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9b0b5469-5fc6-4c8f-80b9-309c1c20df10 | real-time-3d-single-object-tracking-with | 2209.00860 | null | https://arxiv.org/abs/2209.00860v1 | https://arxiv.org/pdf/2209.00860v1.pdf | Real-time 3D Single Object Tracking with Transformer | LiDAR-based 3D single object tracking is a challenging issue in robotics and autonomous driving. Currently, existing approaches usually suffer from the problem that objects at long distance often have very sparse or partially-occluded point clouds, which makes the features extracted by the model ambiguous. Ambiguous features will make it hard to locate the target object and finally lead to bad tracking results. To solve this problem, we utilize the powerful Transformer architecture and propose a Point-Track-Transformer (PTT) module for point cloud-based 3D single object tracking task. Specifically, PTT module generates fine-tuned attention features by computing attention weights, which guides the tracker focusing on the important features of the target and improves the tracking ability in complex scenarios. To evaluate our PTT module, we embed PTT into the dominant method and construct a novel 3D SOT tracker named PTT-Net. In PTT-Net, we embed PTT into the voting stage and proposal generation stage, respectively. PTT module in the voting stage could model the interactions among point patches, which learns context-dependent features. Meanwhile, PTT module in the proposal generation stage could capture the contextual information between object and background. We evaluate our PTT-Net on KITTI and NuScenes datasets. Experimental results demonstrate the effectiveness of PTT module and the superiority of PTT-Net, which surpasses the baseline by a noticeable margin, ~10% in the Car category. Meanwhile, our method also has a significant performance improvement in sparse scenarios. In general, the combination of transformer and tracking pipeline enables our PTT-Net to achieve state-of-the-art performance on both two datasets. Additionally, PTT-Net could run in real-time at 40FPS on NVIDIA 1080Ti GPU. Our code is open-sourced for the research community at https://github.com/shanjiayao/PTT. | ['Zheng Fang', 'Yubo Cui', 'Sifan Zhou', 'Jiayao Shan'] | 2022-09-02 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.63572955e-01 -3.41388792e-01 -1.16576077e-02 -1.42306358e-01
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5a531fd3-3bb8-4749-b47a-0af531c0832e | scalable-computation-of-prediction-intervals | 2205.03194 | null | https://arxiv.org/abs/2205.03194v1 | https://arxiv.org/pdf/2205.03194v1.pdf | Scalable computation of prediction intervals for neural networks via matrix sketching | Accounting for the uncertainty in the predictions of modern neural networks is a challenging and important task in many domains. Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure (e.g., Bayesian neural networks) or dramatically increase the computational cost of predictions such as approaches based on ensembling. This work proposes a new algorithm that can be applied to a given trained neural network and produces approximate prediction intervals. The method is based on the classical delta method in statistics but achieves computational efficiency by using matrix sketching to approximate the Jacobian matrix. The resulting algorithm is competitive with state-of-the-art approaches for constructing predictive intervals on various regression datasets from the UCI repository. | ['Maxim Panov', 'Alexander Fishkov'] | 2022-05-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.1291353e-01 3.9699271e-01 -3.2483464e-01 -8.2654268e-01
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7a95c47e-259f-4e87-be59-d0cca64ab667 | dp-lstm-differential-privacy-inspired-lstm | 1912.10806 | null | https://arxiv.org/abs/1912.10806v1 | https://arxiv.org/pdf/1912.10806v1.pdf | DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News | Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism. First, based on the autoregressive moving average model (ARMA), a sentiment-ARMA is formulated by taking into consideration the information of financial news articles in the model. Then, an LSTM-based deep neural network is designed, which consists of three components: LSTM, VADER model and differential privacy (DP) mechanism. The proposed DP-LSTM scheme can reduce prediction errors and increase the robustness. Extensive experiments on S&P 500 stocks show that (i) the proposed DP-LSTM achieves 0.32% improvement in mean MPA of prediction result, and (ii) for the prediction of the market index S&P 500, we achieve up to 65.79% improvement in MSE. | ['Xiao-Yang Liu', 'Yinchuan Li', 'Liuqing Yang', 'Hongyang Yang', 'Xinyi Li'] | 2019-12-20 | null | null | null | null | ['stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-4.14801806e-01 -1.41098991e-01 -1.86567813e-01 -4.22463000e-01
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b0211b7b-72af-4829-9559-16ea5ef83c56 | ynu-hpcc-at-semeval-2020-task-8-using-a | 2007.13968 | null | https://arxiv.org/abs/2007.13968v1 | https://arxiv.org/pdf/2007.13968v1.pdf | YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis | In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest. However, the classification and recognition of memes is much more complicated than that of social text since it involves visual cues and language understanding. To address this issue, this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes. In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media. Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the features from the pictures. We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining. The officially released results show that our system performs better than the baseline algorithm. Our team won nineteenth place in subtask A (Sentiment Classification). The code of this paper is availabled at : https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis. | ['Xue-jie Zhang', 'Li Yuan', 'Jin Wang'] | 2020-07-28 | null | https://aclanthology.org/2020.semeval-1.116 | https://aclanthology.org/2020.semeval-1.116.pdf | semeval-2020 | ['suggestion-mining'] | ['natural-language-processing'] | [-3.19614202e-01 -2.99789071e-01 8.57670903e-02 -4.29258585e-01
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d28650c1-ac63-4891-9fc1-906ae4f90c19 | sagdre-sequence-aware-graph-based-document | null | null | https://openreview.net/forum?id=Vi9Cj61ZGsR | https://openreview.net/pdf?id=Vi9Cj61ZGsR | SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss | Relation extraction (RE) is an important task for many natural language processing applications. Document-level relation extraction aims to extract the relations within a document and poses many challenges to the RE tasks as it requires reasoning across sentences and handling multiple relations expressed in the same document. Existing state-of-the-art document-level RE models use the graph structure to better connect long-distance correlations. In this work, we propose SagDRE model, which further considers and captures the original sequential information from the text. The proposed model learns sentence-level directional edges to capture the information flow in the document and uses the token-level sequential information to encode the shortest path from one entity to the other. In addition, we propose an adaptive margin loss to maximize the margins to separate positive and negative classes. The experimental results on datasets from various domains demonstrate the effectiveness of our proposed methods. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.37333179e-01 2.48953998e-01 -6.89077020e-01 -3.86492163e-01
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1027d4f4-10eb-4ec4-9c9d-f71d82c29a58 | a-novel-memetic-strategy-for-optimized | 2305.07959 | null | https://arxiv.org/abs/2305.07959v1 | https://arxiv.org/pdf/2305.07959v1.pdf | A Novel Memetic Strategy for Optimized Learning of Classification Trees | Given the increasing interest in interpretable machine learning, classification trees have again attracted the attention of the scientific community because of their glass-box structure. These models are usually built using greedy procedures, solving subproblems to find cuts in the feature space that minimize some impurity measures. In contrast to this standard greedy approach and to the recent advances in the definition of the learning problem through MILP-based exact formulations, in this paper we propose a novel evolutionary algorithm for the induction of classification trees that exploits a memetic approach that is able to handle datasets with thousands of points. Our procedure combines the exploration of the feasible space of solutions with local searches to obtain structures with generalization capabilities that are competitive with the state-of-the-art methods. | ['Tommaso Aldinucci'] | 2023-05-13 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 3.08285266e-01 2.01423138e-01 -4.93917793e-01 -3.03709716e-01
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5d94ab73-0493-4fd4-b8b9-d13b02e0dd0a | unsupervised-learning-of-sampling | 2302.01174 | null | https://arxiv.org/abs/2302.01174v1 | https://arxiv.org/pdf/2302.01174v1.pdf | Unsupervised Learning of Sampling Distributions for Particle Filters | Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them based on their importance weight. For particle filters to be computationally tractable, it must be feasible to simulate the trajectories by drawing from the sampling distribution. Simultaneously, these trajectories need to reflect the reality of the nonlinear dynamical system so that the resulting estimators are accurate. Thus, the crux of particle filters lies in designing sampling distributions that are both easy to sample from and lead to accurate estimators. In this work, we propose to learn the sampling distributions. We put forward four methods for learning sampling distributions from observed measurements. Three of the methods are parametric methods in which we learn the mean and covariance matrix of a multivariate Gaussian distribution; each methods exploits a different aspect of the data (generic, time structure, graph structure). The fourth method is a nonparametric alternative in which we directly learn a transform of a uniform random variable. All four methods are trained in an unsupervised manner by maximizing the likelihood that the states may have produced the observed measurements. Our computational experiments demonstrate that learned sampling distributions exhibit better performance than designed, minimum-degeneracy sampling distributions. | ['Santiago Segarra', 'Richard Baraniuk', 'Martin Sevilla', 'Nicolas Zilberstein', 'Fernando Gama'] | 2023-02-02 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 1.30030528e-01 -1.33679315e-01 -2.40485653e-01 -1.31901935e-01
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e0601c4c-7ff8-45cf-b347-d8c3cd7d13f8 | geoqa-a-geometric-question-answering | 2105.14517 | null | https://arxiv.org/abs/2105.14517v3 | https://arxiv.org/pdf/2105.14517v3.pdf | GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning | Automatic math problem solving has recently attracted increasing attention as a long-standing AI benchmark. In this paper, we focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge. However, the existing methods were highly dependent on handcraft rules and were merely evaluated on small-scale datasets. Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4,998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems. Compared with another publicly available dataset GeoS, GeoQA is 25 times larger, in which the program annotations can provide a practical testbed for future research on explicit and explainable numerical reasoning. Moreover, we introduce a Neural Geometric Solver (NGS) to address geometric problems by comprehensively parsing multimodal information and generating interpretable programs. We further add multiple self-supervised auxiliary tasks on NGS to enhance cross-modal semantic representation. Extensive experiments on GeoQA validate the effectiveness of our proposed NGS and auxiliary tasks. However, the results are still significantly lower than human performance, which leaves large room for future research. Our benchmark and code are released at https://github.com/chen-judge/GeoQA . | ['Liang Lin', 'Eric P. Xing', 'Lingbo Liu', 'Xiaodan Liang', 'Jinghui Qin', 'Jianheng Tang', 'Jiaqi Chen'] | 2021-05-30 | null | https://aclanthology.org/2021.findings-acl.46 | https://aclanthology.org/2021.findings-acl.46.pdf | findings-acl-2021-8 | ['mathematical-reasoning'] | ['natural-language-processing'] | [-1.26100689e-01 1.10908151e-01 2.76171993e-02 -6.22668564e-01
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29e039bf-8b05-4652-bd7a-13067f029bc3 | focus-is-all-you-need-for-chinese-grammatical | 2210.12692 | null | https://arxiv.org/abs/2210.12692v3 | https://arxiv.org/pdf/2210.12692v3.pdf | Focus Is What You Need For Chinese Grammatical Error Correction | Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text. In the long term, researchers regard CGEC as a task with a certain degree of uncertainty, that is, an ungrammatical sentence may often have multiple references. However, we argue that even though this is a very reasonable hypothesis, it is too harsh for the intelligence of the mainstream models in this era. In this paper, we first discover that multiple references do not actually bring positive gains to model training. On the contrary, it is beneficial to the CGEC model if the model can pay attention to small but essential data during the training process. Furthermore, we propose a simple yet effective training strategy called OneTarget to improve the focus ability of the CGEC models and thus improve the CGEC performance. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of our proposed method. | ['Hai-Tao Zheng', 'Wei Wu', 'Rui Xie', 'Shirong Ma', 'Yinghui Li', 'Jingheng Ye'] | 2022-10-23 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 9.88101363e-02 3.27013433e-01 2.45479062e-01 -5.23189127e-01
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38949582-12d4-4add-933c-75a29a269244 | ava-activespeaker-an-audio-visual-dataset-for | 1901.01342 | null | https://arxiv.org/abs/1901.01342v2 | https://arxiv.org/pdf/1901.01342v2.pdf | AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection | Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual dataset for this task has constrained algorithm evaluations with respect to data diversity, environments, and accuracy. This has made comparisons and improvements difficult. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) that will be released publicly to facilitate algorithm development and enable comparisons. The dataset contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. We also present a new audio-visual approach for active speaker detection, and analyze its performance, demonstrating both its strength and the contributions of the dataset. | ['Zhonghua Xi', 'Arkadiusz Stopczynski', 'Sourish Chaudhuri', 'Joseph Roth', 'Caroline Pantofaru', 'Sharadh Ramaswamy', 'Radhika Marvin', 'Cordelia Schmid', 'Andrew Gallagher', 'Ondrej Klejch', 'Liat Kaver'] | 2019-01-05 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 3.78517807e-01 3.59772854e-02 -2.38044575e-01 -5.10730982e-01
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db738991-b291-453f-b318-a20bd7ca7599 | detection-of-gan-synthesized-street-videos | 2109.04991 | null | https://arxiv.org/abs/2109.04991v2 | https://arxiv.org/pdf/2109.04991v2.pdf | Detection of GAN-synthesized street videos | Research on the detection of AI-generated videos has focused almost exclusively on face videos, usually referred to as deepfakes. Manipulations like face swapping, face reenactment and expression manipulation have been the subject of an intense research with the development of a number of efficient tools to distinguish artificial videos from genuine ones. Much less attention has been paid to the detection of artificial non-facial videos. Yet, new tools for the generation of such kind of videos are being developed at a fast pace and will soon reach the quality level of deepfake videos. The goal of this paper is to investigate the detectability of a new kind of AI-generated videos framing driving street sequences (here referred to as DeepStreets videos), which, by their nature, can not be analysed with the same tools used for facial deepfakes. Specifically, we present a simple frame-based detector, achieving very good performance on state-of-the-art DeepStreets videos generated by the Vid2vid architecture. Noticeably, the detector retains very good performance on compressed videos, even when the compression level used during training does not match that used for the test videos. | ['Mauro Barni', 'Omran Alamayreh'] | 2021-09-10 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.88950562e-01 1.77010596e-01 1.79689720e-01 -2.01465636e-01
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22553006-72c6-4b07-9e48-a5327a9cc2f4 | contrastive-prototypical-network-with | null | null | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/121_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136790654.pdf | Contrastive Prototypical Network with Wasserstein Confidence Penalty | Unsupervised few-shot learning aims to learn the inductive bias from unlabeled dataset for solving the novel few-shot tasks. The existing unsupervised few-shot learning models and the contrastive learning models follow a unified paradigm. Therefore, we conduct empirical study under this paradigm and find that pairwise contrast, meta losses and large batch size are the important design factors. This results in our CPN (Contrastive Prototypical Network) model, which combines the prototypical loss with pairwise contrast and outperforms the existing models from this paradigm with modestly large batch size. Furthermore, the one-hot prediction target in CPN could lead to learning the sample-specific information. To this end, we propose Wasserstein Confidence Penalty which can impose appropriate penalty on overconfident predictions based on the semantic relationships among pseudo classes. Our full model, CPNWCP (Contrastive Prototypical Network with Wasserstein Confidence Penalty), achieves state-of-the-art performance on miniImageNet and tieredImageNet under unsupervised setting. Our code is available at https://github.com/Haoqing-Wang/CPNWCP. | ['Zhi-Hong Deng', 'Haoqing Wang'] | 2022-10-21 | null | null | null | european-conference-on-computer-vision-2022 | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.13126367e-01 1.86697826e-01 -3.74095082e-01 -6.45190179e-01
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6e4fc2cd-a66e-439e-a8ee-e9af017a45b9 | come-again-re-query-in-referring-expression | 2110.10206 | null | https://arxiv.org/abs/2110.10206v3 | https://arxiv.org/pdf/2110.10206v3.pdf | Evaluating and Improving Interactions with Hazy Oracles | Many AI systems integrate sensor inputs, world knowledge, and human-provided information to perform inference. While such systems often treat the human input as flawless, humans are better thought of as hazy oracles whose input may be ambiguous or outside of the AI system's understanding. In such situations it makes sense for the AI system to defer its inference while it disambiguates the human-provided information by, for example, asking the human to rephrase the query. Though this approach has been considered in the past, current work is typically limited to application-specific methods and non-standardized human experiments. We instead introduce and formalize a general notion of deferred inference. Using this formulation, we then propose a novel evaluation centered around the Deferred Error Volume (DEV) metric, which explicitly considers the tradeoff between error reduction and the additional human effort required to achieve it. We demonstrate this new formalization and an innovative deferred inference method on the disparate tasks of Single-Target Video Object Tracking and Referring Expression Comprehension, ultimately reducing error by up to 48% without any change to the underlying model or its parameters. | ['Jason J. Corso', 'Stephan J. Lemmer'] | 2021-10-19 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 4.40414041e-01 4.70270663e-01 -1.10398023e-03 -6.85098886e-01
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9b803d15-d8ba-4139-ba0b-53040867aea5 | csat-ftcn-a-fuzzy-oriented-model-with | null | null | https://link.springer.com/article/10.1007/s12559-023-10119-6 | https://link.springer.com/article/10.1007/s12559-023-10119-6 | CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition | Multimodal emotion analysis has become a hot trend because of its wide applications, such as the question-answering
system. However, in a real-world scenario, people usually have mixed or partial emotions about evaluating objects. In this
paper, we introduce a fuzzy temporal convolutional network based on contextual self-attention (CSAT-FTCN) to address
these challenges, which has a membership function modeling various fuzzy emotions for understanding emotions in a more
profound sense. Moreover, the CSAT-FTCN can obtain the dependency relationships of target utterances on internal own
key information and external contextual information to understand emotions in a more profound sense. Additionally, as for
multi-modality data, we introduce an attention fusion (ATF) mechanism to capture the dependency relationship between
different modality information. The experimental results show that our CSAT-FTCN outperforms state-of-the-art models
on tested datasets. The CSAT-FTCN network provides a novel method for multimodal emotion analysis. | ['Geng Tu', 'Runguo Wei', 'Hao liu', 'Dazhi Jiang'] | 2023-01-31 | null | null | null | cognitive-computation-2023-1 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-9.34945568e-02 -4.60094839e-01 1.92460701e-01 -7.16093838e-01
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e6856d6f-57bd-44e4-ac95-c5ba0201c26f | biodex-large-scale-biomedical-adverse-drug | 2305.13395 | null | https://arxiv.org/abs/2305.13395v1 | https://arxiv.org/pdf/2305.13395v1.pdf | BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance | Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to improve drug safety monitoring (pharmacovigilance, PV) through the use of Natural Language Processing (NLP). We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U.S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts. The core features of these reports include the reported weight, age, and biological sex of a patient, a set of drugs taken by the patient, the drug dosages, the reactions experienced, and whether the reaction was life threatening. In this work, we consider the task of predicting the core information of the report given its originating paper. We estimate human performance to be 72.0% F1, whereas our best model achieves 62.3% F1, indicating significant headroom on this task. We also begin to explore ways in which these models could help professional PV reviewers. Our code and data are available: https://github.com/KarelDO/BioDEX. | ['Christopher Potts', 'Jack Collins', 'Simon Ellershaw', 'Aneiss Ghodsi', 'Klim Zaporojets', 'Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'François Remy', "Karel D'Oosterlinck"] | 2023-05-22 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.10429540e-01 4.67302427e-02 -8.13101232e-01 -1.68349832e-01
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40fe2335-cbed-47e3-b615-02250459cd0d | whats-new-identifying-the-unfolding-of-new | 2302.07748 | null | https://arxiv.org/abs/2302.07748v3 | https://arxiv.org/pdf/2302.07748v3.pdf | Whats New? Identifying the Unfolding of New Events in Narratives | Narratives include a rich source of events unfolding over time and context. Automatic understanding of these events provides a summarised comprehension of the narrative for further computation (such as reasoning). In this paper, we study the Information Status (IS) of the events and propose a novel challenging task: the automatic identification of \textit{new} events in a narrative. We define an event as a triplet of subject, predicate, and object. The event is categorized as new with respect to the discourse context and whether it can be inferred through commonsense reasoning. We annotated a publicly available corpus of narratives with the new events at sentence level using human annotators. We present the annotation protocol and study the quality of the annotation and the difficulty of the task. We publish the annotated dataset, annotation materials, and machine learning baseline models for the task of new event extraction for narrative understanding. | ['Giuseppe Riccardi', 'Satoshi Nakamura', 'Koichiro Yoshino', 'Gabriel Roccabruna', 'Shohei Tanaka', 'Seyed Mahed Mousavi'] | 2023-02-15 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 7.28233874e-01 6.39466286e-01 -2.87861973e-01 -4.46241409e-01
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04caf2bd-667b-4866-b3c9-aaa857433730 | shopping-in-the-multiverse-a-counterfactual | 2007.10087 | null | https://arxiv.org/abs/2007.10087v1 | https://arxiv.org/pdf/2007.10087v1.pdf | Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution | We tackle the challenge of in-session attribution for on-site search engines in eCommerce. We phrase the problem as a causal counterfactual inference, and contrast the approach with rule-based systems from industry settings and prediction models from the multi-touch attribution literature. We approach counterfactuals in analogy with treatments in formal semantics, explicitly modeling possible outcomes through alternative shopper timelines; in particular, we propose to learn a generative browsing model over a target shop, leveraging the latent space induced by prod2vec embeddings; we show how natural language queries can be effectively represented in the same space and how "search intervention" can be performed to assess causal contribution. Finally, we validate the methodology on a synthetic dataset, mimicking important patterns emerged in customer interviews and qualitative analysis, and we present preliminary findings on an industry dataset from a partnering shop. | ['Jacopo Tagliabue', 'Bingqing Yu'] | 2020-07-20 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.06318748e-01 7.89278209e-01 -6.46123886e-01 -4.83688235e-01
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394b401c-ac95-4275-88da-fc3b4bb6b10e | semi-supervised-object-detection-for-sorghum | 2305.09810 | null | https://arxiv.org/abs/2305.09810v1 | https://arxiv.org/pdf/2305.09810v1.pdf | Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery | The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10\% of original training data. | ['Edward J. Delp', 'Changye Yang', 'Jiaqi Guo', 'Enyu Cai'] | 2023-05-16 | null | null | null | null | ['plant-phenotyping', 'semi-supervised-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.80151999e-01 1.27253756e-01 -2.74951071e-01 -4.61648047e-01
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c8d0d57f-ec16-4d8e-a25e-c7c522cd8493 | transformer-based-value-function | 2208.07298 | null | https://arxiv.org/abs/2208.07298v1 | https://arxiv.org/pdf/2208.07298v1.pdf | Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft | The StarCraft II Multi-Agent Challenge (SMAC) was created to be a challenging benchmark problem for cooperative multi-agent reinforcement learning (MARL). SMAC focuses exclusively on the problem of StarCraft micromanagement and assumes that each unit is controlled individually by a learning agent that acts independently and only possesses local information; centralized training is assumed to occur with decentralized execution (CTDE). To perform well in SMAC, MARL algorithms must handle the dual problems of multi-agent credit assignment and joint action evaluation. This paper introduces a new architecture TransMix, a transformer-based joint action-value mixing network which we show to be efficient and scalable as compared to the other state-of-the-art cooperative MARL solutions. TransMix leverages the ability of transformers to learn a richer mixing function for combining the agents' individual value functions. It achieves comparable performance to previous work on easy SMAC scenarios and outperforms other techniques on hard scenarios, as well as scenarios that are corrupted with Gaussian noise to simulate fog of war. | ['Gita Sukthankar', 'Syed Hammad Ahmed', 'Muhammad Junaid Khan'] | 2022-08-15 | null | null | null | null | ['starcraft-ii', 'smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games', 'playing-games'] | [-4.36544448e-01 5.63534833e-02 -2.93488115e-01 1.58322856e-01
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7dd31304-7eb4-4d39-ba23-2b02a20b2f6e | on-the-advance-of-making-language-models | 2206.02336 | null | https://arxiv.org/abs/2206.02336v3 | https://arxiv.org/pdf/2206.02336v3.pdf | Making Large Language Models Better Reasoners with Step-Aware Verifier | Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress in this area, but they still face difficulties in reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve their reasoning skills, previous work has proposed to guide the language model with prompts that elicit a series of reasoning steps before giving the final answer, achieving a significant improvement on GSM8K from 17.9% to 58.1% in problem-solving rate. In this paper, we present DIVERSE (Diverse Verifier on Reasoning Step), a novel approach that further enhances the reasoning capability of language models. DIVERSE has three main components: first, it generates diverse prompts to explore different reasoning paths for the same question; second, it uses a verifier to filter out incorrect answers based on a weighted voting scheme; and third, it verifies each reasoning step individually instead of the whole chain. We evaluate DIVERSE on the latest language model code-davinci-002 and show that it achieves new state-of-the-art results on six of eight reasoning benchmarks (e.g., GSM8K 74.4% to 83.2%). | ['Weizhu Chen', 'Jian-Guang Lou', 'Bei Chen', 'Qiang Fu', 'Shizhuo Zhang', 'Zeqi Lin', 'Yifei Li'] | 2022-06-06 | null | null | null | null | ['gsm8k', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [-6.82686120e-02 2.88895309e-01 4.34156768e-02 -3.85100782e-01
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5b248580-ed82-46d8-bffe-9b713f7fb030 | a-deep-bayesian-bandits-approach-for | 2205.02944 | null | https://arxiv.org/abs/2205.02944v2 | https://arxiv.org/pdf/2205.02944v2.pdf | A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior | Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data in preclinical/clinical studies for modeling multiple treatment efficacies is often an expensive, time-consuming process. Moreover, the randomization in treatment allocation proves to be suboptimal since some participants/samples are not receiving the most appropriate treatments during the trial. To address this challenge, we formulate drug screening study as a "contextual bandit" problem, in which an algorithm selects anticancer therapeutics based on contextual information about cancer cell lines while adapting its treatment strategy to maximize treatment response in an "online" fashion. We propose using a novel deep Bayesian bandits framework that uses functional prior to approximate posterior for drug response prediction based on multi-modal information consisting of genomic features and drug structure. We empirically evaluate our method on three large-scale in vitro pharmacogenomic datasets and show that our approach outperforms several benchmarks in identifying optimal treatment for a given cell line. | ['Su-In Lee', 'Yifang Chen', 'Mingyu Lu'] | 2022-05-05 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 6.54715657e-01 -3.38654011e-01 -1.19943917e+00 -2.44491249e-01
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b7030216-b679-496e-972f-118baa686639 | gradient-matching-generative-networks-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Sariyildiz_Gradient_Matching_Generative_Networks_for_Zero-Shot_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Sariyildiz_Gradient_Matching_Generative_Networks_for_Zero-Shot_Learning_CVPR_2019_paper.pdf | Gradient Matching Generative Networks for Zero-Shot Learning | Zero-shot learning (ZSL) is one of the most promising problems where substantial progress can potentially be achieved through unsupervised learning, due to distributional differences between supervised and zero-shot classes. For this reason, several works investigate the incorporation of discriminative domain adaptation techniques into ZSL, which, however, lead to modest improvements in ZSL accuracy. In contrast, we propose a generative model that can naturally learn from unsupervised examples, and synthesize training examples for unseen classes purely based on their class embeddings, and therefore, reduce the zero-shot learning problem into a supervised classification task. The proposed approach consists of two important components: (i) a conditional Generative Adversarial Network that learns to produce samples that mimic the characteristics of unsupervised data examples, and (ii) the Gradient Matching (GM) loss that measures the quality of the gradient signal obtained from the synthesized examples. Using our GM loss formulation, we enforce the generator to produce examples from which accurate classifiers can be trained. Experimental results on several ZSL benchmark datasets show that our approach leads to significant improvements over the state of the art in generalized zero-shot classification.
| [' Ramazan Gokberk Cinbis', 'Mert Bulent Sariyildiz'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['generalized-zero-shot-learning-unseen'] | ['computer-vision'] | [ 5.03587484e-01 2.16479391e-01 -2.28553697e-01 -4.89674151e-01
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3e9e6e6f-9ff7-4d0f-afcf-15208523697e | eico-improving-few-shot-text-classification | null | null | https://aclanthology.org/2022.findings-acl.283 | https://aclanthology.org/2022.findings-acl.283.pdf | EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization | While the prompt-based fine-tuning methods had advanced few-shot natural language understanding tasks, self-training methods are also being explored. This work revisits the consistency regularization in self-training and presents explicit and implicit consistency regularization enhanced language model (EICO). By employing both explicit and implicit consistency regularization, EICO advances the performance of prompt-based few-shot text classification. For implicit consistency regularization, we generate pseudo-label from the weakly-augmented view and predict pseudo-label from the strongly-augmented view. For explicit consistency regularization, we minimize the difference between the prediction of the augmentation view and the prediction of the original view. We conducted extensive experiments on six text classification datasets and found that with sixteen labeled examples, EICO achieves competitive performance compared to existing self-training few-shot learning methods. | ['Cheng Yao', 'Lei Zhao'] | null | null | null | null | findings-acl-2022-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.50025511e-01 2.54651606e-01 -5.91115296e-01 -7.14399934e-01
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3413af2d-42d1-469b-941b-5a20e1f6a04d | solving-the-steiner-tree-problem-with-few | 2011.04593 | null | https://arxiv.org/abs/2011.04593v1 | https://arxiv.org/pdf/2011.04593v1.pdf | Solving the Steiner Tree Problem with few Terminals | The Steiner tree problem is a well-known problem in network design, routing, and VLSI design. Given a graph, edge costs, and a set of dedicated vertices (terminals), the Steiner tree problem asks to output a sub-graph that connects all terminals at minimum cost. A state-of-the-art algorithm to solve the Steiner tree problem by means of dynamic programming is the Dijkstra-Steiner algorithm. The algorithm builds a Steiner tree of the entire instance by systematically searching for smaller instances, based on subsets of the terminals, and combining Steiner trees for these smaller instances. The search heavily relies on a guiding heuristic function in order to prune the search space. However, to ensure correctness, this algorithm allows only for limited heuristic functions, namely, those that satisfy a so-called consistency condition. In this paper, we enhance the Dijkstra-Steiner algorithm and establish a revisited algorithm, called DS*. The DS* algorithm allows for arbitrary lower bounds as heuristics relaxing the previous condition on the heuristic function. Notably, we can now use linear programming based lower bounds. Further, we capture new requirements for a heuristic function in a condition, which we call admissibility. We show that admissibility is indeed weaker than consistency and establish correctness of the DS* algorithm when using an admissible heuristic function. We implement DS* and combine it with modern preprocessing, resulting in an open-source solver (DS* Solve). Finally, we compare its performance on standard benchmarks and observe a competitive behavior. | ['Andre Schidler', 'Markus Hecher', 'Johannes K. Fichte'] | 2020-11-09 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 2.99117744e-01 5.53641796e-01 -5.17588258e-01 -1.03141420e-01
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f0289cfd-4993-4832-a2e1-696b7c1c8ad1 | identifying-risk-factors-for-heart-disease-in | null | null | https://aclanthology.org/W18-2303 | https://aclanthology.org/W18-2303.pdf | Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach | Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are mostly hybrid systems entailing the integration of knowledge-driven and data-driven methods, relying on dictionaries, rules and machine learning methods that require a substantial amount of human effort. This paper proposes a comparative analysis on the applicability of deep learning, a re-emerged data-driven technique, in the context of clinical text classification. Various deep learning architectures were devised and evaluated for extracting heart disease risk factors from clinical documents. The data provided for the 2014 i2b2/UTHealth shared task focusing on identifying risk factors for heart disease was used for system development and evaluation. Results have shown that a relatively simple deep learning model can achieve a high micro-averaged F-measure of 0.9081, which is comparable to the best systems from the shared task. This is highly encouraging given the simplicity of the deep learning approach compared to the heavily feature-engineered hybrid approaches that were required to achieve state-of-the-art performances. | ['Hamed Hassanzadeh', 'Thanat Chokwijitkul', 'Siegfried Perez', 'Anthony Nguyen'] | 2018-07-01 | null | null | null | ws-2018-7 | ['clinical-concept-extraction'] | ['medical'] | [ 5.95114678e-02 3.83524030e-01 -4.49365765e-01 -3.27046782e-01
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a5be6521-76a6-4f57-a3ba-3de068c1819a | stochastic-contextual-bandits-with-long | 2302.00814 | null | https://arxiv.org/abs/2302.00814v2 | https://arxiv.org/pdf/2302.00814v2.pdf | Stochastic Contextual Bandits with Long Horizon Rewards | The growing interest in complex decision-making and language modeling problems highlights the importance of sample-efficient learning over very long horizons. This work takes a step in this direction by investigating contextual linear bandits where the current reward depends on at most $s$ prior actions and contexts (not necessarily consecutive), up to a time horizon of $h$. In order to avoid polynomial dependence on $h$, we propose new algorithms that leverage sparsity to discover the dependence pattern and arm parameters jointly. We consider both the data-poor ($T<h$) and data-rich ($T\ge h$) regimes, and derive respective regret upper bounds $\tilde O(d\sqrt{sT} +\min\{ q, T\})$ and $\tilde O(\sqrt{sdT})$, with sparsity $s$, feature dimension $d$, total time horizon $T$, and $q$ that is adaptive to the reward dependence pattern. Complementing upper bounds, we also show that learning over a single trajectory brings inherent challenges: While the dependence pattern and arm parameters form a rank-1 matrix, circulant matrices are not isometric over rank-1 manifolds and sample complexity indeed benefits from the sparse reward dependence structure. Our results necessitate a new analysis to address long-range temporal dependencies across data and avoid polynomial dependence on the reward horizon $h$. Specifically, we utilize connections to the restricted isometry property of circulant matrices formed by dependent sub-Gaussian vectors and establish new guarantees that are also of independent interest. | ['Samet Oymak', 'Maryam Fazel', 'Fabio Pasqualetti', 'Yingcong Li', 'Yuzhen Qin'] | 2023-02-02 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.02035621e-01 8.92792046e-02 -6.13191783e-01 -5.99275865e-02
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925488a8-fe69-4643-8e2b-80b376da0e13 | single-stage-diffusion-nerf-a-unified | 2304.06714 | null | https://arxiv.org/abs/2304.06714v3 | https://arxiv.org/pdf/2304.06714v3.pdf | Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction | 3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects. Previous studies have used two-stage approaches that rely on pretrained NeRFs as real data to train diffusion models. In contrast, we propose a new single-stage training paradigm with an end-to-end objective that jointly optimizes a NeRF auto-decoder and a latent diffusion model, enabling simultaneous 3D reconstruction and prior learning, even from sparsely available views. At test time, we can directly sample the diffusion prior for unconditional generation, or combine it with arbitrary observations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates robust results comparable to or better than leading task-specific methods in unconditional generation and single/sparse-view 3D reconstruction. | ['Hao Su', 'Lingjie Liu', 'Zhuowen Tu', 'Wei Tian', 'Anpei Chen', 'Jiatao Gu', 'Hansheng Chen'] | 2023-04-13 | null | null | null | null | ['scene-generation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.32691157e-01 2.28201021e-02 2.45398402e-01 -4.76830065e-01
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dd48db85-ecec-43d7-a293-e63887206bad | data-augmentation-for-robust-keyword-spotting | 1808.00563 | null | http://arxiv.org/abs/1808.00563v1 | http://arxiv.org/pdf/1808.00563v1.pdf | Data Augmentation for Robust Keyword Spotting under Playback Interference | Accurate on-device keyword spotting (KWS) with low false accept and false
reject rate is crucial to customer experience for far-field voice control of
conversational agents. It is particularly challenging to maintain low false
reject rate in real world conditions where there is (a) ambient noise from
external sources such as TV, household appliances, or other speech that is not
directed at the device (b) imperfect cancellation of the audio playback from
the device, resulting in residual echo, after being processed by the Acoustic
Echo Cancellation (AEC) system. In this paper, we propose a data augmentation
strategy to improve keyword spotting performance under these challenging
conditions. The training set audio is artificially corrupted by mixing in music
and TV/movie audio, at different signal to interference ratios. Our results
show that we get around 30-45% relative reduction in false reject rates, at a
range of false alarm rates, under audio playback from such devices. | ['Anirudh Raju', 'Nikko Strom', 'Arindam Mandal', 'Xing Liu', 'Sankaran Panchapagesan'] | 2018-08-01 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.68248808e-01 -2.80359268e-01 4.76268440e-01 -9.84515622e-02
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14baff3f-d0f8-4d23-bd7f-7c1a81a74039 | learning-to-superoptimize-real-world-programs | 2109.13498 | null | https://arxiv.org/abs/2109.13498v2 | https://arxiv.org/pdf/2109.13498v2.pdf | Learning to Superoptimize Real-world Programs | Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive search and constraint solving techniques. Generally, these methods do not scale well to programs in real development scenarios, and as a result, superoptimization has largely been confined to small-scale, domain-specific, and/or synthetic program benchmarks. In this paper, we propose a framework to learn to superoptimize real-world programs by using neural sequence-to-sequence models. We created a dataset consisting of over 25K real-world x86-64 assembly functions mined from open-source projects and propose an approach, Self Imitation Learning for Optimization (SILO) that is easy to implement and outperforms a standard policy gradient learning approach on our dataset. Our method, SILO, superoptimizes 5.9% of our test set when compared with the gcc version 10.3 compiler's aggressive optimization level -O3. We also report that SILO's rate of superoptimization on our test set is over five times that of a standard policy gradient approach and a model pre-trained on compiler optimization demonstration. | ['Graham Neubig', 'Edward Schwartz', 'Claire Le Goues', 'Jeremy Lacomis', 'Pengcheng Yin', 'Alex Shypula'] | 2021-09-28 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-7.78853372e-02 5.36421537e-02 -8.78312469e-01 -5.20656049e-01
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818cd3a1-6c7b-4f23-b727-37fd66472802 | deep-fisher-kernels-end-to-end-learning-of | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Sydorov_Deep_Fisher_Kernels_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Sydorov_Deep_Fisher_Kernels_2014_CVPR_paper.pdf | Deep Fisher Kernels - End to End Learning of the Fisher Kernel GMM Parameters | Fisher Kernels and Deep Learning were two developments with significant impact on large-scale object categorization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object categorization datasets, such as ImageNet. Conceptually, however, they are perceived as very different and it is not uncommon for heated debates to spring up when advocates of both paradigms meet at conferences or workshops. In this work, we emphasize the similarities between both architectures rather than their differences and we argue that such a unified view allows us to transfer ideas from one domain to the other. As a concrete example we introduce a method for learning a support vector machine classifier with Fisher kernel at the same time as a task-specific data representation. We reinterpret the setting as a multi-layer feed forward network. Its final layer is the classifier, parameterized by a weight vector, and the two previous layers compute Fisher vectors, parameterized by the coefficients of a Gaussian mixture model. We introduce a gradient descent based learning algorithm that, in contrast to other feature learning techniques, is not just derived from intuition or biological analogy, but has a theoretical justification in the framework of statistical learning theory. Our experiments show that the new training procedure leads to significant improvements in classification accuracy while preserving the modularity and geometric interpretability of a support vector machine setup. | ['Christoph H. Lampert', 'Vladyslav Sydorov', 'Mayu Sakurada'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['object-categorization'] | ['computer-vision'] | [ 1.91255268e-02 1.30940471e-02 -1.27277151e-01 -4.66255963e-01
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8698d938-1ef7-48a4-9bd0-b63ecaba546a | so-net-self-organizing-network-for-point | 1803.04249 | null | http://arxiv.org/abs/1803.04249v4 | http://arxiv.org/pdf/1803.04249v4.pdf | SO-Net: Self-Organizing Network for Point Cloud Analysis | This paper presents SO-Net, a permutation invariant architecture for deep
learning with orderless point clouds. The SO-Net models the spatial
distribution of point cloud by building a Self-Organizing Map (SOM). Based on
the SOM, SO-Net performs hierarchical feature extraction on individual points
and SOM nodes, and ultimately represents the input point cloud by a single
feature vector. The receptive field of the network can be systematically
adjusted by conducting point-to-node k nearest neighbor search. In recognition
tasks such as point cloud reconstruction, classification, object part
segmentation and shape retrieval, our proposed network demonstrates performance
that is similar with or better than state-of-the-art approaches. In addition,
the training speed is significantly faster than existing point cloud
recognition networks because of the parallelizability and simplicity of the
proposed architecture. Our code is available at the project website.
https://github.com/lijx10/SO-Net | ['Ben M. Chen', 'Gim Hee Lee', 'Jiaxin Li'] | 2018-03-12 | so-net-self-organizing-network-for-point-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_SO-Net_Self-Organizing_Network_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_SO-Net_Self-Organizing_Network_CVPR_2018_paper.pdf | cvpr-2018-6 | ['point-cloud-reconstruction', '3d-point-cloud-linear-classification', '3d-part-segmentation', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.17054659e-01 -2.07469687e-01 -2.27333948e-01 -3.34394783e-01
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38a0b8e6-27dd-4a35-8fd2-4ffe416ec880 | curricular-contrastive-regularization-for | 2303.14218 | null | https://arxiv.org/abs/2303.14218v2 | https://arxiv.org/pdf/2303.14218v2.pdf | Curricular Contrastive Regularization for Physics-aware Single Image Dehazing | Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are nonconsensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically imbalanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C2PNet. Extensive experiments demonstrate that our C2PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets. | ['Yong Du', 'Junyu Dong', 'Shengfeng He', 'Jiahui Zhan', 'Yu Zheng'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_Curricular_Contrastive_Regularization_for_Physics-Aware_Single_Image_Dehazing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-dehazing'] | ['computer-vision'] | [ 2.08431154e-01 4.62331064e-02 3.61419886e-01 -3.20994198e-01
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c985f7f2-f8d6-4915-9d87-4d258d3bc83c | fully-automatic-liver-attenuation-estimation | 1906.09549 | null | https://arxiv.org/abs/1906.09549v2 | https://arxiv.org/pdf/1906.09549v2.pdf | Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations | Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, manual tracing is resource intensive. To address these limitations and to expand the availability of a quantitative CT measure of hepatic steatosis, we propose the automatic liver attenuation ROI-based measurement (ALARM) method for automated liver attenuation estimation. The ALARM method consists of two major stages: (1) deep convolutional neural network (DCNN)-based liver segmentation and (2) automated ROI extraction. First, liver segmentation was achieved using our previously developed SS-Net. Then, a single central ROI (center-ROI) and three circles ROI (periphery-ROI) were computed based on liver segmentation and morphological operations. The ALARM method is available as an open source Docker container (https://github.com/MASILab/ALARM).246 subjects with 738 abdomen CT scans from the African American-Diabetes Heart Study (AA-DHS) were used for external validation (testing), independent from the training and validation cohort (100 clinically acquired CT abdominal scans). | ['Yuankai Huo', 'J. Jeffery Carr', 'Barry I. Freedman', 'James G. Terry', 'Thomas A. Lasko', 'Sangeeta Nair', 'Jiachen Wang', 'Bennett A. Landman'] | 2019-06-23 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-2.00872794e-01 -3.64375144e-01 -1.80370227e-01 -3.90580893e-01
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46763a75-c3ca-431e-8796-06425065709d | sparse-distillation-speeding-up-text | 2110.08536 | null | https://arxiv.org/abs/2110.08536v2 | https://arxiv.org/pdf/2110.08536v2.pdf | Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models | Distilling state-of-the-art transformer models into lightweight student models is an effective way to reduce computation cost at inference time. The student models are typically compact transformers with fewer parameters, while expensive operations such as self-attention persist. Therefore, the improved inference speed may still be unsatisfactory for real-time or high-volume use cases. In this paper, we aim to further push the limit of inference speed by distilling teacher models into bigger, sparser student models -- bigger in that they scale up to billions of parameters; sparser in that most of the model parameters are n-gram embeddings. Our experiments on six single-sentence text classification tasks show that these student models retain 97% of the RoBERTa-Large teacher performance on average, and meanwhile achieve up to 600x speed-up on both GPUs and CPUs at inference time. Further investigation reveals that our pipeline is also helpful for sentence-pair classification tasks, and in domain generalization settings. | ['Aaron Jaech', 'Xiang Ren', 'Sinong Wang', 'Mike Lewis', 'Madian Khabsa', 'Qinyuan Ye'] | 2021-10-16 | sparse-distillation-speeding-up-text-1 | https://aclanthology.org/2022.naacl-main.169 | https://aclanthology.org/2022.naacl-main.169.pdf | naacl-2022-7 | ['sentence-pair-classification'] | ['natural-language-processing'] | [-6.78958222e-02 1.30741060e-01 -2.58982390e-01 -4.82847482e-01
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14ba173e-2014-4ecd-9022-2d6616249983 | mdpose-real-time-multi-person-pose-estimation | 2302.08751 | null | https://arxiv.org/abs/2302.08751v2 | https://arxiv.org/pdf/2302.08751v2.pdf | MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model | One of the major challenges in multi-person pose estimation is instance-aware keypoint estimation. Previous methods address this problem by leveraging an off-the-shelf detector, heuristic post-grouping process or explicit instance identification process, hindering further improvements in the inference speed which is an important factor for practical applications. From the statistical point of view, those additional processes for identifying instances are necessary to bypass learning the high-dimensional joint distribution of human keypoints, which is a critical factor for another major challenge, the occlusion scenario. In this work, we propose a novel framework of single-stage instance-aware pose estimation by modeling the joint distribution of human keypoints with a mixture density model, termed as MDPose. Our MDPose estimates the distribution of human keypoints' coordinates using a mixture density model with an instance-aware keypoint head consisting simply of 8 convolutional layers. It is trained by minimizing the negative log-likelihood of the ground truth keypoints. Also, we propose a simple yet effective training strategy, Random Keypoint Grouping (RKG), which significantly alleviates the underflow problem leading to successful learning of relations between keypoints. On OCHuman dataset, which consists of images with highly occluded people, our MDPose achieves state-of-the-art performance by successfully learning the high-dimensional joint distribution of human keypoints. Furthermore, our MDPose shows significant improvement in inference speed with a competitive accuracy on MS COCO, a widely-used human keypoint dataset, thanks to the proposed much simpler single-stage pipeline. | ['Nojun Kwak', 'Jihye Hwang', 'Jaeyoung Yoo', 'Seunghyeon Seo'] | 2023-02-17 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-4.11285877e-01 -2.18631566e-01 -1.51225492e-01 -1.88516498e-01
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c645062c-3835-43e2-b4e8-387baee4b3d8 | supsiam-non-contrastive-auxiliary-loss-for | 2302.07754 | null | https://arxiv.org/abs/2302.07754v1 | https://arxiv.org/pdf/2302.07754v1.pdf | SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers | We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task aids in supervised training of Euclidean neural networks (E3NNs) and increases manifold smoothness (MS) around point-cloud geometries. We demonstrate this property for multiple drug-activity prediction tasks while maintaining relevant performance metrics, and propose an extension of MS to probabilistic and regression settings. We provide an analysis of representation collapse, finding substantial effects of task-weighting, latent dimension, and regularization. We expect the presented protocol to aid in the development of reliable E3NNs from molecular conformers, even for small-data drug discovery programs. | ['Andrew Watkins', 'Nathan C. Frey', 'Jae Hyeon Lee', 'Joshua Yao-Yu Lin', 'Ji Won Park', 'Michael Maser'] | 2023-02-15 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.04796448e-01 -3.80838402e-02 -2.86624581e-01 -4.57282633e-01
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8f9d4ed3-272f-4475-8cda-02efd7d6f499 | hybrid-reinforced-medical-report-generation | 2210.13729 | null | https://arxiv.org/abs/2210.13729v1 | https://arxiv.org/pdf/2210.13729v1.pdf | Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty | To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these state-of-the-art methods mainly suffer from three shortcomings: (i) incomprehensive optimization, (ii) low-order and unidimensional attention mechanisms, and (iii) repeated generation. In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. Specifically, a hybrid reward with different weights is employed to remedy the limitations of single-metric-based rewards. We also propose a search algorithm with linear complexity to approximate the best weight combination. Furthermore, we use m-linear attention modules to explore high-order feature interactions and to achieve multi-modal reasoning, while a repetition penalty applies penalties to repeated terms during the model's training process. Extensive experimental studies on two public datasets show that HReMRG-MR greatly outperforms the state-of-the-art baselines in terms of all metrics. We also conducted a series of ablation experiments to prove the effectiveness of all our proposed components. We also performed a reward search toy experiment to give evidence that our proposed search approach can significantly reduce the search time while approximating the best performance. | ['Thomas Lukasiewicz', 'Chang Qi', 'Junyang Chen', 'Zhenghua Xu', 'Wenting Xu'] | 2022-10-14 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 1.05697721e-01 3.47954750e-01 -1.82685152e-01 -3.56703401e-01
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97be1198-0927-4e68-b967-0ecbeff4222c | piano-timbre-development-analysis-using | 2112.03214 | null | https://arxiv.org/abs/2112.03214v2 | https://arxiv.org/pdf/2112.03214v2.pdf | Piano Timbre Development Analysis using Machine Learning | A data set of recorded single played tones of a concert grand piano is investigated using Machine Learning (ML) on psychoacoustic timbre features. The examined instrument has been recorded at two stages: firstly right after manufacture and secondly after being played in a concert hall for one year. A previous study [Plath2019] revealed that listeners clearly distinguished both stages but no clear correlation with acoustics, signal processing tools or verbalizations of perceived differences could be found. Using a Self-Organizing Map (SOM), training single as well as double feature sets, it can be shown that spectral flux is able to perfectly cluster the two stages. Sound Pressure Level (SPL), roughness, and fractal correlation dimension (as a measure for initial transient chaoticity) are furthermore able to order the keys with respect to high and low notes. Combining spectral flux with the three other features in double-feature training sets maintains stage clustering only for SPL and fractal dimension, showing sub-clusters for both stages. These sub-clusters point to a homogenization of SPL for stage 2 with respect to stage 1 and a pronounced ordering and sub-clustering of key regions with respect to initial transient chaoticity. | ['Rolf Bader', 'Niko Plath'] | 2021-12-06 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-1.04531497e-01 -1.96716383e-01 4.63098884e-01 2.50474513e-02
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867b12dc-e47c-473a-aafa-32fb877fe6c4 | identifying-supporting-facts-for-multi-hop | 1910.00290 | null | https://arxiv.org/abs/1910.00290v1 | https://arxiv.org/pdf/1910.00290v1.pdf | Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks | Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, complex Question Answering (QA) typically requires multi-hop reasoning - i.e. the integration of supporting facts from different sources, to infer the correct answer. This paper proposes Document Graph Network (DGN), a message passing architecture for the identification of supporting facts over a graph-structured representation of text. The evaluation on HotpotQA shows that DGN obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning. | ['Viktor Schlegel', 'Mokanarangan Thayaparan', 'Marco Valentino', 'Andre Freitas'] | 2019-10-01 | identifying-supporting-facts-for-multi-hop-1 | https://aclanthology.org/D19-5306 | https://aclanthology.org/D19-5306.pdf | ws-2019-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.56460607e-01 9.17692125e-01 1.13471560e-01 -3.98653179e-01
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dc0564d3-5b2c-497c-af04-58f54d67cd05 | enabling-a-network-ai-gym-for-autonomous | 2304.01366 | null | https://arxiv.org/abs/2304.01366v1 | https://arxiv.org/pdf/2304.01366v1.pdf | Enabling A Network AI Gym for Autonomous Cyber Agents | This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high-fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL) is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S is capable to train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E showing full decision proficiency in the emulated "real" network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks. | ['Thomas Kunz', 'James Hailing Rao', 'Adrian Taylor', 'Jean-Pierre S. El Rami', 'Li Li'] | 2023-04-03 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.60561228e-01 6.88922703e-01 2.65107393e-01 1.16228275e-01
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9cc54934-5966-43c4-92a7-e1bd68554563 | effective-few-shot-classification-with | null | null | https://aclanthology.org/2020.coling-main.92 | https://aclanthology.org/2020.coling-main.92.pdf | Effective Few-Shot Classification with Transfer Learning | Few-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the Amazon Review Sentiment Classification (ARSC) text dataset for use in the few-shot setting. In this work, we use the ARSC dataset to study a simple application of transfer learning approaches to few-shot classification. We train a single binary classifier to learn all few-shot classes jointly by prefixing class identifiers to the input text. Given the text and class, the model then makes a binary prediction for that text/class pair. Our results show that this simple approach can outperform most published results on this dataset. Surprisingly, we also show that including domain information as part of the task definition only leads to a modest improvement in model accuracy, and zero-shot classification, without further fine-tuning on few-shot domains, performs equivalently to few-shot classification. These results suggest that the classes in the ARSC few-shot task, which are defined by the intersection of domain and rating, are actually very similar to each other, and that a more suitable dataset is needed for the study of few-shot text classification. | ["Neil O{'}Hare", 'Kapil Thadani', 'Aakriti Gupta'] | 2020-12-01 | null | null | null | coling-2020-8 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.37555140e-01 -1.14150971e-01 -4.57876921e-01 -7.77512729e-01
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1c38a390-b31c-4ede-9c85-a8fa488bf445 | causal-inference-out-of-control-estimating | 2302.04989 | null | https://arxiv.org/abs/2302.04989v1 | https://arxiv.org/pdf/2302.04989v1.pdf | Causal Inference out of Control: Estimating the Steerability of Consumption | Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inference problem we call the steerability of consumption that abstracts many settings of interest. Focusing on observational designs and exploiting the structure of the problem, we exhibit a set of assumptions for causal identifiability that significantly weaken the often unrealistic overlap assumptions of standard designs. The key novelty of our approach is to explicitly model the dynamics of consumption over time, viewing the platform as a controller acting on a dynamical system. From this dynamical systems perspective, we are able to show that exogenous variation in consumption and appropriately responsive algorithmic control actions are sufficient for identifying steerability of consumption. Our results illustrate the fruitful interplay of control theory and causal inference, which we illustrate with examples from econometrics, macroeconomics, and machine learning. | ['Celestine Mendler-Dünner', 'Moritz Hardt', 'Gary Cheng'] | 2023-02-10 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 1.10224932e-01 2.10936651e-01 -8.64519417e-01 2.56552368e-01
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b17ab42f-3f67-4dcf-814c-facd99874c6e | representation-learning-with-autoencoders-for | 1908.09174 | null | https://arxiv.org/abs/1908.09174v2 | https://arxiv.org/pdf/1908.09174v2.pdf | Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study | Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this paper, we propose a predictive modeling approach based on deep learning based feature representations and word embedding techniques. Our method uses different deep architectures (stacked sparse autoencoders, deep belief network, adversarial autoencoders and variational autoencoders) for feature representation in higher-level abstraction to obtain effective and robust features from EHRs, and then build prediction models on top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled data is scarce. We investigate the performance of representation learning through a supervised learning approach. Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights in the choice of deep feature representation techniques. Our experiments demonstrate that for small data sets, stacked sparse autoencoder demonstrates a superior generality performance in prediction due to sparsity regularization whereas variational autoencoders outperform the competing approaches for large data sets due to its capability of learning the representation distribution | ['Dongxiao Zhu', 'Najibesadat Sadati', 'Milad Zafar Nezhad', 'Ratna Babu Chinnam'] | 2019-08-24 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.20169692e-01 1.28266707e-01 3.67953372e-03 -3.86202872e-01
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410f246d-49d9-42e7-a55b-d50f4445b6c7 | locoop-few-shot-out-of-distribution-detection | 2306.01293 | null | https://arxiv.org/abs/2306.01293v2 | https://arxiv.org/pdf/2306.01293v2.pdf | LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning | We present a novel vision-language prompt learning approach for few-shot out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from classes that are unseen during training using only a few labeled in-distribution (ID) images. While prompt learning methods such as CoOp have shown effectiveness and efficiency in few-shot ID classification, they still face limitations in OOD detection due to the potential presence of ID-irrelevant information in text embeddings. To address this issue, we introduce a new approach called Local regularized Context Optimization (LoCoOp), which performs OOD regularization that utilizes the portions of CLIP local features as OOD features during training. CLIP's local features have a lot of ID-irrelevant nuisances (e.g., backgrounds), and by learning to push them away from the ID class text embeddings, we can remove the nuisances in the ID class text embeddings and enhance the separation between ID and OOD. Experiments on the large-scale ImageNet OOD detection benchmarks demonstrate the superiority of our LoCoOp over zero-shot, fully supervised detection methods and prompt learning methods. Notably, even in a one-shot setting -- just one label per class, LoCoOp outperforms existing zero-shot and fully supervised detection methods. The code will be available via https://github.com/AtsuMiyai/LoCoOp. | ['Kiyoharu Aizawa', 'Go Irie', 'Qing Yu', 'Atsuyuki Miyai'] | 2023-06-02 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 5.85880093e-02 2.26148982e-02 -4.43804175e-01 -1.87203571e-01
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2323ede0-f07e-413f-9c90-0a2e5ba4f84c | semeval-2017-task-12-clinical-tempeval | null | null | https://aclanthology.org/S17-2093 | https://aclanthology.org/S17-2093.pdf | SemEval-2017 Task 12: Clinical TempEval | Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were evaluated on clinical and pathology notes from Mayo Clinic cancer patients, annotated with an extension of TimeML for the clinical domain. 11 teams participated in the tasks, with the best systems achieving F1 scores above 0.55 for time expressions, above 0.70 for event expressions, and above 0.40 for temporal relations. Most tasks observed about a 20 point drop over Clinical TempEval 2016, where systems were trained and evaluated on the same domain (colon cancer). | ['Guergana Savova', 'Steven Bethard', 'Martha Palmer', 'James Pustejovsky'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 1.70638959e-04 3.91675293e-01 -5.91664791e-01 -5.22924364e-01
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322fed48-0ee7-433f-a938-f89e879b9795 | robo-gym-an-open-source-toolkit-for | 2007.02753 | null | https://arxiv.org/abs/2007.02753v2 | https://arxiv.org/pdf/2007.02753v2.pdf | robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots | Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. In order to increase the use of DRL with real robots and reduce the gap between simulation and real world robotics, we propose an open source toolkit: robo-gym. We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots: a mobile robot and a robot arm. The distributed capabilities of the framework enable several advantages like using distributed algorithms, separating the workload of simulation and training on different physical machines as well as enabling the future opportunity to train in simulation and real world at the same time. Finally we offer an overview and comparison of robo-gym with other frequently used state-of-the-art DRL frameworks. | ['Stephan Mühlbacher-Karrer', 'Matteo Lucchi', 'Friedemann Zindler', 'Horst Pichler'] | 2020-07-06 | null | null | null | null | ['industrial-robots'] | ['robots'] | [-3.05024028e-01 5.29897623e-02 3.16104591e-01 -2.22831637e-01
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2f7169bc-24c6-4997-86d5-859735723b6a | discourse-representation-structure-parsing-1 | null | null | https://aclanthology.org/W19-1203 | https://aclanthology.org/W19-1203.pdf | Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model | We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8{\%} F1 in the DRS parsing shared task. | ['Mirella Lapata', 'Jiangming Liu', 'Shay B. Cohen'] | 2019-05-01 | null | null | null | ws-2019-5 | ['drs-parsing'] | ['natural-language-processing'] | [ 5.28063476e-01 8.31993878e-01 -3.42071474e-01 -4.18473989e-01
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3fa0e55c-f25c-4ac3-9f47-31ac0abee70c | gen-nerf-efficient-and-generalizable-neural | 2304.11842 | null | https://arxiv.org/abs/2304.11842v2 | https://arxiv.org/pdf/2304.11842v2.pdf | Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design | Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural Radiance Fields (NeRFs) have gained increasing popularity thanks to their cross-scene generalization capability. Despite their promise, the real-device deployment of generalizable NeRFs is bottlenecked by their prohibitive complexity due to the required massive memory accesses to acquire scene features, causing their ray marching process to be memory-bounded. To this end, we propose Gen-NeRF, an algorithm-hardware co-design framework dedicated to generalizable NeRF acceleration, which for the first time enables real-time generalizable NeRFs. On the algorithm side, Gen-NeRF integrates a coarse-then-focus sampling strategy, leveraging the fact that different regions of a 3D scene contribute differently to the rendered pixel, to enable sparse yet effective sampling. On the hardware side, Gen-NeRF highlights an accelerator micro-architecture to maximize the data reuse opportunities among different rays by making use of their epipolar geometric relationship. Furthermore, our Gen-NeRF accelerator features a customized dataflow to enhance data locality during point-to-hardware mapping and an optimized scene feature storage strategy to minimize memory bank conflicts. Extensive experiments validate the effectiveness of our proposed Gen-NeRF framework in enabling real-time and generalizable novel view synthesis. | ['Yingyan Lin', 'Haoran You', 'Sixu Li', 'Shunyao Zhang', 'Jiayi Yuan', 'Zhifan Ye', 'Yonggan Fu'] | 2023-04-24 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 2.04869092e-01 -2.62144983e-01 7.95569047e-02 -2.71242231e-01
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1b9d0aa3-bba2-46a3-843c-5426d21b7a3c | robust-dialogue-utterance-rewriting-as | 2012.14535 | null | https://arxiv.org/abs/2012.14535v1 | https://arxiv.org/pdf/2012.14535v1.pdf | Robust Dialogue Utterance Rewriting as Sequence Tagging | The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different domain. We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. As a common issue of most tagging models for text generation, the model's outputs may lack fluency. To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge improvements of our model over the current state-of-the-art systems on domain transfer. | ['Dong Yu', 'Zhaopeng Tu', 'Kun Xu', 'LiWei Wang', 'Linfeng Song', 'Jie Hao'] | 2020-12-29 | null | null | null | null | ['dialogue-rewriting'] | ['natural-language-processing'] | [ 4.67218816e-01 7.41368532e-01 4.85816449e-02 -4.32241023e-01
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d95c5378-a15d-4421-88ad-18db501e11f5 | omnimvs-end-to-end-learning-for | 1908.06257 | null | https://arxiv.org/abs/1908.06257v1 | https://arxiv.org/pdf/1908.06257v1.pdf | OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching | In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with ultra wide field-of-view (FOV) cameras on an omnidirectional rig are processed by the feature extraction module, and then the deep feature maps are warped onto the concentric spheres swept through all candidate depths using the calibrated camera parameters. The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information. In addition, we present large-scale synthetic datasets for training and testing omnidirectional multi-view stereo algorithms. Our datasets consist of 11K ground-truth depth maps and 45K fisheye images in four orthogonal directions with various objects and environments. Experimental results show that the proposed method generates excellent results in both synthetic and real-world environments, and it outperforms the prior art and the omnidirectional versions of the state-of-the-art conventional stereo algorithms. | ['Changhee Won', 'Jongwoo Lim', 'Jongbin Ryu'] | 2019-08-17 | omnimvs-end-to-end-learning-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Won_OmniMVS_End-to-End_Learning_for_Omnidirectional_Stereo_Matching_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Won_OmniMVS_End-to-End_Learning_for_Omnidirectional_Stereo_Matching_ICCV_2019_paper.pdf | iccv-2019-10 | ['stereo-matching'] | ['computer-vision'] | [-8.33149627e-02 -2.17522651e-01 4.01191086e-01 -7.67254174e-01
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c662c102-186e-4b94-9d5b-495c887b6378 | depth-pooling-based-large-scale-3d-action | 1804.01194 | null | http://arxiv.org/abs/1804.01194v2 | http://arxiv.org/pdf/1804.01194v2.pdf | Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks | This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI), for both isolated and continuous action recognition. These dynamic
images are constructed from a segmented sequence of depth maps using
hierarchical bidirectional rank pooling to effectively capture the
spatial-temporal information. Specifically, DDI exploits the dynamics of
postures over time and DDNI and DDMNI exploit the 3D structural information
captured by depth maps. Upon the proposed representations, a ConvNet based
method is developed for action recognition. The image-based representations
enable us to fine-tune the existing Convolutional Neural Network (ConvNet)
models trained on image data without training a large number of parameters from
scratch. The proposed method achieved the state-of-art results on three large
datasets, namely, the Large-scale Continuous Gesture Recognition Dataset (means
Jaccard index 0.4109), the Large-scale Isolated Gesture Recognition Dataset
(59.21%), and the NTU RGB+D Dataset (87.08% cross-subject and 84.22%
cross-view) even though only the depth modality was used. | ['Philip Ogunbona', 'Zhimin Gao', 'Chang Tang', 'Wanqing Li', 'Pichao Wang'] | 2018-03-17 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 5.67477524e-01 -2.53139466e-01 -2.10161939e-01 -4.31546748e-01
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8c2ab131-e716-4df3-a47b-c0c981b3793e | stochastic-second-order-methods-provably-beat | 2205.12856 | null | https://arxiv.org/abs/2205.12856v2 | https://arxiv.org/pdf/2205.12856v2.pdf | Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function | We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with $1\le\alpha\le2$ which holds in a wide range of applications in machine learning and signal processing. This condition ensures that any first-order stationary point is a global optimum. We prove that the total sample complexity of SCRN in achieving $\epsilon$-global optimum is $\mathcal{O}(\epsilon^{-7/(2\alpha)+1})$ for $1\le\alpha< 3/2$ and $\mathcal{\tilde{O}}(\epsilon^{-2/(\alpha)})$ for $3/2\le\alpha\le 2$. SCRN improves the best-known sample complexity of stochastic gradient descent. Even under a weak version of gradient dominance property, which is applicable to policy-based reinforcement learning (RL), SCRN achieves the same improvement over stochastic policy gradient methods. Additionally, we show that the average sample complexity of SCRN can be reduced to ${\mathcal{O}}(\epsilon^{-2})$ for $\alpha=1$ using a variance reduction method with time-varying batch sizes. Experimental results in various RL settings showcase the remarkable performance of SCRN compared to first-order methods. | ['Patrick Thiran', 'Negar Kiyavash', 'Niao He', 'Saber Salehkaleybar', 'Saeed Masiha'] | 2022-05-25 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.65264383e-02 5.79777807e-02 -1.59730747e-01 -7.21062422e-02
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f2d159ae-f58a-4bde-8e67-7f82a9fc8c2f | scribble-supervised-cell-segmentation-using | 2306.14136 | null | https://arxiv.org/abs/2306.14136v1 | https://arxiv.org/pdf/2306.14136v1.pdf | Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization | Current state-of-the-art supervised deep learning-based segmentation approaches have demonstrated superior performance in medical image segmentation tasks. However, such supervised approaches require fully annotated pixel-level ground-truth labels, which are labor-intensive and time-consuming to acquire. Recently, Scribble2Label (S2L) demonstrated that using only a handful of scribbles with self-supervised learning can generate accurate segmentation results without full annotation. However, owing to the relatively small size of scribbles, the model is prone to overfit and the results may be biased to the selection of scribbles. In this work, we address this issue by employing a novel multiscale contrastive regularization term for S2L. The main idea is to extract features from intermediate layers of the neural network for contrastive loss so that structures at various scales can be effectively separated. To verify the efficacy of our method, we conducted ablation studies on well-known datasets, such as Data Science Bowl 2018 and MoNuSeg. The results show that the proposed multiscale contrastive loss is effective in improving the performance of S2L, which is comparable to that of the supervised learning segmentation method. | ['Won-Ki Jeong', 'Kanggeun Lee', 'Hyun-Jic Oh'] | 2023-06-25 | null | null | null | null | ['self-supervised-learning', 'medical-image-segmentation', 'cell-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 3.39550465e-01 2.26614013e-01 -1.62093148e-01 -3.78510416e-01
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87dc3e39-4f7d-4b7b-bec7-fa0da68ee38f | multi-task-self-supervised-learning-for-1 | 2001.09239 | null | https://arxiv.org/abs/2001.09239v2 | https://arxiv.org/pdf/2001.09239v2.pdf | Multi-task self-supervised learning for Robust Speech Recognition | Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation. Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions. | ['Pawel Swietojanski', 'Mirco Ravanelli', 'Jianyuan Zhong', 'Jan Trmal', 'Santiago Pascual', 'Joao Monteiro', 'Yoshua Bengio'] | 2020-01-25 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.14046402e-01 2.84958661e-01 3.79651576e-01 -4.10979122e-01
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fd678add-7607-439f-8bc2-b7de97cce99a | palmtree-learning-an-assembly-language-model | 2103.03809 | null | https://arxiv.org/abs/2103.03809v3 | https://arxiv.org/pdf/2103.03809v3.pdf | PalmTree: Learning an Assembly Language Model for Instruction Embedding | Deep learning has demonstrated its strengths in numerous binary analysis tasks, including function boundary detection, binary code search, function prototype inference, value set analysis, etc. When applying deep learning to binary analysis tasks, we need to decide what input should be fed into the neural network model. More specifically, we need to answer how to represent an instruction in a fixed-length vector. The idea of automatically learning instruction representations is intriguing, however the existing schemes fail to capture the unique characteristics of disassembly. These schemes ignore the complex intra-instruction structures and mainly rely on control flow in which the contextual information is noisy and can be influenced by compiler optimizations. In this paper, we propose to pre-train an assembly language model called PalmTree for generating general-purpose instruction embeddings by conducting self-supervised training on large-scale unlabeled binary corpora. PalmTree utilizes three pre-training tasks to capture various characteristics of assembly language. These training tasks overcome the problems in existing schemes, thus can help to generate high-quality representations. We conduct both intrinsic and extrinsic evaluations, and compare PalmTree with other instruction embedding schemes. PalmTree has the best performance for intrinsic metrics, and outperforms the other instruction embedding schemes for all downstream tasks. | ['Heng Yin', 'Qu Yu', 'Xuezixiang Li'] | 2021-01-21 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.92961323e-03 -3.57492238e-01 -7.46688247e-01 -5.00988722e-01
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efcdd478-40e6-4a9d-a568-118ea9335021 | multitask-learning-and-benchmarking-with | 1703.07771 | null | https://arxiv.org/abs/1703.07771v3 | https://arxiv.org/pdf/1703.07771v3.pdf | Multitask learning and benchmarking with clinical time series data | Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models. | ['Hrant Khachatrian', 'Aram Galstyan', 'Hrayr Harutyunyan', 'Greg Ver Steeg', 'David C. Kale'] | 2017-03-22 | null | null | null | null | ['computational-phenotyping', 'length-of-stay-prediction', 'phenotype-classification'] | ['medical', 'medical', 'medical'] | [ 3.13054800e-01 -5.78374118e-02 -3.89048904e-01 -6.68176532e-01
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d236f824-abe9-4279-ae71-f4c34f59ef50 | graph-based-sinogram-denoising-for | 1603.04203 | null | http://arxiv.org/abs/1603.04203v1 | http://arxiv.org/pdf/1603.04203v1.pdf | Graph Based Sinogram Denoising for Tomographic Reconstructions | Limited data and low dose constraints are common problems in a variety of
tomographic reconstruction paradigms which lead to noisy and incomplete data.
Over the past few years sinogram denoising has become an essential
pre-processing step for low dose Computed Tomographic (CT) reconstructions. We
propose a novel sinogram denoising algorithm inspired by the modern field of
signal processing on graphs. Graph based methods often perform better than
standard filtering operations since they can exploit the signal structure. This
makes the sinogram an ideal candidate for graph based denoising since it
generally has a piecewise smooth structure. We test our method with a variety
of phantoms and different reconstruction methods. Our numerical study shows
that the proposed algorithm improves the performance of analytical filtered
back-projection (FBP) and iterative methods ART (Kaczmarz) and SIRT
(Cimmino).We observed that graph denoised sinogram always minimizes the error
measure and improves the accuracy of the solution as compared to regular
reconstructions. | ['Pierre Vandergheynst', 'Faisal Mahmood', 'Ulf Skoglund', 'Nauman Shahid'] | 2016-03-14 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.37101692e-01 1.10035948e-01 1.86424181e-01 -2.96444505e-01
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8bfb99bf-7ecf-40c9-8e98-8d328dc2aaca | polarity-in-the-classroom-a-case-study | 2108.10068 | null | https://arxiv.org/abs/2108.10068v1 | https://arxiv.org/pdf/2108.10068v1.pdf | Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment | Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has 1) leveraged sentiment analysis in the peer-review process to inform or validate grades or 2) utilized aspect extraction to craft a review form from what students actually communicated. Our work utilizes, rather than discards, student data from review form comments to deliver better information to the instructor. In this work, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm which provides a fine-grained sentiment score from text alone. We end by analyzing validity and discussing conclusions from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses. | ['Paul Rosen', 'Les A. Piegl', 'Zachariah J. Beasley'] | 2021-08-02 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 7.18312338e-02 2.47653350e-01 -2.93502659e-01 -7.29991138e-01
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6d433e65-166d-49df-a665-b9a893bf41ca | contrastive-clustering | 2009.09687 | null | https://arxiv.org/abs/2009.09687v1 | https://arxiv.org/pdf/2009.09687v1.pdf | Contrastive Clustering | In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19\% (39\%) performance improvement compared with the best baseline. | ['Dezhong Peng', 'Zitao Liu', 'Xi Peng', 'Joey Tianyi Zhou', 'Yunfan Li', 'Peng Hu'] | 2020-09-21 | null | null | null | null | ['image-clustering', 'online-clustering'] | ['computer-vision', 'computer-vision'] | [ 3.29879038e-02 -2.96619087e-02 -1.89743266e-01 -5.84190607e-01
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12aad737-e746-4dbc-86dd-31547bd2de01 | graphxnet-chest-x-ray-classification-under | 1907.10085 | null | https://arxiv.org/abs/1907.10085v3 | https://arxiv.org/pdf/1907.10085v3.pdf | GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision | The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data. | ['Carola-Bibiane Schönlieb', 'Robby T. Tan', 'Philip Sellars', 'Ruoteng Li', 'Angelica I. Aviles-Rivero', 'Qingnan Fan', 'Nicolas Papadakis'] | 2019-07-23 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 5.31913102e-01 5.10418117e-01 -2.40150064e-01 -6.19402230e-01
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fa67a126-9b04-47da-b66b-1ec0acb33f3b | on-leveraging-variational-graph-embeddings | 2204.11848 | null | https://arxiv.org/abs/2204.11848v1 | https://arxiv.org/pdf/2204.11848v1.pdf | On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning | Humans are able to identify and categorize novel compositions of known concepts. The task in Compositional Zero-Shot learning (CZSL) is to learn composition of primitive concepts, i.e. objects and states, in such a way that even their novel compositions can be zero-shot classified. In this work, we do not assume any prior knowledge on the feasibility of novel compositions i.e.open-world setting, where infeasible compositions dominate the search space. We propose a Compositional Variational Graph Autoencoder (CVGAE) approach for learning the variational embeddings of the primitive concepts (nodes) as well as feasibility of their compositions (via edges). Such modelling makes CVGAE scalable to real-world application scenarios. This is in contrast to SOTA method, CGE, which is computationally very expensive. e.g.for benchmark C-GQA dataset, CGE requires 3.94 x 10^5 nodes, whereas CVGAE requires only 1323 nodes. We learn a mapping of the graph and image embeddings onto a common embedding space. CVGAE adopts a deep metric learning approach and learns a similarity metric in this space via bi-directional contrastive loss between projected graph and image embeddings. We validate the effectiveness of our approach on three benchmark datasets.We also demonstrate via an image retrieval task that the representations learnt by CVGAE are better suited for compositional generalization. | ['Martin Kleinsteuber', 'Zhihui Pan', 'Muhammad Umer Anwaar'] | 2022-04-23 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 1.12066895e-01 2.85179883e-01 9.30109397e-02 -5.27986921e-02
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bc8a9025-f4f1-4317-b002-50d73d6b6433 | a-detection-and-segmentation-architecture-for | 1809.03917 | null | http://arxiv.org/abs/1809.03917v2 | http://arxiv.org/pdf/1809.03917v2.pdf | A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images | This report summarises our method and validation results for the ISIC
Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1:
Lesion Segmentation. We present a two-stage method for lesion segmentation with
optimised training method and ensemble post-process. Our method achieves
state-of-the-art performance on lesion segmentation and we win the first place
in ISIC 2018 task1. | ['Hao Jiang', 'Ting Liu', 'Chengyao Qian', 'Pengfei Wang', 'Biao Sun', 'Zhe Wang', 'Mingxin Guan'] | 2018-09-11 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 1.08581007e+00 1.63686141e-01 -5.12194753e-01 1.59970112e-02
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5552d977-9bac-4bcd-88c2-5b72fa5e36a1 | controlling-the-risk-of-conversational-search | 2101.06327 | null | https://arxiv.org/abs/2101.06327v1 | https://arxiv.org/pdf/2101.06327v1.pdf | Controlling the Risk of Conversational Search via Reinforcement Learning | Users often formulate their search queries with immature language without well-developed keywords and complete structures. Such queries fail to express their true information needs and raise ambiguity as fragmental language often yield various interpretations and aspects. This gives search engines a hard time processing and understanding the query, and eventually leads to unsatisfactory retrieval results. An alternative approach to direct answer while facing an ambiguous query is to proactively ask clarifying questions to the user. Recent years have seen many works and shared tasks from both NLP and IR community about identifying the need for asking clarifying question and methodology to generate them. An often neglected fact by these works is that although sometimes the need for clarifying questions is correctly recognized, the clarifying questions these system generate are still off-topic and dissatisfaction provoking to users and may just cause users to leave the conversation. In this work, we propose a risk-aware conversational search agent model to balance the risk of answering user's query and asking clarifying questions. The agent is fully aware that asking clarifying questions can potentially collect more information from user, but it will compare all the choices it has and evaluate the risks. Only after then, it will make decision between answering or asking. To demonstrate that our system is able to retrieve better answers, we conduct experiments on the MSDialog dataset which contains real-world customer service conversations from Microsoft products community. We also purpose a reinforcement learning strategy which allows us to train our model on the original dataset directly and saves us from any further data annotation efforts. Our experiment results show that our risk-aware conversational search agent is able to significantly outperform strong non-risk-aware baselines. | ['Qingyao Ai', 'Zhenduo Wang'] | 2021-01-15 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.59413663e-02 5.96728802e-01 -3.13584328e-01 -5.95427454e-01
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f0f9aac6-e546-4f19-83b5-738a2e53b81f | video-instance-shadow-detection | 2211.12827 | null | https://arxiv.org/abs/2211.12827v1 | https://arxiv.org/pdf/2211.12827v1.pdf | Video Instance Shadow Detection | Video instance shadow detection aims to simultaneously detect, segment, associate, and track paired shadow-object associations in videos. This work has three key contributions to the task. First, we design SSIS-Track, a new framework to extract shadow-object associations in videos with paired tracking and without category specification; especially, we strive to maintain paired tracking even the objects/shadows are temporarily occluded for several frames. Second, we leverage both labeled images and unlabeled videos, and explore temporal coherence by augmenting the tracking ability via an association cycle consistency loss to optimize SSIS-Track's performance. Last, we build $\textit{SOBA-VID}$, a new dataset with 232 unlabeled videos of ${5,863}$ frames for training and 60 labeled videos of ${1,182}$ frames for testing. Experimental results show that SSIS-Track surpasses baselines built from SOTA video tracking and instance-shadow-detection methods by a large margin. In the end, we showcase several video-level applications. | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Haoran Wu', 'Xiaowei Hu', 'Tianyu Wang', 'Zhenghao Xing'] | 2022-11-23 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 2.90512234e-01 -2.18398273e-01 -5.90537786e-01 -3.15165430e-01
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a9427190-9333-4cdf-a6d0-403d05fc071b | autocount-unsupervised-segmentation-and | 2007.09178 | null | https://arxiv.org/abs/2007.09178v1 | https://arxiv.org/pdf/2007.09178v1.pdf | AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images | Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications. | ['Steve Shirtliffe', 'Tewodros Ayalew', 'Ian Stavness', 'Curtis Pozniak', 'Jordan Ubbens', 'Anique Josuttes'] | 2020-07-17 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.58137935e-01 -1.28690064e-01 1.30612999e-01 -3.00528795e-01
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6fa2c6a2-b48f-4c7b-b355-5deb46c7a8a7 | zeroc-a-neuro-symbolic-model-for-zero-shot | 2206.15049 | null | https://arxiv.org/abs/2206.15049v3 | https://arxiv.org/pdf/2206.15049v3.pdf | ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time | Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acquire new concepts by parsing and communicating symbolic structures using learned visual concepts and relations. Endowing these capabilities in machines is pivotal in improving their generalization capability at inference time. In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way. ZeroC represents concepts as graphs of constituent concept models (as nodes) and their relations (as edges). To allow inference time composition, we employ energy-based models (EBMs) to model concepts and relations. We design ZeroC architecture so that it allows a one-to-one mapping between a symbolic graph structure of a concept and its corresponding EBM, which for the first time, allows acquiring new concepts, communicating its graph structure, and applying it to classification and detection tasks (even across domains) at inference time. We introduce algorithms for learning and inference with ZeroC. We evaluate ZeroC on a challenging grid-world dataset which is designed to probe zero-shot concept recognition and acquisition, and demonstrate its capability. | ['Jure Leskovec', 'Rok Sosič', 'Kevin Liu', 'Xuelin Yang', 'Zhengxuan Wu', 'Megan Tjandrasuwita', 'Tailin Wu'] | 2022-06-30 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.89560783e-01 3.01347792e-01 -5.16250283e-02 -4.48448539e-01
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194ed220-b624-4301-b07a-ec4fcd26edc8 | overcoming-catastrophic-forgetting-in | 2111.01549 | null | https://arxiv.org/abs/2111.01549v2 | https://arxiv.org/pdf/2111.01549v2.pdf | Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima | This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing methods severely suffer from catastrophic forgetting, a well-known problem in incremental learning, which is aggravated due to data scarcity and imbalance in the few-shot setting. Our analysis further suggests that to prevent catastrophic forgetting, actions need to be taken in the primitive stage -- the training of base classes instead of later few-shot learning sessions. Therefore, we propose to search for flat local minima of the base training objective function and then fine-tune the model parameters within the flat region on new tasks. In this way, the model can efficiently learn new classes while preserving the old ones. Comprehensive experimental results demonstrate that our approach outperforms all prior state-of-the-art methods and is very close to the approximate upper bound. The source code is available at https://github.com/moukamisama/F2M. | ['Xiao-Ming Wu', 'Li-Ming Zhan', 'Wenlong Zhang', 'Jiaxin Chen', 'Guangyuan Shi'] | 2021-10-30 | null | http://proceedings.neurips.cc/paper/2021/hash/357cfba15668cc2e1e73111e09d54383-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/357cfba15668cc2e1e73111e09d54383-Paper.pdf | neurips-2021-12 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.37371534e-01 1.49897709e-01 -3.65795463e-01 -2.89106131e-01
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0e56bec9-c9b5-46ac-a085-aadf9a3a7ea7 | escape-from-cells-deep-kd-networks-for-the | 1704.01222 | null | http://arxiv.org/abs/1704.01222v2 | http://arxiv.org/pdf/1704.01222v2.pdf | Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models | We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed onto them by Kd-trees. Unlike the currently dominant
convolutional architectures that usually require rasterization on uniform
two-dimensional or three-dimensional grids, Kd-networks do not rely on such
grids in any way and therefore avoid poor scaling behaviour. In a series of
experiments with popular shape recognition benchmarks, Kd-networks demonstrate
competitive performance in a number of shape recognition tasks such as shape
classification, shape retrieval and shape part segmentation. | ['Roman Klokov', 'Victor Lempitsky'] | 2017-04-04 | escape-from-cells-deep-kd-networks-for-the-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Klokov_Escape_From_Cells_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Klokov_Escape_From_Cells_ICCV_2017_paper.pdf | iccv-2017-10 | ['3d-part-segmentation'] | ['computer-vision'] | [-1.38606429e-01 2.57565011e-03 6.62004128e-02 -5.21697342e-01
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8d0a524d-56f5-4e2f-a025-0dc31404a53a | perspective-flow-aggregation-for-data-limited | 2203.09836 | null | https://arxiv.org/abs/2203.09836v2 | https://arxiv.org/pdf/2203.09836v2.pdf | Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation | Most recent 6D object pose estimation methods, including unsupervised ones, require many real training images. Unfortunately, for some applications, such as those in space or deep under water, acquiring real images, even unannotated, is virtually impossible. In this paper, we propose a method that can be trained solely on synthetic images, or optionally using a few additional real ones. Given a rough pose estimate obtained from a first network, it uses a second network to predict a dense 2D correspondence field between the image rendered using the rough pose and the real image and infers the required pose correction. This approach is much less sensitive to the domain shift between synthetic and real images than state-of-the-art methods. It performs on par with methods that require annotated real images for training when not using any, and outperforms them considerably when using as few as twenty real images. | ['Mathieu Salzmann', 'Pascal Fua', 'Yinlin Hu'] | 2022-03-18 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 4.43062007e-01 5.86791754e-01 2.76165873e-01 -4.14872259e-01
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94296feb-a401-438a-8b33-8c254acc1dce | estimation-of-low-rank-density-matrices-by | 1610.04811 | null | http://arxiv.org/abs/1610.04811v2 | http://arxiv.org/pdf/1610.04811v2.pdf | Estimation of low rank density matrices by Pauli measurements | Density matrices are positively semi-definite Hermitian matrices with unit
trace that describe the states of quantum systems. Many quantum systems of
physical interest can be represented as high-dimensional low rank density
matrices. A popular problem in {\it quantum state tomography} (QST) is to
estimate the unknown low rank density matrix of a quantum system by conducting
Pauli measurements. Our main contribution is twofold. First, we establish the
minimax lower bounds in Schatten $p$-norms with $1\leq p\leq +\infty$ for low
rank density matrices estimation by Pauli measurements. In our previous paper,
these minimax lower bounds are proved under the trace regression model with
Gaussian noise and the noise is assumed to have common variance. In this paper,
we prove these bounds under the Binomial observation model which meets the
actual model in QST.
Second, we study the Dantzig estimator (DE) for estimating the unknown low
rank density matrix under the Binomial observation model by using Pauli
measurements. In our previous papers, we studied the least squares estimator
and the projection estimator, where we proved the optimal convergence rates for
the least squares estimator in Schatten $p$-norms with $1\leq p\leq 2$ and,
under a stronger condition, the optimal convergence rates for the projection
estimator in Schatten $p$-norms with $1\leq p\leq +\infty$. In this paper, we
show that the results of these two distinct estimators can be simultaneously
obtained by the Dantzig estimator. Moreover, better convergence rates in
Schatten norm distances can be proved for Dantzig estimator under conditions
weaker than those needed in previous papers. When the objective function of DE
is replaced by the negative von Neumann entropy, we obtain sharp convergence
rate in Kullback-Leibler divergence. | ['Dong Xia'] | 2016-10-16 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 1.55580878e-01 1.58620939e-01 5.48221841e-02 -6.96118250e-02
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3553cf61-1ede-4be0-a5d7-441506d6e9d6 | on-the-transferability-of-visual-features-in | 2211.12494 | null | https://arxiv.org/abs/2211.12494v1 | https://arxiv.org/pdf/2211.12494v1.pdf | On the Transferability of Visual Features in Generalized Zero-Shot Learning | Generalized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes, using a set of attributes as auxiliary information, and the visual features extracted from a pre-trained convolutional neural network. While recent GZSL methods have explored various techniques to leverage the capacity of these features, there has been an extensive growth of representation learning techniques that remain under-explored. In this work, we investigate the utility of different GZSL methods when using different feature extractors, and examine how these models' pre-training objectives, datasets, and architecture design affect their feature representation ability. Our results indicate that 1) methods using generative components for GZSL provide more advantages when using recent feature extractors; 2) feature extractors pre-trained using self-supervised learning objectives and knowledge distillation provide better feature representations, increasing up to 15% performance when used with recent GZSL techniques; 3) specific feature extractors pre-trained with larger datasets do not necessarily boost the performance of GZSL methods. In addition, we investigate how GZSL methods fare against CLIP, a more recent multi-modal pre-trained model with strong zero-shot performance. We found that GZSL tasks still benefit from generative-based GZSL methods along with CLIP's internet-scale pre-training to achieve state-of-the-art performance in fine-grained datasets. We release a modular framework for analyzing representation learning issues in GZSL here: https://github.com/uvavision/TV-GZSL | ['Vicente Ordonez', 'Yanjun Qi', 'James Seale Smith', 'Leonid Karlinsky', 'Paola Cascante-Bonilla'] | 2022-11-22 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.44572341e-01 2.08321642e-02 -2.66950309e-01 -3.89587253e-01
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1282ee4d-9a61-4978-99bd-855a79bcf8ef | why-and-how-to-pay-different-attention-to | 1604.06896 | null | http://arxiv.org/abs/1604.06896v2 | http://arxiv.org/pdf/1604.06896v2.pdf | Why and How to Pay Different Attention to Phrase Alignments of Different Intensities | This work studies comparatively two typical sentence pair classification
tasks: textual entailment (TE) and answer selection (AS), observing that phrase
alignments of different intensities contribute differently in these tasks. We
address the problems of identifying phrase alignments of flexible granularity
and pooling alignments of different intensities for these tasks. Examples for
flexible granularity are alignments between two single words, between a single
word and a phrase and between a short phrase and a long phrase. By intensity we
roughly mean the degree of match, it ranges from identity over surface-form
co-occurrence, rephrasing and other semantic relatedness to unrelated words as
in lots of parenthesis text. Prior work (i) has limitations in phrase
generation and representation, or (ii) conducts alignment at word and phrase
levels by handcrafted features or (iii) utilizes a single attention mechanism
over alignment intensities without considering the characteristics of specific
tasks, which limits the system's effectiveness across tasks. We propose an
architecture based on Gated Recurrent Unit that supports (i) representation
learning of phrases of arbitrary granularity and (ii) task-specific focusing of
phrase alignments between two sentences by attention pooling. Experimental
results on TE and AS match our observation and are state-of-the-art. | ['Hinrich Schütze', 'Wenpeng Yin'] | 2016-04-23 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 6.56487823e-01 -3.05421483e-02 -1.39647692e-01 -4.72320646e-01
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3289f217-e438-4747-b09d-4401c6e416c9 | object-recognition-system-on-a-tactile-device | 2307.02211 | null | https://arxiv.org/abs/2307.02211v1 | https://arxiv.org/pdf/2307.02211v1.pdf | Object Recognition System on a Tactile Device for Visually Impaired | People with visual impairments face numerous challenges when interacting with their environment. Our objective is to develop a device that facilitates communication between individuals with visual impairments and their surroundings. The device will convert visual information into auditory feedback, enabling users to understand their environment in a way that suits their sensory needs. Initially, an object detection model is selected from existing machine learning models based on its accuracy and cost considerations, including time and power consumption. The chosen model is then implemented on a Raspberry Pi, which is connected to a specifically designed tactile device. When the device is touched at a specific position, it provides an audio signal that communicates the identification of the object present in the scene at that corresponding position to the visually impaired individual. Conducted tests have demonstrated the effectiveness of this device in scene understanding, encompassing static or dynamic objects, as well as screen contents such as TVs, computers, and mobile phones. | ['Slimane Larabi', 'Mokretar Kraroubi Abderrahmene', 'Souayah Abdelkader'] | 2023-07-05 | null | null | null | null | ['object-recognition', 'object-detection', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.34596997e-01 -2.92712241e-01 1.21975794e-01 2.08932593e-01
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dda541c9-2952-4bde-9bac-62434aae076e | sketchformer-transformer-based-representation | 2002.10381 | null | https://arxiv.org/abs/2002.10381v1 | https://arxiv.org/pdf/2002.10381v1.pdf | Sketchformer: Transformer-based Representation for Sketched Structure | Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i.e. as a sequence of strokes. Sketchformer effectively addresses multiple tasks: sketch classification, sketch based image retrieval (SBIR), and the reconstruction and interpolation of sketches. We report several variants exploring continuous and tokenized input representations, and contrast their performance. Our learned embedding, driven by a dictionary learning tokenization scheme, yields state of the art performance in classification and image retrieval tasks, when compared against baseline representations driven by LSTM sequence to sequence architectures: SketchRNN and derivatives. We show that sketch reconstruction and interpolation are improved significantly by the Sketchformer embedding for complex sketches with longer stroke sequences. | ['John Collomosse', 'Moacir Ponti', 'Tu Bui', 'Leo Sampaio Ferraz Ribeiro'] | 2020-02-24 | sketchformer-transformer-based-representation-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.pdf | cvpr-2020-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.70709872e-01 -4.37438339e-01 -5.55167973e-01 -2.09271178e-01
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bc8df28a-c450-4b1d-9214-b637b714e6d7 | 190107454 | 1901.07454 | null | http://arxiv.org/abs/1901.07454v1 | http://arxiv.org/pdf/1901.07454v1.pdf | Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures | We introduce Arbor, a performance portable library for simulation of large
networks of multi-compartment neurons on HPC systems. Arbor is open source
software, developed under the auspices of the HBP. The performance portability
is by virtue of back-end specific optimizations for x86 multicore, Intel KNL,
and NVIDIA GPUs. When coupled with low memory overheads, these optimizations
make Arbor an order of magnitude faster than the most widely-used comparable
simulation software. The single-node performance can be scaled out to run very
large models at extreme scale with efficient weak scaling.
HPC, GPU, neuroscience, neuron, software | ['Alexander Peyser', 'Anne Küsters', 'Vasileios Karakasis', 'Wouter Klijn', 'Nora Abi Akar', 'Ben Cumming', 'Stuart Yates'] | 2019-01-17 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-5.79615891e-01 -5.70592344e-01 3.96086931e-01 -2.34783031e-02
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f44ef6a8-bbe4-4a98-bf58-001547ab80a8 | teleidoscopic-imaging-system-for-microscale | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kawahara_Teleidoscopic_Imaging_System_for_Microscale_3D_Shape_Reconstruction_CVPR_2023_paper.pdf | Teleidoscopic Imaging System for Microscale 3D Shape Reconstruction | This paper proposes a practical method of microscale 3D shape capturing by a teleidoscopic imaging system. The main challenge in microscale 3D shape reconstruction is to capture the target from multiple viewpoints with a large enough depth-of-field. Our idea is to employ a teleidoscopic measurement system consisting of three planar mirrors and monocentric lens. The planar mirrors virtually define multiple viewpoints by multiple reflections, and the monocentric lens realizes a high magnification with less blurry and surround view even in closeup imaging. Our contributions include, a structured ray-pixel camera model which handles refractive and reflective projection rays efficiently, analytical evaluations of depth of field of our teleidoscopic imaging system, and a practical calibration algorithm of the teleidoscppic imaging system. Evaluations with real images prove the concept of our measurement system. | ['Shohei Nobuhara', 'Meng-Yu Jennifer Kuo', 'Ryo Kawahara'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 1.51511490e-01 -1.47128589e-02 6.45508885e-01 -2.97137231e-01
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acd62b87-c2a3-4faf-a051-1157db6eb94e | multistylegan-multiple-one-shot-face | 2210.04120 | null | https://arxiv.org/abs/2210.04120v2 | https://arxiv.org/pdf/2210.04120v2.pdf | MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN | Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference style. Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image. However, such methods cannot generate multiple stylizations without fine-tuning a new model for each style separately. In this work, we present a MultiStyleGAN method that is capable of producing multiple different stylizations at once by fine-tuning a single generator. The key component of our method is a learnable transformation module called Style Transformation Network. It takes latent codes as input, and learns linear mappings to different regions of the latent space to produce distinct codes for each style, resulting in a multistyle space. Our model inherently mitigates overfitting since it is trained on multiple styles, hence improving the quality of stylizations. Our method can learn upwards of $120$ image stylizations at once, bringing $8\times$ to $60\times$ improvement in training time over recent competing methods. We support our results through user studies and quantitative results that indicate meaningful improvements over existing methods. | ['Svetlana Lazebnik', 'Sudharsan Krishnakumar Anitha', 'Ayush Sarkar', 'Viraj Shah'] | 2022-10-08 | null | null | null | null | ['image-stylization', 'one-shot-face-stylization'] | ['computer-vision', 'computer-vision'] | [ 4.72844690e-01 2.10315689e-01 -1.35496184e-01 -1.96052432e-01
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409311af-85e7-41d7-8216-5cc024875335 | leveraging-pseudo-labeled-data-to-improve | 2205.08993 | null | https://arxiv.org/abs/2205.08993v1 | https://arxiv.org/pdf/2205.08993v1.pdf | Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation | Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently. The task is very challenging due to data scarcity and complex speech-to-speech mapping. In this paper, we report our recent achievements in S2ST. Firstly, we build a S2ST Transformer baseline which outperforms the original Translatotron. Secondly, we utilize the external data by pseudo-labeling and obtain a new state-of-the-art result on the Fisher English-to-Spanish test set. Indeed, we exploit the pseudo data with a combination of popular techniques which are not trivial when applied to S2ST. Moreover, we evaluate our approach on both syntactically similar (Spanish-English) and distant (English-Chinese) language pairs. Our implementation is available at https://github.com/fengpeng-yue/speech-to-speech-translation. | ['Yu Zhang', 'Qibing Bai', 'Mingxuan Wang', 'Tom Ko', 'Fengpeng Yue', 'Qianqian Dong'] | 2022-05-18 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 1.67126045e-01 -1.96485016e-02 -2.15747237e-01 -5.30037403e-01
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1daf6d17-8655-4259-b7ff-7058a16fd1a4 | gait-identification-under-surveillance | 2111.11720 | null | https://arxiv.org/abs/2111.11720v2 | https://arxiv.org/pdf/2111.11720v2.pdf | Gait Identification under Surveillance Environment based on Human Skeleton | As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and can be easily influenced by bagging (BG) and clothing (CL). Since human body skeletons convey essential information about human gaits, a skeleton-based gait identification network is proposed in our project. First, extract skeleton sequences from the video and map them into a gait graph. Then a feature extraction network based on Spatio-Temporal Graph Convolutional Network (ST-GCN) is constructed to learn gait representations. Finally, the probe sample is identified by matching with the most similar piece in the gallery. We tested our method on the CASIA-B dataset. The result shows that our approach is highly adaptive and gets the advanced result in BG, CL conditions, and average. | ['Tanfeng Sun', 'Xinghao Jiang', 'Ke Xu', 'Xirui Li', 'Xingkai Zheng'] | 2021-11-23 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [ 6.61853254e-02 -5.95346451e-01 -2.11967528e-01 -8.10417831e-02
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17fc121c-be52-4899-a88a-f2f83a364cfd | a-security-steganography-scheme-based-on-hdr | 1902.10943 | null | http://arxiv.org/abs/1902.10943v1 | http://arxiv.org/pdf/1902.10943v1.pdf | A security steganography scheme based on hdr image | It is widely recognized that the image format is crucial to steganography for
that each individual format has its unique properities. Nowadays, the most
famous approach of digital image steganography is to combine a well-defined
distortion function with efficient practical codes such as STC. And numerous
researches are concentrated on spatial domain and jpeg domain. However, whether
in spatial domain or jpeg domain, high payload (e.g., 0.5 bit per pixel) is not
secure enough. In this paper, we propose a novel adaptive steganography scheme
based on 32-bit HDR (High dynamic range) format and Norm IEEE 754. Experiments
show that the steganographic method can achieve satisfactory security under
payload from 0.3bpp to 0.5bpp. | ['Wei Gao', 'Yongqing Huo', 'Yan Qiao'] | 2019-02-28 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 5.72010338e-01 -2.23737672e-01 -1.00607648e-02 1.32041052e-01
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944456ca-2407-4968-99ef-a6a81900a86c | radio-astronomical-images-object-detection | 2303.04506 | null | https://arxiv.org/abs/2303.04506v2 | https://arxiv.org/pdf/2303.04506v2.pdf | Radio astronomical images object detection and segmentation: A benchmark on deep learning methods | In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research. | ['Carmelo Pino', 'Cristobal Bordiu', 'Francesco Schillirò', 'Filomena Bufano', 'Andrew M. Hopkins', 'Concetto Spampinato', 'Andrea DeMarco', 'Simone Riggi', 'Eva Sciacca', 'Giuseppe Fiameni', 'Daniel Magro', 'Renato Sortino'] | 2023-03-08 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 2.23388031e-01 -4.68110591e-02 4.71915789e-02 -7.14642182e-02
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d7bf1359-b688-430b-8435-1b203c0a0e8e | local-object-crop-collision-network-for | 2304.09439 | null | https://arxiv.org/abs/2304.09439v2 | https://arxiv.org/pdf/2304.09439v2.pdf | Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators | Our goal is to develop an efficient contact detection algorithm for large-scale GPU-based simulation of non-convex objects. Current GPU-based simulators such as IsaacGym and Brax must trade-off speed with fidelity, generality, or both when simulating non-convex objects. Their main issue lies in contact detection (CD): existing CD algorithms, such as Gilbert-Johnson-Keerthi (GJK), must trade off their computational speed with accuracy which becomes expensive as the number of collisions among non-convex objects increases. We propose a data-driven approach for CD, whose accuracy depends only on the quality and quantity of offline dataset rather than online computation time. Unlike GJK, our method inherently has a uniform computational flow, which facilitates efficient GPU usage based on advanced compilers such as XLA (Accelerated Linear Algebra). Further, we offer a data-efficient solution by learning the patterns of colliding local crop object shapes, rather than global object shapes which are harder to learn. We demonstrate our approach improves the efficiency of existing CD methods by a factor of 5-10 for non-convex objects with comparable accuracy. Using the previous work on contact resolution for a neural-network-based contact detector, we integrate our CD algorithm into the open-source GPU-based simulator, Brax, and show that we can improve the efficiency over IsaacGym and generality over standard Brax. We highly recommend the videos of our simulator included in the supplementary materials. | ['Beomjoon Kim', 'Dongwon Son'] | 2023-04-19 | null | null | null | null | ['contact-detection'] | ['robots'] | [-1.48247883e-01 -3.23261887e-01 3.10790807e-01 1.17717884e-01
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46a67e75-5e47-4b6f-9edd-e0b76f4898a1 | material-classification-in-the-wild-do | 1711.03874 | null | http://arxiv.org/abs/1711.03874v1 | http://arxiv.org/pdf/1711.03874v1.pdf | Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data? | We question the dominant role of real-world training images in the field of
material classification by investigating whether synthesized data can
generalise more effectively than real-world data. Experimental results on three
challenging real-world material databases show that the best performing
pre-trained convolutional neural network (CNN) architectures can achieve up to
91.03% mean average precision when classifying materials in cross-dataset
scenarios. We demonstrate that synthesized data achieve an improvement on mean
average precision when used as training data and in conjunction with
pre-trained CNN architectures, which spans from ~ 5% to ~ 19% across three
widely used material databases of real-world images. | ['Klaus D. McDonald-Maier', 'Anca Sticlaru', 'Shoaib Ehsan', 'Grigorios Kalliatakis', 'Juergen Gall', 'Ales Leonardis', 'George Stamatiadis'] | 2017-11-09 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 5.31830549e-01 -1.16766676e-01 -1.88787654e-01 -2.24029243e-01
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2c4ee1d9-7926-4771-9b01-95e30e52be08 | forecasting-the-2016-2017-central-apennines | 2301.09948 | null | https://arxiv.org/abs/2301.09948v2 | https://arxiv.org/pdf/2301.09948v2.pdf | Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process | Point processes have been dominant in modeling the evolution of seismicity for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most popular. Recent advances in machine learning have constructed highly flexible point process models using neural networks to improve upon existing parametric models. We investigate whether these flexible point process models can be applied to short-term seismicity forecasting by extending an existing temporal neural model to the magnitude domain and we show how this model can forecast earthquakes above a target magnitude threshold. We first demonstrate that the neural model can fit synthetic ETAS data, however, requiring less computational time because it is not dependent on the full history of the sequence. By artificially emulating short-term aftershock incompleteness in the synthetic dataset, we find that the neural model outperforms ETAS. Using a new enhanced catalog from the 2016-2017 Central Apennines earthquake sequence, we investigate the predictive skill of ETAS and the neural model with respect to the lowest input magnitude. Constructing multiple forecasting experiments using the Visso, Norcia and Campotosto earthquakes to partition training and testing data, we target M3+ events. We find both models perform similarly at previously explored thresholds (e.g., above M3), but lowering the threshold to M1.2 reduces the performance of ETAS unlike the neural model. We argue that some of these gains are due to the neural model's ability to handle incomplete data. The robustness to missing data and speed to train the neural model present it as an encouraging competitor in earthquake forecasting. | ['Maxmilian J. Werner', 'Daniel J. Lawson', 'Samuel Stockman'] | 2023-01-24 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 7.17195496e-02 -2.60035396e-02 1.39557347e-01 7.42847621e-02
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982bab17-eacf-4cb6-8206-34ce170d560a | evolving-boxes-for-fast-vehicle-detection | 1702.00254 | null | http://arxiv.org/abs/1702.00254v3 | http://arxiv.org/pdf/1702.00254v3.pdf | Evolving Boxes for Fast Vehicle Detection | We perform fast vehicle detection from traffic surveillance cameras. A novel
deep learning framework, namely Evolving Boxes, is developed that proposes and
refines the object boxes under different feature representations. Specifically,
our framework is embedded with a light-weight proposal network to generate
initial anchor boxes as well as to early discard unlikely regions; a
fine-turning network produces detailed features for these candidate boxes. We
show intriguingly that by applying different feature fusion techniques, the
initial boxes can be refined for both localization and recognition. We evaluate
our network on the recent DETRAC benchmark and obtain a significant improvement
over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network
achieves 9-13 FPS detection speed on a moderate commercial GPU. | ['xiangyang xue', 'Hong Wang', 'Yingbin Zheng', 'Yao Lu', 'Li Wang', 'Hao Ye'] | 2017-02-01 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [ 1.0877198e-01 -1.0721744e-01 -7.5332597e-02 -3.6448750e-01
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979b3d67-249e-4e8a-962e-c8f5907ccafb | beyond-bounding-box-convex-hull-feature | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf | Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection | Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely packed object layouts. CFA is rooted in convex-hull feature representation, which defines a set of dynamically predicted feature points guided by the convex intersection over union (CIoU) to bound the extent of objects. CFA pursues optimal feature assignment by constructing convex-hull sets and dynamically splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA alleviates spatial feature aliasing towards optimal feature adaptation. Experiments on DOTA and SKU110K-R datasets show that CFA significantly outperforms the baseline approach, achieving new state-of-the-art detection performance. | ['Qixiang Ye', 'Xiangyang Ji', 'Jianbin Jiao', 'Xiaosong Zhang', 'Chang Liu', 'Zonghao Guo'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-9.97256637e-02 -4.85288128e-02 2.49375060e-01 -4.24113601e-01
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323006a1-2897-4487-a1df-d2f5768ea5d9 | source-identification-a-self-supervision-task | 2307.02238 | null | https://arxiv.org/abs/2307.02238v1 | https://arxiv.org/pdf/2307.02238v1.pdf | Source Identification: A Self-Supervision Task for Dense Prediction | The paradigm of self-supervision focuses on representation learning from raw data without the need of labor-consuming annotations, which is the main bottleneck of current data-driven methods. Self-supervision tasks are often used to pre-train a neural network with a large amount of unlabeled data and extract generic features of the dataset. The learned model is likely to contain useful information which can be transferred to the downstream main task and improve performance compared to random parameter initialization. In this paper, we propose a new self-supervision task called source identification (SI), which is inspired by the classic blind source separation problem. Synthetic images are generated by fusing multiple source images and the network's task is to reconstruct the original images, given the fused images. A proper understanding of the image content is required to successfully solve the task. We validate our method on two medical image segmentation tasks: brain tumor segmentation and white matter hyperintensities segmentation. The results show that the proposed SI task outperforms traditional self-supervision tasks for dense predictions including inpainting, pixel shuffling, intensity shift, and super-resolution. Among variations of the SI task fusing images of different types, fusing images from different patients performs best. | ['Marleen de Bruijne', 'Subhradeep Kayal', 'Shuai Chen'] | 2023-07-05 | null | null | null | null | ['super-resolution', 'tumor-segmentation', 'medical-image-segmentation', 'brain-tumor-segmentation', 'representation-learning'] | ['computer-vision', 'computer-vision', 'medical', 'medical', 'methodology'] | [ 1.03860033e+00 4.91794080e-01 -1.83156699e-01 -5.88888943e-01
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49ccb369-f4ea-4e27-b2b1-16098cae7722 | sketching-a-linguistically-driven-reasoning | null | null | https://aclanthology.org/2022.acl-srw.14 | https://aclanthology.org/2022.acl-srw.14.pdf | Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk | The capability of holding social talk (or casual conversation) and making sense of conversational content requires context-sensitive natural language understanding and reasoning, which cannot be handled efficiently by the current popular open-domain dialog systems and chatbots. Heavily relying on corpus-based machine learning techniques to encode and decode context-sensitive meanings, these systems focus on fitting a particular training dataset, but not tracking what is actually happening in a conversation, and therefore easily derail in a new context. This work sketches out a more linguistically-informed architecture to handle social talk in English, in which corpus-based methods form the backbone of the relatively context-insensitive components (e.g. part-of-speech tagging, approximation of lexical meaning and constituent chunking), while symbolic modeling is used for reasoning out the context-sensitive components, which do not have any consistent mapping to linguistic forms. All components are fitted into a Bayesian game-theoretic model to address the interactive and rational aspects of conversation. | ['Alex Lưu'] | null | null | null | null | acl-2022-5 | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.63945402e-02 7.21670568e-01 -1.48791656e-01 -5.94940186e-01
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2e8305b2-28c6-4642-b00b-d1ef8d960a62 | image-generation-from-layout | 1811.11389 | null | https://arxiv.org/abs/1811.11389v3 | https://arxiv.org/pdf/1811.11389v3.pdf | Image Generation from Layout | Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for layout-based image generation; we call it Layout2Im. Given the coarse spatial layout (bounding boxes + object categories), our model can generate a set of realistic images which have the correct objects in the desired locations. The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance). The category is encoded using a word embedding and the appearance is distilled into a low-dimensional vector sampled from a normal distribution. Individual object representations are composed together using convolutional LSTM, to obtain an encoding of the complete layout, and then decoded to an image. Several loss terms are introduced to encourage accurate and diverse generation. The proposed Layout2Im model significantly outperforms the previous state of the art, boosting the best reported inception score by 24.66% and 28.57% on the very challenging COCO-Stuff and Visual Genome datasets, respectively. Extensive experiments also demonstrate our method's ability to generate complex and diverse images with multiple objects. | ['Weidong Yin', 'Bo Zhao', 'Lili Meng', 'Leonid Sigal'] | 2018-11-28 | image-generation-from-layout-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Image_Generation_From_Layout_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Image_Generation_From_Layout_CVPR_2019_paper.pdf | cvpr-2019-6 | ['layout-to-image-generation'] | ['computer-vision'] | [ 4.87789869e-01 6.71318844e-02 1.31445482e-01 -2.69004196e-01
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2a4b3b53-fe7e-4f25-8e70-7075e7286e4b | context-dependent-semantic-parsing-for | 2112.00894 | null | https://arxiv.org/abs/2112.00894v1 | https://arxiv.org/pdf/2112.00894v1.pdf | Context-Dependent Semantic Parsing for Temporal Relation Extraction | Extracting temporal relations among events from unstructured text has extensive applications, such as temporal reasoning and question answering. While it is difficult, recent development of Neural-symbolic methods has shown promising results on solving similar tasks. Current temporal relation extraction methods usually suffer from limited expressivity and inconsistent relation inference. For example, in TimeML annotations, the concept of intersection is absent. Additionally, current methods do not guarantee the consistency among the predicted annotations. In this work, we propose SMARTER, a neural semantic parser, to extract temporal information in text effectively. SMARTER parses natural language to an executable logical form representation, based on a custom typed lambda calculus. In the training phase, dynamic programming on denotations (DPD) technique is used to provide weak supervision on logical forms. In the inference phase, SMARTER generates a temporal relation graph by executing the logical form. As a result, our neural semantic parser produces logical forms capturing the temporal information of text precisely. The accurate logical form representations of an event given the context ensure the correctness of the extracted relations. | ['Jane Yung-jen Hsu', 'Kuan-Yin Lai', 'Shang-Ling Hsu', 'Bo-Ying Su'] | 2021-12-02 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.39675602e-01 4.05179918e-01 -5.50736308e-01 -6.77960515e-01
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3dfc545e-bf62-4af9-bf01-7b6a7ea86223 | self-supervised-learning-to-guide | 2204.09854 | null | https://arxiv.org/abs/2204.09854v1 | https://arxiv.org/pdf/2204.09854v1.pdf | Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images | Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability. Existing approaches to label Martian terrain either involve the use of non-expert annotators producing taxonomies of limited granularity (e.g. soil, sand, bedrock, float rock, etc.), or rely on generic class discovery approaches that tend to produce perceptual classes such as rover parts and landscape, which are irrelevant to geologic analysis. Expert-labeled datasets containing granular geological/geomorphological terrain categories are rare or inaccessible to public, and sometimes require the extraction of relevant categorical information from complex annotations. In order to facilitate the creation of a dataset with detailed terrain categories, we present a self-supervised method that can cluster sedimentary textures in images captured from the Mast camera onboard the Curiosity rover (Mars Science Laboratory). We then present a qualitative analysis of these clusters and describe their geologic significance via the creation of a set of granular terrain categories. The precision and geologic validation of these automatically discovered clusters suggest that our methods are promising for the rapid classification of important geologic features and will therefore facilitate our long-term goal of producing a large, granular, and publicly available dataset for Mars terrain recognition. | ['Mario Parente', 'Erik Learned-Miller', 'Ralph Milliken', 'Melissa Meyer', 'Deep Chakraborty', 'Tejas Panambur'] | 2022-04-21 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.70382252e-02 4.38394509e-02 -1.94132440e-02 -5.87555051e-01
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faf75c74-b163-4901-98d1-658a8b483346 | test-time-training-on-nearest-neighbors-for | 2305.18466 | null | https://arxiv.org/abs/2305.18466v2 | https://arxiv.org/pdf/2305.18466v2.pdf | Test-Time Training on Nearest Neighbors for Large Language Models | Many recent efforts aim to augment language models with relevant information retrieved from a database at test time. We avoid the need for prompt engineering by directly fine-tuning the model on data retrieved at test time using its standard training setup. For this purpose, we build a large-scale distributed nearest neighbor index based on text embeddings of the Pile dataset. Given a query to a language model, our system retrieves the neighbors of the query and fine-tunes the model on the text data corresponding to those neighbors. Surprisingly, retrieving and training on as few as 20 neighbors, each for only one gradient iteration, drastically improves performance across more than twenty language modeling tasks in the Pile benchmark. For example, test-time training significantly narrows the performance gap between a small GPT2 model and a GPTNeo model, more than ten times larger, that was specifically trained to convergence on the Pile. Sufficient index quality and size, however, are important. Our work establishes a valuable first baseline for implementing test-time training in the context of large language models, opening the door to numerous promising research avenues. | ['Yu Sun', 'Moritz Hardt'] | 2023-05-29 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [-9.97532010e-02 -1.77224725e-01 -4.53250915e-01 -3.98553133e-01
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10175bf3-4e40-4d85-af60-2eea4579eaf5 | audio-denoising-for-robust-audio | 2212.11277 | null | https://arxiv.org/abs/2212.11277v1 | https://arxiv.org/pdf/2212.11277v1.pdf | Audio Denoising for Robust Audio Fingerprinting | Music discovery services let users identify songs from short mobile recordings. These solutions are often based on Audio Fingerprinting, and rely more specifically on the extraction of spectral peaks in order to be robust to a number of distortions. Few works have been done to study the robustness of these algorithms to background noise captured in real environments. In particular, AFP systems still struggle when the signal to noise ratio is low, i.e when the background noise is strong. In this project, we tackle this problematic with Deep Learning. We test a new hybrid strategy which consists of inserting a denoising DL model in front of a peak-based AFP algorithm. We simulate noisy music recordings using a realistic data augmentation pipeline, and train a DL model to denoise them. The denoising model limits the impact of background noise on the AFP system's extracted peaks, improving its robustness to noise. We further propose a novel loss function to adapt the DL model to the considered AFP system, increasing its precision in terms of retrieved spectral peaks. To the best of our knowledge, this hybrid strategy has not been tested before. | ['Kamil Akesbi'] | 2022-12-21 | null | null | null | null | ['audio-denoising'] | ['audio'] | [ 3.77370566e-01 2.06440762e-02 3.04641575e-01 7.42529258e-02
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dd0fecc8-4127-4206-959f-be0e83e5ed19 | illumination-based-data-augmentation-for | 1910.08470 | null | https://arxiv.org/abs/1910.08470v1 | https://arxiv.org/pdf/1910.08470v1.pdf | Illumination-Based Data Augmentation for Robust Background Subtraction | A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features semantic transformations of illumination which enhance the generalisation of the model. It successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask that is randomly generated. Such data allows us to effectively train an illumination-invariant deep learning model for BGS. Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation. | ['Edmond S. L. Ho', 'Hubert P. H. Shum', 'Dimitrios Sakkos'] | 2019-10-18 | null | null | null | null | ['video-background-subtraction', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.60967571e-01 -1.89341158e-01 5.78753531e-01 -3.81665587e-01
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42ba4a17-174b-40c6-baf5-af0f3d927596 | multi-modal-transformers-excel-at-class | 2111.11430 | null | https://arxiv.org/abs/2111.11430v6 | https://arxiv.org/pdf/2111.11430v6.pdf | Class-agnostic Object Detection with Multi-modal Transformer | What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. For the first time in literature, we demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on the observation that existing MViTs do not include multi-scale feature processing and usually require longer training schedules, we develop an efficient MViT architecture using multi-scale deformable attention and late vision-language fusion. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs can adaptively generate proposals given a specific language query and thus offer enhanced interactability. Code: \url{https://git.io/J1HPY}. | ['Ming-Hsuan Yang', 'Rao Muhammad Anwer', 'Fahad Shahbaz Khan', 'Salman Khan', 'Hanoona Rasheed', 'Muhammad Maaz'] | 2021-11-22 | null | null | null | null | ['object-proposal-generation', 'class-agnostic-object-detection', 'open-world-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.25955069e-01 -2.66958982e-01 -1.96956575e-01 -4.06410575e-01
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20d25d57-272b-412c-b710-564a142a4bc5 | a-corpus-and-evaluation-framework-for-deeper | 1604.01696 | null | http://arxiv.org/abs/1604.01696v1 | http://arxiv.org/pdf/1604.01696v1.pdf | A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories | Representation and learning of commonsense knowledge is one of the
foundational problems in the quest to enable deep language understanding. This
issue is particularly challenging for understanding casual and correlational
relationships between events. While this topic has received a lot of interest
in the NLP community, research has been hindered by the lack of a proper
evaluation framework. This paper attempts to address this problem with a new
framework for evaluating story understanding and script learning: the 'Story
Cloze Test'. This test requires a system to choose the correct ending to a
four-sentence story. We created a new corpus of ~50k five-sentence commonsense
stories, ROCStories, to enable this evaluation. This corpus is unique in two
ways: (1) it captures a rich set of causal and temporal commonsense relations
between daily events, and (2) it is a high quality collection of everyday life
stories that can also be used for story generation. Experimental evaluation
shows that a host of baselines and state-of-the-art models based on shallow
language understanding struggle to achieve a high score on the Story Cloze
Test. We discuss these implications for script and story learning, and offer
suggestions for deeper language understanding. | ['James Allen', 'Nasrin Mostafazadeh', 'Xiaodong He', 'Nathanael Chambers', 'Lucy Vanderwende', 'Dhruv Batra', 'Pushmeet Kohli', 'Devi Parikh'] | 2016-04-06 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 4.40964431e-01 4.66137156e-02 -1.96131945e-01 -6.06682777e-01
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ddc0d5f2-e134-42be-9c19-9fc8ec056c67 | comparing-different-criteria-for-vietnamese | null | null | https://aclanthology.org/W12-5005 | https://aclanthology.org/W12-5005.pdf | Comparing Different Criteria for Vietnamese Word Segmentation | null | ['Yusuke Miyao', 'Ngan L.T. Nguyen', 'Quy T. Nguyen'] | 2012-12-01 | comparing-different-criteria-for-vietnamese-1 | https://aclanthology.org/W12-5005 | https://aclanthology.org/W12-5005.pdf | ws-2012-12 | ['vietnamese-word-segmentation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b55dbbcd-46dd-47dd-a266-d5d92a1c8f0a | autonomous-robotic-drilling-system-for-mice | 2303.12265 | null | https://arxiv.org/abs/2303.12265v1 | https://arxiv.org/pdf/2303.12265v1.pdf | Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model | Robotic assistance for experimental manipulation in the life sciences is expected to enable precise manipulation of valuable samples, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and deformation, and therefore require autonomous robotic control. As an example, we are studying the installation of a cranial window in a mouse. This operation requires the removal of the skull, which is approximately 300 um thick, to cut it into a circular shape 8 mm in diameter, but the shape of the mouse skull varies depending on the strain of mouse, sex and week of age. The thickness of the skull is not uniform, with some areas being thin and others thicker. It is also difficult to ensure that the skulls of the mice are kept in the same position for each operation. It is not realistically possible to measure all these features and pre-program a robotic trajectory for individual mice. The paper therefore proposes an autonomous robotic drilling method. The proposed method consists of drilling trajectory planning and image-based task completion level recognition. The trajectory planning adjusts the z-position of the drill according to the task completion level at each discrete point, and forms the 3D drilling path via constrained cubic spline interpolation while avoiding overshoot. The task completion level recognition uses a DSSD-inspired deep learning model to estimate the task completion level of each discrete point. Since an egg has similar characteristics to a mouse skull in terms of shape, thickness and mechanical properties, removing the egg shell without damaging the membrane underneath was chosen as the simulation task. The proposed method was evaluated using a 6-DOF robotic arm holding a drill and achieved a success rate of 80% out of 20 trials. | ['Kanako Harada', 'Murilo M. Marinho', 'Enduo Zhao'] | 2023-03-22 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.93670285e-01 -2.93863509e-02 2.41075650e-01 3.09308805e-02
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ebc0cdf9-e48f-4436-b04b-cce018763b98 | token-level-supervised-contrastive-learning | 2107.09099 | null | https://arxiv.org/abs/2107.09099v3 | https://arxiv.org/pdf/2107.09099v3.pdf | Token-Level Supervised Contrastive Learning for Punctuation Restoration | Punctuation is critical in understanding natural language text. Currently, most automatic speech recognition (ASR) systems do not generate punctuation, which affects the performance of downstream tasks, such as intent detection and slot filling. This gives rise to the need for punctuation restoration. Recent work in punctuation restoration heavily utilizes pre-trained language models without considering data imbalance when predicting punctuation classes. In this work, we address this problem by proposing a token-level supervised contrastive learning method that aims at maximizing the distance of representation of different punctuation marks in the embedding space. The result shows that training with token-level supervised contrastive learning obtains up to 3.2% absolute F1 improvement on the test set. | ['Bo Wu', 'Xubo Liu', 'H Lilian Tang', 'Tom Ko', 'Qiushi Huang'] | 2021-07-19 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.55843604e-01 1.57270551e-01 -3.52404237e-01 -2.95558482e-01
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d3d4e6c9-1d76-480a-a1d0-1fdf0eef7121 | probabilistic-partition-of-unity-networks | 2107.03066 | null | https://arxiv.org/abs/2107.03066v1 | https://arxiv.org/pdf/2107.03066v1.pdf | Probabilistic partition of unity networks: clustering based deep approximation | Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of training parameters. We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimization of a maximum likelihood loss. The resulting architecture provides spatial representations of both noiseless and noisy data as Gaussian mixtures with closed form expressions for variance which provides an estimator of local error. The training process yields remarkably sharp partitions of input space based upon correlation of function values. This classification of training points is amenable to a hierarchical refinement strategy that significantly improves the localization of the regression, allowing for higher-order polynomial approximation to be utilized. The framework scales more favorably to large data sets as compared to Gaussian process regression and allows for spatially varying uncertainty, leveraging the expressive power of deep neural networks while bypassing expensive training associated with other probabilistic deep learning methods. Compared to standard deep neural networks, the framework demonstrates hp-convergence without the use of regularizers to tune the localization of partitions. We provide benchmarks quantifying performance in high/low-dimensions, demonstrating that convergence rates depend only on the latent dimension of data within high-dimensional space. Finally, we introduce a new open-source data set of PDE-based simulations of a semiconductor device and perform unsupervised extraction of a physically interpretable reduced-order basis. | ['Kookjin Lee', 'Andy Huang', 'Mamikon Gulian', 'Nat Trask'] | 2021-07-07 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.94591749e-02 1.53485641e-01 2.24393476e-02 -2.17011318e-01
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-3.69457453e-01 -8.96846116e-01 -4.00587827e-01 5.44808030e-01
-3.78406823e-01 -2.76508451e-01 3.91095132e-01 8.07761788e-01
-1.01391956e-01 4.20538992e-01 3.23854178e-01 1.29829600e-01
-9.57591683e-02 1.66179717e-01 6.20813429e-01 -6.10834360e-01
-1.13753371e-01 2.66616821e-01 -9.10381451e-02 -6.45827472e-01
-1.99258467e-03 -6.78595781e-01 -1.38935375e+00 -1.61187559e-01
-8.62942338e-02 -2.25018337e-02 6.37991548e-01 1.29757357e+00
3.67523402e-01 7.34430552e-01 1.04044572e-01 -1.43283236e+00
-8.02230716e-01 -7.69877672e-01 -8.13830078e-01 9.02503170e-03
3.85731578e-01 -7.38538384e-01 -3.90156567e-01 -3.60782385e-01] | [7.083524703979492, 3.8291358947753906] |
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