic and real-world scenes, highlighting the effectiveness and generalizability of the proposed approach. Furthermore, our experiments show that the learned slots from the proposed method exhibit superior suitability for downstream reinforcement learning (RL) tasks.	['Yoshua Bengio', 'Anirudh Goyal', 'Aniket Didolkar']	2023-06-03	null	null	null	null	['object-discovery']	['computer-vision']	[ 1.60805941e-01 1.10021934e-01 -3.66401434e-01 -3.55938107e-01 -4.89325196e-01 -3.23721975e-01 7.32449114e-01 2.52099663e-01 -3.10038298e-01 8.05408299e-01 4.27408330e-02 -1.42126918e-01 -4.51966077e-01 -8.15103412e-01 -9.06280398e-01 -6.23765767e-01 -1.16985053e-01 3.89625072e-01 4.38599110e-01 -2.72529162e-02 5.21979451e-01 6.06585205e-01 -1.96633697e+00 -1.13230934e-02 7.45038569e-01 1.13182294e+00 6.85354114e-01 2.20045283e-01 -4.80800539e-01 7.44255602e-01 -7.06836402e-01 -4.89963591e-02 3.99584681e-01 -2.16748863e-01 -8.26367080e-01 3.12037498e-01 3.55336338e-01 -3.75694960e-01 -2.39169389e-01 8.57492328e-01 1.39476404e-01 4.40936983e-01 5.31951427e-01 -1.41893852e+00 -6.71568751e-01 3.25906843e-01 -5.78637719e-01 4.14805055e-01 -1.08064726e-01 1.69518977e-01 1.21129227e+00 -8.02937508e-01 5.93918264e-01 1.20274854e+00 2.47493356e-01 4.63261962e-01 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d0840ec1-9227-4e86-8590-90dcfa1761f8	a-structured-learning-approach-with-neural	1807.09119	null	http://arxiv.org/abs/1807.09119v2	http://arxiv.org/pdf/1807.09119v2.pdf	A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging	Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal. Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.	['Jaideep Srivastava', 'Swaraj Khadanga', 'Louis Kazaglis', 'Shafiq R. Joty', 'Karan Aggarwal']	2018-07-23	null	null	null	null	['sleep-staging']	['medical']	[ 8.03303048e-02 -1.87092140e-01 -2.24697888e-01 -3.03270340e-01 -5.77819422e-02 -2.72661984e-01 -3.63542698e-02 -5.11870123e-02 -4.90360737e-01 5.59964001e-01 4.68500674e-01 -3.39324176e-01 -5.28367907e-02 -5.39343774e-01 1.40601909e-03 -7.96605468e-01 -1.92862779e-01 1.02901921e-01 2.30678156e-01 -6.10458516e-02 5.43876402e-02 3.34141642e-01 -1.45482314e+00 8.47794563e-02 8.00571799e-01 1.15210593e+00 1.97445288e-01 9.61402714e-01 2.80947313e-02 5.91829777e-01 -4.49277937e-01 2.51897007e-01 1.11375578e-01 -8.19255173e-01 -6.48946285e-01 -1.73018605e-01 -1.31578118e-01 -1.21585488e-01 -2.46328637e-01 6.68650627e-01 7.02376842e-01 2.84268469e-01 2.34894633e-01 -8.87991905e-01 -2.68842205e-02 1.60116777e-01 1.64398924e-02 8.89552593e-01 1.07403994e-01 3.20501119e-01 8.48241091e-01 -5.29658496e-01 -4.42901626e-02 5.33059537e-01 7.53415883e-01 8.80765736e-01 -1.22730231e+00 -6.02354527e-01 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1.65516391e-01 1.05921900e+00 -5.68095207e-01 -7.24755228e-01 8.96019265e-02]	[13.582651138305664, 3.461106777191162]
cacaa741-174d-419b-9784-99f3854efd6d	algorithm-unrolling-based-distributed	2301.02360	null	https://arxiv.org/abs/2301.02360v1	https://arxiv.org/pdf/2301.02360v1.pdf	Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks	The user-centric cell-free network has emerged as an appealing technology to improve the next-generation wireless network's capacity thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D-ADMM) network framework. To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D-ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through data-driven end-to-end training.	['Chau Yuen', 'Lu Gan', 'Hongbin Li', 'Jiancheng An', 'Wangyang Xu']	2023-01-06	null	null	null	null	['distributed-optimization']	['methodology']	[ 1.09981097e-01 3.06403935e-01 -1.61620200e-01 2.16034025e-01 -4.55362856e-01 -3.03470552e-01 -1.39807776e-01 -3.58676404e-01 -1.57966286e-01 1.08073473e+00 -1.04516245e-01 -6.58794582e-01 -4.95792598e-01 -1.00543058e+00 -5.61438382e-01 -1.10062158e+00 -3.26687306e-01 1.54663965e-01 -4.12383199e-01 -5.50940871e-01 -1.64208308e-01 4.24246460e-01 -5.78236639e-01 -3.47275883e-01 1.13033056e+00 1.24689865e+00 2.91494519e-01 3.11656445e-01 1.87603429e-01 5.53132176e-01 -3.86447370e-01 -2.65417725e-01 3.96929204e-01 -4.53054905e-01 -3.16461593e-01 3.58867109e-01 -2.69391984e-01 -2.30266541e-01 -6.67294025e-01 7.47006595e-01 9.05167460e-01 -5.64109571e-02 2.63462633e-01 -1.18660939e+00 -1.53671548e-01 5.43588161e-01 -9.55897689e-01 -9.57688838e-02 -2.09523384e-02 -1.40459746e-01 6.18492544e-01 -9.00106370e-01 2.73259073e-01 8.01038146e-01 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95027e2e-4164-421c-b06e-7e815e135b27	panoramic-video-salient-object-detection-with	2211.14419	null	https://arxiv.org/abs/2211.14419v1	https://arxiv.org/pdf/2211.14419v1.pdf	Panoramic Video Salient Object Detection with Ambisonic Audio Guidance	Video salient object detection (VSOD), as a fundamental computer vision problem, has been extensively discussed in the last decade. However, all existing works focus on addressing the VSOD problem in 2D scenarios. With the rapid development of VR devices, panoramic videos have been a promising alternative to 2D videos to provide immersive feelings of the real world. In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios. A multimodal fusion module equipped with two pseudo-siamese audio-visual context fusion (ACF) blocks is proposed to effectively conduct audio-visual interaction. The ACF block equipped with spherical positional encoding enables the fusion in the 3D context to capture the spatial correspondence between pixels and sound sources from the equirectangular frames and ambisonic audios. Experimental results verify the effectiveness of our proposed components and demonstrate that our method achieves state-of-the-art performance on the ASOD60K dataset.	['Bhiksha Raj', 'Li Zhang', 'Junlin Li', 'Shijie Zhao', 'Haoyuan Cao', 'Xiang Li']	2022-11-26	null	null	null	null	['video-salient-object-detection']	['computer-vision']	[ 3.02687377e-01 -5.04076958e-01 1.38732806e-01 2.55704463e-01 -8.15026939e-01 -1.68583170e-01 5.45041680e-01 -1.82925329e-01 -1.57950714e-01 3.28496635e-01 4.59764481e-01 4.06692088e-01 1.79868098e-02 -1.76147163e-01 -6.16342604e-01 -8.96344423e-01 -9.25665647e-02 -3.49574268e-01 5.92679560e-01 -2.08249629e-01 3.77582848e-01 1.68659106e-01 -2.19165206e+00 3.65999877e-01 5.58890760e-01 1.24117422e+00 7.42463708e-01 8.40501189e-01 1.24164730e-01 7.97721803e-01 -4.06145245e-01 -8.17851648e-02 2.51562238e-01 -2.51800269e-01 -1.84650257e-01 1.15847938e-01 9.01244581e-01 -2.47209013e-01 -5.43164730e-01 1.27770674e+00 6.38550103e-01 3.05350035e-01 2.65110821e-01 -1.56437218e+00 -2.58617163e-01 4.89802152e-01 -1.00862134e+00 6.37630701e-01 7.29620695e-01 -1.95397556e-01 9.55621004e-01 -1.28527474e+00 6.44704521e-01 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9dff1118-685e-41f9-b670-e0d8f4369cd9	contextual-bandits-with-budgeted-information	2305.18511	null	https://arxiv.org/abs/2305.18511v1	https://arxiv.org/pdf/2305.18511v1.pdf	Contextual Bandits with Budgeted Information Reveal	Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a limited budget to encourage patients to take these actions and collect additional information. We introduce a novel optimization and learning algorithm to address this problem. This algorithm effectively combines the strengths of two algorithmic approaches in a seamless manner, including 1) an online primal-dual algorithm for deciding the optimal timing to reach out to patients, and 2) a contextual bandit learning algorithm to deliver personalized treatment to the patient. We prove that this algorithm admits a sub-linear regret bound. We illustrate the usefulness of this algorithm on both synthetic and real-world data.	['Susan Murphy', 'Xueqing Liu', 'Esmaeil Keyvanshokooh', 'Kyra Gan']	2023-05-29	null	null	null	null	['multi-armed-bandits']	['miscellaneous']	[ 4.77729231e-01 4.42920178e-01 -1.14166641e+00 -2.94569582e-01 -1.02947187e+00 -4.92907524e-01 -3.28691327e-03 3.55364740e-01 -1.99159503e-01 9.12686110e-01 5.81284165e-01 -5.92221081e-01 -6.87964022e-01 -7.02751160e-01 -6.45499706e-01 -8.23090136e-01 1.44512728e-01 5.67113817e-01 -4.18790251e-01 3.43859732e-01 3.95096466e-02 2.73645073e-01 -7.23750114e-01 4.68268245e-01 8.99930775e-01 9.46433365e-01 -3.79154496e-02 4.84387875e-01 -1.23615656e-02 7.52683342e-01 -4.05975804e-02 -3.74596685e-01 4.58690375e-01 -6.33672893e-01 -8.96335661e-01 4.56549168e-01 -6.78769648e-02 -9.63249505e-01 -2.88679749e-01 7.36380219e-01 4.26790625e-01 2.89937798e-02 3.95260692e-01 -8.96040380e-01 -2.07156807e-01 8.31509113e-01 -6.46675289e-01 6.79108873e-03 4.47685421e-01 1.56151101e-01 1.11691141e+00 2.14225814e-01 3.72724444e-01 1.04502010e+00 4.16088670e-01 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121ae177-b214-4c93-8722-1634a5605a26	fedhm-efficient-federated-learning-for	2111.14655	null	https://arxiv.org/abs/2111.14655v2	https://arxiv.org/pdf/2111.14655v2.pdf	FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization	One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learning framework should address the heterogeneity that clients have different computing capacities and communication capabilities. To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model. Our solution enables the training of heterogeneous models with varying computational complexities and aggregates them into a single global model. Furthermore, FedHM significantly reduces the communication cost by using low-rank models. Extensive experimental results demonstrate that FedHM is superior in the performance and robustness of models of different sizes, compared with state-of-the-art heterogeneous FL methods under various FL settings. Additionally, the convergence guarantee of FL for heterogeneous devices is first theoretically analyzed.	['Lichao Sun', 'Yao Wan', 'Yutong Dai', "Michael J O'Neill", 'Hai Jin', 'Wanning Pan', 'Dezhong Yao']	2021-11-29	null	null	null	null	['low-rank-compression']	['computer-code']	[-1.70620576e-01 -1.79540947e-01 -6.13304317e-01 -2.98556119e-01 -7.91497707e-01 -3.05460304e-01 1.47604749e-01 -2.92472094e-01 1.26382023e-01 6.90920591e-01 -1.11503355e-01 -3.27744931e-02 -4.76581216e-01 -7.65925705e-01 -7.08441079e-01 -8.34215403e-01 -1.91539656e-02 5.96437395e-01 1.06545217e-01 4.04174328e-01 -3.02729875e-01 4.81241077e-01 -1.72494459e+00 3.82230341e-01 7.17408836e-01 1.54424000e+00 3.45141560e-01 4.02834058e-01 -2.15370134e-01 1.25053346e+00 -3.19929004e-01 -7.01302409e-01 4.51337039e-01 1.03357017e-01 -7.60154843e-01 -2.23272189e-01 6.26668274e-01 -5.59495091e-01 -7.14272141e-01 1.08422744e+00 5.12492120e-01 -2.49589458e-01 8.47289637e-02 -1.36689699e+00 -1.96764246e-01 1.04471457e+00 -6.99878111e-02 -2.25278676e-01 -1.00276344e-01 -1.98148549e-01 9.26074862e-01 -6.45737171e-01 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c66fa5ae-b17e-4484-bba1-11d2dde1730a	uniex-an-effective-and-efficient-framework	2305.10306	null	https://arxiv.org/abs/2305.10306v3	https://arxiv.org/pdf/2305.10306v3.pdf	UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective	We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, which uniformly disassembles all extraction targets into joint span detection, classification and association problems with a unified extractive framework, namely UniEX. UniEX can synchronously encode schema-based prompt and textual information, and collaboratively learn the generalized knowledge from pre-defined information using the auto-encoder language models. We develop a traffine attention mechanism to integrate heterogeneous factors including tasks, labels and inside tokens, and obtain the extraction target via a scoring matrix. Experiment results show that UniEX can outperform generative universal IE models in terms of performance and inference-speed on $14$ benchmarks IE datasets with the supervised setting. The state-of-the-art performance in low-resource scenarios also verifies the transferability and effectiveness of UniEX.	['Pingjian Zhang', 'Jiaxing Zhang', 'Yuxiang Zhang', 'Junjie Wang', 'Ruyi Gan', 'Ping Yang', 'Junyu Lu']	2023-05-17	null	null	null	null	['event-extraction', 'relation-extraction']	['natural-language-processing', 'natural-language-processing']	[ 1.99935034e-01 1.10771835e-01 -5.15582383e-01 -3.19942594e-01 -1.25122356e+00 -5.82210958e-01 4.87271786e-01 2.01033100e-01 -4.95902628e-01 8.58901262e-01 4.76477563e-01 -2.38986313e-01 -7.19187334e-02 -1.09889996e+00 -1.00517702e+00 -3.05382967e-01 5.20163625e-02 6.79533005e-01 1.89204421e-03 7.42906611e-03 -2.73902953e-01 2.33573407e-01 -1.25632572e+00 5.93517363e-01 7.91604996e-01 1.32245111e+00 7.03094602e-02 5.36108077e-01 -4.94417876e-01 1.06842482e+00 -5.65007448e-01 -9.82842386e-01 5.66614233e-02 -7.16550509e-03 -1.30103922e+00 -3.09594303e-01 -1.29937053e-01 -1.57347143e-01 -6.33950353e-01 7.05473185e-01 6.87671959e-01 -3.14744383e-01 6.72120571e-01 -1.37553167e+00 -6.07905865e-01 1.38321888e+00 -4.50470775e-01 1.57147601e-01 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dd414345-1750-4712-a12e-dcece6476bd7	lahm-large-annotated-dataset-for-multi-domain	2304.00913	null	https://arxiv.org/abs/2304.00913v1	https://arxiv.org/pdf/2304.00913v1.pdf	LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identification	Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual hate speech analysis dataset for English, Hindi, Arabic, French, German and Spanish languages for multiple domains across hate speech - Abuse, Racism, Sexism, Religious Hate and Extremism. To the best of our knowledge, this paper is the first to address the problem of identifying various types of hate speech in these five wide domains in these six languages. In this work, we describe how we created the dataset, created annotations at high level and low level for different domains and how we use it to test the current state-of-the-art multilingual and multitask learning approaches. We evaluate our dataset in various monolingual, cross-lingual and machine translation classification settings and compare it against open source English datasets that we aggregated and merged for this task. Then we discuss how this approach can be used to create large scale hate-speech datasets and how to leverage our annotations in order to improve hate speech detection and classification in general.	['Anil Bandhakavi', 'Sushant Chatufale', 'Shubham Chandel', 'Ankit Yadav']	2023-04-03	null	null	null	null	['hate-speech-detection']	['natural-language-processing']	[-2.67451853e-01 -3.96382064e-01 -3.54637462e-03 2.34533492e-02 -9.95870411e-01 -9.85162020e-01 1.02574348e+00 2.40540937e-01 -3.74640375e-01 8.71009111e-01 3.68386030e-01 -1.53990388e-01 3.47852856e-01 -5.52610457e-02 -2.83769578e-01 -5.62004387e-01 1.74646437e-01 4.95206326e-01 2.22942501e-01 -4.80077267e-01 2.81230718e-01 2.45864287e-01 -1.08049881e+00 6.14947557e-01 8.12531710e-01 1.09263882e-01 -3.19612712e-01 7.88641751e-01 1.74474731e-01 1.28818774e+00 -1.10096312e+00 -8.89842391e-01 -1.79401204e-01 -1.67328864e-01 -1.10326302e+00 -6.28007948e-02 8.98288548e-01 -3.43504578e-01 -2.34636441e-01 1.00515234e+00 9.94185925e-01 -1.53535530e-01 8.20000648e-01 -1.19942164e+00 -1.06469953e+00 6.72168076e-01 -6.44416213e-01 5.81061244e-01 5.03306150e-01 -2.53372714e-02 5.08136213e-01 -9.76413190e-01 9.31964040e-01 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1a3d7603-cdfe-4daa-9f3a-52688121d126	spain-net-spatially-informed-stereophonic	2202.07523	null	https://arxiv.org/abs/2202.07523v1	https://arxiv.org/pdf/2202.07523v1.pdf	SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation	With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem. While existing deep learning models implicitly absorb the spatial information conveyed by the multi-channel input signals, we argue that a more explicit and active use of spatial information could not only improve the separation process but also provide an entry-point for many user-interaction based tools. To this end, we introduce a control method based on the stereophonic location of the sources of interest, expressed as the panning angle. We present various conditioning mechanisms, including the use of raw angle and its derived feature representations, and show that spatial information helps. Our proposed approaches improve the separation performance compared to location agnostic architectures by 1.8 dB SI-SDR in our Slakh-based simulated experiments. Furthermore, the proposed methods allow for the disentanglement of same-class instruments, for example, in mixtures containing two guitar tracks. Finally, we also demonstrate that our approach is robust to incorrect source panning information, which can be incurred by our proposed user interaction.	['Minje Kim', 'Darius Petermann']	2022-02-15	null	null	null	null	['music-source-separation']	['music']	[ 2.80833393e-01 -4.09916639e-01 -1.55683577e-01 -1.98127121e-01 -9.54166949e-01 -8.24282110e-01 5.06567597e-01 -2.04379857e-02 -1.38128489e-01 4.24886376e-01 2.58609205e-01 3.30126435e-02 -7.69766212e-01 -4.98202145e-01 -4.84361380e-01 -8.37291956e-01 1.27336279e-01 2.34070882e-01 -2.15545461e-01 -4.00788069e-01 2.38610163e-01 6.21141911e-01 -1.79190445e+00 2.08775192e-01 7.71728039e-01 1.15121830e+00 6.84783608e-02 8.00997615e-01 -5.48746325e-02 4.88209575e-01 -9.81501222e-01 7.54661337e-02 3.85789454e-01 -6.02764070e-01 -2.92308182e-01 -2.30610654e-01 5.39879024e-01 -2.19902337e-01 -5.27382419e-02 1.01902747e+00 8.39803755e-01 1.88909501e-01 6.29139185e-01 -1.19988310e+00 -2.38564447e-01 1.02029145e+00 -6.21910393e-01 -8.15131664e-02 1.27553999e-01 -2.44986355e-01 1.28918540e+00 -8.21001232e-01 1.18932799e-01 8.24475348e-01 7.07697332e-01 3.96448784e-02 -1.44647372e+00 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f57cdec9-b7e5-4b53-b611-ce9ecae32a0e	context-aware-block-net-for-small-object	null	null	https://ieeexplore.ieee.org/abstract/document/9151360	https://ieeexplore.ieee.org/abstract/document/9151360	Context-Aware Block Net for Small Object Detection	State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects. In this article, we propose a context-aware block net (CAB Net) to improve small object detection by building high-resolution and strong semantic feature maps. To internally enhance the representation capacity of feature maps with high spatial resolution, we delicately design the context-aware block (CAB). CAB exploits pyramidal dilated convolutions to incorporate multilevel contextual information without losing the original resolution of feature maps. Then, we assemble CAB to the end of the truncated backbone network (e.g., VGG16) with a relatively small downsampling factor (e.g., 8) and cast off all following layers. CAB Net can capture both basic visual patterns as well as semantical information of small objects, thus improving the performance of small object detection. Experiments conducted on the benchmark Tsinghua-Tencent 100K and the Airport dataset show that CAB Net outperforms other top-performing detectors by a large margin while keeping real-time speed, which demonstrates the effectiveness of CAB Net for small object detection.	['Mingliang Xu', 'Ling Shao', 'Luming Zhang', 'Bing Zhou', 'Zhimin Gao', 'Xiaoheng Jiang', 'Pei Lv', 'Lisha Cui']	2023-04-01	null	null	null	ieee-transactions-on-cybernetics-2023-4	['traffic-sign-detection', 'small-object-detection']	['computer-vision', 'computer-vision']	[-4.47990978e-03 -2.92406291e-01 2.37412259e-01 -2.64134884e-01 -2.34888718e-01 -5.02873778e-01 4.52231407e-01 2.74063665e-02 -5.96170962e-01 2.35604629e-01 -5.62886223e-02 -2.24948391e-01 4.13652137e-02 -1.06851089e+00 -8.00237715e-01 -6.02517903e-01 1.04826413e-01 -2.12366655e-01 1.10769904e+00 -2.69190967e-01 1.45000935e-01 6.86033010e-01 -1.56945705e+00 6.32230282e-01 7.67362237e-01 1.25858533e+00 6.89419925e-01 4.18461502e-01 8.77402350e-02 6.26878917e-01 -5.93751132e-01 -1.01013571e-01 4.68455195e-01 6.66814968e-02 -5.45000732e-01 -1.01445825e-03 5.90190351e-01 -5.66002846e-01 -5.73914587e-01 1.04842567e+00 3.13057303e-01 1.81437358e-01 2.39818618e-01 -1.05219114e+00 -6.89056158e-01 5.02328277e-01 -8.22189033e-01 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ddf2d225-5b6e-4c37-aa01-c5a9ac32edca	activitynet-2019-task-3-exploring-contexts	1907.05092	null	https://arxiv.org/abs/1907.05092v1	https://arxiv.org/pdf/1907.05092v1.pdf	Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos	Contextual reasoning is essential to understand events in long untrimmed videos. In this work, we systematically explore different captioning models with various contexts for the dense-captioning events in video task, which aims to generate captions for different events in the untrimmed video. We propose five types of contexts as well as two categories of event captioning models, and evaluate their contributions for event captioning from both accuracy and diversity aspects. The proposed captioning models are plugged into our pipeline system for the dense video captioning challenge. The overall system achieves the state-of-the-art performance on the dense-captioning events in video task with 9.91 METEOR score on the challenge testing set.	['Alexander Hauptmann', 'Jianlong Fu', 'Shizhe Chen', 'Yuqing Song', 'Yida Zhao', 'Zhaoyang Zeng', 'Qin Jin', 'Bei Liu']	2019-07-11	null	null	null	null	['dense-captioning', 'dense-video-captioning']	['computer-vision', 'computer-vision']	[ 4.30423558e-01 1.54563785e-01 -1.37779284e-02 -5.31179190e-01 -1.17162251e+00 -5.06745994e-01 8.46098602e-01 -1.97076187e-01 -1.37767553e-01 8.38278890e-01 9.70313847e-01 8.31069797e-02 4.74530667e-01 -2.87028939e-01 -1.26535690e+00 -4.42881465e-01 -1.31438911e-01 4.68761474e-01 2.67927468e-01 2.44862214e-02 -1.88601002e-01 -2.14219093e-02 -1.43460000e+00 1.20130289e+00 3.87073636e-01 8.86062622e-01 9.56979245e-02 1.15058565e+00 1.36188582e-01 1.35879517e+00 -7.68550158e-01 -6.22364938e-01 -1.13319069e-01 -8.10542822e-01 -7.38855958e-01 8.41288492e-02 8.66067648e-01 -5.08557439e-01 -9.96564269e-01 6.29405379e-01 3.95679146e-01 -2.58612093e-02 5.62017500e-01 -1.50599480e+00 -1.05629528e+00 9.11544085e-01 -2.95802832e-01 8.55233371e-01 7.78065801e-01 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8.43125165e-01 9.22251701e-01 6.54455662e-01 1.50905386e-01 -9.89098251e-01 1.45966813e-01 -1.07848966e+00 1.48781940e-01 9.10533667e-01 3.53866339e-01 -5.06901979e-01 -3.68365586e-01 8.17913234e-01]	[10.482078552246094, 0.7331781387329102]
778f13d4-fdd7-4562-ab91-edd298a0e03d	robust-pivoting-manipulation-using-contact	2303.08965	null	https://arxiv.org/abs/2303.08965v1	https://arxiv.org/pdf/2303.08965v1.pdf	Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization	Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this paper, we study robust optimization for planning of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for inaccuracies in the estimates of the physical properties during manipulation. Under certain assumptions, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a Contact Implicit Bilevel Optimization (CIBO) framework to optimize a trajectory that maximizes this stability margin to provide robustness against uncertainty in several physical parameters of the object. We present analysis of the stability margin with respect to several parameters involved in the underlying bilevel optimization problem. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects.	['Arvind U. Raghunathan', 'Devesh K. Jha', 'Yuki Shirai']	2023-03-15	null	null	null	null	['bilevel-optimization']	['methodology']	[ 3.01728700e-03 3.46078157e-01 -2.55728692e-01 -9.37524438e-03 -2.12628707e-01 -7.66602159e-01 3.97971272e-01 2.69516230e-01 -4.28785026e-01 9.47486877e-01 -3.19262803e-01 6.16241172e-02 -1.10021937e+00 -4.16190237e-01 -1.00833070e+00 -8.03056300e-01 -2.29309425e-01 8.08125913e-01 8.90444778e-03 -4.36670482e-01 4.00012434e-01 9.97962117e-01 -1.05705702e+00 -5.72782516e-01 8.22782040e-01 5.76448023e-01 7.00275958e-01 6.93756580e-01 6.21230960e-01 1.66227072e-01 -3.37017447e-01 1.76670551e-01 5.61681271e-01 2.49325708e-01 -6.04703248e-01 2.90886760e-01 -3.78962606e-01 -2.19867334e-01 -1.48824930e-01 1.07013428e+00 1.89460754e-01 9.25494313e-01 9.66126502e-01 -1.29417455e+00 -1.63290240e-02 5.68292022e-01 -2.92315453e-01 -1.62466139e-01 2.25363195e-01 2.36565173e-01 4.99030232e-01 -7.33477533e-01 8.63033831e-01 1.22931170e+00 2.58165956e-01 1.13835737e-01 -9.63004827e-01 7.04234641e-04 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5.26382446e-01 -1.52610266e+00 -4.27531034e-01 -1.88687190e-01]	[4.861684799194336, 1.462216854095459]
f23bfd21-1225-464e-9b49-6a82bde1156d	drlcomplex-reconstruction-of-protein	2205.13594	null	https://arxiv.org/abs/2205.13594v1	https://arxiv.org/pdf/2205.13594v1.pdf	DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning	Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted inter-chain contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning (DRLComplex) to build protein complex structures using inter-chain contacts as distance constraints. We rigorously tested DRLComplex on two standard datasets of homodimeric and heterodimeric protein complexes (i.e., the CASP-CAPRI homodimer and Std_32 heterodimer datasets) using both true and predicted interchain contacts as inputs. Utilizing true contacts as input, DRLComplex achieved high average TM-scores of 0.9895 and 0.9881 and a low average interface RMSD (I_RMSD) of 0.2197 and 0.92 on the two datasets, respectively. When predicted contacts are used, the method achieves TM-scores of 0.73 and 0.76 for homodimers and heterodimers, respectively. Our experiments find that the accuracy of reconstructed quaternary structures depends on the accuracy of the contact predictions. Compared to other optimization methods for reconstructing quaternary structures from inter-chain contacts, DRLComplex performs similar to an advanced gradient descent method and better than a Markov Chain Monte Carlo simulation method and a simulated annealing-based method, validating the effectiveness of DRLComplex for quaternary reconstruction of protein complexes.	['Jianlin Cheng', 'Alex Morehead', 'Nabin Giri', 'Farhan Quadir', 'Raj S. Roy', 'Elham Soltanikazemi']	2022-05-26	null	null	null	null	['self-learning']	['natural-language-processing']	[-1.68153882e-01 -1.53854741e-02 -1.47942156e-01 -1.30974829e-01 -7.45943844e-01 -5.69755256e-01 5.17344594e-01 3.49126577e-01 -6.44711077e-01 1.70417988e+00 -2.10375488e-01 -5.73778093e-01 2.02466711e-01 -5.23655355e-01 -1.06136680e+00 -1.02856767e+00 -7.18848258e-02 1.03932285e+00 3.51538002e-01 -4.14616168e-01 1.79847643e-01 5.20641327e-01 -1.24956775e+00 3.28667790e-01 1.25085592e+00 3.74233425e-01 3.42746645e-01 5.81085503e-01 6.22735210e-02 3.29453826e-01 -4.92022842e-01 -3.74386311e-01 1.75315067e-02 -6.35639668e-01 -6.82781100e-01 -4.76599604e-01 4.58987504e-02 6.71945214e-02 6.04647845e-02 3.88385266e-01 7.80597389e-01 -2.29012325e-01 8.20510685e-01 -6.70120299e-01 -3.06029487e-02 2.34525919e-01 -4.38391745e-01 -6.39524385e-02 5.72315753e-01 7.01407015e-01 1.05756319e+00 -6.10660255e-01 1.19434524e+00 1.04494750e+00 9.52215731e-01 4.65523154e-01 -1.92580700e+00 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9adaa176-ba6a-4cf1-8ab9-074629ca8011	domain-adaptation-for-deep-entity-resolution	null	null	https://dl.acm.org/doi/10.1145/3514221.3517870	https://dl.acm.org/doi/pdf/10.1145/3514221.3517870	Domain Adaptation for Deep Entity Resolution: A Design Space Exploration	Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-world ER applications, the situation changes dramatically, with a painful issue to collect large-scale labeled datasets. In this paper, we seek to answer: If we have a well-labeled source ER dataset, can we train a DL-based ER model for a target dataset, without any labels or with a few labels? This is known as domain adaptation (DA), which has achieved great successes in computer vision and natural language processing, but is not systematically studied for ER. Our goal is to systematically explore the benefits and limitations of a wide range of DA methods for ER. To this purpose, we develop a DADER (Domain Adaptation for Deep Entity Resolution) framework that significantly advances ER in applying DA. We define a space of design solutions for the three modules of DADER, namely Feature Extractor, Matcher, and Feature Aligner. We conduct so far the most comprehensive experimental study to explore the design space and compare different choices of DA for ER. We provide guidance for selecting appropriate design solutions based on extensive experiments.	['Xiaoyong Du', 'Ruixue Fan', 'Guoliang Li', 'Chengliang Chai', 'Peng Wang', 'Nan Tang', 'Ju Fan', 'Jianhong Tu']	2022-06-01	null	null	null	sigmod-pods-2022-6	['data-integration', 'entity-resolution']	['knowledge-base', 'natural-language-processing']	[-1.06902830e-01 1.27899259e-01 -3.72667402e-01 -4.96265680e-01 -7.78079450e-01 -4.47872400e-01 4.74054426e-01 1.74623758e-01 -5.97426355e-01 8.64990652e-01 1.44526392e-01 -8.93481541e-03 -1.40392780e-01 -9.12155211e-01 -5.84038138e-01 -2.60294735e-01 8.69623795e-02 8.82407308e-01 1.55522794e-01 -5.24087608e-01 -9.83843729e-02 6.15105927e-01 -1.43884909e+00 2.96738058e-01 1.06446528e+00 7.32671678e-01 1.99733432e-02 1.59917325e-01 -5.21021426e-01 5.92506647e-01 -7.36210704e-01 -8.28200400e-01 3.05977970e-01 -2.08450913e-01 -1.03633392e+00 -5.20884097e-01 3.23944151e-01 -4.09727059e-02 -9.93347019e-02 1.01497114e+00 8.38589370e-01 2.64181327e-02 5.32628179e-01 -1.43460202e+00 -8.22888076e-01 9.29066420e-01 -5.15864253e-01 1.47845641e-01 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ab334a96-fd57-44b9-9470-f3994a5076f2	investigating-the-translation-performance-of	2303.01911	null	https://arxiv.org/abs/2303.01911v2	https://arxiv.org/pdf/2303.01911v2.pdf	Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM	The NLP community recently saw the release of a new large open-access multilingual language model, BLOOM (BigScience et al., 2022) covering 46 languages. We focus on BLOOM's multilingual ability by evaluating its machine translation performance across several datasets (WMT, Flores-101 and DiaBLa) and language pairs (high- and low-resourced). Our results show that 0-shot performance suffers from overgeneration and generating in the wrong language, but this is greatly improved in the few-shot setting, with very good results for a number of language pairs. We study several aspects including prompt design, model sizes, cross-lingual transfer and the use of discursive context.	['François Yvon', 'Rachel Bawden']	2023-03-03	null	null	null	null	['cross-lingual-transfer']	['natural-language-processing']	[-6.29842460e-01 -1.59446761e-01 -6.24986410e-01 -9.44942683e-02 -1.49984324e+00 -1.05575144e+00 1.00810981e+00 6.12191036e-02 -8.02788675e-01 1.27093840e+00 7.62896836e-01 -6.55960739e-01 9.66794938e-02 -2.43480757e-01 -6.95352554e-01 3.15217637e-02 1.38844416e-01 9.48410451e-01 -1.33177251e-01 -5.99157810e-01 -2.43713316e-02 -7.83954412e-02 -8.57720494e-01 3.72378796e-01 1.29509270e+00 -6.66767433e-02 4.08412993e-01 4.09272790e-01 -2.35910371e-01 7.23458230e-01 -4.11994308e-01 -8.83807957e-01 3.16415250e-01 -2.12609693e-01 -1.05019999e+00 -5.84574878e-01 5.31232953e-01 -8.36006179e-02 -1.84344295e-02 8.17479134e-01 9.77690637e-01 -9.07907709e-02 4.59569305e-01 -1.02102232e+00 -1.14699948e+00 1.37424421e+00 -3.29257458e-01 2.76386946e-01 8.72124612e-01 3.98754925e-01 1.17764342e+00 -1.19836235e+00 1.28024900e+00 1.53748000e+00 9.30959463e-01 3.48902434e-01 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51334d0e-5424-4b64-82b4-08014fb26ad3	brain-tumor-segmentation-using-synthetic-mr	2306.02986	null	https://arxiv.org/abs/2306.02986v1	https://arxiv.org/pdf/2306.02986v1.pdf	Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models	Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation (e.g. the general data protection regulation (GDPR)). Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing as GDPR should not apply for medical images which do not belong to a specific person. However, in order to share synthetic images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation. Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80\% - 90\% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Furthermore, we demonstrate that common metrics for evaluating synthetic images, Fr\'echet inception distance (FID) and inception score (IS), do not correlate well with the obtained performance when using the synthetic images for training segmentation networks.	['Anders Eklund', 'Måns Larsson', 'Muhammad Usman Akbar']	2023-06-05	null	null	null	null	['tumor-segmentation', 'brain-tumor-segmentation', 'ethics', 'memorization']	['computer-vision', 'medical', 'miscellaneous', 'natural-language-processing']	[ 5.12003481e-01 5.79297662e-01 1.66479379e-01 -3.28172505e-01 -5.72419941e-01 -5.72307169e-01 5.05449533e-01 -1.89574435e-01 -7.29349375e-01 1.08630323e+00 -2.19707210e-02 -4.46283817e-01 1.83756486e-01 -7.33607769e-01 -6.37212813e-01 -6.80485427e-01 2.09042832e-01 5.65571249e-01 -1.25284374e-01 -1.48797911e-02 -2.07574040e-01 6.55627012e-01 -7.98009098e-01 2.31955707e-01 1.09332204e+00 8.88107657e-01 -1.31315485e-01 6.66800499e-01 7.87345394e-02 7.44488716e-01 -1.16515923e+00 -6.87364042e-01 5.47739327e-01 -9.12120104e-01 -8.83125424e-01 -1.41038105e-01 2.87830889e-01 -4.90220636e-01 -3.62447500e-01 1.22775054e+00 4.96725440e-01 -2.97896206e-01 8.23275268e-01 -1.33265400e+00 -9.14786696e-01 6.85049176e-01 -3.60193223e-01 6.06012307e-02 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5e277c35-1c6e-499f-8131-e7a03689a142	cate-embedding-mathcal-alc-ontologies-using	2305.07163	null	https://arxiv.org/abs/2305.07163v1	https://arxiv.org/pdf/2305.07163v1.pdf	CatE: Embedding $\mathcal{ALC}$ ontologies using category-theoretical semantics	Machine learning with Semantic Web ontologies follows several strategies, one of which involves projecting ontologies into graph structures and applying graph embeddings or graph-based machine learning methods to the resulting graphs. Several methods have been developed that project ontology axioms into graphs. However, these methods are limited in the type of axioms they can project (totality), whether they are invertible (injectivity), and how they exploit semantic information. These limitations restrict the kind of tasks to which they can be applied. Category-theoretical semantics of logic languages formalizes interpretations using categories instead of sets, and categories have a graph-like structure. We developed CatE, which uses the category-theoretical formulation of the semantics of the Description Logic $\mathcal{ALC}$ to generate a graph representation for ontology axioms. The CatE projection is total and injective, and therefore overcomes limitations of other graph-based ontology embedding methods which are generally not invertible. We apply CatE to a number of different tasks, including deductive and inductive reasoning, and we demonstrate that CatE improves over state of the art ontology embedding methods. Furthermore, we show that CatE can also outperform model-theoretic ontology embedding methods in machine learning tasks in the biomedical domain.	['Robert Hoehndorf', 'Fernando Zhapa-Camacho']	2023-05-11	null	null	null	null	['ontology-embedding']	['knowledge-base']	[ 1.66746840e-01 7.68168569e-01 -2.56592005e-01 -2.62210429e-01 1.93955570e-01 -6.39106214e-01 7.44134724e-01 3.01207691e-01 -2.69437045e-01 3.37046295e-01 3.66122425e-01 -7.44273841e-01 -5.59915423e-01 -1.48296452e+00 -6.51001632e-01 -1.92745432e-01 -2.03670934e-01 8.24465930e-01 2.76624024e-01 -5.61859846e-01 -2.23809183e-01 1.32657588e-01 -1.50397754e+00 3.44226569e-01 6.32446766e-01 4.87353116e-01 -3.34124506e-01 3.05383027e-01 -5.77573717e-01 1.07490170e+00 -4.01090607e-02 -5.89360178e-01 -2.19921805e-02 -4.20694053e-01 -1.11300933e+00 -2.21660614e-01 2.53562421e-01 1.23007752e-01 -6.99149251e-01 1.26372778e+00 -1.73072904e-01 1.78784113e-02 7.53508031e-01 -1.89921832e+00 -1.30194235e+00 7.38542020e-01 2.01699898e-01 -1.53611317e-01 5.51746905e-01 -5.90109289e-01 1.56347799e+00 -6.99799955e-01 6.72874570e-01 1.81408942e+00 7.03510821e-01 8.65717530e-01 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21f202da-8938-499a-9598-366b82611b52	6-dof-graspnet-variational-grasp-generation	1905.10520	null	https://arxiv.org/abs/1905.10520v2	https://arxiv.org/pdf/1905.10520v2.pdf	6-DOF GraspNet: Variational Grasp Generation for Object Manipulation	Generating grasp poses is a crucial component for any robot object manipulation task. In this work, we formulate the problem of grasp generation as sampling a set of grasps using a variational autoencoder and assess and refine the sampled grasps using a grasp evaluator model. Both Grasp Sampler and Grasp Refinement networks take 3D point clouds observed by a depth camera as input. We evaluate our approach in simulation and real-world robot experiments. Our approach achieves 88\% success rate on various commonly used objects with diverse appearances, scales, and weights. Our model is trained purely in simulation and works in the real world without any extra steps. The video of our experiments can be found at: https://research.nvidia.com/publication/2019-10_6-DOF-GraspNet\%3A-Variational	['Dieter Fox', 'Clemens Eppner', 'Arsalan Mousavian']	2019-05-25	6-dof-graspnet-variational-grasp-generation-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Mousavian_6-DOF_GraspNet_Variational_Grasp_Generation_for_Object_Manipulation_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Mousavian_6-DOF_GraspNet_Variational_Grasp_Generation_for_Object_Manipulation_ICCV_2019_paper.pdf	iccv-2019-10	['grasp-generation']	['computer-vision']	[-3.49531770e-01 -2.96376590e-02 3.09716035e-02 -2.26670802e-01 -2.58877397e-01 -4.99757111e-01 1.91233288e-02 -1.67934865e-01 -1.11703701e-01 4.82416809e-01 -2.46477544e-01 9.32358950e-02 1.75828606e-01 -9.67310965e-01 -1.15025532e+00 -8.13610196e-01 -2.10715428e-01 1.09941852e+00 6.83304369e-02 -4.99967374e-02 1.20281607e-01 6.35324359e-01 -1.45777667e+00 4.29000519e-02 7.09173083e-01 9.39279437e-01 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7ffa5a0e-08e2-42d5-b11d-d51d8056f8c0	utopic-uncertainty-aware-overlap-prediction	2208.02712	null	https://arxiv.org/abs/2208.02712v6	https://arxiv.org/pdf/2208.02712v6.pdf	UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration	High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inherently exists uncertainty between the overlapping and non-overlapping regions, which has always been neglected and significantly affects the registration performance. Beyond the current wisdom, we propose a novel uncertainty-aware overlap prediction network, dubbed UTOPIC, to tackle the ambiguous overlap prediction problem; to our knowledge, this is the first to explicitly introduce overlap uncertainty to point cloud registration. Moreover, we induce the feature extractor to implicitly perceive the shape knowledge through a completion decoder, and present a geometric relation embedding for Transformer to obtain transformation-invariant geometry-aware feature representations. With the merits of more reliable overlap scores and more precise dense correspondences, UTOPIC can achieve stable and accurate registration results, even for the inputs with limited overlapping areas. Extensive quantitative and qualitative experiments on synthetic and real benchmarks demonstrate the superiority of our approach over state-of-the-art methods.	['Mingqiang Wei', 'Jing Qin', 'Yanwen Guo', 'Jun Wang', 'Xuefeng Yan', 'Lina Gong', 'Honghua Chen', 'Zhilei Chen']	2022-08-04	null	null	null	null	['point-cloud-registration']	['computer-vision']	[ 2.29358226e-02 2.59218037e-01 -1.39853045e-01 -4.43810195e-01 -9.58735526e-01 -4.63931203e-01 5.39035559e-01 4.72473502e-02 7.80141130e-02 3.23120415e-01 7.69057572e-02 1.10347390e-01 -2.93855965e-01 -7.28856146e-01 -8.64090860e-01 -3.04551750e-01 1.73034102e-01 7.26939738e-01 2.69377083e-01 -7.87989944e-02 2.20339671e-01 5.71723878e-01 -1.40894282e+00 -6.11432269e-02 1.33561146e+00 1.01144850e+00 2.28960231e-01 -3.17722440e-01 -5.89408018e-02 -9.94865671e-02 2.18163710e-02 -5.11704683e-01 5.61065912e-01 2.51317859e-01 -6.04790628e-01 3.95695493e-02 5.51116109e-01 -2.82389402e-01 -3.32452178e-01 1.07879698e+00 2.00753927e-01 -6.59866706e-02 6.87092602e-01 -1.17427421e+00 -6.53046548e-01 3.02013665e-01 -8.70472312e-01 -3.71094555e-01 3.69387627e-01 2.22305253e-01 1.07458568e+00 -1.38638008e+00 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7fb6e49a-e691-4a60-9eab-7800224c1030	linear-convergence-of-natural-policy-gradient	2210.01400	null	https://arxiv.org/abs/2210.01400v3	https://arxiv.org/pdf/2210.01400v3.pdf	Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies	We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class. Using the compatible function approximation framework, both methods with log-linear policies can be written as inexact versions of the policy mirror descent (PMD) method. We show that both methods attain linear convergence rates and $\tilde{\mathcal{O}}(1/\epsilon^2)$ sample complexities using a simple, non-adaptive geometrically increasing step size, without resorting to entropy or other strongly convex regularization. Lastly, as a byproduct, we obtain sublinear convergence rates for both methods with arbitrary constant step size.	['Lin Xiao', 'Alessandro Lazaric', 'Robert M. Gower', 'Simon S. Du', 'Rui Yuan']	2022-10-04	null	null	null	null	['policy-gradient-methods']	['methodology']	[-1.04919598e-01 5.07228434e-01 -2.90260464e-01 -1.08081378e-01 -1.04254186e+00 -6.38914168e-01 4.45312500e-01 2.86491632e-01 -1.00395489e+00 1.27525365e+00 -7.39468634e-02 -7.99326181e-01 -2.14934722e-01 -5.07679343e-01 -6.58503354e-01 -8.63788188e-01 -2.58617550e-01 4.92615908e-01 8.59915540e-02 -4.03268486e-02 3.14643115e-01 2.18347251e-01 -1.04983151e+00 -4.28594351e-01 9.76244271e-01 1.39091957e+00 -2.42077872e-01 9.01937366e-01 1.06017478e-01 9.03258562e-01 -1.37887299e-01 -4.78842586e-01 6.80766940e-01 -4.32691664e-01 -7.22775817e-01 -3.41421068e-02 2.38980696e-01 -5.55898488e-01 -3.05404276e-01 1.38671672e+00 5.69791615e-01 5.90805471e-01 6.46755695e-01 -8.39460850e-01 -3.66491079e-01 3.72834772e-01 -5.90967000e-01 4.57416087e-01 -9.59486961e-02 2.03771785e-01 7.80191600e-01 -5.01012027e-01 1.67847440e-01 1.35150981e+00 6.33851886e-01 5.72627127e-01 -1.30429542e+00 -3.16367716e-01 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0a1cd9a0-54aa-4ddb-a9c9-6b12215c9f4d	image-to-video-generation-via-3d-facial	2105.14678	null	https://arxiv.org/abs/2105.14678v1	https://arxiv.org/pdf/2105.14678v1.pdf	Image-to-Video Generation via 3D Facial Dynamics	We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to "imagine" a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.	['Jiashi Feng', 'Wei Liu', 'Zhifeng Li', 'Guodong Guo', 'Zhikang Wang', 'Yuan YAO', 'Jian Dong', 'Wenjie Ai', 'Jian Zhao', 'Yingtian Zou', 'Xiaoguang Tu']	2021-05-31	null	null	null	null	['image-to-video']	['computer-vision']	[ 3.52354825e-01 1.56831771e-01 1.27259269e-01 -2.13962734e-01 -4.24131066e-01 -4.48026806e-01 4.69641984e-01 -1.16217959e+00 3.01958412e-01 7.08232045e-01 2.27213815e-01 4.99498695e-01 3.07981044e-01 -6.36070728e-01 -1.01884878e+00 -9.47978854e-01 3.22130114e-01 1.82795629e-01 -5.69482505e-01 -2.66070724e-01 -6.26496002e-02 8.79641116e-01 -1.88898242e+00 3.34541023e-01 4.31533724e-01 1.11160743e+00 1.54866621e-01 4.49320167e-01 4.76662964e-02 7.60992348e-01 -4.67512220e-01 -5.41920543e-01 3.33966851e-01 -6.61350667e-01 -1.44356504e-01 4.88504350e-01 4.59206879e-01 -5.18555224e-01 -6.86433196e-01 1.10380924e+00 4.04317349e-01 7.21372738e-02 5.51377118e-01 -1.42341065e+00 -8.30349147e-01 -6.69135228e-02 -6.81433558e-01 -3.09437931e-01 9.01301324e-01 2.31500834e-01 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32057bc3-40fc-483b-8323-a98ec3ad7291	snakevoxformer-transformer-based-single-image	2303.16293	null	https://arxiv.org/abs/2303.16293v1	https://arxiv.org/pdf/2303.16293v1.pdf	SnakeVoxFormer: Transformer-based Single Image\\Voxel Reconstruction with Run Length Encoding	Deep learning-based 3D object reconstruction has achieved unprecedented results. Among those, the transformer deep neural model showed outstanding performance in many applications of computer vision. We introduce SnakeVoxFormer, a novel, 3D object reconstruction in voxel space from a single image using the transformer. The input to SnakeVoxFormer is a 2D image, and the result is a 3D voxel model. The key novelty of our approach is in using the run-length encoding that traverses (like a snake) the voxel space and encodes wide spatial differences into a 1D structure that is suitable for transformer encoding. We then use dictionary encoding to convert the discovered RLE blocks into tokens that are used for the transformer. The 1D representation is a lossless 3D shape data compression method that converts to 1D data that use only about 1% of the original data size. We show how different voxel traversing strategies affect the effect of encoding and reconstruction. We compare our method with the state-of-the-art for 3D voxel reconstruction from images and our method improves the state-of-the-art methods by at least 2.8% and up to 19.8%.	['Bedrich Benes', 'Jae Joong Lee']	2023-03-28	null	null	null	null	['3d-object-reconstruction', 'object-reconstruction', 'data-compression']	['computer-vision', 'computer-vision', 'time-series']	[ 1.72375575e-01 2.12071031e-01 9.48828682e-02 -2.66423792e-01 -6.16751552e-01 -2.32871458e-01 4.72266912e-01 1.84737936e-01 -4.31118488e-01 1.88081592e-01 7.94079080e-02 -1.98624283e-01 4.13256511e-02 -1.27764595e+00 -1.20509005e+00 -5.60803592e-01 -3.70230615e-01 7.62067616e-01 6.00640416e-01 3.68925929e-02 2.53577232e-01 1.05280781e+00 -1.49197888e+00 3.28013837e-01 3.51867348e-01 1.46001291e+00 3.91072065e-01 3.71100754e-01 -7.10297823e-01 3.99187475e-01 -2.72847056e-01 -2.19045475e-01 7.07134724e-01 -1.73019752e-01 -8.53876770e-01 1.34533765e-02 6.82659328e-01 -5.70328355e-01 -4.12432104e-01 8.80506337e-01 5.79580784e-01 -2.17415079e-01 8.17503870e-01 -6.94741726e-01 -5.23693800e-01 4.93170232e-01 -6.88700318e-01 -1.89144865e-01 7.29202628e-02 -3.02152038e-02 4.91249025e-01 -1.13866735e+00 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8fecf19c-849e-41fb-b1bd-ec81afcbd78e	active-sparse-conversations-for-improved	2306.04047	null	https://arxiv.org/abs/2306.04047v1	https://arxiv.org/pdf/2306.04047v1.pdf	Active Sparse Conversations for Improved Audio-Visual Embodied Navigation	Efficient navigation towards an audio-goal necessitates an embodied agent to not only possess the ability to use audio-visual cues effectively, but also be equipped to actively (but occasionally) seek human/oracle assistance without sacrificing autonomy, e.g., when it is uncertain of where to navigate towards locating a noisy or sporadic audio goal. To this end, we present CAVEN -- a conversational audio-visual embodied navigation agent that is capable of posing navigation questions to a human/oracle and processing the oracle responses; both in free-form natural language. At the core of CAVEN is a multimodal hierarchical reinforcement learning (RL) setup that is equipped with a high-level policy that is trained to choose from one of three low-level policies (at every step), namely: (i) to navigate using audio-visual cues, or (ii) to frame a question to the oracle and receive a short or detailed response, or (iii) ask generic questions (when unsure of what to ask) and receive instructions. Key to generating the agent's questions is our novel TrajectoryNet that forecasts the most likely next steps to the goal and a QuestionNet that uses these steps to produce a question. All the policies are learned end-to-end via the RL setup, with penalties to enforce sparsity in receiving navigation instructions from the oracle. To evaluate the performance of CAVEN, we present extensive experiments on the SoundSpaces framework for the task of semantic audio-visual navigation. Our results show that CAVEN achieves upto 12% gain in performance over competing methods, especially in localizing new sound sources, even in the presence of auditory distractions.	['Anoop Cherian', 'Moitreya Chatterjee', 'Sudipta Paul', 'Xiulong Liu']	2023-06-06	null	null	null	null	['hierarchical-reinforcement-learning', 'navigate', 'visual-navigation']	['methodology', 'reasoning', 'robots']	[ 1.74615130e-01 4.47725773e-01 5.10918140e-01 -4.07716990e-01 -1.62396991e+00 -8.10817242e-01 5.06002188e-01 9.78299901e-02 -7.03801692e-01 4.56978410e-01 5.44902742e-01 -5.53430200e-01 -1.82008013e-01 -7.01188326e-01 -7.64219046e-01 -5.29189289e-01 -1.62731424e-01 7.41209388e-01 -1.46728838e-02 -3.45985293e-01 8.24420303e-02 4.07588575e-03 -1.59668684e+00 1.51393354e-01 4.71033692e-01 1.00657296e+00 6.15122855e-01 1.15781069e+00 3.76071744e-02 1.03920150e+00 -3.85951072e-01 1.45452544e-01 8.87768492e-02 -3.98586303e-01 -1.13721323e+00 1.22578464e-01 -7.53566157e-03 -6.10388041e-01 -2.34817237e-01 6.85891271e-01 6.21075869e-01 6.51615322e-01 4.38240826e-01 -1.36571550e+00 -1.09240010e-01 5.65351844e-01 1.62984207e-01 1.08990043e-01 9.79119956e-01 8.83628488e-01 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c6996ad5-40ad-4f35-af21-dfbf7863c4e2	chartsumm-a-comprehensive-benchmark-for	2304.13620	null	https://arxiv.org/abs/2304.13620v3	https://arxiv.org/pdf/2304.13620v3.pdf	ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries	Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user. A large and well-structured dataset is always a key part for data driven models. In this paper, we propose ChartSumm: a large-scale benchmark dataset consisting of a total of 84,363 charts along with their metadata and descriptions covering a wide range of topics and chart types to generate short and long summaries. Extensive experiments with strong baseline models show that even though these models generate fluent and informative summaries by achieving decent scores in various automatic evaluation metrics, they often face issues like suffering from hallucination, missing out important data points, in addition to incorrect explanation of complex trends in the charts. We also investigated the potential of expanding ChartSumm to other languages using automated translation tools. These make our dataset a challenging benchmark for future research.	['Abu Raihan Mostofa Kamal', 'Md. Hamjajul Ashmafee', 'Md Tahmid Rahman Laskar', 'Abdullah Al Farhad', 'Rizvi Hasan', 'Raian Rahman']	2023-04-26	null	null	null	null	['data-summarization']	['miscellaneous']	[ 6.86431974e-02 1.62368834e-01 -6.04560487e-02 -3.24714005e-01 -1.36284161e+00 -6.39514565e-01 8.08650792e-01 5.44863522e-01 1.94353729e-01 1.13812995e+00 1.47013593e+00 -6.57923445e-02 1.46328330e-01 -4.34759051e-01 -4.79715109e-01 -1.55694917e-01 2.14835152e-01 4.35301483e-01 -1.01458579e-01 -2.57302940e-01 6.73049092e-01 7.44990706e-02 -1.52155221e+00 8.84691477e-01 1.51327884e+00 5.22400081e-01 4.78739701e-02 8.85488451e-01 -6.02888048e-01 1.34473646e+00 -1.20557451e+00 -6.40692770e-01 -1.17429547e-01 -5.92335641e-01 -7.36118138e-01 3.10973555e-01 1.02397597e+00 -6.37288392e-01 -1.13964947e-02 1.05135715e+00 7.54533410e-01 -2.18314171e-01 8.62222672e-01 -1.29004407e+00 -1.00815928e+00 7.28218079e-01 -5.70637822e-01 -2.49728218e-01 9.59776103e-01 2.06761003e-01 9.35625851e-01 -7.06303298e-01 9.13532913e-01 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5ca677c3-fcb9-40f9-b5b5-47dae0f4c86d	grapeqa-graph-augmentation-and-pruning-to	2303.12320	null	https://arxiv.org/abs/2303.12320v2	https://arxiv.org/pdf/2303.12320v2.pdf	GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering	Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks(GNNs). This faces two major challenges: (i) it is difficult to capture all the information from the QA in the WG, and (ii) the WG contains some irrelevant nodes from the KG. To address these, we propose GrapeQA with two simple improvements on the WG: (i) Prominent Entities for Graph Augmentation identifies relevant text chunks from the QA pair and augments the WG with corresponding latent representations from the LM, and (ii) Context-Aware Node Pruning removes nodes that are less relevant to the QA pair. We evaluate our results on OpenBookQA, CommonsenseQA and MedQA-USMLE and see that GrapeQA shows consistent improvements over its LM + KG predecessor (QA-GNN in particular) and large improvements on OpenBookQA.	['Makarand Tapaswi', 'Charu Sharma', 'Vasudeva Varma', 'Pavan Kandru', 'Lakshya Khanna', 'Dhaval Taunk']	2023-03-22	null	null	null	null	['common-sense-reasoning']	['reasoning']	[ 1.43320933e-01 1.12482941e+00 5.67877106e-02 -1.37833245e-02 -1.01372015e+00 -5.87601840e-01 4.21370059e-01 7.10566044e-01 -7.12131187e-02 9.49383736e-01 7.18085289e-01 -3.07305187e-01 -2.66677365e-02 -1.26490164e+00 -7.94087708e-01 -2.06077993e-01 8.07482451e-02 1.07208884e+00 5.93363941e-01 -7.97605574e-01 2.56388076e-02 1.40665069e-01 -1.06886768e+00 3.58315766e-01 1.55760074e+00 7.01775432e-01 7.29487166e-02 6.03971422e-01 -7.57050395e-01 1.59241641e+00 -7.42082059e-01 -1.06430662e+00 -3.46072875e-02 -6.01840198e-01 -1.58381593e+00 -1.89235345e-01 6.74090326e-01 -1.37702569e-01 -5.18670917e-01 1.14861417e+00 2.14759469e-01 2.97169983e-01 4.91276771e-01 -1.49495327e+00 -9.39426243e-01 1.24499631e+00 -3.36818427e-01 2.17447188e-02 3.96129131e-01 -1.19828852e-02 1.47693598e+00 -4.90253359e-01 1.01170766e+00 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4f56ef65-4d15-4b99-8735-4dc328101464	mintrec-a-new-dataset-for-multimodal-intent	2209.04355	null	https://arxiv.org/abs/2209.04355v1	https://arxiv.org/pdf/2209.04355v1.pdf	MIntRec: A New Dataset for Multimodal Intent Recognition	Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec.	['Jiayan Teng', 'Shaojie Zhao', 'Qianrui Zhou', 'Xin Wang', 'Hua Xu', 'Hanlei Zhang']	2022-09-09	null	null	null	null	['multimodal-intent-recognition', 'intent-recognition']	['miscellaneous', 'natural-language-processing']	[ 3.38200301e-01 -4.88327920e-01 -2.52583057e-01 -6.07694328e-01 -1.33236217e+00 -6.12769186e-01 8.24749112e-01 -2.56564528e-01 -3.76185805e-01 1.71916202e-01 9.78197336e-01 4.79697436e-03 2.38946721e-01 -2.13566213e-03 -3.17893088e-01 -6.24307632e-01 -1.00849688e-01 1.94668636e-01 -3.80980879e-01 -1.84994534e-01 4.69846368e-01 1.55074419e-02 -1.64189839e+00 9.83915806e-01 5.39562285e-01 1.05571020e+00 5.55967167e-03 8.66725802e-01 -1.16642140e-01 1.01153493e+00 -4.92338508e-01 -5.11635959e-01 -1.62195116e-01 -3.12757671e-01 -7.27654219e-01 1.60887882e-01 4.84473854e-01 -4.12432253e-01 -3.98032188e-01 6.15394354e-01 5.26448190e-01 1.21148758e-01 8.83613229e-01 -1.66105902e+00 -4.24334645e-01 5.27694702e-01 -4.52952355e-01 -1.77142426e-01 1.33205867e+00 3.17726508e-02 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ca610a23-be87-4b3f-a335-14edcd7c94b3	featurized-density-ratio-estimation	2107.02212	null	https://arxiv.org/abs/2107.02212v1	https://arxiv.org/pdf/2107.02212v1.pdf	Featurized Density Ratio Estimation	Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox. However, such ratios are difficult to estimate for complex, high-dimensional data, particularly when the densities of interest are sufficiently different. In our work, we propose to leverage an invertible generative model to map the two distributions into a common feature space prior to estimation. This featurization brings the densities closer together in latent space, sidestepping pathological scenarios where the learned density ratios in input space can be arbitrarily inaccurate. At the same time, the invertibility of our feature map guarantees that the ratios computed in feature space are equivalent to those in input space. Empirically, we demonstrate the efficacy of our approach in a variety of downstream tasks that require access to accurate density ratios such as mutual information estimation, targeted sampling in deep generative models, and classification with data augmentation.	['Stefano Ermon', 'Madeline Liao', 'Kristy Choi']	2021-07-05	null	null	null	null	['density-ratio-estimation', 'mutual-information-estimation']	['methodology', 'methodology']	[ 8.86770412e-02 1.41804993e-01 -5.27609102e-02 -3.01897913e-01 -7.13415861e-01 -6.86561465e-01 7.74709165e-01 -9.84707102e-02 -2.19489485e-01 8.77514839e-01 2.36031517e-01 -2.90509403e-01 -2.87236512e-01 -8.22633803e-01 -6.20011270e-01 -8.76554787e-01 2.55256325e-01 6.95929229e-01 -2.77395278e-01 2.56870121e-01 2.99883336e-02 4.51917857e-01 -1.32533896e+00 -3.81641656e-01 8.94465387e-01 7.84628630e-01 -5.45542426e-02 7.17726648e-01 -9.35660601e-02 3.14680278e-01 -3.61293107e-01 -4.82330590e-01 3.77124637e-01 -3.72883260e-01 -3.01931322e-01 8.29956755e-02 2.42474139e-01 -4.23589587e-01 -3.32225859e-01 1.37536860e+00 5.98112829e-02 9.62952301e-02 1.43749368e+00 -1.49753511e+00 -4.29326952e-01 5.27387738e-01 -6.21177733e-01 7.28475899e-02 -2.10061640e-01 8.80318061e-02 1.05573511e+00 -8.44117224e-01 1.16270758e-01 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5.06702363e-01 -7.29801834e-01 -8.15894067e-01 -1.03560007e+00 -7.55091786e-01 2.23245651e-01 4.48863298e-01 -9.40521061e-01 -5.57191789e-01 -1.63224444e-03]	[7.442237377166748, 3.928496837615967]
4b012021-91a2-4c2b-8f05-6db7dc3a7a55	leveraging-gpt-2-for-classifying-spam-reviews	2012.13400	null	https://arxiv.org/abs/2012.13400v1	https://arxiv.org/pdf/2012.13400v1.pdf	Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training	Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train spam detection models. We propose an adversarial training mechanism leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited. The proposed model can also generate synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing additional labeled data during training.	['Gray Stanton', 'Anubha Agrawal', 'Yankun Shen', 'Hanfei Yu', 'Athirai A. Irissappane']	2020-12-24	null	null	null	null	['spam-detection']	['natural-language-processing']	[ 1.85794279e-01 2.84762949e-01 -9.32872146e-02 -5.77040315e-01 -6.33179009e-01 -8.40575278e-01 6.71572030e-01 -8.24113786e-02 -5.90816960e-02 8.00390959e-01 -2.82320619e-01 -7.34358609e-01 6.83014691e-01 -9.78417099e-01 -7.85797834e-01 -5.59268951e-01 3.39457989e-01 6.33868217e-01 3.11354786e-01 -5.27537286e-01 1.86687171e-01 4.48835082e-02 -1.26773024e+00 4.91242439e-01 1.52515149e+00 1.01666009e+00 -3.29835147e-01 3.91362607e-01 -2.47071251e-01 5.16424894e-01 -1.20060539e+00 -1.19880450e+00 4.69152004e-01 -5.49366236e-01 -1.99644789e-01 4.00688916e-01 3.69773299e-01 -3.05827767e-01 -1.11840412e-01 1.41375923e+00 3.96087736e-01 -2.44562998e-02 6.27361894e-01 -1.43881214e+00 -1.26051402e+00 5.15432537e-01 -4.30540293e-01 -4.39997986e-02 1.05965778e-01 2.63761878e-01 7.97785699e-01 -8.12111139e-01 5.20616829e-01 1.51541185e+00 4.73647147e-01 7.90420771e-01 -1.13757622e+00 -8.35379183e-01 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2905536b-2b29-4bfc-af09-98a5caac6aa1	diffusion-jump-gnns-homophiliation-via	2306.16976	null	https://arxiv.org/abs/2306.16976v1	https://arxiv.org/pdf/2306.16976v1.pdf	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters	High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the existing HO-GNNs are hop-based, i.e., they rely on the powers of the transition matrix. As a result, these architectures are not fully reactive to the classification loss and the achieved structural filters have static supports. In other words, neither the filters' supports nor their coefficients can be learned with these networks. They are confined, instead, to learn combinations of filters. To address the above concerns, we propose Diffusion-jump GNNs a method relying on asymptotic diffusion distances that operates on jumps. A diffusion-pump generates pairwise distances whose projections determine both the support and coefficients of each structural filter. These filters are called jumps because they explore a wide range of scales in order to find bonds between scattered nodes with the same label. Actually, the full process is controlled by the classification loss. Both the jumps and the diffusion distances react to classification errors (i.e. they are learnable). Homophiliation, i.e., the process of learning piecewise smooth latent spaces in the heterophilic regime, is formulated as a Dirichlet problem: the known labels determine the border nodes and the diffusion-pump ensures a minimal deviation of the semi-supervised grouping from a canonical unsupervised grouping. This triggers the update of both the diffusion distances and, consequently, the jumps in order to minimize the classification error. The Dirichlet formulation has several advantages. It leads to the definition of structural heterophily, a novel measure beyond edge heterophily. It also allows us to investigate links with (learnable) diffusion distances, absorbing random walks and stochastic diffusion.	['Edwin R. Hancock', 'Miguel Angel Lozano', 'Francisco Escolano', 'Ahmed Begga']	2023-06-29	null	null	null	null	['node-classification']	['graphs']	[-5.25837094e-02 2.88238227e-01 -3.63464177e-01 -5.23270592e-02 2.12267593e-01 -4.92032766e-01 8.24067831e-01 1.91320598e-01 -2.42292285e-01 6.41109765e-01 -8.84028599e-02 2.29808185e-02 -4.61450219e-01 -1.37456882e+00 -7.22766459e-01 -1.26680326e+00 -8.62679556e-02 7.75782466e-01 3.28230023e-01 -1.12483375e-01 -5.45535013e-02 4.93125945e-01 -1.33686244e+00 -1.43552106e-02 9.61913526e-01 6.85087740e-01 -5.80593757e-02 3.89126122e-01 -2.07051322e-01 4.22102064e-01 -1.91072926e-01 -2.35173747e-01 1.87156290e-01 -6.27163529e-01 -3.55159938e-01 -4.39673439e-02 9.61583108e-02 2.28749037e-01 -4.03456122e-01 1.36382496e+00 1.75354168e-01 1.12879321e-01 1.01107252e+00 -1.04925561e+00 -7.14583874e-01 9.45311844e-01 -3.03747028e-01 -1.15198493e-01 -1.17054798e-01 -7.55733550e-02 1.38371181e+00 -8.42739463e-01 9.20328319e-01 1.15443718e+00 7.47514486e-01 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1804a8d4-8aa2-4f7b-a1f7-b5ab46455cc9	retrieval-efficiency-trade-off-of	2208.07262	null	https://arxiv.org/abs/2208.07262v1	https://arxiv.org/pdf/2208.07262v1.pdf	Retrieval-efficiency trade-off of Unsupervised Keyword Extraction	Efficiently identifying keyphrases that represent a given document is a challenging task. In the last years, plethora of keyword detection approaches were proposed. These approaches can be based on statistical (frequency-based) properties of e.g., tokens, specialized neural language models, or a graph-based structure derived from a given document. The graph-based methods can be computationally amongst the most efficient ones, while maintaining the retrieval performance. One of the main properties, common to graph-based methods, is their immediate conversion of token space into graphs, followed by subsequent processing. In this paper, we explore a novel unsupervised approach which merges parts of a document in sequential form, prior to construction of the token graph. Further, by leveraging personalized PageRank, which considers frequencies of such sub-phrases alongside token lengths during node ranking, we demonstrate state-of-the-art retrieval capabilities while being up to two orders of magnitude faster than current state-of-the-art unsupervised detectors such as YAKE and MultiPartiteRank. The proposed method's scalability was also demonstrated by computing keyphrases for a biomedical corpus comprised of 14 million documents in less than a minute.	['Senja Pollak', 'Boshko Koloski', 'Blaž Škrlj']	2022-08-15	null	null	null	null	['keyword-extraction']	['natural-language-processing']	[ 3.07969838e-01 -1.32685844e-02 -3.08537930e-01 1.25322863e-01 -9.08528924e-01 -6.87791407e-01 8.14621270e-01 1.31121325e+00 -6.76965237e-01 6.44575953e-01 2.47357830e-01 -2.78344840e-01 -5.12789369e-01 -8.88167202e-01 -3.23473841e-01 -6.93396926e-01 -5.04632056e-01 4.72217411e-01 6.08261883e-01 6.71591461e-02 6.72899008e-01 4.74582136e-01 -1.44458210e+00 7.98571035e-02 6.75220191e-01 7.86967099e-01 2.67022967e-01 7.73423672e-01 -6.33467436e-01 3.24068755e-01 -5.59861898e-01 -1.63193285e-01 -5.13764545e-02 -3.50260995e-02 -7.32260346e-01 -2.02803522e-01 2.74562001e-01 -1.38712618e-02 -5.79370737e-01 1.17840910e+00 4.95671779e-01 -2.28375793e-02 6.97535872e-01 -9.08991218e-01 -2.71431386e-01 8.46372724e-01 -5.29122829e-01 4.35007066e-01 6.21785939e-01 -5.86826146e-01 1.45300078e+00 -9.13279653e-01 8.17873418e-01 1.07022011e+00 4.11384195e-01 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f4cca4c7-97a8-479f-89bf-3d96dff7e5e0	a-persian-benchmark-for-joint-intent	2303.00408	null	https://arxiv.org/abs/2303.00408v1	https://arxiv.org/pdf/2303.00408v1.pdf	A Persian Benchmark for Joint Intent Detection and Slot Filling	Natural Language Understanding (NLU) is important in today's technology as it enables machines to comprehend and process human language, leading to improved human-computer interactions and advancements in fields such as virtual assistants, chatbots, and language-based AI systems. This paper highlights the significance of advancing the field of NLU for low-resource languages. With intent detection and slot filling being crucial tasks in NLU, the widely used datasets ATIS and SNIPS have been utilized in the past. However, these datasets only cater to the English language and do not support other languages. In this work, we aim to address this gap by creating a Persian benchmark for joint intent detection and slot filling based on the ATIS dataset. To evaluate the effectiveness of our benchmark, we employ state-of-the-art methods for intent detection and slot filling.	['Ali Mohades', 'Mohammad Akbari', 'Fatemeh Shamsezat', 'Kiana Ghezelbash', 'Zeinab Saeidi', 'Tayyebeh Saeedi', 'Amir Hossein Karimi', 'Masoud Akbari']	2023-03-01	null	null	null	null	['intent-detection', 'slot-filling']	['natural-language-processing', 'natural-language-processing']	[ 3.04666162e-01 2.62586355e-01 -5.82311988e-01 -1.12119965e-01 -4.67454225e-01 -6.31220460e-01 8.03100765e-01 3.99840266e-01 -8.17902327e-01 8.36658061e-01 3.12605232e-01 -6.09655380e-01 2.41258159e-01 -7.49368608e-01 -1.22906808e-02 1.87970236e-01 1.90043733e-01 7.70341277e-01 2.91396499e-01 -3.11246037e-01 2.92733192e-01 7.60953054e-02 -1.40324569e+00 2.53553748e-01 1.02471137e+00 6.98587716e-01 4.38334525e-01 4.36312139e-01 -7.00991392e-01 9.26117480e-01 -4.91134822e-01 -2.86239624e-01 4.72725891e-02 -3.25827390e-01 -1.26967919e+00 -2.42252588e-01 -2.29166090e-01 -9.41252112e-02 -1.24698922e-01 5.76860249e-01 1.11035928e-01 2.17384681e-01 5.15033484e-01 -1.20991051e+00 -2.97676474e-01 5.63808084e-01 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27914610-4e4f-4719-8d6b-bc16a4d29d64	a-physics-informed-machine-learning-for	2304.00062	null	https://arxiv.org/abs/2304.00062v1	https://arxiv.org/pdf/2304.00062v1.pdf	A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study	This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.	['Michael Chertkov', 'Daniel Bienstock', 'Yury Dvorkin', 'Zhirui Liang', 'Robert Mieth', 'Laurent Pagnier', 'Robert Ferrando']	2023-03-31	null	null	null	null	['physics-informed-machine-learning']	['graphs']	[-2.91220486e-01 -7.14057460e-02 -4.35966790e-01 1.80225223e-01 -2.23531246e-01 -8.05412650e-01 2.53219932e-01 5.59492968e-02 1.18501753e-01 1.43563259e+00 -4.50858206e-01 -7.29619801e-01 -7.94480205e-01 -1.02041829e+00 -2.12171376e-01 -9.91239488e-01 -7.23312974e-01 4.37626392e-01 -4.47775424e-01 -4.31945682e-01 -6.88095838e-02 5.97744226e-01 -1.22024429e+00 -3.00990820e-01 1.30135965e+00 1.17843032e+00 1.38109550e-01 2.86902755e-01 2.54886776e-01 4.70083773e-01 -6.50543094e-01 5.61397374e-01 7.16093898e-01 -9.21333060e-02 -3.36177349e-01 -4.24945772e-01 -5.29808879e-01 -6.41887844e-01 -1.82856441e-01 9.93453026e-01 6.79111004e-01 5.07111728e-01 4.95809376e-01 -1.87675560e+00 -4.79661316e-01 8.68718565e-01 -6.91494346e-01 5.32813013e-01 1.18109351e-02 2.54527390e-01 1.21138060e+00 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8e5acf64-fa00-456d-9f7f-4c34a7eb0b6c	hands-on-detection-for-steering-wheels-with	2306.09044	null	https://arxiv.org/abs/2306.09044v1	https://arxiv.org/pdf/2306.09044v1.pdf	Hands-on detection for steering wheels with neural networks	In this paper the concept of a machine learning based hands-on detection algorithm is proposed. The hand detection is implemented on the hardware side using a capacitive method. A sensor mat in the steering wheel detects a change in capacity as soon as the driver's hands come closer. The evaluation and final decision about hands-on or hands-off situations is done using machine learning. In order to find a suitable machine learning model, different models are implemented and evaluated. Based on accuracy, memory consumption and computational effort the most promising one is selected and ported on a micro controller. The entire system is then evaluated in terms of reliability and response time.	['Andreas Fischer', 'Michael Hollmer']	2023-06-15	null	null	null	null	['hand-detection']	['computer-vision']	[-2.50651836e-01 -2.26155907e-01 -4.05155361e-01 -4.51840848e-01 1.22585006e-01 -3.75884026e-01 1.20949805e-01 2.88220942e-01 -7.78157353e-01 3.10061514e-01 -5.76420903e-01 -8.15295637e-01 -1.87743828e-01 -6.87630117e-01 -4.60004061e-03 -6.30780280e-01 3.48919719e-01 4.06253606e-01 5.79993784e-01 -2.82214373e-01 5.25328517e-01 1.10381639e+00 -1.93815970e+00 1.45903468e-01 1.89265132e-01 8.89888942e-01 2.04212531e-01 8.48921657e-01 3.55732180e-02 6.12656593e-01 -3.55020940e-01 2.61685550e-01 3.86332184e-01 -1.22227922e-01 -2.40909815e-01 -2.35868484e-01 -1.87097445e-01 -2.35335574e-01 -1.22762300e-01 5.22698581e-01 6.73654914e-01 -9.26418304e-02 7.25208521e-01 -1.21808279e+00 6.84805274e-01 2.43462861e-01 -1.51269540e-01 3.20637852e-01 6.07868671e-01 2.06314594e-01 2.45312586e-01 -9.59434748e-01 3.93523872e-01 7.95269728e-01 3.24372977e-01 2.57734299e-01 -9.69370246e-01 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31a18f46-b355-473e-89cd-700ba412ce96	selective-clustering-ensemble-based-on-kappa	2204.11062	null	https://arxiv.org/abs/2204.11062v1	https://arxiv.org/pdf/2204.11062v1.pdf	Selective clustering ensemble based on kappa and F-score	Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the ensemble performance by selecting base partitions or clusters in according to diversity and stability. However, there is a conflict between diversity and stability, and how to make the trade-off between the two is challenging. The key here is how to evaluate the quality of the base partitions and clusters. In this paper, we propose a new evaluation method for partitions and clusters using kappa and F-score, leading to a new SCE method, which uses kappa to select informative base partitions and uses F-score to weight clusters based on stability. The effectiveness and efficiency of the proposed method is empirically validated over real datasets.	['Zhong-Yuan Zhang', 'Tao You', 'Ji Qi', 'Xin Liu', 'Jie Yan']	2022-04-23	null	null	null	null	['clustering-ensemble']	['graphs']	[-1.63140938e-01 -4.63736951e-01 1.11879461e-01 -3.15444887e-01 -3.25181097e-01 -5.48044980e-01 3.92454684e-01 3.84884238e-01 -2.43258551e-01 8.56410146e-01 1.65147811e-01 2.74661332e-02 -7.14115739e-01 -9.14508402e-01 1.53931230e-01 -1.18187666e+00 -3.28936316e-02 4.12016660e-01 4.08328295e-01 1.14586189e-01 5.83935201e-01 2.18221098e-01 -1.80092955e+00 2.51670927e-01 1.55089843e+00 7.90504575e-01 9.08814296e-02 4.34961796e-01 -6.71137124e-02 2.35436946e-01 -6.75693512e-01 -9.65855867e-02 1.24267712e-01 -6.45565212e-01 -5.56548178e-01 -1.57578290e-01 -4.47756201e-01 3.93764675e-01 5.29755712e-01 8.91493678e-01 8.03672969e-01 1.72401235e-01 1.02238035e+00 -1.41209149e+00 -1.63536534e-01 6.54024899e-01 -5.75208127e-01 2.33050719e-01 5.76056959e-03 1.62414310e-03 8.36393833e-01 -5.76392889e-01 1.41001552e-01 1.12936163e+00 7.13907301e-01 5.28897084e-02 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8a82dac6-ac9c-40a7-a4c6-0b8d87e3fd69	a-tale-of-two-features-stable-diffusion	2305.15347	null	https://arxiv.org/abs/2305.15347v1	https://arxiv.org/pdf/2305.15347v1.pdf	A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence	Text-to-image diffusion models have made significant advances in generating and editing high-quality images. As a result, numerous approaches have explored the ability of diffusion model features to understand and process single images for downstream tasks, e.g., classification, semantic segmentation, and stylization. However, significantly less is known about what these features reveal across multiple, different images and objects. In this work, we exploit Stable Diffusion (SD) features for semantic and dense correspondence and discover that with simple post-processing, SD features can perform quantitatively similar to SOTA representations. Interestingly, the qualitative analysis reveals that SD features have very different properties compared to existing representation learning features, such as the recently released DINOv2: while DINOv2 provides sparse but accurate matches, SD features provide high-quality spatial information but sometimes inaccurate semantic matches. We demonstrate that a simple fusion of these two features works surprisingly well, and a zero-shot evaluation using nearest neighbors on these fused features provides a significant performance gain over state-of-the-art methods on benchmark datasets, e.g., SPair-71k, PF-Pascal, and TSS. We also show that these correspondences can enable interesting applications such as instance swapping in two images.	['Ming-Hsuan Yang', 'Deqing Sun', 'Varun Jampani', 'Luisa Polania Cabrera', 'Junhwa Hur', 'Charles Herrmann', 'Junyi Zhang']	2023-05-24	null	null	null	null	['semantic-correspondence']	['computer-vision']	[ 3.70086312e-01 -2.45521128e-01 -2.34266445e-01 -3.33077013e-01 -8.69902372e-01 -7.70635188e-01 1.04618561e+00 3.86471242e-01 -3.88011396e-01 4.19353426e-01 3.59910786e-01 3.44418772e-02 -2.67487347e-01 -7.31654644e-01 -6.40780151e-01 -7.92136550e-01 1.07734911e-01 5.99604607e-01 6.22012794e-01 -2.91181117e-01 5.33516109e-01 5.62452018e-01 -1.66210580e+00 4.10639584e-01 8.57915699e-01 1.03866267e+00 3.12682837e-01 6.32837951e-01 -4.57561433e-01 6.27917290e-01 -4.79379803e-01 -3.56073827e-01 3.67029041e-01 -4.41800535e-01 -1.06606567e+00 1.25871480e-01 7.00287044e-01 -2.11527511e-01 -4.09203112e-01 1.14665270e+00 3.22710037e-01 2.87261069e-01 8.52693856e-01 -1.08805001e+00 -1.15964341e+00 4.71051276e-01 -9.57281649e-01 3.71055901e-01 4.15624708e-01 2.85044581e-01 1.08930421e+00 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715c7a91-dd2a-4771-ba89-556d2f453acc	deep-residual-correction-network-for-partial	2004.04914	null	https://arxiv.org/abs/2004.04914v1	https://arxiv.org/pdf/2004.04914v1.pdf	Deep Residual Correction Network for Partial Domain Adaptation	Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain. Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in many real-world applications. With the emergence of big data, there is a more practical scenario called partial domain adaptation, where we are always accessible to a more large-scale source domain while working on a relative small-scale target domain. In this case, the conventional domain adaptation assumption should be relaxed, and the target label space tends to be a subset of the source label space. Intuitively, reinforcing the positive effects of the most relevant source subclasses and reducing the negative impacts of irrelevant source subclasses are of vital importance to address partial domain adaptation challenge. This paper proposes an efficiently-implemented Deep Residual Correction Network (DRCN) by plugging one residual block into the source network along with the task-specific feature layer, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes. Specifically, the plugged residual block, which consists of several fully-connected layers, could deepen basic network and boost its feature representation capability correspondingly. Moreover, we design a weighted class-wise domain alignment loss to couple two domains by matching the feature distributions of shared classes between source and target. Comprehensive experiments on partial, traditional and fine-grained cross-domain visual recognition demonstrate that DRCN is superior to the competitive deep domain adaptation approaches.	['Limin Su', 'Shuang Li', 'Qiuxia Lin', 'Gao Huang', 'Qi Wen', 'Zhengming Ding', 'Chi Harold Liu']	2020-04-10	null	null	null	null	['partial-domain-adaptation']	['methodology']	[ 2.46910542e-01 -1.23668596e-01 -3.61986041e-01 -5.74678719e-01 -4.22362238e-01 -5.34580350e-01 4.29214716e-01 -8.31054747e-02 -3.86914968e-01 7.59346902e-01 1.22526467e-01 1.73750371e-01 -8.51833448e-02 -9.47973549e-01 -6.64727867e-01 -8.59685719e-01 4.59720969e-01 4.17074531e-01 4.38886970e-01 -2.72918910e-01 -2.46646583e-01 3.78442377e-01 -1.19087315e+00 1.04604572e-01 1.03643119e+00 1.05257201e+00 2.58368611e-01 -1.86393842e-01 -4.16097760e-01 5.10492623e-01 -6.68929935e-01 -2.06659183e-01 3.44537109e-01 -4.08993602e-01 -4.81712282e-01 1.48678377e-01 3.35206300e-01 -1.79906502e-01 -2.03586131e-01 1.22524679e+00 4.59718436e-01 1.84582114e-01 5.85782588e-01 -1.39483607e+00 -1.00593650e+00 3.62623721e-01 -8.63407552e-01 1.12474553e-01 -8.10306817e-02 9.82663184e-02 7.75591195e-01 -6.08578324e-01 4.99758631e-01 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11c13fc3-84cd-4384-98fc-7193d473b09f	arabic-dialect-identification-using-bert	2011.06977	null	https://arxiv.org/abs/2011.06977v1	https://arxiv.org/pdf/2011.06977v1.pdf	Arabic Dialect Identification Using BERT-Based Domain Adaptation	Arabic is one of the most important and growing languages in the world. With the rise of social media platforms such as Twitter, Arabic spoken dialects have become more in use. In this paper, we describe our approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arabic dialects, we introduce a deep learning semi-supervised fashion approach along with pre-processing that was reported on NADI shared Task 1 Corpus. Our system ranks 4th in NADI's shared task competition achieving a 23.09% F1 macro average score with a simple yet efficient approach to differentiating between 21 Arabic Dialects given tweets.	['Omar ElSherief', 'Abdelrahman Wael', 'Ahmad Beltagy']	2020-11-13	null	https://aclanthology.org/2020.wanlp-1.26	https://aclanthology.org/2020.wanlp-1.26.pdf	coling-wanlp-2020-12	['dialect-identification']	['natural-language-processing']	[-3.73991966e-01 -2.21499920e-01 2.31475234e-01 -6.51850998e-01 -9.61056411e-01 -8.63902748e-01 9.88989890e-01 3.27863216e-01 -7.41719544e-01 5.42170823e-01 2.41747588e-01 -2.13464886e-01 7.44269788e-02 -8.05660546e-01 -2.87786156e-01 -4.37135786e-01 -3.09385926e-01 1.01856303e+00 -1.95730366e-02 -1.19061399e+00 3.07301790e-01 4.24343050e-01 -1.14537942e+00 6.12713218e-01 1.13022423e+00 6.73369348e-01 -3.29602003e-01 7.39653528e-01 1.07944615e-01 6.13209307e-01 -6.81772768e-01 -6.40177369e-01 1.50445119e-01 -3.26703608e-01 -1.25785482e+00 -2.08614096e-01 5.24303079e-01 -5.24874091e-01 -1.39650688e-01 6.84901893e-01 7.16337204e-01 2.17859689e-02 9.69460547e-01 -8.53603065e-01 -6.45098448e-01 1.22399986e+00 -4.83064324e-01 9.65684205e-02 4.35234129e-01 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25d366c6-a751-4594-8355-f4554678c431	kernel-conditional-moment-constraints-for	2302.13348	null	https://arxiv.org/abs/2302.13348v1	https://arxiv.org/pdf/2302.13348v1.pdf	Kernel Conditional Moment Constraints for Confounding Robust Inference	We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound of the policy value. It can be shown that our estimator contains the recently proposed sharp estimator by Dorn and Guo (2022) as a special case, and our method enables a novel extension of the classical marginal sensitivity model using f-divergence. To construct our estimator, we leverage the kernel method to obtain a tractable approximation to the conditional moment constraints, which traditional non-sharp estimators failed to take into account. In the theoretical analysis, we provide a condition for the choice of the kernel which guarantees no specification error that biases the lower bound estimation. Furthermore, we provide consistency guarantees of policy evaluation and learning. In the experiments with synthetic and real-world data, we demonstrate the effectiveness of the proposed method.	['Niao He', 'Kei Ishikawa']	2023-02-26	null	null	null	null	['multi-armed-bandits']	['miscellaneous']	[ 1.01184182e-01 6.15143590e-02 -7.99469769e-01 -1.76191479e-01 -1.02421701e+00 -5.56731820e-01 2.24368006e-01 2.73444444e-01 -4.43407893e-01 1.18590772e+00 1.50227383e-01 -8.07671666e-01 -5.69252789e-01 -7.37421334e-01 -9.89050865e-01 -8.98496211e-01 6.74300492e-02 3.63727175e-02 1.77526437e-02 2.95407772e-01 1.42916888e-01 2.40320429e-01 -9.34268057e-01 -4.73483622e-01 1.36268091e+00 1.15468001e+00 5.29939868e-02 1.49748564e-01 4.09913927e-01 2.88002521e-01 -4.18121815e-01 -3.26733112e-01 3.81925106e-01 -3.57133836e-01 -2.34552681e-01 -5.57309575e-02 1.60507724e-01 -6.41203225e-01 1.73115924e-01 1.42437315e+00 3.59767169e-01 2.28699565e-01 7.29399443e-01 -1.08301342e+00 -4.06375438e-01 6.78252757e-01 -8.11306357e-01 1.25784069e-01 -1.87001720e-01 -2.08257616e-01 9.36974943e-01 -2.83932745e-01 2.58236557e-01 1.14605987e+00 5.31914949e-01 2.56345212e-01 -1.21255016e+00 -7.92681694e-01 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39d99060-4e63-4833-84e3-710738a7d39c	spatial-temporal-mitosis-detection-in-phase	2004.12531	null	https://arxiv.org/abs/2004.12531v2	https://arxiv.org/pdf/2004.12531v2.pdf	Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN	Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1- score using a challenging dataset that contains the data under four different conditions.	['Ryoma Bise', 'Kazuya Nishimura']	2020-04-27	null	null	null	null	['mitosis-detection']	['medical']	[ 1.71801388e-01 -2.62293518e-01 -6.91335350e-02 5.53486049e-02 -8.86806488e-01 -5.86292386e-01 4.70896959e-01 4.54107970e-01 -8.00823271e-01 1.12942731e+00 -1.15603223e-01 1.20628938e-01 2.24526584e-01 -6.72838032e-01 -5.81128120e-01 -1.18573666e+00 2.75420398e-01 4.81544375e-01 9.57910657e-01 3.92654151e-01 4.44622189e-01 2.53776491e-01 -1.26626444e+00 -1.19593531e-01 6.71881914e-01 5.68599284e-01 4.24830586e-01 7.08470702e-01 2.45311689e-02 3.80542248e-01 -6.62754416e-01 -7.55767897e-02 -4.78141792e-02 -5.10825276e-01 -4.78488863e-01 4.03255485e-02 4.42756526e-02 -1.61315635e-01 -2.31476486e-01 1.11610949e+00 5.61247647e-01 -1.53894767e-01 7.97260821e-01 -1.18953097e+00 2.50192676e-02 1.34835169e-01 -1.11742175e+00 6.52032435e-01 1.30006760e-01 1.17111094e-01 4.43745464e-01 -7.02864289e-01 9.79060054e-01 8.54437411e-01 6.26232266e-01 4.92149472e-01 -1.00001502e+00 -5.14016688e-01 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9d1c688a-6a0c-4c14-8340-f20e0fbe5471	detecting-recolored-image-by-spatial	2204.10973	null	https://arxiv.org/abs/2204.10973v1	https://arxiv.org/pdf/2204.10973v1.pdf	Detecting Recolored Image by Spatial Correlation	Image forensics, aiming to ensure the authenticity of the image, has made great progress in dealing with common image manipulation such as copy-move, splicing, and inpainting in the past decades. However, only a few researchers pay attention to an emerging editing technique called image recoloring, which can manipulate the color values of an image to give it a new style. To prevent it from being used maliciously, the previous approaches address the conventional recoloring from the perspective of inter-channel correlation and illumination consistency. In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Through theoretical and numerical analysis, we find that the recoloring operation will inevitably destroy the spatial correlation between pixels, implying a new prior of statistical discriminability. Based on such fact, we generate a set of spatial correlation features and learn the informative representation from the set via a convolutional neural network. To train our network, we use three recoloring methods to generate a large-scale and high-quality data set. Extensive experimental results in two recoloring scenes demonstrate that the spatial correlation features are highly discriminative. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.	['Xiaochun Cao', 'Mingfu Xue', 'Shuren Qi', 'Nuo Chen', 'Yushu Zhang']	2022-04-23	null	null	null	null	['image-forensics']	['computer-vision']	[ 4.81113374e-01 -5.70809603e-01 -9.54509377e-02 -4.44899872e-02 -6.04869187e-01 -4.86039251e-01 4.60605532e-01 -2.84127980e-01 -2.82274067e-01 6.76670134e-01 -2.08053783e-01 -2.91263461e-01 -6.91919401e-02 -7.79803216e-01 -7.78037608e-01 -1.10981476e+00 2.44940087e-01 -4.40013856e-01 -1.77622568e-02 -1.17793595e-02 6.32181466e-01 5.97431600e-01 -1.24247384e+00 1.93339109e-01 9.68933523e-01 1.00738156e+00 -6.84389099e-02 3.77090275e-01 -6.56807944e-02 5.29658318e-01 -8.19418550e-01 -5.09242892e-01 5.03413260e-01 -5.15658915e-01 -4.64714468e-01 2.50409305e-01 4.31752831e-01 -4.88210618e-01 -6.43169582e-01 1.44430518e+00 4.63398069e-01 -1.15912452e-01 4.35060322e-01 -1.51453388e+00 -1.28132594e+00 2.79055804e-01 -1.21489394e+00 2.61428267e-01 2.35525027e-01 2.83428580e-01 5.21787703e-01 -8.29069853e-01 4.39014167e-01 1.16730535e+00 6.55997515e-01 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-5.98788440e-01 -9.65421259e-01 7.66309142e-01 3.26407820e-01 -7.38212526e-01 -4.07157481e-01 2.44603083e-02]	[12.363728523254395, 0.921225368976593]
4b2a3c7f-d355-4825-a787-0c9d58f47b8e	exposure-aware-dynamic-weighted-learning-for	null	null	https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670429.pdf	https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670429.pdf	Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging	We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image. First, we recover poorly exposed pixels by developing a network that learns local dynamic filters to exploit local neighboring pixels across color channels. Second, we develop another network that combines only valid features in well-exposed regions by learning exposure-aware feature fusion. Third, we synthesize the raw radiance map by adaptively combining the outputs of the two networks that have different characteristics with complementary information. Finally, a full-color HDR image is obtained by interpolating missing color information. Experimental results show that the proposed algorithm significantly outperforms conventional algorithms on various datasets.	['Chul Lee', 'An Gia Vien']	2022-10-23	null	null	null	european-conference-on-computer-vision-eccv	['single-shot-hdr-reconstruction', 'hdr-reconstruction']	['computer-vision', 'computer-vision']	[ 6.45367920e-01 -5.21777570e-01 2.25396417e-02 -5.84118128e-01 -9.40472901e-01 -2.30846897e-01 1.41589776e-01 -7.03673303e-01 -5.08921325e-01 8.23358059e-01 6.62797987e-02 1.76845402e-01 -2.96114177e-01 -9.63223159e-01 -7.23981321e-01 -1.10853565e+00 3.50071536e-03 -3.23620826e-01 3.84189814e-01 -2.57938415e-01 3.70168239e-02 5.06264985e-01 -1.56065392e+00 7.91027546e-02 9.79782879e-01 1.08339882e+00 5.18013477e-01 8.16119671e-01 1.11834690e-01 1.04341781e+00 -1.45206302e-01 3.84381592e-01 7.81309068e-01 -6.27985477e-01 -4.54461843e-01 2.51311928e-01 7.67134309e-01 -8.71991932e-01 -9.09242213e-01 1.19791305e+00 4.08756495e-01 6.34319186e-01 9.29523930e-02 -6.38055205e-01 -1.11753285e+00 3.57585192e-01 -8.44931185e-01 3.31271499e-01 1.98938727e-01 4.22010005e-01 4.36285287e-01 -8.70071828e-01 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5e521653-19d6-4502-a3e2-d3ac23bad1c0	190412732	1904.12732	null	https://arxiv.org/abs/1904.12732v2	https://arxiv.org/pdf/1904.12732v2.pdf	Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss	Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy progression. We introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. The model first segments on two scales and then the segmentations are refined with a classification model. To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine. The model is evaluated quantitatively to assess the segmentation performance and qualitatively to analyze the model predictions. This approach introduces a 30.29% relative improvement over the fully convolutional neural network.	['Nassir Navab', 'Abouzar Eslami', 'Mehmet Yigitsoy', 'Shadi Albarqouni', 'Mhd Hasan Sarhan']	2019-04-18	null	null	null	null	['diabetic-retinopathy-detection']	['medical']	[ 1.65323257e-01 1.83150172e-01 -1.67521179e-01 -5.59680164e-01 -8.91470015e-01 -2.68893957e-01 2.28233591e-01 6.25922307e-02 -5.57602704e-01 5.50231040e-01 3.37180585e-01 -2.87140489e-01 6.91265240e-02 -6.10852540e-01 -4.85942096e-01 -6.38494015e-01 -2.92827152e-02 2.75714189e-01 2.73282796e-01 1.81418255e-01 3.71201396e-01 7.22499847e-01 -1.20639348e+00 5.09383619e-01 1.32539380e+00 1.12934601e+00 -1.74879938e-01 8.38446975e-01 -1.37420759e-01 7.68535793e-01 -3.32742721e-01 -3.55005592e-01 8.12836766e-01 -3.38064075e-01 -8.95029426e-01 2.84346789e-01 1.03169632e+00 -5.51407456e-01 -2.49746531e-01 1.12951326e+00 7.77787328e-01 -1.64561376e-01 7.10765421e-01 -3.63123536e-01 -3.87324154e-01 2.24201053e-01 -7.62672365e-01 6.66889787e-01 -2.03936741e-01 2.93838382e-01 7.11400509e-01 -5.44644713e-01 5.60630202e-01 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d4385401-4652-47ba-87e5-06e88795b93f	inharmonious-region-localization	2104.09453	null	https://arxiv.org/abs/2104.09453v1	https://arxiv.org/pdf/2104.09453v1.pdf	Inharmonious Region Localization	The advance of image editing techniques allows users to create artistic works, but the manipulated regions may be incompatible with the background. Localizing the inharmonious region is an appealing yet challenging task. Realizing that this task requires effective aggregation of multi-scale contextual information and suppression of redundant information, we design novel Bi-directional Feature Integration (BFI) block and Global-context Guided Decoder (GGD) block to fuse multi-scale features in the encoder and decoder respectively. We also employ Mask-guided Dual Attention (MDA) block between the encoder and decoder to suppress the redundant information. Experiments on the image harmonization dataset demonstrate that our method achieves competitive performance for inharmonious region localization. The source code is available at https://github.com/bcmi/DIRL.	['Liqing Zhang', 'Li Niu', 'Jing Liang']	2021-04-19	null	null	null	null	['image-harmonization']	['computer-vision']	[ 3.08743834e-01 -1.49148628e-01 -8.45094994e-02 -2.29496792e-01 -9.52989221e-01 -4.91315126e-01 4.40513104e-01 -3.30827326e-01 -1.48633972e-01 6.10423028e-01 4.59077120e-01 1.12872072e-01 1.68041185e-01 -5.06395161e-01 -6.29642487e-01 -6.47821486e-01 5.23034036e-01 -2.23133340e-01 2.76035696e-01 -3.51454705e-01 2.65401423e-01 4.49438184e-01 -1.29471278e+00 5.30489087e-01 1.03131866e+00 9.79632199e-01 6.12711847e-01 4.10225391e-01 9.29873586e-02 4.87037569e-01 -6.47399306e-01 -2.53969818e-01 4.07333553e-01 -4.41937715e-01 -3.97620648e-01 7.17934072e-02 5.58964968e-01 -2.92515188e-01 -3.61199617e-01 1.29867423e+00 7.47208476e-01 6.59858659e-02 3.41551185e-01 -1.20141065e+00 -1.00300455e+00 3.14819187e-01 -1.13185072e+00 3.84330750e-01 2.08870068e-01 9.63796005e-02 7.03583479e-01 -1.17862391e+00 5.52820861e-01 1.16576946e+00 2.62978882e-01 3.68906736e-01 -1.18443203e+00 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edd6308e-6ada-49f5-80e9-973f9e022db0	dsmtgcn-a-direction-sensitive-multi-task	2306.10290	null	https://arxiv.org/abs/2306.10290v1	https://arxiv.org/pdf/2306.10290v1.pdf	DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion	To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved more advanced results via exploiting the structure information on KGs with Graph Convolutional Networks (GCN). However, we observe that entity embeddings aggregated from neighbors in different directions are just simply averaged to complete single-tasks by existing GCN based models, ignoring the specific requirements of forward and backward sub-tasks. In this paper, we propose a Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction information, the multi-head self-attention is applied to specifically combine embeddings in different directions based on various entities and sub-tasks, the geometric constraints are imposed to adjust the distribution of embeddings, and the traditional binary cross-entropy loss is modified to reflect the triple uncertainty. Moreover, the competitive experiments results on several benchmark datasets verify the effectiveness of our model.	['Yuren Zhou', 'Zibin Zheng', 'Chuan Chen', 'Jining Wang']	2023-06-17	null	null	null	null	['knowledge-graph-completion', 'knowledge-graphs', 'entity-embeddings']	['knowledge-base', 'knowledge-base', 'methodology']	[-2.75088578e-01 3.03197682e-01 -3.84949297e-01 -4.95219916e-01 -2.09065899e-01 -3.00818354e-01 4.38910425e-01 8.08368102e-02 -2.11462364e-01 9.28899527e-01 3.78731012e-01 -1.08108297e-01 -5.51858246e-01 -9.75887656e-01 -1.08462262e+00 -5.26039302e-01 -1.41319737e-01 3.53692889e-01 3.12075973e-01 -2.07526222e-01 -7.94647262e-02 9.73676611e-03 -1.02287674e+00 3.56945326e-03 1.28810465e+00 9.43046272e-01 1.08136944e-01 -5.70080429e-02 -3.50289285e-01 6.49360180e-01 1.12928681e-01 -8.44445050e-01 1.79415658e-01 9.04183015e-02 -7.85127521e-01 -3.07286173e-01 1.99375987e-01 -3.25207800e-01 -6.92988515e-01 1.28756416e+00 5.22895277e-01 2.48061903e-02 4.60967928e-01 -1.56582367e+00 -1.21374762e+00 9.85427678e-01 -5.09364009e-01 -1.51505306e-01 1.07903257e-01 -5.97292110e-02 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910eab07-79e1-467d-b0fb-4e15bcb2fa93	maple-masking-words-to-generate-blackout	null	null	https://aclanthology.org/2021.icnlsp-1.6	https://aclanthology.org/2021.icnlsp-1.6.pdf	MAPLE – MAsking words to generate blackout Poetry using sequence-to-sequence LEarning	null	['Dr. Mamatha H R', 'Deeksha D', 'Himanshu Jain', 'Aditeya Baral']	null	null	null	null	icnlsp-2021-11	['blackout-poetry-generation']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 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754bd8c0-7b5c-448c-a87d-6c13d544768f	night-to-day-image-translation-for-retrieval	1809.09767	null	http://arxiv.org/abs/1809.09767v2	http://arxiv.org/pdf/1809.09767v2.pdf	Night-to-Day Image Translation for Retrieval-based Localization	Visual localization is a key step in many robotics pipelines, allowing the robot to (approximately) determine its position and orientation in the world. An efficient and scalable approach to visual localization is to use image retrieval techniques. These approaches identify the image most similar to a query photo in a database of geo-tagged images and approximate the query's pose via the pose of the retrieved database image. However, image retrieval across drastically different illumination conditions, e.g. day and night, is still a problem with unsatisfactory results, even in this age of powerful neural models. This is due to a lack of a suitably diverse dataset with true correspondences to perform end-to-end learning. A recent class of neural models allows for realistic translation of images among visual domains with relatively little training data and, most importantly, without ground-truth pairings. In this paper, we explore the task of accurately localizing images captured from two traversals of the same area in both day and night. We propose ToDayGAN - a modified image-translation model to alter nighttime driving images to a more useful daytime representation. We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image. Our approach improves localization performance by over 250% compared the current state-of-the-art, in the context of standard metrics in multiple categories.	['Luc van Gool', 'Marc Pollefeys', 'Radu Timofte', 'Torsten Sattler', 'Asha Anoosheh']	2018-09-26	null	null	null	null	['style-generalization']	['computer-vision']	[-3.17048952e-02 -2.98192084e-01 -1.14500962e-01 -5.44115067e-01 -9.97029483e-01 -1.01161385e+00 8.04822922e-01 7.47948065e-02 -8.25109541e-01 5.12130439e-01 -1.52168304e-01 9.01343152e-02 -1.00155547e-01 -3.89394730e-01 -1.11278892e+00 -5.81903994e-01 1.18354999e-01 7.37802386e-01 3.74856055e-01 -2.50426978e-01 3.71961296e-01 7.69603908e-01 -1.61438966e+00 -3.49596232e-01 1.62396401e-01 9.98252332e-01 6.22978508e-01 6.92316175e-01 2.65492260e-01 4.89041567e-01 -4.25322503e-01 -1.29133329e-01 5.40461361e-01 -9.60759446e-02 -6.20562971e-01 5.88931628e-02 9.99964952e-01 -3.12400222e-01 -5.66797316e-01 1.01505744e+00 3.83339077e-01 2.97500223e-01 3.48753661e-01 -1.38284361e+00 -4.00512755e-01 -2.96594054e-01 -5.21918178e-01 4.97829504e-02 4.45664465e-01 -2.57513952e-02 6.68010414e-01 -8.72628629e-01 8.25203180e-01 9.66440201e-01 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8e3ca535-a855-41b5-9209-a652feeee3ff	deep-slow-motion-video-reconstruction-with	2002.12106	null	https://arxiv.org/abs/2002.12106v2	https://arxiv.org/pdf/2002.12106v2.pdf	Deep Slow Motion Video Reconstruction with Hybrid Imaging System	Slow motion videos are becoming increasingly popular, but capturing high-resolution videos at extremely high frame rates requires professional high-speed cameras. To mitigate this problem, current techniques increase the frame rate of standard videos through frame interpolation by assuming linear object motion which is not valid in challenging cases. In this paper, we address this problem using two video streams as input; an auxiliary video with high frame rate and low spatial resolution, providing temporal information, in addition to the standard main video with low frame rate and high spatial resolution. We propose a two-stage deep learning system consisting of alignment and appearance estimation that reconstructs high resolution slow motion video from the hybrid video input. For alignment, we propose to compute flows between the missing frame and two existing frames of the main video by utilizing the content of the auxiliary video frames. For appearance estimation, we propose to combine the warped and auxiliary frames using a context and occlusion aware network. We train our model on synthetically generated hybrid videos and show high-quality results on a variety of test scenes. To demonstrate practicality, we show the performance of our system on two real dual camera setups with small baseline.	['Nima Khademi Kalantari', 'Avinash Paliwal']	2020-02-27	null	null	null	null	['video-reconstruction']	['computer-vision']	[ 2.38517001e-01 -3.98465872e-01 -4.14742567e-02 -9.07449275e-02 -6.88660085e-01 -3.61900896e-01 3.36952537e-01 -4.28961575e-01 -5.90062618e-01 6.88691258e-01 4.41568866e-02 1.26618827e-02 4.17325050e-01 -5.12453318e-01 -1.02469933e+00 -7.22163975e-01 1.00368343e-01 3.64672462e-03 5.99724472e-01 2.11228102e-01 -8.65679607e-02 3.02695155e-01 -1.55937064e+00 5.97700596e-01 3.01596582e-01 1.00190854e+00 4.20593500e-01 9.68483090e-01 2.38119885e-01 1.08544731e+00 -3.79951864e-01 -1.62023485e-01 6.09455764e-01 -3.61575454e-01 -6.31025136e-01 4.54103559e-01 1.01149642e+00 -1.15142083e+00 -5.18740773e-01 8.12130690e-01 2.38099322e-01 1.55386493e-01 2.93847211e-02 -1.14299822e+00 -2.18028024e-01 2.26196349e-01 -9.53330398e-01 4.74037170e-01 5.42343795e-01 4.33289289e-01 5.72524607e-01 -8.39457691e-01 8.70059252e-01 1.28485823e+00 4.04986471e-01 5.16847074e-01 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36f6ee60-eea3-4a10-81f2-074b2e1924d7	2detect-a-large-2d-expandable-trainable	2306.05907	null	https://arxiv.org/abs/2306.05907v1	https://arxiv.org/pdf/2306.05907v1.pdf	2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning	Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.	['Felix Lucka', 'Tristan van Leeuwen', 'K. Joost Batenburg', 'Sophia B. Coban', 'Maximilian B. Kiss']	2023-06-09	null	null	null	null	['image-reconstruction', 'image-denoising', 'super-resolution', 'computed-tomography-ct']	['computer-vision', 'computer-vision', 'computer-vision', 'methodology']	[ 4.17451680e-01 -2.20921546e-01 -4.50358354e-02 -5.95054150e-01 -1.38889611e+00 -1.76754311e-01 2.93103784e-01 8.10895190e-02 -5.55515647e-01 5.04417777e-01 -5.03552631e-02 -3.63763422e-01 -3.57705683e-01 -7.89440393e-01 -4.86747354e-01 -7.75100648e-01 -1.58038855e-01 1.26692235e+00 5.84104240e-01 2.31563598e-01 1.68457732e-01 8.69259655e-01 -9.70700264e-01 5.05558252e-01 3.74974787e-01 6.42761588e-01 8.19574654e-01 6.97959960e-01 1.95310339e-01 3.62558305e-01 1.08875357e-01 -5.21177799e-02 2.50652641e-01 -6.16223931e-01 -7.71912813e-01 4.34597313e-01 1.11161746e-01 -4.79399443e-01 -5.43214008e-02 5.67525506e-01 8.01111519e-01 -1.51287615e-01 6.76867425e-01 -4.83455151e-01 -1.85640037e-01 4.32672948e-01 -4.66486156e-01 3.08284193e-01 3.19373161e-01 4.26795125e-01 3.32786888e-01 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160971c4-a5b4-4f7b-a5e1-c6c999050b7b	a-dual-level-detection-method-for-video-copy	2305.12361	null	https://arxiv.org/abs/2305.12361v1	https://arxiv.org/pdf/2305.12361v1.pdf	A Dual-level Detection Method for Video Copy Detection	With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms. Meta AI hold Video Similarity Challenge on CVPR 2023 to push the technology forward. In this paper, we share our winner solutions on both tracks to help progress in this area. For Descriptor Track, we propose a dual-level detection method with Video Editing Detection (VED) and Frame Scenes Detection (FSD) to tackle the core challenges on Video Copy Detection. Experimental results demonstrate the effectiveness and efficiency of our proposed method. Code is available at https://github.com/FeipengMa6/VSC22-Submission.	['Fengyun Rao', 'Zhenhua Liu', 'Feipeng Ma', 'Tianyi Wang']	2023-05-21	null	null	null	null	['partial-video-copy-detection', 'video-similarity']	['computer-vision', 'computer-vision']	[ 1.26105800e-01 -5.11588693e-01 -3.85701448e-01 1.32206485e-01 -8.90056372e-01 -4.62889582e-01 5.55477560e-01 -1.87608808e-01 -8.25906098e-02 2.33850494e-01 3.05287212e-01 -3.22002429e-03 3.22985619e-01 -3.75742048e-01 -6.16638303e-01 -1.14048690e-01 -3.02363098e-01 -2.65105754e-01 8.15095723e-01 -1.37933046e-01 6.08657360e-01 1.79928765e-01 -1.80613279e+00 6.77937448e-01 5.63656211e-01 9.93595839e-01 5.08092105e-01 9.42880154e-01 2.43146122e-02 1.14057970e+00 -3.02536190e-01 -3.98173392e-01 4.62301731e-01 -4.03812230e-01 -5.96082091e-01 5.84457591e-02 5.94709933e-01 -6.29114151e-01 -7.95433283e-01 1.23836291e+00 6.39335752e-01 4.18242253e-02 4.41287756e-01 -1.51852751e+00 -3.50503027e-01 5.81679165e-01 -1.06810904e+00 8.40790510e-01 7.43501246e-01 -9.33853462e-02 8.70322704e-01 -1.30257547e+00 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474bbc6a-fd53-4a69-87a7-f100cc619f69	neural-extractive-text-summarization-with	1902.00863	null	https://arxiv.org/abs/1902.00863v2	https://arxiv.org/pdf/1902.00863v2.pdf	Neural Extractive Text Summarization with Syntactic Compression	Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-document summarization based on joint extraction and syntactic compression. Our model chooses sentences from the document, identifies possible compressions based on constituency parses, and scores those compressions with a neural model to produce the final summary. For learning, we construct oracle extractive-compressive summaries, then learn both of our components jointly with this supervision. Experimental results on the CNN/Daily Mail and New York Times datasets show that our model achieves strong performance (comparable to state-of-the-art systems) as evaluated by ROUGE. Moreover, our approach outperforms an off-the-shelf compression module, and human and manual evaluation shows that our model's output generally remains grammatical.	['Jiacheng Xu', 'Greg Durrett']	2019-02-03	neural-extractive-text-summarization-with-1	https://aclanthology.org/D19-1324	https://aclanthology.org/D19-1324.pdf	ijcnlp-2019-11	['extractive-document-summarization']	['natural-language-processing']	[ 4.35869426e-01 6.55401945e-01 -5.42586148e-01 -4.78131294e-01 -1.33702826e+00 -2.99778998e-01 6.19850993e-01 4.37358797e-01 -5.47524929e-01 8.58641863e-01 1.05925953e+00 -2.03206226e-01 1.43627882e-01 -6.81012690e-01 -9.17692125e-01 -1.99981079e-01 2.80667357e-02 8.00864518e-01 -1.90933481e-01 -8.79632607e-02 4.34711128e-01 -4.78243642e-02 -1.31669569e+00 8.07043731e-01 1.12371433e+00 7.82977819e-01 2.29353935e-01 1.14783823e+00 -9.11683440e-02 9.50845420e-01 -8.96811962e-01 -7.78480411e-01 2.46611703e-03 -6.35831833e-01 -9.16510165e-01 9.63116251e-03 6.71439648e-01 -9.35517609e-01 -6.76023424e-01 9.40641046e-01 4.92385864e-01 8.16173702e-02 7.31241941e-01 -4.03427213e-01 -7.52799809e-01 1.57780242e+00 -2.88382530e-01 2.33185783e-01 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9def26e3-965b-4b5a-9822-48fe66c8b610	camlpad-cybersecurity-autonomous-machine	1907.10442	null	https://arxiv.org/abs/1907.10442v1	https://arxiv.org/pdf/1907.10442v1.pdf	CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection	As machine learning and cybersecurity continue to explode in the context of the digital ecosystem, the complexity of cybersecurity data combined with complicated and evasive machine learning algorithms leads to vast difficulties in designing an end to end system for intelligent, automatic anomaly classification. On the other hand, traditional systems use elementary statistics techniques and are often inaccurate, leading to weak centralized data analysis platforms. In this paper, we propose a novel system that addresses these two problems, titled CAMLPAD, for Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection. The CAMLPAD systems streamlined, holistic approach begins with retrieving a multitude of different species of cybersecurity data in real time using elasticsearch, then running several machine learning algorithms, namely Isolation Forest, Histogram Based Outlier Score (HBOS), Cluster Based Local Outlier Factor (CBLOF), and K Means Clustering, to process the data. Next, the calculated anomalies are visualized using Kibana and are assigned an outlier score, which serves as an indicator for whether an alert should be sent to the system administrator that there are potential anomalies in the network. After comprehensive testing of our platform in a simulated environment, the CAMLPAD system achieved an adjusted rand score of 95 percent, exhibiting the reliable accuracy and precision of the system. All in all, the CAMLPAD system provides an accurate, streamlined approach to real time cybersecurity anomaly detection, delivering a novel solution that has the potential to revolutionize the cybersecurity sector.	['Ayush Hariharan', 'Trisha Pal', 'Ankit Gupta']	2019-07-23	null	null	null	null	['anomaly-classification']	['computer-vision']	[-3.48028511e-01 -3.65495026e-01 -6.43575389e-04 3.30175221e-01 -6.38229251e-02 -9.14021790e-01 6.48950279e-01 9.41739857e-01 -8.41443911e-02 1.95671976e-01 -3.80576611e-01 -8.88890028e-01 -5.95728695e-01 -9.39148724e-01 -2.67788440e-01 -7.48259902e-01 -6.61946595e-01 3.14489901e-01 3.16684574e-01 -1.55208679e-02 5.76405406e-01 8.93804431e-01 -1.60324323e+00 2.07876544e-02 7.54996657e-01 1.58849275e+00 -7.21811593e-01 6.37300491e-01 -7.72751197e-02 3.33198637e-01 -9.39565182e-01 -2.01209038e-01 6.90400720e-01 9.80064496e-02 -3.44394922e-01 -6.05024517e-01 1.14151262e-01 -2.95099616e-01 1.78251434e-02 1.12470639e+00 1.25092015e-01 -1.62896246e-01 4.58608598e-01 -1.93517768e+00 2.47044843e-02 3.17082070e-02 -4.56390381e-01 5.50079346e-01 3.60641539e-01 3.24606031e-01 7.31619418e-01 -5.33300340e-01 4.36729759e-01 9.98749673e-01 5.18392265e-01 -1.67324111e-01 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06fa8543-d3df-4996-bfc5-a4438f01669b	mplug-effective-and-efficient-vision-language	2205.12005	null	https://arxiv.org/abs/2205.12005v2	https://arxiv.org/pdf/2205.12005v2.pdf	mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections	Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing pre-trained models suffer from the problems of low computational efficiency and information asymmetry brought by the long visual sequence in cross-modal alignment. To address these problems, mPLUG introduces an effective and efficient vision-language architecture with novel cross-modal skip-connections, which creates inter-layer shortcuts that skip a certain number of layers for time-consuming full self-attention on the vision side. mPLUG is pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering. mPLUG also demonstrates strong zero-shot transferability when directly transferred to multiple video-language tasks.	['Luo Si', 'Jingren Zhou', 'Fei Huang', 'Songfang Huang', 'Ji Zhang', 'Zheng Cao', 'Guohai Xu', 'Hehong Chen', 'Jiabo Ye', 'Bin Bi', 'Ming Yan', 'Wei Wang', 'Junfeng Tian', 'Haiyang Xu', 'Chenliang Li']	2022-05-24	null	null	null	null	['video-text-retrieval']	['computer-vision']	[ 2.73745298e-01 -1.64074488e-02 -2.18894139e-01 -4.22576576e-01 -1.05731583e+00 -4.16592419e-01 1.00091684e+00 -1.97363123e-01 -4.48800355e-01 2.18224794e-01 2.75703132e-01 -3.77834976e-01 3.17949742e-01 -5.45230746e-01 -1.06250834e+00 -3.36983442e-01 4.97085094e-01 5.26586175e-01 2.37975225e-01 -3.23882461e-01 4.63775508e-02 -7.67001063e-02 -1.58917129e+00 8.15023422e-01 6.84123397e-01 8.18463862e-01 6.41680419e-01 8.51388693e-01 -3.10863018e-01 8.29869449e-01 -1.78066343e-01 -7.42960453e-01 3.82225448e-03 -5.60029805e-01 -9.11802351e-01 1.69515565e-01 1.14136028e+00 -5.56655765e-01 -5.77685475e-01 9.33152735e-01 4.83600527e-01 -7.90005997e-02 6.04101658e-01 -1.54645193e+00 -1.51829028e+00 3.99569035e-01 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8ccc19d6-c1bc-4137-bfec-567b0e19b014	neural-sentence-ordering	1607.06952	null	http://arxiv.org/abs/1607.06952v1	http://arxiv.org/pdf/1607.06952v1.pdf	Neural Sentence Ordering	Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream task, such as multi-document summarization. Given its importance, we propose to study it as an isolated task. We collect a large corpus of academic texts, and derive a data driven approach to learn pairwise ordering of sentences, and validate the efficacy with extensive experiments. Source codes and dataset of this paper will be made publicly available.	['Xinchi Chen', 'Xuanjing Huang', 'Xipeng Qiu']	2016-07-23	null	null	null	null	['sentence-ordering']	['natural-language-processing']	[ 5.37014246e-01 2.68085808e-01 -2.73955345e-01 -5.08005738e-01 -1.25405777e+00 -8.17817986e-01 8.38755786e-01 2.51707405e-01 -2.61541456e-01 1.28640902e+00 9.18497086e-01 -3.88491184e-01 4.58269902e-02 -3.47530216e-01 -4.74562138e-01 -2.98814446e-01 -1.53567031e-01 6.22752607e-01 2.21052110e-01 -5.47860265e-01 8.57282162e-01 1.86746955e-01 -1.06140566e+00 5.40762722e-01 1.20004845e+00 2.57820338e-01 1.00502521e-01 8.37805331e-01 7.99399987e-02 7.62543917e-01 -1.17880523e+00 -5.75210512e-01 1.70068979e-01 -8.56966436e-01 -1.26573122e+00 1.51782727e-03 3.78227592e-01 -1.44477829e-01 2.83496231e-02 9.08307970e-01 8.66019726e-01 3.20932180e-01 7.58491993e-01 -1.09199762e+00 -5.46640038e-01 1.11964452e+00 -4.50274944e-01 3.63693595e-01 7.54474342e-01 -4.38831002e-02 1.53965032e+00 -6.28905177e-01 8.13315213e-01 1.09912896e+00 3.31681758e-01 5.61529875e-01 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