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194d24f4-15bf-4274-8d06-f975a325b0c3 | towards-discriminative-representation-multi | 2203.14208 | null | https://arxiv.org/abs/2203.14208v2 | https://arxiv.org/pdf/2203.14208v2.pdf | Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking | Discriminative representation is crucial for the association step in multi-object tracking. Recent work mainly utilizes features in single or neighboring frames for constructing metric loss and empowering networks to extract representation of targets. Although this strategy is effective, it fails to fully exploit the information contained in a whole trajectory. To this end, we propose a strategy, namely multi-view trajectory contrastive learning, in which each trajectory is represented as a center vector. By maintaining all the vectors in a dynamically updated memory bank, a trajectory-level contrastive loss is devised to explore the inter-frame information in the whole trajectories. Besides, in this strategy, each target is represented as multiple adaptively selected keypoints rather than a pre-defined anchor or center. This design allows the network to generate richer representation from multiple views of the same target, which can better characterize occluded objects. Additionally, in the inference stage, a similarity-guided feature fusion strategy is developed for further boosting the quality of the trajectory representation. Extensive experiments have been conducted on MOTChallenge to verify the effectiveness of the proposed techniques. The experimental results indicate that our method has surpassed preceding trackers and established new state-of-the-art performance. | ['Shoudong Han', 'Zhuoling Li', 'En Yu'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_Towards_Discriminative_Representation_Multi-View_Trajectory_Contrastive_Learning_for_Online_Multi-Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_Towards_Discriminative_Representation_Multi-View_Trajectory_Contrastive_Learning_for_Online_Multi-Object_CVPR_2022_paper.pdf | cvpr-2022-1 | ['online-multi-object-tracking'] | ['computer-vision'] | [-5.04200011e-02 -5.95839441e-01 -3.74546289e-01 -2.29526863e-01
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d5c67ffa-c5af-4cb1-bc77-816b97f2d048 | noise-invariant-frame-selection-a-simple | 1805.01259 | null | http://arxiv.org/abs/1805.01259v1 | http://arxiv.org/pdf/1805.01259v1.pdf | Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification | The performance of speaker-related systems usually degrades heavily in
practical applications largely due to the presence of background noise. To
improve the robustness of such systems in unknown noisy environments, this
paper proposes a simple pre-processing method called Noise Invariant Frame
Selection (NIFS). Based on several noisy constraints, it selects noise
invariant frames from utterances to represent speakers. Experiments conducted
on the TIMIT database showed that the NIFS can significantly improve the
performance of Vector Quantization (VQ), Gaussian Mixture Model-Universal
Background Model (GMM-UBM) and i-vector-based speaker verification systems in
different unknown noisy environments with different SNRs, in comparison to
their baselines. Meanwhile, the proposed NIFS-based speaker verification
systems achieves similar performance when we change the constraints
(hyper-parameters) or features, which indicates that it is robust and easy to
reproduce. Since NIFS is designed as a general algorithm, it could be further
applied to other similar tasks. | ['Björn Schuller', 'Siyang Song', 'Shuimei Zhang', 'Michel Valstar', 'Linlin Shen'] | 2018-05-03 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 6.31426200e-02 -7.74082124e-01 2.14222267e-01 -5.86002409e-01
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a935687b-badb-4147-a688-87d948cd8b72 | q-learning-decision-transformer-leveraging | 2209.03993 | null | https://arxiv.org/abs/2209.03993v4 | https://arxiv.org/pdf/2209.03993v4.pdf | Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL | Recent works have shown that tackling offline reinforcement learning (RL) with a conditional policy produces promising results. The Decision Transformer (DT) combines the conditional policy approach and a transformer architecture, showing competitive performance against several benchmarks. However, DT lacks stitching ability -- one of the critical abilities for offline RL to learn the optimal policy from sub-optimal trajectories. This issue becomes particularly significant when the offline dataset only contains sub-optimal trajectories. On the other hand, the conventional RL approaches based on Dynamic Programming (such as Q-learning) do not have the same limitation; however, they suffer from unstable learning behaviours, especially when they rely on function approximation in an off-policy learning setting. In this paper, we propose the Q-learning Decision Transformer (QDT) to address the shortcomings of DT by leveraging the benefits of Dynamic Programming (Q-learning). It utilises the Dynamic Programming results to relabel the return-to-go in the training data to then train the DT with the relabelled data. Our approach efficiently exploits the benefits of these two approaches and compensates for each other's shortcomings to achieve better performance. We empirically show these in both simple toy environments and the more complex D4RL benchmark, showing competitive performance gains. | ['Raul Santos-Rodriguez', 'Ahmed Khalil', 'Taku Yamagata'] | 2022-09-08 | null | null | null | null | ['d4rl'] | ['robots'] | [-2.55216181e-01 1.17899561e-02 -6.46878123e-01 3.78994495e-02
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19836854-0b2f-43a2-ac0a-6ad66e89c749 | streaming-submodular-maximization-with | 2010.04412 | null | https://arxiv.org/abs/2010.04412v2 | https://arxiv.org/pdf/2010.04412v2.pdf | Fair and Representative Subset Selection from Data Streams | We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constraint $k$. In this work, we consider the setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional \emph{fairness} constraint that limits selection to a given number of items from each group. We then propose efficient algorithms for the fairness-aware variant of the streaming submodular maximization problem. In particular, we first give a $ (\frac{1}{2}-\varepsilon) $-approximation algorithm that requires $ O(\frac{1}{\varepsilon} \log \frac{k}{\varepsilon}) $ passes over the stream for any constant $ \varepsilon>0 $. Moreover, we give a single-pass streaming algorithm that has the same approximation ratio of $(\frac{1}{2}-\varepsilon)$ when unlimited buffer sizes and post-processing time are permitted, and discuss how to adapt it to more practical settings where the buffer sizes are bounded. Finally, we demonstrate the efficiency and effectiveness of our proposed algorithms on two real-world applications, namely \emph{maximum coverage on large graphs} and \emph{personalized recommendation}. | ['Michael Mathioudakis', 'Francesco Fabbri', 'Yanhao Wang'] | 2020-10-09 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.05740958e-01 1.19734526e-01 -4.98567402e-01 -3.09252262e-01
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b9ae51bc-3a68-477d-b9a5-cf593db4ad44 | multi-view-attention-learning-for-residual | 2306.14646 | null | https://arxiv.org/abs/2306.14646v1 | https://arxiv.org/pdf/2306.14646v1.pdf | Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer | In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (e.g., laparoscopy) or time-consuming (e.g., manual analysis). Recently, deep learning methods make many efforts in automatic analysis of medical images. Despite the remarkable progress, most of them underestimated the importance of 3D image information of disease, which might brings a limited performance for residual disease prediction, especially in small-scale datasets. To this end, in this paper, we propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction, which focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner. Specifically, we first obtain multi-view of 3D CT images from transverse, coronal and sagittal views. To better represent the image features in a multi-view manner, we further leverage attention mechanism to help find the more relevant slices in each view. Extensive experiments on a dataset of 111 patients show that our method outperforms existing deep-learning methods. | ['Wei Wei', 'Guoqing Hu', 'Jun Shi', 'Shulan Ruan', 'Xiangneng Gao'] | 2023-06-26 | null | null | null | null | ['disease-prediction', 'computed-tomography-ct', 'decision-making'] | ['medical', 'methodology', 'reasoning'] | [-2.19907472e-03 3.19255479e-02 -6.35410190e-01 -3.94906074e-01
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2188741d-2d9b-432e-bfdf-a58e78a7fac1 | silhouette-guided-point-cloud-reconstruction | 1907.12253 | null | https://arxiv.org/abs/1907.12253v1 | https://arxiv.org/pdf/1907.12253v1.pdf | Silhouette Guided Point Cloud Reconstruction beyond Occlusion | One major challenge in 3D reconstruction is to infer the complete shape geometry from partial foreground occlusions. In this paper, we propose a method to reconstruct the complete 3D shape of an object from a single RGB image, with robustness to occlusion. Given the image and a silhouette of the visible region, our approach completes the silhouette of the occluded region and then generates a point cloud. We show improvements for reconstruction of non-occluded and partially occluded objects by providing the predicted complete silhouette as guidance. We also improve state-of-the-art for 3D shape prediction with a 2D reprojection loss from multiple synthetic views and a surface-based smoothing and refinement step. Experiments demonstrate the efficacy of our approach both quantitatively and qualitatively on synthetic and real scene datasets. | ['Derek Hoiem', 'Chuhang Zou'] | 2019-07-29 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 3.92429888e-01 4.14621532e-01 4.04304653e-01 -3.07716459e-01
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f8320fc4-8925-42cb-8720-c61e5b1db8ab | umat-uncertainty-aware-single-image-high-1 | 2305.16312 | null | https://arxiv.org/abs/2305.16312v1 | https://arxiv.org/pdf/2305.16312v1.pdf | UMat: Uncertainty-Aware Single Image High Resolution Material Capture | We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be illposed -more than a single diffuse image might be needed to disambiguate the specular reflection- or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment. | ['Elena Garces', 'David Pascual-Hernandez', 'Henar Dominguez-Elvira', 'Carlos Rodriguez-Pardo'] | 2023-05-25 | umat-uncertainty-aware-single-image-high | http://openaccess.thecvf.com//content/CVPR2023/html/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.pdf | cvpr-2023-1 | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 6.61083698e-01 1.14654623e-01 2.85367936e-01 -4.39343840e-01
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41aa92e4-b8ba-48f5-ad6f-73a6bc77d06b | latent-multi-view-subspace-clustering | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Latent_Multi-View_Subspace_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Latent_Multi-View_Subspace_CVPR_2017_paper.pdf | Latent Multi-View Subspace Clustering | In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and simultaneously performs data reconstruction based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict data themselves more comprehensively than each single view individually, accordingly makes subspace representation more accurate and robust as well. The proposed method is intuitive and can be optimized efficiently by using the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) algorithm. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
| ['QinGhua Hu', 'Huazhu Fu', 'Xiaochun Cao', 'Pengfei Zhu', 'Changqing Zhang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.82657266e-01 -3.17583561e-01 -5.35189807e-01 -6.85405284e-02
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8609e4e1-9133-4ee4-97ab-ccad7e82155f | multi-model-hypothesize-and-verify-approach | 1608.02052 | null | http://arxiv.org/abs/1608.02052v1 | http://arxiv.org/pdf/1608.02052v1.pdf | Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification | Loop closure detection, which is the task of identifying locations revisited
by a robot in a sequence of odometry and perceptual observations, is typically
formulated as a visual place recognition (VPR) task. However, even
state-of-the-art VPR techniques generate a considerable number of false
positives as a result of confusing visual features and perceptual aliasing. In
this paper, we propose a robust incremental framework for loop closure
detection, termed incremental loop closure verification. Our approach
reformulates the problem of loop closure detection as an instance of a
multi-model hypothesize-and-verify framework, in which multiple loop closure
hypotheses are generated and verified in terms of the consistency between loop
closure hypotheses and VPR constraints at multiple viewpoints along the robot's
trajectory. Furthermore, we consider the general incremental setting of loop
closure detection, in which the system must update both the set of VPR
constraints and that of loop closure hypotheses when new constraints or
hypotheses arrive during robot navigation. Experimental results using a stereo
SLAM system and DCNN features and visual odometry validate effectiveness of the
proposed approach. | ['Kanji Tanaka'] | 2016-08-06 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.08214632e-01 7.01957047e-02 -6.88651875e-02 -2.56400287e-01
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cf0b6e3c-16f9-41fe-a5bc-6a3e1643103f | predicting-visual-features-from-text-for | 1709.01362 | null | http://arxiv.org/abs/1709.01362v3 | http://arxiv.org/pdf/1709.01362v3.pdf | Predicting Visual Features from Text for Image and Video Caption Retrieval | This paper strives to find amidst a set of sentences the one best describing
the content of a given image or video. Different from existing works, which
rely on a joint subspace for their image and video caption retrieval, we
propose to do so in a visual space exclusively. Apart from this conceptual
novelty, we contribute \emph{Word2VisualVec}, a deep neural network
architecture that learns to predict a visual feature representation from
textual input. Example captions are encoded into a textual embedding based on
multi-scale sentence vectorization and further transferred into a deep visual
feature of choice via a simple multi-layer perceptron. We further generalize
Word2VisualVec for video caption retrieval, by predicting from text both 3-D
convolutional neural network features as well as a visual-audio representation.
Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and
the very recent NIST TrecVid challenge for video caption retrieval detail
Word2VisualVec's properties, its benefit over textual embeddings, the potential
for multimodal query composition and its state-of-the-art results. | ['Xirong Li', 'Jianfeng Dong', 'Cees G. M. Snoek'] | 2017-09-05 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.08909059e-01 -1.28284052e-01 -1.83403924e-01 -4.41739708e-01
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acaa270d-2d07-420b-a378-11a66662b762 | conjugated-discrete-distributions-for | 2112.07424 | null | https://arxiv.org/abs/2112.07424v1 | https://arxiv.org/pdf/2112.07424v1.pdf | Conjugated Discrete Distributions for Distributional Reinforcement Learning | In this work we continue to build upon recent advances in reinforcement learning for finite Markov processes. A common approach among previous existing algorithms, both single-actor and distributed, is to either clip rewards or to apply a transformation method on Q-functions to handle a large variety of magnitudes in real discounted returns. We theoretically show that one of the most successful methods may not yield an optimal policy if we have a non-deterministic process. As a solution, we argue that distributional reinforcement learning lends itself to remedy this situation completely. By the introduction of a conjugated distributional operator we may handle a large class of transformations for real returns with guaranteed theoretical convergence. We propose an approximating single-actor algorithm based on this operator that trains agents directly on unaltered rewards using a proper distributional metric given by the Cram\'er distance. To evaluate its performance in a stochastic setting we train agents on a suite of 55 Atari 2600 games using sticky-actions and obtain state-of-the-art performance compared to other well-known algorithms in the Dopamine framework. | ['Karl-Olof Lindahl', 'Jonas Nordqvist', 'Björn Lindenberg'] | 2021-12-14 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 5.32963574e-02 -3.23999189e-02 6.39475584e-02 -1.76493570e-01
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e5d15c56-705c-4851-a4c1-52e46f146be9 | langevin-dynamics-based-algorithm-e-th | 2210.13193 | null | https://arxiv.org/abs/2210.13193v1 | https://arxiv.org/pdf/2210.13193v1.pdf | Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient | We introduce a new Langevin dynamics based algorithm, called e-TH$\varepsilon$O POULA, to solve optimization problems with discontinuous stochastic gradients which naturally appear in real-world applications such as quantile estimation, vector quantization, CVaR minimization, and regularized optimization problems involving ReLU neural networks. We demonstrate both theoretically and numerically the applicability of the e-TH$\varepsilon$O POULA algorithm. More precisely, under the conditions that the stochastic gradient is locally Lipschitz in average and satisfies a certain convexity at infinity condition, we establish non-asymptotic error bounds for e-TH$\varepsilon$O POULA in Wasserstein distances and provide a non-asymptotic estimate for the expected excess risk, which can be controlled to be arbitrarily small. Three key applications in finance and insurance are provided, namely, multi-period portfolio optimization, transfer learning in multi-period portfolio optimization, and insurance claim prediction, which involve neural networks with (Leaky)-ReLU activation functions. Numerical experiments conducted using real-world datasets illustrate the superior empirical performance of e-TH$\varepsilon$O POULA compared to SGLD, ADAM, and AMSGrad in terms of model accuracy. | ['Ying Zhang', 'Sotirios Sabanis', 'Ariel Neufeld', 'Dong-Young Lim'] | 2022-10-24 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.03376013e-01 -8.46119523e-02 -2.52920482e-02 -3.75653148e-01
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5a98c8e5-cf19-4716-a921-9e4744f4a544 | engraf-net-multiple-granularity-branch | null | null | https://link.springer.com/chapter/10.1007/978-3-030-89128-2_38 | http://artelab.dista.uninsubria.it/res/research/papers/2021/2021_CAIP_Lagrassa_EnGraf_Net.pdf | EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task | Fine-Grained classification models can expressly focus on the relevant details useful to distinguish highly similar classes typically when the intra-class variance is high and the inter-class variance is low given a dataset. Most of these models use part annotations as bounding box, location part, text attributes to enhance the performance of classification and other models use sophisticated techniques to extract an attention map automatically. We assume that part-based approaches as the automatic cropping method suffers from a missing representation of local features, which are fundamental to distinguish similar objects. While Fine-Grained classification endeavours to recognize the leaf of a graph, humans recognize an object trying also to make a semantic association. In this paper, we use the semantic association structured as a hierarchy (taxonomy) as supervised signals and used them in an end-to-end deep neural network model termed as EnGraf-Net. Extensive experiments on three well-known datasets: Cifar-100, CUB-200-2011 and FGVC-Aircraft prove the superiority of EnGraf-Net over many Fine-Grained models and it is competitive with the most recent best models without using any cropping technique or manual annotations. | ['Nicola Landro', 'Ignazio Gallo', 'Riccardo La Grassa'] | 2021-10-21 | null | null | null | caip-computer-analysis-of-images-and-patterns | ['fine-grained-image-classification'] | ['computer-vision'] | [ 7.83876032e-02 1.34248391e-01 9.39838588e-03 -5.65749288e-01
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f0387190-cfc6-48f6-9fea-bd1ced17ae8b | to-what-extent-does-lexical-normalization | null | null | https://aclanthology.org/2021.wnut-1.50 | https://aclanthology.org/2021.wnut-1.50.pdf | To What Extent Does Lexical Normalization Help English-as-a-Second Language Learners to Read Noisy English Texts? | How difficult is it for English-as-a-second language (ESL) learners to read noisy English texts? Do ESL learners need lexical normalization to read noisy English texts? These questions may also affect community formation on social networking sites where differences can be attributed to ESL learners and native English speakers. However, few studies have addressed these questions. To this end, we built highly accurate readability assessors to evaluate the readability of texts for ESL learners. We then applied these assessors to noisy English texts to further assess the readability of the texts. The experimental results showed that although intermediate-level ESL learners can read most noisy English texts in the first place, lexical normalization significantly improves the readability of noisy English texts for ESL learners. | ['Yo Ehara'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [-5.23667037e-01 3.06769431e-01 1.77132607e-01 -1.74005598e-01
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9f865b84-4ddd-4feb-8d98-a4ab6fae0660 | taveer-an-interpretable-topic-agnostic | null | null | https://dl.acm.org/doi/10.1145/3407023.3409194 | https://dl.acm.org/doi/10.1145/3407023.3409194 | TAVeer: An Interpretable Topic-Agnostic Authorship Verification Method | A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author. Authorship verification (AV) is a research branch in this field that deals with this question. Over the years, research activities in the context of AV have steadily increased, which has led to a variety of approaches trying to solve this problem. Many of these approaches, however, make use of features that are related to or influenced by the topic of the documents. Therefore, it may accidentally happen that their verification results are based not on the writing style (the actual focus of AV), but on the topic of the documents. To address this problem, we propose an alternative AV approach that considers only topic-agnostic features in its classification decision. In addition, we present a post-hoc interpretation method that allows to understand which particular features have contributed to the prediction of the proposed AV method. To evaluate the performance of our AV method, we compared it with eight competing baselines (including the current state of the art) on four challenging data sets. The results show that our approach outperforms all baselines in two cases (with a maximum accuracy of 84%), while in the other two cases it performs as well as the strongest baseline. | ['Roey Regev', 'Lukas Graner', 'Oren Halvani'] | 2020-08-01 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.43135071e-01 -6.48890957e-02 -1.95326820e-01 -1.09289207e-01
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26d98431-55ac-4a7f-bf1a-4a1105462bc8 | test-time-adaptation-for-real-image-denoising | 2207.02066 | null | https://arxiv.org/abs/2207.02066v1 | https://arxiv.org/pdf/2207.02066v1.pdf | Test-time Adaptation for Real Image Denoising via Meta-transfer Learning | In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a better network architecture. We explore a different direction where we propose to improve real image denoising performance through a better learning strategy that can enable test-time adaptation on the multi-task network. The learning strategy is two stages where the first stage pre-train the network using meta-auxiliary learning to get better meta-initialization. Meanwhile, we use meta-learning for fine-tuning (meta-transfer learning) the network as the second stage of our training to enable test-time adaptation on real noisy images. To exploit a better learning strategy, we also propose a network architecture with self-supervised masked reconstruction loss. Experiments on a real noisy dataset show the contribution of the proposed method and show that the proposed method can outperform other SOTA methods. | ['Se Jin Park', 'Muhammad Adi Nugroho', 'Agus Gunawan'] | 2022-07-05 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 5.16658425e-01 -4.21307571e-02 2.76775360e-01 -4.95934695e-01
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cfbc6d1b-fcc0-43fd-807e-54427c6e6d70 | low-rank-multi-view-clustering-in-third-order | 1608.08336 | null | http://arxiv.org/abs/1608.08336v2 | http://arxiv.org/pdf/1608.08336v2.pdf | Low-rank Multi-view Clustering in Third-Order Tensor Space | The plenty information from multiple views data as well as the complementary
information among different views are usually beneficial to various tasks,
e.g., clustering, classification, de-noising. Multi-view subspace clustering is
based on the fact that the multi-view data are generated from a latent
subspace. To recover the underlying subspace structure, the success of the
sparse and/or low-rank subspace clustering has been witnessed recently. Despite
some state-of-the-art subspace clustering approaches can numerically handle
multi-view data, by simultaneously exploring all possible pairwise correlation
within views, the high order statistics is often disregarded which can only be
captured by simultaneously utilizing all views. As a consequence, the
clustering performance for multi-view data is compromised. To address this
issue, in this paper, a novel multi-view clustering method is proposed by using
\textit{t-product} in third-order tensor space. Based on the circular
convolution operation, multi-view data can be effectively represented by a
\textit{t-linear} combination with sparse and low-rank penalty using
"self-expressiveness". Our extensive experimental results on facial, object,
digits image and text data demonstrate that the proposed method outperforms the
state-of-the-art methods in terms of many criteria. | ['Ming Yin', 'Shengli Xie', 'Yi Guo', 'Junbin Gao'] | 2016-08-30 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.12280929e-01 -5.63794136e-01 -2.70101409e-02 -2.08870634e-01
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a3501e53-5c40-4cdf-bc2c-d764c9e5bf02 | lmr-cbt-learning-modality-fused | 2112.01697 | null | https://arxiv.org/abs/2112.01697v1 | https://arxiv.org/pdf/2112.01697v1.pdf | LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences | Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition. Existing approaches use directional pairwise attention or a message hub to fuse language, visual, and audio modalities. However, those approaches introduce information redundancy when fusing features and are inefficient without considering the complementarity of modalities. In this paper, we propose an efficient neural network to learn modality-fused representations with CB-Transformer (LMR-CBT) for multimodal emotion recognition from unaligned multimodal sequences. Specifically, we first perform feature extraction for the three modalities respectively to obtain the local structure of the sequences. Then, we design a novel transformer with cross-modal blocks (CB-Transformer) that enables complementary learning of different modalities, mainly divided into local temporal learning,cross-modal feature fusion and global self-attention representations. In addition, we splice the fused features with the original features to classify the emotions of the sequences. Finally, we conduct word-aligned and unaligned experiments on three challenging datasets, IEMOCAP, CMU-MOSI, and CMU-MOSEI. The experimental results show the superiority and efficiency of our proposed method in both settings. Compared with the mainstream methods, our approach reaches the state-of-the-art with a minimum number of parameters. | ['Aimin Zhou', 'Xiangling Fu', 'Jiayin Qi', 'Jiahao Zhang', 'Siyuan Shen', 'HanYang Wang', 'Feng Liu', 'Ziwang Fu'] | 2021-12-03 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.62707642e-01 -6.58516288e-01 -4.33638357e-02 -2.94595569e-01
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1b89627e-e19b-4bf1-b1c7-576e6363da11 | divide-evaluate-and-refine-evaluating-and | 2307.04749 | null | https://arxiv.org/abs/2307.04749v1 | https://arxiv.org/pdf/2307.04749v1.pdf | Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback | The field of text-conditioned image generation has made unparalleled progress with the recent advent of latent diffusion models. While remarkable, as the complexity of given text input increases, the state-of-the-art diffusion models may still fail in generating images which accurately convey the semantics of the given prompt. Furthermore, it has been observed that such misalignments are often left undetected by pretrained multi-modal models such as CLIP. To address these problems, in this paper we explore a simple yet effective decompositional approach towards both evaluation and improvement of text-to-image alignment. In particular, we first introduce a Decompositional-Alignment-Score which given a complex prompt decomposes it into a set of disjoint assertions. The alignment of each assertion with generated images is then measured using a VQA model. Finally, alignment scores for different assertions are combined aposteriori to give the final text-to-image alignment score. Experimental analysis reveals that the proposed alignment metric shows significantly higher correlation with human ratings as opposed to traditional CLIP, BLIP scores. Furthermore, we also find that the assertion level alignment scores provide a useful feedback which can then be used in a simple iterative procedure to gradually increase the expression of different assertions in the final image outputs. Human user studies indicate that the proposed approach surpasses previous state-of-the-art by 8.7% in overall text-to-image alignment accuracy. Project page for our paper is available at https://1jsingh.github.io/divide-evaluate-and-refine | ['Liang Zheng', 'Jaskirat Singh'] | 2023-07-10 | null | null | null | null | ['visual-question-answering', 'image-generation'] | ['computer-vision', 'computer-vision'] | [ 3.54334325e-01 1.46801576e-01 5.47725940e-03 -4.28840756e-01
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5d87c645-bcb3-42e4-a7d8-b9a0ca576025 | iterative-scene-graph-generation | 2207.13440 | null | https://arxiv.org/abs/2207.13440v1 | https://arxiv.org/pdf/2207.13440v1.pdf | Iterative Scene Graph Generation | The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large solution space, existing approaches to scene graph generation assume certain factorization of the joint distribution to make the estimation feasible (e.g., assuming that objects are conditionally independent of predicate predictions). However, this fixed factorization is not ideal under all scenarios (e.g., for images where an object entailed in interaction is small and not discernible on its own). In this work, we propose a novel framework for scene graph generation that addresses this limitation, as well as introduces dynamic conditioning on the image, using message passing in a Markov Random Field. This is implemented as an iterative refinement procedure wherein each modification is conditioned on the graph generated in the previous iteration. This conditioning across refinement steps allows joint reasoning over entities and relations. This framework is realized via a novel and end-to-end trainable transformer-based architecture. In addition, the proposed framework can improve existing approach performance. Through extensive experiments on Visual Genome and Action Genome benchmark datasets we show improved performance on the scene graph generation. | ['Leonid Sigal', 'Siddhesh Khandelwal'] | 2022-07-27 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 7.60213673e-01 6.23453856e-01 8.81366357e-02 -3.52044165e-01
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23335d65-16be-4562-bb88-ac83598c4d37 | dual-node-and-edge-fairness-aware-graph | 2306.10123 | null | https://arxiv.org/abs/2306.10123v1 | https://arxiv.org/pdf/2306.10123v1.pdf | Dual Node and Edge Fairness-Aware Graph Partition | Fair graph partition of social networks is a crucial step toward ensuring fair and non-discriminatory treatments in unsupervised user analysis. Current fair partition methods typically consider node balance, a notion pursuing a proportionally balanced number of nodes from all demographic groups, but ignore the bias induced by imbalanced edges in each cluster. To address this gap, we propose a notion edge balance to measure the proportion of edges connecting different demographic groups in clusters. We analyze the relations between node balance and edge balance, then with line graph transformations, we propose a co-embedding framework to learn dual node and edge fairness-aware representations for graph partition. We validate our framework through several social network datasets and observe balanced partition in terms of both nodes and edges along with good utility. Moreover, we demonstrate our fair partition can be used as pseudo labels to facilitate graph neural networks to behave fairly in node classification and link prediction tasks. | ['Hongfu Liu', 'Peizhao Li', 'TingWei Liu'] | 2023-06-16 | null | null | null | null | ['node-classification', 'link-prediction'] | ['graphs', 'graphs'] | [-5.53577058e-02 6.14831328e-01 -7.98397839e-01 -4.04717714e-01
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38a0c40c-4978-4a14-943f-80e3e5707c42 | sentiment-and-knowledge-based-algorithmic | 2001.09403 | null | https://arxiv.org/abs/2001.09403v1 | https://arxiv.org/pdf/2001.09403v1.pdf | Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning | Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real world which make it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that dictate the ups and downs of the market, has hindered the supervised learning attempts for dependable predictions. To learn a good policy for trading, we formulate an approach using reinforcement learning which uses traditional time series stock price data and combines it with news headline sentiments, while leveraging knowledge graphs for exploiting news about implicit relationships. | ['Osmar R. Zaiane', 'Anandh Perumal', 'Abhishek Nan'] | 2020-01-26 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-3.09231013e-01 -1.24945164e-01 -5.62809050e-01 -1.50260359e-01
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e7ba4a7b-866a-4582-b6d3-d234d03fa475 | uncertainty-aware-multi-modal-ensembling-for | 2010.01440 | null | https://arxiv.org/abs/2010.01440v2 | https://arxiv.org/pdf/2010.01440v2.pdf | Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia | Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment. In this work, we propose an uncertainty-aware boosting technique for multi-modal ensembling to predict Alzheimer's Dementia Severity. The propagation of uncertainty across acoustic, cognitive, and linguistic features produces an ensemble system robust to heteroscedasticity in the data. Weighing the different modalities based on the uncertainty estimates, we experiment on the benchmark ADReSS dataset, a subject-independent and balanced dataset, to show that our method outperforms the state-of-the-art methods while also reducing the overall entropy of the system. This work aims to encourage fair and aware models. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia | ['Pattie Maes', 'Rishab Khincha', 'Wazeer Zulfikar', 'Utkarsh Sarawgi'] | 2020-10-03 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-3.03040028e-01 1.15531914e-01 1.50905430e-01 -9.44743931e-01
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3e16d35d-8099-4af0-97a2-6cd271905dc4 | normalization-of-relative-and-incomplete | 1510.04972 | null | http://arxiv.org/abs/1510.04972v1 | http://arxiv.org/pdf/1510.04972v1.pdf | Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives | We analyze the RI-TIMEXes in temporally annotated corpora and propose two
hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative
domain: the anchor point hypothesis and the anchor relation hypothesis. We
annotate the RI-TIMEXes in three corpora to study the characteristics of
RI-TMEXes in different domains. This informed the design of our RI-TIMEX
normalization system for the clinical domain, which consists of an anchor point
classifier, an anchor relation classifier and a rule-based RI-TIMEX text span
parser. We experiment with different feature sets and perform error analysis
for each system component. The annotation confirmed the hypotheses that we can
simplify the RI-TIMEXes normalization task using two multi-label classifiers.
Our system achieves anchor point classification, anchor relation classification
and rule-based parsing accuracy of 74.68%, 87.71% and 57.2% (82.09% under
relaxed matching criteria) respectively on the held-out test set of the 2012
i2b2 temporal relation challenge. Experiments with feature sets reveals some
interesting findings such as the verbal tense feature does not inform the
anchor relation classification in clinical narratives as much as the tokens
near the RI-TIMEX. Error analysis shows that underrepresented anchor point and
anchor relation classes are difficult to detect. We formulate the RI-TIMEX
normalization problem as a pair of multi-label classification problems.
Considering only the RI-TIMEX extraction and normalization, the system achieves
statistically significant improvement over the RI-TIMEX results of the best
systems in the 2012 i2b2 challenge. | ['Weiyi Sun', 'Anna Rumshisky', 'Ozlem Uzuner'] | 2015-10-16 | null | null | null | null | ['timex-normalization'] | ['natural-language-processing'] | [ 3.59847844e-01 5.23949504e-01 -5.61017334e-01 -5.84846258e-01
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ce582067-a720-49f1-9286-553ae90f312c | a-language-model-for-grammatical-error | 2307.01609 | null | https://arxiv.org/abs/2307.01609v1 | https://arxiv.org/pdf/2307.01609v1.pdf | A Language Model for Grammatical Error Correction in L2 Russian | Grammatical error correction is one of the fundamental tasks in Natural Language Processing. For the Russian language, most of the spellcheckers available correct typos and other simple errors with high accuracy, but often fail when faced with non-native (L2) writing, since the latter contains errors that are not typical for native speakers. In this paper, we propose a pipeline involving a language model intended for correcting errors in L2 Russian writing. The language model proposed is trained on untagged texts of the Newspaper subcorpus of the Russian National Corpus, and the quality of the model is validated against the RULEC-GEC corpus. | ['Anastasia Vyrenkova', 'Ivan Smirnov', 'Ekaterina Rakhilina', 'Sergei Obiedkov', 'Nikita Remnev'] | 2023-07-04 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-1.11890018e-01 2.52728373e-01 8.91944468e-02 -3.29673916e-01
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5be1eb5a-ec8d-469f-8937-956cf2d31a43 | recurrent-memory-decision-transformer | 2306.09459 | null | https://arxiv.org/abs/2306.09459v2 | https://arxiv.org/pdf/2306.09459v2.pdf | Recurrent Memory Decision Transformer | Originally developed for natural language problems, transformer models have recently been widely used in offline reinforcement learning tasks. This is because the agent's history can be represented as a sequence, and the whole task can be reduced to the sequence modeling task. However, the quadratic complexity of the transformer operation limits the potential increase in context. Therefore, different versions of the memory mechanism are used to work with long sequences in a natural language. This paper proposes the Recurrent Memory Decision Transformer (RMDT), a model that uses a recurrent memory mechanism for reinforcement learning problems. We conduct thorough experiments on Atari games and MuJoCo control problems and show that our proposed model is significantly superior to its counterparts without the recurrent memory mechanism on Atari games. We also carefully study the effect of memory on the performance of the proposed model. These findings shed light on the potential of incorporating recurrent memory mechanisms to improve the performance of large-scale transformer models in offline reinforcement learning tasks. The Recurrent Memory Decision Transformer code is publicly available in the repository \url{https://anonymous.4open.science/r/RMDT-4FE4}. | ['Aleksandr I. Panov', 'Dmitry Yudin', 'Alexey K. Kovalev', 'Huzhenyu Zhang', 'Alexey Staroverov', 'Arkadii Bessonov'] | 2023-06-15 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-1.72677003e-02 -9.55036730e-02 -3.23756963e-01 3.17517808e-03
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96074683-474d-4243-9883-4e9b1f232ce0 | towards-explainable-evaluation-metrics-for | 2203.11131 | null | https://arxiv.org/abs/2203.11131v1 | https://arxiv.org/pdf/2203.11131v1.pdf | Towards Explainable Evaluation Metrics for Natural Language Generation | Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such as BERTScore or MoverScore) are based on black-box language models such as BERT or XLM-R. They often achieve strong correlations with human judgments, but recent research indicates that the lower-quality classical metrics remain dominant, one of the potential reasons being that their decision processes are transparent. To foster more widespread acceptance of the novel high-quality metrics, explainability thus becomes crucial. In this concept paper, we identify key properties and propose key goals of explainable machine translation evaluation metrics. We also provide a synthesizing overview over recent approaches for explainable machine translation metrics and discuss how they relate to those goals and properties. Further, we conduct own novel experiments, which (among others) find that current adversarial NLP techniques are unsuitable for automatically identifying limitations of high-quality black-box evaluation metrics, as they are not meaning-preserving. Finally, we provide a vision of future approaches to explainable evaluation metrics and their evaluation. We hope that our work can help catalyze and guide future research on explainable evaluation metrics and, mediately, also contribute to better and more transparent text generation systems. | ['Steffen Eger', 'Yang Gao', 'Wei Zhao', 'Marina Fomicheva', 'Piyawat Lertvittayakumjorn', 'Christoph Leiter'] | 2022-03-21 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 3.40889305e-01 6.68130755e-01 -5.76216102e-01 -5.16402841e-01
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b69d1f1b-cf0e-4f9d-b46e-26514ed3718d | unsupervised-learning-of-depth-optical-flow-1 | 2003.00766 | null | https://arxiv.org/abs/2003.00766v3 | https://arxiv.org/pdf/2003.00766v3.pdf | Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry | In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the middle frame are modeled into three parts: the rigid region, the non-rigid region, and the occluded region. In joint unsupervised training of depth and pose, we can segment the occluded region explicitly. The occlusion information is used in unsupervised learning of depth, pose and optical flow, as the image reconstructed by depth-pose and optical flow will be invalid in occluded regions. A less-than-mean mask is designed to further exclude the mismatched pixels interfered with by motion or illumination change in the training of depth and pose networks. This method is also used to exclude some trivial mismatched pixels in the training of the optical flow network. Maximum normalization is proposed for depth smoothness term to restrain depth degradation in textureless regions. In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of optical flow. Our experiments in KITTI dataset demonstrate that the model based on three regions, full and explicit segmentation of the occlusion region, the rigid region, and the non-rigid region with corresponding unsupervised losses can improve performance on three tasks significantly. The source code is available at: https://github.com/guangmingw/DOPlearning. | ['Xinlei Wang', 'Hesheng Wang', 'Yong Wang', 'Jingchuan Wang', 'Guangming Wang', 'Chi Zhang'] | 2020-03-02 | unsupervised-learning-of-depth-optical-flow | null | null | arxiv-2020-3 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 6.18650094e-02 -4.04329523e-02 -3.30870569e-01 -4.69353884e-01
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07e3ff0f-3f88-44f7-9502-9e27431d9c95 | a-novel-framework-based-on-unknown-estimation | 2209.09616 | null | https://arxiv.org/abs/2209.09616v7 | https://arxiv.org/pdf/2209.09616v7.pdf | Provably Uncertainty-Guided Universal Domain Adaptation | Universal domain adaptation (UniDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain without any assumptions of the label sets, which requires distinguishing the unknown samples from the known ones in the target domain. A main challenge of UniDA is that the nonidentical label sets cause the misalignment between the two domains. Moreover, the domain discrepancy and the supervised objectives in the source domain easily lead the whole model to be biased towards the common classes and produce overconfident predictions for unknown samples. To address the above challenging problems, we propose a new uncertainty-guided UniDA framework. Firstly, we introduce an empirical estimation of the probability of a target sample belonging to the unknown class which fully exploits the distribution of the target samples in the latent space. Then, based on the estimation, we propose a novel neighbors searching scheme in a linear subspace with a $\delta$-filter to estimate the uncertainty score of a target sample and discover unknown samples. It fully utilizes the relationship between a target sample and its neighbors in the source domain to avoid the influence of domain misalignment. Secondly, this paper well balances the confidences of predictions for both known and unknown samples through an uncertainty-guided margin loss based on the confidences of discovered unknown samples, which can reduce the gap between the intra-class variances of known classes with respect to the unknown class. Finally, experiments on three public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods. | ['Wei zhang', 'Yibin Li', 'Paul L. Rosin', 'Ran Song', 'Lin Zhang', 'Yifan Wang'] | 2022-09-19 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 2.82474924e-02 9.48070288e-02 -4.48603600e-01 -4.71300006e-01
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ee246fd3-0e39-4c38-9366-6642c8b467c0 | coupling-global-and-local-context-for | null | null | https://aclanthology.org/D19-1465 | https://aclanthology.org/D19-1465.pdf | Coupling Global and Local Context for Unsupervised Aspect Extraction | Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions. To identify aspects, most previous efforts focus on using sequence tagging models trained on human-annotated data. This work studies unsupervised aspect extraction and explores how words appear in global context (on sentence level) and local context (conveyed by neighboring words). We propose a novel neural model, capable of coupling global and local representation to discover aspect words. Experimental results on two benchmarks, laptop and restaurant reviews, show that our model significantly outperforms the state-of-the-art models from previous studies evaluated with varying metrics. Analysis on model output show our ability to learn meaningful and coherent aspect representations. We further investigate how words distribute in global and local context, and find that aspect and non-aspect words do exhibit different context, interpreting our superiority in unsupervised aspect extraction. | ['Kam-Fai Wong', 'Xixin Wu', 'Lingzhi Wang', 'Jing Li', 'Ming Liao', 'Haisong Zhang'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['aspect-extraction'] | ['natural-language-processing'] | [ 3.49374682e-01 4.71890420e-02 -5.77572048e-01 -6.05354071e-01
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5fe9885d-409b-4814-8ed7-bfce1f534b37 | multi-view-mera-subspace-clustering | 2305.09095 | null | https://arxiv.org/abs/2305.09095v1 | https://arxiv.org/pdf/2305.09095v1.pdf | Multi-view MERA Subspace Clustering | Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network, namely, multi-scale entanglement renormalization ansatz (MERA), we propose a low-rank MERA based MSC (MERA-MSC) algorithm, where MERA factorizes a tensor into contractions of one top core factor and the rest orthogonal/semi-orthogonal factors. Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor. The alternating direction method of multipliers is adopted to solve the optimization model. Experimental results on five multi-view datasets demonstrate MERA-MSC has superiority against the compared algorithms on six evaluation metrics. Furthermore, we extend MERA-MSC by incorporating anchor learning to develop a scalable low-rank MERA based multi-view clustering method (sMREA-MVC). The effectiveness and efficiency of sMERA-MVC have been validated on three large-scale multi-view datasets. To our knowledge, this is the first work to introduce MERA to the multi-view clustering topic. The codes of MERA-MSC and sMERA-MVC are publicly available at https://github.com/longzhen520/MERA-MSC. | ['Yipeng Liu', 'Yazhou Ren', 'Zihan Li', 'Jie Chen', 'Ce Zhu', 'Zhen Long'] | 2023-05-16 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.47182924e-01 -4.05015022e-01 -8.19542408e-02 7.57187158e-02
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9a530e17-777e-44ab-a7b3-34e50ed56d99 | exploiting-points-and-lines-in-regression | 1710.10519 | null | http://arxiv.org/abs/1710.10519v3 | http://arxiv.org/pdf/1710.10519v3.pdf | Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization | Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance. | ['Clarence de Silva', 'Julien Valentin', 'James J. Little', 'Lili Meng', 'Frederick Tung'] | 2017-10-28 | null | null | null | null | ['camera-relocalization', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 1.31735608e-01 -4.01043773e-01 -3.19461882e-01 -1.53148264e-01
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d35dc249-b968-4910-93af-0d73fcee7d29 | solving-geometry-problems-combining-text-and | null | null | https://aclanthology.org/D15-1171 | https://aclanthology.org/D15-1171.pdf | Solving Geometry Problems: Combining Text and Diagram Interpretation | null | ['Hannaneh Hajishirzi', 'Minjoon Seo', 'Ali Farhadi', 'Clint Malcolm', 'Oren Etzioni'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['mathematical-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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50ec9f93-24d4-42c3-91af-ad27d6eb9e86 | dual-stream-shallow-networks-for-facial-micro | null | null | https://ieeexplore.ieee.org/abstract/document/8802965 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8802965 | Dual-stream shallow networks for facial micro-expression recognition | Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions. Motivated by recent exploits into deep learning for micro-expression analysis, we propose a lightweight dual-stream shallow network in the form of a pair of truncated CNNs with heterogeneous input features. The merging of the convolutional features allows for discriminative learning of micro-expression classes stemming from both streams. Using activation heatmaps, we further demonstrate that salient facial areas are well emphasized, and correspond closely to relevant action units belonging to emotion classes. We empirically validate the proposed network on three benchmark databases, obtaining state-of-the-art performance on the CASME II and SAMM while remaining competitive on the SMIC. Further observations point towards the sufficiency of utilizing shallower deep networks for micro-expression recognition. | ['Raphael C. -W. Phan', 'Sze-Teng Liong', 'Huai-Qian Khor', 'John See', 'Weiyao Lin'] | 2019-08-26 | null | null | null | 2019-ieee-international-conference-on-image | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.78044155e-01 2.71873981e-01 -3.93204600e-01 -6.96419775e-01
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b4b8fe18-1640-4939-b223-5461bcfc99b5 | query-embedding-pruning-for-dense-retrieval | 2108.10341 | null | https://arxiv.org/abs/2108.10341v1 | https://arxiv.org/pdf/2108.10341v1.pdf | Query Embedding Pruning for Dense Retrieval | Recent advances in dense retrieval techniques have offered the promise of being able not just to re-rank documents using contextualised language models such as BERT, but also to use such models to identify documents from the collection in the first place. However, when using dense retrieval approaches that use multiple embedded representations for each query, a large number of documents can be retrieved for each query, hindering the efficiency of the method. Hence, this work is the first to consider efficiency improvements in the context of a dense retrieval approach (namely ColBERT), by pruning query term embeddings that are estimated not to be useful for retrieving relevant documents. Our proposed query embeddings pruning reduces the cost of the dense retrieval operation, as well as reducing the number of documents that are retrieved and hence require to be fully scored. Experiments conducted on the MSMARCO passage ranking corpus demonstrate that, when reducing the number of query embeddings used from 32 to 3 based on the collection frequency of the corresponding tokens, query embedding pruning results in no statistically significant differences in effectiveness, while reducing the number of documents retrieved by 70%. In terms of mean response time for the end-to-end to end system, this results in a 2.65x speedup. | ['Craig Macdonald', 'Nicola Tonellotto'] | 2021-08-23 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-4.88391099e-03 -2.65037835e-01 2.88480334e-02 1.29788488e-01
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a3376d9c-c68c-45cc-b8f9-0cc21e5ed245 | outlier-detection-on-network-flow-analysis | 1808.02024 | null | http://arxiv.org/abs/1808.02024v1 | http://arxiv.org/pdf/1808.02024v1.pdf | Outlier detection on network flow analysis | It is important to be able to detect and classify malicious network traffic
flows such as DDoS attacks from benign flows. Normally the task is performed by
using supervised classification algorithms. In this paper we analyze the usage
of outlier detection algorithms for the network traffic classification problem. | ['Quang-Vinh Dang'] | 2018-08-06 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-1.56044349e-01 -3.24653089e-01 -1.14206634e-01 -5.63693464e-01
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637116da-53dd-45c8-80d6-6297d58b0405 | the-ntnu-yzu-system-in-the-aesw-shared-task | null | null | https://aclanthology.org/W16-0513 | https://aclanthology.org/W16-0513.pdf | The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network | null | ['Yuen-Hsien Tseng', 'Liang-Chih Yu', 'Bo-Lin Lin', 'Lung-Hao Lee'] | 2016-06-01 | null | null | null | ws-2016-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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e3848d65-950f-4936-8093-f5c82df99324 | a-re-balancing-strategy-for-class-imbalanced | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.pdf | A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty | Real-world data often exhibits class-imbalanced distributions, where a few classes (a.k.a. majority classes) occupy most instances and lots of classes (a.k.a. minority classes) have few instances. Neural classification models usually perform poorly on minority classes when training on such imbalanced datasets. To improve the performance on minority classes, existing methods typically re-balance the data distribution at the class level, i.e., assigning higher weights to minority classes and lower weights to majority classes during the training process. However, we observe that even the majority classes contain difficult instances to learn. By reducing the weights of the majority classes, such instances would become more difficult to learn and hurt the overall performance consequently. To tackle this problem, we propose a novel instance-level re-balancing strategy, which dynamically adjusts the sampling probabilities of instances according to the instance difficulty. Here the instance difficulty is measured based on the learning speed of instance, which is inspired by the human-leaning process (i.e., easier instances will be learned faster). We theoretically prove the correctness and convergence of our re-sampling algorithm. Empirical experiments demonstrate that our method significantly outperforms state-of-the-art re-balancing methods on the class-imbalanced datasets. | ['Xueqi Cheng', 'Zizhen Wang', 'Yixing Fan', 'Ruqing Zhang', 'Jiafeng Guo', 'Sihao Yu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.41888991e-01 7.09574297e-02 -5.61783016e-01 -6.23468101e-01
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530099c1-d838-4690-b9ce-fe50ac98bd93 | asynchronous-multi-view-slam | 2101.06562 | null | https://arxiv.org/abs/2101.06562v3 | https://arxiv.org/pdf/2101.06562v3.pdf | Asynchronous Multi-View SLAM | Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing. For evaluation, we collected AMV-Bench, a challenging new SLAM dataset covering 482 km of driving recorded using our asynchronous multi-camera robotic platform. AMV-Bench is over an order of magnitude larger than previous multi-view HD outdoor SLAM datasets, and covers diverse and challenging motions and environments. Our experiments emphasize the necessity of asynchronous sensor modeling, and show that the use of multiple cameras is critical towards robust and accurate SLAM in challenging outdoor scenes. For additional information, please see the project website at: https://www.cs.toronto.edu/~ajyang/amv-slam | ['Shenlong Wang', 'Raquel Urtasun', 'Ioan Andrei Bârsan', 'Can Cui', 'Anqi Joyce Yang'] | 2021-01-17 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [-9.77816209e-02 -5.49374342e-01 -9.33475122e-02 -4.36047196e-01
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df5eaffa-4f2d-41a7-9a45-b2a75076b0be | morphological-disambiguation-and-text | null | null | https://aclanthology.org/W14-5305 | https://aclanthology.org/W14-5305.pdf | Morphological Disambiguation and Text Normalization for Southern Quechua Varieties | null | ['er', 'Annette Rios Gonzales', 'Richard Alex Castro Mamani'] | 2014-08-01 | null | null | null | ws-2014-8 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ca508f42-8ea5-4667-9bdb-f5a6a656a73a | gpo-global-plane-optimization-for-fast-and | 2004.12051 | null | https://arxiv.org/abs/2004.12051v2 | https://arxiv.org/pdf/2004.12051v2.pdf | GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization | Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time. | ['Fei-Yue Wang', 'Sicong Du', 'Linfu Wen', 'Yao Chen', 'Hengkai Guo', 'Yilun Lin', 'Xiangbing Meng'] | 2020-04-25 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.72313284e-02 -4.71585244e-01 -1.59294829e-01 -3.69767994e-01
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0612f44c-dedd-4907-8fa6-589411cb33c3 | cone-an-efficient-coarse-to-fine-alignment | 2209.10918 | null | https://arxiv.org/abs/2209.10918v2 | https://arxiv.org/pdf/2209.10918v2.pdf | CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding | This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query. Compared with short videos, long videos are also highly demanded but less explored, which brings new challenges in higher inference computation cost and weaker multi-modal alignment. To address these challenges, we propose CONE, an efficient COarse-to-fiNE alignment framework. CONE is a plug-and-play framework on top of existing VTG models to handle long videos through a sliding window mechanism. Specifically, CONE (1) introduces a query-guided window selection strategy to speed up inference, and (2) proposes a coarse-to-fine mechanism via a novel incorporation of contrastive learning to enhance multi-modal alignment for long videos. Extensive experiments on two large-scale long VTG benchmarks consistently show both substantial performance gains (e.g., from 3.13% to 6.87% on MAD) and state-of-the-art results. Analyses also reveal higher efficiency as the query-guided window selection mechanism accelerates inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results. Codes have been released at https://github.com/houzhijian/CONE. | ['Nan Duan', 'Zheng Shou', 'Chong-Wah Ngo', 'Wing-Kwong Chan', 'Kun Yan', 'Difei Gao', 'Lei Ji', 'Wanjun Zhong', 'Zhijian Hou'] | 2022-09-22 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-1.54001191e-01 -4.19645876e-01 -8.45607042e-01 -2.16139659e-01
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c89dd6a0-deb6-48c6-8dae-503183395f55 | on-sentence-representations-for-propaganda | null | null | https://aclanthology.org/D19-5015 | https://aclanthology.org/D19-5015.pdf | On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings | Bias is ubiquitous in most online sources of natural language, from news media to social networks. Given the steady shift in news consumption behavior from traditional outlets to online sources, the automatic detection of propaganda, in which information is shaped to purposefully foster a predetermined agenda, is an increasingly crucial task. To this goal, we explore the task of sentence-level propaganda detection, and experiment with both handcrafted features and learned dense semantic representations. We also experiment with random undersampling of the majority class (non-propaganda) to curb the influence of class distribution on the system{'}s performance, leading to marked improvements on the minority class (propaganda). Our best performing system uses pre-trained ELMo word embeddings, followed by a bidirectional LSTM and an attention layer. We have submitted a 5-model ensemble of our best performing system to the NLP4IF shared task on sentence-level propaganda detection (team LIACC), achieving rank 10 among 25 participants, with 59.5 F1-score. | ["Andr{\\'e} Ferreira Cruz", 'Gil Rocha', 'Henrique Lopes Cardoso'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 3.59018654e-01 1.65516019e-01 -3.65976185e-01 -1.54435694e-01
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86c52558-2253-4c90-a877-43580da7ad48 | mvp-seg-multi-view-prompt-learning-for-open | 2304.06957 | null | https://arxiv.org/abs/2304.06957v1 | https://arxiv.org/pdf/2304.06957v1.pdf | MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation | CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations. In this work, we first demonstrate the necessity of image-pixel CLIP feature adaption, then provide Multi-View Prompt learning (MVP-SEG) as an effective solution to achieve image-pixel adaptation and to solve open-vocabulary semantic segmentation. Concretely, MVP-SEG deliberately learns multiple prompts trained by our Orthogonal Constraint Loss (OCLoss), by which each prompt is supervised to exploit CLIP feature on different object parts, and collaborative segmentation masks generated by all prompts promote better segmentation. Moreover, MVP-SEG introduces Global Prompt Refining (GPR) to further eliminate class-wise segmentation noise. Experiments show that the multi-view prompts learned from seen categories have strong generalization to unseen categories, and MVP-SEG+ which combines the knowledge transfer stage significantly outperforms previous methods on several benchmarks. Moreover, qualitative results justify that MVP-SEG does lead to better focus on different local parts. | ['Baochang Zhang', 'Yao Hu', 'Xu Tang', 'XiaoLong Jiang', 'Yan Gao', 'Qimeng Wang', 'Jie Guo'] | 2023-04-14 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 5.60465753e-01 1.24160059e-01 -4.22995627e-01 -3.61321509e-01
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4c59552d-3cda-4a29-ad1d-b09dfbf8cca1 | meta-learning-curiosity-algorithms-1 | 2003.05325 | null | https://arxiv.org/abs/2003.05325v1 | https://arxiv.org/pdf/2003.05325v1.pdf | Meta-learning curiosity algorithms | We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime. We formulate the problem of generating curious behavior as one of meta-learning: an outer loop will search over a space of curiosity mechanisms that dynamically adapt the agent's reward signal, and an inner loop will perform standard reinforcement learning using the adapted reward signal. However, current meta-RL methods based on transferring neural network weights have only generalized between very similar tasks. To broaden the generalization, we instead propose to meta-learn algorithms: pieces of code similar to those designed by humans in ML papers. Our rich language of programs combines neural networks with other building blocks such as buffers, nearest-neighbor modules and custom loss functions. We demonstrate the effectiveness of the approach empirically, finding two novel curiosity algorithms that perform on par or better than human-designed published curiosity algorithms in domains as disparate as grid navigation with image inputs, acrobot, lunar lander, ant and hopper. | ['Tomas Lozano-Perez', 'Leslie Pack Kaelbling', 'Martin F. Schneider', 'Ferran Alet'] | 2020-03-11 | null | https://openreview.net/forum?id=BygdyxHFDS | https://openreview.net/pdf?id=BygdyxHFDS | iclr-2020-1 | ['acrobot'] | ['playing-games'] | [-1.21914841e-01 3.15633953e-01 -1.58064365e-01 -2.62642950e-01
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4f190da4-18db-4c75-b433-22490a79c6ea | affect-analysis-in-the-wild-valence-arousal | 2103.15792 | null | https://arxiv.org/abs/2103.15792v1 | https://arxiv.org/pdf/2103.15792v1.pdf | Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework | Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This paper exploits these advances and presents significant contributions for affect analysis and recognition in-the-wild. Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets. The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes of deep neural networks trained with these databases. The first class refers to uni-task affect recognition, focusing on prediction of the valence and arousal dimensional variables. The second class refers to estimation of all main behavior tasks, i.e. valence-arousal prediction; categorical emotion classification in seven basic facial expressions; facial Action Unit detection. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition over all existing in-the-wild databases. Large experimental studies illustrate the achieved performance improvement over the existing state-of-the-art in affect recognition. | ['Stefanos Zafeiriou', 'Dimitrios Kollias'] | 2021-03-29 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 6.33101016e-02 -1.11714043e-01 2.00725451e-01 -7.79247105e-01
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4ff0226e-eb59-4878-bc5f-7adc74243e89 | deep-metric-learning-for-unsupervised-remote | 2303.09536 | null | https://arxiv.org/abs/2303.09536v1 | https://arxiv.org/pdf/2303.09536v1.pdf | Deep Metric Learning for Unsupervised Remote Sensing Change Detection | Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cover, land use, human development analysis, and disaster response. The performance of existing RS-CD methods is attributed to training on large annotated datasets. Furthermore, most of these models are less transferable in the sense that the trained model often performs very poorly when there is a domain gap between training and test datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues. Given an MT-RSI, the proposed method generates corresponding change probability map by iteratively optimizing an unsupervised CD loss without training it on a large dataset. Our unsupervised CD method consists of two interconnected deep networks, namely Deep-Change Probability Generator (D-CPG) and Deep-Feature Extractor (D-FE). The D-CPG is designed to predict change and no change probability maps for a given MT-RSI, while D-FE is used to extract deep features of MT-RSI that will be further used in the proposed unsupervised CD loss. We use transfer learning capability to initialize the parameters of D-FE. We iteratively optimize the parameters of D-CPG and D-FE for a given MT-RSI by minimizing the proposed unsupervised ``similarity-dissimilarity loss''. This loss is motivated by the principle of metric learning where we simultaneously maximize the distance between change pair-wise pixels while minimizing the distance between no-change pair-wise pixels in bi-temporal image domain and their deep feature domain. The experiments conducted on three CD datasets show that our unsupervised CD method achieves significant improvements over the state-of-the-art supervised and unsupervised CD methods. Code available at https://github.com/wgcban/Metric-CD | ['Vishal M. Patel', 'Wele Gedara Chaminda Bandara'] | 2023-03-16 | null | null | null | null | ['change-detection', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.30207011e-01 -3.19057345e-01 1.63704157e-01 -6.23338580e-01
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7db5c9e1-380b-45c3-ae37-5d980f33c7cf | real-rawvsr-real-world-raw-video-super | 2209.12475 | null | https://arxiv.org/abs/2209.12475v1 | https://arxiv.org/pdf/2209.12475v1.pdf | Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset | In recent years, real image super-resolution (SR) has achieved promising results due to the development of SR datasets and corresponding real SR methods. In contrast, the field of real video SR is lagging behind, especially for real raw videos. Considering the superiority of raw image SR over sRGB image SR, we construct a real-world raw video SR (Real-RawVSR) dataset and propose a corresponding SR method. We utilize two DSLR cameras and a beam-splitter to simultaneously capture low-resolution (LR) and high-resolution (HR) raw videos with 2x, 3x, and 4x magnifications. There are 450 video pairs in our dataset, with scenes varying from indoor to outdoor, and motions including camera and object movements. To our knowledge, this is the first real-world raw VSR dataset. Since the raw video is characterized by the Bayer pattern, we propose a two-branch network, which deals with both the packed RGGB sequence and the original Bayer pattern sequence, and the two branches are complementary to each other. After going through the proposed co-alignment, interaction, fusion, and reconstruction modules, we generate the corresponding HR sRGB sequence. Experimental results demonstrate that the proposed method outperforms benchmark real and synthetic video SR methods with either raw or sRGB inputs. Our code and dataset are available at https://github.com/zmzhang1998/Real-RawVSR. | ['Jingyu Yang', 'Zhiming Zhang', 'Huanjing Yue'] | 2022-09-26 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 7.76021361e-01 -3.68182778e-01 -1.05581731e-02 -1.30269289e-01
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b1b9fa5f-f805-4a08-9bb2-6ec7fbc64715 | sienet-siamese-expansion-network-for-image | 2007.03851 | null | https://arxiv.org/abs/2007.03851v1 | https://arxiv.org/pdf/2007.03851v1.pdf | SiENet: Siamese Expansion Network for Image Extrapolation | Different from image inpainting, image outpainting has relative less context in the image center to capture and more content at the image border to predict. Therefore, classical encoder-decoder pipeline of existing methods may not predict the outstretched unknown content perfectly. In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed. In two stages, a novel border sensitive convolution named adaptive filling convolution is designed for allowing encoder to predict the unknown content, alleviating the burden of decoder. Besides, to introduce prior knowledge to network and reinforce the inferring ability of encoder, siamese adversarial mechanism is designed to enable our network to model the distribution of covered long range feature for that of uncovered image feature. The results on four datasets has demonstrated that our method outperforms existing state-of-the-arts and could produce realistic results. | ['Xiaofeng Zhang', 'Songsong Wu', 'Ming Tao', 'Guoping Jiang', 'Feng Chen', 'Cailing Wang'] | 2020-07-08 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 3.29779953e-01 4.44351196e-01 -2.60188609e-01 -2.16097206e-01
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965766e5-cc84-41c1-ab23-f442849191d6 | application-of-machine-learning-regression | 2212.04279 | null | https://arxiv.org/abs/2212.04279v1 | https://arxiv.org/pdf/2212.04279v1.pdf | Application of machine learning regression models to inverse eigenvalue problems | In this work, we study the numerical solution of inverse eigenvalue problems from a machine learning perspective. Two different problems are considered: the inverse Strum-Liouville eigenvalue problem for symmetric potentials and the inverse transmission eigenvalue problem for spherically symmetric refractive indices. Firstly, we solve the corresponding direct problems to produce the required eigenvalues datasets in order to train the machine learning algorithms. Next, we consider several examples of inverse problems and compare the performance of each model to predict the unknown potentials and refractive indices respectively, from a given small set of the lowest eigenvalues. The supervised regression models we use are k-Nearest Neighbours, Random Forests and Multi-Layer Perceptron. Our experiments show that these machine learning methods, under appropriate tuning on their parameters, can numerically solve the examined inverse eigenvalue problems. | ['Andreas Ntargaras', 'Nikolaos Pallikarakis'] | 2022-12-08 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [ 5.40905893e-01 6.32560700e-02 1.08693436e-01 -1.95849035e-02
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7d58c651-8eb8-4c79-9546-c35d3c07f989 | meta-cotgan-a-meta-cooperative-training | 2003.11530 | null | https://arxiv.org/abs/2003.11530v1 | https://arxiv.org/pdf/2003.11530v1.pdf | Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation | Training generative models that can generate high-quality text with sufficient diversity is an important open problem for Natural Language Generation (NLG) community. Recently, generative adversarial models have been applied extensively on text generation tasks, where the adversarially trained generators alleviate the exposure bias experienced by conventional maximum likelihood approaches and result in promising generation quality. However, due to the notorious defect of mode collapse for adversarial training, the adversarially trained generators face a quality-diversity trade-off, i.e., the generator models tend to sacrifice generation diversity severely for increasing generation quality. In this paper, we propose a novel approach which aims to improve the performance of adversarial text generation via efficiently decelerating mode collapse of the adversarial training. To this end, we introduce a cooperative training paradigm, where a language model is cooperatively trained with the generator and we utilize the language model to efficiently shape the data distribution of the generator against mode collapse. Moreover, instead of engaging the cooperative update for the generator in a principled way, we formulate a meta learning mechanism, where the cooperative update to the generator serves as a high level meta task, with an intuition of ensuring the parameters of the generator after the adversarial update would stay resistant against mode collapse. In the experiment, we demonstrate our proposed approach can efficiently slow down the pace of mode collapse for the adversarial text generators. Overall, our proposed method is able to outperform the baseline approaches with significant margins in terms of both generation quality and diversity in the testified domains. | ['Haiyan Yin', 'Xu Li', 'Dingcheng Li', 'Ping Li'] | 2020-03-12 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.00624108e-01 4.55556244e-01 4.27400954e-02 1.88868478e-01
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28f20d2d-babc-4c6b-9323-afefa5ae2efb | acoustics-based-intent-recognition-using | 2011.03646 | null | https://arxiv.org/abs/2011.03646v2 | https://arxiv.org/pdf/2011.03646v2.pdf | Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages | With recent advancements in language technologies, humans are now speaking to devices. Increasing the reach of spoken language technologies requires building systems in local languages. A major bottleneck here are the underlying data-intensive parts that make up such systems, including automatic speech recognition (ASR) systems that require large amounts of labelled data. With the aim of aiding development of spoken dialog systems in low resourced languages, we propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification. The system is made up of two blocks - the first block is a universal phone recognition system that generates a transcript of discovered phonetic units for the input audio, and the second block performs intent classification from the generated phonetic transcripts. We propose a CNN+LSTM based architecture and present results for two languages families - Indic languages and Romance languages, for two different intent recognition tasks. We also perform multilingual training of our intent classifier and show improved cross-lingual transfer and zero-shot performance on an unknown language within the same language family. | ['Alan W Black', 'Sai Krishna Rallabandi', 'Xinjian Li', 'Akshat Gupta'] | 2020-11-07 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 1.64432853e-01 2.11374424e-02 1.61827266e-01 -8.59964013e-01
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7976f961-85e9-4125-a73b-3c2de59e5a7a | on-the-robustness-of-deep-learning-based-mri | 2211.04930 | null | https://arxiv.org/abs/2211.04930v2 | https://arxiv.org/pdf/2211.04930v2.pdf | On the Robustness of deep learning-based MRI Reconstruction to image transformations | Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities of DL-based MRI reconstruction models. However, the approaches of robustifying these models are underdeveloped. Compared to image classification, it could be much more challenging to achieve a robust MRI image reconstruction network considering its regression-based learning objective, limited amount of training data, and lack of efficient robustness metrics. To circumvent the above limitations, our work revisits the problem of DL-based image reconstruction through the lens of robust machine learning. We find a new instability source of MRI image reconstruction, i.e., the lack of reconstruction robustness against spatial transformations of an input, e.g., rotation and cutout. Inspired by this new robustness metric, we develop a robustness-aware image reconstruction method that can defend against both pixel-wise adversarial perturbations as well as spatial transformations. Extensive experiments are also conducted to demonstrate the effectiveness of our proposed approaches. | ['Sijia Liu', 'Mehmet Akçakaya', 'Yimeng Zhang', 'Mingyi Hong', 'Jinghan Jia'] | 2022-11-09 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 5.59800923e-01 7.63152093e-02 3.58741768e-02 -2.17863202e-01
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1dbc81b3-5236-4b79-8370-74b51761467c | safe-reinforcement-learning-with-dead-ends | 2306.13944 | null | https://arxiv.org/abs/2306.13944v1 | https://arxiv.org/pdf/2306.13944v1.pdf | Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery | Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend to adopt overly conservative policy to avoid unsafe situations. However, overly conservative policy severely hinders the exploration, and makes the algorithms substantially less rewarding. In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states. The boundary we construct is equivalent to distinguishing dead-end states, indicating the maximum extent to which safe exploration is guaranteed, and thus has minimum limitation on exploration. Similar to Recovery Reinforcement Learning, we utilize a decoupled RL framework to learn two policies, (1) a task policy that only considers improving the task performance, and (2) a recovery policy that maximizes safety. The recovery policy and a corresponding safety critic are pretrained on an offline dataset, in which the safety critic evaluates upper bound of safety in each state as awareness of environmental safety for the agent. During online training, a behavior correction mechanism is adopted, ensuring the agent to interact with the environment using safe actions only. Finally, experiments of continuous control tasks demonstrate that our approach has better task performance with less safety violations than state-of-the-art algorithms. | ['Junqiao Zhao', 'Chen Ye', 'Di Zhang', 'Chang Huang', 'Hongtu Zhou', 'Hai Zhang', 'Xiao Zhang'] | 2023-06-24 | null | null | null | null | ['continuous-control', 'safe-exploration'] | ['playing-games', 'robots'] | [ 2.14930952e-01 3.83324623e-01 -4.11402196e-01 -1.24515750e-01
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307789a0-71e0-456d-94f8-de2379915f75 | understanding-graph-embedding-methods-and | 2012.08019 | null | https://arxiv.org/abs/2012.08019v1 | https://arxiv.org/pdf/2012.08019v1.pdf | Understanding graph embedding methods and their applications | Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques can be effective in converting high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces, preserving maximally the graph structure properties. Another type of emerging graph embedding employs Gaussian distribution-based graph embedding with important uncertainty estimation. The main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension, hence, node similarity in the original complex irregular spaces can be easily quantified in the embedded vector spaces using standard metrics. The generated nonlinear and highly informative graph embeddings in the latent space can be conveniently used to address different downstream graph analytics tasks (e.g., node classification, link prediction, community detection, visualization, etc.). In this Review, we present some fundamental concepts in graph analytics and graph embedding methods, focusing in particular on random walk-based and neural network-based methods. We also discuss the emerging deep learning-based dynamic graph embedding methods. We highlight the distinct advantages of graph embedding methods in four diverse applications, and present implementation details and references to open-source software as well as available databases in the Appendix for the interested readers to start their exploration into graph analytics. | ['Mengjia Xu'] | 2020-12-15 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-3.08029413e-01 2.88356870e-01 -2.45863467e-01 7.35616013e-02
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304382ea-806e-4290-b5a9-010f4566e40a | telesto-a-graph-neural-network-model-for | 2102.12877 | null | https://arxiv.org/abs/2102.12877v2 | https://arxiv.org/pdf/2102.12877v2.pdf | TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services | Deployment, operation and maintenance of large IT systems becomes increasingly complex and puts human experts under extreme stress when problems occur. Therefore, utilization of machine learning (ML) and artificial intelligence (AI) is applied on IT system operation and maintenance - summarized in the term AIOps. One specific direction aims at the recognition of re-occurring anomaly types to enable remediation automation. However, due to IT system specific properties, especially their frequent changes (e.g. software updates, reconfiguration or hardware modernization), recognition of reoccurring anomaly types is challenging. Current methods mainly assume a static dimensionality of provided data. We propose a method that is invariant to dimensionality changes of given data. Resource metric data such as CPU utilization, allocated memory and others are modelled as multivariate time series. The extraction of temporal and spatial features together with the subsequent anomaly classification is realized by utilizing TELESTO, our novel graph convolutional neural network (GCNN) architecture. The experimental evaluation is conducted in a real-world cloud testbed deployment that is hosting two applications. Classification results of injected anomalies on a cassandra database node show that TELESTO outperforms the alternative GCNNs and achieves an overall classification accuracy of 85.1%. Classification results for the other nodes show accuracy values between 85% and 60%. | ['Alexander Acker', 'Dominik Scheinert'] | 2021-02-25 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [-1.89852864e-01 -5.14526367e-01 1.00783594e-01 -7.57482275e-02
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493bf001-d352-48f4-b65e-6a24e975094d | beyond-fair-pay-ethical-implications-of-nlp | 2104.10097 | null | https://arxiv.org/abs/2104.10097v1 | https://arxiv.org/pdf/2104.10097v1.pdf | Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing | The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers. | ['Lun-Wei Ku', 'Soumya Ray', 'Jan Fell', 'Boaz Shmueli'] | 2021-04-20 | null | https://aclanthology.org/2021.naacl-main.295 | https://aclanthology.org/2021.naacl-main.295.pdf | naacl-2021-4 | ['misconceptions'] | ['miscellaneous'] | [ 3.76437344e-02 4.47040826e-01 -6.17699437e-02 -4.70866203e-01
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910c35e3-299b-44fb-8bbf-1d76418bf242 | event-based-timestamp-image-encoding-network | 2104.05145 | null | https://arxiv.org/abs/2104.05145v2 | https://arxiv.org/pdf/2104.05145v2.pdf | Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation | Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable for human action understanding task. It is vital to encode the spatial-temporal information of event data properly and use standard computer vision tool to learn from the data. In this work, we propose a timestamp image encoding 2D network, which takes the encoded spatial-temporal images with polarity information of the event data as input and output the action label. In addition, we propose a future timestamp image generator to generate futureaction information to aid the model to anticipate the human action when the action is not completed. Experiment results show that our method can achieve the same level of performance as those RGB-based benchmarks on real world action recognition,and also achieve the state of the art (SOTA) result on gesture recognition. Our future timestamp image generating model can effectively improve the prediction accuracy when the action is not completed. We also provide insight discussion on the importance of motion and appearance information in action recognition and anticipation. | ['Chaoxing Huang'] | 2021-04-12 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 7.64763117e-01 -3.00284296e-01 -3.17495376e-01 -5.65999210e-01
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519dea7f-f8c7-457e-8c8e-4e8cea6df9fa | few-shot-table-to-text-generation-with | 2108.12516 | null | https://arxiv.org/abs/2108.12516v2 | https://arxiv.org/pdf/2108.12516v2.pdf | Few-Shot Table-to-Text Generation with Prototype Memory | Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly rely on large-scale training examples, limiting their applicability in real-world applications. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts. Experimental results on three benchmark datasets with three state-of-the-art models demonstrate that the proposed framework significantly improves the model performance across various evaluation metrics. | ['Nigel Collier', 'Simon Baker', 'Zaiqiao Meng', 'Yixuan Su'] | 2021-08-27 | null | https://aclanthology.org/2021.findings-emnlp.77 | https://aclanthology.org/2021.findings-emnlp.77.pdf | findings-emnlp-2021-11 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.41099471e-01 -3.42638940e-02 -3.65948468e-01 -2.94507116e-01
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ce8b7724-476f-4cd7-b4a9-1315744560ce | submodlib-a-submodular-optimization-library | 2202.10680 | null | https://arxiv.org/abs/2202.10680v2 | https://arxiv.org/pdf/2202.10680v2.pdf | Submodlib: A Submodular Optimization Library | Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib. | ['Rishabh Iyer', 'Ganesh Ramakrishnan', 'Vishal Kaushal'] | 2022-02-22 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-2.48877227e-01 1.39188647e-01 -7.33602583e-01 -4.90204811e-01
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b016dc1a-6cea-44b7-870c-295809deb534 | minimax-optimal-reward-agnostic-exploration | 2304.07278 | null | https://arxiv.org/abs/2304.07278v1 | https://arxiv.org/pdf/2304.07278v1.pdf | Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning | This paper studies reward-agnostic exploration in reinforcement learning (RL) -- a scenario where the learner is unware of the reward functions during the exploration stage -- and designs an algorithm that improves over the state of the art. More precisely, consider a finite-horizon non-stationary Markov decision process with $S$ states, $A$ actions, and horizon length $H$, and suppose that there are no more than a polynomial number of given reward functions of interest. By collecting an order of \begin{align*} \frac{SAH^3}{\varepsilon^2} \text{ sample episodes (up to log factor)} \end{align*} without guidance of the reward information, our algorithm is able to find $\varepsilon$-optimal policies for all these reward functions, provided that $\varepsilon$ is sufficiently small. This forms the first reward-agnostic exploration scheme in this context that achieves provable minimax optimality. Furthermore, once the sample size exceeds $\frac{S^2AH^3}{\varepsilon^2}$ episodes (up to log factor), our algorithm is able to yield $\varepsilon$ accuracy for arbitrarily many reward functions (even when they are adversarially designed), a task commonly dubbed as ``reward-free exploration.'' The novelty of our algorithm design draws on insights from offline RL: the exploration scheme attempts to maximize a critical reward-agnostic quantity that dictates the performance of offline RL, while the policy learning paradigm leverages ideas from sample-optimal offline RL paradigms. | ['Jianqing Fan', 'Yuxin Chen', 'Yuling Yan', 'Gen Li'] | 2023-04-14 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 9.31369737e-02 4.86224413e-01 -6.34558201e-01 1.08334832e-01
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ba7c9b05-0d04-4710-851b-d3c4f4979ce3 | a-graph-framework-for-multimodal-medical | 1608.00134 | null | http://arxiv.org/abs/1608.00134v2 | http://arxiv.org/pdf/1608.00134v2.pdf | A Graph Framework for Multimodal Medical Information Processing | Multimodal medical information processing is currently the epicenter of
intense interdisciplinary research, as proper data fusion may lead to more
accurate diagnoses. Moreover, multimodality may disambiguate cases of
co-morbidity. This paper presents a framework for retrieving, analyzing, and
storing medical information as a multilayer graph, an abstract format suitable
for data fusion and further processing. At the same time, this paper addresses
the need for reliable medical information through co-author graph ranking. A
use case pertaining to frailty based on Python and Neo4j serves as an
illustration of the proposed framework. | ['Megalooikonomou Vasileios', 'Drakopoulos Georgios'] | 2017-02-22 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [-1.31967634e-01 1.63244709e-01 -1.15878530e-01 -3.37559164e-01
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42760d2d-cf7e-4e43-848b-d3c9117f8abd | ls-iq-implicit-reward-regularization-for | 2303.00599 | null | https://arxiv.org/abs/2303.00599v1 | https://arxiv.org/pdf/2303.00599v1.pdf | LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning | Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states. Previous works show that a squared norm regularization on the implicit reward function is effective, but do not provide a theoretical analysis of the resulting properties of the algorithms. In this work, we show that using this regularizer under a mixture distribution of the policy and the expert provides a particularly illuminating perspective: the original objective can be understood as squared Bellman error minimization, and the corresponding optimization problem minimizes a bounded $\chi^2$-Divergence between the expert and the mixture distribution. This perspective allows us to address instabilities and properly treat absorbing states. We show that our method, Least Squares Inverse Q-Learning (LS-IQ), outperforms state-of-the-art algorithms, particularly in environments with absorbing states. Finally, we propose to use an inverse dynamics model to learn from observations only. Using this approach, we retain performance in settings where no expert actions are available. | ['Jan Peters', 'Guoping Zhao', 'Oleg Arenz', 'Davide Tateo', 'Firas Al-Hafez'] | 2023-03-01 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 1.69024877e-02 2.82933474e-01 -4.09645140e-01 8.79992098e-02
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c0f24d49-b71a-41aa-bfc2-4d6aae39ab89 | you-only-look-at-one-category-level-object | 2305.12626 | null | https://arxiv.org/abs/2305.12626v1 | https://arxiv.org/pdf/2305.12626v1.pdf | You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example | In order to meaningfully interact with the world, robot manipulators must be able to interpret objects they encounter. A critical aspect of this interpretation is pose estimation: inferring quantities that describe the position and orientation of an object in 3D space. Most existing approaches to pose estimation make limiting assumptions, often working only for specific, known object instances, or at best generalising to an object category using large pose-labelled datasets. In this work, we present a method for achieving category-level pose estimation by inspection of just a single object from a desired category. We show that we can subsequently perform accurate pose estimation for unseen objects from an inspected category, and considerably outperform prior work by exploiting multi-view correspondences. We demonstrate that our method runs in real-time, enabling a robot manipulator equipped with an RGBD sensor to perform online 6D pose estimation for novel objects. Finally, we showcase our method in a continual learning setting, with a robot able to determine whether objects belong to known categories, and if not, use active perception to produce a one-shot category representation for subsequent pose estimation. | ['Ingmar Posner', 'Ioannis Havoutis', 'Walter Goodwin'] | 2023-05-22 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 5.37897646e-01 4.32294846e-01 -5.15937805e-04 -2.73999244e-01
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5ac64b1d-26de-4a4c-ace6-2147ad466827 | smart-roi-detection-for-alzheimer-s-disease | 2303.10401 | null | https://arxiv.org/abs/2303.10401v1 | https://arxiv.org/pdf/2303.10401v1.pdf | Smart ROI Detection for Alzheimer's disease prediction using explainable AI | Purpose Predicting the progression of MCI to Alzheimer's disease is an important step in reducing the progression of the disease. Therefore, many methods have been introduced for this task based on deep learning. Among these approaches, the methods based on ROIs are in a good position in terms of accuracy and complexity. In these techniques, some specific parts of the brain are extracted as ROI manually for all of the patients. Extracting ROI manually is time-consuming and its results depend on human expertness and precision. Method To overcome these limitations, we propose a novel smart method for detecting ROIs automatically based on Explainable AI using Grad-Cam and a 3DCNN model that extracts ROIs per patient. After extracting the ROIs automatically, Alzheimer's disease is predicted using extracted ROI-based 3D CNN. Results We implement our method on 176 MCI patients of the famous ADNI dataset and obtain remarkable results compared to the state-of-the-art methods. The accuracy acquired using 5-fold cross-validation is 98.6 and the AUC is 1. We also compare the results of the ROI-based method with the whole brain-based method. The results show that the performance is impressively increased. Conclusion The experimental results show that the proposed smart ROI extraction, which extracts the ROIs automatically, performs well for Alzheimer's disease prediction. The proposed method can also be used for Alzheimer's disease classification and diagnosis. | ['Mohsen Ebrahimi Moghaddam', 'Atefe Aghaei'] | 2023-03-18 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-1.34640962e-01 8.89398381e-02 -1.75100416e-02 -5.95514596e-01
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2ec3b5bd-af1f-48b4-9303-8ab0468278f6 | exit-extrapolation-and-interpolation-based | 2204.08771 | null | https://arxiv.org/abs/2204.08771v2 | https://arxiv.org/pdf/2204.08771v2.pdf | EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting | Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins. | ['Noseong Park', 'Jayoung Kim', 'Jihyeon Hyeong', 'Jinsung Jeon', 'Seungji Kook', 'Minju Jo', 'Jaehoon Lee', 'Sheo Yon Jhin'] | 2022-04-19 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.67539306e-02 -2.03297108e-01 -1.67420641e-01 -1.54459476e-01
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7d140bf6-1d29-444e-824c-184fbe434f33 | deceptive-decision-making-under-uncertainty | 2109.06740 | null | https://arxiv.org/abs/2109.06740v1 | https://arxiv.org/pdf/2109.06740v1.pdf | Deceptive Decision-Making Under Uncertainty | We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals while deceiving outside observers about its true goal. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies via linear programming. The proposed approach enables the agent to exhibit a variety of tunable deceptive behaviors while ensuring the satisfaction of probabilistic constraints on the behavior. We evaluate the performance of the proposed approach via comparative user studies and present a case study on the streets of Manhattan, New York, using real travel time distributions. | ['Ufuk Topcu', 'Christos K. Verginis', 'Yagiz Savas'] | 2021-09-14 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-2.87793819e-02 7.15197802e-01 1.09943196e-01 -4.28593457e-01
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e11d5722-aa91-4845-b161-6d75021bebc6 | collage-diffusion | 2303.00262 | null | https://arxiv.org/abs/2303.00262v1 | https://arxiv.org/pdf/2303.00262v1.pdf | Collage Diffusion | Text-conditional diffusion models generate high-quality, diverse images. However, text is often an ambiguous specification for a desired target image, creating the need for additional user-friendly controls for diffusion-based image generation. We focus on having precise control over image output for scenes with several objects. Users control image generation by defining a collage: a text prompt paired with an ordered sequence of layers, where each layer is an RGBA image and a corresponding text prompt. We introduce Collage Diffusion, a collage-conditional diffusion algorithm that allows users to control both the spatial arrangement and visual attributes of objects in the scene, and also enables users to edit individual components of generated images. To ensure that different parts of the input text correspond to the various locations specified in the input collage layers, Collage Diffusion modifies text-image cross-attention with the layers' alpha masks. To maintain characteristics of individual collage layers that are not specified in text, Collage Diffusion learns specialized text representations per layer. Collage input also enables layer-based controls that provide fine-grained control over the final output: users can control image harmonization on a layer-by-layer basis, and they can edit individual objects in generated images while keeping other objects fixed. Collage-conditional image generation requires harmonizing the input collage to make objects fit together--the key challenge involves minimizing changes in the positions and key visual attributes of objects in the input collage while allowing other attributes of the collage to change in the harmonization process. By leveraging the rich information present in layer input, Collage Diffusion generates globally harmonized images that maintain desired object locations and visual characteristics better than prior approaches. | ['Kayvon Fatahalian', 'Christopher Ré', 'Arden Ma', 'Linden Li', 'Vishnu Sarukkai'] | 2023-03-01 | null | null | null | null | ['image-harmonization', 'conditional-image-generation'] | ['computer-vision', 'computer-vision'] | [ 4.14851755e-01 -1.59161761e-01 8.87913108e-02 -2.81242698e-01
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f6b294b8-1ddb-40ac-8212-3bb50acf2633 | context-aware-hypergraph-construction-for | 1401.0764 | null | http://arxiv.org/abs/1401.0764v1 | http://arxiv.org/pdf/1401.0764v1.pdf | Context-Aware Hypergraph Construction for Robust Spectral Clustering | Spectral clustering is a powerful tool for unsupervised data analysis. In
this paper, we propose a context-aware hypergraph similarity measure (CAHSM),
which leads to robust spectral clustering in the case of noisy data. We
construct three types of hypergraph---the pairwise hypergraph, the
k-nearest-neighbor (kNN) hypergraph, and the high-order over-clustering
hypergraph. The pairwise hypergraph captures the pairwise similarity of data
points; the kNN hypergraph captures the neighborhood of each point; and the
clustering hypergraph encodes high-order contexts within the dataset. By
combining the affinity information from these three hypergraphs, the CAHSM
algorithm is able to explore the intrinsic topological information of the
dataset. Therefore, data clustering using CAHSM tends to be more robust.
Considering the intra-cluster compactness and the inter-cluster separability of
vertices, we further design a discriminative hypergraph partitioning criterion
(DHPC). Using both CAHSM and DHPC, a robust spectral clustering algorithm is
developed. Theoretical analysis and experimental evaluation demonstrate the
effectiveness and robustness of the proposed algorithm. | ['Zhongfei Zhang', 'Xi Li', 'Chunhua Shen', 'Anthony Dick', 'Weiming Hu'] | 2014-01-04 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [-2.81928983e-02 -1.31717533e-01 -1.75435811e-01 -1.47288501e-01
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4d128179-3651-4dd1-9a69-f3ffba8d46e0 | a-framework-for-the-identification-and | null | null | https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0162-8?ref=https://githubhelp.com | https://ij-healthgeographics.biomedcentral.com/counter/pdf/10.1186/s12942-018-0162-8.pdf | A framework for the identification and classification of homogeneous socioeconomic areas in the analysis of health care variation | Background
Detecting the variation of health indicators across similar areas or peer geographies is often useful if the spatial units are socially and economically meaningful, so that there is a degree of homogeneity in each unit. Indices are frequently constructed to generate summaries of socioeconomic status or other measures in geographic small areas. Larger areas may be built to be homogenous using regionalization algorithms. However, there are no explicit guidelines in the literature for the grouping of peer geographies based on measures such as area level socioeconomic indices. Moreover, the use of an index score becomes less meaningful as the size of an area increases. This paper introduces an easy to use statistical framework for the identification and classification of homogeneous areas. We propose the Homogeneity and Location indices to measure the concentration and central value respectively of an areas’ socioeconomic distribution. We also provide a transparent set of criteria that a researcher can follow to establish whether a set of proposed geographies are acceptably homogeneous or need further refining.
Results
We applied our framework to assess the socioeconomic homogeneity of the commonly used SA3 Australian census geography. These results showed that almost 60% of the SA3 census units are likely to be socioeconomically heterogeneous and hence inappropriate for presenting area level socioeconomic disadvantage. We also showed that the Location Index is a more robust descriptive measure of the distribution compared to other measures of central tendency. Finally, the methodology proposed was used to analyse the age-standardized variation of GP attenders in a metropolitan area. The results suggest that very high GP attenders (20+ visits) live in SA3s with the most socioeconomic disadvantage. The findings revealed that households with low income and families with children and jobless parents are the major drivers for discerning disadvantaged communities.
Conclusion
Reporting indicators rates for geographies grouped according to similarity may be useful for the analysis of geographic variation. The use of a framework for the identification of meaningful peer geographies would be beneficial to health planners and policy makers by providing realistic and valid peer group geographies. | ['Soumya Mazumdar & Federico Girosi', 'Ludovico Pinzari'] | 2018-12-04 | null | null | null | international-journal-of-health-geographics | ['unsupervised-spatial-clustering'] | ['time-series'] | [ 3.00831371e-03 1.05013169e-01 -2.05359414e-01 -2.71467894e-01
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b3a10213-ce85-4213-bf52-84244280c901 | the-role-of-computational-stylometry-in | null | null | https://aclanthology.org/2020.trac-1.11 | https://aclanthology.org/2020.trac-1.11.pdf | The Role of Computational Stylometry in Identifying (Misogynistic) Aggression in English Social Media Texts | In this paper, we describe UniOr{\_}ExpSys team participation in TRAC-2 (Trolling, Aggression and Cyberbullying) shared task, a workshop organized as part of LREC 2020. TRAC-2 shared task is organized in two sub-tasks: Aggression Identification (a 3-way classification between {``}Overtly Aggressive{''}, {``}Covertly Aggressive{''} and {``}Non-aggressive{''} text data) and Misogynistic Aggression Identification (a binary classifier for classifying the texts as {``}gendered{''} or {``}non-gendered{''}). Our approach is based on linguistic rules, stylistic features extraction through stylometric analysis and Sequential Minimal Optimization algorithm in building the two classifiers. | ['Vincenzo Masucci', 'Johanna Monti', 'Antonio Pascucci', 'Raffaele Manna'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['misogynistic-aggression-identification', 'aggression-identification'] | ['natural-language-processing', 'natural-language-processing'] | [-3.55053782e-01 8.88571441e-02 9.13090706e-02 -4.17166531e-01
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1eefff13-590d-4790-8208-51164213b51a | enrichment-score-a-better-quantitative-metric | 2210.10905 | null | https://arxiv.org/abs/2210.10905v4 | https://arxiv.org/pdf/2210.10905v4.pdf | Enrichment Score: a better quantitative metric for evaluating the enrichment capacity of molecular docking models | The standard quantitative metric for evaluating enrichment capacity known as $\textit{LogAUC}$ depends on a cutoff parameter that controls what the minimum value of the log-scaled x-axis is. Unless this parameter is chosen carefully for a given ROC curve, one of the two following problems occurs: either (1) some fraction of the first inter-decoy intervals of the ROC curve are simply thrown away and do not contribute to the metric at all, or (2) the very first inter-decoy interval contributes too much to the metric at the expense of all following inter-decoy intervals. We fix this problem with LogAUC by showing a simple way to choose the cutoff parameter based on the number of decoys which forces the first inter-decoy interval to always have a stable, sensible contribution to the total value. Moreover, we introduce a normalized version of LogAUC known as $\textit{enrichment score}$, which (1) enforces stability by selecting the cutoff parameter in the manner described, (2) yields scores which are more intuitively meaningful, and (3) allows reliably accurate comparison of the enrichment capacities exhibited by different ROC curves, even those produced using different numbers of decoys. Finally, we demonstrate the advantage of enrichment score over unbalanced metrics using data from a real retrospective docking study performed using the program $\textit{DOCK 3.7}$ on the target receptor TRYB1 included in the $\textit{DUDE-Z}$ benchmark. | ['John J. Irwin', 'Slava Naprienko', 'Ian Scott Knight'] | 2022-10-19 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.74272120e-01 -2.36595601e-01 -2.15966582e-01 -2.85699517e-01
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599f0fdf-1395-4e2d-bcfb-08164c8a2f3c | spherical-image-inpainting-with-frame | 2209.14604 | null | https://arxiv.org/abs/2209.14604v1 | https://arxiv.org/pdf/2209.14604v1.pdf | Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks | Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging. It is non-trivial to extend an algorithm developed for flat images to the spherical ones. In this work, we focus on the challenging task of spherical image inpainting with deep learning-based regularizer. Instead of a naive application of existing models for planar images, we employ a fast directional spherical Haar framelet transform and develop a novel optimization framework based on a sparsity assumption of the framelet transform. Furthermore, by employing progressive encoder-decoder architecture, a new and better-performed deep CNN denoiser is carefully designed and works as an implicit regularizer. Finally, we use a plug-and-play method to handle the proposed optimization model, which can be implemented efficiently by training the CNN denoiser prior. Numerical experiments are conducted and show that the proposed algorithms can greatly recover damaged spherical images and achieve the best performance over purely using deep learning denoiser and plug-and-play model. | ['Tieyong Zeng', 'Micheal Ng', 'Han Feng', 'Raymond Chan', 'Chaoyan Huang', 'Jianfei Li'] | 2022-09-29 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 2.45916918e-01 3.17233771e-01 4.52351570e-01 -2.78514326e-01
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42dedb4b-fcf8-4c50-a579-d057fa754ada | prediction-intervals-and-confidence-regions | 2209.06454 | null | https://arxiv.org/abs/2209.06454v1 | https://arxiv.org/pdf/2209.06454v1.pdf | Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles | Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and for decision making. The linear approximation and so-called likelihood profiles are well-known possibilities for the calculation of confidence and prediction intervals for nonlinear regression models. These simple and effective techniques have been completely ignored so far in the genetic programming literature. In this work we describe the calculation of likelihood profiles in details and also provide some illustrative examples with models created with three different symbolic regression algorithms on two different datasets. The examples highlight the importance of the likelihood profiles to understand the limitations of symbolic regression models and to help the user taking an informed post-prediction decision. | ['Gabriel Kronberger', 'Fabricio Olivetti de Franca'] | 2022-09-14 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.74844131e-01 1.96904898e-01 -3.85282822e-02 -7.05034673e-01
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52e72b66-92a8-4a2a-912d-01b7e565c382 | data-driven-and-physics-informed-modelling-of | 2305.03257 | null | https://arxiv.org/abs/2305.03257v1 | https://arxiv.org/pdf/2305.03257v1.pdf | Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors | Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical inconsistencies. Here, we propose a physically-informed data-driven hybrid model (a "gray box") to learn models of the dynamical evolution of Chinese Hamster Ovary (CHO) cell bioreactors from process data. The approach incorporates physical laws (e.g. mass balances) as well as kinetic expressions for metabolic fluxes. Machine learning (ML) is then used to (a) directly learn evolution equations (black-box modelling); (b) recover unknown physical parameters ("white-box" parameter fitting) or -- importantly -- (c) learn partially unknown kinetic expressions (gray-box modelling). We encode the convex optimization step of the overdetermined metabolic biophysical system as a differentiable, feed-forward layer into our architectures, connecting partial physical knowledge with data-driven machine learning. | ['Ioannis G. Kevrekidis', 'Costas Maranas', 'Michael Betenbaugh', 'Pratik Khare', 'Nelson Ndahiro', 'Tom S. Bertalan', 'Tianqi Cui'] | 2023-05-05 | null | null | null | null | ['culture'] | ['speech'] | [ 3.38460766e-02 -1.13158017e-01 -4.05014344e-02 5.21902042e-03
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93ecf85e-646b-4021-aff4-07b1e9d8ae5a | machine-learning-repurposing-of-drugbank | 2303.00240 | null | https://arxiv.org/abs/2303.00240v1 | https://arxiv.org/pdf/2303.00240v1.pdf | Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder | Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment. | ['Guo-Wei Wei', 'Jian Jiang', 'Hongsong Feng'] | 2023-03-01 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.63291946e-01 -4.57448483e-01 -1.08634591e+00 -1.73291370e-01
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fdfc4742-1f26-4088-a3e4-83b0ba037e38 | viteraser-harnessing-the-power-of-vision | 2306.12106 | null | https://arxiv.org/abs/2306.12106v1 | https://arxiv.org/pdf/2306.12106v1.pdf | ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining | Scene text removal (STR) aims at replacing text strokes in natural scenes with visually coherent backgrounds. Recent STR approaches rely on iterative refinements or explicit text masks, resulting in higher complexity and sensitivity to the accuracy of text localization. Moreover, most existing STR methods utilize convolutional neural networks (CNNs) for feature representation while the potential of vision Transformers (ViTs) remains largely unexplored. In this paper, we propose a simple-yet-effective ViT-based text eraser, dubbed ViTEraser. Following a concise encoder-decoder framework, different types of ViTs can be easily integrated into ViTEraser to enhance the long-range dependencies and global reasoning. Specifically, the encoder hierarchically maps the input image into the hidden space through ViT blocks and patch embedding layers, while the decoder gradually upsamples the hidden features to the text-erased image with ViT blocks and patch splitting layers. As ViTEraser implicitly integrates text localization and inpainting, we propose a novel end-to-end pretraining method, termed SegMIM, which focuses the encoder and decoder on the text box segmentation and masked image modeling tasks, respectively. To verify the effectiveness of the proposed methods, we comprehensively explore the architecture, pretraining, and scalability of the ViT-based encoder-decoder for STR, which provides deep insights into the application of ViT to STR. Experimental results demonstrate that ViTEraser with SegMIM achieves state-of-the-art performance on STR by a substantial margin. Furthermore, the extended experiment on tampered scene text detection demonstrates the generality of ViTEraser to other tasks. We believe this paper can inspire more research on ViT-based STR approaches. Code will be available at https://github.com/shannanyinxiang/ViTEraser. | ['Lianwen Jin', 'Yuliang Liu', 'Chongyu Liu', 'Dezhi Peng'] | 2023-06-21 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 5.36963701e-01 -1.09369561e-01 -4.26753536e-02 -2.37348750e-01
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e3575919-acd9-4514-be0a-a79e62c9633d | to-risk-or-not-to-risk-learning-with-risk | 2302.07399 | null | https://arxiv.org/abs/2302.07399v1 | https://arxiv.org/pdf/2302.07399v1.pdf | To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs | A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT) environment. The task offloading technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm. Such consequences include an undetected fire, pest infestation, or a UAV being unusable. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rule-based techniques. The simulation results show that the CVaR-based risk quantifying method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task. As a result, it reduced the total number of deadline violations with a negligible increase in energy consumption. | ['Melike Erol-Kantarci', 'W. Sean Kennedy', 'Aisha Syed', 'Turgay Pamuklu', 'Anne Catherine Nguyen'] | 2023-02-14 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 1.67303592e-01 3.53440583e-01 8.12361166e-02 2.10210323e-01
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c5e6e638-cf9d-4fa0-a8ec-45308195ce68 | scalable-safe-exploration-for-global | 2201.09562 | null | https://arxiv.org/abs/2201.09562v5 | https://arxiv.org/pdf/2201.09562v5.pdf | GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems | Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for high-dimensional systems while giving safety and optimality guarantees. We demonstrate the superiority of GoSafeOpt over competing model-free safe learning methods on a robot arm that would be prohibitive for GoSafe. | ['Dominik Baumann', 'Sebastian Trimpe', 'Andreas Krause', 'David Lindner', 'Matteo Turchetta', 'Bhavya Sukhija'] | 2022-01-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-2.94409513e-01 3.26412827e-01 -6.75729334e-01 3.60278815e-01
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29a6595a-05e4-42b6-9c51-17383fa5ccc3 | improved-logical-reasoning-of-language-models | 2305.03742 | null | https://arxiv.org/abs/2305.03742v1 | https://arxiv.org/pdf/2305.03742v1.pdf | Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming | Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning. In contrast to works that rely on hand-crafted logic rules, our differentiable symbolic reasoning framework efficiently learns weighted rules and applies semantic loss to further improve LMs. DSR-LM is scalable, interpretable, and allows easy integration of prior knowledge, thereby supporting extensive symbolic programming to robustly derive a logical conclusion. The results of our experiments suggest that DSR-LM improves the logical reasoning abilities of pre-trained language models, resulting in a significant increase in accuracy of over 20% on deductive reasoning benchmarks. Furthermore, DSR-LM outperforms a variety of competitive baselines when faced with systematic changes in sequence length. | ['Eric Xing', 'Mayur Naik', 'Ziyang Li', 'Jiani Huang', 'HANLIN ZHANG'] | 2023-05-05 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.70348191e-01 6.16691828e-01 -5.01414120e-01 -2.99840301e-01
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37d26282-dcec-4892-a2c3-6d69ff11b6c9 | simplifyur-unsupervised-lexical-text | null | null | https://aclanthology.org/2020.lrec-1.428 | https://aclanthology.org/2020.lrec-1.428.pdf | SimplifyUR: Unsupervised Lexical Text Simplification for Urdu | This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu. Urdu is a morphologically rich language that requires unique considerations such as proper handling of inflectional case and honorifics. We present an unsupervised method for lexical simplification of complex Urdu text. Our method only requires plain Urdu text and makes use of word embeddings together with a set of morphological features to generate simplifications. Our system achieves a BLEU score of 80.15 and SARI score of 42.02 upon automatic evaluation on manually crafted simplified corpora. We also report results for human evaluations for correctness, grammaticality, meaning-preservation and simplicity of the output. Our code and corpus are publicly available to make our results reproducible. | ['Agha Ali Raza', 'Awais Athar', 'Namoos Hayat Qasmi', 'Haris Bin Zia'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['lexical-simplification'] | ['natural-language-processing'] | [-1.25475436e-01 1.74226403e-01 1.75678849e-01 -2.61332989e-01
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4578e67e-ae44-40df-a636-e369634e8712 | lot-a-benchmark-for-evaluating-chinese-long | 2108.12960 | null | https://arxiv.org/abs/2108.12960v2 | https://arxiv.org/pdf/2108.12960v2.pdf | LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation | Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing (NLP) usually focus only on understanding or generating short texts. However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of generation. The lack of standardized benchmarks makes it difficult to assess these abilities of a model and fairly compare different models, especially Chinese models. Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks. We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. Furthermore, we release an encoder-decoder-based Chinese long text pretraining model named LongLM with up to 1 billion parameters. We pretrain LongLM on 120G Chinese novels with two generative tasks including text infilling and conditional continuation. Extensive experiments show that LongLM outperforms similar-sized pretraining models substantially on both the understanding and generation tasks in LOT. | ['Minlie Huang', 'Changjie Fan', 'Xiaoxi Mao', 'Ruilin He', 'Yamei Chen', 'Zhuoer Feng', 'Jian Guan'] | 2021-08-30 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 3.06180358e-01 4.95967299e-01 2.56656017e-02 -5.72596073e-01
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ddbf6a1c-fff0-49cc-86a3-39f98bd008f5 | leco-lightweight-compression-via-learning | 2306.15374 | null | https://arxiv.org/abs/2306.15374v1 | https://arxiv.org/pdf/2306.15374v1.pdf | LeCo: Lightweight Compression via Learning Serial Correlations | Lightweight data compression is a key technique that allows column stores to exhibit superior performance for analytical queries. Despite a comprehensive study on dictionary-based encodings to approach Shannon's entropy, few prior works have systematically exploited the serial correlation in a column for compression. In this paper, we propose LeCo (i.e., Learned Compression), a framework that uses machine learning to remove the serial redundancy in a value sequence automatically to achieve an outstanding compression ratio and decompression performance simultaneously. LeCo presents a general approach to this end, making existing (ad-hoc) algorithms such as Frame-of-Reference (FOR), Delta Encoding, and Run-Length Encoding (RLE) special cases under our framework. Our microbenchmark with three synthetic and six real-world data sets shows that a prototype of LeCo achieves a Pareto improvement on both compression ratio and random access speed over the existing solutions. When integrating LeCo into widely-used applications, we observe up to 3.9x speed up in filter-scanning a Parquet file and a 16% increase in Rocksdb's throughput. | ['Huanchen Zhang', 'Xinyu Zeng', 'Yihao Liu'] | 2023-06-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 1.98766083e-01 -4.22675431e-01 -6.02319837e-01 -1.23794265e-01
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ca196c03-d89b-4479-b801-a0eb9f71580f | trip-triangular-document-level-pre-training | 2212.07752 | null | https://arxiv.org/abs/2212.07752v2 | https://arxiv.org/pdf/2212.07752v2.pdf | Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models | Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use `bilingual corpora' to denote parallel corpora with `bilingual translation pairs' in many different language pairs, each consisting of two sentences/documents with the same meaning written in different languages. We use `trilingual corpora' to denote parallel corpora with `trilingual translation pairs' in many different language combinations, each consisting of three sentences/documents.} and sometimes synthetic document-level bilingual corpora. This hampers the performance with cross-lingual document-level tasks such as document-level translation. Therefore, we propose to mine and leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training. We present \textbf{Tri}angular Document-level \textbf{P}re-training (\textbf{TRIP}), which is the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. Experiments show that TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points. | ['Furu Wei', 'Wai Lam', 'Dongdong Zhang', 'Shuming Ma', 'Haoyang Huang', 'Hongyuan Lu'] | 2022-12-15 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.60357434e-01 -5.44857502e-01 -6.68842971e-01 -2.40054995e-01
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73af7867-de37-440e-b7bd-048e399d5196 | divide-and-adapt-active-domain-adaptation-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.pdf | Divide and Adapt: Active Domain Adaptation via Customized Learning | Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating the active learning (AL) techniques to label a maximally-informative subset of target samples. Conventional AL methods do not consider the existence of domain shift, and hence, fail to identify the truly valuable samples in the context of domain adaptation. To accommodate active learning and domain adaption, the two naturally different tasks, in a collaborative framework, we advocate that a customized learning strategy for the target data is the key to the success of ADA solutions. We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties. With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples. While sending the informative instances for annotation, DiaNA employs tailored learning strategies for the remaining categories. Furthermore, we propose an informativeness score that unifies the data partitioning criteria. This enables the use of a Gaussian mixture model (GMM) to automatically sample unlabeled data into the proposed four categories. Thanks to the "divide-and-adapt" spirit, DiaNA can handle data with large variations of domain gap. In addition, we show that DiaNA can generalize to different domain adaptation settings, such as unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA), source-free domain adaptation (SFDA), etc. | ['Guanbin Li', 'Zhenhua Chai', 'Junshi Huang', 'Weikai Chen', 'Jichang Li', 'Duojun Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 2.78161943e-01 4.12338376e-01 -6.44747734e-01 -4.41309690e-01
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552af5f5-ca05-4e17-aede-a8c2f75ee8af | approximate-stein-classes-for-truncated | 2306.00602 | null | https://arxiv.org/abs/2306.00602v1 | https://arxiv.org/pdf/2306.00602v1.pdf | Approximate Stein Classes for Truncated Density Estimation | Estimating truncated density models is difficult, as these models have intractable normalising constants and hard to satisfy boundary conditions. Score matching can be adapted to solve the truncated density estimation problem, but requires a continuous weighting function which takes zero at the boundary and is positive elsewhere. Evaluation of such a weighting function (and its gradient) often requires a closed-form expression of the truncation boundary and finding a solution to a complicated optimisation problem. In this paper, we propose approximate Stein classes, which in turn leads to a relaxed Stein identity for truncated density estimation. We develop a novel discrepancy measure, truncated kernelised Stein discrepancy (TKSD), which does not require fixing a weighting function in advance, and can be evaluated using only samples on the boundary. We estimate a truncated density model by minimising the Lagrangian dual of TKSD. Finally, experiments show the accuracy of our method to be an improvement over previous works even without the explicit functional form of the boundary. | ['Song Liu', 'Daniel J. Williams'] | 2023-06-01 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 8.45383629e-02 2.60034084e-01 -1.15362406e-01 -3.40520650e-01
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45f4feb2-1d9e-4376-bdbc-6f6bb17b4ae3 | l3cube-mahasbert-and-hindsbert-sentence-bert | 2211.11187 | null | https://arxiv.org/abs/2211.11187v2 | https://arxiv.org/pdf/2211.11187v2.pdf | L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi | Sentence representation from vanilla BERT models does not work well on sentence similarity tasks. Sentence-BERT models specifically trained on STS or NLI datasets are shown to provide state-of-the-art performance. However, building these models for low-resource languages is not straightforward due to the lack of these specialized datasets. This work focuses on two low-resource Indian languages, Hindi and Marathi. We train sentence-BERT models for these languages using synthetic NLI and STS datasets prepared using machine translation. We show that the strategy of NLI pre-training followed by STSb fine-tuning is effective in generating high-performance sentence-similarity models for Hindi and Marathi. The vanilla BERT models trained using this simple strategy outperform the multilingual LaBSE trained using a complex training strategy. These models are evaluated on downstream text classification and similarity tasks. We evaluate these models on real text classification datasets to show embeddings obtained from synthetic data training are generalizable to real datasets as well and thus represent an effective training strategy for low-resource languages. We also provide a comparative analysis of sentence embeddings from fast text models, multilingual BERT models (mBERT, IndicBERT, xlm-RoBERTa, MuRIL), multilingual sentence embedding models (LASER, LaBSE), and monolingual BERT models based on L3Cube-MahaBERT and HindBERT. We release L3Cube-MahaSBERT and HindSBERT, the state-of-the-art sentence-BERT models for Marathi and Hindi respectively. Our work also serves as a guide to building low-resource sentence embedding models. | ['Raviraj Joshi', 'Samruddhi Deode', 'Janhavi Gadre', 'Aditi Kajale', 'Ananya Joshi'] | 2022-11-21 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 5.09133190e-02 2.44745798e-02 1.08575255e-01 -6.37861490e-01
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472b8d4b-d917-4127-a754-8e7a8731b8fc | multi-task-lung-nodule-detection-in-chest | 2207.03050 | null | https://arxiv.org/abs/2207.03050v1 | https://arxiv.org/pdf/2207.03050v1.pdf | Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network | Lung nodules can be an alarming precursor to potential lung cancer. Missed nodule detections during chest radiograph analysis remains a common challenge among thoracic radiologists. In this work, we present a multi-task lung nodule detection algorithm for chest radiograph analysis. Unlike past approaches, our algorithm predicts a global-level label indicating nodule presence along with local-level labels predicting nodule locations using a Dual Head Network (DHN). We demonstrate the favorable nodule detection performance that our multi-task formulation yields in comparison to conventional methods. In addition, we introduce a novel Dual Head Augmentation (DHA) strategy tailored for DHN, and we demonstrate its significance in further enhancing global and local nodule predictions. | ['Yu-Shao Peng', 'Chen-Han Tsai'] | 2022-07-07 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 5.88255584e-01 4.24807549e-01 -5.21816194e-01 -2.25590318e-01
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01fb1142-42f7-426e-97e2-eeac06077750 | color-cerberus | 1907.06483 | null | https://arxiv.org/abs/1907.06483v1 | https://arxiv.org/pdf/1907.06483v1.pdf | Color Cerberus | Simple convolutional neural network was able to win ISISPA color constancy competition. Partial reimplementation of (Bianco, 2017) neural architecture would have shown even better results in this setup. | ['S. ~Karpenko', 'E. ~Ershov', 'A. ~Savchik'] | 2019-07-15 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-7.12800920e-02 1.15720443e-01 3.82936388e-01 -4.57285583e-01
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3ece3d53-d2ef-4ee0-be5c-097ab30894ff | embodiedgpt-vision-language-pre-training-via | 2305.15021 | null | https://arxiv.org/abs/2305.15021v1 | https://arxiv.org/pdf/2305.15021v1.pdf | EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought | Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset. | ['Ping Luo', 'Yu Qiao', 'Jifeng Dai', 'Bin Wang', 'Jun Jin', 'Mingyu Ding', 'Wenhai Wang', 'Mengkang Hu', 'Qinglong Zhang', 'Yao Mu'] | 2023-05-24 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 7.56140575e-02 4.19946969e-01 4.23691086e-02 -5.52336499e-02
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a986e26f-44a7-4e91-8c94-b6cea9d99cf1 | a-minimal-solution-to-the-generalized-pose | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Ventura_A_Minimal_Solution_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Ventura_A_Minimal_Solution_2014_CVPR_paper.pdf | A Minimal Solution to the Generalized Pose-and-Scale Problem | We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter. This further generalization of the well-known absolute camera pose problem has applications in multi-frame loop closure. While a well-calibrated camera rig has a fixed and known scale, camera trajectories produced by monocular motion estimation necessarily lack a scale estimate. Thus, when performing loop closure in monocular visual odometry, or registering separate structure-from-motion reconstructions, we must estimate a seven degree-of-freedom similarity transform from corresponding observations. Existing approaches solve this problem, in specialized configurations, by aligning 3D triangulated points or individual camera pose estimates. Our approach handles general configurations of rays and points and directly estimates the full similarity transformation from the 2D-3D correspondences. Four correspondences are needed in the minimal case, which has eight possible solutions. The minimal solver can be used in a hypothesize-and-test architecture for robust transformation estimation. Our solver also produces a least-squares estimate in the overdetermined case. The approach is evaluated experimentally on synthetic and real datasets, and is shown to produce higher accuracy solutions to multi-frame loop closure than existing approaches. | ['Dieter Schmalstieg', 'Jonathan Ventura', 'Gerhard Reitmayr', 'Clemens Arth'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['monocular-visual-odometry'] | ['robots'] | [-9.10351984e-03 -4.44423296e-02 -1.99446633e-01 -1.56260699e-01
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09412613-192a-47b2-b06c-47ee6c4e94b9 | few-shots-portrait-generation-with-style | 2303.00377 | null | https://arxiv.org/abs/2303.00377v1 | https://arxiv.org/pdf/2303.00377v1.pdf | Few-shots Portrait Generation with Style Enhancement and Identity Preservation | Nowadays, the wide application of virtual digital human promotes the comprehensive prosperity and development of digital culture supported by digital economy. The personalized portrait automatically generated by AI technology needs both the natural artistic style and human sentiment. In this paper, we propose a novel StyleIdentityGAN model, which can ensure the identity and artistry of the generated portrait at the same time. Specifically, the style-enhanced module focuses on artistic style features decoupling and transferring to improve the artistry of generated virtual face images. Meanwhile, the identity-enhanced module preserves the significant features extracted from the input photo. Furthermore, the proposed method requires a small number of reference style data. Experiments demonstrate the superiority of StyleIdentityGAN over state-of-art methods in artistry and identity effects, with comparisons done qualitatively, quantitatively and through a perceptual user study. Code has been released on Github3. | ['Fan Zhang', 'Youbing Zhao', 'Naye Ji', 'Runchuan Zhu'] | 2023-03-01 | null | null | null | null | ['culture'] | ['speech'] | [ 1.60128504e-01 1.12871870e-01 -1.75636448e-02 -1.75064772e-01
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68c9c50a-bf0f-4c68-92fc-ee822a32e0e8 | camo-mot-combined-appearance-motion | 2209.02540 | null | https://arxiv.org/abs/2209.02540v3 | https://arxiv.org/pdf/2209.02540v3.pdf | CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion | 3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. Some fusion methods work well but do not consider the untrustworthy issue of appearance features under occlusion. At the same time, the false detection problem also significantly affects tracking. As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection. For occlusion problems, we are the first to propose an occlusion head to select the best object appearance features multiple times effectively, reducing the influence of occlusions. To decrease the impact of false detection in tracking, we design a motion cost matrix based on confidence scores which improve the positioning and object prediction accuracy in 3D space. As existing multi-object tracking methods only consider a single category, we also propose to build a multi-category loss to implement multi-object tracking in multi-category scenes. A series of validation experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our proposed method achieves state-of-the-art performance and the lowest identity switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal MOT methods on the KITTI test dataset. And our proposed method achieves state-of-the-art performance among all algorithms on the nuScenes test dataset with 75.3% AMOTA. | ['Huaping Liu', 'Jun Li', 'Hong Wang', 'Lei Zhu', 'Zhiwei Li', 'Lei Yang', 'Xiaoyu Li', 'Wenyuan Qin', 'Xinyu Zhang', 'Li Wang'] | 2022-09-06 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.84876138e-01 -6.87010825e-01 -1.68032140e-01 -1.53952092e-01
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ca6cd9f9-7914-4de1-8669-c6f460f52e4b | fusion-of-inverse-synthetic-aperture-radar | 2209.13512 | null | https://arxiv.org/abs/2209.13512v1 | https://arxiv.org/pdf/2209.13512v1.pdf | Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking | Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploited for generating inverse synthetic aperture radar (ISAR) images that offer rich information regarding the target vehicle's size, shape, and trajectory which is useful for object recognition and classification. However, a key requirement for ISAR is translation motion compensation and estimation of the turning velocity of the target. State-of-the-art algorithms for motion compensation trade-off between computational complexity and accuracy. An alternative low complexity method is to use an additional sensor for tracking the target motion. In this work, we propose to exploit computer vision algorithms to identify the radar target object in the sensor field-of-view (FoV) with high accuracy. Further, we propose to track the target vehicle's motion through fusion of vision and radar data. Vision data facilitates the accurate estimation of the lateral position of the target which complements the radar capability of accurate estimation of range and radial velocity. Through simulations and experimental evaluations with a monocular camera and Texas Instrument millimeter wave radar we demonstrate the effectiveness of sensor fusion for accurate target tracking for translational motion compensation and the generation of high-quality ISAR images. | ['Shobha Sundar Ram'] | 2022-09-27 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.83467764e-01 -4.71007288e-01 -8.52439739e-03 -4.42054719e-01
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4f12970d-359e-4653-805b-39673d0776be | selective-hearing-through-lip-reading | 2106.07150 | null | https://arxiv.org/abs/2106.07150v2 | https://arxiv.org/pdf/2106.07150v2.pdf | Selective Listening by Synchronizing Speech with Lips | A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre-enrolled speech utterance, or an accompanying video track. Visual cues are particularly useful when a pre-enrolled speech is not available. In this work, we don't rely on the target speaker's pre-enrolled speech, but rather use the target speaker's face track as the speaker cue, that is referred to as the auxiliary reference, to form an attractor towards the target speaker. We advocate that the temporal synchronization between the speech and its accompanying lip movements is a direct and dominant audio-visual cue. Therefore, we propose a self-supervised pre-training strategy, to exploit the speech-lip synchronization cue for target speaker extraction, which allows us to leverage abundant unlabeled in-domain data. We transfer the knowledge from the pre-trained model to the attractor encoder of the speaker extraction network. We show that the proposed speaker extraction network outperforms various competitive baselines in terms of signal quality, perceptual quality, and intelligibility, achieving state-of-the-art performance. | ['Haizhou Li', 'Chenglin Xu', 'Ruijie Tao', 'Zexu Pan'] | 2021-06-14 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 2.35163078e-01 1.73226252e-01 -4.34092849e-01 -1.07306391e-01
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86f9f664-a043-46fd-949d-956e4996dcb6 | rebotnet-fast-real-time-video-enhancement | 2303.13504 | null | https://arxiv.org/abs/2303.13504v1 | https://arxiv.org/pdf/2303.13504v1.pdf | ReBotNet: Fast Real-time Video Enhancement | Most video restoration networks are slow, have high computational load, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework to perform real-time video enhancement for practical use-cases like live video calls and video streams. Our proposed method, called Recurrent Bottleneck Mixer Network (ReBotNet), employs a dual-branch framework. The first branch learns spatio-temporal features by tokenizing the input frames along the spatial and temporal dimensions using a ConvNext-based encoder and processing these abstract tokens using a bottleneck mixer. To further improve temporal consistency, the second branch employs a mixer directly on tokens extracted from individual frames. A common decoder then merges the features form the two branches to predict the enhanced frame. In addition, we propose a recurrent training approach where the last frame's prediction is leveraged to efficiently enhance the current frame while improving temporal consistency. To evaluate our method, we curate two new datasets that emulate real-world video call and streaming scenarios, and show extensive results on multiple datasets where ReBotNet outperforms existing approaches with lower computations, reduced memory requirements, and faster inference time. | ['Anne Menini', 'Vishal M. Patel', 'Andreas Lugmayr', 'Weijuan Xi', 'Xin Tong', 'Andeep Toor', 'Rahul Garg', 'Jeya Maria Jose Valanarasu'] | 2023-03-23 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.12921111e-01 -3.85853797e-01 -2.50984967e-01 -2.35273346e-01
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a5f4fff7-fdaa-4c55-8e06-9d36c968e424 | perception-aware-multi-sensor-fusion-for-3d | 2106.15277 | null | https://arxiv.org/abs/2106.15277v2 | https://arxiv.org/pdf/2106.15277v2.pdf | Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation | 3D LiDAR (light detection and ranging) semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinates to provide spatio-depth information for RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately, and fuse the features by effective residual-based fusion modules. Moreover, we propose additional perception-aware losses to measure the perceptual difference between the two modalities. Extensive experiments on two benchmark data sets show the superiority of our method. For example, on nuScenes, our PMF outperforms the state-of-the-art method by 0.8 in mIoU. | ['Yuanqing Li', 'Qicheng Wang', 'Mingkui Tan', 'Kui Jia', 'Rong Li', 'Zhuangwei Zhuang'] | 2021-06-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhuang_Perception-Aware_Multi-Sensor_Fusion_for_3D_LiDAR_Semantic_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhuang_Perception-Aware_Multi-Sensor_Fusion_for_3D_LiDAR_Semantic_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 4.15124625e-01 -4.72643167e-01 3.16465609e-02 -6.03975594e-01
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8080be2a-4edf-4087-95fe-b5b94eb9197c | accurate-facial-parts-localization-and-deep | 1803.05846 | null | http://arxiv.org/abs/1803.05846v1 | http://arxiv.org/pdf/1803.05846v1.pdf | Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition | Meaningful facial parts can convey key cues for both facial action unit
detection and expression prediction. Textured 3D face scan can provide both
detailed 3D geometric shape and 2D texture appearance cues of the face which
are beneficial for Facial Expression Recognition (FER). However, accurate
facial parts extraction as well as their fusion are challenging tasks. In this
paper, a novel system for 3D FER is designed based on accurate facial parts
extraction and deep feature fusion of facial parts. In particular, each
textured 3D face scan is firstly represented as a 2D texture map and a depth
map with one-to-one dense correspondence. Then, the facial parts of both
texture map and depth map are extracted using a novel 4-stage process consists
of facial landmark localization, facial rotation correction, facial resizing,
facial parts bounding box extraction and post-processing procedures. Finally,
deep fusion Convolutional Neural Networks (CNNs) features of all facial parts
are learned from both texture maps and depth maps, respectively and nonlinear
SVMs are used for expression prediction. Experiments are conducted on the
BU-3DFE database, demonstrating the effectiveness of combing different facial
parts, texture and depth cues and reporting the state-of-the-art results in
comparison with all existing methods under the same setting. | ['Hongy-ing Meng', 'Huaxiong Ding', 'Liming Chen', 'Huibin Li', 'Asim Jan'] | 2018-03-04 | null | null | null | null | ['3d-facial-expression-recognition', 'action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.46794915e-01 -3.09234988e-02 -1.19607225e-01 -7.67681420e-01
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e3010962-657a-45a4-bd00-4ea332172190 | evaluation-of-deep-convolutional-nets-for | 1502.07058 | null | http://arxiv.org/abs/1502.07058v1 | http://arxiv.org/pdf/1502.07058v1.pdf | Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval | This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis. | ['Konstantinos G. Derpanis', 'Alex Ufkes', 'Adam W. Harley'] | 2015-02-25 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 4.27249938e-01 -3.75276804e-01 -2.91014671e-01 -5.53246140e-01
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a080c904-1670-4046-9369-5dfc30d81367 | cardiac-arrhythmia-detection-using-artificial | 2304.08162 | null | https://arxiv.org/abs/2304.08162v1 | https://arxiv.org/pdf/2304.08162v1.pdf | Cardiac Arrhythmia Detection using Artificial Neural Network | The prime purpose of this project is to develop a portable cardiac abnormality monitoring device which can drastically improvise the quality of the monitoring and the overall safety of the device. While a generic, low cost, wearable battery powered device for such applications may not yield sufficient performance, such devices combined with the capabilities of Artificial Neural Network algorithms can however, prove to be as competent as high end flexible and wearable monitoring devices fabricated using advanced manufacturing technologies. This paper evaluates the feasibility of the Levenberg-Marquardt ANN algorithm for use in any generic low power wearable devices implemented either as a pure real-time embedded system or as an IoT device capable of uploading the monitored readings to the cloud. | ['Vishal Kumar A', 'Sreevatsan B', 'Kishore Anand K', 'Prof Sangeetha R G'] | 2023-04-17 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 1.87600628e-01 7.11281151e-02 2.23683298e-01 -2.48677611e-01
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