Split (2)

train · 44.6k rows

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7d220314-1cf6-4123-b503-b3f2e47c7aad	fera-2017-addressing-head-pose-in-the-third	1702.04174	null	http://arxiv.org/abs/1702.04174v1	http://arxiv.org/pdf/1702.04174v1.pdf	FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge	The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.	['László A. Jeni', 'Enrique Sánchez-Lozano', 'Jeffrey M. Girard', 'Jeffrey F. Cohn', 'Maja Pantic', 'Michel F. Valstar', 'Lijun Yin', 'Zheng Zhang']	2017-02-14	null	null	null	null	['facial-action-unit-detection']	['computer-vision']	[ 5.05815089e-01 -3.34956527e-01 -5.75102381e-02 -7.97768533e-01 -7.09686100e-01 -5.72331309e-01 6.62472844e-01 -6.01229370e-01 -4.25242186e-01 5.36585689e-01 2.75914997e-01 3.80254656e-01 2.61160046e-01 -2.41014212e-02 -1.02690093e-01 -7.58560359e-01 -3.94421369e-01 -1.26896665e-01 -5.48233330e-01 -1.20915316e-01 -5.51176257e-02 8.76169801e-01 -1.98274076e+00 5.04334092e-01 -9.34462994e-02 1.43841636e+00 -5.10328233e-01 7.13598490e-01 1.49971008e-01 8.37759852e-01 -8.29847634e-01 -5.73241234e-01 1.41893640e-01 -5.43510556e-01 -8.36100698e-01 3.52964938e-01 8.79846096e-01 -6.14791036e-01 1.76399469e-01 9.22778964e-01 8.28017592e-01 2.87350956e-02 5.83076894e-01 -1.58979785e+00 4.75879870e-02 -1.80932850e-01 -7.20362544e-01 1.11722142e-01 9.00706828e-01 -7.06529692e-02 7.10889816e-01 -1.12168157e+00 8.52485180e-01 1.33125317e+00 6.51995122e-01 8.44175994e-01 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d3b57d45-c1e9-4619-9efe-6102ef6b2e5a	unsupervised-feature-rich-clustering	null	null	https://aclanthology.org/C12-2028	https://aclanthology.org/C12-2028.pdf	Unsupervised Feature-Rich Clustering	null	['Vladimir Eidelman']	2012-12-01	unsupervised-feature-rich-clustering-1	https://aclanthology.org/C12-2028	https://aclanthology.org/C12-2028.pdf	coling-2012-12	['text-clustering']	['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 9.28529128e-02 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ad619c90-7d0a-4f63-a845-232860210a39	improving-traffic-sign-recognition-by-active	2111.14426	null	https://arxiv.org/abs/2111.14426v1	https://arxiv.org/pdf/2111.14426v1.pdf	Improving traffic sign recognition by active search	We describe an iterative active-learning algorithm to recognise rare traffic signs. A standard ResNet is trained on a training set containing only a single sample of the rare class. We demonstrate that by sorting the samples of a large, unlabeled set by the estimated probability of belonging to the rare class, we can efficiently identify samples from the rare class. This works despite the fact that this estimated probability is usually quite low. A reliable active-learning loop is obtained by labeling these candidate samples, including them in the training set, and iterating the procedure. Further, we show that we get similar results starting from a single synthetic sample. Our results are important as they indicate a straightforward way of improving traffic-sign recognition for automated driving systems. In addition, they show that we can make use of the information hidden in low confidence outputs, which is usually ignored.	['N. Gustafsson', 'E. Werner', 'B. Mehlig', 'H. Gustafsson', 'S. Jaghouar']	2021-11-29	null	null	null	null	['traffic-sign-recognition']	['computer-vision']	[ 4.38847154e-01 3.37916672e-01 -5.19903362e-01 -5.06092846e-01 -9.49846864e-01 -3.01401943e-01 6.11450016e-01 -6.06712289e-02 -6.91282749e-01 9.61792409e-01 -1.87092572e-02 -2.49747381e-01 1.19082741e-01 -6.97579920e-01 -7.82493055e-01 -9.80875611e-01 5.82027473e-02 6.92172587e-01 6.91782653e-01 1.69679940e-01 2.51660317e-01 6.31457865e-01 -2.14369583e+00 1.25823781e-01 1.00651670e+00 8.84482443e-01 2.04366129e-02 7.72368312e-01 -3.68625909e-01 1.06071389e+00 -7.27106988e-01 -2.16028929e-01 2.82149732e-01 -6.80284798e-01 -6.19339347e-01 3.22623551e-01 6.00035608e-01 -2.77251035e-01 -6.50324300e-02 1.00751030e+00 3.79351564e-02 3.84444833e-01 4.94978338e-01 -1.46473551e+00 2.18057677e-01 2.94289380e-01 -2.47098476e-01 1.76078394e-01 -1.29799902e-01 2.28557140e-01 9.84973371e-01 -9.90568638e-01 7.41799831e-01 7.23406971e-01 5.08705080e-01 6.72936380e-01 -1.07008660e+00 -8.16943526e-01 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c2fd8fe5-cc85-4b4c-be25-d17bf25743a3	hodinet-high-order-discrepant-interaction	2307.00954	null	https://arxiv.org/abs/2307.00954v1	https://arxiv.org/pdf/2307.00954v1.pdf	HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection	RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the multimodality and multistage features. However, these features contribute differently to the final saliency results, which raises two issues: 1) how to model discrepant characteristics of RGB images and depth maps; 2) how to fuse these cross-modality features in different stages. In this paper, we propose a high-order discrepant interaction network (HODINet) for RGB-D SOD. Concretely, we first employ transformer-based and CNN-based architectures as backbones to encode RGB and depth features, respectively. Then, the high-order representations are delicately extracted and embedded into spatial and channel attentions for cross-modality feature fusion in different stages. Specifically, we design a high-order spatial fusion (HOSF) module and a high-order channel fusion (HOCF) module to fuse features of the first two and the last two stages, respectively. Besides, a cascaded pyramid reconstruction network is adopted to progressively decode the fused features in a top-down pathway. Extensive experiments are conducted on seven widely used datasets to demonstrate the effectiveness of the proposed approach. We achieve competitive performance against 24 state-of-the-art methods under four evaluation metrics.	['Yan-Feng Wu', 'Fu Guo', 'Xiao Jin', 'Jing Xu', 'Kang Yi']	2023-07-03	null	null	null	null	['rgb-d-salient-object-detection', 'salient-object-detection-1']	['computer-vision', 'computer-vision']	[ 7.79929459e-02 -6.62529692e-02 -4.80276858e-03 -3.99349272e-01 -8.45148683e-01 -1.65548325e-01 5.19084275e-01 9.79243517e-02 -3.01155627e-01 3.35026979e-01 3.92963797e-01 3.27594317e-02 2.02560425e-02 -5.45078278e-01 -6.97232008e-01 -7.71955967e-01 3.39319617e-01 -4.02465433e-01 7.57444561e-01 -4.24493015e-01 2.91805863e-01 3.91756445e-01 -1.75735605e+00 5.19742131e-01 9.38284099e-01 1.47423613e+00 4.74480003e-01 3.52700144e-01 -5.86437844e-02 7.43811607e-01 -9.56076756e-02 -2.69429356e-01 1.99131876e-01 -4.34728116e-01 -5.57512105e-01 1.29325300e-01 2.43411362e-01 -5.00387251e-01 -5.52247524e-01 1.09136045e+00 5.97662926e-01 -1.01115309e-01 4.39911753e-01 -1.38384199e+00 -6.75743043e-01 2.36205503e-01 -9.87465322e-01 4.20352936e-01 3.52864832e-01 3.35805774e-01 9.06229973e-01 -1.21197271e+00 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cf77bae4-bfcb-44b0-8f75-ebbd7b45bb43	multi-channel-target-speech-extraction-with	2010.09191	null	https://arxiv.org/abs/2010.09191v1	https://arxiv.org/pdf/2010.09191v1.pdf	Multi-channel target speech extraction with channel decorrelation and target speaker adaptation	The end-to-end approaches for single-channel target speech extraction have attracted widespread attention. However, the studies for end-to-end multi-channel target speech extraction are still relatively limited. In this work, we propose two methods for exploiting the multi-channel spatial information to extract the target speech. The first one is using a target speech adaptation layer in a parallel encoder architecture. The second one is designing a channel decorrelation mechanism to extract the inter-channel differential information to enhance the multi-channel encoder representation. We compare the proposed methods with two strong state-of-the-art baselines. Experimental results on the multi-channel reverberant WSJ0 2-mix dataset demonstrate that our proposed methods achieve up to 11.2% and 11.5% relative improvements in SDR and SiSDR, respectively.	['Yijie Li', 'Yanhua Long', 'Xinyuan Zhou', 'Jiangyu Han']	2020-10-19	null	null	null	null	['speech-extraction']	['speech']	[ 2.95431077e-01 -9.58045274e-02 -1.63212046e-01 -1.95401534e-01 -1.84254730e+00 -5.03561437e-01 4.28824484e-01 -3.46632779e-01 -3.98674637e-01 6.38979375e-01 6.97148740e-01 -4.70647484e-01 3.54977101e-01 1.42845809e-01 -5.22250712e-01 -9.35266137e-01 1.87984202e-02 -4.38594669e-01 1.83495656e-01 -3.46176662e-02 -1.60671435e-02 1.20038413e-01 -8.91280055e-01 4.78478879e-01 7.11043477e-01 1.24356878e+00 5.74645519e-01 1.07058144e+00 1.94750011e-01 6.11954629e-01 -7.46284604e-01 -1.45101070e-01 1.62058443e-01 -8.98843765e-01 -1.65788129e-01 -5.46364002e-02 5.91696128e-02 -2.72777885e-01 -7.75191963e-01 1.12115347e+00 1.37484908e+00 -7.06235915e-02 4.21526819e-01 -5.77290356e-01 -2.42670536e-01 9.94300008e-01 -6.23541415e-01 4.74832326e-01 7.52314180e-02 2.70052522e-04 1.12829280e+00 -1.27157283e+00 1.18241400e-01 1.15430391e+00 5.14857650e-01 3.05371225e-01 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-1.41558933e+00 -7.79565632e-01 -4.92024980e-02 8.07280421e-01 -2.93559551e-01 -3.35956305e-01 9.92479548e-02]	[14.805537223815918, 5.99949312210083]
76b0dfb9-a7fc-477e-8bba-304faf345a9c	attention-mechanism-exploits-temporal	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.pdf	Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction	We propose a novel attention-based framework for 3D human pose estimation from a monocular video. Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be remarkably reduced by increasing the receptive field in a video. Therefore, we design an attentional mechanism to adaptively identify significant frames and tensor outputs from each deep neural net layer, leading to a more optimal estimation. To achieve large temporal receptive fields, multi-scale dilated convolutions are employed to model long-range dependencies among frames. The architecture is straightforward to implement and can be flexibly adopted for real-time applications. Any off-the-shelf 2D pose estimation system, e.g. Mocap libraries, can be easily integrated in an ad-hoc fashion. We both quantitatively and qualitatively evaluate our method on various standard benchmark datasets (e.g. Human3.6M, HumanEva). Our method considerably outperforms all the state-of-the-art algorithms up to 8% error reduction (average mean per joint position error: 34.7) as compared to the best-reported results. Code is available at: (https://github.com/lrxjason/Attention3DHumanPose)	[' Vijayan Asari', ' Sen-ching Cheung', ' Chen Chen', ' He Wang', ' Ju Shen', 'Ruixu Liu']	2020-06-01	null	null	null	cvpr-2020-6	['monocular-3d-human-pose-estimation']	['computer-vision']	[-2.15280369e-01 -3.11194569e-01 5.89838177e-02 -2.69691527e-01 -4.87462342e-01 -3.64352167e-01 3.75402778e-01 -3.49743098e-01 -8.37203264e-01 5.40371358e-01 1.37246937e-01 2.75254756e-01 1.31526574e-01 -1.73163742e-01 -9.53143954e-01 -5.29522121e-01 -2.16683656e-01 3.13204914e-01 3.05672020e-01 6.65055290e-02 1.11181930e-01 5.78456402e-01 -1.49862802e+00 8.42749476e-02 3.48513186e-01 1.17820787e+00 3.38644505e-01 8.44196916e-01 5.38865447e-01 7.35159516e-01 -3.84498209e-01 -2.68608660e-01 3.54333937e-01 -1.33493453e-01 -6.91384315e-01 9.89907309e-02 6.47793233e-01 -6.61919713e-01 -8.05324435e-01 8.64347517e-01 8.45970333e-01 3.46495479e-01 2.58736134e-01 -8.40286672e-01 -3.93952876e-01 -2.09811866e-01 -4.60451603e-01 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5342213a-642f-4406-82a4-656cc0745484	modeling-multi-hop-question-answering-as-2	2205.09226	null	https://arxiv.org/abs/2205.09226v1	https://arxiv.org/pdf/2205.09226v1.pdf	Modeling Multi-hop Question Answering as Single Sequence Prediction	Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work, we propose a simple generative approach (PathFid) that extends the task beyond just answer generation by explicitly modeling the reasoning process to resolve the answer for multi-hop questions. By linearizing the hierarchical reasoning path of supporting passages, their key sentences, and finally the factoid answer, we cast the problem as a single sequence prediction task. To facilitate complex reasoning with multiple clues, we further extend the unified flat representation of multiple input documents by encoding cross-passage interactions. Our extensive experiments demonstrate that PathFid leads to strong performance gains on two multi-hop QA datasets: HotpotQA and IIRC. Besides the performance gains, PathFid is more interpretable, which in turn yields answers that are more faithfully grounded to the supporting passages and facts compared to the baseline Fid model.	['Caiming Xiong', 'Nitish Shirish Keskar', 'Yingbo Zhou', 'Kazuma Hashimoto', 'Semih Yavuz']	2022-05-18	modeling-multi-hop-question-answering-as-1	https://aclanthology.org/2022.acl-long.69	https://aclanthology.org/2022.acl-long.69.pdf	acl-2022-5	['multi-hop-question-answering', 'generative-question-answering', 'passage-retrieval']	['knowledge-base', 'natural-language-processing', 'natural-language-processing']	[-1.64659947e-01 5.72726130e-01 1.65149987e-01 -2.08409280e-01 -1.69665611e+00 -7.76507556e-01 8.46621513e-01 6.48983046e-02 8.79535675e-02 9.70142126e-01 9.47264612e-01 -6.37941539e-01 -1.31609932e-01 -9.90723372e-01 -8.25522006e-01 -1.47434762e-02 5.05673826e-01 8.50523591e-01 5.63930631e-01 -8.59613180e-01 6.78480789e-02 -3.81477743e-01 -1.24680889e+00 8.90247822e-01 1.28801930e+00 6.07299268e-01 7.53810480e-02 9.16089594e-01 -5.69218993e-01 1.48092413e+00 -7.46330142e-01 -1.07173932e+00 -1.12925276e-01 -8.13698649e-01 -1.43445003e+00 -4.17154104e-01 4.94426072e-01 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19bfc6ee-0a9c-4a2c-bb4a-7862842d49a6	star-net-improving-single-image-desnowing	2303.09988	null	https://arxiv.org/abs/2303.09988v1	https://arxiv.org/pdf/2303.09988v1.pdf	Star-Net: Improving Single Image Desnowing Model With More Efficient Connection and Diverse Feature Interaction	Compared to other severe weather image restoration tasks, single image desnowing is a more challenging task. This is mainly due to the diversity and irregularity of snow shape, which makes it extremely difficult to restore images in snowy scenes. Moreover, snow particles also have a veiling effect similar to haze or mist. Although current works can effectively remove snow particles with various shapes, they also bring distortion to the restored image. To address these issues, we propose a novel single image desnowing network called Star-Net. First, we design a Star type Skip Connection (SSC) to establish information channels for all different scale features, which can deal with the complex shape of snow particles.Second, we present a Multi-Stage Interactive Transformer (MIT) as the base module of Star-Net, which is designed to better understand snow particle shapes and to address image distortion by explicitly modeling a variety of important image recovery features. Finally, we propose a Degenerate Filter Module (DFM) to filter the snow particle and snow fog residual in the SSC on the spatial and channel domains. Extensive experiments show that our Star-Net achieves state-of-the-art snow removal performances on three standard snow removal datasets and retains the original sharpness of the images.	['Binling Nie', 'Xuesong Yin', 'Yuanqi Chang', 'Jiawei Mao']	2023-03-17	null	null	null	null	['single-image-desnowing']	['computer-vision']	[ 2.49164939e-01 -4.33109373e-01 5.34070671e-01 -1.48589820e-01 -2.55241305e-01 -3.82971525e-01 1.45436794e-01 -2.82765865e-01 -4.08451259e-02 5.66943884e-01 2.54358530e-01 -2.76867539e-01 1.21805035e-01 -1.06630087e+00 -6.40806317e-01 -1.05046308e+00 1.98081702e-01 -1.10809349e-01 6.07748747e-01 -6.96401238e-01 5.21492623e-02 4.75463301e-01 -1.90993261e+00 1.86431006e-01 1.74727774e+00 8.55379105e-01 6.79331422e-01 4.42850381e-01 -1.94997892e-01 4.62034941e-01 -5.98203480e-01 -3.79679263e-01 5.77683866e-01 -2.92748421e-01 -1.67013869e-01 4.72497821e-01 5.99001944e-01 -5.34285843e-01 -5.90083420e-01 1.40360594e+00 4.91101474e-01 1.42860502e-01 4.57945317e-01 -1.09897864e+00 -5.00495136e-01 3.04371327e-01 -6.91643655e-01 3.48275214e-01 4.56965715e-02 4.23022717e-01 5.00674963e-01 -9.75054204e-01 1.72928184e-01 1.22502112e+00 7.24294841e-01 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091f870e-27fb-465e-b1ab-0c2beb9dbc95	a-self-boosting-framework-for-automated	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.pdf	A Self-Boosting Framework for Automated Radiographic Report Generation	Automated radiographic report generation is a challenging task since it requires to generate paragraphs describing fine-grained visual differences of cases, especially for those between the diseased and the healthy. Existing image captioning methods commonly target at generic images, and lack mechanism to meet this requirement. To bridge this gap, in this paper, we propose a self-boosting framework that improves radiographic report generation based on the cooperation of the main task of report generation and anauxiliary task of image-text matching. The two tasks are built as the two branches of a network model and influence each other in a cooperative way. On one hand, the image-text matching branch helps to learn highly text-correlated visual features for the report generation branch to output high quality reports. One the other hand, the improved reports produced by the report generation branch provideadditional harder samples for the image-text matching task and enforce the latter to improve itself by learning better visual and text feature representations. This, in turn, helps improve the report generation branch again. These two branches are jointly trained to help improve each other iteratively and progressively, so that the whole model is self-boosted without requiring any external resources. Additionally, in the loss function, our model evaluates the quality of the generated reports not only on the word similarity as common approaches do (via minimizing a cross-entropy loss), but also on the feature similarity at high-level, while the latter is provided by the text-encoder of the image-text matching branch. Experimental results demonstrate the effectiveness of our method on two public datasets, showing its superior performance over other state-of-the-art medical report generation methods.	['Xiu Li', 'Lei Wang', 'Luping Zhou', 'Zhanyu Wang']	2021-06-19	null	null	null	cvpr-2021-1	['medical-report-generation', 'word-similarity']	['medical', 'natural-language-processing']	[ 3.67268324e-01 4.44195837e-01 -2.91413873e-01 -2.60862291e-01 -1.24858510e+00 -1.84204906e-01 4.58273292e-01 2.66553342e-01 -1.42825007e-01 7.74395943e-01 5.05345881e-01 -4.91077626e-05 1.76935971e-01 -8.76451969e-01 -7.38248050e-01 -9.38375294e-01 2.72927016e-01 2.53934115e-01 1.48348778e-01 -1.21015441e-02 5.23373485e-02 5.76300323e-02 -1.37503791e+00 5.48206687e-01 9.44592416e-01 1.04330611e+00 3.25213522e-01 5.52234292e-01 -2.42063850e-01 1.11911583e+00 -4.25063908e-01 -6.67591512e-01 -1.51068032e-01 -6.33923352e-01 -6.41986609e-01 3.95358622e-01 3.67851287e-01 -2.90823400e-01 -2.79898763e-01 8.70997250e-01 5.23308158e-01 -1.97894916e-01 6.80518389e-01 -9.96406436e-01 -7.31970847e-01 5.24189591e-01 -7.19960511e-01 9.93416533e-02 2.62258857e-01 2.16949642e-01 1.10225701e+00 -7.44521141e-01 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ac38ded2-23cc-463f-9cf1-2a3c8c2f4aaa	conditionally-invariant-representation	2307.00558	null	https://arxiv.org/abs/2307.00558v1	https://arxiv.org/pdf/2307.00558v1.pdf	Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity	This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. Our approach identifies both spurious and invariant latent features necessary for achieving accurate reconstruction by placing distinct conditional priors on latent features. The invariant signals are disentangled from noise by enforcing independence which facilitates the construction of an interpretable model with a causal semantic. By exploiting the interplay between data domains and labels, our method simultaneously identifies invariant features and builds invariant predictors. We apply our method to grand biological challenges, such as data integration in single-cell genomics with the aim of capturing biological variations across datasets with many samples, obtained from different conditions or multiple laboratories. Our approach allows for the incorporation of specific biological mechanisms, including gene programs, disease states, or treatment conditions into the data integration process, bridging the gap between the theoretical assumptions and real biological applications. Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest. Through extensive benchmarking using large-scale human hematopoiesis and human lung cancer data, we validate the superiority of our approach over existing methods and demonstrate that it can empower deeper insights into cellular heterogeneity and the identification of disease cell states.	['Fabian J. Theis', 'Soroor Hediyeh-Zadeh', 'Ferdinand Kapl', 'Hananeh Aliee']	2023-07-02	null	null	null	null	['data-integration', 'benchmarking', 'benchmarking']	['knowledge-base', 'miscellaneous', 'robots']	[ 4.57570761e-01 -1.77798405e-01 -4.71861452e-01 -4.21002150e-01 -8.44944060e-01 -5.91779888e-01 8.86815727e-01 4.80725288e-01 2.38898601e-02 1.02674806e+00 5.31008303e-01 4.18303674e-03 -4.37021911e-01 -6.32553637e-01 -8.55942428e-01 -1.10980463e+00 4.27295417e-02 5.41794240e-01 -2.60830522e-01 2.80446529e-01 2.83209421e-02 3.93991351e-01 -1.16359007e+00 2.14142278e-01 5.33770204e-01 6.38980985e-01 -2.60051578e-01 4.68154877e-01 3.66343260e-02 4.32448983e-01 -4.13314730e-01 1.52547553e-01 5.72355576e-02 -3.99352103e-01 -3.37365508e-01 1.08357176e-01 1.34264082e-01 3.49463969e-01 -9.50799584e-02 7.15197444e-01 4.12934691e-01 -4.08190370e-01 9.23377872e-01 -1.14794242e+00 -4.32228774e-01 4.77908015e-01 -5.46448410e-01 5.64755760e-02 -5.23573719e-02 4.33412552e-01 1.33569658e+00 -7.19399035e-01 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39670fcb-55ff-4fc0-83a1-f39159fc85a9	saliency-guided-textured-contact-lens-aware	null	null	https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Parzianello_Saliency-Guided_Textured_Contact_Lens-Aware_Iris_Recognition_WACVW_2022_paper.html	https://openaccess.thecvf.com/content/WACV2022W/MAP-A/papers/Parzianello_Saliency-Guided_Textured_Contact_Lens-Aware_Iris_Recognition_WACVW_2022_paper.pdf	Saliency-Guided Textured Contact Lens-Aware Iris Recognition	Iris recognition requires an adequate level of the iris texture being visible to perform a reliable matching. In case when a textured contact lens covers the iris, a false non-match is reported or a presentation attack is detected. There are, however, scenarios in which one wants to maximize the probability of a correct match despite the iris texture being being partially or mostly obscured, for instance when a non-cooperative subject conceals their identity by purposely wearing textured contact lenses. This paper proposes an iris recognition method designed to detect and match portions of live iris tissue still visible when a person wears textured contact lenses. The proposed method includes (a) a convolutional neural network-based segmenter detecting partial live iris patterns, and (b) a Siamese network-based feature extraction model, trained in a novel way with images having non-iris information removed by blurring, to guide the network towards salient live iris features. Experiments matching pairs of iris images in which the iris is not wearing a lens in one image and is wearing a textured contact lens in the other, show a lower EER= 10.6% for the proposed algorithm, compared to state-of-the-art iris code-based iris recognition (EER= 33.6%). The source codes of the method are offered along with the paper.	['Adam Czajka', 'Lucas Parzianello']	2022-01-03	null	null	null	proceedings-of-the-ieee-cvf-winter-conference-1	['iris-segmentation']	['medical']	[ 8.04268122e-01 2.51283288e-01 -2.59820342e-01 -9.84830707e-02 -3.13964397e-01 -4.57347542e-01 3.19564342e-01 -1.99875578e-01 -1.74701229e-01 3.17371190e-01 1.76831096e-01 -2.56082863e-01 -3.45110059e-01 -2.73229450e-01 -6.03977501e-01 -9.29306805e-01 -9.32321846e-02 2.82260895e-01 -2.92403042e-01 3.71490300e-01 5.52973449e-01 6.55508459e-01 -1.81306386e+00 3.15733850e-01 8.46912444e-01 9.51646209e-01 -3.34534913e-01 9.86528337e-01 4.58578527e-01 5.21243572e-01 -6.87709808e-01 -3.95150065e-01 8.44185710e-01 -7.63409436e-01 -6.10706925e-01 6.25154674e-01 1.46415412e+00 -4.44618195e-01 -1.25229537e-01 1.37310922e+00 5.12667716e-01 -2.27962866e-01 4.94592994e-01 -4.97952044e-01 -3.29378486e-01 7.77480528e-02 -9.64920759e-01 1.19373731e-01 5.56399286e-01 4.30565625e-01 3.24602872e-01 -3.92403811e-01 4.59865302e-01 7.64715135e-01 4.63082582e-01 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bf7b43d5-e1e0-4c08-8b9b-c36e9c573d42	gal-geometric-adversarial-loss-for-single	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.pdf	GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction	In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single view image. Distance metrics, like Chamfer distance, were used in previous work to measure the difference of two point sets and serve as the loss function in point-based reconstruction. However, such point-point loss does not constrain the 3D model from a global perspective. We propose to add geometric adversarial loss (GAL). It is composed of two terms where the geometric loss ensures consistent shape of reconstructed 3D models close to ground-truth from different viewpoints, and the conditional adversarial loss generates a semantically-meaningful point cloud. GAL benefits predicting the obscured part of objects and maintaining geometric structure of the predicted 3D model. Both the qualitative results and quantitative analysis manifest the generality and suitability of our method.	['Shaoshuai Shi', 'Xiaojuan Qi', 'Jiaya Jia', 'Li Jiang']	2018-09-01	null	null	null	eccv-2018-9	['3d-object-reconstruction']	['computer-vision']	[-3.94152477e-03 3.91470164e-01 3.11050117e-01 -2.10146070e-01 -6.63702905e-01 -7.09120631e-01 4.81712490e-01 -1.72511145e-01 1.32253721e-01 3.04419786e-01 -1.91946372e-01 2.87885349e-02 4.19588536e-02 -9.50248241e-01 -1.05085289e+00 -4.43504781e-01 2.72412926e-01 4.89554495e-01 5.11573493e-01 -1.35458753e-01 1.79268077e-01 1.26808941e+00 -1.19258964e+00 -2.84788646e-02 6.94295406e-01 1.03570879e+00 -7.15673417e-02 1.94692999e-01 1.68947846e-01 2.71249831e-01 -4.47099477e-01 -8.03325534e-01 7.86455572e-01 -1.53142035e-01 -4.24911588e-01 1.40356317e-01 5.35773873e-01 -4.50539231e-01 -4.76393491e-01 1.08251631e+00 1.59541741e-01 -2.64864415e-01 7.48945892e-01 -1.37031937e+00 -4.95666414e-01 -1.85280874e-01 -7.01485932e-01 -5.03706276e-01 6.75947547e-01 1.06669843e-01 6.45017445e-01 -8.95440161e-01 8.99087787e-01 1.41005540e+00 9.96155858e-01 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a6a46fb5-f60b-48c0-9c8a-9dabf89740b0	attribution-aware-weight-transfer-a-warm	2210.07207	null	https://arxiv.org/abs/2210.07207v1	https://arxiv.org/pdf/2210.07207v1.pdf	Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation	In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift. Although recent works focused on these issues, existing classifier initialization methods do not address the background shift problem and assign the same initialization weights to both background and new foreground class classifiers. We propose to address the background shift with a novel classifier initialization method which employs gradient-based attribution to identify the most relevant weights for new classes from the classifier's weights for the previous background and transfers these weights to the new classifier. This warm-start weight initialization provides a general solution applicable to several CISS methods. Furthermore, it accelerates learning of new classes while mitigating forgetting. Our experiments demonstrate significant improvement in mIoU compared to the state-of-the-art CISS methods on the Pascal-VOC 2012, ADE20K and Cityscapes datasets.	['Didier Stricker', 'Joost Van de Weijer', 'René Schuster', 'Dipam Goswami']	2022-10-13	null	null	null	null	['overlapped-100-5', 'overlapped-5-3', 'overlapped-10-1', 'overlapped-15-5', 'overlapped-100-10', 'class-incremental-semantic-segmentation', 'overlapped-15-1', 'overlapped-50-50', 'overlapped-100-50', 'overlapped-14-1']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 6.43532872e-01 -1.79790750e-01 1.10923275e-02 -4.44670290e-01 -3.91079664e-01 -2.97443599e-01 3.76765907e-01 1.64310232e-01 -7.76216805e-01 8.40763748e-01 -4.47447002e-01 -2.58695364e-01 3.64382446e-01 -7.26022482e-01 -6.45688593e-01 -8.23625326e-01 3.65441829e-01 4.54665691e-01 1.14580214e+00 -1.26229227e-01 1.06197253e-01 3.18438053e-01 -1.79522610e+00 5.72127402e-01 1.07612240e+00 9.65452909e-01 3.38275075e-01 6.15336001e-01 -4.97048974e-01 6.85071826e-01 -1.01387763e+00 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38f43025-8c5b-488b-b1f9-5af0f9539df6	autoregressive-language-model-for-zero-shot	null	null	https://openreview.net/forum?id=5cU6H8EAxpO	https://openreview.net/pdf?id=5cU6H8EAxpO	Autoregressive Language Model for Zero-shot Constrained Keyphrase Generation	Recently, most of the state-of-the-art keyphrase prediction models are based on a supervised generative model. It shows significantly better than before. Nevertheless, it still faces domain robustness and building datasets on high-resource. To overcome these limitations, unsupervised methods have also been critical and studied. We analyzed it also have a defect in a necessary process, which extracts candidates beforehand selecting keyphrase. As not including various forms of phrases, we note that the unsupervised method can't ensure oracle keyphrase. In this paper, we present zero-shot constrained keyphrase generation by leveraging a large-scale language model. To generate diverse keyphrases, we explore controlling a phrase during the generation. Finally, we evaluate benchmark datasets of the scholar domain. It results in better performances than unsupervised methods on several datasets without going through the candidate extraction stage. For domain robustness, we evaluate out-of-domain DUC compare with NUS. Since our method doesn't fine-tune to a corpus of a specific domain, it's better than supervised methods based on Sequence-to-Sequence.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['keyphrase-generation']	['natural-language-processing']	[ 1.03907675e-01 -3.54715511e-02 -4.13789451e-01 1.36921540e-01 -9.42202806e-01 -8.58948469e-01 9.78544176e-01 3.08889002e-01 -4.07976776e-01 1.10412288e+00 4.10700798e-01 -2.50001580e-01 -5.39786369e-02 -8.63569856e-01 -6.08866990e-01 -4.34867144e-01 3.24196398e-01 7.42311418e-01 6.91097140e-01 -5.72119653e-01 6.44176245e-01 2.42330536e-01 -1.55383408e+00 3.48073363e-01 8.78361821e-01 5.36737263e-01 4.27763045e-01 4.64410335e-01 -4.76997942e-01 9.27871168e-01 -6.39950991e-01 -3.37289572e-01 1.49720177e-01 -5.61542392e-01 -7.83966541e-01 1.10751418e-02 2.80127913e-01 -1.80216193e-01 -1.57825023e-01 9.59539175e-01 4.55375254e-01 -4.00794186e-02 8.65449250e-01 -1.20855582e+00 -7.26589620e-01 1.07653880e+00 -2.27466583e-01 6.74498500e-04 6.64084077e-01 -2.71749437e-01 1.39759147e+00 -9.77452219e-01 1.12538028e+00 9.70231652e-01 3.84896576e-01 3.17260653e-01 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-1.07674158e+00 -5.69617629e-01 3.15638453e-01 -1.40465349e-01 -4.11086649e-01 -1.60981566e-01 2.84772873e-01]	[12.265238761901855, 8.894455909729004]
f69b6453-1489-4a49-aa02-eb843169d34d	towards-preemptive-detection-of-depression	2011.05249	null	https://arxiv.org/abs/2011.05249v1	https://arxiv.org/pdf/2011.05249v1.pdf	Towards Preemptive Detection of Depression and Anxiety in Twitter	Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not explicitly talk about their mental state, and if they do, contextual cues such as immediacy must be taken into account. When available, linguistic flags pointing to probable anxiety or depression could be used by medical experts to write better guidelines and treatments. In this paper, we develop a dataset designed to foster research in depression and anxiety detection in Twitter, framing the detection task as a binary tweet classification problem. We then apply state-of-the-art classification models to this dataset, providing a competitive set of baselines alongside qualitative error analysis. Our results show that language models perform reasonably well, and better than more traditional baselines. Nonetheless, there is clear room for improvement, particularly with unbalanced training sets and in cases where seemingly obvious linguistic cues (keywords) are used counter-intuitively.	['Luis Espinosa-Anke', 'Jose Camacho Collados', 'David Owen']	2020-11-10	null	https://aclanthology.org/2020.smm4h-1.12	https://aclanthology.org/2020.smm4h-1.12.pdf	smm4h-coling-2020-12	['anxiety-detection']	['medical']	[ 1.66070566e-01 2.27510825e-01 -4.51274693e-01 -6.88671410e-01 -1.04412055e+00 -6.02119088e-01 7.24582136e-01 1.10657966e+00 -5.83594680e-01 4.73866791e-01 7.70739973e-01 -5.89663327e-01 -1.04861468e-01 -5.97306967e-01 1.24812596e-01 -5.92520870e-02 -9.46347639e-02 5.30077755e-01 -2.83328325e-01 -2.62106776e-01 2.84326971e-01 3.77383590e-01 -9.81546283e-01 4.93046552e-01 7.15872884e-01 6.92270160e-01 -3.85188222e-01 2.71010995e-01 -2.76069552e-01 1.10056686e+00 -5.23167551e-01 -6.29433155e-01 -4.49573874e-01 -2.90253907e-01 -1.02369010e+00 4.69714731e-01 3.60046268e-01 -5.77567399e-01 7.79039338e-02 9.71521437e-01 5.11843383e-01 -9.52940285e-02 7.39015818e-01 -8.85027945e-01 -6.36951685e-01 6.84335172e-01 -7.17592776e-01 4.05656725e-01 9.54290032e-01 4.79341745e-02 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3.44620526e-01 2.41232544e-01 2.02420563e-01 -9.91123468e-02 -3.80559266e-01 -9.07977283e-01 -5.14189303e-01 4.14262444e-01 7.17276335e-01 -4.85800207e-01 -4.00563389e-01 -2.95673162e-01]	[8.801128387451172, 10.309511184692383]
b7175e5d-c915-4832-917f-ab0074e7875f	0-nu-beta-beta-nuclear-matrix-elements	1808.05016	null	http://arxiv.org/abs/1808.05016v1	http://arxiv.org/pdf/1808.05016v1.pdf	$0\nu\beta\beta$ nuclear matrix elements, neutrino potentials and $\mathrm{SU}(4)$ symmetry	Intimate relation between the Gamow-Teller part of the matrix element $M^{0\nu}_\mathrm{GT}$ and the $2\nu\beta\beta$ closure matrix element $M^{2\nu}_\mathrm{cl}$ is explained and explored. If the corresponding radial dependence $C^{2\nu}_\mathrm{cl}(r)$ would be known, $M^{0\nu}$ corresponding to any mechanism responsible for the $0\nu\beta\beta$ decay can be obtained as a simple integral. However, the $M^{2\nu}_\mathrm{cl}$ values sensitively depend on the properties of higher lying $1^+$ states in the intermediate odd-odd nuclei. We show that the $\beta^-$ and $\beta^+$ amplitudes of such states typically have opposite relative signs, and their contributions reduce severally the $M^{2\nu}_\mathrm{cl}$ values. Vanishing values of $M^{2\nu}_\mathrm{cl}$ are signs of a partial restoration of the spin-isospin $\mathrm{SU}(4)$ symmetry. We suggest that demanding that $M^{2\nu}_\mathrm{cl}$ = 0 is a sensible way, within the method of the Quasi-particle Random Phase Approximation (QRPA), of determining the amount of renormalization of isoscalar particle-particle interaction strength $g^{T=0}_{pp}$. Using such prescription, the matrix elements $M^{0\nu}$ are evaluated; their values are not very different ($\le$ 20\%) from the usual QRPA values when $g^{T=0}_{pp}$ is related to the known $2\nu\beta\beta$ half-lives.	[]	2018-08-15	null	null	null	null	['2d-semantic-segmentation']	['computer-vision']	[ 1.31859571e-01 3.03750575e-01 -1.22730680e-01 -1.48840055e-01 -5.77319264e-01 -1.08847618e-01 4.80031133e-01 -1.65734544e-01 -6.24720812e-01 1.04936504e+00 -3.92916024e-01 -7.00107396e-01 -4.85772163e-01 -1.15609288e+00 -4.56517994e-01 -1.53905058e+00 -3.29952925e-01 4.83830631e-01 1.94780439e-01 -7.03646541e-01 2.70367235e-01 3.28398764e-01 -1.88382924e+00 -1.93360746e-01 6.94923460e-01 1.56992340e+00 -8.07214677e-02 2.84866601e-01 -7.11609498e-02 6.52403355e-01 -3.08216184e-01 -2.18540788e-01 4.42246109e-01 -1.20752144e+00 -5.06726384e-01 -5.99653363e-01 -2.68350571e-01 4.46492791e-01 -3.25602323e-01 1.62792897e+00 1.70681685e-01 4.83826429e-01 1.07793319e+00 -6.92198217e-01 -4.93635654e-01 5.46124935e-01 -1.00149918e+00 2.89400160e-01 -5.48732094e-02 1.80454418e-01 8.09205890e-01 -5.03902614e-01 5.23246050e-01 5.40396392e-01 3.03856581e-01 5.16841352e-01 -1.04582918e+00 -1.15022254e+00 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5e61f00c-999b-4b17-9218-fb06a4c9d9b2	spring-a-high-resolution-high-detail-dataset	2303.01943	null	https://arxiv.org/abs/2303.01943v1	https://arxiv.org/pdf/2303.01943v1.pdf	Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo	While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology. Hence, we introduce Spring $-$ a large, high-resolution, high-detail, computer-generated benchmark for scene flow, optical flow, and stereo. Based on rendered scenes from the open-source Blender movie "Spring", it provides photo-realistic HD datasets with state-of-the-art visual effects and ground truth training data. Furthermore, we provide a website to upload, analyze and compare results. Using a novel evaluation methodology based on a super-resolved UHD ground truth, our Spring benchmark can assess the quality of fine structures and provides further detailed performance statistics on different image regions. Regarding the number of ground truth frames, Spring is 60$\times$ larger than the only scene flow benchmark, KITTI 2015, and 15$\times$ larger than the well-established MPI Sintel optical flow benchmark. Initial results for recent methods on our benchmark show that estimating fine details is indeed challenging, as their accuracy leaves significant room for improvement. The Spring benchmark and the corresponding datasets are available at http://spring-benchmark.org.	['Andrés Bruhn', 'Yaroslava Nalivayko', 'Azin Jahedi', 'Jenny Schmalfuss', 'Lukas Mehl']	2023-03-03	null	http://openaccess.thecvf.com//content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.pdf	cvpr-2023-1	['stereo-depth-estimation', 'scene-flow-estimation']	['computer-vision', 'computer-vision']	[-2.00758308e-01 -5.62824011e-01 -1.23017328e-02 -5.98198809e-02 -8.25976670e-01 -6.93951368e-01 6.72872305e-01 -2.55209774e-01 -1.85990557e-01 9.74361479e-01 5.60564280e-01 -4.42940481e-02 1.79843932e-01 -7.03104317e-01 -5.31752110e-01 -5.20757258e-01 -3.42850506e-01 2.06097007e-01 4.06351328e-01 -3.18545312e-01 2.71364599e-01 4.67217058e-01 -1.67037773e+00 3.29743028e-01 7.22324729e-01 9.94717300e-01 -6.61426317e-03 1.10414696e+00 8.44760165e-02 1.19918060e+00 -1.74583629e-01 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23140b88-2143-4a35-9655-31d0dfb0bdb6	distributed-dynamic-safe-screening-algorithms	2204.10981	null	https://arxiv.org/abs/2204.10981v1	https://arxiv.org/pdf/2204.10981v1.pdf	Distributed Dynamic Safe Screening Algorithms for Sparse Regularization	Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional features widely exist in the era of big data. Safe screening is a popular technique to speed up high-dimensional models by discarding the inactive features with zero coefficients. Nevertheless, existing safe screening methods are limited to the sequential setting. In this paper, we propose a new distributed dynamic safe screening (DDSS) method for sparsity regularized models and apply it on shared-memory and distributed-memory architecture respectively, which can achieve significant speedup without any loss of accuracy by simultaneously enjoying the sparsity of the model and dataset. To the best of our knowledge, this is the first work of distributed safe dynamic screening method. Theoretically, we prove that the proposed method achieves the linear convergence rate with lower overall complexity and can eliminate almost all the inactive features in a finite number of iterations almost surely. Finally, extensive experimental results on benchmark datasets confirm the superiority of our proposed method.	['Heng Huang', 'Wenhan Xian', 'Xidong Wu', 'Runxue Bao']	2022-04-23	null	null	null	null	['distributed-optimization']	['methodology']	[-1.58039004e-01 -4.08767343e-01 -2.73374736e-01 -2.67819583e-01 -7.08321214e-01 -7.50815570e-02 -5.73220477e-02 1.39114812e-01 -3.08099866e-01 8.29985559e-01 -2.65817493e-01 -5.25690168e-02 -3.71859521e-01 -6.44702435e-01 -6.81310475e-01 -9.13041472e-01 -1.14596769e-01 4.72503006e-01 2.93235242e-01 2.00937688e-01 8.56879540e-03 2.22540647e-01 -1.52620709e+00 6.25796840e-02 1.11006403e+00 1.28363979e+00 2.67111123e-01 1.31295756e-01 9.14487764e-02 8.17509830e-01 -3.54766130e-01 -7.90926069e-02 5.59346199e-01 -2.39873573e-01 -5.69175422e-01 2.62679368e-01 3.19607794e-01 -4.57553983e-01 -1.41830191e-01 1.19208670e+00 5.72949648e-01 1.44576699e-01 -4.01193909e-02 -1.02942574e+00 -2.88735092e-01 5.63098848e-01 -8.20821881e-01 2.42502376e-01 -6.31652847e-02 -1.06192395e-01 8.52682292e-01 -1.25840831e+00 3.27923983e-01 1.11376333e+00 4.97745544e-01 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adc8682c-f3cf-464c-8a8b-f6c9dadac9a9	explicit-box-detection-unifies-end-to-end	2302.01593	null	https://arxiv.org/abs/2302.01593v1	https://arxiv.org/pdf/2302.01593v1.pdf	Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation	This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. First, we introduce a human detection decoder from encoded tokens to extract global features. It can provide a good initialization for the latter keypoint detection, making the training process converge fast. Second, to bring in contextual information near keypoints, we regard pose estimation as a keypoint box detection problem to learn both box positions and contents for each keypoint. A human-to-keypoint detection decoder adopts an interactive learning strategy between human and keypoint features to further enhance global and local feature aggregation. In general, ED-Pose is conceptually simple without post-processing and dense heatmap supervision. It demonstrates its effectiveness and efficiency compared with both two-stage and one-stage methods. Notably, explicit box detection boosts the pose estimation performance by 4.5 AP on COCO and 9.9 AP on CrowdPose. For the first time, as a fully end-to-end framework with a L1 regression loss, ED-Pose surpasses heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO and achieves the state-of-the-art with 76.6 AP on CrowdPose without bells and whistles. Code is available at https://github.com/IDEA-Research/ED-Pose.	['Lei Zhang', 'Ruimao Zhang', 'Feng Li', 'Shilong Liu', 'Ailing Zeng', 'Jie Yang']	2023-02-03	null	null	null	null	['keypoint-detection', '2d-human-pose-estimation', 'human-detection', 'multi-person-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[-3.23537976e-01 3.32083628e-02 1.03103079e-01 -1.73457608e-01 -1.12449682e+00 -4.08920765e-01 4.86067653e-01 2.28382006e-01 -8.71693313e-01 3.11237276e-01 3.10761422e-01 3.33862484e-01 3.78353059e-01 -5.70577741e-01 -8.29965472e-01 -3.21327984e-01 -1.12313963e-01 6.62714660e-01 6.38854265e-01 -2.40168422e-01 -1.12676390e-01 2.05063000e-01 -1.42415786e+00 8.22939351e-02 7.82134950e-01 9.60577786e-01 2.12783962e-01 9.18467522e-01 4.25644368e-01 2.83380181e-01 -6.39883220e-01 -7.27540672e-01 3.60869676e-01 -1.26033789e-02 -3.94542247e-01 -5.34291565e-02 1.13576758e+00 -6.82746649e-01 -4.87028509e-01 7.25087523e-01 1.01047575e+00 2.03593656e-01 4.27080423e-01 -1.26685917e+00 -1.89037696e-01 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6304e45f-ebe6-490b-b376-211e3238f598	optimizing-temporal-resolution-of	2210.10208	null	https://arxiv.org/abs/2210.10208v1	https://arxiv.org/pdf/2210.10208v1.pdf	Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection	In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely related to the baseline provided for this problem, as they are essentially convolutional recurrent neural networks trained in a mean teacher setting to deal with the heterogeneous annotation of the supplied data. However, the time resolution of the predictions was adapted to deal with the fact that these systems are evaluated using two intersection-based metrics involving different needs in terms of temporal localization. This was done by optimizing the pooling operations. For the first of the defined evaluation scenarios, imposing relatively strict requirements on the temporal localization accuracy, our best model achieved a PSDS score of 0.3609 on the validation data. This is only marginally better than the performance obtained by the baseline system (0.342): The amount of pooling in the baseline network already turned out to be optimal, and thus, no substantial changes were made, explaining this result. For the second evaluation scenario, imposing relatively lax restrictions on the localization accuracy, our best-performing system achieved a PSDS score of 0.7312 on the validation data. This is significantly better than the performance obtained by the baseline model (0.527), which can effectively be attributed to the changes that were applied to the pooling operations of the network.	['Hugo Van hamme', 'Wim Boes']	2022-10-18	null	null	null	null	['sound-event-detection']	['audio']	[-7.23980889e-02 4.07596350e-01 1.84834808e-01 -2.54444569e-01 -1.16287208e+00 -5.87371707e-01 6.03226125e-01 9.96772274e-02 -8.29846740e-01 5.65086842e-01 2.32642710e-01 -1.51142359e-01 -1.01834744e-01 -3.08097035e-01 -6.16528809e-01 -7.19771981e-01 -2.51039565e-01 1.15773212e-02 6.19446278e-01 -9.43046510e-02 7.41812140e-02 4.29995805e-01 -1.70775139e+00 4.85148996e-01 2.32688680e-01 1.17014682e+00 5.02457395e-02 8.82331908e-01 3.85335207e-01 7.51872659e-01 -1.03217411e+00 -2.04390302e-01 4.25031222e-02 -2.50339448e-01 -8.32723439e-01 -5.14235616e-01 5.42720258e-01 -1.97362930e-01 -4.12908643e-01 7.01441646e-01 8.78028333e-01 3.54691625e-01 3.25227827e-01 -9.21519876e-01 -1.06537119e-01 9.00616705e-01 -3.74588259e-02 5.34473598e-01 3.07012767e-01 -1.35449648e-01 1.17554224e+00 -9.07988727e-01 5.32338560e-01 9.26940262e-01 9.01636422e-01 4.14523512e-01 -1.19023788e+00 -4.52945530e-01 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72d2d1ad-bec8-486d-98b8-f51bdf0f2c8d	ssgan-secure-steganography-based-on	1707.01613	null	http://arxiv.org/abs/1707.01613v4	http://arxiv.org/pdf/1707.01613v4.pdf	SSGAN: Secure Steganography Based on Generative Adversarial Networks	In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.	['Xiao-Yu Zhang', 'Yinlong Qian', 'Jing Dong', 'Haichao Shi', 'Wei Wang']	2017-07-06	null	null	null	null	['steganalysis']	['computer-vision']	[ 4.29175526e-01 3.77127938e-02 2.62259752e-01 9.22547802e-02 -2.28602141e-01 -3.41712177e-01 5.37193954e-01 -8.25025439e-01 -1.06374197e-01 5.68912864e-01 -8.60054642e-02 -4.57551837e-01 3.94851953e-01 -1.40526450e+00 -4.91626322e-01 -1.20220816e+00 9.45809297e-03 -2.14047998e-01 2.74132580e-01 -4.05648500e-01 2.02096820e-01 3.02659839e-01 -9.69032824e-01 9.43925306e-02 9.26516414e-01 8.95758450e-01 -9.87949688e-03 5.88672221e-01 3.60789299e-01 6.18002594e-01 -7.80778706e-01 -2.75809735e-01 4.07495797e-01 -7.70776987e-01 -2.57415682e-01 4.62872908e-02 -3.95224899e-01 -5.98621726e-01 -8.01374555e-01 1.37374771e+00 6.25875115e-01 -1.81982547e-01 4.34398532e-01 -1.03431165e+00 -6.45038784e-01 7.04475760e-01 -1.39627188e-01 -1.07758209e-01 1.22365043e-01 5.17946839e-01 4.27310079e-01 -4.56932098e-01 3.17446440e-01 1.35797822e+00 4.11653221e-01 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8a51ebce-868f-4133-86ca-b20c03c73efa	semantic-parsing-via-staged-query-graph	null	null	https://aclanthology.org/P15-1128	https://aclanthology.org/P15-1128.pdf	Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base	null	['Ming-Wei Chang', 'Wen-tau Yih', 'Xiaodong He', 'Jianfeng Gao']	2015-07-01	semantic-parsing-via-staged-query-graph-1	https://aclanthology.org/P15-1128	https://aclanthology.org/P15-1128.pdf	ijcnlp-2015-7	['knowledge-base-question-answering']	['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 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bad92d84-db2a-445c-b069-940c40d9b35b	analysis-of-cnn-based-remote-ppg-to	1911.02736	null	https://arxiv.org/abs/1911.02736v2	https://arxiv.org/pdf/1911.02736v2.pdf	Analysis of CNN-based remote-PPG to understand limitations and sensitivities	Deep learning based on Convolutional Neural Network (CNN) has shown promising results in various vision-based applications, recently also in camera-based vital signs monitoring. The CNN-based Photoplethysmography (PPG) extraction has, so far, been focused on performance rather than understanding. In this paper, we try to answer four questions with experiments aiming at improving our understanding of this methodology as it gains popularity. We conclude that the network exploits the blood absorption variation to extract the physiological signals, and that the choice and parameters (phase, spectral content, etc.) of the reference-signal may be more critical than anticipated. The availability of multiple convolutional kernels is necessary for CNN to arrive at a flexible channel combination through the spatial operation, but may not provide the same motion-robustness as a multi-site measurement using knowledge-based PPG extraction. Finally, we conclude that the PPG-related prior knowledge is still helpful for the CNN-based PPG extraction. Consequently, we recommend further investigation of hybrid CNN-based methods to include prior knowledge in their design.	['Gerard de Haan', 'Wenjin Wang', 'Qi Zhan']	2019-11-07	null	null	null	null	['photoplethysmography-ppg']	['medical']	[ 7.00854361e-02 -1.96182474e-01 1.61473304e-01 -2.69988328e-01 -3.93627852e-01 -1.76061362e-01 7.57907405e-02 -7.28780851e-02 -5.46755731e-01 7.10887372e-01 3.62497307e-02 -1.77160412e-01 -1.25162303e-01 -3.56594890e-01 -2.04460353e-01 -1.06151664e+00 -2.24704117e-01 -2.76191175e-01 -1.53498892e-02 -5.81369363e-02 1.76824227e-01 7.73820162e-01 -1.25361705e+00 3.50354947e-02 5.43614805e-01 1.31237328e+00 -2.24436641e-01 8.23858142e-01 1.63798869e-01 6.63414478e-01 -6.78688347e-01 -1.97959021e-01 3.47896934e-01 -8.20516586e-01 -4.30566043e-01 -2.98077673e-01 3.36896241e-01 -3.85517389e-01 -3.08997154e-01 7.12694705e-01 9.89270329e-01 -8.64890367e-02 4.22006488e-01 -8.69736373e-01 -2.17372522e-01 2.82353401e-01 -3.49683911e-01 4.52141970e-01 2.40674708e-02 5.40795207e-01 4.21010703e-01 -6.18376493e-01 2.61584044e-01 5.53128183e-01 8.06126356e-01 5.76935947e-01 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044872b8-b350-4d06-a797-8fc079b00dc1	3d-shape-generation-with-grid-based-implicit-1	2107.10607	null	https://arxiv.org/abs/2107.10607v1	https://arxiv.org/pdf/2107.10607v1.pdf	3D Shape Generation with Grid-based Implicit Functions	Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it is difficult to add spatial supervision into the generation process, as the AE only gives us a global representation. To remedy these issues, we propose to train the GAN on grids (i.e. each cell covers a part of a shape). In this representation each cell is equipped with a latent vector provided by an AE. This localized representation enables more expressiveness (since the cell-based latent vectors can be combined in novel ways) as well as spatial control of the generation process (e.g. via bounding boxes). Our method outperforms the current state of the art on all established evaluation measures, proposed for quantitatively evaluating the generative capabilities of GANs. We show limitations of these measures and propose the adaptation of a robust criterion from statistical analysis as an alternative.	['Leif Kobbelt', 'Isaak Lim', 'Moritz Ibing']	2021-07-22	3d-shape-generation-with-grid-based-implicit	http://openaccess.thecvf.com//content/CVPR2021/html/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.pdf	cvpr-2021-1	['3d-shape-generation']	['computer-vision']	[ 1.46724686e-01 4.30001497e-01 2.75290489e-01 1.33620664e-01 -6.80775285e-01 -8.52727652e-01 1.05335951e+00 -1.55319959e-01 -2.56078858e-02 1.09390855e+00 2.27348462e-01 1.74608767e-01 5.29943146e-02 -1.28653228e+00 -8.72950792e-01 -1.11012924e+00 2.24884957e-01 7.84362435e-01 5.52125499e-02 -1.03195824e-01 4.69228588e-02 1.02290893e+00 -1.67087698e+00 1.02536350e-01 8.42368305e-01 8.39555502e-01 2.00869754e-01 4.55923706e-01 -3.41483392e-02 2.46147230e-01 -9.82305706e-01 -2.71223873e-01 3.42277735e-01 -7.79240489e-01 -4.96457756e-01 2.05741316e-01 2.81456143e-01 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1bbd18d7-3ab5-4594-9e71-88a367527290	report-from-the-nsf-future-directions-1	2203.10012	null	https://arxiv.org/abs/2203.10012v1	https://arxiv.org/pdf/2203.10012v1.pdf	Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges	This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggested promising directions for future work in this important and very rapidly changing area of research.	['Chen Zhang', 'Yizhe Zhang', 'Zhou Yu', 'Yi-Ting Yeh', 'David Traum', 'Samira Shaikh', 'Verena Rieser', 'Zekang Li', 'Dilek Hakkani-Tur', 'Kallirroi Georgila', 'Milica Gasic', 'Maxine Eskenazi', 'Jan Deriu', "Luis Fernando D'Haro", 'Jinho Choi', 'Shikib Mehri']	2022-03-18	null	null	null	null	['dialogue-evaluation']	['natural-language-processing']	[-1.50486277e-02 4.57698733e-01 -2.71798819e-01 -9.58905280e-01 -7.95062244e-01 -8.33455741e-01 7.32522786e-01 1.45277828e-02 -6.47355735e-01 9.67465401e-01 8.23444307e-01 -4.22887385e-01 2.99911737e-01 -2.50836790e-01 2.73475237e-02 5.84276356e-02 1.05775136e-04 1.03762591e+00 6.66632414e-01 -7.19994545e-01 7.97109425e-01 4.44463372e-01 -1.25880671e+00 7.68311024e-01 4.93311942e-01 3.16228211e-01 -7.45963156e-02 1.14084280e+00 -5.05343676e-01 9.99246538e-01 -1.15188718e+00 -7.53897190e-01 -1.90913349e-01 -4.68699634e-01 -1.55227292e+00 1.65011555e-01 5.95503092e-01 -8.23370337e-01 -2.64429390e-01 8.63751650e-01 5.53769052e-01 6.82660758e-01 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7d2c4931-bbe0-48e6-b6d9-f26ef9bc42db	unsupervised-anomaly-detection-in-medical-1	2305.19867	null	https://arxiv.org/abs/2305.19867v1	https://arxiv.org/pdf/2305.19867v1.pdf	Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model	It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called masked-DDPM (mDPPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in DPPM models for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Code is available at https://github.com/hasan1292/mDDPM.	['Chen Chen', 'Jing Hua', 'Umar Khalid', 'Hasan Iqbal']	2023-05-31	null	null	null	null	['unsupervised-anomaly-detection', 'anatomy']	['methodology', 'miscellaneous']	[ 4.89589125e-01 5.49281180e-01 -6.06514402e-02 -4.09469783e-01 -9.26400065e-01 -1.81745827e-01 8.02422106e-01 1.10361040e-01 -2.27044031e-01 5.30418932e-01 2.71120787e-01 -3.19658279e-01 2.17540696e-01 -4.93842155e-01 -6.55972183e-01 -7.53147006e-01 -2.55351931e-01 5.08277833e-01 1.69296965e-01 2.30837330e-01 1.38721138e-01 3.97110283e-01 -1.23674917e+00 6.19866967e-01 1.16340983e+00 8.54789138e-01 3.62901539e-01 4.82010007e-01 -4.15747553e-01 1.18486571e+00 -3.18804801e-01 -1.53978959e-01 2.24288404e-01 -6.74278855e-01 -8.52571666e-01 3.38505089e-01 5.56086123e-01 -3.19706857e-01 -3.24543267e-01 1.26712918e+00 4.26804066e-01 -2.19961643e-01 1.19833779e+00 -1.01952720e+00 -7.20322490e-01 6.60571992e-01 -9.16416407e-01 6.60869658e-01 -1.89171508e-01 2.15896606e-01 6.12160087e-01 -9.80546653e-01 6.74486458e-01 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f186a9ce-2bb4-48db-b5c5-cfd47bcdf92f	waco-word-aligned-contrastive-learning-for	2212.09359	null	https://arxiv.org/abs/2212.09359v3	https://arxiv.org/pdf/2212.09359v3.pdf	WACO: Word-Aligned Contrastive Learning for Speech Translation	End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are available for training. We observe that an ST model's performance closely correlates with its embedding similarity between speech and source transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation. Our key idea is bridging word-level representations for both speech and text modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark, and on a low-resource direction Maltese-English from IWSLT 2023. Our experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. Code is available at https://github.com/owaski/WACO.	['Lei LI', 'Rong Ye', 'Siqi Ouyang']	2022-12-19	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 2.63009280e-01 1.34671196e-01 -3.52986336e-01 -3.23362589e-01 -1.81762397e+00 -7.26102233e-01 9.13850248e-01 -3.06474358e-01 -3.68570685e-01 6.58793688e-01 7.43573010e-01 -7.68932819e-01 6.85212851e-01 -1.16204366e-01 -6.53929472e-01 -4.00514811e-01 2.93141544e-01 7.07024932e-01 1.13529118e-03 -3.56980741e-01 -3.50893617e-01 -2.69089360e-02 -6.07332051e-01 6.72656536e-01 8.40349317e-01 6.23702168e-01 3.65628093e-01 8.86399388e-01 -1.27410889e-01 5.64680874e-01 -4.93910074e-01 -7.07151115e-01 2.19823360e-01 -9.82243598e-01 -9.32222724e-01 -2.05328345e-01 4.12511319e-01 -7.88801759e-02 -4.82206643e-01 6.88549399e-01 9.43882704e-01 -2.88054775e-02 6.17751181e-01 -1.06105983e+00 -7.12465107e-01 9.26297784e-01 -1.88482985e-01 4.43170160e-01 3.70882332e-01 1.31486341e-01 1.35510552e+00 -1.56715047e+00 6.46704674e-01 1.20223248e+00 4.21855897e-01 8.22936535e-01 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96e3845d-c5ad-4490-b922-81686d1b2926	nlx-gpt-a-model-for-natural-language	2203.05081	null	https://arxiv.org/abs/2203.05081v1	https://arxiv.org/pdf/2203.05081v1.pdf	NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks	Natural language explanation (NLE) models aim at explaining the decision-making process of a black box system via generating natural language sentences which are human-friendly, high-level and fine-grained. Current NLE models explain the decision-making process of a vision or vision-language model (a.k.a., task model), e.g., a VQA model, via a language model (a.k.a., explanation model), e.g., GPT. Other than the additional memory resources and inference time required by the task model, the task and explanation models are completely independent, which disassociates the explanation from the reasoning process made to predict the answer. We introduce NLX-GPT, a general, compact and faithful language model that can simultaneously predict an answer and explain it. We first conduct pre-training on large scale data of image-caption pairs for general understanding of images, and then formulate the answer as a text prediction task along with the explanation. Without region proposals nor a task model, our resulting overall framework attains better evaluation scores, contains much less parameters and is 15$\times$ faster than the current SoA model. We then address the problem of evaluating the explanations which can be in many times generic, data-biased and can come in several forms. We therefore design 2 new evaluation measures: (1) explain-predict and (2) retrieval-based attack, a self-evaluation framework that requires no labels. Code is at: https://github.com/fawazsammani/nlxgpt.	['Nikos Deligiannis', 'Tanmoy Mukherjee', 'Fawaz Sammani']	2022-03-09	null	http://openaccess.thecvf.com//content/CVPR2022/html/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.pdf	cvpr-2022-1	['visual-entailment']	['reasoning']	[ 2.51807570e-01 8.28175902e-01 -7.87569433e-02 -6.65701926e-01 -6.84119999e-01 -4.25769746e-01 8.36004794e-01 -1.44560516e-01 1.52178749e-01 5.84264874e-01 4.07607675e-01 -7.57479727e-01 -1.40319504e-02 -5.76567769e-01 -8.74024272e-01 -3.53207082e-01 5.68501294e-01 8.37674797e-01 -4.60168384e-02 -2.49772612e-02 5.69899678e-01 2.15242773e-01 -1.51233923e+00 4.85270202e-01 1.10394716e+00 9.10616040e-01 4.44443434e-01 8.73926461e-01 -5.37715495e-01 1.00015330e+00 -2.08663657e-01 -7.81908631e-01 2.70230342e-02 -7.04037428e-01 -1.29620433e+00 4.13414270e-01 5.60331166e-01 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62b27646-608f-407e-a85b-b1514463b690	random-projections-for-adversarial-attack	2012.06405	null	https://arxiv.org/abs/2012.06405v2	https://arxiv.org/pdf/2012.06405v2.pdf	Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis	Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be well-defined, attacker strategies may still vary widely within those constraints. Therefore, detection should be considered as an open-set problem, standing in contrast to most current detection approaches. These methods take a closed-set view and train binary detectors, thus biasing detection toward attacks seen during detector training. Second, limited information is available at test time and typically confounded by nuisance factors including the label and underlying content of the image. We address these challenges via a novel strategy based on random subspace analysis. We present a technique that utilizes properties of random projections to characterize the behavior of clean and adversarial examples across a diverse set of subspaces. The self-consistency (or inconsistency) of model activations is leveraged to discern clean from adversarial examples. Performance evaluations demonstrate that our technique ($AUC\in[0.92, 0.98]$) outperforms competing detection strategies ($AUC\in[0.30,0.79]$), while remaining truly agnostic to the attack strategy (for both targeted/untargeted attacks). It also requires significantly less calibration data (composed only of clean examples) than competing approaches to achieve this performance.	['Philippe Burlina', 'Neil Fendley', 'Nathan Drenkow']	2020-12-11	null	null	null	null	['adversarial-attack-detection', 'adversarial-attack-detection']	['computer-vision', 'knowledge-base']	[ 4.24071133e-01 -3.41029555e-01 -1.28385857e-01 -6.61525526e-04 -1.11386979e+00 -1.48378325e+00 8.17482710e-01 3.20581309e-02 -2.41544858e-01 3.04161251e-01 -3.87950912e-02 -3.99233788e-01 -9.32301357e-02 -5.16281903e-01 -5.32577157e-01 -7.08125651e-01 -3.70054603e-01 1.36926910e-03 1.57177165e-01 -1.22734383e-01 9.91427153e-02 5.79495311e-01 -9.54248428e-01 5.60875610e-02 5.80536664e-01 8.96450579e-01 -4.77365613e-01 6.65989161e-01 3.79135519e-01 4.68501300e-01 -8.68483007e-01 -5.56376278e-01 8.26350331e-01 -4.26479131e-01 -2.31066793e-01 -7.39544407e-02 7.51389146e-01 -1.76619366e-01 -4.37131822e-01 1.65035343e+00 5.05900800e-01 -6.35608584e-02 6.64887965e-01 -1.12929773e+00 -3.66281897e-01 3.95438910e-01 -5.20478964e-01 4.84738410e-01 4.71498370e-01 3.70922565e-01 9.81305778e-01 -7.21088290e-01 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c3a63fe9-e392-4c8d-bca4-1eadf2294f09	a-multi-view-context-aware-approach-to	1704.01759	null	http://arxiv.org/abs/1704.01759v2	http://arxiv.org/pdf/1704.01759v2.pdf	A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization	Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall.	['Yang Liu', 'Mahinthan Chandramohan', 'Annamalai Narayanan', 'Lihui Chen']	2017-04-06	null	null	null	null	['android-malware-detection']	['miscellaneous']	[ 1.01807222e-01 -5.60534537e-01 -9.14058268e-01 9.38667208e-02 -6.40481412e-01 -1.09448624e+00 8.09032679e-01 1.59837455e-01 2.83803910e-01 -1.25485435e-02 -2.87162475e-02 -6.24774337e-01 -1.59331590e-01 -6.21834397e-01 -3.82173836e-01 -4.39865947e-01 -3.56493562e-01 -9.26862955e-02 6.35766983e-01 -4.54210117e-02 4.82672334e-01 4.13583368e-01 -1.40300846e+00 5.24257958e-01 4.98598427e-01 8.74342084e-01 -1.19385585e-01 7.21450090e-01 -3.63343060e-02 9.40339863e-01 -6.75063074e-01 -7.55637705e-01 8.12316984e-02 -1.16872407e-01 -5.72604775e-01 -2.38897175e-01 5.61115146e-01 -3.15833062e-01 -4.88034934e-02 1.10138643e+00 6.72783190e-03 -5.72895586e-01 5.78437030e-01 -1.57219410e+00 -2.75777280e-01 3.13088685e-01 -8.10002744e-01 4.90203857e-01 6.82468593e-01 1.44359320e-01 1.15190649e+00 -5.28608024e-01 3.95751387e-01 1.12562346e+00 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3c477458-02d9-478b-b71f-095bc1736061	causal-discovery-for-gene-regulatory-network	2301.01110	null	https://arxiv.org/abs/2301.01110v1	https://arxiv.org/pdf/2301.01110v1.pdf	Causal Discovery for Gene Regulatory Network Prediction	Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and edges represent intermolecular interactions (inhibition, regulation, promotion, coexpression, etc.). In this work, a novel algorithm for the discovery of latent graph structures given experimental data is presented.	['Jacob Rast']	2023-01-03	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 4.86616850e-01 1.58101887e-01 -2.97652543e-01 -1.76367909e-02 6.47346258e-01 -7.47231901e-01 1.04919565e+00 6.13740385e-01 1.62187800e-01 1.24077630e+00 2.86293328e-01 -6.17845297e-01 -4.10851717e-01 -8.83846700e-01 -5.17543256e-01 -7.94295847e-01 -4.80592012e-01 3.16444188e-01 1.06476948e-01 2.13702093e-03 4.53267992e-02 7.94236541e-01 -6.69490933e-01 -2.09889501e-01 5.03828645e-01 2.74233613e-02 -2.58967489e-01 6.73368096e-01 -4.74971175e-01 7.43994355e-01 -5.56097329e-01 -1.71568975e-01 -1.96107596e-01 -8.09238315e-01 -3.95813316e-01 1.31363824e-01 -5.08866966e-01 6.09201849e-01 -4.89537001e-01 7.99897850e-01 -1.24102773e-03 3.85608673e-02 1.19342983e+00 -1.23668766e+00 -4.78451610e-01 5.66933453e-01 -4.66819286e-01 -1.32722378e-01 5.39322197e-01 -1.73552126e-01 9.45772529e-01 -7.68759727e-01 9.95866001e-01 1.39309335e+00 7.47568831e-02 1.97574735e-01 -1.69001079e+00 -3.60423654e-01 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978ba999-71ad-4235-9d0d-52f8411c5750	estimation-beyond-data-reweighting-kernel	2305.10898	null	https://arxiv.org/abs/2305.10898v2	https://arxiv.org/pdf/2305.10898v2.pdf	Estimation Beyond Data Reweighting: Kernel Method of Moments	Moment restrictions and their conditional counterparts emerge in many areas of machine learning and statistics ranging from causal inference to reinforcement learning. Estimators for these tasks, generally called methods of moments, include the prominent generalized method of moments (GMM) which has recently gained attention in causal inference. GMM is a special case of the broader family of empirical likelihood estimators which are based on approximating a population distribution by means of minimizing a $\varphi$-divergence to an empirical distribution. However, the use of $\varphi$-divergences effectively limits the candidate distributions to reweightings of the data samples. We lift this long-standing limitation and provide a method of moments that goes beyond data reweighting. This is achieved by defining an empirical likelihood estimator based on maximum mean discrepancy which we term the kernel method of moments (KMM). We provide a variant of our estimator for conditional moment restrictions and show that it is asymptotically first-order optimal for such problems. Finally, we show that our method achieves competitive performance on several conditional moment restriction tasks.	['Jia-Jie Zhu', 'Bernhard Schölkopf', 'Yassine Nemmour', 'Heiner Kremer']	2023-05-18	null	null	null	null	['causal-inference', 'causal-inference']	['knowledge-base', 'miscellaneous']	[ 1.45500168e-01 2.42955670e-01 -5.91723144e-01 -5.57059348e-01 -9.96610343e-01 -2.81982392e-01 7.97563612e-01 6.84989765e-02 -6.48041666e-01 1.04571784e+00 1.44042403e-01 -3.78100365e-01 -5.97113550e-01 -5.70130706e-01 -8.39604855e-01 -6.87270463e-01 -2.78460354e-01 4.42832738e-01 -4.88787070e-02 3.16845745e-01 7.67483234e-01 3.55530739e-01 -1.30636656e+00 -2.69399166e-01 1.10767817e+00 8.32924902e-01 -4.41564955e-02 5.06290436e-01 1.67906452e-02 3.44147354e-01 -4.24969316e-01 -4.49249625e-01 -1.21723175e-01 -4.89956856e-01 -7.61024117e-01 9.06801149e-02 3.51522475e-01 -2.92708635e-01 7.15061799e-02 1.16879380e+00 3.12589765e-01 4.52968121e-01 1.25898933e+00 -1.51065552e+00 -6.41377687e-01 6.82837486e-01 -1.06404901e+00 3.11623275e-01 3.09040368e-01 -5.21606922e-01 1.20731843e+00 -7.54234374e-01 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7e291a25-f073-47b7-abfa-ecfb3e02884b	an-asymmetric-loss-with-anomaly-detection	2302.10889	null	https://arxiv.org/abs/2302.10889v1	https://arxiv.org/pdf/2302.10889v1.pdf	An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction	Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load forecasting task, to remove any present oultiers. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary containing hourly power consumption, weather, and calendar features. Root-mean-square error (RMSE) results show that removing the anomalies efficiently reduces the underestimation and overestimation errors in all the seasonal datasets. Additionally, asymmetric loss functions and seasonality splitting effectively minimize underestimations despite increasing the overestimation error to some degree. Reducing underpredictions of electricity consumption is essential to prevent power outages that can be damaging to the community.	['Mariette Awad', 'Maha Issa', 'Jihan Ghanim']	2023-02-05	null	null	null	null	['load-forecasting']	['miscellaneous']	[-6.28426224e-02 -1.51371330e-01 3.63425255e-01 -3.29804540e-01 -3.67699236e-01 -4.76725310e-01 5.52738845e-01 5.14463186e-01 -1.58869103e-01 1.16951358e+00 1.21002994e-01 -3.93983006e-01 -6.54772818e-01 -1.13649750e+00 -6.05341136e-01 -1.15788114e+00 -3.71990412e-01 4.44810092e-01 -2.24849224e-01 -7.26479143e-02 6.88942224e-02 6.84284210e-01 -1.72134018e+00 7.23822489e-02 1.52178311e+00 1.14290583e+00 2.17444941e-01 2.59523571e-01 -1.13396123e-01 6.22146130e-01 -8.43685746e-01 1.12368740e-01 2.67743170e-01 -5.62994964e-02 -3.06850970e-01 -1.17105491e-01 -2.63005793e-01 -4.72144783e-01 2.33408019e-01 9.77937281e-01 5.51731169e-01 3.07166815e-01 7.08292842e-01 -1.40843999e+00 -2.00518116e-01 6.32211387e-01 -3.81131679e-01 4.32475954e-01 5.33727789e-03 -1.51529714e-01 6.50569737e-01 -5.22168994e-01 -2.26805210e-01 6.30552590e-01 7.12984622e-01 -1.98285729e-01 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6bb1dacb-df14-449b-9fd5-22421d737438	generating-a-graph-colouring-heuristic-with	2304.04051	null	https://arxiv.org/abs/2304.04051v1	https://arxiv.org/pdf/2304.04051v1.pdf	Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks	The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same colour. In this work we investigate whether deep reinforcement learning can be used to discover a competitive construction heuristic for graph colouring. Our proposed approach, ReLCol, uses deep Q-learning together with a graph neural network for feature extraction, and employs a novel way of parameterising the graph that results in improved performance. Using standard benchmark graphs with varied topologies, we empirically evaluate the benefits and limitations of the heuristic learned by ReLCol relative to existing construction algorithms, and demonstrate that reinforcement learning is a promising direction for further research on the graph colouring problem.	['Juergen Branke', 'Giovanni Montana', 'George Watkins']	2023-04-08	null	null	null	null	['q-learning']	['methodology']	[ 1.91286922e-01 5.30392826e-01 -3.73383552e-01 -6.85342704e-04 -2.47777015e-01 -5.58032155e-01 4.41423774e-01 2.78469563e-01 -8.02722797e-02 7.20769167e-01 -6.75344989e-02 -6.19121552e-01 -5.69382668e-01 -1.05248547e+00 -6.25037253e-01 -7.19904900e-01 -6.25801504e-01 7.73605227e-01 1.52601287e-01 -1.67926386e-01 4.22920823e-01 5.75451314e-01 -1.25015879e+00 -1.24974800e-02 4.47714001e-01 8.93401802e-01 -1.14774801e-01 8.66040468e-01 2.08805455e-03 8.14291298e-01 -6.28638804e-01 -4.08857018e-01 6.25700414e-01 -6.62185252e-01 -1.28104699e+00 3.95475179e-01 3.08041543e-01 1.08934201e-01 -2.73120731e-01 7.44836152e-01 3.14136863e-01 1.27690379e-02 4.43733484e-01 -1.62862265e+00 -6.44604862e-01 8.48792195e-01 -5.69660544e-01 1.91242933e-01 5.26219070e-01 2.41792113e-01 1.54504311e+00 4.67314534e-02 6.69482112e-01 1.21519208e+00 6.96901083e-01 3.63406599e-01 -1.40611076e+00 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5.38028419e-01 2.72097766e-01 -8.04037631e-01 -1.23978205e-01 -4.67959970e-01]	[5.270968914031982, 3.116010904312134]
8c6787d9-b652-4741-8aba-806f6eb2dcbc	voxformer-sparse-voxel-transformer-for-camera	2302.12251	null	https://arxiv.org/abs/2302.12251v2	https://arxiv.org/pdf/2302.12251v2.pdf	VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion	Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images. Our framework adopts a two-stage design where we start from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones. A key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces. Therefore, starting with the featurization and prediction of the visible structures is more reliable. Once we obtain the set of sparse queries, we apply a masked autoencoder design to propagate the information to all the voxels by self-attention. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training to less than 16GB. Our code is available on https://github.com/NVlabs/VoxFormer.	['Anima Anandkumar', 'Chen Feng', 'Sanja Fidler', 'Jose M. Alvarez', 'Chaowei Xiao', 'Christopher Choy', 'Zhiding Yu', 'Yiming Li']	2023-02-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-semantic-scene-completion']	['computer-vision']	[ 1.17933087e-01 2.53163636e-01 2.47249246e-01 -3.90539527e-01 -3.57103765e-01 -1.16304301e-01 5.22342622e-01 2.54976917e-02 -8.56444836e-02 4.59398180e-01 3.25770915e-01 1.42298630e-02 3.19623649e-01 -1.04956138e+00 -8.30874324e-01 -7.30635762e-01 7.54755959e-02 6.42204523e-01 3.64252269e-01 3.65606025e-02 2.28754178e-01 5.73436677e-01 -1.80889773e+00 5.15796363e-01 6.69346750e-01 1.31804824e+00 7.05284834e-01 3.40132147e-01 -5.24933875e-01 8.99175704e-01 -2.36755386e-01 1.16638333e-01 1.98912516e-01 5.06335944e-02 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5aa0ac73-8c03-4c88-a0b2-e99f2077ac25	bionavi-np-biosynthesis-navigator-for-natural	2105.13121	null	https://arxiv.org/abs/2105.13121v1	https://arxiv.org/pdf/2105.13121v1.pdf	BioNavi-NP: Biosynthesis Navigator for Natural Products	Nature, a synthetic master, creates more than 300,000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs. To date, there are fewer than 30,000 validated NPs compounds involved in about 33,000 known enzyme catalytic reactions, and even fewer biosynthetic pathways are known with complete cascade-connected enzyme catalysis. Therefore, it is valuable to make computer-aided bio-retrosynthesis predictions. Here, we develop BioNavi-NP, a navigable and user-friendly toolkit, which is capable of predicting the biosynthetic pathways for NPs and NP-like compounds through a novel (AND-OR Tree)-based planning algorithm, an enhanced molecular Transformer neural network, and a training set that combines general organic transformations and biosynthetic steps. Extensive evaluations reveal that BioNavi-NP generalizes well to identifying the reported biosynthetic pathways for 90% of test compounds and recovering the verified building blocks for 73%, significantly outperforming conventional rule-based approaches. Moreover, BioNavi-NP also shows an outstanding capacity of biologically plausible pathways enumeration. In this sense, BioNavi-NP is a leading-edge toolkit to redesign complex biosynthetic pathways of natural products with applications to total or semi-synthesis and pathway elucidation or reconstruction.	['Ruibo Wu', 'Yuedong Yang', 'Connor W. Coley', 'Binghong Chen', 'Chengtao Li', 'Tao Zeng', 'Shuangjia Zheng']	2021-05-26	null	null	null	null	['retrosynthesis']	['medical']	[ 6.04532540e-01 9.52245221e-02 -4.39959764e-01 1.79153949e-01 -2.48037547e-01 -1.31596279e+00 6.75093591e-01 4.15964693e-01 1.31034940e-01 1.20960104e+00 3.92096341e-02 -8.52490187e-01 -4.47911210e-02 -6.89745188e-01 -8.26447427e-01 -7.41563320e-01 -9.81340930e-02 6.19486630e-01 3.33431065e-01 -4.32083517e-01 3.46592575e-01 1.03626704e+00 -1.01041579e+00 2.73840785e-01 9.73008454e-01 7.82999635e-01 4.16598320e-01 2.36341625e-01 -2.12501138e-01 4.19515371e-01 -1.21971883e-01 -7.53350556e-01 5.07007055e-02 -5.55665791e-01 -7.75384545e-01 -5.73247671e-01 -2.20174283e-01 1.90035239e-01 3.09907943e-02 4.96897191e-01 3.20753336e-01 2.07423240e-01 7.64140487e-01 -8.14062238e-01 -7.67200530e-01 4.43593770e-01 1.32230505e-01 -1.08209059e-01 7.04718053e-01 5.30753672e-01 8.39002311e-01 -1.28353620e+00 8.84032547e-01 1.00444484e+00 5.02576709e-01 5.19087195e-01 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eed64737-9909-40fd-b6e1-4a27227def33	ea-2e-improving-consistency-with-event	null	null	https://aclanthology.org/2022.findings-naacl.202	https://aclanthology.org/2022.findings-naacl.202.pdf	EA^2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction	Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events, which will further cause discrepancy for downstream applications such as event knowledge base population, question answering, and hypothesis generation. In this work, we formulate event argument consistency as the constraints from event-event relations under the document-level setting. To improve consistency we introduce the Event-Aware Argument Extraction (EA^2E) model with augmented context for training and inference. Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA^2E compared to baseline methods.	['Heng Ji', 'Qiusi Zhan', 'Qi Zeng']	null	null	null	null	findings-naacl-2022-7	['knowledge-base-population']	['natural-language-processing']	[ 3.22911322e-01 7.39020586e-01 -6.24324799e-01 -4.58249152e-01 -9.81959999e-01 -6.62270367e-01 1.19477046e+00 9.12273943e-01 -3.70360345e-01 1.28208339e+00 8.93663764e-01 -4.47335839e-01 -2.99034387e-01 -9.09558654e-01 -9.10412371e-01 7.74153471e-02 -1.86589081e-02 4.46656495e-01 7.08615661e-01 -9.36108604e-02 1.40233800e-01 -1.72679931e-01 -1.41931129e+00 9.07127798e-01 6.74583375e-01 4.62377548e-01 -7.45249614e-02 4.68442380e-01 -3.89775723e-01 1.44065619e+00 -8.58732045e-01 -6.88643396e-01 -3.87358487e-01 -6.93337798e-01 -1.31282759e+00 -4.27844465e-01 1.14644475e-01 -4.51558232e-02 -4.34806347e-02 6.67070150e-01 2.22875953e-01 2.14225635e-01 6.52274668e-01 -1.33011889e+00 -2.40478352e-01 1.40010607e+00 -2.93568909e-01 6.21432304e-01 5.94978452e-01 -6.29181564e-01 1.49600768e+00 -8.74104440e-01 1.32320404e+00 1.23761070e+00 5.76667845e-01 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f121a176-1128-4d5f-9c93-253742b06b7d	uncertainty-estimation-for-deep-learning	2305.07618	null	https://arxiv.org/abs/2305.07618v1	https://arxiv.org/pdf/2305.07618v1.pdf	Uncertainty Estimation for Deep Learning Image Reconstruction using a Local Lipschitz Metric	The use of deep learning approaches for image reconstruction is of contemporary interest in radiology, especially for approaches that solve inverse problems associated with imaging. In deployment, these models may be exposed to input distributions that are widely shifted from training data, due in part to data biases or drifts. We propose a metric based on local Lipschitz determined from a single trained model that can be used to estimate the model uncertainty for image reconstructions. We demonstrate a monotonic relationship between the local Lipschitz value and Mean Absolute Error and show that this method can be used to provide a threshold that determines whether a given DL reconstruction approach was well suited to the task. Our uncertainty estimation method can be used to identify out-of-distribution test samples, relate information regarding epistemic uncertainties, and guide proper data augmentation. Quantifying uncertainty of learned reconstruction approaches is especially pertinent to the medical domain where reconstructed images must remain diagnostically accurate.	['Matthew S. Rosen', 'Bruce R. Rosen', 'Neha Koonjoo', 'Jeremiah Z. Liu', 'Bo Zhu', 'Danyal F. Bhutto']	2023-05-12	null	null	null	null	['image-reconstruction']	['computer-vision']	[ 3.95922780e-01 4.55866575e-01 -2.05530033e-01 -6.88476324e-01 -1.57155216e+00 -4.40164328e-01 3.93200815e-01 4.40210074e-01 -6.46719277e-01 8.91787410e-01 2.47318119e-01 -4.84956563e-01 -5.46361566e-01 -6.00380242e-01 -1.03040504e+00 -9.51212883e-01 -3.55979949e-02 6.92633212e-01 2.76435409e-02 3.81699741e-01 2.39767641e-01 5.36913276e-01 -8.07576299e-01 3.34631115e-01 6.79588139e-01 1.04679787e+00 1.00044683e-01 5.84699690e-01 1.33302167e-01 8.21577489e-01 -4.93831843e-01 -3.00362799e-02 9.26423669e-02 -6.02278829e-01 -7.95125782e-01 -2.99969465e-01 -6.11263961e-02 -4.96837109e-01 -8.80090669e-02 1.22089982e+00 6.92894518e-01 -1.87233475e-03 1.06060886e+00 -4.93742287e-01 -2.11703673e-01 9.12740171e-01 -2.57909834e-01 5.40010214e-01 1.30426809e-01 7.69214109e-02 3.30449849e-01 -5.22924721e-01 5.55759132e-01 9.49685156e-01 8.34690690e-01 4.82552320e-01 -1.39995348e+00 -2.90639102e-01 -1.38337433e-01 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f0c54348-182d-4ff2-bd15-5d90ddbde840	is-chatgpt-a-good-nlg-evaluator-a-preliminary	2303.04048	null	https://arxiv.org/abs/2303.04048v2	https://arxiv.org/pdf/2303.04048v2.pdf	Is ChatGPT a Good NLG Evaluator? A Preliminary Study	Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable performance on various NLP tasks in terms of automatic evaluation metrics. However, the ability of ChatGPT to serve as an evaluation metric is still underexplored. Considering assessing the quality of natural language generation (NLG) models is an arduous task and NLG metrics notoriously show their poor correlation with human judgments, we wonder whether ChatGPT is a good NLG evaluation metric. In this report, we provide a preliminary meta-evaluation on ChatGPT to show its reliability as an NLG metric. In detail, we regard ChatGPT as a human evaluator and give task-specific (e.g., summarization) and aspect-specific (e.g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models. We conduct experiments on five NLG meta-evaluation datasets (including summarization, story generation and data-to-text tasks). Experimental results show that compared with previous automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation with human judgments in most cases. In addition, we find that the effectiveness of the ChatGPT evaluator might be influenced by the creation method of the meta-evaluation datasets. For the meta-evaluation datasets which are created greatly depending on the reference and thus are biased, the ChatGPT evaluator might lose its effectiveness. We hope our preliminary study could prompt the emergence of a general-purposed reliable NLG metric.	['Jianfeng Qu', 'Zengkui Sun', 'Jie zhou', 'Jinan Xu', 'Zhixu Li', 'Haoxiang Shi', 'Fandong Meng', 'Yunlong Liang', 'Jiaan Wang']	2023-03-07	null	null	null	null	['story-generation']	['natural-language-processing']	[ 1.16184326e-02 3.25615138e-01 -1.06237233e-01 -2.67108291e-01 -1.20281315e+00 -6.27843559e-01 9.91993845e-01 3.98118079e-01 -3.77936184e-01 8.82272720e-01 6.53654218e-01 -3.48154396e-01 1.13883786e-01 -7.32015073e-01 -3.19456398e-01 -4.97051775e-01 3.79943192e-01 8.29729378e-01 2.81543285e-01 -3.02867889e-01 7.77709126e-01 -2.25876659e-01 -1.33994532e+00 5.70425093e-01 1.30808306e+00 5.21309316e-01 2.56719738e-01 7.91069627e-01 -2.50448078e-01 7.74091065e-01 -1.30366850e+00 -6.01253688e-01 -2.28633329e-01 -8.56447935e-01 -1.13905156e+00 -1.82615429e-01 4.21318263e-01 -5.45200109e-02 1.77165493e-01 6.97863519e-01 8.86339664e-01 2.31980961e-02 8.78043592e-01 -1.21077836e+00 -7.71225631e-01 9.81475949e-01 -1.63295567e-01 1.43041424e-02 7.10649133e-01 3.64232987e-01 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27f8be08-8f3a-42f5-825b-b5504f34c325	knowledge-guided-representation-learning-and	2306.09302	null	https://arxiv.org/abs/2306.09302v1	https://arxiv.org/pdf/2306.09302v1.pdf	Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science	An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different soil processes that limit our understanding of soil. Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes. Collecting observed data is cost-prohibitive but reflects real-world behavior, while process-based models can be used to generate ample synthetic data which may not be representative of reality. We propose a framework, knowledge-guided representation learning, and causal structure learning (KGRCL), to accelerate scientific discoveries in soil science. The framework improves representation learning for simulated soil processes via conditional distribution matching with observed soil processes. Simultaneously, the framework leverages both observed and simulated data to learn a causal structure among the soil processes. The learned causal graph is more representative of ground truth than other graphs generated from other causal discovery methods. Furthermore, the learned causal graph is leveraged in a supervised learning setup to predict the impact of fertilizer use and changing weather on soil carbon. We present the results in five different locations to show the improvement in the prediction performance in out-of-sample and few-shots setting.	['Ranveer Chandra', 'Emre Kiciman', 'John Crawford', 'Robert Ness', 'Andy Neal', 'Rishabh Tushir', 'Licheng Liu', 'Swati Sharma', 'Somya Sharma']	2023-06-15	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 5.93092203e-01 3.40494782e-01 -5.84677756e-01 -5.58118485e-02 -1.96905226e-01 -4.47322339e-01 8.32134724e-01 6.39386714e-01 2.09632978e-01 1.16464174e+00 6.38910532e-01 -8.24584484e-01 -5.91839075e-01 -1.69351113e+00 -1.18776345e+00 -6.93725228e-01 -5.33033729e-01 3.06027770e-01 2.32271090e-01 -2.32370794e-01 -6.73956200e-02 3.27183247e-01 -1.57133806e+00 3.69891137e-01 9.99127567e-01 1.70098886e-01 9.18659985e-01 7.13230729e-01 -1.58341542e-01 7.06227124e-01 1.36395663e-01 2.30069309e-01 -8.79997164e-02 -3.68063658e-01 -4.38025415e-01 -2.30498970e-01 -4.04585600e-02 -2.34151140e-01 -5.20255804e-01 8.75011325e-01 1.70358807e-01 -1.56916991e-01 9.90985870e-01 -9.72451508e-01 -4.90784854e-01 1.00463998e+00 -9.45358038e-01 -5.69419935e-02 1.84913754e-01 1.39076948e-01 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362c5c73-6ab2-48a0-8d0b-445c5ce4c7d4	beyond-chemical-language-a-multimodal	2306.14919	null	https://arxiv.org/abs/2306.14919v1	https://arxiv.org/pdf/2306.14919v1.pdf	Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction	We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal multistage feature selection method that identifies physicochemical features based on their direct causal effect on a specific target property. These causal features are then integrated with the vector space generated by molecular embeddings from MOLFORMER. In particular, we employ Mordred descriptors as physicochemical features and identify the Markov blanket of the target property, which theoretically contains the most relevant features for accurate prediction. Our results demonstrate a superior performance of our proposed approach compared to existing state-of-the-art algorithms, including the chemical language-based MOLFORMER and graph neural networks, in predicting complex tasks such as biodegradability and PFAS toxicity estimation. Moreover, we demonstrate the effectiveness of our feature selection method in reducing the dimensionality of the Mordred feature space while maintaining or improving the model's performance. Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.	['Dmitry Zubarev', 'Kristin Schmidt', 'Dan Sanders', 'Renato Cerqueira', 'Karen Fiorela Aquino Gutierrez', 'Emilio Vital Brazil', 'Eduardo Soares']	2023-06-22	null	null	null	null	['property-prediction', 'molecular-property-prediction']	['medical', 'miscellaneous']	[ 5.55273294e-01 -5.01859844e-01 -4.88471150e-01 7.67448321e-02 -6.84186876e-01 -6.52877748e-01 5.47334313e-01 1.09159553e+00 -2.17325091e-01 8.91272247e-01 4.08491552e-01 -4.78319377e-01 -6.20272994e-01 -9.37180579e-01 -7.59797275e-01 -1.18341756e+00 -4.27577555e-01 -6.84736818e-02 -8.50003436e-02 -9.99406800e-02 4.71462548e-01 7.11269557e-01 -1.23138368e+00 3.40946764e-01 9.44437683e-01 1.04318404e+00 3.02387714e-01 3.75082940e-01 1.26960069e-01 8.75187933e-01 -3.19031291e-02 -1.93935826e-01 -2.68871248e-01 -2.31450960e-01 -4.62791592e-01 -5.55802464e-01 1.81155607e-01 2.43270203e-01 -3.96423608e-01 7.04574049e-01 4.76192325e-01 4.02195454e-01 1.10104978e+00 -7.65046000e-01 -7.52169311e-01 3.71983200e-01 -3.34663093e-01 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ed282fb4-5177-4857-a779-43cecf44e78f	dspdet3d-dynamic-spatial-pruning-for-3d-small	2305.03716	null	https://arxiv.org/abs/2305.03716v2	https://arxiv.org/pdf/2305.03716v2.pdf	DSPDet3D: Dynamic Spatial Pruning for 3D Small Object Detection	Fine-grained 3D object detection is a core ability for agents to understand their 3D environment and interact with surrounding objects. However, current methods and benchmarks mainly focus on relatively large stuff. 3D object detectors still struggle on small objects due to weak geometric information. With in-depth study, we find increasing the spatial resolution of the feature maps significantly boosts the performance of 3D small object detection. And more interestingly, though the computational overhead increases dramatically with resolution, the growth mainly comes from the upsampling operation of the decoder. Inspired by this, we present a high-resolution multi-level detector with dynamic spatial pruning named DSPDet3D, which detects objects from large to small by iterative upsampling and meanwhile prunes the spatial representation of the scene at regions where there is no smaller object to be detected in higher resolution. We organize two benchmarks on ScanNet and TO-SCENE dataset to evaluate the ability of fine-grained 3D object detection, where our DSPDet3D improves the detection performance of small objects to a new level while achieving leading inference speed compared with existing 3D object detection methods. Moreover, DSPDet3D trained with only ScanNet rooms can generalize well to scenes in larger scale. It takes less than 2s for DSPDet3D to directly process a whole house or building consisting of dozens of rooms while detecting out almost all objects, ranging from bottles to beds, on a single RTX 3090 GPU. Project page: https://xuxw98.github.io/DSPDet3D/.	['Jiwen Lu', 'Jie zhou', 'Hongmin Liu', 'Ziwei Wang', 'Zhihao Sun', 'Xiuwei Xu']	2023-05-05	null	null	null	null	['small-object-detection']	['computer-vision']	[-2.06224144e-01 -2.81068474e-01 2.15476066e-01 -1.08488396e-01 -2.34337181e-01 -4.70527440e-01 5.15411019e-01 6.96786568e-02 -5.30184150e-01 1.19482286e-01 -9.93387252e-02 -3.27517748e-01 2.38410920e-01 -1.03977704e+00 -8.59154642e-01 -3.98512691e-01 -2.52341151e-01 7.56811142e-01 1.03882194e+00 -1.31075472e-01 -6.29472435e-02 6.61092818e-01 -1.63414192e+00 3.00636381e-01 5.12604535e-01 8.63681138e-01 7.12325513e-01 7.11989760e-01 1.98040828e-01 5.42974710e-01 -7.76378155e-01 1.46338806e-01 7.91346788e-01 1.34013638e-01 -3.33671927e-01 1.41445488e-01 6.93972051e-01 -9.73275900e-01 -5.52472472e-01 1.16571951e+00 5.24675250e-01 5.18848151e-02 3.72683913e-01 -1.09575367e+00 -3.84009182e-01 4.10567701e-01 -9.90440965e-01 7.80074418e-01 3.61200601e-01 6.52754724e-01 6.87525988e-01 -1.14327133e+00 4.99476820e-01 1.76387763e+00 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cc24d388-1648-4fb2-9f25-c5e43024de00	vernacular-search-query-translation-with	2208.03711	null	https://arxiv.org/abs/2208.03711v1	https://arxiv.org/pdf/2208.03711v1.pdf	Vernacular Search Query Translation with Unsupervised Domain Adaptation	With the democratization of e-commerce platforms, an increasingly diversified user base is opting to shop online. To provide a comfortable and reliable shopping experience, it's important to enable users to interact with the platform in the language of their choice. An accurate query translation is essential for Cross-Lingual Information Retrieval (CLIR) with vernacular queries. Due to internet-scale operations, e-commerce platforms get millions of search queries every day. However, creating a parallel training set to train an in-domain translation model is cumbersome. This paper proposes an unsupervised domain adaptation approach to translate search queries without using any parallel corpus. We use an open-domain translation model (trained on public corpus) and adapt it to the query data using only the monolingual queries from two languages. In addition, fine-tuning with a small labeled set further improves the result. For demonstration, we show results for Hindi to English query translation and use mBART-large-50 model as the baseline to improve upon. Experimental results show that, without using any parallel corpus, we obtain more than 20 BLEU points improvement over the baseline while fine-tuning with a small 50k labeled set provides more than 27 BLEU points improvement over the baseline.	['Nikesh Garera', 'Mandar Kulkarni']	2022-08-07	null	null	null	null	['cross-lingual-information-retrieval']	['natural-language-processing']	[-2.73542613e-01 -4.81868058e-01 -5.39070845e-01 -5.37444711e-01 -1.66624713e+00 -1.04724789e+00 4.13501531e-01 -8.47364366e-02 -8.15141022e-01 6.34529650e-01 1.34907097e-01 -6.02219701e-01 3.37629318e-01 -5.43092370e-01 -6.05853677e-01 -1.39365613e-01 5.83180249e-01 1.05663037e+00 2.16992751e-01 -9.40262794e-01 -8.72497857e-02 9.41142067e-02 -6.78991437e-01 4.82990950e-01 1.07431448e+00 8.05551708e-01 3.67624342e-01 2.64070272e-01 -3.22023690e-01 1.01309925e-01 -4.52998906e-01 -9.24735963e-01 6.21898353e-01 -4.34004307e-01 -1.02785981e+00 -2.01216713e-01 1.49120539e-01 -3.10708553e-01 -5.46105765e-02 9.00433660e-01 6.18255973e-01 2.93038320e-02 3.27158123e-01 -8.18167627e-01 -8.81605804e-01 5.86961329e-01 -4.17631269e-01 -5.08833416e-02 4.07489061e-01 -1.84426174e-01 1.14543128e+00 -9.19999003e-01 8.91732752e-01 9.37271774e-01 3.03785801e-01 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34b5d72a-64b9-47e0-95a2-72e460bb7cae	an-end-to-end-strategy-for-recovering-a-free	2305.16845	null	https://arxiv.org/abs/2305.16845v1	https://arxiv.org/pdf/2305.16845v1.pdf	An end-to-end strategy for recovering a free-form potential from a snapshot of stellar coordinates	New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning. Here we present an end-to-end strategy for recovering a free-form analytical potential from a mere snapshot of stellar positions and velocities. First we show how auto-differentiation can be used to capture an agnostic map of the gravitational potential and its underlying dark matter distribution in the form of a neural network. However, in the context of physics, neural networks are both a plague and a blessing as they are extremely flexible for modeling physical systems but largely consist in non-interpretable black boxes. Therefore, in addition, we show how a complementary symbolic regression approach can be used to open up this neural network into a physically meaningful expression. We demonstrate our strategy by recovering the potential of a toy isochrone system.	['Foivos I. Diakogiannis', 'Rodrigo Ibata', 'Wassim Tenachi']	2023-05-26	null	null	null	null	['symbolic-regression']	['knowledge-base']	[-1.31849468e-01 1.41765684e-01 8.52296650e-02 -1.59360111e-01 -2.36661881e-01 -6.42645001e-01 9.52478409e-01 -1.98906541e-01 -1.61587528e-03 7.71785855e-01 -3.04605484e-01 -6.03739381e-01 -1.38798431e-01 -8.35720420e-01 -7.53713429e-01 -9.51062799e-01 -1.37425318e-01 7.15957284e-01 -4.19680448e-03 -3.63107502e-01 1.78916261e-01 6.11064017e-01 -1.19172287e+00 -5.90998471e-01 7.91341305e-01 9.56280768e-01 -9.56933349e-02 5.21768451e-01 5.98321809e-03 6.84816480e-01 -1.49219468e-01 -1.85834005e-01 5.09885371e-01 -4.33464855e-01 -6.43476605e-01 -2.98119336e-01 2.78836221e-01 -6.87613934e-02 -6.17649674e-01 1.04915535e+00 1.68620154e-01 3.36037070e-01 7.98267841e-01 -9.93382931e-01 -4.91989136e-01 2.54422903e-01 -3.06811959e-01 -1.60390213e-01 -4.96375822e-02 5.44906020e-01 9.65363741e-01 -5.45089722e-01 3.88809592e-01 1.00817370e+00 7.33187914e-01 2.32813165e-01 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-5.46025753e-01 8.32641572e-02 5.23340642e-01 -6.98362887e-01 -5.56426346e-01 -2.53408879e-01]	[6.591766834259033, 3.498807668685913]
95eecb1c-b77d-4d7c-a531-cec114c23b01	iterative-refinement-graph-neural-network-for-1	2110.04624	null	https://arxiv.org/abs/2110.04624v3	https://arxiv.org/pdf/2110.04624v3.pdf	Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design	Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a sequence autoregressively while iteratively refining its predicted global structure. The inferred structure in turn guides subsequent residue choices. For efficiency, we model the conditional dependence between residues inside and outside of a CDR in a coarse-grained manner. Our method achieves superior log-likelihood on the test set and outperforms previous baselines in designing antibodies capable of neutralizing the SARS-CoV-2 virus.	['Tommi Jaakkola', 'Regina Barzilay', 'Jeremy Wohlwend', 'Wengong Jin']	2021-10-09	iterative-refinement-graph-neural-network-for	https://openreview.net/forum?id=LI2bhrE_2A	https://openreview.net/pdf?id=LI2bhrE_2A	iclr-2022-4	['protein-design']	['medical']	[ 4.04485017e-01 -7.31869647e-03 -2.00413510e-01 -4.82904196e-01 -4.11050439e-01 -1.12838781e+00 2.54824519e-01 -3.78503241e-02 -1.27632409e-01 1.14883983e+00 3.97610664e-01 -9.02031243e-01 2.34329030e-01 -5.52847266e-01 -9.53191817e-01 -8.18882287e-01 -1.84866950e-01 9.90338385e-01 -3.61855025e-04 -3.31118971e-01 2.06601411e-01 8.93916547e-01 -8.18594694e-01 3.52376908e-01 1.05388629e+00 1.64282471e-01 3.87311667e-01 7.52111495e-01 -9.95289609e-02 2.82265902e-01 -2.82964289e-01 -1.59331784e-01 8.23027864e-02 -7.46404171e-01 -3.58893037e-01 -1.24171970e-03 2.27634069e-02 -4.97726426e-02 2.30824813e-01 5.09076178e-01 3.07360083e-01 -2.27885857e-01 1.06325650e+00 -7.17265785e-01 -8.73956919e-01 -1.67309687e-01 -5.58717787e-01 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8aa820d7-1a9e-4c7f-93d8-ca8898456c27	gsrformer-grounded-situation-recognition	2208.08965	null	https://arxiv.org/abs/2208.08965v4	https://arxiv.org/pdf/2208.08965v4.pdf	GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement	Grounded Situation Recognition (GSR) aims to generate structured semantic summaries of images for "human-like" event understanding. Specifically, GSR task not only detects the salient activity verb (e.g. buying), but also predicts all corresponding semantic roles (e.g. agent and goods). Inspired by object detection and image captioning tasks, existing methods typically employ a two-stage framework: 1) detect the activity verb, and then 2) predict semantic roles based on the detected verb. Obviously, this illogical framework constitutes a huge obstacle to semantic understanding. First, pre-detecting verbs solely without semantic roles inevitably fails to distinguish many similar daily activities (e.g., offering and giving, buying and selling). Second, predicting semantic roles in a closed auto-regressive manner can hardly exploit the semantic relations among the verb and roles. To this end, in this paper we propose a novel two-stage framework that focuses on utilizing such bidirectional relations within verbs and roles. In the first stage, instead of pre-detecting the verb, we postpone the detection step and assume a pseudo label, where an intermediate representation for each corresponding semantic role is learned from images. In the second stage, we exploit transformer layers to unearth the potential semantic relations within both verbs and semantic roles. With the help of a set of support images, an alternate learning scheme is designed to simultaneously optimize the results: update the verb using nouns corresponding to the image, and update nouns using verbs from support images. Extensive experimental results on challenging SWiG benchmarks show that our renovated framework outperforms other state-of-the-art methods under various metrics.	['Alexander G. Hauptmann', 'Teruko Mitamura', 'SiYao Li', 'Qi Dai', 'Zhi-Qi Cheng']	2022-08-18	null	null	null	null	['grounded-situation-recognition']	['computer-vision']	[ 7.08274901e-01 2.63036102e-01 -1.34901568e-01 -5.90444028e-01 -3.90515268e-01 -4.85394478e-01 7.79400706e-01 2.88087279e-01 -4.47585344e-01 5.36471188e-01 4.47039276e-01 -6.99200332e-02 -6.22683065e-03 -9.00164664e-01 -8.13312650e-01 -5.95432162e-01 3.02334696e-01 3.80326509e-01 3.35248053e-01 -2.84733832e-01 1.97193369e-01 2.35623226e-01 -1.65192962e+00 5.76968431e-01 7.82713234e-01 1.06587851e+00 7.66192138e-01 9.40768942e-02 -2.80906498e-01 1.27250314e+00 -4.50922906e-01 -3.81336570e-01 4.51089814e-03 -7.14312851e-01 -8.22638929e-01 6.50187254e-01 -1.06214046e-01 -2.75633335e-01 -3.28436583e-01 1.19149995e+00 7.59349689e-02 4.07763809e-01 3.32341135e-01 -1.28063118e+00 -6.16435528e-01 7.08972394e-01 -5.57367444e-01 1.47997737e-01 5.82727432e-01 7.21730143e-02 1.26388657e+00 -9.53573585e-01 6.35665953e-01 1.42572379e+00 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6a643a70-9449-4ba8-a5d9-e734edfcd4c4	changer-feature-interaction-is-what-you-need	2209.08290	null	https://arxiv.org/abs/2209.08290v1	https://arxiv.org/pdf/2209.08290v1.pdf	Changer: Feature Interaction is What You Need for Change Detection	Change detection is an important tool for long-term earth observation missions. It takes bi-temporal images as input and predicts "where" the change has occurred. Different from other dense prediction tasks, a meaningful consideration for change detection is the interaction between bi-temporal features. With this motivation, in this paper we propose a novel general change detection architecture, MetaChanger, which includes a series of alternative interaction layers in the feature extractor. To verify the effectiveness of MetaChanger, we propose two derived models, ChangerAD and ChangerEx with simple interaction strategies: Aggregation-Distribution (AD) and "exchange". AD is abstracted from some complex interaction methods, and "exchange" is a completely parameter\&computation-free operation by exchanging bi-temporal features. In addition, for better alignment of bi-temporal features, we propose a flow dual-alignment fusion (FDAF) module which allows interactive alignment and feature fusion. Crucially, we observe Changer series models achieve competitive performance on different scale change detection datasets. Further, our proposed ChangerAD and ChangerEx could serve as a starting baseline for future MetaChanger design.	['Zhe Li', 'Kaiyu Li', 'Sheng Fang']	2022-09-17	null	null	null	null	['building-change-detection-for-remote-sensing']	['miscellaneous']	[-1.04618989e-01 -4.72577631e-01 2.82869011e-01 -6.63559020e-01 -2.45684475e-01 -6.58947289e-01 1.09521282e+00 1.29548982e-01 -4.73749608e-01 4.44733411e-01 2.01534718e-01 -1.66628912e-01 -2.11714029e-01 -1.05574834e+00 -5.19237041e-01 -7.04904795e-01 -2.97125727e-01 2.24549044e-02 4.53117996e-01 -6.75187647e-01 1.04726233e-01 6.78373277e-01 -1.71401465e+00 2.97297686e-01 7.93808818e-01 1.16467667e+00 2.51712471e-01 5.94744682e-01 -4.98922775e-03 5.02227724e-01 -3.08057219e-01 -1.01051845e-01 5.57682335e-01 -3.59808981e-01 -6.71075344e-01 -2.76596814e-01 3.94732952e-01 -2.61619508e-01 -3.95239830e-01 1.05754173e+00 6.58495843e-01 1.43907949e-01 3.97944719e-01 -1.24394917e+00 -1.66859418e-01 5.46295166e-01 -5.91832042e-01 7.35551715e-01 2.70821273e-01 3.07743877e-01 9.60775793e-01 -9.82162118e-01 6.48108423e-01 1.11893475e+00 9.32014346e-01 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39accfca-1187-40df-b63c-b6ba92e0e55a	how-to-guide-your-learner-imitation-learning	2303.02073	null	https://arxiv.org/abs/2303.02073v1	https://arxiv.org/pdf/2303.02073v1.pdf	How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement	Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from compounding error, low sample efficiency, and high hyper-parameter sensitivity. In contrast, active imitation learning methods solicit expert interventions to address the limitations. However, recent active imitation learning methods are designed based on human intuitions or empirical experience without theoretical guarantee. In this paper, we propose a novel active imitation learning framework based on a teacher-student interaction model, in which the teacher's goal is to identify the best teaching behavior and actively affect the student's learning process. By solving the optimization objective of this framework, we propose a practical implementation, naming it AdapMen. Theoretical analysis shows that AdapMen can improve the error bound and avoid compounding error under mild conditions. Experiments on the MetaDrive benchmark and Atari 2600 games validate our theoretical analysis and show that our method achieves near-expert performance with much less expert involvement and total sampling steps than previous methods. The code is available at https://github.com/liuxhym/AdapMen.	['Yang Yu', 'Zongzhang Zhang', 'Ruifeng Chen', 'Shengyi Jiang', 'Tianyuan Liu', 'Xinyu Zhang', 'Feng Xu', 'Xu-Hui Liu']	2023-03-03	null	null	null	null	['atari-games']	['playing-games']	[-2.26151526e-01 4.51591879e-01 -6.37490809e-01 8.38615671e-02 -7.75421858e-01 -5.64397037e-01 3.03598136e-01 -1.74397483e-01 -6.90477192e-01 9.01487947e-01 -4.11550999e-01 -2.89294183e-01 -3.96760195e-01 -4.83137071e-01 -8.50766003e-01 -8.60828340e-01 2.41461605e-01 4.38033998e-01 3.53933126e-01 -6.09404966e-02 4.84525859e-01 8.81748423e-02 -1.34644949e+00 -3.78925055e-01 1.59274590e+00 5.69693923e-01 2.93608814e-01 5.63527822e-01 2.94610739e-01 1.22246158e+00 -7.01347470e-01 -1.37289718e-01 3.26819181e-01 -5.12257814e-01 -7.28352249e-01 4.76464778e-02 -2.12478176e-01 -7.21588612e-01 -2.85895288e-01 1.15329671e+00 8.08421433e-01 3.60163510e-01 3.44646573e-01 -1.41891503e+00 -4.92572039e-01 7.80636966e-01 -3.72419477e-01 -2.32928321e-02 3.25950354e-01 6.44328594e-01 7.95894563e-01 -6.34694278e-01 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9af0cbde-315e-4025-a4f3-2590890b49c7	self-evolution-learning-for-mixup-enhance	2305.13547	null	https://arxiv.org/abs/2305.13547v1	https://arxiv.org/pdf/2305.13547v1.pdf	Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks	Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effective on various text classification tasks. However, most of the mixup methods do not consider the varying degree of learning difficulty in different stages of training and generate new samples with one hot labels, resulting in the model over confidence. In this paper, we propose a self evolution learning (SE) based mixup approach for data augmentation in text classification, which can generate more adaptive and model friendly pesudo samples for the model training. SE focuses on the variation of the model's learning ability. To alleviate the model confidence, we introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up. Through experimental analysis, in addition to improving classification accuracy, we demonstrate that SE also enhances the model's generalize ability.	['DaCheng Tao', 'Dongsheng Li', 'Xin Niu', 'Zhiliang Tian', 'Liang Ding', 'Qihuang Zhong', 'Haoqi Zheng']	2023-05-22	null	null	null	null	['few-shot-text-classification']	['natural-language-processing']	[ 3.44741583e-01 2.24346176e-01 -3.68376285e-01 -3.68232191e-01 -3.18817794e-01 -1.88118219e-01 5.19545019e-01 4.93197590e-01 -4.79969323e-01 7.63860524e-01 -7.32237007e-04 -9.17823240e-02 5.83534352e-02 -8.65042090e-01 -1.83948338e-01 -8.20611298e-01 5.74657381e-01 5.09560645e-01 1.63767666e-01 -1.72649264e-01 2.14667812e-01 3.63808721e-02 -1.71981907e+00 3.63309443e-01 1.50994062e+00 8.96146476e-01 1.13684043e-01 5.47231495e-01 -5.89704990e-01 6.65237367e-01 -8.03519607e-01 -3.09690207e-01 8.36846679e-02 -5.85146010e-01 -5.12572587e-01 1.73416525e-01 5.06320670e-02 7.73559287e-02 -2.99206600e-02 9.86264527e-01 4.97085094e-01 4.28533673e-01 8.07177186e-01 -1.43789554e+00 -6.68077648e-01 8.41925085e-01 -5.69116831e-01 1.12286724e-01 -2.80303478e-01 1.59983058e-02 5.81009984e-01 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7733e2e2-568e-4bf1-bc5e-3ee21151768f	document-layout-analysis-via-dynamic-residual	2104.02874	null	https://arxiv.org/abs/2104.02874v1	https://arxiv.org/pdf/2104.02874v1.pdf	Document Layout Analysis via Dynamic Residual Feature Fusion	The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval. However, it is a challenge to build a DLA system because the training data is very limited and lacks an efficient model. In this paper, we propose an end-to-end united network named Dynamic Residual Fusion Network (DRFN) for the DLA task. Specifically, we design a dynamic residual feature fusion module which can fully utilize low-dimensional information and maintain high-dimensional category information. Besides, to deal with the model overfitting problem that is caused by lacking enough data, we propose the dynamic select mechanism for efficient fine-tuning in limited train data. We experiment with two challenging datasets and demonstrate the effectiveness of the proposed module.	['Liang He', 'Jing Yang', 'Xiangcheng Du', 'Ziling Hu', 'Xingjiao Wu']	2021-04-07	null	null	null	null	['document-layout-analysis']	['computer-vision']	[ 8.09303522e-02 -6.50761902e-01 -1.73722319e-02 -3.36759388e-01 -4.35696512e-01 -4.91050899e-01 4.27716315e-01 -2.91664153e-01 -1.64581329e-01 2.16036901e-01 2.55212098e-01 -1.96552038e-01 -4.05548364e-01 -6.49609268e-01 -4.03561264e-01 -6.38111115e-01 4.40401256e-01 4.52889279e-02 1.89876840e-01 1.99797135e-02 5.50576031e-01 6.29618287e-01 -1.35745692e+00 4.46748525e-01 1.24782789e+00 8.70413005e-01 7.00791478e-01 5.03737569e-01 -4.95084584e-01 6.56541765e-01 -7.29344726e-01 1.99236497e-02 -9.26957354e-02 -3.07124317e-01 -5.60895622e-01 4.88122284e-01 4.11201000e-01 -4.56508011e-01 -4.21197295e-01 8.97184610e-01 6.44734979e-01 2.82164007e-01 4.90759462e-01 -1.03380620e+00 -8.05567384e-01 3.80463183e-01 -8.09747815e-01 -6.06690720e-02 8.88339728e-02 -8.09956342e-02 5.77323437e-01 -1.08769214e+00 6.18097425e-01 1.50335550e+00 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250744fd-7d38-4cb8-a4ee-b70734a73c9b	self-supervised-learning-of-geometrically	1804.01552	null	http://arxiv.org/abs/1804.01552v1	http://arxiv.org/pdf/1804.01552v1.pdf	Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection	Self-supervision can dramatically cut back the amount of manually-labelled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection. We do so by building on several recent ideas in unsupervised landmark detection. Our approach learns dense distinctive visual descriptors from an unlabelled dataset of images using synthetic image transformations. It does so by means of a robust probabilistic formulation that can introspectively determine which image regions are likely to result in stable image matching. We show empirically that a network pre-trained in this manner requires significantly less supervision to learn semantic object parts compared to numerous pre-training alternatives. We also show that the pre-trained representation is excellent for semantic object matching.	['Diane Larlus', 'Andrea Vedaldi', 'Samuel Albanie', 'David Novotny']	2018-04-04	self-supervised-learning-of-geometrically-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Novotny_Self-Supervised_Learning_of_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Novotny_Self-Supervised_Learning_of_CVPR_2018_paper.pdf	cvpr-2018-6	['unsupervised-landmark-detection']	['computer-vision']	[ 6.06528878e-01 5.03305733e-01 -1.76614538e-01 -7.67695129e-01 -6.92056477e-01 -4.22890872e-01 8.80648911e-01 3.12342972e-01 -6.59909308e-01 3.44657421e-01 1.36740297e-01 -1.50534838e-01 8.54173973e-02 -8.88394415e-01 -1.11957252e+00 -5.81230700e-01 1.45276338e-01 6.91362083e-01 3.75507355e-01 -1.76912472e-01 3.04200828e-01 9.13556457e-01 -1.71932471e+00 1.11388639e-02 5.13926864e-01 8.53010893e-01 1.40701029e-02 5.46667874e-01 -1.20280847e-01 5.49380541e-01 -2.66386032e-01 -2.37139851e-01 5.06006718e-01 -3.82766545e-01 -1.20631862e+00 6.00153565e-01 7.98132718e-01 -1.22135922e-01 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d39490c4-2457-45d1-935b-31691beda649	how-to-memorize-a-random-60-bit-string	null	null	https://aclanthology.info/papers/N15-1180/n15-1180	https://www.aclweb.org/anthology/N15-1180	How to Memorize a Random 60-Bit String	null	['Marjan Ghazvininejad', 'Kevin Knight']	2015-05-01	null	null	null	hlt-2015-5	['2048']	['playing-games']	[-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 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b0982b7c-9aec-48d3-be6e-69342f4963a9	parsing-entire-discourses-as-very-long	null	null	https://aclanthology.org/Q13-1026	https://aclanthology.org/Q13-1026.pdf	Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning	Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, however, utterances in a conversation are treated independently and discourse structure information is largely ignored. In the context of language acquisition, this independence assumption discards cues that are important to the learner, e.g., the fact that consecutive utterances are likely to share the same referent (Frank et al., 2013). The current paper describes an approach to the problem of simultaneously modeling grounded language at the sentence and discourse levels. We combine ideas from parsing and grammar induction to produce a parser that can handle long input strings with thousands of tokens, creating parse trees that represent full discourses. By casting grounded language learning as a grammatical inference task, we use our parser to extend the work of Johnson et al. (2012), investigating the importance of discourse continuity in children{'}s language acquisition and its interaction with social cues. Our model boosts performance in a language acquisition task and yields good discourse segmentations compared with human annotators.	['Mark Johnson', 'Minh-Thang Luong', 'Michael C. Frank']	2013-01-01	null	null	null	tacl-2013-1	['grounded-language-learning']	['natural-language-processing']	[ 4.93445933e-01 9.10069346e-01 -1.82467937e-01 -7.26912618e-01 -8.10177982e-01 -7.01888621e-01 4.29075181e-01 9.28208888e-01 -4.16828632e-01 6.91883743e-01 6.97071493e-01 -5.10457695e-01 1.97686747e-01 -8.59069645e-01 -9.54964519e-01 -3.21958214e-01 -9.30715259e-03 5.23428261e-01 3.85297924e-01 -3.00367802e-01 2.00584665e-01 -1.21114813e-01 -1.37159181e+00 6.33227050e-01 1.06475997e+00 1.72537751e-02 4.52207834e-01 6.89025044e-01 -5.00354707e-01 1.39870334e+00 -7.01359689e-01 -4.47651654e-01 -3.98751467e-01 -7.50591457e-01 -1.43793511e+00 1.62826087e-02 5.90485454e-01 -4.38115269e-01 2.18973041e-01 9.23244476e-01 2.00368375e-01 1.72630511e-02 4.79501426e-01 -8.61308098e-01 -5.23718476e-01 1.55196249e+00 -2.45802909e-01 1.41275093e-01 5.84879756e-01 -2.85972923e-01 1.20116973e+00 -4.45522875e-01 8.36108506e-01 1.57307327e+00 3.64642709e-01 7.49568641e-01 -1.30421567e+00 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-2.17370525e-01 2.19374523e-01 5.52380204e-01 -1.80968627e-01 -7.10206851e-02 2.22673997e-01]	[10.6866455078125, 9.406256675720215]
4c25b312-22a9-4c69-a613-d1ac2cb158f6	visual-detection-with-context-for-document	null	null	https://aclanthology.org/D19-1348	https://aclanthology.org/D19-1348.pdf	Visual Detection with Context for Document Layout Analysis	We present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset of region-labeled articles. A continuing challenge in scientific literature mining is the difficulty of consistently extracting high-quality text from formatted PDFs. To address this, we adapt the object-detection technique Faster R-CNN for document layout detection, incorporating contextual information that leverages the inherently localized nature of article contents to improve the region detection performance. Due to the limited availability of high-quality region-labels for scientific articles, we also contribute a novel dataset of region annotations, the first version of which covers 9 region classes and 822 article pages. Initial experimental results demonstrate a 23.9{\%} absolute improvement in mean average precision over the baseline model by incorporating contextual features, and a processing speed 14x faster than a text-based technique. Ongoing work on further improvements is also discussed.	['Shinjae Yoo', 'Carlos Soto']	2019-11-01	null	null	null	ijcnlp-2019-11	['document-layout-analysis', 'literature-mining']	['computer-vision', 'natural-language-processing']	[ 1.54441625e-01 -1.72877405e-02 -3.92338604e-01 -2.13873133e-01 -1.22387803e+00 -1.15644026e+00 5.99642873e-01 6.22752905e-01 -2.90366352e-01 5.94591200e-01 2.82605618e-01 -8.47398043e-01 -1.49821620e-02 -4.59431797e-01 -9.01999116e-01 -1.86337397e-01 4.38414589e-02 1.70642599e-01 2.08406284e-01 4.79269773e-01 1.22218788e+00 1.15800369e+00 -1.05370128e+00 5.23825943e-01 7.24559903e-01 8.06397557e-01 3.24364752e-01 9.83203530e-01 -5.98151624e-01 5.59240699e-01 -9.75337386e-01 1.15157530e-01 2.22799227e-01 -1.06060773e-01 -7.27700233e-01 -7.19119459e-02 1.00657141e+00 -2.70019561e-01 -1.43980697e-01 6.87029243e-01 2.67110229e-01 1.71583414e-01 7.85047650e-01 -7.05367982e-01 -1.09385443e+00 5.45242488e-01 -1.27989590e+00 7.14151144e-01 3.83535922e-01 -2.34720677e-01 1.04041076e+00 -1.07905650e+00 1.11739874e+00 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ee06f84f-0039-4087-9455-1e0234052719	promptkg-a-prompt-learning-framework-for	2210.00305	null	https://arxiv.org/abs/2210.00305v2	https://arxiv.org/pdf/2210.00305v2.pdf	LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings	Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained language models, but no open-sourced library is specifically designed for KGs with PLMs at present. In this paper, we present LambdaKG, a library for KGE that equips with many pre-trained language models (e.g., BERT, BART, T5, GPT-3), and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). LambdaKG is publicly open-sourced at https://github.com/zjunlp/PromptKG/tree/main/lambdaKG, with a demo video at http://deepke.zjukg.cn/lambdakg.mp4 and long-term maintenance.	['Huajun Chen', 'Feiyu Xiong', 'Shumin Deng', 'Bozhong Tian', 'Siyuan Cheng', 'Jintian Zhang', 'Yuqi Zhu', 'Ningyu Zhang', 'Xiaohan Wang', 'Zhoubo Li', 'Xin Xie']	2022-10-01	null	null	null	null	['knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'methodology']	[-0.793565 0.5608073 -0.5951951 0.03425942 -0.38141155 -0.47682914 0.41430163 0.52183646 0.04666169 0.77993083 0.5102051 -0.46707144 -0.20703803 -1.3824457 -0.7334117 -0.25340602 -0.23344518 0.768559 0.29616833 -0.33746204 -0.13246228 0.02527981 -1.0487984 0.08226863 0.9345312 0.90652627 0.41026473 0.68504906 -0.48147306 1.157331 -0.06992529 -1.0643362 -0.21725942 0.13046162 -1.3015832 -0.21525343 0.12953909 -0.16808183 -1.0373112 0.8836711 0.4911027 0.18127161 0.60004246 -1.4966654 -1.4048992 0.9681012 0.00926181 -0.06382216 0.5253251 0.01508925 1.394334 -1.1043354 0.88592196 1.0812051 0.50703806 0.24885108 -0.683636 -0.45772 0.03093906 0.5715434 -1.7160107 -0.27585047 0.5602446 -0.34489822 1.1664003 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48a4220d-ee8a-4ae9-8a80-1fd2b50e2be7	learning-dynamic-word-embeddings-with-drift	1907.09169	null	https://arxiv.org/abs/1907.09169v1	https://arxiv.org/pdf/1907.09169v1.pdf	Learning dynamic word embeddings with drift regularisation	Word usage, meaning and connotation change throughout time. Diachronic word embeddings are used to grasp these changes in an unsupervised way. In this paper, we use variants of the Dynamic Bernoulli Embeddings model to learn dynamic word embeddings, in order to identify notable properties of the model. The comparison is made on the New York Times Annotated Corpus in English and a set of articles from the French newspaper Le Monde covering the same period. This allows us to define a pipeline to analyse the evolution of words use across two languages.	['Alexandre Allauzen', 'Syrielle Montariol']	2019-07-22	null	null	null	null	['diachronic-word-embeddings']	['natural-language-processing']	[-3.93331200e-01 -2.19698980e-01 -4.83587205e-01 -3.41995358e-01 7.33674839e-02 -1.00283897e+00 1.25608277e+00 4.38894004e-01 -1.26741457e+00 4.56930310e-01 8.43677521e-01 -4.29449052e-01 -8.18357840e-02 -6.38161242e-01 1.94695853e-02 -3.29383850e-01 -1.72470674e-01 4.09805864e-01 1.20076753e-01 -4.89967704e-01 3.56549442e-01 2.31098145e-01 -1.20540535e+00 4.05130237e-02 1.68814093e-01 2.47735769e-01 2.68033415e-01 7.07695246e-01 -7.40465224e-01 1.64068401e-01 -3.84389520e-01 -6.39264405e-01 7.02645928e-02 8.98414180e-02 -7.16636777e-01 -3.39551687e-01 -1.06344014e-01 2.12953791e-01 -5.74663699e-01 9.37584639e-01 2.58310199e-01 2.24427328e-01 8.80853713e-01 -6.72069669e-01 -9.83812213e-01 1.12199533e+00 -3.08232307e-01 9.27192211e-01 3.00551027e-01 -6.10885881e-02 1.49366772e+00 -7.89669573e-01 1.06916380e+00 1.24430847e+00 7.14658201e-01 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c7ca8eab-a8c1-4cdd-a8af-e0270abe517b	better-long-range-dependency-by-bootstrapping	1905.11978	null	https://arxiv.org/abs/1905.11978v2	https://arxiv.org/pdf/1905.11978v2.pdf	Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer	In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data. Applied on language modelling, our regularizer expresses the inductive bias that sequence variables should have high mutual information even though the model might not see abundant observations for complex long-range dependency. We show how the `next sentence prediction (classification)' heuristic can be derived in a principled way from our mutual information estimation framework, and be further extended to maximize the mutual information of sequence variables. The proposed approach not only is effective at increasing the mutual information of segments under the learned model but more importantly, leads to a higher likelihood on holdout data, and improved generation quality. Code is released at https://github.com/BorealisAI/BMI.	['Peng Xu', 'Yanshuai Cao']	2019-05-28	null	null	null	null	['mutual-information-estimation']	['methodology']	[ 2.73095369e-01 4.83013421e-01 -4.48610127e-01 -5.38372517e-01 -7.17846215e-01 -6.74355268e-01 4.19611096e-01 8.87687132e-02 -2.44560152e-01 1.14063668e+00 3.82282913e-01 -5.61938345e-01 -2.86129676e-02 -7.04971313e-01 -8.33473146e-01 -8.13333273e-01 -1.15333319e-01 3.09190124e-01 5.66707458e-04 -1.35816380e-01 1.94282070e-01 1.35288358e-01 -1.14070499e+00 1.64834395e-01 1.05482578e+00 3.11008483e-01 7.33792841e-01 7.17402577e-01 -1.09990537e-01 9.41593587e-01 -4.70218003e-01 -5.45884728e-01 -8.46821442e-02 -7.52168298e-01 -9.98685539e-01 -3.06857914e-01 -2.25982517e-01 1.53007641e-01 -7.13776499e-02 9.44739938e-01 4.85383242e-01 -6.61377385e-02 6.38089836e-01 -8.83819759e-01 -5.44618249e-01 1.17903018e+00 -4.27270263e-01 3.18510801e-01 3.11489791e-01 2.00332195e-01 1.56690717e+00 -5.68453968e-01 6.49395704e-01 1.07798529e+00 4.05601919e-01 5.81140041e-01 -1.30311251e+00 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-4.01836365e-01 4.19368774e-01 2.03052789e-01 -1.85815841e-01 -4.16014135e-01 1.50247231e-01]	[11.388154983520508, 9.207175254821777]
219b5e20-2827-4e37-9901-38f99e510e18	data-augmentation-with-symbolic-to-real-image	1907.12902	null	https://arxiv.org/abs/1907.12902v1	https://arxiv.org/pdf/1907.12902v1.pdf	Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition	Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of traffic signs, but in our experiments it does not reach high accuracy, and we believe this is due to lack of data, requiring data augmentation. Generative adversarial networks can learn the high dimensional distribution of empirical data, allowing the generation of new data points. In this paper we apply pix2pix GANs architecture to generate new traffic sign images and evaluate the use of these images in data augmentation. We were motivated to use pix2pix to translate symbolic sign images to real ones due to the mode collapse in Conditional GANs. Through our experiments we found that data augmentation using GAN can increase classification accuracy for circular traffic signs from 92.1% to 94.0%, and for triangular traffic signs from 93.8% to 95.3%, producing an overall improvement of 2%. However some traditional augmentation techniques can outperform GAN data augmentation, for example contrast variation in circular traffic signs (95.5%) and displacement on triangular traffic signs (96.7 %). Our negative results shows that while GANs can be naively used for data augmentation, they are not always the best choice, depending on the problem and variability in the data.	['Matias Valdenegro-Toro', 'Nour Soufi']	2019-07-17	null	null	null	null	['traffic-sign-recognition']	['computer-vision']	[ 3.34343553e-01 1.08171083e-01 -8.03561136e-02 -3.21759135e-01 -5.31495750e-01 -4.41721946e-01 6.79377675e-01 -8.64017189e-01 -2.46254370e-01 1.00115752e+00 -2.32124940e-01 -5.46257913e-01 2.72434354e-01 -9.31596875e-01 -8.02503645e-01 -8.58323216e-01 2.36807331e-01 4.81776267e-01 3.09241176e-01 -5.52363455e-01 2.08675638e-01 6.58966601e-01 -1.84348929e+00 2.46035397e-01 9.72325265e-01 9.51371968e-01 -2.96916485e-01 8.12447786e-01 -4.27497953e-01 8.28932941e-01 -1.08227813e+00 -5.08538961e-01 5.79131663e-01 -6.91259146e-01 -2.30130419e-01 -1.92348793e-01 6.68134332e-01 -3.40204209e-01 -2.63230354e-01 7.54416466e-01 3.92075360e-01 -2.90601850e-02 7.97765970e-01 -1.90051723e+00 -4.70088363e-01 2.00346932e-01 -5.54224491e-01 -1.61631316e-01 -2.25464031e-01 5.21055818e-01 6.03586972e-01 -4.97910082e-01 5.68435788e-01 8.78938317e-01 6.67436540e-01 9.31466639e-01 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94f4cd5d-9f6a-4a8b-ac32-dfbc753e59a9	rating-distributions-and-bayesian-inference	null	null	https://aclanthology.org/W18-2807	https://aclanthology.org/W18-2807.pdf	Rating Distributions and Bayesian Inference: Enhancing Cognitive Models of Spatial Language Use	We present two methods that improve the assessment of cognitive models. The first method is applicable to models computing average acceptability ratings. For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings. Our second method enables Bayesian inference for models generating individual data. To this end, we propose to use the cross-match test (Rosenbaum, 2005) as a likelihood function. We exemplarily present both methods using cognitive models from the domain of spatial language use. For spatial language use, determining linguistic acceptability judgments of a spatial preposition for a depicted spatial relation is assumed to be a crucial process (Logan and Sadler, 1996). Existing models of this process compute an average acceptability rating. We extend the models and {--} based on existing data {--} show that the extended models allow extracting more information from the empirical data and yield more readily interpretable information about model successes and failures. Applying Bayesian inference, we find that model performance relies less on mechanisms of capturing geometrical aspects than on mapping the captured geometry to a rating interval.	['Thomas Kluth', 'Holger Schultheis']	2018-07-01	null	null	null	ws-2018-7	['linguistic-acceptability']	['natural-language-processing']	[-7.16077909e-02 4.17283326e-01 1.05233140e-01 -7.69654572e-01 -8.17499638e-01 -9.90752637e-01 7.41882026e-01 4.53704834e-01 -5.09846330e-01 3.31434727e-01 3.58864397e-01 -8.53738487e-01 -6.73702180e-01 -9.71561849e-01 -5.16807616e-01 -1.16709536e-02 -2.39547924e-03 4.68500584e-01 3.00509185e-01 -2.34803677e-01 8.38741958e-01 5.92521787e-01 -1.62337029e+00 4.67460006e-01 1.03075290e+00 9.34376597e-01 3.51008683e-01 5.18146873e-01 5.60356118e-02 6.86478317e-01 -4.25948948e-01 -5.86792946e-01 4.75007556e-02 -8.81660953e-02 -7.32774854e-01 -4.73567098e-01 3.28555435e-01 -5.48272550e-01 1.85832426e-01 7.49476850e-01 2.28871971e-01 2.15174481e-01 1.31174493e+00 -8.90751302e-01 -1.03352582e+00 7.85086334e-01 -1.17284935e-02 7.90504068e-02 9.03801799e-01 -8.61762371e-03 1.01019371e+00 -1.08852935e+00 4.74415332e-01 1.49572098e+00 7.20342219e-01 1.65444277e-02 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-6.19384408e-01 2.39341289e-01 -3.63663286e-01 -1.08461761e+00 -3.93036544e-01 -1.56606674e-01]	[9.691948890686035, 7.451372146606445]
5e661236-7cc9-4ea0-8d12-bb8e0b127bf9	robust-single-image-reflection-removal	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.pdf	Robust Single Image Reflection Removal Against Adversarial Attacks	This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR^2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.	['Jianfeng Lu', 'Wenqi Ren', 'Zhaoxin Fan', 'Wenhan Luo', 'Kaihao Zhang', 'Zhenyuan Zhang', 'Zhenbo Song']	2023-01-01	null	null	null	cvpr-2023-1	['reflection-removal']	['computer-vision']	[ 4.36807573e-01 -4.12909955e-01 2.87350535e-01 1.22630410e-01 -1.31074035e+00 -9.77505028e-01 5.45366585e-01 -5.88069618e-01 -6.56106994e-02 4.24684644e-01 3.87799263e-01 -1.82824120e-01 6.06948584e-02 -5.63859940e-01 -7.51932144e-01 -1.12460661e+00 5.97010814e-02 -4.95439202e-01 4.06149104e-02 -5.36725700e-01 1.07627898e-01 9.15588975e-01 -9.42317069e-01 2.34370738e-01 8.46609414e-01 8.91639650e-01 -3.03642720e-01 9.29057837e-01 7.67029166e-01 8.59854400e-01 -1.09567273e+00 -4.58539009e-01 8.22005212e-01 -2.61175632e-01 -2.41330639e-01 -8.65996629e-02 5.43060660e-01 -4.41032231e-01 -1.17888033e+00 1.29438615e+00 1.01383805e+00 2.81956047e-01 5.34155726e-01 -1.11620939e+00 -1.29715383e+00 3.21618915e-01 -8.21522892e-01 3.61455232e-01 2.42088526e-01 6.78918302e-01 4.67018574e-01 -5.51706016e-01 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cf1f6487-f3e7-4f1c-80cd-c119ec037f0f	transducing-sentences-to-syntactic-feature	null	null	https://aclanthology.org/W13-3205	https://aclanthology.org/W13-3205.pdf	Transducing Sentences to Syntactic Feature Vectors: an Alternative Way to ``Parse''?	null	["Lorenzo Dell{'}Arciprete", 'Fabio Massimo Zanzotto']	2013-08-01	null	null	null	ws-2013-8	['graph-similarity']	['graphs']	[-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 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 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8aba4ddf-d1c4-4f0a-9260-32c20912d28f	mmd-b-fair-learning-fair-representations-with	2211.07907	null	https://arxiv.org/abs/2211.07907v3	https://arxiv.org/pdf/2211.07907v3.pdf	MMD-B-Fair: Learning Fair Representations with Statistical Testing	We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing. We find neural features of our data where a maximum mean discrepancy (MMD) test cannot distinguish between representations of different sensitive groups, while preserving information about the target attributes. Minimizing the power of an MMD test is more difficult than maximizing it (as done in previous work), because the test threshold's complex behavior cannot be simply ignored. Our method exploits the simple asymptotics of block testing schemes to efficiently find fair representations without requiring complex adversarial optimization or generative modelling schemes widely used by existing work on fair representation learning. We evaluate our approach on various datasets, showing its ability to ``hide'' information about sensitive attributes, and its effectiveness in downstream transfer tasks.	['Danica J. Sutherland', 'Namrata Deka']	2022-11-15	null	null	null	null	['hypothesis-testing', 'hypothesis-testing']	['methodology', 'miscellaneous']	[ 4.31698650e-01 5.12079060e-01 -2.79471278e-01 -5.95093846e-01 -1.24568284e+00 -8.16312432e-01 5.30400038e-01 9.50526670e-02 -4.64673638e-01 1.21627831e+00 -2.42505819e-02 -6.65178001e-01 -2.04962984e-01 -8.96808386e-01 -9.76600170e-01 -9.53538477e-01 -3.20390671e-01 4.60526258e-01 9.74529088e-02 -6.55844286e-02 1.95649981e-01 4.93815243e-01 -1.30614758e+00 4.02972341e-01 8.48345995e-01 9.67999041e-01 -4.05364007e-01 6.12437606e-01 2.04779819e-01 8.70597005e-01 -6.84406698e-01 -8.54381025e-01 6.20889723e-01 -7.60737717e-01 -7.49496162e-01 -4.39170003e-01 6.82349920e-01 -5.05206704e-01 -3.57728422e-01 1.16049993e+00 6.82605743e-01 1.76948547e-01 1.07233083e+00 -1.51776767e+00 -9.61042821e-01 7.49208391e-01 -4.38085496e-01 1.23237789e-01 -1.71305999e-01 1.59932107e-01 1.31889331e+00 -3.40093434e-01 3.33522111e-01 1.33452916e+00 6.90377474e-01 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400fab34-7401-4b4c-adbb-e37f98d7f09f	smartchoices-augmenting-software-with-learned	2304.13033	null	https://arxiv.org/abs/2304.13033v1	https://arxiv.org/pdf/2304.13033v1.pdf	SmartChoices: Augmenting Software with Learned Implementations	We are living in a golden age of machine learning. Powerful models are being trained to perform many tasks far better than is possible using traditional software engineering approaches alone. However, developing and deploying those models in existing software systems remains difficult. In this paper we present SmartChoices, a novel approach to incorporating machine learning into mature software stacks easily, safely, and effectively. We explain the overall design philosophy and present case studies using SmartChoices within large scale industrial systems.	['Vincent Tjeng', 'Justin Sybrandt', 'Nikhil Sarda', 'Ruoyan Qin', 'Efi Kokiopoulou', 'Tzu-Kuo Huang', 'Emily Donahue', 'Eric Chen', 'Gabor Bartok', 'Daniel Golovin']	2023-04-12	null	null	null	null	['philosophy']	['miscellaneous']	[-1.84276223e-01 1.99055359e-01 -3.35986376e-01 -5.39576292e-01 -3.46463531e-01 -4.37625021e-01 1.48962989e-01 -1.45769820e-01 3.54451984e-01 1.80490837e-01 -6.02722466e-01 -1.03610718e+00 5.12892939e-02 -2.86648482e-01 -4.44778949e-01 -2.43244711e-02 8.12592655e-02 1.19581409e-01 3.03687632e-01 -3.41455132e-01 3.75324607e-01 3.24303478e-01 -1.56741905e+00 6.42127275e-01 4.22739714e-01 8.50933611e-01 5.99477179e-02 9.52449620e-01 7.78839514e-02 1.00584543e+00 -8.82535934e-01 -5.19501686e-04 4.07376289e-01 1.12808459e-01 -6.01079583e-01 4.53195088e-02 5.17541409e-01 -2.69759208e-01 2.20998928e-01 6.84466779e-01 -6.94677234e-02 -6.05420530e-01 -1.17818438e-01 -1.75121784e+00 -5.23998290e-02 7.81130135e-01 -4.75432277e-01 -5.17326333e-02 1.90211728e-01 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d281e4b4-b13d-4e6b-a3a4-b01586b16cbd	audio-representation-learning-by-distilling	2302.02845	null	https://arxiv.org/abs/2302.02845v1	https://arxiv.org/pdf/2302.02845v1.pdf	Audio Representation Learning by Distilling Video as Privileged Information	Deep audio representation learning using multi-modal audio-visual data often leads to a better performance compared to uni-modal approaches. However, in real-world scenarios both modalities are not always available at the time of inference, leading to performance degradation by models trained for multi-modal inference. In this work, we propose a novel approach for deep audio representation learning using audio-visual data when the video modality is absent at inference. For this purpose, we adopt teacher-student knowledge distillation under the framework of learning using privileged information (LUPI). While the previous methods proposed for LUPI use soft-labels generated by the teacher, in our proposed method we use embeddings learned by the teacher to train the student network. We integrate our method in two different settings: sequential data where the features are divided into multiple segments throughout time, and non-sequential data where the entire features are treated as one whole segment. In the non-sequential setting both the teacher and student networks are comprised of an encoder component and a task header. We use the embeddings produced by the encoder component of the teacher to train the encoder of the student, while the task header of the student is trained using ground-truth labels. In the sequential setting, the networks have an additional aggregation component that is placed between the encoder and task header. We use two sets of embeddings produced by the encoder and aggregation component of the teacher to train the student. Similar to the non-sequential setting, the task header of the student network is trained using ground-truth labels. We test our framework on two different audio-visual tasks, namely speaker recognition and speech emotion recognition and show considerable improvements over sole audio-based recognition as well as prior works that use LUPI.	['Ali Etemad', 'Amirhossein Hajavi']	2023-02-06	null	null	null	null	['speaker-recognition', 'speech-emotion-recognition']	['speech', 'speech']	[ 4.18310910e-01 1.78737924e-01 1.11154772e-01 -3.06367010e-01 -1.11116159e+00 -5.53043008e-01 5.80862224e-01 6.29269779e-02 -4.34107214e-01 5.65727413e-01 1.02312081e-01 -1.29793838e-01 1.46464810e-01 -6.59615159e-01 -9.14410174e-01 -9.25649047e-01 2.49017760e-01 5.22650957e-01 9.93203446e-02 1.25710428e-01 -2.12208271e-01 2.73602158e-02 -1.88852501e+00 5.97471774e-01 3.81423384e-01 1.39192533e+00 6.46805316e-02 8.05953145e-01 -7.51551837e-02 9.40828443e-01 -4.97899711e-01 -1.89987525e-01 1.35505214e-01 -3.10248315e-01 -7.22452402e-01 1.93090528e-01 5.18499076e-01 -3.31239760e-01 -2.97886342e-01 8.05345178e-01 6.92934334e-01 3.42952311e-01 6.30671144e-01 -1.50644755e+00 -2.87806332e-01 5.16511798e-01 -4.30191070e-01 -2.24990189e-01 5.73016822e-01 -1.36074215e-01 1.21575749e+00 -8.26861620e-01 4.18920308e-01 1.14099038e+00 3.75738859e-01 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-5.68977058e-01 3.38886589e-01 8.84557784e-01 -9.06651735e-01 -6.68955743e-01 1.43411472e-01]	[14.99648666381836, 5.077201843261719]
6b74ce97-9f6a-4dd8-ad1f-23efa6bdaaf4	deep-reinforcement-learning-based-optimal-1	2304.03489	null	https://arxiv.org/abs/2304.03489v1	https://arxiv.org/pdf/2304.03489v1.pdf	Deep Reinforcement Learning Based Optimal Infinite-Horizon Control of Probabilistic Boolean Control Networks	In this paper, a deep reinforcement learning based method is proposed to obtain optimal policies for optimal infinite-horizon control of probabilistic Boolean control networks (PBCNs). Compared with the existing literatures, the proposed method is model-free, namely, the system model and the initial states needn't to be known. Meanwhile, it is suitable for large-scale PBCNs. First, we establish the connection between deep reinforcement learning and optimal infinite-horizon control, and structure the problem into the framework of the Markov decision process. Then, PBCNs are defined as large-scale or small-scale, depending on whether the memory of the action-values exceeds the RAM of the computer. Based on the newly introduced definition, Q-learning (QL) and double deep Q-network (DDQN) are applied to the optimal infinite-horizon control of small-scale and large-scale PBCNs, respectively. Meanwhile, the optimal state feedback controllers are designed. Finally, two examples are presented, which are a small-scale PBCN with 3 nodes, and a large-scale one with 28 nodes. To verify the convergence of QL and DDQN, the optimal control policy and the optimal action-values, which are obtained from both the algorithms, are compared with the ones based on a model-based method named policy iteration. Meanwhile, the performance of QL is compared with DDQN in the small-scale PBCN.	['Zheng-Guang Wu', 'Fangfei Li', 'Jingjie Ni']	2023-04-07	null	null	null	null	['q-learning']	['methodology']	[-2.02037036e-01 1.26312271e-01 -4.11502838e-01 3.60780925e-01 -3.72833401e-01 -2.38072336e-01 1.57349810e-01 -6.42689764e-02 -5.08504868e-01 1.28260183e+00 -2.73738980e-01 -5.21343887e-01 -6.38145506e-01 -1.20170534e+00 -6.26345217e-01 -1.12464476e+00 -1.92241207e-01 4.06205863e-01 5.43355703e-01 -1.74921572e-01 4.89430688e-02 2.99279541e-01 -1.10089982e+00 -4.13477749e-01 6.27442539e-01 1.14232755e+00 2.11454466e-01 4.66836512e-01 9.33316275e-02 6.64076567e-01 -5.59985995e-01 2.96026736e-01 6.65706098e-02 -3.75410020e-01 -5.33768117e-01 9.11056399e-02 -7.09075332e-01 -5.30879259e-01 -7.12173998e-01 1.22851968e+00 5.87934554e-01 3.48854750e-01 4.18730259e-01 -1.36418080e+00 -2.70703495e-01 7.22356260e-01 -4.13448870e-01 6.66955262e-02 -1.02287307e-01 5.28249025e-01 7.12368190e-01 -3.43185849e-02 2.53641516e-01 1.68119156e+00 4.83250678e-01 6.67308331e-01 -9.72743273e-01 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-3.37069333e-01 1.12119883e-01 -8.31982851e-01 -3.23196858e-01 -6.79296196e-01]	[4.463194847106934, 2.2165398597717285]
1ffb21e5-f710-42f9-9ad8-dfafa1ea2635	describing-image-focused-in-cognitive-and	2202.05331	null	https://arxiv.org/abs/2202.05331v2	https://arxiv.org/pdf/2202.05331v2.pdf	Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs	Several services for people with visual disabilities have emerged recently due to achievements in Assistive Technologies and Artificial Intelligence areas. Despite the growth in assistive systems availability, there is a lack of services that support specific tasks, such as understanding the image context presented in online content, e.g., webinars. Image captioning techniques and their variants are limited as Assistive Technologies as they do not match the needs of visually impaired people when generating specific descriptions. We propose an approach for generating context of webinar images combining a dense captioning technique with a set of filters, to fit the captions in our domain, and a language model for the abstractive summary task. The results demonstrated that we can produce descriptions with higher interpretability and focused on the relevant information for that group of people by combining image analysis methods and neural language models.	['Michel Melo Silva', 'Fabio Ribeiro Cerqueira', 'Marcos Henrique Fonseca Ribeiro', 'Daniel Louzada Fernandes']	2022-02-10	null	null	null	null	['dense-captioning']	['computer-vision']	[ 1.39600232e-01 3.64473522e-01 2.68394619e-01 -4.31376576e-01 -4.36916143e-01 -3.41815531e-01 5.68238258e-01 1.38000816e-01 -3.16717595e-01 9.04746771e-01 9.18208003e-01 -1.29432723e-01 -3.80177423e-02 -3.95739824e-01 -5.47381461e-01 1.42107569e-02 4.01899278e-01 4.02320027e-01 2.28080839e-01 -3.23991239e-01 4.34216499e-01 3.25998724e-01 -2.02528977e+00 9.78598356e-01 1.10402608e+00 7.18695939e-01 9.19523537e-01 6.14487708e-01 -8.12271118e-01 7.25388765e-01 -6.29952908e-01 -3.74983370e-01 -3.51017788e-02 -4.34109300e-01 -6.48149967e-01 3.39090049e-01 7.90368855e-01 -9.06436145e-01 -4.17382926e-01 9.07906353e-01 5.12084186e-01 -3.09696555e-01 6.16644740e-01 -1.26442957e+00 -1.17752481e+00 1.75358579e-01 3.38743441e-02 -1.22177057e-01 1.02258480e+00 2.01359391e-01 3.93997729e-01 -8.00567985e-01 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-2.87455786e-02 -8.04167211e-01 -2.73001492e-02 -1.20573151e+00 -1.65344507e-01 6.32086396e-01 1.46328345e-01 -2.72353411e-01 -7.25182816e-02 4.07372355e-01]	[10.878764152526855, 0.8369085192680359]
d208443d-590d-44e7-8739-682b474e8c2b	thunder-a-fast-coordinate-selection-solver	null	null	http://proceedings.neurips.cc/paper/2020/hash/11348e03e23b137d55d94464250a67a2-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/11348e03e23b137d55d94464250a67a2-Paper.pdf	Thunder: a Fast Coordinate Selection Solver for Sparse Learning	L1 regularization has been broadly employed to pursue model sparsity. Despite the non-smoothness, people have developed efficient algorithms by leveraging the sparsity and convexity of the problems. In this paper, we propose a novel active incremental approach to further improve the efficiency of the solvers. We show that our method performs well even when the existing methods fail due to the low sparseness or high solution accuracy request. Theoretical analysis and experimental results on synthetic and real-world data sets validate the advantages of the method.	['Ping Li', 'Weijie Zhao', 'Shaogang Ren']	2020-12-01	null	null	null	neurips-2020-12	['sparse-learning']	['methodology']	[-1.44049674e-01 5.76674156e-02 -6.81550443e-01 -3.08278669e-02 -7.30697811e-01 -2.00283244e-01 1.81726933e-01 -2.40244523e-01 3.33477706e-02 1.09409487e+00 1.68548033e-01 -2.95914188e-02 -2.54406363e-01 -3.49089772e-01 -5.47837973e-01 -6.92788959e-01 -2.37212554e-01 -5.52704073e-02 -1.32817209e-01 1.23017058e-01 3.54886115e-01 9.95330811e-02 -1.10729790e+00 -5.98801039e-02 1.40438545e+00 1.02459049e+00 1.10665351e-01 -6.11873064e-03 5.83534688e-03 9.09561813e-01 1.68584734e-01 9.28371325e-02 6.94872618e-01 -3.99239272e-01 -6.79808378e-01 4.46258008e-01 -6.37216866e-02 -8.68981630e-02 -1.17276862e-01 1.24391723e+00 1.31898895e-01 2.67643452e-01 6.51899055e-02 -1.32317448e+00 -3.46817702e-01 2.05887601e-01 -9.62866843e-01 6.01756871e-02 2.92527407e-01 -8.01484510e-02 1.14681351e+00 -1.49366653e+00 4.58962381e-01 9.29151714e-01 1.01719832e+00 1.95404574e-01 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3a092a4d-ba6f-48d9-8f03-fa382f649563	introducing-rezojdm16k-a-french	null	null	https://aclanthology.org/2022.lrec-1.553	https://aclanthology.org/2022.lrec-1.553.pdf	Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction	Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM. With 16k nodes, 832k triplets, and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering, and recommendation systems. Moreover, we provide strong knowledge graph embedding baselines that are used in link prediction tasks for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.	['Mathieu Lafourcade', 'Lawrence Carbon', 'Helene Jacquenet', 'Kevin Cousot', 'Mohammad Javad Saeedizade', 'Waleed Ragheb', 'Mehdi Mirzapour']	null	null	null	null	lrec-2022-6	['knowledge-graph-embedding']	['graphs']	[-3.15212667e-01 5.26435435e-01 -1.00838804e+00 1.08230084e-01 -2.26677433e-01 -8.17363322e-01 8.17643762e-01 5.44002354e-01 -1.86181724e-01 1.12344193e+00 3.23193192e-01 -6.60593450e-01 -6.85431242e-01 -1.31667137e+00 -5.53160548e-01 3.83541584e-02 4.52288464e-02 8.53353441e-01 6.48589253e-01 -4.02634531e-01 -2.96734343e-03 3.82913440e-01 -5.79924226e-01 2.21511796e-01 7.14744091e-01 7.41017520e-01 -1.41160756e-01 2.69901782e-01 -4.38849270e-01 1.01586843e+00 -1.80093363e-01 -1.46883059e+00 4.88182232e-02 -1.15226991e-01 -1.40467417e+00 -5.23350120e-01 5.72010815e-01 1.52218089e-01 -1.08591866e+00 8.78209054e-01 2.25436896e-01 4.22663949e-02 7.84571528e-01 -1.34899139e+00 -1.21503580e+00 1.06622672e+00 -2.56033510e-01 1.98651016e-01 4.77048248e-01 -3.10811311e-01 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d9c0478e-94b6-454c-873a-66ae656df6ba	vision-based-vehicle-speed-estimation-for-its	2101.06159	null	https://arxiv.org/abs/2101.06159v2	https://arxiv.org/pdf/2101.06159v2.pdf	Vision-based Vehicle Speed Estimation: A Survey	The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.	['Iván García Daza', 'Antonio Hernández Martínez', 'David Fernández Llorca']	2021-01-15	null	null	null	null	['vehicle-speed-estimation']	['computer-vision']	[-1.08476639e-01 -4.90358382e-01 -6.50063276e-01 -3.00741732e-01 -3.20496887e-01 -3.39273542e-01 6.65684581e-01 1.47014009e-02 -6.23569787e-01 6.41759992e-01 -3.02535534e-01 -3.67747962e-01 -4.10526991e-01 -1.02191234e+00 -1.15476474e-01 -7.55039036e-01 1.98402584e-01 3.37386400e-01 5.64610481e-01 -7.22779632e-02 2.51252085e-01 1.01763618e+00 -2.09378695e+00 -5.54371297e-01 1.01058865e+00 1.17535126e+00 3.58701289e-01 5.86562395e-01 -2.48587474e-01 1.81837678e-01 -2.99118906e-01 -4.02466923e-01 5.49926311e-02 -1.06828688e-02 -2.90186882e-01 1.35347499e-02 1.80488721e-01 -5.04962564e-01 -5.06326675e-01 9.96278346e-01 6.99482206e-03 1.51333511e-01 5.73641479e-01 -1.53423476e+00 7.65624568e-02 1.55266339e-03 -4.37789440e-01 4.22884494e-01 -1.70416445e-01 2.22691283e-01 5.46613753e-01 -2.92205900e-01 1.46978348e-01 9.60976183e-01 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1d131e13-7691-49dc-aad0-08cead05966e	background-matters-enhancing-out-of	2303.08727	null	https://arxiv.org/abs/2303.08727v1	https://arxiv.org/pdf/2303.08727v1.pdf	Background Matters: Enhancing Out-of-distribution Detection with Domain Features	Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-world scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground semantic features (e.g., vehicle images vs. ID samples in fruit classification) and background domain features (e.g., textural images vs. ID samples in object recognition). Existing methods focus on detecting OOD samples based on the semantic features, while neglecting the other dimensions such as the domain features. This paper considers the importance of the domain features in OOD detection and proposes to leverage them to enhance the semantic-feature-based OOD detection methods. To this end, we propose a novel generic framework that can learn the domain features from the ID training samples by a dense prediction approach, with which different existing semantic-feature-based OOD detection methods can be seamlessly combined to jointly learn the in-distribution features from both the semantic and domain dimensions. Extensive experiments show that our approach 1) can substantially enhance the performance of four different state-of-the-art (SotA) OOD detection methods on multiple widely-used OOD datasets with diverse domain features, and 2) achieves new SotA performance on these benchmarks.	['Chunhua Shen', 'Guansong Pang', 'Choubo Ding']	2023-03-15	null	null	null	null	['object-recognition']	['computer-vision']	[ 2.57094562e-01 -1.24207832e-01 -5.40871561e-01 -3.50245595e-01 -7.54484117e-01 -7.57125497e-01 8.12084019e-01 -6.30341051e-03 3.92419577e-01 2.74229366e-02 -1.22650117e-01 -3.34759444e-01 1.25231966e-02 -8.60025823e-01 -8.26917648e-01 -8.81344259e-01 8.27213284e-03 3.79598975e-01 4.05895472e-01 3.77805531e-01 2.00771406e-01 5.94086826e-01 -1.98323834e+00 3.78729016e-01 8.64846945e-01 1.62094498e+00 1.17598988e-01 5.37810147e-01 -5.84649265e-01 6.61955655e-01 -8.52094769e-01 -1.31898135e-01 5.36359668e-01 -9.12917331e-02 -1.93475876e-02 4.32466745e-01 8.13347459e-01 -7.08877325e-01 -4.60197598e-01 1.20572388e+00 4.02460635e-01 -2.79795349e-01 1.44720161e+00 -1.70440590e+00 -9.96166289e-01 -1.23981230e-01 -7.87215173e-01 2.04797626e-01 -1.75226822e-01 2.49068052e-01 6.40686154e-01 -1.00941026e+00 6.09032154e-01 1.58870113e+00 4.85073596e-01 3.16495478e-01 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bafcfe63-686a-402d-80d2-19bef70adcd0	hknas-classification-of-hyperspectral-imagery	2304.11701	null	https://arxiv.org/abs/2304.11701v1	https://arxiv.org/pdf/2304.11701v1.pdf	HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search	Recent neural architecture search (NAS) based approaches have made great progress in hyperspectral image (HSI) classification tasks. However, the architectures are usually optimized independently of the network weights, increasing searching time and restricting model performances. To tackle these issues, in this paper, different from previous methods that extra define structural parameters, we propose to directly generate structural parameters by utilizing the specifically designed hyper kernels, ingeniously converting the original complex dual optimization problem into easily implemented one-tier optimizations, and greatly shrinking searching costs. Then, we develop a hierarchical multi-module search space whose candidate operations only contain convolutions, and these operations can be integrated into unified kernels. Using the above searching strategy and searching space, we obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. In addition, by combining the proposed hyper kernel searching scheme with the 3-D convolution decomposition mechanism, we obtain diverse architectures to simulate 3-D convolutions, greatly improving network flexibilities. A series of quantitative and qualitative experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results compared with other advanced NAS-based HSI classification approaches.	['DaCheng Tao', 'Liangpei Zhang', 'Bo Du', 'Di Wang']	2023-04-23	null	null	null	null	['architecture-search']	['methodology']	[ 2.76848704e-01 -5.97005069e-01 -9.90199819e-02 -3.20722997e-01 -1.91947475e-01 -4.76809323e-01 3.00091580e-02 -2.84925878e-01 -5.26951551e-01 3.33019048e-01 -1.44008011e-01 -5.00731230e-01 -5.22604525e-01 -9.39395964e-01 -3.10403824e-01 -1.03364491e+00 1.68807268e-01 -1.96600825e-01 2.45015964e-01 -1.04907371e-01 2.88879156e-01 7.23357975e-01 -1.56836236e+00 1.15079433e-01 1.39167869e+00 1.41575396e+00 3.88577461e-01 3.38768512e-01 -1.52491018e-01 3.99116188e-01 -3.32882941e-01 -2.55454630e-01 4.37262386e-01 -5.07352576e-02 -5.77480376e-01 9.68428850e-02 2.44204208e-01 -2.50080436e-01 -5.57489455e-01 1.31969917e+00 7.04996347e-01 2.22734272e-01 4.38583910e-01 -9.17649627e-01 -9.59390998e-01 5.35010338e-01 -7.27128506e-01 1.19077176e-01 -2.61510968e-01 3.32939595e-01 9.08730090e-01 -8.23211610e-01 -1.86923042e-01 1.05763495e+00 7.72330701e-01 1.88526183e-01 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eb3be988-1770-4d5a-a2da-1b48ba6b5693	cyber-resilient-automatic-generation-control	2208.11163	null	https://arxiv.org/abs/2208.11163v2	https://arxiv.org/pdf/2208.11163v2.pdf	Cyber-resilient Automatic Generation Control for Systems of AC Microgrids	In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (Micro-AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the Micro-AGC. To address the sensory challenges, we introduce a new microgrid model by exploiting the rank-one deficiency property of microgrid dynamics. This model is used to pose an optimal Micro-AGC control problem that is easily implemented, because it does not require fast frequency measurements. An end-to-end cyber security solution to the False Data Injection (FDI) attack detection and mitigation is developed for the proposed Micro-AGC. The front-end barrier of applying off-the-shelf algorithms for cyber attack detection is removed by introducing a data-driven modeling approach. Finally, we propose an observer-based corrective control for an islanded microgrid and a collaborative mitigation schemes in systems of AC microgrids. We demonstrate a collaborative role of systems of microgrids during cyber attacks. The performance of the proposed cyber-resilient Micro-AGC is tested in a system of two networked microgrids.	['Marija Ilic', 'Dan Wu', 'Tong Huang']	2022-08-23	null	null	null	null	['cyber-attack-detection']	['miscellaneous']	[-2.44395465e-01 1.64631531e-01 4.56580609e-01 4.69688118e-01 -3.54924560e-01 -1.32616127e+00 6.30835116e-01 2.42254540e-01 3.29688281e-01 7.52778530e-01 -4.47177291e-02 -3.67609441e-01 -4.07694608e-01 -6.80697501e-01 -6.27094328e-01 -1.03483415e+00 -6.58618093e-01 -1.21020228e-01 2.00565532e-01 -3.81430507e-01 1.80253573e-02 4.44679618e-01 -1.43879616e+00 -3.40358019e-01 9.86147225e-01 1.06637347e+00 -1.13693096e-01 5.77595651e-01 9.42263007e-01 2.83442557e-01 -1.22042072e+00 4.02815610e-01 4.52959925e-01 -5.36686480e-01 -3.06211382e-01 -4.34577502e-02 -5.25018752e-01 -4.03699219e-01 1.68257505e-01 1.49829173e+00 6.78840280e-01 -4.65268530e-02 7.80012190e-01 -2.19082427e+00 -1.04872525e-01 4.61811304e-01 -2.35716790e-01 5.81650995e-02 6.22887611e-01 3.91273052e-01 5.68867445e-01 -3.85596007e-01 3.30306232e-01 8.38272095e-01 2.06173554e-01 1.70575023e-01 -1.43405282e+00 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25017358-834b-4ffb-a948-b385bbb311b8	botsim-an-end-to-end-bot-simulation-toolkit	2211.15916	null	https://arxiv.org/abs/2211.15916v2	https://arxiv.org/pdf/2211.15916v2.pdf	BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systems	We introduce BotSIM, a modular, open-source Bot SIMulation environment with dialog generation, user simulation and conversation analytics capabilities. BotSIM aims to serve as a one-stop solution for large-scale data-efficient end-to-end evaluation, diagnosis and remediation of commercial task-oriented dialog (TOD) systems to significantly accelerate commercial bot development and evaluation, reduce cost and time-to-market. BotSIM adopts a layered design comprising the infrastructure layer, the adaptor layer and the application layer. The infrastructure layer hosts key models and components to support BotSIM's major functionalities via a streamlined "generation-simulation-remediation" pipeline. The adaptor layer is used to extend BotSIM to accommodate new bot platforms. The application layer provides a suite of command line tools and a Web App to significantly lower the entry barrier for BotSIM users such as bot admins or practitioners. In this report, we focus on the technical designs of various system components. A detailed case study using Einstein BotBuilder is also presented to show how to apply BotSIM pipeline for bot evaluation and remediation. The detailed system descriptions can be found in our system demo paper. The toolkit is available at: https://github.com/salesforce/BotSIM .	['Steven Hoi', 'Junnan Li', 'Shafiq Joty', 'Guangsen Wang']	2022-11-29	null	null	null	null	['user-simulation']	['natural-language-processing']	[-8.33881199e-01 -3.07331264e-01 -1.54753262e-02 6.54340312e-02 -1.98142499e-01 -8.59852672e-01 5.15581965e-01 -2.47631624e-01 -3.02843720e-01 4.65929776e-01 -1.23636566e-01 -9.23378110e-01 1.67331621e-01 -4.14501756e-01 3.99764925e-01 -3.15224946e-01 2.75973022e-01 8.60120773e-01 7.68358409e-01 -5.71361959e-01 3.63140047e-01 6.10327184e-01 -1.32359791e+00 1.86212465e-01 5.95170557e-01 3.40255648e-01 3.92644107e-01 1.08698177e+00 -4.60731350e-02 1.30470395e+00 -1.30773365e+00 -3.42605174e-01 1.72145128e-01 2.71603558e-02 -9.37310576e-01 -2.04899698e-01 -3.24781269e-01 -9.44847405e-01 -3.72712672e-01 6.90181911e-01 9.66573775e-01 -1.53326243e-01 8.16279650e-02 -1.95303988e+00 3.93686183e-02 4.58437055e-01 -1.19643755e-01 2.02596962e-01 3.26317757e-01 1.37474501e+00 5.92691839e-01 -4.51964825e-01 6.75534070e-01 1.34926379e+00 6.07620239e-01 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dd1a17e7-6942-4ba1-8676-b6968ac0b7d5	flag3d-a-3d-fitness-activity-dataset-with	2212.04638	null	https://arxiv.org/abs/2212.04638v2	https://arxiv.org/pdf/2212.04638v2.pdf	FLAG3D: A 3D Fitness Activity Dataset with Language Instruction	With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and diverse environments. In this paper, we present FLAG3D, a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Extensive experiments and in-depth analysis show that FLAG3D contributes great research value for various challenges, such as cross-domain human action recognition, dynamic human mesh recovery, and language-guided human action generation. Our dataset and source code are publicly available at https://andytang15.github.io/FLAG3D.	['Xiu Li', 'Jie zhou', 'Jiwen Lu', 'Yongming Rao', 'Wenxun Dai', 'Bin Yang', 'Aoyang Liu', 'Jinpeng Liu', 'Yansong Tang']	2022-12-09	null	http://openaccess.thecvf.com//content/CVPR2023/html/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.pdf	cvpr-2023-1	['human-action-generation', 'human-mesh-recovery', 'action-generation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 2.54572630e-02 -5.98194063e-01 -2.86412686e-01 4.60033752e-02 -7.52251387e-01 -1.16267405e-01 1.93002313e-01 -3.43005270e-01 -2.72055268e-01 6.28533423e-01 6.67428136e-01 3.29902202e-01 -6.48182184e-02 -5.29304743e-01 -6.97004139e-01 -5.35208106e-01 -1.71254605e-01 4.62675214e-01 5.62648952e-01 -3.80755395e-01 -1.04298005e-02 2.53022105e-01 -2.16207123e+00 8.33720416e-02 6.94468141e-01 1.18357253e+00 3.96929011e-02 7.26564646e-01 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474537bb-2acd-4b11-82cf-b8c5981a3d9c	lggnet-learning-from-local-global-graph	2105.02786	null	https://arxiv.org/abs/2105.02786v3	https://arxiv.org/pdf/2105.02786v3.pdf	LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface	Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a novel neurologically inspired graph neural network, to learn local-global-graph representations of electroencephalography (EEG) for Brain-Computer Interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multi-scale 1D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely, the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, RGNN, AMCNN-DGCN, HRNN and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant (p<0.05) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG	['Qiuhao Zeng', 'Chengxuan Tong', 'Cuntai Guan', 'Neethu Robinson', 'Yi Ding']	2021-05-05	null	null	null	null	['eeg-emotion-recognition']	['miscellaneous']	[-1.12396970e-01 -2.23854795e-01 3.83330643e-01 -1.78500712e-01 1.77766964e-01 -2.27550179e-01 3.66885811e-01 5.20973317e-02 -1.35984734e-01 8.11488986e-01 1.67198792e-01 -1.65601626e-01 -8.58980060e-01 -6.67452753e-01 -5.04203916e-01 -6.82766318e-01 -7.04090655e-01 -3.74784879e-02 -8.66203979e-02 -1.82852313e-01 2.69959599e-01 6.16080463e-01 -1.27659500e+00 3.20247233e-01 1.04594421e+00 1.27101851e+00 2.38932312e-01 4.37463164e-01 4.27059978e-01 6.64934993e-01 -4.80423480e-01 -3.23737830e-01 -1.45205811e-01 -5.95168889e-01 -6.47480607e-01 -2.57028192e-01 2.03359537e-02 4.59767848e-01 -7.93483377e-01 1.28878760e+00 8.14580202e-01 4.14144963e-01 6.76476181e-01 -1.34913063e+00 -9.81412530e-01 4.12242085e-01 -1.57540351e-01 7.95734763e-01 2.29987442e-01 2.80327260e-01 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28ca0aba-92ce-4695-876d-584cb5bea756	image-processing-using-multi-code-gan-prior	1912.07116	null	https://arxiv.org/abs/1912.07116v2	https://arxiv.org/pdf/1912.07116v2.pdf	Image Processing Using Multi-Code GAN Prior	Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typically invert a target image back to the latent space either by back-propagation or by learning an additional encoder. However, the reconstructions from both of the methods are far from ideal. In this work, we propose a novel approach, called mGANprior, to incorporate the well-trained GANs as effective prior to a variety of image processing tasks. In particular, we employ multiple latent codes to generate multiple feature maps at some intermediate layer of the generator, then compose them with adaptive channel importance to recover the input image. Such an over-parameterization of the latent space significantly improves the image reconstruction quality, outperforming existing competitors. The resulting high-fidelity image reconstruction enables the trained GAN models as prior to many real-world applications, such as image colorization, super-resolution, image inpainting, and semantic manipulation. We further analyze the properties of the layer-wise representation learned by GAN models and shed light on what knowledge each layer is capable of representing.	['Yujun Shen', 'Bolei Zhou', 'Jinjin Gu']	2019-12-15	image-processing-using-multi-code-gan-prior-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.pdf	cvpr-2020-6	['blind-face-restoration']	['computer-vision']	[ 8.66972208e-01 2.10224301e-01 -9.36395079e-02 -2.11634204e-01 -8.63949537e-01 -5.67180693e-01 7.90700316e-01 -6.30491138e-01 7.21267052e-03 8.62502694e-01 3.08055311e-01 -1.01306848e-01 3.58814806e-01 -9.88775074e-01 -1.14627314e+00 -8.79333794e-01 5.79460680e-01 1.49461284e-01 -2.97188282e-01 -6.40481338e-02 6.16912954e-02 3.82324725e-01 -1.28430343e+00 3.83398801e-01 9.75558519e-01 9.33981359e-01 3.02654833e-01 5.99007249e-01 -2.18135968e-01 9.58145559e-01 -5.95338821e-01 -5.41877210e-01 3.91777188e-01 -1.04015410e+00 -4.42055404e-01 3.06482852e-01 1.70069784e-01 -5.83134770e-01 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aded787b-ce7f-4450-ade2-8761f81b4c79	vision-language-models-in-remote-sensing	2305.05726	null	https://arxiv.org/abs/2305.05726v1	https://arxiv.org/pdf/2305.05726v1.pdf	Vision-Language Models in Remote Sensing: Current Progress and Future Trends	The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide us with intelligent solutions that are more similar to human thinking, enabling us to use general artificial intelligence to solve problems in various applications. However, in the field of remote sensing, the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-language models excel, as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. Vision-language models can go beyond recognizing the objects in an image and can infer the relationships between them, as well as generate natural language descriptions of the image. This makes them better suited for tasks that require both visual and textual understanding, such as image captioning, text-based image retrieval, and visual question answering. This paper provides a comprehensive review of the research on vision-language models in remote sensing, summarizing the latest progress, highlighting the current challenges, and identifying potential research opportunities. Specifically, we review the application of vision-language models in several mainstream remote sensing tasks, including image captioning, text-based image generation, text-based image retrieval, visual question answering, scene classification, semantic segmentation, and object detection. For each task, we briefly describe the task background and review some representative works. Finally, we summarize the limitations of existing work and provide some possible directions for future development.	['Xiao Xiang Zhu', 'Zhenghang Yuan', 'Xiang Li', 'Yuan Hu', 'Congcong Wen']	2023-05-09	null	null	null	null	['scene-classification']	['computer-vision']	[ 6.87758803e-01 1.42067537e-01 6.72132745e-02 -3.84558439e-01 -4.06686187e-01 -7.73456514e-01 6.37613416e-01 1.85274303e-01 -6.20450377e-02 3.49323988e-01 8.60043839e-02 -8.80471468e-01 -4.91623804e-02 -1.13081098e+00 -5.78828692e-01 -7.45160580e-01 1.47876784e-01 4.93997514e-01 2.86758970e-02 -1.38266250e-01 3.01051736e-01 4.61971849e-01 -1.83673418e+00 5.20485878e-01 8.17975402e-01 8.05192173e-01 8.74354124e-01 8.57405901e-01 -6.35765135e-01 1.06617808e+00 -5.96384585e-01 -3.98321711e-02 -5.41910715e-02 -4.47326183e-01 -1.13510990e+00 5.37832558e-01 4.92341012e-01 -2.39640445e-01 -5.98462746e-02 1.15003204e+00 2.34476969e-01 1.67864844e-01 7.28123248e-01 -1.26210928e+00 -1.21474743e+00 2.75193512e-01 -4.49939638e-01 1.00478962e-01 2.54383236e-01 2.02935472e-01 1.09593892e+00 -8.27499211e-01 4.57266182e-01 1.39641583e+00 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838ea1c9-13f8-4cc9-a50b-adb1d00d3b7c	monoloco-monocular-3d-pedestrian-localization	1906.06059	null	https://arxiv.org/abs/1906.06059v2	https://arxiv.org/pdf/1906.06059v2.pdf	MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation	We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss function based on the Laplace distribution. Our architecture is a light-weight feed-forward neural network that predicts 3D locations and corresponding confidence intervals given 2D human poses. The design is particularly well suited for small training data, cross-dataset generalization, and real-time applications. Our experiments show that we (i) outperform state-of-the-art results on KITTI and nuScenes datasets, (ii) even outperform a stereo-based method for far-away pedestrians, and (iii) estimate meaningful confidence intervals. We further share insights on our model of uncertainty in cases of limited observations and out-of-distribution samples.	['Sven Kreiss', 'Alexandre Alahi', 'Lorenzo Bertoni']	2019-06-14	monoloco-monocular-3d-pedestrian-localization-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Bertoni_MonoLoco_Monocular_3D_Pedestrian_Localization_and_Uncertainty_Estimation_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Bertoni_MonoLoco_Monocular_3D_Pedestrian_Localization_and_Uncertainty_Estimation_ICCV_2019_paper.pdf	iccv-2019-10	['3d-depth-estimation']	['computer-vision']	[-6.35053590e-02 -9.73440055e-03 -4.23640087e-02 -6.33288980e-01 -1.03127861e+00 -4.57917988e-01 4.84846205e-01 5.24437346e-04 -5.59945464e-01 1.19461167e+00 -5.04624322e-02 -3.59820813e-01 9.08296630e-02 -4.31657344e-01 -1.19749045e+00 -3.03183913e-01 -5.23938119e-01 5.98458529e-01 3.14786881e-02 4.22920972e-01 -2.07318049e-02 5.23067713e-01 -1.58334804e+00 -1.34756684e-01 6.60025060e-01 1.49294782e+00 -3.48732948e-01 8.32722127e-01 3.11012626e-01 4.50596690e-01 -6.25201166e-01 -4.49372530e-01 2.89424092e-01 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c5625381-7498-4558-bf6e-bf73f4d65928	achieving-better-kinship-recognition-through	2006.11739	null	https://arxiv.org/abs/2006.11739v1	https://arxiv.org/pdf/2006.11739v1.pdf	Achieving Better Kinship Recognition Through Better Baseline	Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions. These restrictions proved to be difficult to deal with, however, recent advancements in face verification show that there is still much to gain using more data and novel ideas. As a result face recognition is a great source domain from which we can transfer the knowledge to get better performance in kinship recognition as a source domain. We present a new baseline for an automatic kinship recognition task and relatives search based on RetinaFace[1] for face registration and ArcFace[2] face verification model. With the approach described above as the foundation, we constructed a pipeline that achieved state-of-the-art performance on two tracks in the recent Recognizing Families In the Wild Data Challenge.	['Andrei Shadrikov']	2020-06-21	null	null	null	null	['face-image-retrieval']	['computer-vision']	[ 9.15050283e-02 9.94879007e-02 -7.24089891e-02 -1.17004681e+00 -4.15394783e-01 -4.43125337e-01 7.18866944e-01 -3.85411024e-01 -1.24361098e-01 5.56850672e-01 1.34411663e-01 1.11391217e-01 -1.58329293e-01 -5.61593473e-01 -5.91841698e-01 -7.86894083e-01 -3.06228608e-01 7.56286800e-01 2.79311184e-02 -4.09911901e-01 1.24141015e-01 9.09636855e-01 -1.84598053e+00 4.87867028e-01 4.77546841e-01 7.66879797e-01 -6.06961846e-01 4.00094867e-01 1.70450937e-02 2.42174968e-01 -3.24114233e-01 -9.51157510e-01 3.97451550e-01 -4.94907737e-01 -1.15021360e+00 -3.55831146e-01 1.21981049e+00 -3.06531131e-01 -2.91489959e-01 7.28197157e-01 7.27371812e-01 -2.07115486e-01 5.34684956e-01 -1.34789824e+00 -5.90540230e-01 5.12184024e-01 -6.98214650e-01 1.42940298e-01 5.41079760e-01 7.98935362e-04 6.11544788e-01 -8.36578190e-01 8.15937996e-01 1.66128051e+00 9.64142025e-01 7.43053198e-01 -1.35480690e+00 -8.96477580e-01 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558511ed-89d0-41a7-b47b-2cef27348ca0	deep-face-video-inpainting-via-uv-mapping	2109.00681	null	https://arxiv.org/abs/2109.00681v2	https://arxiv.org/pdf/2109.00681v2.pdf	Deep Face Video Inpainting via UV Mapping	This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of any prior knowledge of the face to help retrieve correspondences for the corrupted face. They therefore only achieve sub-optimal results, particularly for faces under large pose and expression variations where face components appear very differently across frames. In this paper, we propose a two-stage deep learning method for face video inpainting. We employ 3DMM as our 3D face prior to transform a face between the image space and the UV (texture) space. In Stage I, we perform face inpainting in the UV space. This helps to largely remove the influence of face poses and expressions and makes the learning task much easier with well aligned face features. We introduce a frame-wise attention module to fully exploit correspondences in neighboring frames to assist the inpainting task. In Stage II, we transform the inpainted face regions back to the image space and perform face video refinement that inpaints any background regions not covered in Stage I and also refines the inpainted face regions. Extensive experiments have been carried out which show our method can significantly outperform methods based merely on 2D information, especially for faces under large pose and expression variations. Project page: https://ywq.github.io/FVIP	['Kwan-Yee K. Wong', 'GuanYing Chen', 'Chaofeng Chen', 'Zhenfang Chen', 'Wenqi Yang']	2021-09-02	null	null	null	null	['facial-inpainting', 'video-inpainting']	['computer-vision', 'computer-vision']	[ 1.77511424e-01 -8.79683942e-02 -1.42242268e-01 -4.49830621e-01 -6.16222322e-01 -2.98600852e-01 2.91785717e-01 -7.28700757e-01 -1.72671303e-01 6.41334713e-01 1.52024522e-01 2.71535903e-01 4.50962484e-01 -3.44636947e-01 -9.74226713e-01 -6.17696345e-01 3.16730440e-01 1.96427643e-01 -2.44460732e-01 -6.31007403e-02 -4.08829823e-02 7.15923131e-01 -1.55895376e+00 3.81614029e-01 3.72995615e-01 8.99753571e-01 1.71303347e-01 5.50348997e-01 -1.39755502e-01 6.75462306e-01 -5.25545180e-01 -4.22859192e-01 7.33352959e-01 -6.44174337e-01 -6.96818411e-01 7.93403268e-01 9.40507412e-01 -7.38153040e-01 -6.22055352e-01 1.17425358e+00 3.34357291e-01 1.02790363e-01 3.50062013e-01 -1.12087822e+00 -4.28631485e-01 -1.48526073e-01 -1.19499826e+00 2.69157533e-02 6.65065765e-01 1.22470155e-01 4.16095883e-01 -1.25533605e+00 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d0535116-d520-4129-8311-3cf2bf029c40	brain-in-a-vat-on-missing-pieces-towards	2307.03762	null	https://arxiv.org/abs/2307.03762v1	https://arxiv.org/pdf/2307.03762v1.pdf	Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models	In this perspective paper, we first comprehensively review existing evaluations of Large Language Models (LLMs) using both standardized tests and ability-oriented benchmarks. We pinpoint several problems with current evaluation methods that tend to overstate the capabilities of LLMs. We then articulate what artificial general intelligence should encompass beyond the capabilities of LLMs. We propose four characteristics of generally intelligent agents: 1) they can perform unlimited tasks; 2) they can generate new tasks within a context; 3) they operate based on a value system that underpins task generation; and 4) they have a world model reflecting reality, which shapes their interaction with the world. Building on this viewpoint, we highlight the missing pieces in artificial general intelligence, that is, the unity of knowing and acting. We argue that active engagement with objects in the real world delivers more robust signals for forming conceptual representations. Additionally, knowledge acquisition isn't solely reliant on passive input but requires repeated trials and errors. We conclude by outlining promising future research directions in the field of artificial general intelligence.	['Song-Chun Zhu', 'Chi Zhang', 'Yuxi Ma']	2023-07-07	null	null	null	null	['unity']	['computer-vision']	[ 1.18988469e-01 5.43915153e-01 -6.36507198e-02 -1.28153443e-01 -2.87150055e-01 -7.88717151e-01 1.14987671e+00 1.34390011e-01 -3.75805914e-01 5.25015593e-01 6.04172409e-01 -1.19094290e-01 -6.98278010e-01 -9.57952678e-01 -4.67994303e-01 -3.49157989e-01 1.50291011e-01 8.41518223e-01 -1.10208832e-01 -5.03104031e-01 6.12382352e-01 5.88901997e-01 -1.61434782e+00 3.45918506e-01 9.91061330e-01 6.09831393e-01 5.36850393e-01 5.22402167e-01 -2.49932483e-01 1.39741290e+00 -9.35510218e-01 -3.19037735e-01 -1.29535023e-04 -3.44971299e-01 -1.17749810e+00 -7.15649351e-02 7.96608478e-02 -3.77971798e-01 -1.20136559e-01 7.05124497e-01 2.33510733e-01 4.02764827e-01 5.93321919e-01 -1.23380244e+00 -1.17275560e+00 8.34265292e-01 5.24387717e-01 6.42831102e-02 7.93618739e-01 4.96976107e-01 7.52989948e-01 -7.90871203e-01 7.58579791e-01 1.31003189e+00 4.75157470e-01 6.74541652e-01 -1.07888508e+00 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c87135a3-99cf-45b3-a48d-3a123729f8c8	gkd-generalized-knowledge-distillation-for	2306.13649	null	https://arxiv.org/abs/2306.13649v1	https://arxiv.org/pdf/2306.13649v1.pdf	GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models	Knowledge distillation is commonly used for compressing neural networks to reduce their inference cost and memory footprint. However, current distillation methods for auto-regressive models, such as generative language models (LMs), suffer from two key issues: (1) distribution mismatch between output sequences during training and the sequences generated by the student during its deployment, and (2) model under-specification, where the student model may not be expressive enough to fit the teacher's distribution. To address these issues, we propose Generalized Knowledge Distillation (GKD). GKD mitigates distribution mismatch by sampling output sequences from the student during training. Furthermore, GKD handles model under-specification by optimizing alternative divergences, such as reverse KL, that focus on generating samples from the student that are likely under the teacher's distribution. We demonstrate that GKD outperforms commonly-used approaches for distilling LLMs on summarization, machine translation, and arithmetic reasoning tasks.	['Olivier Bachem', 'Matthieu Geist', 'Sabela Ramos', 'Piotr Stanczyk', 'Nino Vieillard', 'Rishabh Agarwal']	2023-06-23	null	null	null	null	['machine-translation', 'arithmetic-reasoning']	['natural-language-processing', 'reasoning']	[ 3.35307270e-01 2.85980076e-01 -2.71278769e-01 -2.34011158e-01 -8.59999120e-01 -6.34283721e-01 4.57819760e-01 1.31082237e-01 -5.84456027e-01 8.98795426e-01 2.12958902e-01 -7.24282384e-01 7.62156993e-02 -8.64951015e-01 -1.06548774e+00 -6.06298149e-01 3.73610824e-01 9.29730415e-01 -5.71714938e-02 5.67413233e-02 -1.28132049e-02 2.98577160e-01 -1.43664455e+00 1.02765389e-01 1.25837207e+00 5.45790076e-01 4.51440156e-01 9.21469688e-01 -3.92725915e-01 1.05158603e+00 -9.85567868e-01 -6.78125978e-01 -5.69889434e-02 -8.63404632e-01 -6.85127079e-01 -4.37143862e-01 4.29479331e-01 -5.23802340e-01 -5.04549384e-01 1.31892407e+00 5.65130293e-01 4.23617065e-01 1.00930572e+00 -1.20626771e+00 -8.33127379e-01 1.41286230e+00 -2.99028009e-01 2.32394308e-01 -1.84180900e-01 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1ace3a0c-1f87-4865-ade9-8be726551b55	survivalgan-generating-time-to-event-data-for	2302.12749	null	https://arxiv.org/abs/2302.12749v1	https://arxiv.org/pdf/2302.12749v1.pdf	SurvivalGAN: Generating Time-to-Event Data for Survival Analysis	Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods for generating synthetic tabular data, the task remains non-trivial and unexplored for specific scenarios. One such scenario is survival data. Here, the key difficulty is censoring: for some instances, we are not aware of the time of event, or if one even occurred. Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis: (1) generating too few at-risk members; (2) generating too many at-risk members; and (3) censoring too early. We formalize these failure modes and provide three new generative metrics to quantify them. Following this, we propose SurvivalGAN, a generative model that handles survival data firstly by addressing the imbalance in the censoring and event horizons, and secondly by using a dedicated mechanism for approximating time-to-event/censoring. We evaluate this method via extensive experiments on medical datasets. SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.	['Mihaela van der Schaar', 'Pietro Lio', 'Fergus Imrie', 'Bogdan Cebere', 'Alexander Norcliffe']	2023-02-24	null	null	null	null	['survival-analysis']	['miscellaneous']	[ 2.32882816e-02 2.47439176e-01 -4.52299386e-01 -3.77814472e-01 -1.13983953e+00 -6.04706287e-01 6.72875285e-01 4.11347210e-01 -1.39286280e-01 1.17705429e+00 6.10759437e-01 -5.51014006e-01 -2.63828307e-01 -8.84871483e-01 -4.73812282e-01 -7.68182814e-01 -3.43211740e-01 7.18897581e-01 -3.87682289e-01 9.73961949e-02 -1.25343651e-01 2.58643806e-01 -1.11466289e+00 5.36753051e-02 9.60032403e-01 7.71246910e-01 -7.02023089e-01 3.96069199e-01 4.10709530e-02 6.93588376e-01 -8.30030680e-01 -5.16380310e-01 1.11885652e-01 -7.85120189e-01 -5.44676602e-01 -1.65962741e-01 -1.55747026e-01 -3.03456932e-01 -3.28219354e-01 7.20795512e-01 5.79455912e-01 -1.36668041e-01 9.57547605e-01 -1.51555598e+00 -4.92294788e-01 7.77957559e-01 -3.79740089e-01 -1.35816127e-01 2.51899242e-01 1.65271088e-01 7.81657875e-01 -5.23394525e-01 3.70401889e-01 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cba72fb7-7c7e-43be-9331-08b76a70403d	ahp-learning-to-negative-sample-for-hyperedge	2204.06353	null	https://arxiv.org/abs/2204.06353v2	https://arxiv.org/pdf/2204.06353v2.pdf	AHP: Learning to Negative Sample for Hyperedge Prediction	Hypergraphs (i.e., sets of hyperedges) naturally represent group relations (e.g., researchers co-authoring a paper and ingredients used together in a recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes). Predicting future or missing hyperedges bears significant implications for many applications (e.g., collaboration and recipe recommendation). What makes hyperedge prediction particularly challenging is the vast number of non-hyperedge subsets, which grows exponentially with the number of nodes. Since it is prohibitive to use all of them as negative examples for model training, it is inevitable to sample a very small portion of them, and to this end, heuristic sampling schemes have been employed. However, trained models suffer from poor generalization capability for examples of different natures. In this paper, we propose AHP, an adversarial training-based hyperedge-prediction method. It learns to sample negative examples without relying on any heuristic schemes. Using six real hypergraphs, we show that AHP generalizes better to negative examples of various natures. It yields up to 28.2% higher AUROC than the best existing methods and often even outperforms its variants with sampling schemes tailored to test sets.	['Kijung Shin', 'Chanyoung Park', 'Seungwoo Lee', 'Hyunjin Hwang']	2022-04-13	null	null	null	null	['hyperedge-prediction']	['graphs']	[ 5.97640015e-02 5.31799972e-01 -4.39268023e-01 -1.22290030e-01 -1.93787709e-01 -7.94459760e-01 4.63322699e-01 6.77202269e-02 1.17536359e-01 9.93319809e-01 -7.07477480e-02 -4.31031495e-01 -7.68092871e-02 -1.37596357e+00 -8.38408113e-01 -7.07593501e-01 -1.43958718e-01 5.62791884e-01 1.48787335e-01 -3.42839122e-01 9.58777685e-03 2.96517074e-01 -1.04833579e+00 2.01938793e-01 8.78989100e-01 5.64724922e-01 -9.26808938e-02 4.34102386e-01 -4.23840992e-02 5.10695398e-01 -6.08607113e-01 -1.04189110e+00 2.68106371e-01 -4.64044392e-01 -6.20308876e-01 1.81687891e-01 3.20668221e-01 1.04516104e-01 -5.59719324e-01 1.21278024e+00 2.94176191e-01 1.06362931e-01 7.66188741e-01 -1.60165679e+00 -8.12614679e-01 8.53985250e-01 -7.08328903e-01 -1.41388461e-01 3.68738651e-01 1.34471893e-01 1.12092268e+00 -7.06242740e-01 8.36371005e-01 8.77442360e-01 7.93735087e-01 6.65193558e-01 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0936d963-e457-4186-b808-33da6791694d	rademacher-random-projections-with-tensor	2110.13970	null	https://arxiv.org/abs/2110.13970v3	https://arxiv.org/pdf/2110.13970v3.pdf	Rademacher Random Projections with Tensor Networks	Random projection (RP) have recently emerged as popular techniques in the machine learning community for their ability in reducing the dimension of very high-dimensional tensors. Following the work in [30], we consider a tensorized random projection relying on Tensor Train (TT) decomposition where each element of the core tensors is drawn from a Rademacher distribution. Our theoretical results reveal that the Gaussian low-rank tensor represented in compressed form in TT format in [30] can be replaced by a TT tensor with core elements drawn from a Rademacher distribution with the same embedding size. Experiments on synthetic data demonstrate that tensorized Rademacher RP can outperform the tensorized Gaussian RP studied in [30]. In addition, we show both theoretically and experimentally, that the tensorized RP in the Matrix Product Operator (MPO) format is not a Johnson-Lindenstrauss transform (JLT) and therefore not a well-suited random projection map	['Guillaume Rabusseau', 'Beheshteh T. Rakhshan']	2021-10-26	null	null	null	null	['tensor-networks']	['methodology']	[-5.97659200e-02 1.61361292e-01 1.63042799e-01 1.69737577e-01 -5.25160193e-01 -8.30309331e-01 5.58061242e-01 -4.92747873e-01 -2.62210310e-01 4.02746290e-01 7.86348403e-01 -5.95307767e-01 -5.42986333e-01 -5.34516037e-01 -6.61046565e-01 -1.05039740e+00 -4.99786496e-01 8.90450597e-01 -7.20391870e-02 -1.23766497e-01 -4.91732024e-02 5.04374146e-01 -1.13794160e+00 2.80664504e-01 6.72311366e-01 8.23072016e-01 -1.67459920e-01 8.31630945e-01 3.54548618e-02 8.56046259e-01 -1.07695416e-01 -8.50792766e-01 8.14794481e-01 5.78293167e-02 -9.91995454e-01 1.77517161e-02 7.16515005e-01 -1.46479383e-01 -5.96033990e-01 1.11411393e+00 1.48448899e-01 1.88887373e-01 8.38595450e-01 -1.06358349e+00 -5.34610093e-01 9.27430332e-01 -4.71863627e-01 -8.47109854e-02 1.59707531e-01 -2.75351286e-01 1.24917185e+00 -1.45278656e+00 6.86802864e-01 1.24719548e+00 6.91074252e-01 1.90753028e-01 -1.73695588e+00 -4.98262167e-01 -4.73865479e-01 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e6e4c72f-a36e-4c9d-ad0d-b49f75af4262	improved-automated-lesion-segmentation-in	2210.07761	null	https://arxiv.org/abs/2210.07761v1	https://arxiv.org/pdf/2210.07761v1.pdf	Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation	Numerous oncology indications have extensively quantified metabolically active tumors using positron emission tomography (PET) and computed tomography (CT). F-fluorodeoxyglucose-positron emission tomography (FDG-PET) is frequently utilized in clinical practice and clinical drug research to detect and measure metabolically active malignancies. The assessment of tumor burden using manual or computer-assisted tumor segmentation in FDG-PET images is widespread. Deep learning algorithms have also produced effective solutions in this area. However, there may be a need to improve the performance of a pre-trained deep learning network without the opportunity to modify this network. We investigate the potential benefits of test-time augmentation for segmenting tumors from PET-CT pairings. We applied a new framework of multilevel and multimodal tumor segmentation techniques that can simultaneously consider PET and CT data. In this study, we improve the network using a learnable composition of test time augmentations. We trained U-Net and Swin U-Netr on the training database to determine how different test time augmentation improved segmentation performance. We also developed an algorithm that finds an optimal test time augmentation contribution coefficient set. Using the newly trained U-Net and Swin U-Netr results, we defined an optimal set of coefficients for test-time augmentation and utilized them in combination with a pre-trained fixed nnU-Net. The ultimate idea is to improve performance at the time of testing when the model is fixed. Averaging the predictions with varying ratios on the augmented data can improve prediction accuracy. Our code will be available at \url{https://github.com/sepidehamiri/pet\_seg\_unet}	['Bulat Ibragimov', 'Sepideh Amiri']	2022-10-14	null	null	null	null	['tumor-segmentation']	['computer-vision']	[ 0.38892177 -0.06699484 -0.6774278 -0.5189342 -1.0396608 -0.57858974 0.24651226 0.18766946 -0.84926 1.0005683 -0.12257627 -0.7884153 -0.01401369 -0.9453487 -0.37602606 -0.8584934 0.16273479 0.8133331 0.2468079 0.171901 -0.12898013 0.766147 -0.7917309 0.37142637 0.6188783 0.8577867 0.47697338 0.7838701 -0.01544255 0.4852379 -0.14530203 0.09624629 0.2685582 -0.58334225 -0.7649138 -0.09048862 -0.1000751 -0.29123396 -0.19565399 0.8148529 0.49675873 0.0298707 0.4609226 -1.0501391 0.19108956 0.6761868 -0.479112 0.7696091 -0.25766417 0.26165643 0.4819637 -0.677048 0.4488155 0.38809416 0.7544815 0.444294 -1.071218 -0.62336075 -0.14235644 0.08348231 -1.3350322 -0.07211374 0.3992241 -0.31160375 1.0045763 0.49224243 0.92376846 0.95924526 0.4472055 0.5335344 1.0634254 -0.4954251 0.09648205 0.11183685 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00c78745-7a87-4390-8898-a63e8a40f787	efficient-person-search-an-anchor-free	2109.00211	null	https://arxiv.org/abs/2109.00211v1	https://arxiv.org/pdf/2109.00211v1.pdf	Efficient Person Search: An Anchor-Free Approach	Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id branch upon two-stage detectors like Faster R-CNN. Owing to the ROI-Align operation, this pipeline yields promising accuracy as re-id features are explicitly aligned with the corresponding object regions, but in the meantime, it introduces high computational overhead due to dense object anchors. In this work, we present an anchor-free approach to efficiently tackling this challenging task, by introducing the following dedicated designs. First, we select an anchor-free detector (i.e., FCOS) as the prototype of our framework. Due to the lack of dense object anchors, it exhibits significantly higher efficiency compared with existing person search models. Second, when directly accommodating this anchor-free detector for person search, there exist several major challenges in learning robust re-id features, which we summarize as the misalignment issues in different levels (i.e., scale, region, and task). To address these issues, we propose an aligned feature aggregation module to generate more discriminative and robust feature embeddings. Accordingly, we name our model as Feature-Aligned Person Search Network (AlignPS). Third, by investigating the advantages of both anchor-based and anchor-free models, we further augment AlignPS with an ROI-Align head, which significantly improves the robustness of re-id features while still keeping our model highly efficient. Extensive experiments conducted on two challenging benchmarks (i.e., CUHK-SYSU and PRW) demonstrate that our framework achieves state-of-the-art or competitive performance, while displaying higher efficiency. All the source codes, data, and trained models are available at: https://github.com/daodaofr/alignps.	['Xiaokang Yang', 'Shengcai Liao', 'Jie Qin', 'Jinpeng Li', 'Yichao Yan']	2021-09-01	null	null	null	null	['person-search']	['computer-vision']	[-4.17713404e-01 -3.61729294e-01 -1.37290552e-01 -2.56291091e-01 -7.47081935e-01 -3.70665878e-01 6.64515555e-01 -1.08279191e-01 -6.50044739e-01 2.61799604e-01 3.64788771e-01 3.78559440e-01 -5.45356274e-02 -5.20831525e-01 -4.69193369e-01 -4.01509285e-01 9.27284360e-02 3.90824914e-01 3.07437867e-01 -3.31876092e-02 -4.74765114e-02 4.91969973e-01 -1.61193991e+00 -2.09129408e-01 8.96333992e-01 9.71405447e-01 5.63907735e-02 2.38220856e-01 3.87175918e-01 1.75776243e-01 -5.45488179e-01 -5.94960511e-01 4.93109852e-01 -8.26318935e-02 -6.60316288e-01 5.04349871e-03 6.31228328e-01 -6.31596267e-01 -7.08729446e-01 8.82904708e-01 8.72772872e-01 3.28228593e-01 4.77417380e-01 -1.31364703e+00 -9.80717361e-01 2.21368417e-01 -5.92810214e-01 2.63612837e-01 6.52211905e-01 3.52419823e-01 1.18747163e+00 -1.40191185e+00 2.27220431e-01 1.30385447e+00 6.67147875e-01 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-2.71667391e-01 5.41366518e-01 2.26118073e-01 -1.05511856e+00 -2.90618926e-01 -3.75514418e-01]	[14.787038803100586, 0.8036001324653625]
f332bd38-1208-4033-b473-4d5287296cce	resolving-spatial-time-conflicts-in-a-set-of	1608.02763	null	http://arxiv.org/abs/1608.02763v1	http://arxiv.org/pdf/1608.02763v1.pdf	Resolving Spatial-Time Conflicts In A Set Of Any-angle Or Angle-constrained Grid Paths	We study the multi-agent path finding problem (MAPF) for a group of agents which are allowed to move into arbitrary directions on a 2D square grid. We focus on centralized conflict resolution for independently computed plans. We propose an algorithm that eliminates conflicts by using local re-planning and introducing time offsets to the execution of paths by different agents. Experimental results show that the algorithm can find high quality conflict-free solutions at low computational cost.	['Konstantin Yakovlev', 'Anton Andreychuk']	2016-08-09	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[-4.52285931e-02 2.68141091e-01 -3.85160595e-02 7.47217163e-02 -6.81222856e-01 -7.66948819e-01 5.45202136e-01 4.58663046e-01 -7.08672822e-01 1.53532696e+00 2.04787366e-02 -1.79709822e-01 -7.28760540e-01 -1.16850269e+00 -1.56254932e-01 -5.24924874e-01 -8.97135317e-01 1.37690723e+00 8.87916327e-01 -4.45118994e-01 4.60696638e-01 6.99342191e-01 -7.06110179e-01 -2.17811078e-01 6.48354709e-01 1.01474315e-01 2.08954513e-01 8.21797609e-01 2.60941297e-01 5.72521865e-01 -7.82748461e-01 3.16164225e-01 6.43272460e-01 -3.47837508e-01 -1.20058787e+00 3.81856889e-01 -3.77769232e-01 -3.91466409e-01 -7.36302137e-03 9.26548064e-01 2.19921827e-01 4.62701112e-01 5.49911976e-01 -1.82902277e+00 3.95702899e-01 5.08162439e-01 -8.23612869e-01 2.32779369e-01 8.74916553e-01 -8.87562484e-02 6.05482996e-01 -7.84931481e-02 1.07778442e+00 1.05307460e+00 3.55295867e-01 3.37868065e-01 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2652486a-116b-448a-886d-226b3b86f434	one-shot-domain-adaptation-for-person-re	1811.10144	null	https://arxiv.org/abs/1811.10144v3	https://arxiv.org/pdf/1811.10144v3.pdf	Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification	Domain adaptation in person re-identification (re-ID) has always been a challenging task. In this work, we explore how to harness the natural similar characteristics existing in the samples from the target domain for learning to conduct person re-ID in an unsupervised manner. Concretely, we propose a Self-similarity Grouping (SSG) approach, which exploits the potential similarity (from global body to local parts) of unlabeled samples to automatically build multiple clusters from different views. These independent clusters are then assigned with labels, which serve as the pseudo identities to supervise the training process. We repeatedly and alternatively conduct such a grouping and training process until the model is stable. Despite the apparent simplify, our SSG outperforms the state-of-the-arts by more than 4.6% (DukeMTMC to Market1501) and 4.4% (Market1501 to DukeMTMC) in mAP, respectively. Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i.e. the number of independent identities from the target domain is unknown). Without spending much effort on labeling, our SSG ++ can further promote the mAP upon SSG by 10.7% and 6.9%, respectively. Our Code is available at: https://github.com/OasisYang/SSG .	['Yuqian Zhou', 'Yunchao Wei', 'Honghui Shi', 'Guanshuo Wang', 'Yang Fu', 'Thomas Huang']	2018-11-26	self-similarity-grouping-a-simple	http://openaccess.thecvf.com/content_ICCV_2019/html/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf	iccv-2019-10	['unsupervised-person-re-identification']	['computer-vision']	[-2.06107134e-03 -4.19158153e-02 -1.24871828e-01 -4.41571981e-01 -5.78655958e-01 -6.61618471e-01 8.15867901e-01 -1.36000523e-02 -4.43520129e-01 6.49473548e-01 2.87857622e-01 2.46057644e-01 1.25902385e-01 -4.69026893e-01 -5.05150259e-01 -5.52299917e-01 2.22808525e-01 7.82773316e-01 1.19156636e-01 -9.74375382e-03 -7.56346136e-02 1.46551251e-01 -1.46354234e+00 -7.67748505e-02 1.14127254e+00 3.68000686e-01 -9.39996913e-02 3.23183388e-01 1.61062136e-01 1.44187018e-01 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f8135c70-3aec-4c5b-baf6-ce1cc8f6f92a	pp-yolov2-a-practical-object-detector	2104.10419	null	https://arxiv.org/abs/2104.10419v1	https://arxiv.org/pdf/2104.10419v1.pdf	PP-YOLOv2: A Practical Object Detector	Being effective and efficient is essential to an object detector for practical use. To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged. This paper will analyze a collection of refinements and empirically evaluate their impact on the final model performance through incremental ablation study. Things we tried that didn't work will also be discussed. By combining multiple effective refinements, we boost PP-YOLO's performance from 45.9% mAP to 49.5% mAP on COCO2017 test-dev. Since a significant margin of performance has been made, we present PP-YOLOv2. In terms of speed, PP-YOLOv2 runs in 68.9FPS at 640x640 input size. Paddle inference engine with TensorRT, FP16-precision, and batch size = 1 further improves PP-YOLOv2's infer speed, which achieves 106.5 FPS. Such a performance surpasses existing object detectors with roughly the same amount of parameters (i.e., YOLOv4-CSP, YOLOv5l). Besides, PP-YOLOv2 with ResNet101 achieves 50.3% mAP on COCO2017 test-dev. Source code is at https://github.com/PaddlePaddle/PaddleDetection.	['Osamu Yoshie', 'Yanjun Ma', 'dianhai yu', 'Xiaoguang Hu', 'Qiwen Liu', 'Shumin Han', 'Qingqing Dang', 'Kaipeng Deng', 'Xiang Long', 'Xiaying Bai', 'Wenyu Lv', 'Xinxin Wang', 'Xin Huang']	2021-04-21	null	null	null	null	['real-time-object-detection']	['computer-vision']	[-5.37196994e-01 -1.33364454e-01 -1.25495449e-01 -1.58452764e-01 -7.65225708e-01 -3.62576693e-01 3.24788928e-01 -1.10865682e-01 -6.53044403e-01 2.80049235e-01 -3.59588891e-01 -4.44658905e-01 5.38405001e-01 -6.54020965e-01 -8.69443119e-01 -4.30909693e-01 1.35480165e-01 1.11041032e-01 9.22004938e-01 6.76639527e-02 7.92850181e-02 4.02872741e-01 -1.49851429e+00 5.01081765e-01 4.08486158e-01 1.09668791e+00 2.71281034e-01 1.07086861e+00 2.87025601e-01 9.12200630e-01 -6.93962216e-01 -5.26825726e-01 4.43824142e-01 1.43880755e-01 -5.46760798e-01 -4.01666760e-01 9.05473173e-01 -7.09150434e-01 -4.51895714e-01 9.98515546e-01 6.06574237e-01 -1.81400284e-01 3.33197206e-01 -1.48699701e+00 -5.92539310e-01 6.38467014e-01 -7.53621876e-01 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6253219d-d97e-447c-8055-f2e8bfed1d11	on-approximate-nearest-neighbour-selection	2108.11480	null	https://arxiv.org/abs/2108.11480v1	https://arxiv.org/pdf/2108.11480v1.pdf	On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval	Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) techniques, has been increasing in popularity. Two families of approaches have emerged, depending on whether documents and queries are represented by single or multiple embeddings. ColBERT, the exemplar of the latter, uses an ANN index and approximate scores to identify a set of candidate documents for each query embedding, which are then re-ranked using accurate document representations. In this manner, a large number of documents can be retrieved for each query, hindering the efficiency of the approach. In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored. Experiments conducted on the MSMARCO passage ranking corpus demonstrate that, by cutting of the candidate set by using the approximate scores to only 200 documents, we can still obtain an effective ranking without statistically significant differences in effectiveness, and resulting in a 2x speedup in efficiency.	['Nicola Tonellotto', 'Craig Macdonald']	2021-08-25	null	null	null	null	['passage-ranking']	['natural-language-processing']	[-2.41685122e-01 -3.23118031e-01 -1.86396122e-01 -1.24723896e-01 -1.19827604e+00 -7.84975648e-01 8.70389402e-01 9.80386138e-01 -8.41021001e-01 5.75963914e-01 5.11872709e-01 -1.02154255e-01 -6.14777148e-01 -7.77504206e-01 -2.28378922e-01 -4.75987464e-01 -2.17860550e-01 7.53456652e-01 4.76572037e-01 -1.82274491e-01 7.55458534e-01 5.81303060e-01 -1.88585544e+00 3.90191346e-01 6.86414301e-01 7.64163136e-01 8.27741176e-02 7.64904439e-01 -6.60872340e-01 4.71828610e-01 -8.35514188e-01 -2.27047384e-01 7.07269982e-02 -1.70329198e-01 -6.94854856e-01 -5.58322966e-01 3.22706908e-01 -4.57881033e-01 -3.40157002e-01 7.00805068e-01 6.18190825e-01 3.46660823e-01 8.99231791e-01 -7.50223041e-01 -4.24777299e-01 5.64418972e-01 -1.92774624e-01 2.54814535e-01 6.25060737e-01 -7.00132787e-01 1.33031821e+00 -1.20200050e+00 5.40118337e-01 1.15786314e+00 4.05816734e-01 2.46864617e-01 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0b867a97-8792-488e-8385-81ee5bffb4ae	a-self-reasoning-framework-for-anomaly	2008.11887	null	https://arxiv.org/abs/2008.11887v1	https://arxiv.org/pdf/2008.11887v1.pdf	A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels	Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations of anomalous events, obtaining video-level annotations is quite fast and cheap though such high-level labels may contain significant noise. More specifically, an anomalous labeled video may actually contain anomaly only in a short duration while the rest of the video frames may be normal. In the current work, we propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. To carry out the self-reasoning based training, we generate pseudo labels by using binary clustering of spatio-temporal video features which helps in mitigating the noise present in the labels of anomalous videos. Our proposed formulation encourages both the main network and the clustering to complement each other in achieving the goal of more accurate anomaly detection. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate superiority of our proposed framework over the current state-of-the-art methods.	['Seung-Ik Lee', 'Arif Mahmood', 'Hochul Shin', 'Muhammad Zaigham Zaheer']	2020-08-27	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[ 2.85485983e-01 -1.90793350e-01 1.86014563e-01 -4.34722424e-01 -3.14277440e-01 -2.53903151e-01 4.89711732e-01 4.01990980e-01 -3.84992033e-01 4.52394366e-01 1.05761349e-01 -2.08426356e-01 2.03197211e-01 -5.33509374e-01 -8.51708531e-01 -7.76068032e-01 -2.96743572e-01 -7.32409135e-02 5.44021726e-01 2.03431547e-01 1.96452141e-01 3.05173844e-01 -1.72206461e+00 4.34051126e-01 8.05043042e-01 1.42258358e+00 -4.21000957e-01 6.48473620e-01 -5.27010560e-02 1.53002453e+00 -5.34879327e-01 -1.44427553e-01 5.07335544e-01 -2.90259451e-01 -4.80953783e-01 6.05719984e-01 6.45208955e-01 -7.28426754e-01 -5.03087401e-01 1.30561411e+00 -2.69382149e-02 3.38370144e-01 4.95459586e-01 -1.53869581e+00 -4.16127861e-01 1.10609859e-01 -7.84851611e-01 9.92543757e-01 2.61638403e-01 2.01423317e-01 7.31599510e-01 -6.26895308e-01 3.55002880e-01 9.78667557e-01 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3bcb1410-a6ad-40aa-871b-f7811aed6e5b	generation-guided-multi-level-unified-network	2303.07748	null	https://arxiv.org/abs/2303.07748v1	https://arxiv.org/pdf/2303.07748v1.pdf	Generation-Guided Multi-Level Unified Network for Video Grounding	Video grounding aims to locate the timestamps best matching the query description within an untrimmed video. Prevalent methods can be divided into moment-level and clip-level frameworks. Moment-level approaches directly predict the probability of each transient moment to be the boundary in a global perspective, and they usually perform better in coarse grounding. On the other hand, clip-level ones aggregate the moments in different time windows into proposals and then deduce the most similar one, leading to its advantage in fine-grained grounding. In this paper, we propose a multi-level unified framework to enhance performance by leveraging the merits of both moment-level and clip-level methods. Moreover, a novel generation-guided paradigm in both levels is adopted. It introduces a multi-modal generator to produce the implicit boundary feature and clip feature, later regarded as queries to calculate the boundary scores by a discriminator. The generation-guided solution enhances video grounding from a two-unique-modals' match task to a cross-modal attention task, which steps out of the previous framework and obtains notable gains. The proposed Generation-guided Multi-level Unified network (GMU) surpasses previous methods and reaches State-Of-The-Art on various benchmarks with disparate features, e.g., Charades-STA, ActivityNet captions.	['Fan Yang', 'Hezheng Lin', 'Dong Shen', 'Xiangyu Wu', 'Xing Cheng']	2023-03-14	null	null	null	null	['video-grounding']	['computer-vision']	[ 1.27049275e-02 -4.40361261e-01 -6.26940370e-01 -1.75927445e-01 -1.20518863e+00 -3.29165012e-01 5.96474767e-01 2.91480720e-01 -2.26442024e-01 5.78446388e-01 3.67182672e-01 1.57953113e-01 -1.68540418e-01 -8.74533534e-01 -6.91093028e-01 -6.47402823e-01 -2.52846897e-01 1.46397457e-01 7.83502638e-01 -2.37592697e-01 1.46815091e-01 1.86538532e-01 -1.68932772e+00 6.11944616e-01 8.38290930e-01 1.41361296e+00 1.93545327e-01 3.16330850e-01 -2.28993788e-01 6.11252904e-01 -4.39194441e-01 -3.01322728e-01 4.74678092e-02 -5.79407275e-01 -5.96792221e-01 -1.70028672e-01 6.14889860e-01 -1.60800308e-01 -5.19905746e-01 1.06250644e+00 4.22701389e-01 3.14270437e-01 2.79084146e-01 -1.50671697e+00 -4.50885803e-01 5.51750541e-01 -7.90137529e-01 6.50462985e-01 5.65015733e-01 -8.53959769e-02 1.40154469e+00 -6.09894097e-01 7.77385354e-01 1.04966402e+00 6.15024865e-01 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5799e1a6-be83-4795-b3e0-7dfe46df7e70	span-based-joint-entity-and-relation-1	null	null	https://aclanthology.org/2020.coling-main.8	https://aclanthology.org/2020.coling-main.8.pdf	Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations	Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.	['Huijun Liu', 'Yusong Tan', 'Qingbo Wu', 'Jun Ma', 'Shasha Li', 'Jie Yu', 'Bin Ji']	2020-12-01	null	null	null	coling-2020-8	['joint-entity-and-relation-extraction']	['natural-language-processing']	[-9.64505449e-02 3.78719240e-01 -6.72923625e-01 -4.64427918e-01 -8.66839051e-01 -2.86532938e-01 5.85856199e-01 5.85733831e-01 -3.03370297e-01 1.19918990e+00 7.19260991e-01 -7.37933666e-02 -4.37609144e-02 -1.33550525e+00 -6.38408840e-01 2.49362871e-01 -9.13052857e-02 7.78217852e-01 3.22163522e-01 -2.75486946e-01 1.36119244e-03 4.65701252e-01 -1.40454257e+00 7.35182166e-01 9.62922096e-01 1.13703012e+00 -3.91153723e-01 5.23978949e-01 -6.90898955e-01 7.83385634e-01 -8.49823773e-01 -7.62781024e-01 -2.21519366e-01 -3.09231393e-02 -1.41817939e+00 -1.65943697e-01 2.20578358e-01 6.90346956e-02 -6.55996203e-01 7.48280883e-01 3.24650675e-01 3.13961625e-01 7.42354751e-01 -1.22327673e+00 -1.15188897e+00 1.19610238e+00 -3.95794004e-01 2.51493663e-01 7.40969479e-01 -4.79530126e-01 1.58054924e+00 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5d9b7c7c-df01-4def-9032-2254f7c4fc0c	clta-contents-and-length-based-temporal	2103.10567	null	https://arxiv.org/abs/2103.10567v1	https://arxiv.org/pdf/2103.10567v1.pdf	CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition	Few-shot action recognition has attracted increasing attention due to the difficulty in acquiring the properly labelled training samples. Current works have shown that preserving spatial information and comparing video descriptors are crucial for few-shot action recognition. However, the importance of preserving temporal information is not well discussed. In this paper, we propose a Contents and Length-based Temporal Attention (CLTA) model, which learns customized temporal attention for the individual video to tackle the few-shot action recognition problem. CLTA utilizes the Gaussian likelihood function as the template to generate temporal attention and trains the learning matrices to study the mean and standard deviation based on both frame contents and length. We show that even a not fine-tuned backbone with an ordinary softmax classifier can still achieve similar or better results compared to the state-of-the-art few-shot action recognition with precisely captured temporal attention.	['Wenbo He', 'Yangdi Lu', 'Yang Bo']	2021-03-18	null	null	null	null	['few-shot-action-recognition']	['computer-vision']	[ 4.81747210e-01 -4.05961931e-01 -4.24017847e-01 -2.38727614e-01 -8.43241632e-01 -7.54694194e-02 7.29670644e-01 -3.25864255e-01 -5.54939687e-01 4.46542054e-01 2.47284248e-01 3.08802873e-01 -2.37779841e-01 -2.22867072e-01 -7.18244731e-01 -9.93595898e-01 -1.95931450e-01 3.75793837e-02 4.99785244e-01 3.48893888e-02 4.56049353e-01 3.81561309e-01 -1.77160096e+00 4.00023192e-01 6.07283831e-01 1.19407952e+00 2.33135030e-01 8.03246856e-01 2.63441019e-02 1.15870452e+00 -5.49504697e-01 -8.28385353e-02 3.45290750e-01 -8.10081482e-01 -5.39921701e-01 2.67745346e-01 5.57357848e-01 -3.65712047e-01 -5.47205806e-01 8.83430481e-01 3.98525327e-01 6.23325765e-01 6.01970434e-01 -1.27402365e+00 -6.56958759e-01 2.97808141e-01 -5.70158541e-01 6.05261862e-01 2.91659594e-01 2.74260700e-01 8.31245244e-01 -6.35480940e-01 5.64036429e-01 9.67998803e-01 4.96561378e-01 4.83826309e-01 -7.64396727e-01 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7.75355875e-01 1.83699340e-01 -1.03123844e+00 -4.99373555e-01 2.01966807e-01]	[8.366703033447266, 0.6829637885093689]
3fa21ff2-eec9-4c1a-a6f6-e900beb6d6bb	language-agnostic-representation-from	null	null	https://aclanthology.org/2021.emnlp-main.612	https://aclanthology.org/2021.emnlp-main.612.pdf	Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation	We propose a method to distill a language-agnostic meaning embedding from a multilingual sentence encoder. By removing language-specific information from the original embedding, we retrieve an embedding that fully represents the sentence’s meaning. The proposed method relies only on parallel corpora without any human annotations. Our meaning embedding allows efficient cross-lingual sentence similarity estimation by simple cosine similarity calculation. Experimental results on both quality estimation of machine translation and cross-lingual semantic textual similarity tasks reveal that our method consistently outperforms the strong baselines using the original multilingual embedding. Our method consistently improves the performance of any pre-trained multilingual sentence encoder, even in low-resource language pairs where only tens of thousands of parallel sentence pairs are available.	['Makoto Onizuka', 'Yuki Arase', 'Tomoyuki Kajiwara', 'Nattapong Tiyajamorn']	null	null	null	null	emnlp-2021-11	['cross-lingual-semantic-textual-similarity']	['natural-language-processing']	[-2.45809625e-03 -1.48557112e-01 -2.35642970e-01 -5.98658919e-01 -1.48846734e+00 -8.83800089e-01 6.25686049e-01 7.49642670e-01 -8.32484365e-01 7.88401902e-01 6.30167365e-01 -3.49054933e-01 3.19728583e-01 -6.71729684e-01 -7.92772055e-01 -3.27029139e-01 3.20640922e-01 3.28306019e-01 -1.98448360e-01 -5.10603726e-01 1.05642855e-01 -1.12509355e-01 -9.46581960e-01 4.79680240e-01 1.04927456e+00 7.41956413e-01 5.50823987e-01 4.15353119e-01 -3.77164692e-01 4.77362663e-01 -4.29460257e-01 -1.01105583e+00 8.57593790e-02 -4.27290827e-01 -9.94739413e-01 -3.84119898e-01 6.80494130e-01 1.61436796e-01 -2.16464683e-01 1.29594994e+00 5.85278571e-01 -1.92945912e-01 3.83886546e-01 -6.69360161e-01 -1.21281266e+00 8.13762188e-01 -1.19989939e-01 4.25848782e-01 7.48289526e-01 5.42084919e-03 1.48039353e+00 -1.26002300e+00 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f9c17d92-5ea6-4537-b8dc-130232db6315	ellipse-r-cnn-learning-to-infer-elliptical	2001.11584	null	https://arxiv.org/abs/2001.11584v2	https://arxiv.org/pdf/2001.11584v2.pdf	Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion	Images of heavily occluded objects in cluttered scenes, such as fruit clusters in trees, are hard to segment. To further retrieve the 3D size and 6D pose of each individual object in such cases, bounding boxes are not reliable from multiple views since only a little portion of the object's geometry is captured. We introduce the first CNN-based ellipse detector, called Ellipse R-CNN, to represent and infer occluded objects as ellipses. We first propose a robust and compact ellipse regression based on the Mask R-CNN architecture for elliptical object detection. Our method can infer the parameters of multiple elliptical objects even they are occluded by other neighboring objects. For better occlusion handling, we exploit refined feature regions for the regression stage, and integrate the U-Net structure for learning different occlusion patterns to compute the final detection score. The correctness of ellipse regression is validated through experiments performed on synthetic data of clustered ellipses. We further quantitatively and qualitatively demonstrate that our approach outperforms the state-of-the-art model (i.e., Mask R-CNN followed by ellipse fitting) and its three variants on both synthetic and real datasets of occluded and clustered elliptical objects.	['Volkan Isler', 'Cheng Peng', 'Pravakar Roy', 'Wenbo Dong']	2020-01-30	null	null	null	null	['occlusion-handling']	['computer-vision']	[-5.57966642e-02 3.81120592e-01 1.27855897e-01 -1.99593797e-01 -4.57683116e-01 -8.37221444e-01 1.68157116e-01 -7.25586638e-02 -4.13726233e-02 1.64740637e-01 -3.81127208e-01 -2.49778330e-01 7.54403323e-02 -4.44581300e-01 -9.71453726e-01 -4.19664323e-01 -7.14190464e-05 7.97553003e-01 5.43654799e-01 3.42171133e-01 -8.08685645e-02 1.12675965e+00 -1.23903537e+00 -9.61603150e-02 7.80108333e-01 1.06261539e+00 2.51648009e-01 5.26478112e-01 2.63397276e-01 2.61680037e-01 -5.95427454e-01 -6.32428348e-01 3.48357111e-01 3.75050128e-01 -2.15623438e-01 7.94500113e-01 5.18841743e-01 -8.09275270e-01 -1.56895116e-01 8.45073879e-01 7.33092949e-02 -3.31372380e-01 9.22863960e-01 -9.95202661e-01 -4.87082362e-01 4.11191970e-01 -1.11743975e+00 -2.37938389e-01 -1.20262600e-01 1.25450566e-01 7.93339610e-01 -1.21309876e+00 8.06326568e-01 1.29631841e+00 7.88801551e-01 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8d717434-f222-4247-ac20-c793f30d917f	survey-image-mixing-and-deleting-for-data	2106.07085	null	https://arxiv.org/abs/2106.07085v4	https://arxiv.org/pdf/2106.07085v4.pdf	Survey: Image Mixing and Deleting for Data Augmentation	Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting and enhance their generalization and performance, various methods have been suggested in the literature, including dropout, regularization, label smoothing, etc. One such method is augmentation which introduces different types of corruption in the data to prevent the model from overfitting and to memorize patterns present in the data. A sub-area of data augmentation is image mixing and deleting. This specific type of augmentation either deletes image regions or mixes two images to hide or make particular characteristics of images confusing for the network, forcing it to emphasize the overall structure of the object in an image. Models trained with this approach have proven to perform and generalize well compared to those trained without image mixing or deleting. An added benefit that comes with this method of training is robustness against image corruption. Due to its low computational cost and recent success, researchers have proposed many image mixing and deleting techniques. We furnish an in-depth survey of image mixing and deleting techniques and provide categorization via their most distinguishing features. We initiate our discussion with some fundamental relevant concepts. Next, we present essentials, such as each category's strengths and limitations, describing their working mechanism, basic formulations, and applications. We also discuss the general challenges and recommend possible future research directions for image mixing and deleting data augmentation techniques. Datasets and codes for evaluation are publicly available here.	['Ajmal Mian', 'Kashif Javed', 'Munawar Hayat', 'Saeed Anwar', 'Humza Naveed']	2021-06-13	null	null	null	null	['image-augmentation']	['computer-vision']	[ 4.96137410e-01 8.55188295e-02 -2.63859183e-01 -5.15651584e-01 -1.08431280e-02 -2.54250526e-01 3.60384375e-01 8.05719048e-02 -5.66552877e-01 6.29253924e-01 5.39678372e-02 -4.42687832e-02 1.11826502e-01 -5.81607401e-01 -7.13634253e-01 -1.06090665e+00 4.34231311e-02 -1.26631811e-01 -8.10745060e-02 1.95393890e-01 2.50401586e-01 6.35127544e-01 -1.91504812e+00 3.23848724e-01 1.07577300e+00 8.23299348e-01 2.19906911e-01 3.21036667e-01 -3.04831237e-01 8.17610085e-01 -8.95152926e-01 -2.85747260e-01 1.33179769e-01 -3.65155607e-01 -6.21299803e-01 6.37700081e-01 7.73464561e-01 -1.81655720e-01 -5.04592001e-01 1.05535996e+00 3.85694951e-01 2.80555308e-01 6.07605219e-01 -1.34287715e+00 -8.51897359e-01 1.62831545e-01 -5.68241358e-01 3.05864841e-01 -2.60424078e-01 1.66575804e-01 2.05275565e-01 -8.45925570e-01 3.05507094e-01 9.81834471e-01 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-1.50567338e-01 -1.27643383e+00 -4.66716498e-01 6.36087239e-01 4.44453627e-01 -7.82373428e-01 -3.99422258e-01 3.01254004e-01]	[9.39210033416748, 2.0842912197113037]
7a7f6d7e-bbc3-4c9f-9249-097e1667fd19	multi-scale-deformable-alignment-and-content	2306.16544	null	https://arxiv.org/abs/2306.16544v1	https://arxiv.org/pdf/2306.16544v1.pdf	Multi-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression	The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional video compression. In particular, we propose two novelties: i) a multi-scale deformable alignment scheme at the feature level combined with multi-scale conditional coding, ii) motion-content adaptive inference. In addition, we employ a gain unit, which enables a single model to operate at multiple rate-distortion operating points. We also exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding.	['A. Murat Tekalp', 'O. Ugur Ulas', 'M. Akin Yilmaz']	2023-06-28	null	null	null	null	['video-compression', 'motion-compensation']	['computer-vision', 'computer-vision']	[ 5.77509642e-01 -1.01903059e-01 -6.81249499e-01 -5.09895205e-01 -8.61297905e-01 -2.00239450e-01 3.78569037e-01 -4.00177479e-01 -1.41426027e-01 4.00785267e-01 8.54853868e-01 5.68131842e-02 -2.45309740e-01 -4.83117819e-01 -8.20727110e-01 -6.33412957e-01 -4.16051745e-01 9.13437232e-02 4.26577330e-01 -1.97871421e-02 4.18170720e-01 3.30777586e-01 -1.43387938e+00 8.17099452e-01 4.67718869e-01 1.18105030e+00 6.48203731e-01 1.20004570e+00 4.64692712e-01 9.69439268e-01 8.01935606e-03 -2.33828753e-01 4.11459625e-01 -5.17153025e-01 -4.79327589e-01 2.28588387e-01 6.89966321e-01 -1.01495671e+00 -8.44284594e-01 7.77489483e-01 6.05920434e-01 2.54216902e-02 5.17193198e-01 -8.20382178e-01 -6.60764515e-01 4.46746081e-01 -4.98627990e-01 3.78932863e-01 2.59599447e-01 1.09077387e-01 9.70160544e-01 -7.43722558e-01 6.86791539e-01 1.36668742e+00 3.99484694e-01 8.49767745e-01 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-2.08735615e-01 4.56851333e-01 6.34057879e-01 -4.73314106e-01 -1.77625969e-01 7.37432003e-01]	[11.353140830993652, -1.6269787549972534]
704dd086-76f4-469b-8a33-2e14b3af10fd	zero-shot-learning-for-audio-based-music	1907.02670	null	https://arxiv.org/abs/1907.02670v2	https://arxiv.org/pdf/1907.02670v2.pdf	Zero-shot Learning for Audio-based Music Classification and Tagging	Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels. This intrinsically cannot handle unseen labels such as newly added music genres or semantic words that users arbitrarily choose for music retrieval. Zero-shot learning can address this problem by leveraging an additional semantic space of labels where side information about the labels is used to unveil the relationship between each other. In this work, we investigate the zero-shot learning in the music domain and organize two different setups of side information. One is using human-labeled attribute information based on Free Music Archive and OpenMIC-2018 datasets. The other is using general word semantic information based on Million Song Dataset and Last.fm tag annotations. Considering a music track is usually multi-labeled in music classification and tagging datasets, we also propose a data split scheme and associated evaluation settings for the multi-label zero-shot learning. Finally, we report experimental results and discuss the effectiveness and new possibilities of zero-shot learning in the music domain.	['Juhan Nam', 'Jiyoung Park', 'Jeong Choi', 'Jongpil Lee']	2019-07-05	null	null	null	null	['multi-label-zero-shot-learning', 'music-classification']	['computer-vision', 'music']	[ 3.70084316e-01 -1.00760102e-01 -3.57016504e-01 -4.62873906e-01 -1.04288852e+00 -8.21956038e-01 2.70115495e-01 2.06569448e-01 -5.52981973e-01 4.67937112e-01 4.17115718e-01 3.63601089e-01 -5.23027480e-01 -6.57853067e-01 -3.24464262e-01 -7.23283470e-01 -5.21412725e-03 5.97580791e-01 2.15373188e-01 1.22454213e-02 1.38923183e-01 -5.04267156e-01 -2.07618237e+00 4.39877570e-01 2.39813805e-01 1.23897195e+00 2.72980958e-01 3.83468330e-01 -4.33618337e-01 6.46526754e-01 -3.51605237e-01 -1.93161711e-01 4.06053185e-01 -3.97657901e-01 -1.01936316e+00 3.35307047e-02 4.93957162e-01 1.88229084e-01 1.88291550e-01 1.09756362e+00 9.51265693e-01 5.44903100e-01 7.77233422e-01 -1.46356976e+00 -3.64764333e-01 1.17273438e+00 -2.62344956e-01 -5.57435825e-02 3.77131969e-01 -4.36856121e-01 1.61444366e+00 -7.54350305e-01 6.50616109e-01 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