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8cb62944-5092-4acf-a3de-bf3747a42cbc | context-aware-frame-semantic-role-labeling | null | null | https://aclanthology.org/Q15-1032 | https://aclanthology.org/Q15-1032.pdf | Context-aware Frame-Semantic Role Labeling | Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling. | ['Michael Roth', 'Mirella Lapata'] | 2015-01-01 | null | null | null | tacl-2015-1 | ['scene-generation', 'stock-price-prediction'] | ['computer-vision', 'time-series'] | [ 6.01451159e-01 5.61262786e-01 -3.96606326e-01 -7.04569697e-01
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1ff16031-728b-4a8f-8ec0-a0c86c2cb492 | one-class-knowledge-distillation-for-face | 2205.03792 | null | https://arxiv.org/abs/2205.03792v1 | https://arxiv.org/pdf/2205.03792v1.pdf | One-Class Knowledge Distillation for Face Presentation Attack Detection | Face presentation attack detection (PAD) has been extensively studied by research communities to enhance the security of face recognition systems. Although existing methods have achieved good performance on testing data with similar distribution as the training data, their performance degrades severely in application scenarios with data of unseen distributions. In situations where the training and testing data are drawn from different domains, a typical approach is to apply domain adaptation techniques to improve face PAD performance with the help of target domain data. However, it has always been a non-trivial challenge to collect sufficient data samples in the target domain, especially for attack samples. This paper introduces a teacher-student framework to improve the cross-domain performance of face PAD with one-class domain adaptation. In addition to the source domain data, the framework utilizes only a few genuine face samples of the target domain. Under this framework, a teacher network is trained with source domain samples to provide discriminative feature representations for face PAD. Student networks are trained to mimic the teacher network and learn similar representations for genuine face samples of the target domain. In the test phase, the similarity score between the representations of the teacher and student networks is used to distinguish attacks from genuine ones. To evaluate the proposed framework under one-class domain adaptation settings, we devised two new protocols and conducted extensive experiments. The experimental results show that our method outperforms baselines under one-class domain adaptation settings and even state-of-the-art methods with unsupervised domain adaptation. | ['Alex C. Kot', 'Yongjian Hu', 'Kwok-Yan Lam', 'Haoliang Li', 'Rizhao Cai', 'Zhi Li'] | 2022-05-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.19970161e-01 -3.02429438e-01 -1.25719100e-01 -5.08464754e-01
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868c3dfc-5edf-4f6c-b291-4a1f46814daf | summarizing-videos-using-concentrated | null | null | https://dl.acm.org/doi/10.1145/3512527.3531404 | https://www.iti.gr/~bmezaris/publications/icmr2022_preprint.pdf | Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames | In this work, we describe a new method for unsupervised video summarization. To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames' dependencies and the ability to parallelize the training process of RNN-based network architectures, the developed method relies solely on the use of a self-attention mechanism to estimate the importance of video frames. Instead of simply modeling the frames' dependencies based on global attention, our method integrates a concentrated attention mechanism that is able to focus on non-overlapping blocks in the main diagonal of the attention matrix, and to enrich the existing information by extracting and exploiting knowledge about the uniqueness and diversity of the associated frames of the video. In this way, our method makes better estimates about the significance of different parts of the video, and drastically reduces the number of learnable parameters. Experimental evaluations using two benchmarking datasets (SumMe and TVSum) show the competitiveness of the proposed method against other state-of-the-art unsupervised summarization approaches, and demonstrate its ability to produce video summaries that are very close to the human preferences. An ablation study that focuses on the introduced components, namely the use of concentrated attention in combination with attention-based estimates about the frames' uniqueness and diversity, shows their relative contributions to the overall summarization performance. | ['Ioannis Patras', 'Vasileios Mezaris', 'Georgios Balaouras', 'Evlampios Apostolidis'] | 2022-06-29 | null | null | null | acm-icmr-2022-6 | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 3.11623245e-01 4.02376801e-01 -8.38439167e-02 -7.43714496e-02
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5072c0f9-719e-439c-ae35-598dabfd88f9 | extending-phrase-grounding-with-pronouns-in | 2210.12658 | null | https://arxiv.org/abs/2210.12658v1 | https://arxiv.org/pdf/2210.12658v1.pdf | Extending Phrase Grounding with Pronouns in Visual Dialogues | Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently, sole noun phrase grounding is not enough for cross-modal visual language understanding. Here we extend the task by considering pronouns as well. First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions. Based on the dataset, we test the performance of phrase grounding by using a state-of-the-art literature model of this line. Then, we enhance the baseline grounding model with coreference information which should help our task potentially, modeling the coreference structures with graph convolutional networks. Experiments on our dataset, interestingly, show that pronouns are easier to ground than noun phrases, where the possible reason might be that these pronouns are much less ambiguous. Additionally, our final model with coreference information can significantly boost the grounding performance of both noun phrases and pronouns. | ['Min Zhang', 'Meishan Zhang', 'Xin Zhang', 'Panzhong Lu'] | 2022-10-23 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.33856609e-01 6.83345973e-01 -2.97804207e-01 -3.44860762e-01
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9fd46629-9e46-4478-921e-5a686689557c | graph-convolutional-module-for-temporal | 2112.00302 | null | https://arxiv.org/abs/2112.00302v1 | https://arxiv.org/pdf/2112.00302v1.pdf | Graph Convolutional Module for Temporal Action Localization in Videos | Temporal action localization has long been researched in computer vision. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individually, without explicitly exploiting their relations during learning. In this paper, we claim that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it. To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms. To be specific, we first construct a graph, where each action unit is represented as a node and their relations between two action units as an edge. Here, we use two types of relations, one for capturing the temporal connections between different action units, and the other one for characterizing their semantic relationship. Particularly for the temporal connections in two-stage methods, we further explore two different kinds of edges, one connecting the overlapping action units and the other one connecting surrounding but disjointed units. Upon the graph we built, we then apply graph convolutional networks (GCNs) to model the relations among different action units, which is able to learn more informative representations to enhance action localization. Experimental results show that our GCM consistently improves the performance of existing action localization methods, including two-stage methods (e.g., CBR and R-C3D) and one-stage methods (e.g., D-SSAD), verifying the generality and effectiveness of our GCM. | ['Chuang Gan', 'Junzhou Huang', 'Peilin Zhao', 'Yu Rong', 'Mingkui Tan', 'Wenbing Huang', 'Runhao Zeng'] | 2021-12-01 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 3.02929908e-01 -1.62962882e-03 -6.13292158e-01 -7.39122159e-04
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9ce1c165-97d6-4d5a-a664-e61df092ed09 | ns-hunter-bert-cloze-based-semantic-denoising | null | null | https://aclanthology.org/2021.ccl-1.99 | https://aclanthology.org/2021.ccl-1.99.pdf | NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification | “Distant supervision can generate large-scale relation classification data quickly and economi-cally. However a great number of noise sentences are introduced which can not express their labeled relations. By means of pre-trained language model BERT’s powerful function in this paper we propose a BERT-based semantic denoising approach for distantly supervised relation classification. In detail we define an entity pair as a source entity and a target entity. For the specific sentences whose target entities in BERT-vocabulary (one-token word) we present the differences of dependency between two entities for noise and non-noise sentences. For general sentences whose target entity is multi-token word we further present the differences of last hid-den states of [MASK]-entity (MASK-lhs for short) in BERT for noise and non-noise sentences.We regard the dependency and MASK-lhs in BERT as two semantic features of sentences. With BERT we capture the dependency feature to discriminate specific sentences first then capturethe MASK-lhs feature to denoise distant supervision datasets. We propose NS-Hunter a noveldenoising model which leverages BERT-cloze ability to capture the two semantic features andintegrates above functions. According to the experiment on NYT data our NS-Hunter modelachieves the best results in distant supervision denoising and sentence-level relation classification. Keywords: Distant supervision relation classification semantic denoisingIntroduction” | ['Zhang Yifei', 'Feng Shi', 'Wang Daling', 'Shen Tielin'] | null | null | null | null | ccl-2021-8 | ['relation-classification'] | ['natural-language-processing'] | [ 7.58449435e-02 5.48579514e-01 -1.60401151e-01 -7.29906976e-01
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05e26082-271c-4013-9890-46ef2d756620 | knowledge-based-analysis-for-mortality | 1902.07687 | null | https://arxiv.org/abs/1902.07687v2 | https://arxiv.org/pdf/1902.07687v2.pdf | Knowledge-based Analysis for Mortality Prediction from CT Images | Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality. Low-Dose CT (LCDT) scans have led to significant improvements in the accuracy of lung cancer diagnosis and thus the reduction of cancer deaths. However, the high correlation between lung cancer and CVD has not been well explored for mortality prediction. This paper introduces a knowledge-based analytical method using deep convolutional neural network (CNN) for all-cause mortality prediction. The underlying approach combines structural image features extracted from CNNs, based on LDCT volume in different scale, and clinical knowledge obtained from quantitative measurements, to comprehensively predict the mortality risk of lung cancer screening subjects. The introduced method is referred to here as the Knowledge-based Analysis of Mortality Prediction Network, or KAMP-Net. It constitutes a collaborative framework that utilizes both imaging features and anatomical information, instead of completely relying on automatic feature extraction. Our work demonstrates the feasibility of incorporating quantitative clinical measurements to assist CNNs in all-cause mortality prediction from chest LDCT images. The results of this study confirm that radiologist defined features are an important complement to CNNs to achieve a more comprehensive feature extraction. Thus, the proposed KAMP-Net has shown to achieve a superior performance when compared to other methods. Our code is available at https://github.com/DIAL-RPI/KAMP-Net. | ['Pingkun Yan', 'Mannudeep K. Kalra', 'Ge Wang', 'Hengtao Guo', 'Uwe Kruger'] | 2019-02-20 | null | null | null | null | ['lung-cancer-diagnosis', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [-2.41649583e-01 -1.78462341e-01 -4.33042169e-01 -1.34102046e-01
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16a85575-a18e-4b5c-bb81-e3560b7110fd | treeqn-and-atreec-differentiable-tree | 1710.11417 | null | http://arxiv.org/abs/1710.11417v2 | http://arxiv.org/pdf/1710.11417v2.pdf | TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning | Combining deep model-free reinforcement learning with on-line planning is a
promising approach to building on the successes of deep RL. On-line planning
with look-ahead trees has proven successful in environments where transition
models are known a priori. However, in complex environments where transition
models need to be learned from data, the deficiencies of learned models have
limited their utility for planning. To address these challenges, we propose
TreeQN, a differentiable, recursive, tree-structured model that serves as a
drop-in replacement for any value function network in deep RL with discrete
actions. TreeQN dynamically constructs a tree by recursively applying a
transition model in a learned abstract state space and then aggregating
predicted rewards and state-values using a tree backup to estimate Q-values. We
also propose ATreeC, an actor-critic variant that augments TreeQN with a
softmax layer to form a stochastic policy network. Both approaches are trained
end-to-end, such that the learned model is optimised for its actual use in the
tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a
box-pushing task, as well as n-step DQN and value prediction networks (Oh et
al. 2017) on multiple Atari games. Furthermore, we present ablation studies
that demonstrate the effect of different auxiliary losses on learning
transition models. | ['Tim Rocktäschel', 'Gregory Farquhar', 'Shimon Whiteson', 'Maximilian Igl'] | 2017-10-31 | treeqn-and-atreec-differentiable-tree-1 | https://openreview.net/forum?id=H1dh6Ax0Z | https://openreview.net/pdf?id=H1dh6Ax0Z | iclr-2018-1 | ['value-prediction'] | ['computer-code'] | [ 1.39850587e-01 5.40074825e-01 -5.59425354e-01 -3.03302437e-01
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855f82ca-97db-430c-bb44-ec67eca79970 | speech-separation-with-large-scale-self | 2211.05172 | null | https://arxiv.org/abs/2211.05172v2 | https://arxiv.org/pdf/2211.05172v2.pdf | Speech separation with large-scale self-supervised learning | Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the exploration of the SSL-based SS by massively scaling up both the pre-training data (more than 300K hours) and fine-tuning data (10K hours). We also investigate various techniques to efficiently integrate the pre-trained model with the SS network under a limited computation budget, including a low frame rate SSL model training setup and a fine-tuning scheme using only the part of the pre-trained model. Compared with a supervised baseline and the WavLM-based SS model using feature embeddings obtained with the previously released 94K hours trained WavLM, our proposed model obtains 15.9% and 11.2% of relative word error rate (WER) reductions, respectively, for a simulated far-field speech mixture test set. For conversation transcription on real meeting recordings using continuous speech separation, the proposed model achieves 6.8% and 10.6% of relative WER reductions over the purely supervised baseline on AMI and ICSI evaluation sets, respectively, while reducing the computational cost by 38%. | ['Sefik Emre Eskimez', 'Sunit Sivasankaran', 'Jinyu Li', 'Takuya Yoshioka', 'Xiaofei Wang', 'Yu Wu', 'Jian Wu', 'Naoyuki Kanda', 'Zhuo Chen'] | 2022-11-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.53549120e-01 3.26413989e-01 1.99364468e-01 -6.20212078e-01
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64e209d6-5605-48ae-bcab-cc0cd4517a78 | a-constructive-gan-based-approach-to-exact | 2206.06116 | null | https://arxiv.org/abs/2206.06116v1 | https://arxiv.org/pdf/2206.06116v1.pdf | A Constructive GAN-based Approach to Exact Estimate Treatment Effect without Matching | Matching has become the mainstream in counterfactual inference, with which selection bias between sample groups can be significantly eliminated. However in practice, when estimating average treatment effect on the treated (ATT) via matching, no matter which method, the trade-off between estimation accuracy and information loss constantly exist. Attempting to completely replace the matching process, this paper proposes the GAN-ATT estimator that integrates generative adversarial network (GAN) into counterfactual inference framework. Through GAN machine learning, the probability density functions (PDFs) of samples in both treatment group and control group can be approximated. By differentiating conditional PDFs of the two groups with identical input condition, the conditional average treatment effect (CATE) can be estimated, and the ensemble average of corresponding CATEs over all treatment group samples is the estimate of ATT. Utilizing GAN-based infinite sample augmentations, problems in the case of insufficient samples or lack of common support domains can be easily solved. Theoretically, when GAN could perfectly learn the PDFs, our estimators can provide exact estimate of ATT. To check the performance of the GAN-ATT estimator, three sets of data are used for ATT estimations: Two toy data sets with 1/2 dimensional covariate inputs and constant/covariate-dependent treatment effect are tested. The estimates of GAN-ATT are proved close to the ground truth and are better than traditional matching approaches; A real firm-level data set with high-dimensional input is tested and the applicability towards real data sets is evaluated by comparing matching approaches. Through the evidences obtained from the three tests, we believe that the GAN-ATT estimator has significant advantages over traditional matching methods in estimating ATT. | ['Kerry Papps', 'Boyang You'] | 2022-06-13 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 2.72938251e-01 1.96416304e-01 -7.03436017e-01 -1.78964213e-01
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df174195-02a4-45d7-98ec-73573f7ac75e | a-new-knowledge-distillation-network-for | 2209.00519 | null | https://arxiv.org/abs/2209.00519v1 | https://arxiv.org/pdf/2209.00519v1.pdf | A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection | Surface defect detection is one of the most essential processes for industrial quality inspection. Deep learning-based surface defect detection methods have shown great potential. However, the well-performed models usually require large training data and can only detect defects that appeared in the training stage. When facing incremental few-shot data, defect detection models inevitably suffer from catastrophic forgetting and misclassification problem. To solve these problems, this paper proposes a new knowledge distillation network, called Dual Knowledge Align Network (DKAN). The proposed DKAN method follows a pretraining-finetuning transfer learning paradigm and a knowledge distillation framework is designed for fine-tuning. Specifically, an Incremental RCNN is proposed to achieve decoupled stable feature representation of different categories. Under this framework, a Feature Knowledge Align (FKA) loss is designed between class-agnostic feature maps to deal with catastrophic forgetting problems, and a Logit Knowledge Align (LKA) loss is deployed between logit distributions to tackle misclassification problems. Experiments have been conducted on the incremental Few-shot NEU-DET dataset and results show that DKAN outperforms other methods on various few-shot scenes, up to 6.65% on the mean Average Precision metric, which proves the effectiveness of the proposed method. | ['Yiping Gao', 'Xinyu Li', 'Liang Gao', 'Chen Sun'] | 2022-09-01 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.65208125e-01 1.62317068e-03 2.87815064e-01 -3.55272919e-01
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4458a7c5-038b-42b9-8ba4-f371f605154b | the-dependence-of-machine-learning-on | 1703.08251 | null | http://arxiv.org/abs/1703.08251v1 | http://arxiv.org/pdf/1703.08251v1.pdf | The Dependence of Machine Learning on Electronic Medical Record Quality | There is growing interest in applying machine learning methods to Electronic
Medical Records (EMR). Across different institutions, however, EMR quality can
vary widely. This work investigated the impact of this disparity on the
performance of three advanced machine learning algorithms: logistic regression,
multilayer perceptron, and recurrent neural network. The EMR disparity was
emulated using different permutations of the EMR collected at Children's
Hospital Los Angeles (CHLA) Pediatric Intensive Care Unit (PICU) and
Cardiothoracic Intensive Care Unit (CTICU). The algorithms were trained using
patients from the PICU to predict in-ICU mortality for patients in a held out
set of PICU and CTICU patients. The disparate patient populations between the
PICU and CTICU provide an estimate of generalization errors across different
ICUs. We quantified and evaluated the generalization of these algorithms on
varying EMR size, input types, and fidelity of data. | ['David Ledbetter', 'Randall Wetzel', 'Melissa Aczon', 'Long Ho'] | 2017-03-23 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 2.23417073e-01 -3.38678479e-01 -1.01187967e-01 -1.81053832e-01
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31d19ef4-5206-4a93-95b8-d443002df0a6 | current-shortcomings-of-machine-translation | null | null | https://aclanthology.org/2022.clib-1.20 | https://aclanthology.org/2022.clib-1.20.pdf | Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English | In late 2016, Google Translate (GT), widely considered a machine translation leader, replaced its statistical machine translation (SMT) functions with a neural machine translation (NMT) model for many large languages, including Spanish, with other languages following thereafter. Whereas the capabilities of GT had previously advanced incrementally, this switch to NMT resulted in seemingly exponential improvement. However, half a dozen years later, while recognizing GT’s usefulness, it is also imperative to systematically evaluate ongoing shortcomings, including determining which challenges may reasonably be presumed as superable over time and those which, following a multiyear tracking study, prove unlikely ever to be fully resolved. While the research in question principally explores Spanish-English-Spanish machine translation, this paper examines similar problems with Bulgarian-English-Bulgarian GT renditions. Better understanding both the strengths and weaknesses of current machine translation applications is fundamental to knowing when such non-human natural language processing (NLP) technology is capable of performing all or most of a given task, and when heavy, perhaps even exclusive human intervention is still required. | ['Travis Sorenson'] | null | null | null | null | clib-2022-9 | ['nmt'] | ['computer-code'] | [ 4.18585062e-01 2.34204784e-01 -4.33784902e-01 -2.33670294e-01
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bafd7588-fd32-49f4-8487-2ca1953f0e08 | self-supervised-matting-specific-portrait | 2208.06601 | null | https://arxiv.org/abs/2208.06601v1 | https://arxiv.org/pdf/2208.06601v1.pdf | Self-supervised Matting-specific Portrait Enhancement and Generation | We resolve the ill-posed alpha matting problem from a completely different perspective. Given an input portrait image, instead of estimating the corresponding alpha matte, we focus on the other end, to subtly enhance this input so that the alpha matte can be easily estimated by any existing matting models. This is accomplished by exploring the latent space of GAN models. It is demonstrated that interpretable directions can be found in the latent space and they correspond to semantic image transformations. We further explore this property in alpha matting. Particularly, we invert an input portrait into the latent code of StyleGAN, and our aim is to discover whether there is an enhanced version in the latent space which is more compatible with a reference matting model. We optimize multi-scale latent vectors in the latent spaces under four tailored losses, ensuring matting-specificity and subtle modifications on the portrait. We demonstrate that the proposed method can refine real portrait images for arbitrary matting models, boosting the performance of automatic alpha matting by a large margin. In addition, we leverage the generative property of StyleGAN, and propose to generate enhanced portrait data which can be treated as the pseudo GT. It addresses the problem of expensive alpha matte annotation, further augmenting the matting performance of existing models. Code is available at~\url{https://github.com/cnnlstm/StyleGAN_Matting}. | ['Shengfeng He', 'Yangyang Xu Zeyang Zhou'] | 2022-08-13 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 7.40586162e-01 3.60439360e-01 -1.72202364e-01 -1.57733709e-01
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3bff30b9-106f-4ec8-8aa4-92f86105dd4b | boosting-monocular-depth-estimation-with-1 | 2202.01470 | null | https://arxiv.org/abs/2202.01470v4 | https://arxiv.org/pdf/2202.01470v4.pdf | Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth | Existing monocular depth estimation methods have achieved excellent robustness in diverse scenes, but they can only retrieve affine-invariant depth, up to an unknown scale and shift. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shift residing in per-frame prediction may cause the depth inconsistency. To solve this problem, we propose a locally weighted linear regression method to recover the scale and shift with very sparse anchor points, which ensures the scale consistency along consecutive frames. Extensive experiments show that our method can boost the performance of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks. Besides, we merge over 6.3 million RGBD images to train strong and robust depth models. Our produced ResNet50-backbone model even outperforms the state-of-the-art DPT ViT-Large model. Combining with geometry-based reconstruction methods, we formulate a new dense 3D scene reconstruction pipeline, which benefits from both the scale consistency of sparse points and the robustness of monocular methods. By performing the simple per-frame prediction over a video, the accurate 3D scene shape can be recovered. | ['Feng Wu', 'Chunhua Shen', 'Feng Zhao', 'Kai Cheng', 'Hao Chen', 'Wei Yin', 'Guangkai Xu'] | 2022-02-03 | null | null | null | null | ['3d-scene-reconstruction', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 1.32053196e-01 -2.90313214e-01 -1.37593180e-01 -4.02982354e-01
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6459e11a-0aa5-4fee-a26a-83527f7f3d73 | focal-onset-seizure-prediction-using | 1805.11576 | null | http://arxiv.org/abs/1805.11576v1 | http://arxiv.org/pdf/1805.11576v1.pdf | Focal onset seizure prediction using convolutional networks | Objective: This work investigates the hypothesis that focal seizures can be
predicted using scalp electroencephalogram (EEG) data. Our first aim is to
learn features that distinguish between the interictal and preictal regions.
The second aim is to define a prediction horizon in which the prediction is as
accurate and as early as possible, clearly two competing objectives. Methods:
Convolutional filters on the wavelet transformation of the EEG signal are used
to define and learn quantitative signatures for each period: interictal,
preictal, and ictal. The optimal seizure prediction horizon is also learned
from the data as opposed to making an a priori assumption. Results:
Computational solutions to the optimization problem indicate a ten-minute
seizure prediction horizon. This result is verified by measuring
Kullback-Leibler divergence on the distributions of the automatically extracted
features. Conclusion: The results on the EEG database of 204 recordings
demonstrate that (i) the preictal phase transition occurs approximately ten
minutes before seizure onset, and (ii) the prediction results on the test set
are promising, with a sensitivity of 87.8% and a low false prediction rate of
0.142 FP/h. Our results significantly outperform a random predictor and other
seizure prediction algorithms. Significance: We demonstrate that a robust set
of features can be learned from scalp EEG that characterize the preictal state
of focal seizures. | ['Bülent Yener', 'Madeline Fields', 'Lara Marcuse', 'Kalina Swann', 'Haidar Khan'] | 2018-05-29 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.31970161e-01 -4.34730761e-02 1.11877047e-01 -5.03169894e-01
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c6eced68-964a-4837-86c3-f166dea238a0 | lipkey-a-large-scale-news-dataset-for-absent | null | null | https://aclanthology.org/2022.coling-1.303 | https://aclanthology.org/2022.coling-1.303.pdf | LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization | Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. We jointly use the three elements via multi-task training and training as joint structured inputs, in the context of document summarization. We find that including absent keyphrases and titles as additional context to the source document improves transformer-based summarization models. | ['Jey Han Lau', 'Timothy Baldwin', 'Fajri Koto'] | null | null | null | null | coling-2022-10 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.04943168e-01 2.31420442e-01 -6.27868772e-01 -4.33078073e-02
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17988190-112e-4cfa-9640-ed151315e4fe | robust-kernelized-multi-view-self | 1709.05083 | null | http://arxiv.org/abs/1709.05083v1 | http://arxiv.org/pdf/1709.05083v1.pdf | Robust Kernelized Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization | Most recently, tensor-SVD is implemented on multi-view self-representation
clustering and has achieved the promising results in many real-world
applications such as face clustering, scene clustering and generic object
clustering. However, tensor-SVD based multi-view self-representation clustering
is proposed originally to solve the clustering problem in the multiple linear
subspaces, leading to unsatisfactory results when dealing with the case of
non-linear subspaces. To handle data clustering from the non-linear subspaces,
a kernelization method is designed by mapping the data from the original input
space to a new feature space in which the transformed data can be clustered by
a multiple linear clustering method. In this paper, we make an optimization
model for the kernelized multi-view self-representation clustering problem. We
also develop a new efficient algorithm based on the alternation direction
method and infer a closed-form solution. Since all the subproblems can be
solved exactly, the proposed optimization algorithm is guaranteed to obtain the
optimal solution. In particular, the original tensor-based multi-view
self-representation clustering problem is a special case of our approach and
can be solved by our algorithm. Experimental results on several popular
real-world clustering datasets demonstrate that our approach achieves the
state-of-the-art performance. | ['Yanyun Qu', 'Jinyan Liu', 'Yuan Xie', 'Wensheng Zhang'] | 2017-09-15 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-2.84067392e-01 -4.48907971e-01 -9.94966924e-02 -2.57647932e-01
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5093ae0b-86d0-4fdc-ac49-d2904f5f11ea | boundary-aware-supervoxel-level-iteratively | 2303.10692 | null | https://arxiv.org/abs/2303.10692v1 | https://arxiv.org/pdf/2303.10692v1.pdf | Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning | Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints. While iterative in nature, most existing interactive segmentation methods tend to ignore the dynamics of successive interactions and take each interaction independently. We here propose to model iterative interactive image segmentation with a Markov decision process (MDP) and solve it with reinforcement learning (RL) where each voxel is treated as an agent. Considering the large exploration space for voxel-wise prediction and the dependence among neighboring voxels for the segmentation tasks, multi-agent reinforcement learning is adopted, where the voxel-level policy is shared among agents. Considering that boundary voxels are more important for segmentation, we further introduce a boundary-aware reward, which consists of a global reward in the form of relative cross-entropy gain, to update the policy in a constrained direction, and a boundary reward in the form of relative weight, to emphasize the correctness of boundary predictions. To combine the advantages of different types of interactions, i.e., simple and efficient for point-clicking, and stable and robust for scribbles, we propose a supervoxel-clicking based interaction design. Experimental results on four benchmark datasets have shown that the proposed method significantly outperforms the state-of-the-arts, with the advantage of fewer interactions, higher accuracy, and enhanced robustness. | ['Ya zhang', 'Yanfeng Wang', 'Xiaoyun Zhang', 'Bo Jin', 'Xiangfeng Wang', 'Qisen Xu', 'Chaofan Ma'] | 2023-03-19 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 1.25442043e-01 3.00088137e-01 -2.54721701e-01 -2.82013595e-01
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dc38a52a-17d4-4233-8cf9-3e382999c89a | stecformer-spatio-temporal-encoding-cascaded | 2305.16370 | null | https://arxiv.org/abs/2305.16370v1 | https://arxiv.org/pdf/2305.16370v1.pdf | Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting | Multivariate long-term time series forecasting is of great application across many domains, such as energy consumption and weather forecasting. With the development of transformer-based methods, the performance of multivariate long-term time series forecasting has been significantly improved, however, the study of spatial features extracting in transformer-based model is rare and the consistency of different prediction periods is unsatisfactory due to the large span. In this work, we propose a complete solution to address these problems in terms of feature extraction and target prediction. For extraction, we design an efficient spatio-temporal encoding extractor including a semi-adaptive graph to acquire sufficient spatio-temporal information. For prediction, we propose a Cascaded Decoding Predictor (CDP) to strengthen the correlation between different intervals, which can also be utilized as a generic component to improve the performance of transformer-based methods. The proposed method, termed as Spatio-temporal Encoding Cascaded Transformer (Stecformer), achieving a notable gap over the baseline model and is comparable with the state-of-the-art performance of transformer-based methods on five benchmark datasets. We hope our attempt will serve as a regular configuration in multivariate long-term time series forecasting in the future. | ['Long Yu', 'Wenxiao Jia', 'Yi Wei', 'Zheng Sun'] | 2023-05-25 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 1.52781457e-01 -5.60186505e-01 -1.47107154e-01 -2.93643028e-01
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90715363-00ff-49b7-b028-38b181e1a88b | probabilistic-program-induction-for-intuitive | null | null | https://openreview.net/forum?id=HJMsiiRctX | https://openreview.net/pdf?id=HJMsiiRctX | Probabilistic Program Induction for Intuitive Physics Game Play | Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects. We propose a framework for bots to deploy similar tools for interacting with intuitive physics environments. The framework employs a physics simulation in a probabilistic way to infer about moves performed by an agent in a setting governed by Newtonian laws of motion. However, methods of probabilistic programs can be slow in such setting due to their need to generate many samples. We complement the model with a model-free approach to aid the sampling procedures in becoming more efficient through learning from experience during game playing. We present an approach where a myriad of model-free approaches (a convolutional neural network in our model) and model-based approaches (probabilistic physics simulation) is able to achieve what neither could alone. This way the model outperforms an all model-free or all model-based approach. We discuss a case study showing empirical results of the performance of the model on the game of Flappy Bird. | ['Fahad Alhasoun'] | 2018-09-27 | null | null | null | null | ['program-induction'] | ['computer-code'] | [-3.38763177e-01 7.04595670e-02 4.79125887e-01 5.95416725e-02
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6c2d8852-8109-47f3-aab2-c10029f219ea | accelerated-componentwise-gradient-boosting | 2110.03513 | null | https://arxiv.org/abs/2110.03513v2 | https://arxiv.org/pdf/2110.03513v2.pdf | Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization | Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability. CWB is thus often used in research areas where models are employed as tools to explain relationships in data. One downside of CWB is its computational complexity in terms of memory and runtime. In this paper, we propose two techniques to overcome these issues without losing the properties of CWB: feature discretization of numerical features and incorporating Nesterov momentum into functional gradient descent. As the latter can be prone to early overfitting, we also propose a hybrid approach that prevents a possibly diverging gradient descent routine while ensuring faster convergence. We perform extensive benchmarks on multiple simulated and real-world data sets to demonstrate the improvements in runtime and memory consumption while maintaining state-of-the-art estimation and prediction performance. | ['David Rügamer', 'Bernd Bischl', 'Daniel Schalk'] | 2021-10-07 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.95929110e-01 -2.77495980e-01 -3.76440853e-01 -6.22122109e-01
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69e83ce3-4f29-4917-9996-25369d6beeaf | treating-motion-as-option-to-reduce-motion | 2209.03138 | null | https://arxiv.org/abs/2209.03138v5 | https://arxiv.org/pdf/2209.03138v5.pdf | Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation | Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods leverage motion cues obtained from optical flow maps in addition to appearance cues to exploit the property that salient objects usually have distinctive movements compared to the background. However, as they are overly dependent on motion cues, which may be unreliable in some cases, they cannot achieve stable prediction. To reduce this motion dependency of existing two-stream VOS methods, we propose a novel motion-as-option network that optionally utilizes motion cues. Additionally, to fully exploit the property of the proposed network that motion is not always required, we introduce a collaborative network learning strategy. On all the public benchmark datasets, our proposed network affords state-of-the-art performance with real-time inference speed. | ['Sangyoun Lee', 'Donghyeong Kim', 'Chaewon Park', 'Seunghoon Lee', 'Minhyeok Lee', 'Suhwan Cho'] | 2022-09-04 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 3.79103035e-01 -2.08643183e-01 -6.34625793e-01 -1.45193741e-01
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ec918eec-15f4-46cf-b68f-30d16d0bddc0 | one-class-meta-learning-towards-generalizable | 2109.06859 | null | https://arxiv.org/abs/2109.06859v1 | https://arxiv.org/pdf/2109.06859v1.pdf | One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification | Real-world classification tasks are frequently required to work in an open-set setting. This is especially challenging for few-shot learning problems due to the small sample size for each known category, which prevents existing open-set methods from working effectively; however, most multiclass few-shot methods are limited to closed-set scenarios. In this work, we address the problem of few-shot open-set classification by first proposing methods for few-shot one-class classification and then extending them to few-shot multiclass open-set classification. We introduce two independent few-shot one-class classification methods: Meta Binary Cross-Entropy (Meta-BCE), which learns a separate feature representation for one-class classification, and One-Class Meta-Learning (OCML), which learns to generate one-class classifiers given standard multiclass feature representation. Both methods can augment any existing few-shot learning method without requiring retraining to work in a few-shot multiclass open-set setting without degrading its closed-set performance. We demonstrate the benefits and drawbacks of both methods in different problem settings and evaluate them on three standard benchmark datasets, miniImageNet, tieredImageNet, and Caltech-UCSD-Birds-200-2011, where they surpass the state-of-the-art methods in the few-shot multiclass open-set and few-shot one-class tasks. | ['Matthew Turk', 'Jedrzej Kozerawski'] | 2021-09-14 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 5.29371679e-01 -1.50110554e-02 -5.31064749e-01 -4.40358013e-01
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a0810d74-425f-49fa-a3b7-013eb823c670 | 190602392 | 1906.02392 | null | https://arxiv.org/abs/1906.02392v1 | https://arxiv.org/pdf/1906.02392v1.pdf | Generative Model-Based Ischemic Stroke Lesion Segmentation | CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and time of peak (Tmax) could also be computed from CTP data. However, CTP data or the perfusion parameters, are ambiguous to locate the infarct core or tissue at risk (penumbra), which is normally confirmed by the follow-up Diffusion Weighted Imaging (DWI) or perfusion diffusion mismatch. In this paper, we propose a novel generative modelbased segmentation framework composed of an extractor, a generator and a segmentor for ischemic stroke lesion segmentation. First, an extractor is used to directly extract the representative feature images from the CTP feature images. Second, a generator is used to generate the clinical relevant DWI images using the output from the extractor and perfusion parameters. Finally, the segmentor is used to precisely segment the ischemic stroke lesion using the generated DWI from the generator. Meanwhile, a novel pixel-region loss function, generalized dice combined with weighted cross entropy, is used to handle data unbalance problem which is commonly encountered in medical image segmentation. All networks are trained end-to-end from scratch using the 2018 Ischemic Stroke Lesion Segmentation Challenge (ISLES) dataset and our method won the first place in the 2018 ischemic stroke lesions segmentation challenge in the test stage. | ['Tao Song'] | 2019-06-06 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 6.56636953e-02 -3.07857066e-01 -1.85635000e-01 -3.99568886e-01
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4b2c9b53-b244-4295-82ef-87d468534bf4 | automatic-aortic-valve-pathology-detection | 2304.05885 | null | https://arxiv.org/abs/2304.05885v2 | https://arxiv.org/pdf/2304.05885v2.pdf | Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps | The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precession (bSSFP) scans (Cine MRI). We propose a 3D classification neural network (NN) to automatically identify aortic valve pathology (aortic regurgitation, aortic stenosis, mixed valve disease) from Cine MR images. We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice 3-chamber cine MRI from N = 576 patients. Our classification model accurately predicts the presence of aortic valve pathology (AVD) with an accuracy of 0.85 +/- 0.03 and can also correctly discriminate the type of AVD pathology (accuracy: 0.75 +/- 0.03). Gradient-weighted class activation mapping (Grad-CAM) confirms that the blood pool voxels close to the aortic root contribute the most to the classification. Our approach can be used to improve the diagnosis of AVD and optimise clinical CMR protocols for accurate and efficient AVD detection. | ['M. Varela', 'A. A. Bharath', 'G. Cole', 'N. Peters', 'N. Linton', 'J. Howard', 'S. Zaman', 'C. Galazis', 'K. Vimalesvaran', 'Y. On'] | 2023-04-12 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-1.61057862e-03 3.16825975e-03 -1.40863676e-02 -1.43026903e-01
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2036baf2-9b8a-4026-823d-f01075200994 | a-spatio-temporal-spot-forecasting-framework | 2003.13977 | null | https://arxiv.org/abs/2003.13977v2 | https://arxiv.org/pdf/2003.13977v2.pdf | A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction | Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while remaining easy to understand and interpret. Our proposal is based on an interpretable attention-based neural network in which several modules are combined in order to capture key spatio-temporal time series components. Through extensive experimentation, we show how the results of our approach are stable and better than those of other state-of-the-art alternatives. | ['José L. Aznarte', 'Rodrigo de Medrano'] | 2020-03-31 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-2.00361311e-01 -3.49719584e-01 -1.64476350e-01 -5.80804169e-01
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cf752237-0f4b-4e6c-9528-ae9159b3014c | controlvideo-adding-conditional-control-for | 2305.17098 | null | https://arxiv.org/abs/2305.17098v1 | https://arxiv.org/pdf/2305.17098v1.pdf | ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing | In this paper, we present ControlVideo, a novel method for text-driven video editing. Leveraging the capabilities of text-to-image diffusion models and ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency of videos that align with a given text while preserving the structure of the source video. This is achieved by incorporating additional conditions such as edge maps, fine-tuning the key-frame and temporal attention on the source video-text pair with carefully designed strategies. An in-depth exploration of ControlVideo's design is conducted to inform future research on one-shot tuning video diffusion models. Quantitatively, ControlVideo outperforms a range of competitive baselines in terms of faithfulness and consistency while still aligning with the textual prompt. Additionally, it delivers videos with high visual realism and fidelity w.r.t. the source content, demonstrating flexibility in utilizing controls containing varying degrees of source video information, and the potential for multiple control combinations. The project page is available at \href{https://ml.cs.tsinghua.edu.cn/controlvideo/}{https://ml.cs.tsinghua.edu.cn/controlvideo/}. | ['Jun Zhu', 'Chongxuan Li', 'Fan Bao', 'Rongzhen Wang', 'Min Zhao'] | 2023-05-26 | null | null | null | null | ['text-to-video-editing'] | ['computer-vision'] | [ 1.11776315e-01 -2.34391838e-01 -4.25044626e-01 -1.24979265e-01
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23641db4-c0b2-4a14-be58-742cab8aa5df | color-aware-deep-temporal-backdrop-duplex | 2306.02954 | null | https://arxiv.org/abs/2306.02954v1 | https://arxiv.org/pdf/2306.02954v1.pdf | Color-aware Deep Temporal Backdrop Duplex Matting System | Deep learning-based alpha matting showed tremendous improvements in recent years, yet, feature film production studios still rely on classical chroma keying including costly post-production steps. This perceived discrepancy can be explained by some missing links necessary for production which are currently not adequately addressed in the alpha matting community, in particular foreground color estimation or color spill compensation. We propose a neural network-based temporal multi-backdrop production system that combines beneficial features from chroma keying and alpha matting. Given two consecutive frames with different background colors, our one-encoder-dual-decoder network predicts foreground colors and alpha values using a patch-based overlap-blend approach. The system is able to handle imprecise backdrops, dynamic cameras, and dynamic foregrounds and has no restrictions on foreground colors. We compare our method to state-of-the-art algorithms using benchmark datasets and a video sequence captured by a demonstrator setup. We verify that a dual backdrop input is superior to the usually applied trimap-based approach. In addition, the proposed studio set is actor friendly, and produces high-quality, temporal consistent alpha and color estimations that include a superior color spill compensation. | ['Bodo Rosenhahn', 'Hendrik Hachmann'] | 2023-06-05 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.61699247e-01 -3.96476120e-01 1.71703085e-01 -1.49897024e-01
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108c8412-d7be-4771-b8b3-2c65a12bf53d | multispanqa-a-dataset-for-multi-span-question | null | null | https://aclanthology.org/2022.naacl-main.90 | https://aclanthology.org/2022.naacl-main.90.pdf | MultiSpanQA: A Dataset for Multi-Span Question Answering | Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage. Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common real life but are less studied. In this paper, we present MultiSpanQA, a new dataset that focuses on multi-span questions. Raw questions and contexts are extracted from the Natural Questions dataset. After multi-span re-annotation, MultiSpanQA consists of over a total of 6,000 multi-span questions in the basic version, and over 19,000 examples with unanswerable questions, and questions with single-, and multi-span answers in the expanded version. We introduce new metrics for the purposes of multi-span question answering evaluation, and establish several baselines using advanced models. Finally, we propose a new model which beats all baselines and achieves state-of-the-art on our dataset. | ['Timothy Baldwin', 'Maria Vasardani', 'Martin Tomko', 'Haonan Li'] | null | null | null | null | naacl-2022-7 | ['natural-questions'] | ['miscellaneous'] | [ 1.26724929e-01 2.58885533e-01 -2.83518154e-02 -5.63970268e-01
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985cef4a-371a-4b2c-8c79-fc97555be7ef | better-automatic-evaluation-of-open-domain | 1904.10635 | null | http://arxiv.org/abs/1904.10635v1 | http://arxiv.org/pdf/1904.10635v1.pdf | Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings | Despite advances in open-domain dialogue systems, automatic evaluation of
such systems is still a challenging problem. Traditional reference-based
metrics such as BLEU are ineffective because there could be many valid
responses for a given context that share no common words with reference
responses. A recent work proposed Referenced metric and Unreferenced metric
Blended Evaluation Routine (RUBER) to combine a learning-based metric, which
predicts relatedness between a generated response and a given query, with
reference-based metric; it showed high correlation with human judgments. In
this paper, we explore using contextualized word embeddings to compute more
accurate relatedness scores, thus better evaluation metrics. Experiments show
that our evaluation metrics outperform RUBER, which is trained on static
embeddings. | ['Johnny Tian-Zheng Wei', 'Nanyun Peng', 'Aram Galstyan', 'Sarik Ghazarian'] | 2019-04-24 | better-automatic-evaluation-of-open-domain-1 | https://aclanthology.org/W19-2310 | https://aclanthology.org/W19-2310.pdf | ws-2019-6 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.02651663e-01 -4.51800413e-02 4.27134410e-02 -6.29840374e-01
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1018698e-f9cd-42a0-a22f-d5cf271ca5b7 | dual-gated-fusion-with-prefix-tuning-for | 2306.11020 | null | https://arxiv.org/abs/2306.11020v1 | https://arxiv.org/pdf/2306.11020v1.pdf | Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction | Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but existing works cannot well model finer associations among different modalities, failing to capture the truly helpful visual information and thus limiting relation extraction performance. In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. We first propose a prompt-based autoregressive encoder, which builds the associations of intra-modal and inter-modal features related to the task, respectively by entity-oriented and object-oriented prefixes. To better integrate helpful visual information, we design a dual-gated fusion module to distinguish the importance of image/objects and further enrich text representations. In addition, a generative decoder is introduced with entity type restriction on relations, better filtering out candidates. Extensive experiments conducted on the benchmark dataset show that our approach achieves excellent performance compared to strong competitors, even in the few-shot situation. | ['JianXin Li', 'Shiyao Cui', 'Xutan Peng', 'Cheng Ji', 'Shu Guo', 'Qian Li'] | 2023-06-19 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 8.80910307e-02 -2.13983506e-02 -1.31614983e-01 -1.20789029e-01
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e0ed0b26-b334-4a94-8b1c-2293267fdeb6 | wider-closer-mixture-of-short-channel | 2212.03506 | null | https://arxiv.org/abs/2212.03506v1 | https://arxiv.org/pdf/2212.03506v1.pdf | WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition | Zero-shot cross-lingual named entity recognition (NER) aims at transferring knowledge from annotated and rich-resource data in source languages to unlabeled and lean-resource data in target languages. Existing mainstream methods based on the teacher-student distillation framework ignore the rich and complementary information lying in the intermediate layers of pre-trained language models, and domain-invariant information is easily lost during transfer. In this study, a mixture of short-channel distillers (MSD) method is proposed to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently. Concretely, a multi-channel distillation framework is designed for sufficient information transfer by aggregating multiple distillers as a mixture. Besides, an unsupervised method adopting parallel domain adaptation is proposed to shorten the channels between the teacher and student models to preserve domain-invariant features. Experiments on four datasets across nine languages demonstrate that the proposed method achieves new state-of-the-art performance on zero-shot cross-lingual NER and shows great generalization and compatibility across languages and fields. | ['Cong Liu', 'Zhigang Chen', 'Quan Liu', 'Wu Guo', 'Zhen-Hua Ling', 'Jia-Chen Gu', 'Beiduo Chen', 'Jun-Yu Ma'] | 2022-12-07 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.54119760e-01 -5.02692945e-02 -3.33035856e-01 -6.65885329e-01
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e2fc265b-3411-4066-8bfc-b53686ecfbf0 | accurate-open-set-recognition-for-memory | 2212.08817 | null | https://arxiv.org/abs/2212.08817v1 | https://arxiv.org/pdf/2212.08817v1.pdf | Accurate Open-set Recognition for Memory Workload | How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the process is open-set recognition which aims to detect new workloads not seen in the training phase. Despite its importance, however, existing open-set recognition methods are unsatisfactory in terms of accuracy since they fail to exploit the characteristics of workload sequences. In this paper, we propose Acorn, an accurate open-set recognition method capturing the characteristics of workload sequences. Acorn extracts two types of feature vectors to capture sequential patterns and spatial locality patterns in memory access. Acorn then uses the feature vectors to accurately classify a subsequence into one of the known classes or identify it as the unknown class. Experiments show that Acorn achieves state-of-the-art accuracy, giving up to 37% points higher unknown class detection accuracy while achieving comparable known class classification accuracy than existing methods. | ['U Kang', 'Suhyun Chae', 'Jongmin Park', 'Jeeyong Lee', 'Vladimir Egay', 'Sooyeon Shim', 'Jun-Gi Jang'] | 2022-12-17 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 7.49732740e-03 -7.46032715e-01 -7.32784629e-01 -1.47867680e-01
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5444abe6-b134-490d-9121-76f0ea202ce5 | mixture-of-prompt-experts-for-generalizable | 2305.14628 | null | https://arxiv.org/abs/2305.14628v1 | https://arxiv.org/pdf/2305.14628v1.pdf | Mixture of Prompt Experts for Generalizable and Interpretable Question Answering | One of the ultimate quests of question answering (QA) is to deploy a system that can answer any type of question from the users, and refrain from answering when it does not know the answer. While recent advancements in scaling large language models (LLMs) brought significant improvements on various QA datasets, it remains difficult for a single model to generalize across question types that require distinct reasoning abilities. In this paper, we first provide empirical evidence that state-of-the-art LLMs such as Codex suffer from poor generalizability on question types beyond those seen in the prompt. To address this, we propose a Mixture-of-Prompt-Experts (MOPE) system that ensembles multiple specialized LLMs. We first implement each specialized model based on the same backbone model (Codex) but with prompts optimized for different reasoning categories including factual, multihop, mathematical, and commonsense reasoning. By strategically selecting the best specialized model for each given question, our MOPE system significantly outperforms any single specialized model on a collection of 12 QA datasets from four reasoning types. Moreover, the attribution and agreement among specialized expert models offer greater interpretability, allowing for better selective question answering. Our human study further confirms that presenting the expert predictions and answer selection process helps annotators more accurately decide when to trust the system's output. We release all code and data to facilitate future work. | ['Jordan Boyd-Graber', 'Luke Zettlemoyer', 'Chen Zhao', 'Weijia Shi', 'Chenglei Si'] | 2023-05-24 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 5.92720089e-03 6.06703699e-01 8.47179815e-03 -4.97362763e-01
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d4ba8e9b-9ecb-4254-9f81-0aaf5c725ea1 | parformer-transformer-based-multi-task | 2304.07230 | null | https://arxiv.org/abs/2304.07230v1 | https://arxiv.org/pdf/2304.07230v1.pdf | PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition | Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis. Extracting robust feature representation is one of the key challenges in this task. The existing methods mainly use the convolutional neural network (CNN) as the backbone network to extract features. However, these methods mainly focus on small discriminative regions while ignoring the global perspective. To overcome these limitations, we propose a pure transformer-based multi-task PAR network named PARFormer, which includes four modules. In the feature extraction module, we build a transformer-based strong baseline for feature extraction, which achieves competitive results on several PAR benchmarks compared with the existing CNN-based baseline methods. In the feature processing module, we propose an effective data augmentation strategy named batch random mask (BRM) block to reinforce the attentive feature learning of random patches. Furthermore, we propose a multi-attribute center loss (MACL) to enhance the inter-attribute discriminability in the feature representations. In the viewpoint perception module, we explore the impact of viewpoints on pedestrian attributes, and propose a multi-view contrastive loss (MCVL) that enables the network to exploit the viewpoint information. In the attribute recognition module, we alleviate the negative-positive imbalance problem to generate the attribute predictions. The above modules interact and jointly learn a highly discriminative feature space, and supervise the generation of the final features. Extensive experimental results show that the proposed PARFormer network performs well compared to the state-of-the-art methods on several public datasets, including PETA, RAP, and PA100K. Code will be released at https://github.com/xwf199/PARFormer. | ['Hanzi Wang', 'Yang Lu', 'Yukang Zhang', 'Xinwen Fan'] | 2023-04-14 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-3.04189622e-02 -5.79015017e-01 -9.11136866e-02 -7.42458403e-01
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b1216be2-43bd-4ea5-8155-0e591359edaa | efficient-sampling-in-pomdps-with-lipschitz | 2106.04206 | null | https://arxiv.org/abs/2106.04206v1 | https://arxiv.org/pdf/2106.04206v1.pdf | Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces | Decision making under uncertainty can be framed as a partially observable Markov decision process (POMDP). Finding exact solutions of POMDPs is generally computationally intractable, but the solution can be approximated by sampling-based approaches. These sampling-based POMDP solvers rely on multi-armed bandit (MAB) heuristics, which assume the outcomes of different actions to be uncorrelated. In some applications, like motion planning in continuous spaces, similar actions yield similar outcomes. In this paper, we utilize variants of MAB heuristics that make Lipschitz continuity assumptions on the outcomes of actions to improve the efficiency of sampling-based planning approaches. We demonstrate the effectiveness of this approach in the context of motion planning for automated driving. | ['Martin Lauer', 'Felix Hauser', 'Ömer Şahin Taş'] | 2021-06-08 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.35949880e-01 5.46402276e-01 -7.07680166e-01 -2.92606831e-01
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34d49ac2-c14a-4ebe-90f3-97709a0b49ea | a-transfer-learning-based-approach-for | 2111.00976 | null | https://arxiv.org/abs/2111.00976v2 | https://arxiv.org/pdf/2111.00976v2.pdf | A transfer learning based approach for pronunciation scoring | Phone-level pronunciation scoring is a challenging task, with performance far from that of human annotators. Standard systems generate a score for each phone in a phrase using models trained for automatic speech recognition (ASR) with native data only. Better performance has been shown when using systems that are trained specifically for the task using non-native data. Yet, such systems face the challenge that datasets labelled for this task are scarce and usually small. In this paper, we present a transfer learning-based approach that leverages a model trained for ASR, adapting it for the task of pronunciation scoring. We analyze the effect of several design choices and compare the performance with a state-of-the-art goodness of pronunciation (GOP) system. Our final system is 20% better than the GOP system on EpaDB, a database for pronunciation scoring research, for a cost function that prioritizes low rates of unnecessary corrections. | ['Luciana Ferrer', 'Cyntia Bonomi', 'Jazmin Vidal', 'Marcelo Sancinetti'] | 2021-11-01 | null | null | null | null | ['phone-level-pronunciation-scoring'] | ['speech'] | [ 1.00030221e-01 1.08830921e-01 6.45942427e-03 -6.53342485e-01
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255de3cb-114d-461c-8320-c2a5d2baa4fa | mama-edha-at-semeval-2017-task-8-stance | null | null | https://aclanthology.org/S17-2084 | https://aclanthology.org/S17-2084.pdf | Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules | For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9{\%} on the competition{'}s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification. | ['Edward Tj{\\"o}rnhammar', "Marianela Garc{\\'\\i}a Lozano", 'Hanna Lilja', 'Maja Karasalo'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-1.47570670e-01 6.24314547e-01 -4.65597451e-01 -6.41117036e-01
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cc898207-3ec4-4d8b-afce-4aafb77d37d9 | identifying-source-speakers-for-voice | 2206.09103 | null | https://arxiv.org/abs/2206.09103v2 | https://arxiv.org/pdf/2206.09103v2.pdf | Identifying Source Speakers for Voice Conversion based Spoofing Attacks on Speaker Verification Systems | An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person's speech signal to make it sound like another speaker's voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofing attacks are designed to discriminate bona fide speech from spoofed speech for speaker verification systems. In this paper, we investigate the problem of source speaker identification -- inferring the identity of the source speaker given the voice converted speech. To perform source speaker identification, we simply add voice-converted speech data with the label of source speaker identity to the genuine speech dataset during speaker embedding network training. Experimental results show the feasibility of source speaker identification when training and testing with converted speeches from the same voice conversion model(s). In addition, our results demonstrate that having more converted utterances from various voice conversion model for training helps improve the source speaker identification performance on converted utterances from unseen voice conversion models. | ['Ming Li', 'Zexin Cai', 'Danwei Cai'] | 2022-06-18 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.52243245e-01 2.33044580e-01 -6.04710728e-02 -4.95552212e-01
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d9afe96e-5b88-488e-a8b4-fcd4a064c9c4 | pseudolikelihood-reranking-with-masked | 1910.14659 | null | https://arxiv.org/abs/1910.14659v3 | https://arxiv.org/pdf/1910.14659v3.pdf | Masked Language Model Scoring | Pretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one. We show that PLLs outperform scores from autoregressive language models like GPT-2 in a variety of tasks. By rescoring ASR and NMT hypotheses, RoBERTa reduces an end-to-end LibriSpeech model's WER by 30% relative and adds up to +1.7 BLEU on state-of-the-art baselines for low-resource translation pairs, with further gains from domain adaptation. We attribute this success to PLL's unsupervised expression of linguistic acceptability without a left-to-right bias, greatly improving on scores from GPT-2 (+10 points on island effects, NPI licensing in BLiMP). One can finetune MLMs to give scores without masking, enabling computation in a single inference pass. In all, PLLs and their associated pseudo-perplexities (PPPLs) enable plug-and-play use of the growing number of pretrained MLMs; e.g., we use a single cross-lingual model to rescore translations in multiple languages. We release our library for language model scoring at https://github.com/awslabs/mlm-scoring. | ['Toan Q. Nguyen', 'Katrin Kirchhoff', 'Julian Salazar', 'Davis Liang'] | 2019-10-31 | masked-language-model-scoring | https://aclanthology.org/2020.acl-main.240 | https://aclanthology.org/2020.acl-main.240.pdf | acl-2020-6 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.28576130e-01 1.86662972e-01 -4.92632031e-01 -5.53234816e-01
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37ac919d-0bd1-4767-bc16-63ae20a27305 | exploration-in-feature-space-for | 1710.02210 | null | http://arxiv.org/abs/1710.02210v1 | http://arxiv.org/pdf/1710.02210v1.pdf | Exploration in Feature Space for Reinforcement Learning | The infamous exploration-exploitation dilemma is one of the oldest and most
important problems in reinforcement learning (RL). Deliberate and effective
exploration is necessary for RL agents to succeed in most environments.
However, until very recently even very sophisticated RL algorithms employed
simple, undirected exploration strategies in large-scale RL tasks.
We introduce a new optimistic count-based exploration algorithm for RL that
is feasible in high-dimensional MDPs. The success of RL algorithms in these
domains depends crucially on generalization from limited training experience.
Function approximation techniques enable RL agents to generalize in order to
estimate the value of unvisited states, but at present few methods have
achieved generalization about the agent's uncertainty regarding unvisited
states. We present a new method for computing a generalized state visit-count,
which allows the agent to estimate the uncertainty associated with any state.
In contrast to existing exploration techniques, our
$\phi$-$\textit{pseudocount}$ achieves generalization by exploiting the feature
representation of the state space that is used for value function
approximation. States that have less frequently observed features are deemed
more uncertain. The resulting $\phi$-$\textit{Exploration-Bonus}$ algorithm
rewards the agent for exploring in feature space rather than in the original
state space. This method is simpler and less computationally expensive than
some previous proposals, and achieves near state-of-the-art results on
high-dimensional RL benchmarks. In particular, we report world-class results on
several notoriously difficult Atari 2600 video games, including Montezuma's
Revenge. | ['Suraj Narayanan Sasikumar'] | 2017-10-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.12998533e-01 2.08929121e-01 -6.14423573e-01 -7.48048052e-02
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c89a93fb-3dbb-43c4-8926-a2b7e4f55822 | boosting-semi-supervised-few-shot-object | 2303.05739 | null | https://arxiv.org/abs/2303.05739v2 | https://arxiv.org/pdf/2303.05739v2.pdf | Boosting Semi-Supervised Few-Shot Object Detection with SoftER Teacher | Few-shot object detection (FSOD) is an emerging problem aimed at detecting novel concepts from few exemplars. Existing approaches to FSOD assume abundant base labels to adapt to novel objects. This paper studies the task of semi-supervised FSOD by considering a realistic scenario in which both base and novel labels are simultaneously scarce. We explore the utility of unlabeled data and discover its remarkable ability to boost semi-supervised FSOD by way of region proposals. Motivated by this finding, we introduce SoftER Teacher, a robust detector combining pseudo-labeling with representation learning on region proposals, to harness unlabeled data for improved FSOD without relying on abundant labels. Extensive experiments show that SoftER Teacher surpasses the novel performance of a strong supervised detector using only 10% of required base labels, without experiencing catastrophic forgetting observed in prior approaches. Our work also sheds light on a potential relationship between semi-supervised and few-shot detection suggesting that a stronger semi-supervised detector leads to a more effective few-shot detector. The code and models are available at https://github.com/lexisnexis-risk-open-source/ledetection | ['Phi Vu Tran'] | 2023-03-10 | null | null | null | null | ['few-shot-object-detection', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 1.98552832e-01 3.98535371e-01 -3.58239979e-01 -3.51122290e-01
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864a41e3-8dbc-4db4-a2a0-bd877ea63894 | coordinated-multi-agent-pathfinding-for | 2110.08802 | null | https://arxiv.org/abs/2110.08802v2 | https://arxiv.org/pdf/2110.08802v2.pdf | Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks | We address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., truck travel distance and drone flight distance, compared to operating drones and trucks independently. But it comes at the potentially prohibitive computational cost of deciding which trucks and drones should coordinate and when and where it is most beneficial to do so. We tackle this fundamental trade-off by decoupling our overall intractable problem into tractable sub-problems that we solve stage-wise. The first stage solves only for trucks, by computing paths that make them more likely to be useful transit options for drones. The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage. We design a comprehensive algorithmic framework that frames each stage as a multi-agent path-finding problem and implement two distinct methods for solving them. We evaluate our approach on extensive simulations with up to $100$ agents on the real-world Manhattan road network containing nearly $4500$ vertices and $10000$ edges. Our framework saves on more than $50\%$ of vehicle distance traveled compared to independently solving for trucks and drones, and computes solutions for all settings within $5$ minutes on commodity hardware. | ['Marco Pavone', 'Mykel Kochenderfer', 'Kiril Solovey', 'Shushman Choudhury'] | 2021-10-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.43025705e-01 2.57249951e-01 -3.13478589e-01 5.09854406e-03
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69706b87-af74-4c04-81d8-42a6c5a628e8 | polibertweet-a-pre-trained-language-model-for | null | null | https://aclanthology.org/2022.lrec-1.801 | https://aclanthology.org/2022.lrec-1.801.pdf | PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter | Transformer-based models have become the state-of-the-art for numerous natural language processing (NLP) tasks, especially for noisy data sets, including social media posts. For example, BERTweet, pre-trained RoBERTa on a large amount of Twitter data, has achieved state-of-the-art results on several Twitter NLP tasks. We argue that it is not only important to have general pre-trained models for a social media platform, but also domain-specific ones that better capture domain-specific language context. Domain-specific resources are not only important for NLP tasks associated with a specific domain, but they are also useful for understanding language differences across domains. One domain that receives a large amount of attention is politics, more specifically political elections. Towards that end, we release PoliBERTweet, a pre-trained language model trained from BERTweet on over 83M US 2020 election-related English tweets. While the construction of the resource is fairly straightforward, we believe that it can be used for many important downstream tasks involving language, including political misinformation analysis and election public opinion analysis. To show the value of this resource, we evaluate PoliBERTweet on different NLP tasks. The results show that our model outperforms general-purpose language models in domain-specific contexts, highlighting the value of domain-specific models for more detailed linguistic analysis. We also extend other existing language models with a sample of these data and show their value for presidential candidate stance detection, a context-specific task. We release PoliBERTweet and these other models to the community to advance interdisciplinary research related to Election 2020. | ['Lisa Singh', 'Kornraphop Kawintiranon'] | null | null | null | null | lrec-2022-6 | ['stance-detection'] | ['natural-language-processing'] | [-1.37585491e-01 2.07722455e-01 -6.39070690e-01 -7.31531262e-01
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3c0f4f39-90e6-4461-928d-0020c49d6fc1 | compilation-based-solvers-for-multi-agent | 2104.11809 | null | https://arxiv.org/abs/2104.11809v1 | https://arxiv.org/pdf/2104.11809v1.pdf | Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities | Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents can navigate from their starting positions to specified individual goal positions. The combination of two additional requirements makes the problem computationally challenging: (i) agents must not collide with each other and (ii) the paths must be optimal with respect to some objective. Two major approaches to optimal MAPF solving include (1) dedicated search-based methods, which solve MAPF directly, and (2) compilation-based methods that reduce a MAPF instance to an instance in a different well established formalism, for which an efficient solver exists. The compilation-based MAPF solving can benefit from advancements accumulated during the development of the target solver often decades long. We summarize and compare contemporary compilation-based solvers for MAPF using formalisms like ASP, MIP, and SAT. We show the lessons learned from past developments and current trends in the topic and discuss its wider impact. | ['Pavel Surynek'] | 2021-04-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.74824247e-02 3.43915820e-01 -6.54623657e-02 -3.23881775e-01
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ba036587-4668-4c07-946a-6a2b20ad098a | a-polynomial-time-iterative-algorithm-for | 2212.13677 | null | https://arxiv.org/abs/2212.13677v1 | https://arxiv.org/pdf/2212.13677v1.pdf | A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation | Motivated by the problem of matching vertices in two correlated Erd\H{o}s-R\'enyi graphs, we study the problem of matching two correlated Gaussian Wigner matrices. We propose an iterative matching algorithm, which succeeds in polynomial time as long as the correlation between the two Gaussian matrices does not vanish. Our result is the first polynomial time algorithm that solves a graph matching type of problem when the correlation is an arbitrarily small constant. | ['Zhangsong Li', 'Jian Ding'] | 2022-12-28 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.66279483e-01 3.30187649e-01 -5.26966453e-02 1.26269341e-01
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c0bae23a-ecf1-4bf3-be7b-eb035aafdff4 | robustscanner-dynamically-enhancing | 2007.07542 | null | https://arxiv.org/abs/2007.07542v2 | https://arxiv.org/pdf/2007.07542v2.pdf | RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition | The attention-based encoder-decoder framework has recently achieved impressive results for scene text recognition, and many variants have emerged with improvements in recognition quality. However, it performs poorly on contextless texts (e.g., random character sequences) which is unacceptable in most of real application scenarios. In this paper, we first deeply investigate the decoding process of the decoder. We empirically find that a representative character-level sequence decoder utilizes not only context information but also positional information. Contextual information, which the existing approaches heavily rely on, causes the problem of attention drift. To suppress such side-effect, we propose a novel position enhancement branch, and dynamically fuse its outputs with those of the decoder attention module for scene text recognition. Specifically, it contains a position aware module to enable the encoder to output feature vectors encoding their own spatial positions, and an attention module to estimate glimpses using the positional clue (i.e., the current decoding time step) only. The dynamic fusion is conducted for more robust feature via an element-wise gate mechanism. Theoretically, our proposed method, dubbed \emph{RobustScanner}, decodes individual characters with dynamic ratio between context and positional clues, and utilizes more positional ones when the decoding sequences with scarce context, and thus is robust and practical. Empirically, it has achieved new state-of-the-art results on popular regular and irregular text recognition benchmarks while without much performance drop on contextless benchmarks, validating its robustness in both contextual and contextless application scenarios. | ['Hongbin Sun', 'Chenhao Lin', 'Wayne Zhang', 'Zhanghui Kuang', 'Xiaoyu Yue'] | 2020-07-15 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3160_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640137.pdf | eccv-2020-8 | ['irregular-text-recognition'] | ['computer-vision'] | [ 6.88252330e-01 -6.06118381e-01 -9.53188986e-02 -1.73541948e-01
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bcf96a90-8aeb-4a24-9211-2f7b44e21335 | hiding-your-signals-a-security-analysis-of | 2207.04434 | null | https://arxiv.org/abs/2207.04434v1 | https://arxiv.org/pdf/2207.04434v1.pdf | Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication | Recently, physiological signal-based biometric systems have received wide attention. Unlike traditional biometric features, physiological signals can not be easily compromised (usually unobservable to human eyes). Photoplethysmography (PPG) signal is easy to measure, making it more attractive than many other physiological signals for biometric authentication. However, with the advent of remote PPG (rPPG), unobservability has been challenged when the attacker can remotely steal the rPPG signals by monitoring the victim's face, subsequently posing a threat to PPG-based biometrics. In PPG-based biometric authentication, current attack approaches mandate the victim's PPG signal, making rPPG-based attacks neglected. In this paper, we firstly analyze the security of PPG-based biometrics, including user authentication and communication protocols. We evaluate the signal waveforms, heart rate and inter-pulse-interval information extracted by five rPPG methods, including four traditional optical computing methods (CHROM, POS, LGI, PCA) and one deep learning method (CL_rPPG). We conducted experiments on five datasets (PURE, UBFC_rPPG, UBFC_Phys, LGI_PPGI, and COHFACE) to collect a comprehensive set of results. Our empirical studies show that rPPG poses a serious threat to the authentication system. The success rate of the rPPG signal spoofing attack in the user authentication system reached 0.35. The bit hit rate is 0.6 in inter-pulse-interval-based security protocols. Further, we propose an active defence strategy to hide the physiological signals of the face to resist the attack. It reduces the success rate of rPPG spoofing attacks in user authentication to 0.05. The bit hit rate was reduced to 0.5, which is at the level of a random guess. Our strategy effectively prevents the exposure of PPG signals to protect users' sensitive physiological data. | ['Yang Xiang', 'Jun Zhang', 'Yonghang Tai', 'Lei Pan', 'Chao Chen', 'Lin Li'] | 2022-07-10 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 2.25856230e-01 -1.92619652e-01 2.43976146e-01 8.39749128e-02
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5cc3376d-8aca-4ee4-9b0e-09a59f3adef9 | ct-sgan-computed-tomography-synthesis-gan | 2110.09288 | null | https://arxiv.org/abs/2110.09288v2 | https://arxiv.org/pdf/2110.09288v2.pdf | CT-SGAN: Computed Tomography Synthesis GAN | Diversity in data is critical for the successful training of deep learning models. Leveraged by a recurrent generative adversarial network, we propose the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes ($\geq 224\times224\times224$) when trained on a small dataset of chest CT-scans. CT-SGAN offers an attractive solution to two major challenges facing machine learning in medical imaging: a small number of given i.i.d. training data, and the restrictions around the sharing of patient data preventing to rapidly obtain larger and more diverse datasets. We evaluate the fidelity of the generated images qualitatively and quantitatively using various metrics including Fr\'echet Inception Distance and Inception Score. We further show that CT-SGAN can significantly improve lung nodule detection accuracy by pre-training a classifier on a vast amount of synthetic data. | ['Mohammad Havaei', 'Yiping Wang', 'Ahmad Pesaranghader'] | 2021-10-14 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.47070754e-01 6.27215922e-01 -2.07600300e-03 -2.76336908e-01
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34598a57-3dfa-4c56-8e0b-470b1b0c5b67 | a-transformer-based-approach-for-source-code | 2005.00653 | null | https://arxiv.org/abs/2005.00653v1 | https://arxiv.org/pdf/2005.00653v1.pdf | A Transformer-based Approach for Source Code Summarization | Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens' position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available to facilitate future research. | ['Kai-Wei Chang', 'Baishakhi Ray', 'Wasi Uddin Ahmad', 'Saikat Chakraborty'] | 2020-05-01 | a-transformer-based-approach-for-source-code-1 | https://aclanthology.org/2020.acl-main.449 | https://aclanthology.org/2020.acl-main.449.pdf | acl-2020-6 | ['code-summarization'] | ['computer-code'] | [ 4.23002481e-01 4.39598888e-01 -5.76488137e-01 -3.51796627e-01
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00f3adbf-d24f-40e9-b230-ea25e13dee5c | pu-mfa-point-cloud-up-sampling-via-multi | 2208.10968 | null | https://arxiv.org/abs/2208.10968v1 | https://arxiv.org/pdf/2208.10968v1.pdf | PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention | Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with the high cost of obtaining high-quality point clouds. Therefore, with the recent remarkable development of deep learning, point cloud up-sampling research, which uses deep learning to generate high-quality point clouds from low-quality point clouds, is one of the fields attracting considerable attention. This paper proposes a new point cloud up-sampling method called Point cloud Up-sampling via Multi-scale Features Attention (PU-MFA). Inspired by previous studies that reported good performance using the multi-scale features or attention mechanisms, PU-MFA merges the two through a U-Net structure. In addition, PU-MFA adaptively uses multi-scale features to refine the global features effectively. The performance of PU-MFA was compared with other state-of-the-art methods through various experiments using the PU-GAN dataset, which is a synthetic point cloud dataset, and the KITTI dataset, which is the real-scanned point cloud dataset. In various experimental results, PU-MFA showed superior performance in quantitative and qualitative evaluation compared to other state-of-the-art methods, proving the effectiveness of the proposed method. The attention map of PU-MFA was also visualized to show the effect of multi-scale features. | ['Sejoon Lim', 'Hyungjun Lee'] | 2022-08-22 | null | null | null | null | ['point-cloud-reconstruction', 'point-cloud-super-resolution'] | ['computer-vision', 'computer-vision'] | [-2.01738775e-01 -4.83986855e-01 1.07776858e-01 -2.11356029e-01
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15120d5d-4e8f-48c7-80e9-a11212624d93 | behaviour-diverse-automatic-penetration | 2202.10630 | null | https://arxiv.org/abs/2202.10630v1 | https://arxiv.org/pdf/2202.10630v1.pdf | Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach | Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of penetration tests by reducing human effort and improving reliability. Existing RL solutions focus on finding a specific attack path to impact the target hosts. However, in reality, a diverse range of attack variations are needed to provide comprehensive assessments of the target network's security level. Hence, the attack agents must consider multiple objectives when penetrating the network. Nevertheless, this challenge is not adequately addressed in the existing literature. To this end, we formulate the automatic penetration testing in the Multi-Objective Reinforcement Learning (MORL) framework and propose a Chebyshev decomposition critic to find diverse adversary strategies that balance different objectives in the penetration test. Additionally, the number of available actions increases with the agent consistently probing the target network, making the training process intractable in many practical situations. Thus, we introduce a coverage-based masking mechanism that reduces attention on previously selected actions to help the agent adapt to future exploration. Experimental evaluation on a range of scenarios demonstrates the superiority of our proposed approach when compared to adapted algorithms in terms of multi-objective learning and performance efficiency. | ['Xin Liu', 'Yizhou Yang'] | 2022-02-22 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.01701775e-01 -3.64063382e-01 -2.64429241e-01 1.97817296e-01
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40a250cf-a5a8-4669-8202-d85d13e10998 | refind-relation-extraction-financial-dataset | 2305.18322 | null | https://arxiv.org/abs/2305.18322v1 | https://arxiv.org/pdf/2305.18322v1.pdf | REFinD: Relation Extraction Financial Dataset | A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with $\sim$29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We also provide an empirical evaluation with various state-of-the-art models as benchmarks for the RE task and highlight the challenges posed by our dataset. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity. | ['Sameena Shah', 'Toyin Aguda', 'Suchetha Siddagangappa', 'Dongsheng Wang', 'Joy Sain', 'Akshat Gupta', 'Charese Smiley', 'Simerjot Kaur'] | 2023-05-22 | null | null | null | null | ['general-knowledge', 'relation-extraction', 'information-retrieval'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-3.33089679e-01 6.18214846e-01 -5.23211479e-01 -4.36791480e-01
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eb85a1f7-185f-4441-99b4-175a7e41327a | cross-domain-few-shot-classification-via | 2104.14385 | null | https://arxiv.org/abs/2104.14385v2 | https://arxiv.org/pdf/2104.14385v2.pdf | Cross-Domain Few-Shot Classification via Adversarial Task Augmentation | Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classification elaborately design various task-shared inductive bias (meta-knowledge) to solve such tasks, and achieve impressive performance. However, when there exists the domain shift between the training tasks and the test tasks, the obtained inductive bias fails to generalize across domains, which degrades the performance of the meta-learning models. In this work, we aim to improve the robustness of the inductive bias through task augmentation. Concretely, we consider the worst-case problem around the source task distribution, and propose the adversarial task augmentation method which can generate the inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple plug-and-play module for various meta-learning models, and improve their cross-domain generalization capability. We conduct extensive experiments under the cross-domain setting, using nine few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and ChestX. Experimental results show that our method can effectively improve the few-shot classification performance of the meta-learning models under domain shift, and outperforms the existing works. Our code is available at https://github.com/Haoqing-Wang/CDFSL-ATA. | ['Zhi-Hong Deng', 'Haoqing Wang'] | 2021-04-29 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.26721212e-01 -1.89894915e-01 -3.75797212e-01 -3.11742336e-01
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2d36748b-7d42-4aa4-a65d-b485e0a36492 | density-map-guided-object-detection-in-aerial | 2004.05520 | null | https://arxiv.org/abs/2004.05520v1 | https://arxiv.org/pdf/2004.05520v1.pdf | Density Map Guided Object Detection in Aerial Images | Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects. A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop. In this paper, we investigate the image cropping strategy to address these challenges. Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map. As pixel intensity varies, it is able to tell whether a region has objects or not, which in turn provides guidance for cropping images statistically. DMNet has three key components: a density map generation module, an image cropping module and an object detector. DMNet generates a density map and learns scale information based on density intensities to form cropping regions. Extensive experiments show that DMNet achieves state-of-the-art performance on two popular aerial image datasets, i.e. VisionDrone and UAVDT. | ['Taojiannan Yang', 'Shanyue Guan', 'Chen Chen', 'Changlin Li', 'Sijie Zhu'] | 2020-04-12 | null | null | null | null | ['rgb-d-salient-object-detection', 'object-detection-in-aerial-images', 'image-cropping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.01091731e-01 -3.35937023e-01 4.50730920e-02 -3.65010649e-02
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2b45521b-f008-4750-93c0-bb361811e13e | towards-realistic-few-shot-relation | null | null | https://aclanthology.org/2021.emnlp-main.433 | https://aclanthology.org/2021.emnlp-main.433.pdf | Towards Realistic Few-Shot Relation Extraction | In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification is solved. In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types. | ['Adrian Benton', 'Sichao Wu', 'Sam Brody'] | null | null | null | null | emnlp-2021-11 | ['few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 9.85504910e-02 6.96083307e-01 -6.83736205e-01 -2.62025952e-01
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96a65992-9e49-431e-b868-2efa8af53a08 | sgd-with-adagrad-stepsizes-full-adaptivity | 2302.08783 | null | https://arxiv.org/abs/2302.08783v2 | https://arxiv.org/pdf/2302.08783v2.pdf | SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance | We study Stochastic Gradient Descent with AdaGrad stepsizes: a popular adaptive (self-tuning) method for first-order stochastic optimization. Despite being well studied, existing analyses of this method suffer from various shortcomings: they either assume some knowledge of the problem parameters, impose strong global Lipschitz conditions, or fail to give bounds that hold with high probability. We provide a comprehensive analysis of this basic method without any of these limitations, in both the convex and non-convex (smooth) cases, that additionally supports a general ``affine variance'' noise model and provides sharp rates of convergence in both the low-noise and high-noise~regimes. | ['Tomer Koren', 'Amit Attia'] | 2023-02-17 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-4.00381237e-02 -1.87328950e-01 -1.41051307e-01 -3.15956533e-01
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09396964-ac6e-4661-ae2e-96c78cdc1e88 | telescoping-density-ratio-estimation | 2006.12204 | null | https://arxiv.org/abs/2006.12204v2 | https://arxiv.org/pdf/2006.12204v2.pdf | Telescoping Density-Ratio Estimation | Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and generative modelling, with the number of use-cases continuing to proliferate. However, it suffers from a critical limitation: it fails to accurately estimate ratios p/q for which the two densities differ significantly. Empirically, we find this occurs whenever the KL divergence between p and q exceeds tens of nats. To resolve this limitation, we introduce a new framework, telescoping density-ratio estimation (TRE), that enables the estimation of ratios between highly dissimilar densities in high-dimensional spaces. Our experiments demonstrate that TRE can yield substantial improvements over existing single-ratio methods for mutual information estimation, representation learning and energy-based modelling. | ['Michael U. Gutmann', 'Benjamin Rhodes', 'Kai Xu'] | 2020-06-22 | null | http://proceedings.neurips.cc/paper/2020/hash/33d3b157ddc0896addfb22fa2a519097-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/33d3b157ddc0896addfb22fa2a519097-Paper.pdf | neurips-2020-12 | ['density-ratio-estimation', 'mutual-information-estimation'] | ['methodology', 'methodology'] | [ 3.00050288e-01 -2.82966435e-01 -3.66947353e-01 -2.46354938e-01
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15541347-a785-4437-96d8-59364c5cc40b | scale-prior-deformable-convolution-for | null | null | https://bmvc2022.mpi-inf.mpg.de/313/ | https://bmvc2022.mpi-inf.mpg.de/0313.pdf | Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting | Class-agnostic counting has recently emerged as a more practical counting task, which aims to predict the number and distribution of any exemplar objects, instead of counting specific categories like pedestrians or cars. However, recent methods are developed by designing suitable similarity matching rules between exemplars and query images, but ignoring the robustness of extracted features. To address this issue, we propose a scale-prior deformable convolution by integrating exemplars' information, \eg, scale, into the counting network backbone. As a result, the proposed counting network can extract semantic features of objects similar to the given exemplars and effectively filter irrelevant backgrounds. Besides, we find that traditional L2 and generalized loss are not suitable for class-agnostic counting due to the variety of object scales in different samples. Here we propose a scale-sensitive generalized loss to tackle this problem. It can adjust the cost function formulation according to the given exemplars, making the difference between prediction and ground truth more prominent. Extensive experiments show that our model obtains remarkable improvement and achieves state-of-the-art performance on a public class-agnostic counting benchmark. the source code is available at https://github.com/Elin24/SPDCN-CAC. | ['Antoni B. Chan', 'Shuai Yi', 'Jun Hou', 'Shinan Liu', 'Lingbo Liu', 'Junyu Gao', 'Xinzhu Ma', 'Kunlin Yang', 'Wei Lin'] | 2022-12-21 | null | null | null | conference-2022-12 | ['object-counting'] | ['computer-vision'] | [-9.62845609e-02 -5.63762844e-01 5.28111123e-02 -6.80817604e-01
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ba763190-7d6e-46b4-a54b-5bcca2b91119 | sentence-encoding-for-dialogue-act | null | null | https://www.cambridge.org/core/journals/natural-language-engineering/article/sentence-encoding-for-dialogue-act-classification/2EF3DC8E57D1019960D18FDE685B1EBA | https://www.cambridge.org/core/journals/natural-language-engineering/article/sentence-encoding-for-dialogue-act-classification/2EF3DC8E57D1019960D18FDE685B1EBA | Sentence encoding for Dialogue Act classification | In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or underreported within the literature, for example, the number of words to keep in the vocabulary or input sequences. We assess each of these with respect to two DA-labelled corpora, using a range of supervised models, which represent those most frequently applied to the task. Additionally, we compare context-free word embedding models with that of transfer learning via pre-trained language models, including several based on the transformer architecture, such as Bidirectional Encoder Representations from Transformers (BERT) and XLNET, which have thus far not been widely explored for the DA classification task. Our findings indicate that these text pre-processing considerations do have a statistically significant effect on classification accuracy. Notably, we found that viable input sequence lengths, and vocabulary sizes, can be much smaller than is typically used in DA classification experiments, yielding no significant improvements beyond certain thresholds. We also show that in some cases the contextual sentence representations generated by language models do not reliably outperform supervised methods. Though BERT, and its derivative models, do represent a significant improvement over supervised approaches, and much of the previous work on DA classification. | ['Jim Smith', 'Steve Battle', 'Nathan Duran'] | 2021-11-02 | null | null | null | natural-language-engineering-2021-11 | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.55414140e-01 3.32002252e-01 -1.47036329e-01 -4.59593356e-01
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cf40a9b7-5bce-4e51-94ab-4ee5651f3afd | sleepnet-automated-sleep-staging-system-via | 1707.08262 | null | http://arxiv.org/abs/1707.08262v1 | http://arxiv.org/pdf/1707.08262v1.pdf | SLEEPNET: Automated Sleep Staging System via Deep Learning | Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect
50-70 million adults in the United States (Hillman et al., 2006). Overnight
polysomnography (PSG), including brain monitoring using electroencephalography
(EEG), is a central component of the diagnostic evaluation for sleep disorders.
While PSG is conventionally performed by trained technologists, the recent rise
of powerful neural network learning algorithms combined with large
physiological datasets offers the possibility of automation, potentially making
expert-level sleep analysis more widely available. We propose SLEEPNET (Sleep
EEG neural network), a deployed annotation tool for sleep staging. SLEEPNET
uses a deep recurrent neural network trained on the largest sleep physiology
database assembled to date, consisting of PSGs from over 10,000 patients from
the Massachusetts General Hospital (MGH) Sleep Laboratory. SLEEPNET achieves
human-level annotation performance on an independent test set of 1,000 EEGs,
with an average accuracy of 85.76% and algorithm-expert inter-rater agreement
(IRA) of kappa = 79.46%, comparable to expert-expert IRA. | ['M. Brandon Westover', 'Matt T. Bianchi', 'Joshua Kulas', 'Balaji Goparaju', 'Haoqi Sun', 'Siddharth Biswal', 'Jimeng Sun'] | 2017-07-26 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-7.88825825e-02 -1.18933260e-01 -7.66149983e-02 -4.81826782e-01
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70386c09-3106-46fe-992d-c20f1fca56c9 | spherical-regression-learning-viewpoints | 1904.05404 | null | http://arxiv.org/abs/1904.05404v1 | http://arxiv.org/pdf/1904.05404v1.pdf | Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres | Many computer vision challenges require continuous outputs, but tend to be
solved by discrete classification. The reason is classification's natural
containment within a probability $n$-simplex, as defined by the popular softmax
activation function. Regular regression lacks such a closed geometry, leading
to unstable training and convergence to suboptimal local minima. Starting from
this insight we revisit regression in convolutional neural networks. We observe
many continuous output problems in computer vision are naturally contained in
closed geometrical manifolds, like the Euler angles in viewpoint estimation or
the normals in surface normal estimation. A natural framework for posing such
continuous output problems are $n$-spheres, which are naturally closed
geometric manifolds defined in the $\mathbb{R}^{(n+1)}$ space. By introducing a
spherical exponential mapping on $n$-spheres at the regression output, we
obtain well-behaved gradients, leading to stable training. We show how our
spherical regression can be utilized for several computer vision challenges,
specifically viewpoint estimation, surface normal estimation and 3D rotation
estimation. For all these problems our experiments demonstrate the benefit of
spherical regression. All paper resources are available at
https://github.com/leoshine/Spherical_Regression. | ['Shuai Liao', 'Efstratios Gavves', 'Cees G. M. Snoek'] | 2019-04-10 | spherical-regression-learning-viewpoints-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liao_Spherical_Regression_Learning_Viewpoints_Surface_Normals_and_3D_Rotations_on_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liao_Spherical_Regression_Learning_Viewpoints_Surface_Normals_and_3D_Rotations_on_CVPR_2019_paper.pdf | cvpr-2019-6 | ['surface-normals-estimation', '3d-rotation-estimation', 'viewpoint-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.46002942e-02 3.28592032e-01 3.07871327e-02 -4.37409520e-01
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126def7a-c96f-439c-a710-72226dbe128d | multi-domain-aspect-extraction-using-support | null | null | https://aclanthology.org/O17-1029 | https://aclanthology.org/O17-1029.pdf | Multi-Domain Aspect Extraction Using Support Vector Machines | null | ['Yasas Senarath', 'Surangika Ranathunga', 'Nadheesh Jihan', 'Dulanjaya Tennekoon', 'Mithila Wickramarathne'] | 2017-11-01 | multi-domain-aspect-extraction-using-support-1 | https://aclanthology.org/O17-1029 | https://aclanthology.org/O17-1029.pdf | roclingijclclp-2017-11 | ['aspect-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ce918a97-9992-44d5-92f9-6cbb0f3c0437 | gmm-based-synthetic-samples-for | 1712.04778 | null | http://arxiv.org/abs/1712.04778v1 | http://arxiv.org/pdf/1712.04778v1.pdf | GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data | The amount of training data that is required to train a classifier scales
with the dimensionality of the feature data. In hyperspectral remote sensing,
feature data can potentially become very high dimensional. However, the amount
of training data is oftentimes limited. Thus, one of the core challenges in
hyperspectral remote sensing is how to perform multi-class classification using
only relatively few training data points.
In this work, we address this issue by enriching the feature matrix with
synthetically generated sample points. This synthetic data is sampled from a
GMM fitted to each class of the limited training data. Although, the true
distribution of features may not be perfectly modeled by the fitted GMM, we
demonstrate that a moderate augmentation by these synthetic samples can
effectively replace a part of the missing training samples. We show the
efficacy of the proposed approach on two hyperspectral datasets. The median
gain in classification performance is $5\%$. It is also encouraging that this
performance gain is remarkably stable for large variations in the number of
added samples, which makes it much easier to apply this method to real-world
applications. | ['Christian Riess', 'AmirAbbas Davari', 'Erchan Aptoula', 'Berrin Yanikoglu', 'Andreas Maier'] | 2017-12-13 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 6.00679398e-01 -2.58106053e-01 7.67182782e-02 -4.53780562e-01
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9c03a4f5-aeec-4242-a25f-955783b84d1c | a-comprehensive-survey-on-source-free-domain | 2302.11803 | null | https://arxiv.org/abs/2302.11803v1 | https://arxiv.org/pdf/2302.11803v1.pdf | A Comprehensive Survey on Source-free Domain Adaptation | Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the source domain. Conventional domain adaptation methods often assume access to both source and target domain data simultaneously, which may not be feasible in real-world scenarios due to privacy and confidentiality concerns. As a result, the research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years, which only utilizes the source-trained model and unlabeled target data to adapt to the target domain. Despite the rapid explosion of SFDA work, yet there has no timely and comprehensive survey in the field. To fill this gap, we provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme based on the framework of transfer learning. Instead of presenting each approach independently, we modularize several components of each method to more clearly illustrate their relationships and mechanics in light of the composite properties of each method. Furthermore, we compare the results of more than 30 representative SFDA methods on three popular classification benchmarks, namely Office-31, Office-home, and VisDA, to explore the effectiveness of various technical routes and the combination effects among them. Additionally, we briefly introduce the applications of SFDA and related fields. Drawing from our analysis of the challenges facing SFDA, we offer some insights into future research directions and potential settings. | ['Heng Tao Shen', 'Lei Zhu', 'Zhekai Du', 'Jingjing Li', 'Zhiqi Yu'] | 2023-02-23 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-2.22492311e-03 -3.52426618e-01 -7.06009984e-01 -5.04683316e-01
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77671719-c911-48f8-8103-3202fdd1ab04 | metaportrait-identity-preserving-talking-head | 2212.08062 | null | https://arxiv.org/abs/2212.08062v3 | https://arxiv.org/pdf/2212.08062v3.pdf | MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation | In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait. Although the proposed model surpasses prior generation fidelity on established benchmarks, to further make the talking head generation qualified for real usage, personalized fine-tuning is usually needed. However, this process is rather computationally demanding that is unaffordable to standard users. To solve this, we propose a fast adaptation model using a meta-learning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not the least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings. | ['Fang Wen', 'Yong Wang', 'Qifeng Chen', 'Dong Chen', 'HsiangTao Wu', 'Bo Zhang', 'Pan Zhang', 'Chenyang Qi', 'BoWen Zhang'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-head-generation', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 9.06196758e-02 1.27632126e-01 -4.04702984e-02 -3.72047901e-01
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072f720e-574e-4b77-85c7-1c1f8b7f09ba | symmetric-saliency-based-adversarial-attack | 2210.16777 | null | https://arxiv.org/abs/2210.16777v1 | https://arxiv.org/pdf/2210.16777v1.pdf | Symmetric Saliency-based Adversarial Attack To Speaker Identification | Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this paper, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. It contains two novel components. First, it uses a novel saliency map decoder to learn the importance of speech samples to the decision of a targeted speaker identification system, so as to make the attacker focus on generating artificial noise to the important samples. It also proposes an angular loss function to push the speaker embedding far away from the source speaker. Our experimental results demonstrate that the proposed SSED yields the state-of-the-art performance, i.e. over 97% targeted attack success rate and a signal-to-noise level of over 39 dB on both the open-set and close-set speaker identification tasks, with a low computational cost. | ['Kunde Yang', 'Wei-Qiang Zhang', 'Xiao-Lei Zhang', 'Xing Chen', 'Jiadi Yao'] | 2022-10-30 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.46222460e-01 2.57898867e-01 2.20818877e-01 -3.06290984e-01
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b74fffc9-2f86-48a2-b724-3fc97b5332a9 | noisy-neural-network-compression-for-analog | null | null | https://openreview.net/forum?id=APvrboUZS7w | https://openreview.net/pdf?id=APvrboUZS7w | Noisy Neural Network Compression for Analog Storage Devices | Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications. Although several methods for NN compression have been developed, there has been considerably less work in the efficient storage of NN weights. While analog storage devices are promising alternatives to digital systems, the fact that they are noisy presents challenges for model compression as slight perturbations of the weights may significantly compromise the network’s overall performance. In this work, we study an analog NVM array fabricated in hardware (Phase Change Memory (PCM)) and develop a variety of robust coding strategies for NN weights that work well in practice. We demonstrate the efficacy of our approach on MNIST and CIFAR-10 datasets for pruning and knowledge distillation. | ['Armin Alaghi', 'Tsachy Weissman', 'Stefano Ermon', 'H.-S. Philip Wong', 'Xin Zheng', 'Kristy Choi', 'Berivan Isik'] | 2020-10-19 | null | null | null | neurips-workshop-dl-ig-2020-12 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 7.01804578e-01 -1.67429283e-01 -1.74699739e-01 -3.76146048e-01
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844ddd49-0710-4b6a-b78c-782a5fa87013 | cnnpred-cnn-based-stock-market-prediction | 1810.08923 | null | http://arxiv.org/abs/1810.08923v1 | http://arxiv.org/pdf/1810.08923v1.pdf | CNNPred: CNN-based stock market prediction using several data sources | Feature extraction from financial data is one of the most important problems
in market prediction domain for which many approaches have been suggested.
Among other modern tools, convolutional neural networks (CNN) have recently
been applied for automatic feature selection and market prediction. However, in
experiments reported so far, less attention has been paid to the correlation
among different markets as a possible source of information for extracting
features. In this paper, we suggest a CNN-based framework with specially
designed CNNs, that can be applied on a collection of data from a variety of
sources, including different markets, in order to extract features for
predicting the future of those markets. The suggested framework has been
applied for predicting the next day's direction of movement for the indices of
S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of
initial features. The evaluations show a significant improvement in
prediction's performance compared to the state of the art baseline algorithms. | ['Saman Haratizadeh', 'Ehsan Hoseinzade'] | 2018-10-21 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-4.30911750e-01 -5.38722038e-01 5.87611087e-02 -3.01468611e-01
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40de009d-a6f8-4ee8-b826-2537a361abd0 | a-model-aggregation-approach-for-high | 2205.07525 | null | https://arxiv.org/abs/2205.07525v2 | https://arxiv.org/pdf/2205.07525v2.pdf | A model aggregation approach for high-dimensional large-scale optimization | Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields, high-dimensional and large-scale problems are becoming increasingly common. In this study, we propose a model aggregation method in the Bayesian optimization (MamBO) algorithm for efficiently solving high-dimensional large-scale optimization problems. MamBO uses a combination of subsampling and subspace embeddings to collectively address high dimensionality and large-scale issues; in addition, a model aggregation method is employed to address the surrogate model uncertainty issue that arises when embedding is applied. This surrogate model uncertainty issue is largely ignored in the embedding literature and practice, and it is exacerbated when the problem is high-dimensional and data are limited. Our proposed model aggregation method reduces these lower-dimensional surrogate model risks and improves the robustness of the BO algorithm. We derive an asymptotic bound for the proposed aggregated surrogate model and prove the convergence of MamBO. Benchmark numerical experiments indicate that our algorithm achieves superior or comparable performance to other commonly used high-dimensional BO algorithms. Moreover, we apply MamBO to a cascade classifier of a machine learning algorithm for face detection, and the results reveal that MamBO finds settings that achieve higher classification accuracy than the benchmark settings and is computationally faster than other high-dimensional BO algorithms. | ['Giulia Pedrielli', 'Szu Hui Ng', 'Ercong Zhang', 'Haowei Wang'] | 2022-05-16 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-2.65280843e-01 -3.54632944e-01 -1.75419703e-01 -1.14923887e-01
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e6d528b2-dda4-4aef-888d-b7a8dbc0b0a5 | using-channel-state-information-for-physical | 2011.03573 | null | https://arxiv.org/abs/2011.03573v3 | https://arxiv.org/pdf/2011.03573v3.pdf | Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach | This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional autoencoder (DCAE) to tackle it. The past observations of the estimated channel state information (CSI) are used to train the DCAE. Then, a post-processing is deployed on the trained DCAE output to perform the physical tamper detection. Our experimental results show that the proposed approach, deployed in an office and a hall environment, is able to detect on average 99.6% of tamper events (TPR = 99.6%) while creating zero false alarms (FPR = 0%). | ['Andreas Springer', 'Núria Ballber Torres', 'Bernhard Etzlinger', 'Eshagh Dehmollaian'] | 2020-11-06 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.26940149e-01 -4.53694761e-02 3.27347606e-01 -6.04501031e-02
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a92503e8-5595-404a-bc91-901f6c6bea9e | seer-language-instructed-video-prediction | 2303.14897 | null | https://arxiv.org/abs/2303.14897v2 | https://arxiv.org/pdf/2303.14897v2.pdf | Seer: Language Instructed Video Prediction with Latent Diffusion Models | Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning, i.e., predicting future video frames with a given language instruction and reference frames. It is a highly challenging task to ground task-level goals specified by instructions and high-fidelity frames together, requiring large-scale data and computation. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We inflate the denoising U-Net and language conditioning model with two novel techniques, Autoregressive Spatial-Temporal Attention and Frame Sequential Text Decomposer, to propagate the rich prior knowledge in the pretrained T2I models across the frames. With the well-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2) and Bridgedata datasets demonstrate our superior video prediction performance with around 210-hour training on 4 RTX 3090 GPUs: decreasing the FVD of the current SOTA model from 290 to 200 on SSv2 and achieving at least 70\% preference in the human evaluation. | ['Yang Gao', 'Jiaming Song', 'Chuan Wen', 'Xianfan Gu'] | 2023-03-27 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.37814873e-01 2.11295970e-02 -1.97155863e-01 -3.10764700e-01
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624d1f76-3870-420e-a9e2-c975a01b5b55 | balancing-test-accuracy-and-security-in | 2305.18312 | null | https://arxiv.org/abs/2305.18312v1 | https://arxiv.org/pdf/2305.18312v1.pdf | Balancing Test Accuracy and Security in Computerized Adaptive Testing | Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length. Bilevel optimization-based CAT (BOBCAT) is a recent framework that learns a data-driven question selection algorithm to effectively reduce test length and improve test accuracy. However, it suffers from high question exposure and test overlap rates, which potentially affects test security. This paper introduces a constrained version of BOBCAT to address these problems by changing its optimization setup and enabling us to trade off test accuracy for question exposure and test overlap rates. We show that C-BOBCAT is effective through extensive experiments on two real-world adult testing datasets. | ['Andrew S. Lan', 'Stephen Sireci', 'Aritra Ghosh', 'Wanyong Feng'] | 2023-05-18 | null | null | null | null | ['bilevel-optimization', 'question-selection'] | ['methodology', 'natural-language-processing'] | [ 2.80346647e-02 -6.00435920e-02 -4.15680230e-01 -6.48857653e-01
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b032c3b2-7647-46d5-ac44-496842f6edb6 | social-navigation-with-human-empowerment | 2003.08158 | null | https://arxiv.org/abs/2003.08158v3 | https://arxiv.org/pdf/2003.08158v3.pdf | Social Navigation with Human Empowerment driven Deep Reinforcement Learning | Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical \acf{RL} and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants. | ['Herke van Hoof', 'Florian Mirus', 'Tessa van der Heiden'] | 2020-03-18 | null | null | null | null | ['social-navigation'] | ['robots'] | [-1.46551311e-01 9.17969704e-01 -3.42491157e-02 -1.63856372e-01
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fa0aae62-808b-4ff6-aa42-71f2866794a9 | amnet-deep-atrous-multiscale-stereo-disparity | 1904.09099 | null | http://arxiv.org/abs/1904.09099v1 | http://arxiv.org/pdf/1904.09099v1.pdf | AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks | In this paper, a new deep learning architecture for stereo disparity
estimation is proposed. The proposed atrous multiscale network (AMNet) adopts
an efficient feature extractor with depthwise-separable convolutions and an
extended cost volume that deploys novel stereo matching costs on the deep
features. A stacked atrous multiscale network is proposed to aggregate rich
multiscale contextual information from the cost volume which allows for
estimating the disparity with high accuracy at multiple scales. AMNet can be
further modified to be a foreground-background aware network, FBA-AMNet, which
is capable of discriminating between the foreground and the background objects
in the scene at multiple scales. An iterative multitask learning method is
proposed to train FBA-AMNet end-to-end. The proposed disparity estimation
networks, AMNet and FBA-AMNet, show accurate disparity estimates and advance
the state of the art on the challenging Middlebury, KITTI 2012, KITTI 2015, and
Sceneflow stereo disparity estimation benchmarks. | ['Mostafa El-Khamy', 'Jungwon Lee', 'Xianzhi Du'] | 2019-04-19 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 2.35194623e-01 -4.04852748e-01 1.67804524e-01 -5.28778553e-01
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78f3e86a-3736-4928-87af-0555ce390a83 | mrdet-a-multi-head-network-for-accurate | 2012.13135 | null | https://arxiv.org/abs/2012.13135v2 | https://arxiv.org/pdf/2012.13135v2.pdf | MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images | Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected. Many recently developed methods attempt to solve these issues by estimating an extra orientation parameter and placing dense anchors, which will result in high model complexity and computational costs. In this paper, we propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. The AO-RPN is very efficient with only a few amounts of parameters increase than the original RPN. Furthermore, to obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network to accomplish them. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately. We name it MRDet short for Multi-head Rotated object Detector for convenience. We test the proposed MRDet on two challenging benchmarks, i.e., DOTA and HRSC2016, and compare it with several state-of-the-art methods. Our method achieves very promising results which clearly demonstrate its effectiveness. | ['Yunhong Wang', 'Di Huang', 'Guangshuai Gao', 'Qingjie Liu', 'Ran Qin'] | 2020-12-24 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-7.60845244e-02 -8.08193609e-02 7.37074837e-02 -2.67578125e-01
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7c4923d3-099e-4eaf-b007-d955c9e0c1f1 | convolution-in-the-cloud-learning-deformable | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.pdf | Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis | Point clouds are among the popular geometry representations for 3D vision applications. However, without regular structures like 2D images, processing and summarizing information over these unordered data points are very challenging. Although a number of previous works attempt to analyze point clouds and achieve promising performances, their performances would degrade significantly when data variations like shift and scale changes are presented. In this paper, we propose 3D Graph Convolution Networks (3D-GCN), which is designed to extract local 3D features from point clouds across scales, while shift and scale-invariance properties are introduced. The novelty of our 3D-GCN lies in the definition of learnable kernels with a graph max-pooling mechanism. We show that 3D-GCN can be applied to 3D classification and segmentation tasks, with ablation studies and visualizations verifying the design of 3D-GCN.
| [' Yu-Chiang Frank Wang', ' Sheng-Yu Huang', 'Zhi-Hao Lin'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-9.61102545e-02 -1.30544081e-01 1.02924012e-01 -3.22533429e-01
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62aed925-566a-45a6-8c54-ee131155973f | towards-open-domain-topic-classification-1 | 2306.17290 | null | https://arxiv.org/abs/2306.17290v1 | https://arxiv.org/pdf/2306.17290v1.pdf | Towards Open-Domain Topic Classification | We introduce an open-domain topic classification system that accepts user-defined taxonomy in real time. Users will be able to classify a text snippet with respect to any candidate labels they want, and get instant response from our web interface. To obtain such flexibility, we build the backend model in a zero-shot way. By training on a new dataset constructed from Wikipedia, our label-aware text classifier can effectively utilize implicit knowledge in the pretrained language model to handle labels it has never seen before. We evaluate our model across four datasets from various domains with different label sets. Experiments show that the model significantly improves over existing zero-shot baselines in open-domain scenarios, and performs competitively with weakly-supervised models trained on in-domain data. | ['Dan Roth', 'Hongming Zhang', 'Yuqian Deng', 'Jinrui Yang', 'Hantian Ding'] | 2023-06-29 | towards-open-domain-topic-classification | https://aclanthology.org/2022.naacl-demo.10 | https://aclanthology.org/2022.naacl-demo.10.pdf | naacl-acl-2022-7 | ['classification-1'] | ['methodology'] | [ 1.18136287e-01 2.15476498e-01 -7.93217838e-01 -4.93509114e-01
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7177c5cb-911d-472f-8f31-6bd26f7fdf6f | improving-the-inference-of-topic-models-via | 2301.12974 | null | https://arxiv.org/abs/2301.12974v1 | https://arxiv.org/pdf/2301.12974v1.pdf | Improving the Inference of Topic Models via Infinite Latent State Replications | In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus. One of the most popular inference approaches to topic models is perhaps collapsed Gibbs sampling (CGS), which typically samples one single topic label for each observed document-word pair. In this paper, we aim at improving the inference of CGS for topic models. We propose to leverage state augmentation technique by maximizing the number of topic samples to infinity, and then develop a new inference approach, called infinite latent state replication (ILR), to generate robust soft topic assignment for each given document-word pair. Experimental results on the publicly available datasets show that ILR outperforms CGS for inference of existing established topic models. | ['Gao Cong', 'Manoranjan Dash', 'Juan Felipe Carmona', 'Zhen Hai', 'Daniel Rugeles'] | 2023-01-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 1.39061198e-01 3.26796651e-01 -5.28169036e-01 -3.99722546e-01
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ce9025f0-bf25-4ee7-9e4d-e8859c378796 | lagrangian-motion-magnification-with-double | 2204.07636 | null | https://arxiv.org/abs/2204.07636v1 | https://arxiv.org/pdf/2204.07636v1.pdf | Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition | Motion magnification techniques aim at amplifying and hence revealing subtle motion in videos. There are basically two main approaches to reach this goal, namely via Eulerian or Lagrangian techniques. While the first one magnifies motion implicitly by operating directly on image pixels, the Lagrangian approach uses optical flow techniques to extract and amplify pixel trajectories. Microexpressions are fast and spatially small facial expressions that are difficult to detect. In this paper, we propose a novel approach for local Lagrangian motion magnification of facial micromovements. Our contribution is three-fold: first, we fine-tune the recurrent all-pairs field transforms for optical flows (RAFT) deep learning approach for faces by adding ground truth obtained from the variational dense inverse search (DIS) for optical flow algorithm applied to the CASME II video set of faces. This enables us to produce optical flows of facial videos in an efficient and sufficiently accurate way. Second, since facial micromovements are both local in space and time, we propose to approximate the optical flow field by sparse components both in space and time leading to a double sparse decomposition. Third, we use this decomposition to magnify micro-motions in specific areas of the face, where we introduce a new forward warping strategy using a triangular splitting of the image grid and barycentric interpolation of the RGB vectors at the corners of the transformed triangles. We demonstrate the very good performance of our approach by various examples. | ['Daniel J. Strauss', 'Gabriele Steidl', 'Cosmas Heiss', 'Philipp Flotho'] | 2022-04-15 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 1.72627479e-01 6.69393167e-02 8.81405771e-02 1.18939400e-01
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99dfffe5-3103-4613-ad9a-ebe08e836eb6 | ml-leaks-model-and-data-independent | 1806.01246 | null | http://arxiv.org/abs/1806.01246v2 | http://arxiv.org/pdf/1806.01246v2.pdf | ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models | Machine learning (ML) has become a core component of many real-world
applications and training data is a key factor that drives current progress.
This huge success has led Internet companies to deploy machine learning as a
service (MLaaS). Recently, the first membership inference attack has shown that
extraction of information on the training set is possible in such MLaaS
settings, which has severe security and privacy implications.
However, the early demonstrations of the feasibility of such attacks have
many assumptions on the adversary, such as using multiple so-called shadow
models, knowledge of the target model structure, and having a dataset from the
same distribution as the target model's training data. We relax all these key
assumptions, thereby showing that such attacks are very broadly applicable at
low cost and thereby pose a more severe risk than previously thought. We
present the most comprehensive study so far on this emerging and developing
threat using eight diverse datasets which show the viability of the proposed
attacks across domains.
In addition, we propose the first effective defense mechanisms against such
broader class of membership inference attacks that maintain a high level of
utility of the ML model. | ['Michael Backes', 'Ahmed Salem', 'Yang Zhang', 'Pascal Berrang', 'Mathias Humbert', 'Mario Fritz'] | 2018-06-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.87298733e-01 2.36997321e-01 -2.51445323e-01 -3.45683068e-01
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4c7944a5-e884-4124-944a-054a33c6734e | recovering-and-simulating-pedestrians-in-the | 2011.08106 | null | https://arxiv.org/abs/2011.08106v1 | https://arxiv.org/pdf/2011.08106v1.pdf | Recovering and Simulating Pedestrians in the Wild | Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on artists to create both 3D assets and their animations to generate a new scenario. This, however, does not scale. In contrast, we propose to recover the shape and motion of pedestrians from sensor readings captured in the wild by a self-driving car driving around. Towards this goal, we formulate the problem as energy minimization in a deep structured model that exploits human shape priors, reprojection consistency with 2D poses extracted from images, and a ray-caster that encourages the reconstructed mesh to agree with the LiDAR readings. Importantly, we do not require any ground-truth 3D scans or 3D pose annotations. We then incorporate the reconstructed pedestrian assets bank in a realistic LiDAR simulation system by performing motion retargeting, and show that the simulated LiDAR data can be used to significantly reduce the amount of annotated real-world data required for visual perception tasks. | ['Raquel Urtasun', 'Wei-Chiu Ma', 'Bin Yang', 'Ming Liang', 'Siva Manivasagam', 'Ze Yang'] | 2020-11-16 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 2.44758680e-01 3.19274038e-01 3.61469030e-01 -5.51106572e-01
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838cc422-263b-435e-ad0e-1d5fd587395b | straight-to-the-facts-learning-knowledge-base | 1809.01124 | null | http://arxiv.org/abs/1809.01124v1 | http://arxiv.org/pdf/1809.01124v1.pdf | Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering | Question answering is an important task for autonomous agents and virtual
assistants alike and was shown to support the disabled in efficiently
navigating an overwhelming environment. Many existing methods focus on
observation-based questions, ignoring our ability to seamlessly combine
observed content with general knowledge. To understand interactions with a
knowledge base, a dataset has been introduced recently and keyword matching
techniques were shown to yield compelling results despite being vulnerable to
misconceptions due to synonyms and homographs. To address this issue, we
develop a learning-based approach which goes straight to the facts via a
learned embedding space. We demonstrate state-of-the-art results on the
challenging recently introduced fact-based visual question answering dataset,
outperforming competing methods by more than 5%. | ['Medhini Narasimhan', 'Alexander G. Schwing'] | 2018-09-04 | straight-to-the-facts-learning-knowledge-base-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.pdf | eccv-2018-9 | ['factual-visual-question-answering', 'misconceptions'] | ['computer-vision', 'miscellaneous'] | [-7.16917291e-02 3.66212249e-01 -1.15357563e-01 -4.28377837e-01
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48b3a044-06fd-4aa1-a9d2-6ea4cdab2952 | matching-latent-encoding-for-audio-text-based | 2306.05245 | null | https://arxiv.org/abs/2306.05245v1 | https://arxiv.org/pdf/2306.05245v1.pdf | Matching Latent Encoding for Audio-Text based Keyword Spotting | Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word keywords of different sequence lengths remains largely unsolved. In this paper, we propose an audio-text-based end-to-end model architecture for flexible keyword spotting (KWS), which builds upon learned audio and text embeddings. Our architecture uses a novel dynamic programming-based algorithm, Dynamic Sequence Partitioning (DSP), to optimally partition the audio sequence into the same length as the word-based text sequence using the monotonic alignment of spoken content. Our proposed model consists of an encoder block to get audio and text embeddings, a projector block to project individual embeddings to a common latent space, and an audio-text aligner containing a novel DSP algorithm, which aligns the audio and text embeddings to determine if the spoken content is the same as the text. Experimental results show that our DSP is more effective than other partitioning schemes, and the proposed architecture outperformed the state-of-the-art results on the public dataset in terms of Area Under the ROC Curve (AUC) and Equal-Error-Rate (EER) by 14.4 % and 28.9%, respectively. | ['Devang Naik', 'Minsik Cho', 'Kumari Nishu'] | 2023-06-08 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.95383757e-01 -3.50901335e-01 -4.81901504e-03 -3.68592411e-01
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b9255eca-4f7f-4685-98be-5e14f38de010 | deep-learning-for-human-parsing-a-survey | 2301.12416 | null | https://arxiv.org/abs/2301.12416v1 | https://arxiv.org/pdf/2301.12416v1.pdf | Deep Learning for Human Parsing: A Survey | Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others. Recently, due to the success of deep learning in computer vision, there are a number of works aimed at developing human parsing algorithms using deep learning models. As methods have been proposed, a comprehensive survey of this topic is of great importance. In this survey, we provide an analysis of state-of-the-art human parsing methods, covering a broad spectrum of pioneering works for semantic human parsing. We introduce five insightful categories: (1) structure-driven architectures exploit the relationship of different human parts and the inherent hierarchical structure of a human body, (2) graph-based networks capture the global information to achieve an efficient and complete human body analysis, (3) context-aware networks explore useful contexts across all pixel to characterize a pixel of the corresponding class, (4) LSTM-based methods can combine short-distance and long-distance spatial dependencies to better exploit abundant local and global contexts, and (5) combined auxiliary information approaches use related tasks or supervision to improve network performance. We also discuss the advantages/disadvantages of the methods in each category and the relationships between methods in different categories, examine the most widely used datasets, report performances, and discuss promising future research directions in this area. | ['Zhen Lei', 'Ming Tang', 'Xiangyu Zhu', 'Xiaomei Zhang'] | 2023-01-29 | null | null | null | null | ['human-parsing', 'person-search'] | ['computer-vision', 'computer-vision'] | [ 3.96334141e-01 1.43610895e-01 -2.76311219e-01 -6.15760148e-01
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6b7a98ce-6d29-4638-afc6-b96c0520023e | paste-a-tagging-free-decoding-framework-using | 2110.04794 | null | https://arxiv.org/abs/2110.04794v1 | https://arxiv.org/pdf/2110.04794v1.pdf | PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans. A recent grid tagging approach on the other hand fails to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair. Different from these, we present a tagging-free solution for the task, while addressing the limitations of the existing works. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step thereby making our solution end-to-end. Interactions between the aspects and opinions are effectively captured by the decoder by considering their entire detected spans while predicting their connecting sentiment. Extensive experiments on several benchmark datasets establish the better efficacy of our proposed approach, especially in the recall, and in predicting multiple and aspect/opinion-overlapped triplets from the same review sentence. We report our results both with and without BERT and also demonstrate the utility of domain-specific BERT post-training for the task. | ['Pawan Goyal', 'Sourangshu Bhattacharya', 'Yash Butala', 'Tapas Nayak', 'Rajdeep Mukherjee'] | 2021-10-10 | null | https://aclanthology.org/2021.emnlp-main.731 | https://aclanthology.org/2021.emnlp-main.731.pdf | emnlp-2021-11 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 2.54603177e-01 1.36919737e-01 -2.29946852e-01 -5.17010093e-01
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407ba94b-2d49-4619-ac8f-da8f692fd442 | increased-complexity-and-fitness-of | 2103.08406 | null | https://arxiv.org/abs/2103.08406v1 | https://arxiv.org/pdf/2103.08406v1.pdf | Increased Complexity and Fitness of Artificial Cells that Reproduce Using Spatially Distributed Asynchronous Parallel Processes | Replication time is among the most important components of a bacterial cell's reproductive fitness. Paradoxically, larger cells replicate in less time than smaller cells despite the fact that building a larger cell requires increased quantities of raw materials and energy. This feat is primarily accomplished by the massive over expression of ribosomes, which permits translation of mRNA into protein, the limiting step in reproduction, to occur at a scale that would be impossible were it not for the use of parallel processing. In computer science, spatial parallelism is the distribution of work across the nodes of a distributed-memory multicomputer system. Despite the fact that a non-negligible fraction of artificial life research is grounded in formulations based on spatially parallel substrates, there have been no examples of artificial organisms that use spatial parallelism to replicate in less time than smaller organisms. This paper describes artificial cells defined using a combinator-based artificial chemistry that replicate in less time than smaller cells. This is achieved by employing extra copies of programs implementing the limiting steps in the process used by the cells to synthesize their component parts. Significant speedup is demonstrated, despite the increased complexity of control and export processes necessitated by the use of a parallel replication strategy. | ['Lance R. Williams'] | 2021-03-15 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.11095297e-01 5.27692437e-02 3.61483604e-01 4.78525609e-01
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8ecb5fba-3fc7-4491-a458-e73ee83986d5 | teaching-probabilistic-logical-reasoning-to | 2305.13179 | null | https://arxiv.org/abs/2305.13179v1 | https://arxiv.org/pdf/2305.13179v1.pdf | Teaching Probabilistic Logical Reasoning to Transformers | Recent research on transformer-based language models investigates their reasoning ability over logical rules expressed in natural language text. However, their logic is not yet well-understood as we cannot explain the abstractions made by the models that help them in reasoning. These models are criticized for merely memorizing complex patterns in the data, which often creates issues for their generalizability in unobserved situations. In this work, we analyze the use of probabilistic logical rules in transformer-based language models. In particular, we propose a new approach, Probabilistic Constraint Training (PCT), that explicitly models probabilistic logical reasoning by imposing the rules of reasoning as constraints during training. We create a new QA benchmark for evaluating probabilistic reasoning over uncertain textual rules, which creates instance-specific rules, unlike the only existing relevant benchmark. Experimental results show that our proposed technique improves the base language models' accuracy and explainability when probabilistic logical reasoning is required for question answering. Moreover, we show that the learned probabilistic reasoning abilities are transferable to novel situations. | ['Parisa Kordjamshidi', 'Kristen Brent Venable', 'Aliakbar Nafar'] | 2023-05-22 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.17648849e-02 1.09134746e+00 -1.55633226e-01 -8.37935865e-01
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2abcfe8f-794d-4e61-b114-300ed279c727 | dyntypo-example-based-dynamic-text-effects | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Men_DynTypo_Example-Based_Dynamic_Text_Effects_Transfer_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Men_DynTypo_Example-Based_Dynamic_Text_Effects_Transfer_CVPR_2019_paper.pdf | DynTypo: Example-Based Dynamic Text Effects Transfer | In this paper, we present a novel approach for dynamic text effects transfer by using example-based texture synthesis. In contrast to previous works that require an input video of the target to provide motion guidance, we aim to animate a still image of the target text by transferring the desired dynamic effects from an observed exemplar. Due to the simplicity of target guidance and complexity of realistic effects, it is prone to producing temporal artifacts such as flickers and pulsations. To address the problem, our core idea is to find a common Nearest-neighbor Field (NNF) that would optimize the textural coherence across all keyframes simultaneously. With the static NNF for video sequences, we implicitly transfer motion properties from source to target. We also introduce a guided NNF search by employing the distance-based weight map and Simulated Annealing (SA) for deep direction-guided propagation to allow intense dynamic effects to be completely transferred with no semantic guidance provided. Experimental results demonstrate the effectiveness and superiority of our method in dynamic text effects transfer through extensive comparisons with state-of-the-art algorithms. We also show the potentiality of our method via multiple experiments for various application domains.
| [' Jianguo Xiao', ' Yingmin Tang', ' Zhouhui Lian', 'Yifang Men'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['text-effects-transfer'] | ['natural-language-processing'] | [ 6.82992637e-01 -1.37847379e-01 1.95383027e-01 -6.75836727e-02
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372ab0c7-3ade-40da-81da-0f7903dfe159 | adaptive-least-mean-squares-estimation-of | 1602.05703 | null | http://arxiv.org/abs/1602.05703v3 | http://arxiv.org/pdf/1602.05703v3.pdf | Adaptive Least Mean Squares Estimation of Graph Signals | The aim of this paper is to propose a least mean squares (LMS) strategy for
adaptive estimation of signals defined over graphs. Assuming the graph signal
to be band-limited, over a known bandwidth, the method enables reconstruction,
with guaranteed performance in terms of mean-square error, and tracking from a
limited number of observations over a subset of vertices. A detailed mean
square analysis provides the performance of the proposed method, and leads to
several insights for designing useful sampling strategies for graph signals.
Numerical results validate our theoretical findings, and illustrate the
performance of the proposed method. Furthermore, to cope with the case where
the bandwidth is not known beforehand, we propose a method that performs a
sparse online estimation of the signal support in the (graph) frequency domain,
which enables online adaptation of the graph sampling strategy. Finally, we
apply the proposed method to build the power spatial density cartography of a
given operational region in a cognitive network environment. | ['Paolo Di Lorenzo', 'Stefania Sardellitti', 'Sergio Barbarossa', 'Paolo Banelli'] | 2016-02-18 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 5.70656121e-01 4.53710884e-01 -9.67218280e-02 3.27038050e-01
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5bc104b9-92a6-41d2-8039-f70aba28405d | neural-graph-matching-network-learning | 1911.11308 | null | https://arxiv.org/abs/1911.11308v3 | https://arxiv.org/pdf/1911.11308v3.pdf | Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching | Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task. The association graph is learned by an embedding network for vertex classification, followed by Sinkhorn normalization and a cross-entropy loss for end-to-end learning. We further improve the embedding model on association graph by introducing Sinkhorn based matching-aware constraint, as well as dummy nodes to deal with unequal sizes of graphs. To our best knowledge, this is one of the first network to directly learn with the general Lawler's QAP. In contrast, recent deep matching methods focus on the learning of node/edge features in two graphs respectively. We also show how to extend our network to hypergraph matching, and matching of multiple graphs. Experimental results on both synthetic graphs and real-world images show its effectiveness. For pure QAP tasks on synthetic data and QAPLIB benchmark, our method can perform competitively and even surpass state-of-the-art graph matching and QAP solvers with notable less time cost. We provide a project homepage at http://thinklab.sjtu.edu.cn/project/NGM/index.html. | ['Junchi Yan', 'Xiaokang Yang', 'Runzhong Wang'] | 2019-11-26 | neural-graph-matching-network-learning-lawler | https://ieeexplore.ieee.org/document/9426408 | https://arxiv.org/pdf/1911.11308.pdf | null | ['hypergraph-matching'] | ['graphs'] | [ 2.18733639e-01 4.37010437e-01 -3.68285537e-01 -3.19529742e-01
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eb758fae-b455-4b1e-a34d-8c4621ba1e31 | large-scale-decipherment-for-out-of-domain | null | null | https://aclanthology.org/D12-1025 | https://aclanthology.org/D12-1025.pdf | Large Scale Decipherment for Out-of-Domain Machine Translation | null | ['Qing Dou', 'Kevin Knight'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['decipherment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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90759603-41ff-4d2a-9be1-f75f7edb1c2e | modeling-local-geometric-structure-of-3d | 1811.07782 | null | http://arxiv.org/abs/1811.07782v1 | http://arxiv.org/pdf/1811.07782v1.pdf | Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN | Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling
local structure has been proven to be important for the success of
convolutional architectures, and researchers exploited the modeling of local
point sets in the feature extraction hierarchy. However, limited attention has
been paid to explicitly model the geometric structure amongst points in a local
region. To address this problem, we propose Geo-CNN, which applies a generic
convolution-like operation dubbed as GeoConv to each point and its local
neighborhood. Local geometric relationships among points are captured when
extracting edge features between the center and its neighboring points. We
first decompose the edge feature extraction process onto three orthogonal
bases, and then aggregate the extracted features based on the angles between
the edge vector and the bases. This encourages the network to preserve the
geometric structure in Euclidean space throughout the feature extraction
hierarchy. GeoConv is a generic and efficient operation that can be easily
integrated into 3D point cloud analysis pipelines for multiple applications. We
evaluate Geo-CNN on ModelNet40 and KITTI and achieve state-of-the-art
performance. | ['Ruichi Yu', 'Shiyi Lan', 'Larry S. Davis', 'Gang Yu'] | 2018-11-19 | modeling-local-geometric-structure-of-3d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lan_Modeling_Local_Geometric_Structure_of_3D_Point_Clouds_Using_Geo-CNN_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lan_Modeling_Local_Geometric_Structure_of_3D_Point_Clouds_Using_Geo-CNN_CVPR_2019_paper.pdf | cvpr-2019-6 | ['modeling-local-geometric-structure'] | ['miscellaneous'] | [-6.69994354e-01 -3.69143188e-01 1.50187999e-01 -4.09188062e-01
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6c4ccb2e-f2f4-4c86-9d88-2eafec661588 | data-driven-pronunciation-modeling-of-swiss | null | null | https://aclanthology.org/L18-1498 | https://aclanthology.org/L18-1498.pdf | Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition | null | ['Christoph Schmidt', 'Michael Stadtschnitzer'] | 2018-05-01 | data-driven-pronunciation-modeling-of-swiss-1 | https://aclanthology.org/L18-1498 | https://aclanthology.org/L18-1498.pdf | lrec-2018-5 | ['robust-speech-recognition'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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e273e68a-458f-49c3-a7c2-337ab40a07f6 | what-do-they-capture-a-structural-analysis-of | 2202.06840 | null | https://arxiv.org/abs/2202.06840v1 | https://arxiv.org/pdf/2202.06840v1.pdf | What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code | Recently, many pre-trained language models for source code have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code completion, code search, and code summarization. These models leverage masked pre-training and Transformer and have achieved promising results. However, currently there is still little progress regarding interpretability of existing pre-trained code models. It is not clear why these models work and what feature correlations they can capture. In this paper, we conduct a thorough structural analysis aiming to provide an interpretation of pre-trained language models for source code (e.g., CodeBERT, and GraphCodeBERT) from three distinctive perspectives: (1) attention analysis, (2) probing on the word embedding, and (3) syntax tree induction. Through comprehensive analysis, this paper reveals several insightful findings that may inspire future studies: (1) Attention aligns strongly with the syntax structure of code. (2) Pre-training language models of code can preserve the syntax structure of code in the intermediate representations of each Transformer layer. (3) The pre-trained models of code have the ability of inducing syntax trees of code. Theses findings suggest that it may be helpful to incorporate the syntax structure of code into the process of pre-training for better code representations. | ['Hai Jin', 'Guandong Xu', 'Yulei Sui', 'Hongyu Zhang', 'Wei Zhao', 'Yao Wan'] | 2022-02-14 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 1.03561148e-01 4.60376233e-01 -4.52678680e-01 -3.50855380e-01
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fe37c344-8ab9-41e5-9e9e-5c8c07447bb3 | question-generation-and-answering-for | null | null | https://aclanthology.org/2022.lrec-1.486 | https://aclanthology.org/2022.lrec-1.486.pdf | Question Generation and Answering for exploring Digital Humanities collections | This paper introduces the question answering paradigm as a way to explore digitized archive collections for Social Science studies. In particular, we are interested in evaluating largely studied question generation and question answering approaches on a new type of documents, as a step forward beyond traditional benchmark evaluations. Question generation can be used as a way to provide enhanced training material for Machine Reading Question Answering algorithms but also has its own purpose in this paradigm, where relevant questions can be used as a way to create explainable links between documents. To this end, generating large amounts of question is not the only motivation, but we need to include qualitative and semantic control to the generation process. We propose a new approach for question generation, relying on a BART Transformer based generative model, for which input data are enriched by semantic constraints. Question generation and answering are evaluated on several French corpora, and the whole approach is validated on a new corpus of digitized archive collection of a French Social Science journal. | ['Géraldine Damnati', 'Jérémy Auguste', 'Elie Antoine', 'Frederic Bechet'] | null | null | null | null | lrec-2022-6 | ['question-generation'] | ['natural-language-processing'] | [ 2.10688248e-01 7.48632371e-01 2.77106464e-01 -3.38226348e-01
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2ec3db4d-ca3d-4ec4-af83-918db14c2d14 | invariance-adapted-decomposition-and-lasso | 2210.07413 | null | https://arxiv.org/abs/2210.07413v1 | https://arxiv.org/pdf/2210.07413v1.pdf | Invariance-adapted decomposition and Lasso-type contrastive learning | Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the mechanism that describes this effectiveness have not been thoroughly analyzed, and many studies have been conducted to investigate the data structures captured by contrastive learning. In particular, the recent study of \citet{content_isolate} has shown that contrastive learning is capable of decomposing the data space into the space that is invariant to all augmentations and its complement. In this paper, we introduce the notion of invariance-adapted latent space that decomposes the data space into the intersections of the invariant spaces of each augmentation and their complements. This decomposition generalizes the one introduced in \citet{content_isolate}, and describes a structure that is analogous to the frequencies in the harmonic analysis of a group. We experimentally show that contrastive learning with lasso-type metric can be used to find an invariance-adapted latent space, thereby suggesting a new potential for the contrastive learning. We also investigate when such a latent space can be identified up to mixings within each component. | ['Kenji Fukumizu', 'Takeru Miyato', 'Masanori Koyama'] | 2022-10-13 | null | null | null | null | ['type'] | ['speech'] | [ 3.34882885e-01 2.38358006e-02 -3.86913240e-01 -1.95518151e-01
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3d5b6c4f-90a5-43e9-90d3-2abf17ae011a | spectral-illumination-correction-achieving | null | null | https://ieeexplore.ieee.org/document/8642637 | https://pureadmin.qub.ac.uk/ws/portalfiles/portal/163783181/sic_converted.pdf | Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral Domain | Achieving color constancy between and within images, i.e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition. Most current methods attempt to solve this by illumination correction on perceptual color spaces. In this paper, we proposed two pixel-wise algorithms to achieve relative color constancy by working under the spectral domain. That is, the proposed algorithms map each pixel to the reflectance ratio of objects appeared in the scene and perform illumination correction in this spectral domain. Also, we proposed a camera calibration technique that calculates the characteristics of a camera without the need of a standard reference. We show that both of the proposed algorithms achieved the best performance on nonuniform illumination correction and relative illumination matching respectively compared to the benchmarked algorithms. | ['Huiyu Zhou', 'Yunfeng Zhao', 'Karen Rafferty', 'Chris Elliott'] | 2018-12-06 | null | null | null | 2018-ieee-international-symposium-on-signal | ['color-constancy'] | ['computer-vision'] | [ 7.37953782e-01 -6.74574375e-01 3.64968061e-01 -3.41679305e-01
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6a133c7b-83a4-43ea-a4b7-c800d62c3665 | sam-iqa-can-segment-anything-boost-image | 2307.04455 | null | https://arxiv.org/abs/2307.04455v1 | https://arxiv.org/pdf/2307.04455v1.pdf | SAM-IQA: Can Segment Anything Boost Image Quality Assessment? | Image Quality Assessment (IQA) is a challenging task that requires training on massive datasets to achieve accurate predictions. However, due to the lack of IQA data, deep learning-based IQA methods typically rely on pre-trained networks trained on massive datasets as feature extractors to enhance their generalization ability, such as the ResNet network trained on ImageNet. In this paper, we utilize the encoder of Segment Anything, a recently proposed segmentation model trained on a massive dataset, for high-level semantic feature extraction. Most IQA methods are limited to extracting spatial-domain features, while frequency-domain features have been shown to better represent noise and blur. Therefore, we leverage both spatial-domain and frequency-domain features by applying Fourier and standard convolutions on the extracted features, respectively. Extensive experiments are conducted to demonstrate the effectiveness of all the proposed components, and results show that our approach outperforms the state-of-the-art (SOTA) in four representative datasets, both qualitatively and quantitatively. Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks. Code: https://github.com/Hedlen/SAM-IQA | ['Shuaicheng Liu', 'Haoqiang Fan', 'Ting Jiang', 'Xinpeng Li'] | 2023-07-10 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 7.40620196e-02 -3.67683113e-01 -4.17939126e-02 -4.29740369e-01
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6.45389855e-01 -8.41658115e-01 -2.43757665e-01 8.76329839e-03] | [11.793706893920898, -1.8068432807922363] |
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