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3d0b8781-b03d-46dc-bc46-aae8e3864b4b | ris-assisted-jamming-rejection-and-path | 2302.04994 | null | https://arxiv.org/abs/2302.04994v1 | https://arxiv.org/pdf/2302.04994v1.pdf | RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework | This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio environment, suppress jamming signals, and enhance the desired signals. The UAV is designed to learn its trajectory and the RIS configuration based solely on changes in its received data rate, using the latest deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models. Simulations show that the proposed DRL algorithms give the UAV with strong resistance against jamming and that the TD3 algorithm exhibits faster and smoother convergence than the DDPG algorithm, and suits better for larger RISs. This DRL-based approach eliminates the need for knowledge of the channels involving the RIS and jammer, thereby offering significant practical value. | ['Abbas Jamalipour', 'Xin Wang', 'Wei Ni', 'Xin Yuan', 'Shuyan Hu'] | 2023-02-10 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 6.37516286e-03 2.25146398e-01 -6.33675829e-02 3.39405149e-01
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027bafbc-2db6-4b20-afbb-f9ca0eed5f05 | verbs-in-action-improving-verb-understanding | 2304.06708 | null | https://arxiv.org/abs/2304.06708v1 | https://arxiv.org/pdf/2304.06708v1.pdf | Verbs in Action: Improving verb understanding in video-language models | Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding and to rely extensively on nouns, restricting their performance in real-world video applications that require action and temporal understanding. In this work, we improve verb understanding for CLIP-based video-language models by proposing a new Verb-Focused Contrastive (VFC) framework. This consists of two main components: (1) leveraging pretrained large language models (LLMs) to create hard negatives for cross-modal contrastive learning, together with a calibration strategy to balance the occurrence of concepts in positive and negative pairs; and (2) enforcing a fine-grained, verb phrase alignment loss. Our method achieves state-of-the-art results for zero-shot performance on three downstream tasks that focus on verb understanding: video-text matching, video question-answering and video classification. To the best of our knowledge, this is the first work which proposes a method to alleviate the verb understanding problem, and does not simply highlight it. | ['Cordelia Schmid', 'Andrew Zisserman', 'Arsha Nagrani', 'Mathilde Caron', 'Liliane Momeni'] | 2023-04-13 | null | null | null | null | ['video-classification', 'video-question-answering', 'text-matching'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.71036470e-01 -1.74330607e-01 -4.39272791e-01 -3.37518007e-01
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50b818fc-2c04-4f41-a102-c02cf1e546a1 | in-the-eye-of-transformer-global-local | 2208.04464 | null | https://arxiv.org/abs/2208.04464v2 | https://arxiv.org/pdf/2208.04464v2.pdf | In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation | In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scene context and local visual information is vital for localizing the gaze fixation from egocentric video frames. To this end, we design the transformer encoder to embed the global context as one additional visual token and further propose a novel Global-Local Correlation (GLC) module to explicitly model the correlation of the global token and each local token. We validate our model on two egocentric video datasets - EGTEA Gaze+ and Ego4D. Our detailed ablation studies demonstrate the benefits of our method. In addition, our approach exceeds previous state-of-the-arts by a large margin. We also provide additional visualizations to support our claim that global-local correlation serves a key representation for predicting gaze fixation from egocentric videos. More details can be found in our website (https://bolinlai.github.io/GLC-EgoGazeEst). | ['James M. Rehg', 'Fiona Ryan', 'Miao Liu', 'Bolin Lai'] | 2022-08-08 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-2.83809096e-01 -1.11417718e-01 -4.14860845e-01 -3.72716963e-01
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8b78c919-3713-48bb-9e54-541a658c9d39 | event-transformer-a-sparse-aware-solution-for | 2204.03355 | null | https://arxiv.org/abs/2204.03355v2 | https://arxiv.org/pdf/2204.03355v2.pdf | Event Transformer. A sparse-aware solution for efficient event data processing | Event cameras are sensors of great interest for many applications that run in low-resource and challenging environments. They log sparse illumination changes with high temporal resolution and high dynamic range, while they present minimal power consumption. However, top-performing methods often ignore specific event-data properties, leading to the development of generic but computationally expensive algorithms. Efforts toward efficient solutions usually do not achieve top-accuracy results for complex tasks. This work proposes a novel framework, Event Transformer (EvT), that effectively takes advantage of event-data properties to be highly efficient and accurate. We introduce a new patch-based event representation and a compact transformer-like architecture to process it. EvT is evaluated on different event-based benchmarks for action and gesture recognition. Evaluation results show better or comparable accuracy to the state-of-the-art while requiring significantly less computation resources, which makes EvT able to work with minimal latency both on GPU and CPU. | ['Ana C. Murillo', 'Luis Montesano', 'Alberto Sabater'] | 2022-04-07 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 3.93092394e-01 -7.15323091e-01 9.88504197e-03 -4.16443527e-01
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47ecf9d0-a385-450d-82d1-ad66b8bb05a6 | improving-neural-machine-translation-models | 1511.06709 | null | http://arxiv.org/abs/1511.06709v4 | http://arxiv.org/pdf/1511.06709v4.pdf | Improving Neural Machine Translation Models with Monolingual Data | Neural Machine Translation (NMT) has obtained state-of-the art performance
for several language pairs, while only using parallel data for training.
Target-side monolingual data plays an important role in boosting fluency for
phrase-based statistical machine translation, and we investigate the use of
monolingual data for NMT. In contrast to previous work, which combines NMT
models with separately trained language models, we note that encoder-decoder
NMT architectures already have the capacity to learn the same information as a
language model, and we explore strategies to train with monolingual data
without changing the neural network architecture. By pairing monolingual
training data with an automatic back-translation, we can treat it as additional
parallel training data, and we obtain substantial improvements on the WMT 15
task English<->German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task
Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We
also show that fine-tuning on in-domain monolingual and parallel data gives
substantial improvements for the IWSLT 15 task English->German. | ['Alexandra Birch', 'Barry Haddow', 'Rico Sennrich'] | 2015-11-20 | improving-neural-machine-translation-models-1 | https://aclanthology.org/P16-1009 | https://aclanthology.org/P16-1009.pdf | acl-2016-8 | ['cross-lingual-bitext-mining'] | ['natural-language-processing'] | [ 4.00207937e-02 -8.00787956e-02 -5.09606063e-01 -3.34283084e-01
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b30b079b-81ae-4132-8261-c84713505995 | using-automatically-extracted-minimum-spans | 1906.06703 | null | https://arxiv.org/abs/1906.06703v1 | https://arxiv.org/pdf/1906.06703v1.pdf | Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection | The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We will integrate MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans. | ['Leo Born', 'Nafise Sadat Moosavi', 'Michael Strube', 'Massimo Poesio'] | 2019-06-16 | using-automatically-extracted-minimum-spans-1 | https://aclanthology.org/P19-1408 | https://aclanthology.org/P19-1408.pdf | acl-2019-7 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 5.21918237e-02 2.86102444e-01 -2.55038917e-01 -3.44729841e-01
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16148ec4-bab6-4277-99cb-944daa50ee3e | use-of-transformer-based-models-for-word | 2205.11370 | null | https://arxiv.org/abs/2205.11370v2 | https://arxiv.org/pdf/2205.11370v2.pdf | Use of Transformer-Based Models for Word-Level Transliteration of the Book of the Dean of Lismore | The Book of the Dean of Lismore (BDL) is a 16th-century Scottish Gaelic manuscript written in a non-standard orthography. In this work, we outline the problem of transliterating the text of the BDL into a standardised orthography, and perform exploratory experiments using Transformer-based models for this task. In particular, we focus on the task of word-level transliteration, and achieve a character-level BLEU score of 54.15 with our best model, a BART architecture pre-trained on the text of Scottish Gaelic Wikipedia and then fine-tuned on around 2,000 word-level parallel examples. Our initial experiments give promising results, but we highlight the shortcomings of our model, and discuss directions for future work. | ['Roibeard Ó Maolalaigh', 'Jade Scott', 'William Gillies', 'Mark McConville', 'Edward Gow-Smith'] | 2022-05-23 | null | https://aclanthology.org/2022.cltw-1.13 | https://aclanthology.org/2022.cltw-1.13.pdf | cltw-lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [ 2.23741680e-01 1.38692454e-01 -3.48874144e-02 -2.25832611e-01
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fd81957e-39f1-47f8-ae6e-94f917881e02 | on-training-flexible-robots-using-deep | 1907.00269 | null | https://arxiv.org/abs/1907.00269v2 | https://arxiv.org/pdf/1907.00269v2.pdf | On Training Flexible Robots using Deep Reinforcement Learning | The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest into developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL) in other areas, we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn. | ['Mariano Phielipp', 'Madhavun Candadai', 'Zach Dwiel'] | 2019-06-29 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 8.34904537e-02 6.70510083e-02 -2.47874737e-01 1.65888127e-02
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8060d252-cbe6-44a1-9663-98c1ee1626e9 | every-picture-tells-a-story-image-grounded | 2209.01638 | null | https://arxiv.org/abs/2209.01638v2 | https://arxiv.org/pdf/2209.01638v2.pdf | Every picture tells a story: Image-grounded controllable stylistic story generation | Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriately assessing the quality of the story, steering the generated story into a certain style, and addressing the scarcity of image-story pair reference datasets limiting supervision during training. In this work, we introduce Plug-and-Play Story Teller (PPST) and improve image-to-story generation by: 1) alleviating the data scarcity problem by incorporating large pre-trained models, namely CLIP and GPT-2, to facilitate a fluent image-to-text generation with minimal supervision, and 2) enabling a more style-relevant generation by incorporating stylistic adapters to control the story generation. We conduct image-to-story generation experiments with non-styled, romance-styled, and action-styled PPST approaches and compare our generated stories with those of previous work over three aspects, i.e., story coherence, image-story relevance, and style fitness, using both automatic and human evaluation. The results show that PPST improves story coherence and has better image-story relevance, but has yet to be adequately stylistic. | ['Pascale Fung', 'Willy Chung', 'Samuel Cahyawijaya', 'Romain Barraud', 'Bryan Wilie', 'Holy Lovenia'] | 2022-09-04 | null | https://aclanthology.org/2022.latechclfl-1.6 | https://aclanthology.org/2022.latechclfl-1.6.pdf | latechclfl-coling-2022-10 | ['story-generation'] | ['natural-language-processing'] | [ 6.88057601e-01 3.16991895e-01 1.26103863e-01 -3.10759157e-01
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c9a8bc2b-88d7-4149-9b57-165b4337689b | cosplade-contextualizing-splade-for | 2301.04413 | null | https://arxiv.org/abs/2301.04413v1 | https://arxiv.org/pdf/2301.04413v1.pdf | CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval | Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history. Most of the previous methods have focused on a multi-stage ranking approach relying on query reformulation, a critical intermediate step that might lead to a sub-optimal retrieval. Other approaches have tried to use a fully neural IR first-stage, but are either zero-shot or rely on full learning-to-rank based on a dataset with pseudo-labels. In this work, leveraging the CANARD dataset, we propose an innovative lightweight learning technique to train a first-stage ranker based on SPLADE. By relying on SPLADE sparse representations, we show that, when combined with a second-stage ranker based on T5Mono, the results are competitive on the TREC CAsT 2020 and 2021 tracks. | ['Laure Soulier', 'Benjamin Piwowarski', 'Jian-Yun Nie', 'Thibault Formal', 'Thomas Gerald', 'Nam Le Hai'] | 2023-01-11 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.45476589e-01 -7.08947107e-02 -4.14023072e-01 -3.97406161e-01
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75cfe55d-7585-428a-b66f-a298b6eabeb6 | arabic-scene-text-recognition-in-the-deep | null | null | https://ieeexplore.ieee.org/abstract/document/9499028 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9499028 | Arabic Scene Text Recognition in the Deep Learning Era: Analysis on A Novel Dataset | The problem of scene text recognition has recently gained extra attention, being an essential part of scene
understanding systems. The broad scope of applications and the unresolved challenges has given this
problem its popularity. However, the research focus has long been on languages with Latin characters
while leaving behind other languages with different characteristics, such as the Arabic language. In this
paper, we focus on Arabic scene text recognition and attempt to fill two main gaps regarding this research
task. First, the Arabic language is lacking a publicly available benchmark dataset to compare different
proposed methods on the same grounds. Therefore, we introduce a novel Arabic/English dataset: Everyday
Arabic-English Scene Text dataset (EvArEST), to fill that need. Second, while deep learning methods have
continuously evolved and pushed the sate of the art in languages with Latin characters, their use for the
Arabic language has been very limited. Therefore, we use our new dataset to evaluate the problem of
Arabic scene text recognition from three perspectives: (1) using deep learning techniques and studying
their suitability for Arabic scene text recognition, where we identify essential components required for
the model to obtain good performance; (2) identifying Arabic text challenges that differ from Latin text
and require special attention; (3) investigating a bilingual model that concurrently deals with Arabic and
English words, since Arabic text is usually found along with other languages. We determine the best model
to handle bidirectional text, its challenges, and possible ways to overcome them. We offer both Arabic and
Bilingual text recognition results using EvArEST dataset for upcoming research to build upon and improve.
We also point to directions for future research based on the analysis performed on the dataset. The dataset
is publicly available at https://github.com/HGamal11/EvArEST-dataset. | ['Mohamed E. Hussein', 'Ahmed El-Mahdy', 'HEBA HASSAN1'] | 2021-07-27 | null | null | null | ieee-access-2021-7 | ['scene-text-recognition'] | ['computer-vision'] | [ 5.00123203e-02 -5.27469933e-01 1.15037430e-02 -3.67533803e-01
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f0c9a112-c715-4385-9e07-fd583c6fe719 | adversarial-counterfactual-visual | 2303.09962 | null | https://arxiv.org/abs/2303.09962v1 | https://arxiv.org/pdf/2303.09962v1.pdf | Adversarial Counterfactual Visual Explanations | Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable and understandable image modifications. Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain. The proposed approach hypothesizes that Denoising Diffusion Probabilistic Models are excellent regularizers for avoiding high-frequency and out-of-distribution perturbations when generating adversarial attacks. The paper's key idea is to build attacks through a diffusion model to polish them. This allows studying the target model regardless of its robustification level. Extensive experimentation shows the advantages of our counterfactual explanation approach over current State-of-the-Art in multiple testbeds. | ['Frédéric Jurie', 'Loïc Simon', 'Guillaume Jeanneret'] | 2023-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.87450778e-01 7.94831336e-01 -2.23437145e-01 1.78223883e-03
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4454aff7-b15d-4259-bf69-cefa5f57db13 | treating-dialogue-quality-evaluation-as-an | null | null | https://aclanthology.org/2020.lrec-1.64 | https://aclanthology.org/2020.lrec-1.64.pdf | Treating Dialogue Quality Evaluation as an Anomaly Detection Problem | Dialogue systems for interaction with humans have been enjoying increased popularity in the research and industry fields. To this day, the best way to estimate their success is through means of human evaluation and not automated approaches, despite the abundance of work done in the field. In this paper, we investigate the effectiveness of perceiving dialogue evaluation as an anomaly detection task. The paper looks into four dialogue modeling approaches and how their objective functions correlate with human annotation scores. A high-level perspective exhibits negative results. However, a more in-depth look shows some potential for using anomaly detection for evaluating dialogues. | ['Rostislav Nedelchev', 'Ricardo Usbeck', 'Jens Lehmann'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-9.89400446e-02 5.91800630e-01 9.45816562e-02 -7.36506462e-01
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67e7b28b-bd71-47f1-8bee-fea42639f62d | the-devil-is-in-the-points-weakly-semi | 2303.15062 | null | https://arxiv.org/abs/2303.15062v1 | https://arxiv.org/pdf/2303.15062v1.pdf | The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation | In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation. Namely, we consider a dataset setting consisting of a few fully-labeled images and a lot of point-labeled images. Motivated by the main challenge of semi-supervised approaches mainly derives from the trade-off between false-negative and false-positive instance proposals, we propose a method for WSSIS that can effectively leverage the budget-friendly point labels as a powerful weak supervision source to resolve the challenge. Furthermore, to deal with the hard case where the amount of fully-labeled data is extremely limited, we propose a MaskRefineNet that refines noise in rough masks. We conduct extensive experiments on COCO and BDD100K datasets, and the proposed method achieves promising results comparable to those of the fully-supervised model, even with 50% of the fully labeled COCO data (38.8% vs. 39.7%). Moreover, when using as little as 5% of fully labeled COCO data, our method shows significantly superior performance over the state-of-the-art semi-supervised learning method (33.7% vs. 24.9%). The code is available at https://github.com/clovaai/PointWSSIS. | ['Sung Ju Hwang', 'Dongyoon Han', 'JoonHyun Jeong', 'Beomyoung Kim'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_The_Devil_Is_in_the_Points_Weakly_Semi-Supervised_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_The_Devil_Is_in_the_Points_Weakly_Semi-Supervised_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [-1.21901196e-03 5.28146803e-01 -6.66918278e-01 -4.88463908e-01
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94f4cd23-f972-47a9-8594-a65208c4172d | learning-multi-object-tracking-and | 1912.02096 | null | https://arxiv.org/abs/1912.02096v3 | https://arxiv.org/pdf/1912.02096v3.pdf | Learning Multi-Object Tracking and Segmentation from Automatic Annotations | In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet - a deep learning, tracking-by-detection architecture for MOTS - deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data. | ['Samuel Rota Bulò', 'Joan Serrat', 'Peter Kontschieder', 'Lorenzo Porzi', 'Idoia Ruiz', 'Markus Hofinger'] | 2019-12-04 | learning-multi-object-tracking-and-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Porzi_Learning_Multi-Object_Tracking_and_Segmentation_From_Automatic_Annotations_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Porzi_Learning_Multi-Object_Tracking_and_Segmentation_From_Automatic_Annotations_CVPR_2020_paper.pdf | cvpr-2020-6 | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 3.59865159e-01 5.89830987e-02 -8.30129236e-02 -1.97809190e-01
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0c14ac80-8acb-46c2-840c-ea067943518d | experiments-in-language-variety-geolocation | null | null | https://aclanthology.org/2020.vardial-1.21 | https://aclanthology.org/2020.vardial-1.21.pdf | Experiments in Language Variety Geolocation and Dialect Identification | In this paper we describe the systems we used when participating in the VarDial Evaluation Campaign organized as part of the 7th workshop on NLP for similar languages, varieties and dialects. The shared tasks we participated in were the second edition of the Romanian Dialect Identification (RDI) and the first edition of the Social Media Variety Geolocation (SMG). The submissions of our SUKI team used generative language models based on Naive Bayes and character n-grams. | ['Krister Lindén', 'Heidi Jauhiainen', 'Tommi Jauhiainen'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-5.85910439e-01 -2.88421750e-01 -1.31698981e-01 -8.48441839e-01
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f45019fd-fae7-4d8b-9723-897a993c219c | advances-on-the-classification-of-radio-image | 2305.03435 | null | https://arxiv.org/abs/2305.03435v1 | https://arxiv.org/pdf/2305.03435v1.pdf | Advances on the classification of radio image cubes | Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of intensive machine intelligence to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of artificial intelligence in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study presents a succinct, but comprehensive review of the application of machine intelligence techniques on radio images with emphasis on the morphological classification of radio galaxies. It aims to present a detailed synthesis of the relevant papers summarizing the literature based on data complexity, data pre-processing, and methodological novelty in radio astronomy. The rapid advancement and application of computer intelligence in radio astronomy has resulted in a revolution and a new paradigm shift in the automation of daunting data processes. However, the optimal exploitation of artificial intelligence in radio astronomy, calls for continued collaborative efforts in the creation of annotated data sets. Additionally, in order to quickly locate radio galaxies with similar or dissimilar physical characteristics, it is necessary to index the identified radio sources. Nonetheless, this issue has not been adequately addressed in the literature, making it an open area for further study. | ['George Azzopardi', 'Dimka Karastoyanova', 'Stefan J. Wijnholds', 'Trienko Grobler', "Steven Ndung'u"] | 2023-05-05 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 7.59643614e-02 -3.34764659e-01 2.27405414e-01 -6.08394854e-02
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2faed590-b4e8-4d5f-b26a-cbbc9d5e605b | table-tennis-stroke-recognition-using-two | 2104.09907 | null | https://arxiv.org/abs/2104.09907v2 | https://arxiv.org/pdf/2104.09907v2.pdf | Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation | We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player's strokes, which is extremely insightful for performance improvement. | ['Sucheth Shenoy', 'Kaustubh Milind Kulkarni'] | 2021-04-20 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 1.05950728e-01 -1.96671695e-01 -3.57511401e-01 -1.61408305e-01
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653d2e52-ea84-4d38-9afb-3c2f26f10c80 | perceptual-conversational-head-generation | 2206.12837 | null | https://arxiv.org/abs/2206.12837v2 | https://arxiv.org/pdf/2206.12837v2.pdf | Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer | This paper reports our solution for ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, which aims to generate vivid face-to-face conversation videos based on audio and reference images. Our solution focuses on training a generalized audio-to-head driver using regularization and assembling a high-visual quality renderer. We carefully tweak the audio-to-behavior model and post-process the generated video using our foreground-background fusion module. We get first place in the listening head generation track and second place in the talking head generation track on the official leaderboard. Our code is available at https://github.com/megvii-research/MM2022-ViCoPerceptualHeadGeneration. | ['Shuchang Zhou', 'Zhewei Huang', 'Ailin Huang'] | 2022-06-26 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 6.28447309e-02 6.12776697e-01 2.99839288e-01 -4.21542257e-01
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dde95574-3305-401f-8ead-0f5e9337a5eb | multimodal-emotion-recognition-with | null | null | https://ieeexplore.ieee.org/document/9206016 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9206016 | Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion | Emotion Recognition is a challenging research area given its complex nature, and humans express emotional cues across various modalities such as language, facial expressions, and speech. Representation and fusion of features are the most crucial tasks in multimodal emotion recognition research. Self Supervised Learning (SSL) has become a prominent and influential research direction in representation learning, where researchers have access to pre-trained SSL models that represent different data modalities. For the first time in the literature, we represent three input modalities of text, audio (speech), and vision with features extracted from independently pre-trained SSL models in this paper. Given the high dimensional nature of SSL features, we introduce a novel Transformers and Attention-based fusion mechanism that can combine multimodal SSL features and achieve state-of-the-art results for the task of multimodal emotion recognition. We benchmark and evaluate our work to show that our model is robust and outperforms the state-of-the-art models on four datasets. | ['Shamane Siriwardhana ; Tharindu Kaluarachchi ; Mark Billinghurst ; Suranga Nanayakkara'] | 2020-10-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.54688287e-01 -4.29951876e-01 -2.93796837e-01 -6.48192108e-01
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d4aa1693-80ff-4d32-88f3-f43a9504e33c | fuzzy-c-means-clustering-and-sonification-of | 1908.07107 | null | https://arxiv.org/abs/1908.07107v2 | https://arxiv.org/pdf/1908.07107v2.pdf | Fuzzy C-Means Clustering and Sonification of HRV Features | Linear and non-linear measures of heart rate variability (HRV) are widely investigated as non-invasive indicators of health. Stress has a profound impact on heart rate, and different meditation techniques have been found to modulate heartbeat rhythm. This paper aims to explore the process of identifying appropriate metrices from HRV analysis for sonification. Sonification is a type of auditory display involving the process of mapping data to acoustic parameters. This work explores the use of auditory display in aiding the analysis of HRV leveraged by unsupervised machine learning techniques. Unsupervised clustering helps select the appropriate features to improve the sonification interpretability. Vocal synthesis sonification techniques are employed to increase comprehension and learnability of the processed data displayed through sound. These analyses are early steps in building a real-time sound-based biofeedback training system. | ['Paul Batchelor', 'Victoria Grace', 'Kunal Mankodiya', 'Harishchandra Dubey', 'Debanjan Borthakur'] | 2019-08-19 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.06462318e-01 -1.54579610e-01 1.34424064e-02 -4.76773351e-01
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6cb5d306-9a97-4640-b05a-976feb3466e4 | simbow-at-semeval-2017-task-3-soft-cosine | null | null | https://aclanthology.org/S17-2051 | https://aclanthology.org/S17-2051.pdf | SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering | This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a soft-cosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B. | ["G{\\'e}raldine Damnati", 'Delphine Charlet'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [ 1.26671091e-01 3.42429131e-02 2.29487151e-01 -5.26596665e-01
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598fcaaf-80fe-42bd-946c-27fb0e54c149 | few-shot-open-set-recognition-using | 2207.09059 | null | https://arxiv.org/abs/2207.09059v1 | https://arxiv.org/pdf/2207.09059v1.pdf | Few-shot Open-set Recognition Using Background as Unknowns | Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required not only to learn a discriminative classifier to classify the pre-defined classes with few training data but also to reject inputs from unseen classes that never appear at training time. In this paper, we propose to solve the problem from two novel aspects. First, instead of learning the decision boundaries between seen classes, as is done in standard close-set classification, we reserve space for unseen classes, such that images located in these areas are recognized as the unseen classes. Second, to effectively learn such decision boundaries, we propose to utilize the background features from seen classes. As these background regions do not significantly contribute to the decision of close-set classification, it is natural to use them as the pseudo unseen classes for classifier learning. Our extensive experiments show that our proposed method not only outperforms multiple baselines but also sets new state-of-the-art results on three popular benchmarks, namely tieredImageNet, miniImageNet, and Caltech-USCD Birds-200-2011 (CUB). | ['Guosheng Lin', 'Chi Zhang', 'Nan Song'] | 2022-07-19 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.94326437e-01 -3.48453484e-02 -1.81147754e-01 -5.77868104e-01
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65655ab3-bc52-4811-bc2f-26e6d13791c4 | finding-short-signals-in-long-irregular-time | 2302.04052 | null | https://arxiv.org/abs/2302.04052v1 | https://arxiv.org/pdf/2302.04052v1.pdf | Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks | Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals. However, for many classification problems, only small portions of long time series are often relevant to the class label. In this case, existing ITS models often fail to classify long series since they rely on careful imputation, which easily over- or under-samples the relevant regions. Using this insight, we then propose CAT, a model that classifies multivariate ITS by explicitly seeking highly-relevant portions of an input series' timeline. CAT achieves this by integrating three components: (1) A Moment Network learns to seek relevant moments in an ITS's continuous timeline using reinforcement learning. (2) A Receptor Network models the temporal dynamics of both observations and their timing localized around predicted moments. (3) A recurrent Transition Model models the sequence of transitions between these moments, cultivating a representation with which the series is classified. Using synthetic and real data, we find that CAT outperforms ten state-of-the-art methods by finding short signals in long irregular time series. | ['Elke Rundensteiner', 'Xiangnan Kong', 'Jidapa Thadajarassiri', 'Thomas Hartvigsen'] | 2023-02-08 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 3.00555021e-01 -1.32317439e-01 -6.24721825e-01 -3.79836977e-01
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aafdfceb-1a58-4001-9ee8-1fc04a1f6760 | joint-brain-tumor-segmentation-from-multi-mr | 2203.03338 | null | https://arxiv.org/abs/2203.03338v1 | https://arxiv.org/pdf/2203.03338v1.pdf | Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network | Brain tumor segmentation is highly contributive in diagnosing and treatment planning. The manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologists skill. Automated brain tumor segmentation is of high importance, and does not depend on either inter or intra-observation. The objective of this study is to automate the delineation of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted contrast-enhanced MR sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein. | ['Hossein Arabi', 'Alireza Karimian', 'Farzaneh Dehghani'] | 2022-03-07 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 2.15788528e-01 -2.09599093e-01 2.82906163e-02 -3.28923047e-01
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57987970-48d7-4877-b26b-961b9b09dc41 | more-comprehensive-facial-inversion-for-more | 2211.13564 | null | https://arxiv.org/abs/2211.13564v2 | https://arxiv.org/pdf/2211.13564v2.pdf | More comprehensive facial inversion for more effective expression recognition | Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the image inversion mechanism for the FER task, termed Inversion FER (IFER). Particularly, we devise a novel Adversarial Style Inversion Transformer (ASIT) towards IFER to comprehensively extract features of generated facial images. In addition, ASIT is equipped with an image inversion discriminator that measures the cosine similarity of semantic features between source and generated images, constrained by a distribution alignment loss. Finally, we introduce a feature modulation module to fuse the structural code and latent codes from ASIT for the subsequent FER work. We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance. IFER also achieves competitive results in facial expression recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models are available at https://github.com/Talented-Q/IFER-master. | ['Rui Xu', 'Xiaogang Peng', 'Xuesong Yin', 'Yuanqi Chang', 'Guangyi Zhao', 'Jiawei Mao'] | 2022-11-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.72992957e-01 4.93172742e-02 1.78612351e-01 -6.72692180e-01
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aa1a477c-ba1d-4af1-8d57-d1760f82258d | 3c-net-category-count-and-center-loss-for | 1908.08216 | null | https://arxiv.org/abs/1908.08216v2 | https://arxiv.org/pdf/1908.08216v2.pdf | 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization | Temporal action localization is a challenging computer vision problem with numerous real-world applications. Most existing methods require laborious frame-level supervision to train action localization models. In this work, we propose a framework, called 3C-Net, which only requires video-level supervision (weak supervision) in the form of action category labels and the corresponding count. We introduce a novel formulation to learn discriminative action features with enhanced localization capabilities. Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization. Comprehensive experiments are performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our approach sets a new state-of-the-art for weakly-supervised temporal action localization on both datasets. On the THUMOS14 dataset, the proposed method achieves an absolute gain of 4.6% in terms of mean average precision (mAP), compared to the state-of-the-art. Source code is available at https://github.com/naraysa/3c-net. | ['Hisham Cholakkal', 'Fahad Shahbaz Khan', 'Sanath Narayan', 'Ling Shao'] | 2019-08-22 | 3c-net-category-count-and-center-loss-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Narayan_3C-Net_Category_Count_and_Center_Loss_for_Weakly-Supervised_Action_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Narayan_3C-Net_Category_Count_and_Center_Loss_for_Weakly-Supervised_Action_Localization_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.45984393e-01 -3.93798321e-01 -7.60215580e-01 -2.52751201e-01
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2e237e89-bb30-44ba-97c8-9196f1ab4aa1 | inferring-causal-effects-under-heterogeneous | 2305.17479 | null | https://arxiv.org/abs/2305.17479v1 | https://arxiv.org/pdf/2305.17479v1.pdf | Inferring Causal Effects Under Heterogeneous Peer Influence | Causal inference in networks should account for interference, which occurs when a unit's outcome is influenced by treatments or outcomes of peers. There can be heterogeneous peer influence between units when a unit's outcome is subjected to variable influence from different peers based on their attributes and relationships, or when each unit has a different susceptibility to peer influence. Existing solutions to causal inference under interference consider either homogeneous influence from peers or specific heterogeneous influence mechanisms (e.g., based on local neighborhood structure). This paper presents a methodology for estimating individual causal effects in the presence of heterogeneous peer influence due to arbitrary mechanisms. We propose a structural causal model for networks that can capture arbitrary assumptions about network structure, interference conditions, and causal dependence. We identify potential heterogeneous contexts using the causal model and propose a novel graph neural network-based estimator to estimate individual causal effects. We show that existing state-of-the-art methods for individual causal effect estimation produce biased results in the presence of heterogeneous peer influence, and that our proposed estimator is robust. | ['Elena Zheleva', 'Shishir Adhikari'] | 2023-05-27 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.45093215e-01 3.73483628e-01 -6.06270194e-01 -2.37318471e-01
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e7e1818a-32bc-42cd-9dcb-bd99fabdd945 | two-stream-transformer-for-multi-label-image | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548343 | https://dl.acm.org/doi/abs/10.1145/3503161.3548343 | Two-Stream Transformer for Multi-Label Image Classification | Multi-label image classification is a fundamental yet challenging task in computer vision that aims to identify multiple objects from a given image. Recent studies on this task mainly focus on learning cross-modal interactions between label semantics and high-level visual representations via an attention operation. However, these one-shot attention based approaches generally perform poorly in establishing accurate and robust alignments between vision and text due to the acknowledged semantic gap. In this paper, we propose a two-stream transformer (TSFormer) learning framework, in which the spatial stream focuses on extracting patch features with a global perception, while the semantic stream aims to learn vision-aware label semantics as well as their correlations via a multi-shot attention mechanism. Specifically, in each layer of TSFormer, a cross-modal attention module is developed to aggregate visual features from spatial stream into semantic stream and update label semantics via a residual connection. In this way, the semantic gap between two streams gradually narrows as the procedure progresses layer by layer, allowing the semantic stream to produce sophisticated visual representations for each label towards accurate label recognition. Extensive experiments on three visual benchmarks, including Pascal VOC 2007, Microsoft COCO and NUS-WIDE, consistently demonstrate that our proposed TSFormer achieves state-of-the-art performance on the multi-label image classification task. | ['Bo Liu', 'Weijia Liu', 'Jiawei Ge', 'Jiuxin Cao', 'Xuelin Zhu'] | 2022-10-01 | null | null | null | acmmm-2022-10 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.83624482e-01 -4.11078244e-01 -1.91540554e-01 -4.36265737e-01
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d3f099af-79c3-48bb-8f90-cdfacca7a7f4 | non-invasive-urinary-bladder-volume | 2303.14028 | null | https://arxiv.org/abs/2303.14028v1 | https://arxiv.org/pdf/2303.14028v1.pdf | Non-invasive urinary bladder volume estimation with artefact-suppressed bio-impedance measurements | Urine output is a vital parameter to gauge kidney health. Current monitoring methods include manually written records, invasive urinary catheterization or ultrasound measurements performed by highly skilled personnel. Catheterization bears high risks of infection while intermittent ultrasound measures and manual recording are time consuming and might miss early signs of kidney malfunction. Bioimpedance (BI) measurements may serve as a non-invasive alternative for measuring urine volume in vivo. However, limited robustness have prevented its clinical translation. Here, a deep learning-based algorithm is presented that processes the local BI of the lower abdomen and suppresses artefacts to measure the bladder volume quantitatively, non-invasively and without the continuous need for additional personnel. A tetrapolar BI wearable system called ANUVIS was used to collect continuous bladder volume data from three healthy subjects to demonstrate feasibility of operation, while clinical gold standards of urodynamic (n=6) and uroflowmetry tests (n=8) provided the ground truth. Optimized location for electrode placement and a model for the change in BI with changing bladder volume is deduced. The average error for full bladder volume estimation and for residual volume estimation was -29 +/-87.6 ml, thus, comparable to commercial portable ultrasound devices (Bland Altman analysis showed a bias of -5.2 ml with LoA between 119.7 ml to -130.1 ml), while providing the additional benefit of hands-free, non-invasive, and continuous bladder volume estimation. The combination of the wearable BI sensor node and the presented algorithm provides an attractive alternative to current standard of care with potential benefits in providing insights into kidney function. | ['Michele Magno', 'Simone Schürle', 'Hugo Sax', 'Thomas Hermanns', 'Denise Franke', 'Marko Kozomara', 'Manuel Eggimann', 'Philipp Mayer', 'Stefan Walser', 'Kanika Dheman'] | 2023-03-24 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 2.60632783e-01 5.52751757e-02 -2.47595042e-01 -9.55777019e-02
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9b6d5dac-ec94-4787-a8a0-8866623f1c9d | learning-span-level-interactions-for-aspect | 2107.12214 | null | https://arxiv.org/abs/2107.12214v1 | https://arxiv.org/pdf/2107.12214v1.pdf | Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our span-level approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions. | ['Lidong Bing', 'Yew Ken Chia', 'Lu Xu'] | 2021-07-26 | null | https://aclanthology.org/2021.acl-long.367 | https://aclanthology.org/2021.acl-long.367.pdf | acl-2021-5 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.18577808e-01 -2.16144651e-01 -3.99457604e-01 -5.10439634e-01
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3ff4b77e-550e-4787-9a08-a1a81937b91c | scalable-statistical-inference-of-photometric | 2103.16041 | null | https://arxiv.org/abs/2103.16041v2 | https://arxiv.org/pdf/2103.16041v2.pdf | Scalable Statistical Inference of Photometric Redshift via Data Subsampling | Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift -- the stretching effect in observing the light of far-away galaxies -- given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models. | ['Jonas Chaves-Montero', 'Stefan M. Wild', 'Arindam Fadikar'] | 2021-03-30 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [-9.16051120e-02 1.41641587e-01 4.13804084e-01 -5.83709776e-01
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523f3171-612c-44b1-b247-e8428e4a9d31 | k-order-graph-oriented-transformer-with | 2208.11328 | null | https://arxiv.org/abs/2208.11328v2 | https://arxiv.org/pdf/2208.11328v2.pdf | K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation | We propose a novel attention-based 2D-to-3D pose estimation network for graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation network for hand data, named GASE-Net. Previous 3D pose estimation methods have focused on various modifications to the graph convolution kernel, such as abandoning weight sharing or increasing the receptive field. Some of these methods employ attention-based non-local modules as auxiliary modules. In order to better model the relationship between nodes in graph-structured data and fuse the information of different neighbor nodes in a differentiated way, we make targeted modifications to the attention module and propose two modules designed for graph-structured data, graph relative positional encoding multi-head self-attention (GR-MSA) and K-order graph-oriented multi-head self-attention (KOG-MSA). By stacking GR-MSA and KOG-MSA, we propose a novel network KOG-Transformer for 2D-to-3D pose estimation. Furthermore, we propose a network for shape estimation on hand data, called GraAttention shape estimation network (GASE-Net), which takes a 3D pose as input and gradually models the shape of the hand from sparse to dense. We have empirically shown the superiority of KOG-Transformer through extensive experiments. Experimental results show that KOG-Transformer significantly outperforms the previous state-of-the-art methods on the benchmark dataset Human3.6M. We evaluate the effect of GASE-Net on two public available hand datasets, ObMan and InterHand2.6M. GASE-Net can predict the corresponding shape for input pose with strong generalization ability. | ['Weiqiang Wang', 'Weixi Zhao'] | 2022-08-24 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-3.86318356e-01 2.22050488e-01 -3.62214446e-02 -3.27400535e-01
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66ea3fd6-d99a-436e-bebf-e0880620a226 | cryptocurrency-price-prediction-using-twitter | 2303.09397 | null | https://arxiv.org/abs/2303.09397v1 | https://arxiv.org/pdf/2303.09397v1.pdf | Cryptocurrency Price Prediction using Twitter Sentiment Analysis | The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%. | ['Sahana N. B', 'Haritha GB'] | 2023-03-03 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-6.26667678e-01 -8.99664685e-02 -2.75099605e-01 -2.55477190e-01
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0bfcbcb7-344c-4fa0-ab89-3be8ecbe344a | directed-acyclic-graphs-with-tears | 2302.02160 | null | https://arxiv.org/abs/2302.02160v1 | https://arxiv.org/pdf/2302.02160v1.pdf | Directed Acyclic Graphs With Tears | Bayesian network is a frequently-used method for fault detection and diagnosis in industrial processes. The basis of Bayesian network is structure learning which learns a directed acyclic graph (DAG) from data. However, the search space will scale super-exponentially with the increase of process variables, which makes the data-driven structure learning a challenging problem. To this end, the DAGs with NOTEARs methods are being well studied not only for their conversion of the discrete optimization into continuous optimization problem but also their compatibility with deep learning framework. Nevertheless, there still remain challenges for NOTEAR-based methods: 1) the infeasible solution results from the gradient descent-based optimization paradigm; 2) the truncation operation to promise the learned graph acyclic. In this work, the reason for challenge 1) is analyzed theoretically, and a novel method named DAGs with Tears method is proposed based on mix-integer programming to alleviate challenge 2). In addition, prior knowledge is able to incorporate into the new proposed method, making structure learning more practical and useful in industrial processes. Finally, a numerical example and an industrial example are adopted as case studies to demonstrate the superiority of the developed method. | ['Zhiqiang Ge', 'Zhichao Chen'] | 2023-02-04 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.56682611e-01 1.97043255e-01 -7.83053134e-03 -2.70670295e-01
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389f0a5d-8256-4007-b8ba-91e89aa61693 | protecting-global-properties-of-datasets-with | 2207.08367 | null | https://arxiv.org/abs/2207.08367v2 | https://arxiv.org/pdf/2207.08367v2.pdf | Protecting Global Properties of Datasets with Distribution Privacy Mechanisms | We consider the problem of ensuring confidentiality of dataset properties aggregated over many records of a dataset. Such properties can encode sensitive information, such as trade secrets or demographic data, while involving a notion of data protection different to the privacy of individual records typically discussed in the literature. In this work, we demonstrate how a distribution privacy framework can be applied to formalize such data confidentiality. We extend the Wasserstein Mechanism from Pufferfish privacy and the Gaussian Mechanism from attribute privacy to this framework, then analyze their underlying data assumptions and how they can be relaxed. We then empirically evaluate the privacy-utility tradeoffs of these mechanisms and apply them against a practical property inference attack which targets global properties of datasets. The results show that our mechanisms can indeed reduce the effectiveness of the attack while providing utility substantially greater than a crude group differential privacy baseline. Our work thus provides groundwork for theoretical mechanisms for protecting global properties of datasets along with their evaluation in practice. | ['Olga Ohrimenko', 'Michelle Chen'] | 2022-07-18 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 5.50665855e-01 2.68394500e-01 -2.48187572e-01 -7.17898428e-01
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664f7c57-e48f-43f3-b2cf-20e1e6aa5957 | multi-adversarial-discriminative-deep-domain | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Shao_Multi-Adversarial_Discriminative_Deep_Domain_Generalization_for_Face_Presentation_Attack_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Shao_Multi-Adversarial_Discriminative_Deep_Domain_Generalization_for_Face_Presentation_Attack_Detection_CVPR_2019_paper.pdf | Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection | Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.
| [' Pong C. Yuen', ' Jiawei Li', ' Xiangyuan Lan', 'Rui Shao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.88946778e-01 -3.23356390e-01 -1.19540937e-01 -5.04746258e-01
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e4f0ae14-b528-4252-a3ea-ae1926898a8f | speech-emotion-recognition-based-on-self | null | null | https://ieeexplore.ieee.org/document/9936641 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9936641 | Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features | Speech emotion recognition (SER) is essential for understanding a speaker’s intention. Recently, some groups have attempted to improve SER performance using a bidirectional long short-term memory (BLSTM) to extract features from speech sequences and a self-attention mechanism to focus on the important parts of the speech sequences. SER also benefits from combining the information in speech with text, which can be accomplished automatically using an automatic speech recognizer (ASR), further improving its performance. However, ASR performance deteriorates in the presence of emotion in speech. Although there is a method to improve ASR performance in the presence of emotional speech, it requires the fine-tuning of ASR, which has a high computational cost and leads to the loss of cues important for determining the presence of emotion in speech segments, which can be helpful in SER. To solve these problems, we propose a BLSTM-and-self-attention-based SER method using self-attention weight correction (SAWC) with confidence measures. This method is applied to acoustic and text feature extractors in SER to adjust the importance weights of speech segments and words with a high possibility of ASR error. Our proposed SAWC reduces the importance of words with speech recognition error in the text feature while emphasizing the importance of speech segments containing these words in acoustic features. Our experimental results on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset reveal that our proposed method achieves a weighted average accuracy of 76.6%, outperforming other state-of-the-art methods. Furthermore, we investigated the behavior of our proposed SAWC in each of the feature extractors. | ['Shoji Makino', 'Taiichi Hashimoto', 'Kenkichi Ishizuka', 'Takeshi Yamada', 'JENNIFER SANTOSO'] | 2022-11-08 | null | null | null | ieee-access-2022-11 | ['multimodal-emotion-recognition', 'speech-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech', 'speech'] | [ 1.94398552e-01 -3.64920199e-02 2.29972467e-01 -2.36365005e-01
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032b5cbc-3426-4cf1-af5e-ed433b8a589d | examining-the-state-of-the-art-in-news | 2005.10107 | null | https://arxiv.org/abs/2005.10107v1 | https://arxiv.org/pdf/2005.10107v1.pdf | Examining the State-of-the-Art in News Timeline Summarization | Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the state-of-the-art on all tested benchmarks. For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets. | ['Georgiana Ifrim', 'Demian Gholipour Ghalandari'] | 2020-05-20 | examining-the-state-of-the-art-in-news-1 | https://aclanthology.org/2020.acl-main.122 | https://aclanthology.org/2020.acl-main.122.pdf | acl-2020-6 | ['timeline-summarization'] | ['natural-language-processing'] | [ 1.43380880e-01 -5.78435436e-02 -4.00377542e-01 -3.86857301e-01
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46137501-86bf-42f1-b4b1-fda658b68dd7 | a-closer-look-at-geometric-temporal-dynamics | 2306.14313 | null | https://arxiv.org/abs/2306.14313v1 | https://arxiv.org/pdf/2306.14313v1.pdf | A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing | Face anti-spoofing (FAS) is indispensable for a face recognition system. Many texture-driven countermeasures were developed against presentation attacks (PAs), but the performance against unseen domains or unseen spoofing types is still unsatisfactory. Instead of exhaustively collecting all the spoofing variations and making binary decisions of live/spoof, we offer a new perspective on the FAS task to distinguish between normal and abnormal movements of live and spoof presentations. We propose Geometry-Aware Interaction Network (GAIN), which exploits dense facial landmarks with spatio-temporal graph convolutional network (ST-GCN) to establish a more interpretable and modularized FAS model. Additionally, with our cross-attention feature interaction mechanism, GAIN can be easily integrated with other existing methods to significantly boost performance. Our approach achieves state-of-the-art performance in the standard intra- and cross-dataset evaluations. Moreover, our model outperforms state-of-the-art methods by a large margin in the cross-dataset cross-type protocol on CASIA-SURF 3DMask (+10.26% higher AUC score), exhibiting strong robustness against domain shifts and unseen spoofing types. | ['Trista Pei-Chun Chen', 'Shang-Hong Lai', 'Chien-Yi Wang', 'Min-Hung Chen', 'Shih-Hsuan Yao', 'Yaw-Chern Lee', 'Chih-Jung Chang'] | 2023-06-25 | null | null | null | null | ['face-recognition', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 2.79958755e-01 -1.65934473e-01 -2.69124687e-01 -2.68177420e-01
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2b4e6378-7e09-442e-ac9f-49a6708ba8d4 | graph-generation-with-variational-recurrent | 1910.01743 | null | https://arxiv.org/abs/1910.01743v1 | https://arxiv.org/pdf/1910.01743v1.pdf | Graph Generation with Variational Recurrent Neural Network | Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurrent Neural Network (GraphVRNN), a probabilistic autoregressive model for graph generation. Through modeling the latent variables of graph data, GraphVRNN can capture the joint distributions of graph structures and the underlying node attributes. We conduct experiments on the proposed GraphVRNN in both graph structure learning and attribute generation tasks. The evaluation results show that the variational component allows our network to model complicated distributions, as well as generate plausible structures and node attributes. | ['Shih-Yang Su', 'Hossein Hajimirsadeghi', 'Greg Mori'] | 2019-10-02 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-8.31504688e-02 7.52409995e-01 -1.27071947e-01 -1.88595399e-01
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e7d9f039-a36c-4af5-9ca1-9b3a42e37190 | multimodal-classification-for-analysing | 1708.02099 | null | http://arxiv.org/abs/1708.02099v1 | http://arxiv.org/pdf/1708.02099v1.pdf | Multimodal Classification for Analysing Social Media | Classification of social media data is an important approach in understanding
user behavior on the Web. Although information on social media can be of
different modalities such as texts, images, audio or videos, traditional
approaches in classification usually leverage only one prominent modality.
Techniques that are able to leverage multiple modalities are often complex and
susceptible to the absence of some modalities. In this paper, we present simple
models that combine information from different modalities to classify social
media content and are able to handle the above problems with existing
techniques. Our models combine information from different modalities using a
pooling layer and an auxiliary learning task is used to learn a common feature
space. We demonstrate the performance of our models and their robustness to the
missing of some modalities in the emotion classification domain. Our
approaches, although being simple, can not only achieve significantly higher
accuracies than traditional fusion approaches but also have comparable results
when only one modality is available. | ['Remi Lebret', 'Karl Aberer', 'Chi Thang Duong'] | 2017-08-07 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 8.67436454e-02 -3.87121230e-01 -3.05964172e-01 -5.80472171e-01
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1f829267-17ed-4f01-a03c-baa89f506cd1 | point-e-a-system-for-generating-3d-point | 2212.08751 | null | https://arxiv.org/abs/2212.08751v1 | https://arxiv.org/pdf/2212.08751v1.pdf | Point-E: A System for Generating 3D Point Clouds from Complex Prompts | While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image. While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at https://github.com/openai/point-e. | ['Mark Chen', 'Pamela Mishkin', 'Prafulla Dhariwal', 'Heewoo Jun', 'Alex Nichol'] | 2022-12-16 | null | null | null | null | ['generating-3d-point-clouds'] | ['computer-vision'] | [ 1.22797422e-01 2.28882089e-01 3.29183072e-01 -6.26351461e-02
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09c133b6-a3f0-49fa-9e36-3d8f3c0a6877 | speaker-profiling-in-multiparty-conversations | 2304.08801 | null | https://arxiv.org/abs/2304.08801v2 | https://arxiv.org/pdf/2304.08801v2.pdf | Speaker Profiling in Multiparty Conversations | In conversational settings, individuals exhibit unique behaviors, rendering a one-size-fits-all approach insufficient for generating responses by dialogue agents. Although past studies have aimed to create personalized dialogue agents using speaker persona information, they have relied on the assumption that the speaker's persona is already provided. However, this assumption is not always valid, especially when it comes to chatbots utilized in industries like banking, hotel reservations, and airline bookings. This research paper aims to fill this gap by exploring the task of Speaker Profiling in Conversations (SPC). The primary objective of SPC is to produce a summary of persona characteristics for each individual speaker present in a dialogue. To accomplish this, we have divided the task into three subtasks: persona discovery, persona-type identification, and persona-value extraction. Given a dialogue, the first subtask aims to identify all utterances that contain persona information. Subsequently, the second task evaluates these utterances to identify the type of persona information they contain, while the third subtask identifies the specific persona values for each identified type. To address the task of SPC, we have curated a new dataset named SPICE, which comes with specific labels. We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper. Furthermore, we present a comprehensive analysis of SPOT, examining the limitations of individual modules both quantitatively and qualitatively. | ['Tanmoy Chakraborty', 'Md Shad Akhtar', 'Rishabh Gupta', 'Shivani Kumar'] | 2023-04-18 | null | null | null | null | ['speaker-profiling'] | ['speech'] | [ 1.64069593e-01 4.51525420e-01 8.21624994e-02 -7.85153389e-01
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e4ed8dcb-c21b-45b3-91b7-5a1ce0c2bb7f | training-free-neural-matte-extraction-for | 2306.17321 | null | https://arxiv.org/abs/2306.17321v1 | https://arxiv.org/pdf/2306.17321v1.pdf | Training-Free Neural Matte Extraction for Visual Effects | Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suited to video conferencing due to the consistent subject matter (front-facing humans), however training-based approaches are somewhat pointless for entertainment videos where varied subjects (spaceships, monsters, etc.) may appear only a few times in a single movie -- if a method of creating ground truth for training exists, just use that method to produce the desired mattes. We introduce a training-free high quality neural matte extraction approach that specifically targets the assumptions of visual effects production. Our approach is based on the deep image prior, which optimizes a deep neural network to fit a single image, thereby providing a deep encoding of the particular image. We make use of the representations in the penultimate layer to interpolate coarse and incomplete "trimap" constraints. Videos processed with this approach are temporally consistent. The algorithm is both very simple and surprisingly effective. | ['Christoph Bregler', 'Nori Kanazawa', 'J. P. Lewis', 'Sharif Elcott'] | 2023-06-29 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.49804014e-01 -3.76126021e-02 1.68300644e-01 -4.13878471e-01
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89f9318e-f945-4ed3-8b64-23bc80feb4c4 | bounded-future-ms-tcn-for-surgical-gesture | 2209.14647 | null | https://arxiv.org/abs/2209.14647v2 | https://arxiv.org/pdf/2209.14647v2.pdf | Bounded Future MS-TCN++ for surgical gesture recognition | In recent times there is a growing development of video based applications for surgical purposes. Part of these applications can work offline after the end of the procedure, other applications must react immediately. However, there are cases where the response should be done during the procedure but some delay is acceptable. In the literature, the online-offline performance gap is known. Our goal in this study was to learn the performance-delay trade-off and design an MS-TCN++-based algorithm that can utilize this trade-off. To this aim, we used our open surgery simulation data-set containing 96 videos of 24 participants that perform a suturing task on a variable tissue simulator. In this study, we used video data captured from the side view. The Networks were trained to identify the performed surgical gestures. The naive approach is to reduce the MS-TCN++ depth, as a result, the receptive field is reduced, and also the number of required future frames is also reduced. We showed that this method is sub-optimal, mainly in the small delay cases. The second method was to limit the accessible future in each temporal convolution. This way, we have flexibility in the network design and as a result, we achieve significantly better performance than in the naive approach. | ['Shlomi Laufer', 'Carla M. Pugh', 'Netanell Avisdris', 'Adam Goldbraikh'] | 2022-09-29 | null | null | null | null | ['gesture-recognition', 'surgical-gesture-recognition'] | ['computer-vision', 'medical'] | [ 3.46425891e-01 1.96031749e-01 1.58773083e-02 -9.53201354e-02
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2fa3aa5a-1afa-4f84-b48c-e9fe72cd89ab | dmsa-dynamic-multi-scale-unsupervised | 2303.00199 | null | https://arxiv.org/abs/2303.00199v1 | https://arxiv.org/pdf/2303.00199v1.pdf | DMSA: Dynamic Multi-scale Unsupervised Semantic Segmentation Based on Adaptive Affinity | The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions. The framework uses Atrous Spatial Pyramid Pooling (ASPP) module to enhance feature extraction. At the same time, a dynamic dilation strategy is designed to better capture multi-scale context information. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced, which can adaptively refine the initial pseudo labels after feature fusion to obtain high quality pseudo labels. Experiments show that the proposed DSMA framework is superior to the existing methods on the saliency dataset. On the COCO 80 dataset, the MIoU is improved by 2.0, and the accuracy is improved by 5.39. On the Pascal VOC 2012 Augmented dataset, the MIoU is improved by 4.9, and the accuracy is improved by 3.4. In addition, the convergence speed of the model is also greatly improved after the introduction of the PAR module. | ['Jun Lu', 'Kun Yang'] | 2023-03-01 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 2.60588944e-01 4.88298051e-02 4.61908542e-02 -4.23582137e-01
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2cec829f-dda7-4b7b-b04f-339ac136cc52 | multi-field-de-interlacing-using-deformable | 2209.10192 | null | https://arxiv.org/abs/2209.10192v1 | https://arxiv.org/pdf/2209.10192v1.pdf | Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention | Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry. This is despite deinterlacing is well-suited for supervised learning from synthetic data since the degradation model is known and fixed. In this paper, we propose a novel multi-field full frame-rate deinterlacing network, which adapts the state-of-the-art superresolution approaches to the deinterlacing task. Our model aligns features from adjacent fields to a reference field (to be deinterlaced) using both deformable convolution residual blocks and self attention. Our extensive experimental results demonstrate that the proposed method provides state-of-the-art deinterlacing results in terms of both numerical and perceptual performance. At the time of writing, our model ranks first in the Full FrameRate LeaderBoard at https://videoprocessing.ai/benchmarks/deinterlacer.html | ['A. Murat Tekalp', 'Ronglei Ji'] | 2022-09-21 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 4.91677284e-01 -4.04649645e-01 -1.91670999e-01 -2.70662636e-01
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b2ddb24e-497b-423f-8ab9-5192517cc75a | artifact-a-large-scale-dataset-with | 2302.11970 | null | https://arxiv.org/abs/2302.11970v2 | https://arxiv.org/pdf/2302.11970v2.pdf | ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection | Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images that can be exploited to detect them. However, the fundamental problem of generalization remains, as even state-of-the-art detectors encounter difficulty when facing generators never seen during training. To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges. Moreover, the proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. The proposed solution significantly outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy metric. | ['Shaikh Anowarul Fattah', 'Zaber Ibn Abdul Hakim', 'Najibul Haque Sarker', 'Bishmoy Paul', 'Md Awsafur Rahman'] | 2023-02-23 | null | null | null | null | ['synthetic-image-detection', 'synthetic-image-attribution'] | ['computer-vision', 'computer-vision'] | [ 4.95668292e-01 3.51827666e-02 3.18266332e-01 2.85235904e-02
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53606d14-50b8-4189-8476-33604134933c | sekd-self-evolving-keypoint-detection-and | 2006.05077 | null | https://arxiv.org/abs/2006.05077v1 | https://arxiv.org/pdf/2006.05077v1.pdf | SEKD: Self-Evolving Keypoint Detection and Description | Researchers have attempted utilizing deep neural network (DNN) to learn novel local features from images inspired by its recent successes on a variety of vision tasks. However, existing DNN-based algorithms have not achieved such remarkable progress that could be partly attributed to insufficient utilization of the interactive characters between local feature detector and descriptor. To alleviate these difficulties, we emphasize two desired properties, i.e., repeatability and reliability, to simultaneously summarize the inherent and interactive characters of local feature detector and descriptor. Guided by these properties, a self-supervised framework, namely self-evolving keypoint detection and description (SEKD), is proposed to learn an advanced local feature model from unlabeled natural images. Additionally, to have performance guarantees, novel training strategies have also been dedicatedly designed to minimize the gap between the learned feature and its properties. We benchmark the proposed method on homography estimation, relative pose estimation, and structure-from-motion tasks. Extensive experimental results demonstrate that the proposed method outperforms popular hand-crafted and DNN-based methods by remarkable margins. Ablation studies also verify the effectiveness of each critical training strategy. We will release our code along with the trained model publicly. | ['Mingyang Li', 'Ling Cai', 'Yonghong Tian', 'Yafei Song', 'Jia Li'] | 2020-06-09 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-9.64946812e-04 -2.15154991e-01 -3.77959937e-01 -3.88062030e-01
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06600e94-5360-4d54-9ad6-df87025536d3 | a-multi-phase-gammatone-filterbank-for-speech | 1910.11615 | null | https://arxiv.org/abs/1910.11615v2 | https://arxiv.org/pdf/1910.11615v2.pdf | A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet | In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well be replaced by a deterministic hand-crafted filterbank. Motivated by the resemblance of the trained encoder of Conv-TasNet to auditory filterbanks, we propose to employ a deterministic gammatone filterbank. In contrast to a common gammatone filterbank, our filters are restricted to 2 ms length to allow for low-latency processing. Inspired by the encoder learned by Conv-TasNet, in addition to the logarithmically spaced filters, the proposed filterbank holds multiple gammatone filters at the same center frequency with varying phase shifts. We show that replacing the learned encoder with our proposed multi-phase gammatone filterbank (MP-GTF) even leads to a scale-invariant source-to-noise ratio (SI-SNR) improvement of 0.7 dB. Furthermore, in contrast to using the learned encoder we show that the number of filters can be reduced from 512 to 128 without loss of performance. | ['David Ditter', 'Timo Gerkmann'] | 2019-10-25 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 2.08848312e-01 2.74106503e-01 3.12508434e-01 -8.24515000e-02
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7035cdda-638a-4133-b2b5-78616d165036 | counterfactual-learning-to-rank-for-additive | 1805.00065 | null | https://arxiv.org/abs/1805.00065v3 | https://arxiv.org/pdf/1805.00065v3.pdf | A General Framework for Counterfactual Learning-to-Rank | Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias. For the special case of optimizing average rank for linear ranking functions, however, the recently developed SVM-PropRank method has shown that counterfactual inference techniques can be used to provably overcome the distorting effect of presentation bias. Going beyond this special case, this paper provides a general and theoretically rigorous framework for counterfactual learning-to-rank that enables unbiased training for a broad class of additive ranking metrics (e.g., Discounted Cumulative Gain (DCG)) as well as a broad class of models (e.g., deep networks). Specifically, we derive a relaxation for propensity-weighted rank-based metrics which is subdifferentiable and thus suitable for gradient-based optimization. We demonstrate the effectiveness of this general approach by instantiating two new learning methods. One is a new type of unbiased SVM that optimizes DCG -- called SVM PropDCG --, and we show how the resulting optimization problem can be solved via the Convex Concave Procedure (CCP). The other is Deep PropDCG, where the ranking function can be an arbitrary deep network. In addition to the theoretical support, we empirically find that SVM PropDCG significantly outperforms existing linear rankers in terms of DCG. Moreover, the ability to train non-linear ranking functions via Deep PropDCG further improves performance. | ['Kenta Takatsu', 'Aman Agarwal', 'Thorsten Joachims', 'Ivan Zaitsev'] | 2018-04-30 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 7.29247481e-02 1.62771091e-01 -5.62615037e-01 -5.02318919e-01
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a91fc65a-cea3-4922-abc3-4aa9183ee07c | zero-shot-image-restoration-using-denoising | 2212.00490 | null | https://arxiv.org/abs/2212.00490v2 | https://arxiv.org/pdf/2212.00490v2.pdf | Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model | Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, colorization, inpainting, compressed sensing, and deblurring. DDNM only needs a pre-trained off-the-shelf diffusion model as the generative prior, without any extra training or network modifications. By refining only the null-space contents during the reverse diffusion process, we can yield diverse results satisfying both data consistency and realness. We further propose an enhanced and robust version, dubbed DDNM+, to support noisy restoration and improve restoration quality for hard tasks. Our experiments on several IR tasks reveal that DDNM outperforms other state-of-the-art zero-shot IR methods. We also demonstrate that DDNM+ can solve complex real-world applications, e.g., old photo restoration. | ['Jian Zhang', 'Jiwen Yu', 'Yinhuai Wang'] | 2022-12-01 | null | null | null | null | ['colorization', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 5.91160297e-01 -2.73894221e-01 1.22552603e-01 8.05620197e-03
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ea5e26dd-0bc8-4fdf-b4d1-4160698e0b42 | diverse-embedding-expansion-network-and-low | 2303.14481 | null | https://arxiv.org/abs/2303.14481v1 | https://arxiv.org/pdf/2303.14481v1.pdf | Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification | For the visible-infrared person re-identification (VIReID) task, one of the major challenges is the modality gaps between visible (VIS) and infrared (IR) images. However, the training samples are usually limited, while the modality gaps are too large, which leads that the existing methods cannot effectively mine diverse cross-modality clues. To handle this limitation, we propose a novel augmentation network in the embedding space, called diverse embedding expansion network (DEEN). The proposed DEEN can effectively generate diverse embeddings to learn the informative feature representations and reduce the modality discrepancy between the VIS and IR images. Moreover, the VIReID model may be seriously affected by drastic illumination changes, while all the existing VIReID datasets are captured under sufficient illumination without significant light changes. Thus, we provide a low-light cross-modality (LLCM) dataset, which contains 46,767 bounding boxes of 1,064 identities captured by 9 RGB/IR cameras. Extensive experiments on the SYSU-MM01, RegDB and LLCM datasets show the superiority of the proposed DEEN over several other state-of-the-art methods. The code and dataset are released at: https://github.com/ZYK100/LLCM | ['Hanzi Wang', 'Yukang Zhang'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'cross-view-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 2.11331435e-02 -5.88179171e-01 -3.59710231e-02 -2.43427172e-01
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64398340-c83e-4cd6-9204-4afce7870098 | adversarial-multi-task-learning-for | 2206.11558 | null | https://arxiv.org/abs/2206.11558v1 | https://arxiv.org/pdf/2206.11558v1.pdf | Adversarial Multi-Task Learning for Disentangling Timbre and Pitch in Singing Voice Synthesis | Recently, deep learning-based generative models have been introduced to generate singing voices. One approach is to predict the parametric vocoder features consisting of explicit speech parameters. This approach has the advantage that the meaning of each feature is explicitly distinguished. Another approach is to predict mel-spectrograms for a neural vocoder. However, parametric vocoders have limitations of voice quality and the mel-spectrogram features are difficult to model because the timbre and pitch information are entangled. In this study, we propose a singing voice synthesis model with multi-task learning to use both approaches -- acoustic features for a parametric vocoder and mel-spectrograms for a neural vocoder. By using the parametric vocoder features as auxiliary features, the proposed model can efficiently disentangle and control the timbre and pitch components of the mel-spectrogram. Moreover, a generative adversarial network framework is applied to improve the quality of singing voices in a multi-singer model. Experimental results demonstrate that our proposed model can generate more natural singing voices than the single-task models, while performing better than the conventional parametric vocoder-based model. | ['Gyeong-Hoon Lee', 'Min-Su Kang', 'Tae-Woo Kim'] | 2022-06-23 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.02179490e-01 -1.35612473e-01 1.17041849e-01 5.89980185e-02
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4a430e3b-1d29-4dc6-8757-a49bcbdc1b5d | orders-of-magnitude-speedup-in-atmospheric | 1808.03874 | null | http://arxiv.org/abs/1808.03874v1 | http://arxiv.org/pdf/1808.03874v1.pdf | Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation | Chemical transport models (CTMs), which simulate air pollution transport,
transformation, and removal, are computationally expensive, largely because of
the computational intensity of the chemical mechanisms: systems of coupled
differential equations representing atmospheric chemistry. Here we investigate
the potential for machine learning to reproduce the behavior of a chemical
mechanism, yet with reduced computational expense. We create a 17-layer
residual multi-target regression neural network to emulate the Carbon Bond
Mechanism Z (CBM-Z) gas-phase chemical mechanism. We train the network to match
CBM-Z predictions of changes in concentrations of 77 chemical species after one
hour, given a range of chemical and meteorological input conditions, which it
is able to do with root-mean-square error (RMSE) of less than 1.97 ppb (median
RMSE = 0.02 ppb), while achieving a 250x computational speedup. An additional
17x speedup (total 4250x speedup) is achieved by running the neural network on
a graphics-processing unit (GPU). The neural network is able to reproduce the
emergent behavior of the chemical system over diurnal cycles using Euler
integration, but additional work is needed to constrain the propagation of
errors as simulation time progresses. | ['Christopher W. Tessum', 'Julian D. Marshall', 'Makoto M. Kelp'] | 2018-08-11 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 2.16224328e-01 -3.88128757e-01 2.80136049e-01 1.44511521e-01
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b43332e8-0724-4a5f-b149-21a368f25930 | efficient-large-scale-nonstationary-spatial | 2306.11487 | null | https://arxiv.org/abs/2306.11487v1 | https://arxiv.org/pdf/2306.11487v1.pdf | Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks | Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we utilize the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data. We employ a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. In order to distinguish between stationary and nonstationary random fields, we conducted training on ConvNet using various simulated data. These simulations are generated from Gaussian processes with Mat\'ern covariance models under a wide range of parameter settings, ensuring adequate representation of both stationary and nonstationary spatial data. We assess the performance of the proposed method with synthetic and real datasets at a large scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches. | ['Ying Sun', 'Marc G. Genton', 'Ghulam A. Qadir', 'Sameh Abdulah', 'Yiping Hong', 'Pratik Nag'] | 2023-06-20 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 7.33301714e-02 -7.21804023e-01 4.16034937e-01 -3.68472427e-01
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3c341433-9fc9-4c56-80df-caeb5b85f733 | majority-voting-with-bidirectional-pre | 2103.06369 | null | https://arxiv.org/abs/2103.06369v2 | https://arxiv.org/pdf/2103.06369v2.pdf | Majority Voting with Bidirectional Pre-translation For Bitext Retrieval | Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called "pseudo-parallel" sentences from paired documents in two languages. In this paper, we outline some problems with current methods, propose computationally economical solutions to those problems, and demonstrate success with novel methods on the Tatoeba similarity search benchmark and on a downstream task, namely NMT. We uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining approach, and echo problems with the oft-used BUCC dataset that have been observed by others. We make the code and data used for our experiments publicly available. | ['Derry Tanti Wijaya', 'Alex Jones'] | 2021-03-10 | null | https://aclanthology.org/2021.bucc-1.7 | https://aclanthology.org/2021.bucc-1.7.pdf | ranlp-bucc-2021-9 | ['cross-lingual-bitext-mining'] | ['natural-language-processing'] | [ 0.2283297 -0.47464997 -0.17218296 -0.44476646 -1.341767 -0.7659033
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f81b7926-9fdf-4e4d-afbe-5b9c05e5b6ea | knowledge-base-completion-for-long-tail | 2306.17472 | null | https://arxiv.org/abs/2306.17472v1 | https://arxiv.org/pdf/2306.17472v1.pdf | Knowledge Base Completion for Long-Tail Entities | Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps. Language models (LMs) have been proposed as a source for filling these gaps. However, prior works have focused on prominent entities with rich coverage by LMs, neglecting the crucial case of long-tail entities. In this paper, we present a novel method for LM-based-KB completion that is specifically geared for facts about long-tail entities. The method leverages two different LMs in two stages: for candidate retrieval and for candidate verification and disambiguation. To evaluate our method and various baselines, we introduce a novel dataset, called MALT, rooted in Wikidata. Our method outperforms all baselines in F1, with major gains especially in recall. | ['Gerhard Weikum', 'Simon Razniewski', 'Lihu Chen'] | 2023-06-30 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion', 'retrieval'] | ['graphs', 'knowledge-base', 'methodology'] | [-5.62148154e-01 4.04013366e-01 -7.16699243e-01 -2.65052356e-02
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f55d3b89-2fa5-4e4b-b014-0a01c59d527f | learning-cross-task-attribute-attribute | null | null | https://aclanthology.org/2021.ecnlp-1.10 | https://aclanthology.org/2021.ecnlp-1.10.pdf | Learning Cross-Task Attribute - Attribute Similarity for Multi-task Attribute-Value Extraction | Automatic extraction of product attribute-value pairs from unstructured text like product descriptions is an important problem for e-commerce companies. The attribute schema typically varies from one category of products (which will be referred as vertical) to another. This leads to extreme annotation efforts for training of supervised deep sequence labeling models such as LSTM-CRF, and consequently not enough labeled data for some vertical-attribute pairs. In this work, we propose a technique for alleviating this problem by using annotated data from related verticals in a multi-task learning framework. Our approach relies on availability of similar attributes (labels) in another related vertical. Our model jointly learns the similarity between attributes of the two verticals along with the model parameters for the sequence tagging model. The main advantage of our approach is that it does not need any prior annotation of attribute similarity. Our system has been tested with datasets of size more than 10000 from a large e-commerce company in India. We perform detailed experiments to show that our method indeed increases the macro-F1 scores for attribute value extraction in general, and for labels with low training data in particular. We also report top labels from other verticals that contribute towards learning of particular labels. | ['Muthusamy Chelliah', 'Karimulla Shaik', 'Harshit Jain', 'Sourangshu Bhattacharya', 'Mayank Jain'] | null | null | null | null | acl-ecnlp-2021-8 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.87465602e-01 1.99561864e-01 -2.44688362e-01 -9.82579231e-01
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0f9140b2-35cf-4878-9c7a-40d63f25ce26 | classification-regression-for-chart | 2111.14792 | null | https://arxiv.org/abs/2111.14792v2 | https://arxiv.org/pdf/2111.14792v2.pdf | Classification-Regression for Chart Comprehension | Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the relationships between the textual and the visual components of a chart, in order to answer general questions or infer numerical values. Most existing CQA datasets and models are based on simplifying assumptions that often enable surpassing human performance. In this work, we address this outcome and propose a new model that jointly learns classification and regression. Our language-vision setup uses co-attention transformers to capture the complex real-world interactions between the question and the textual elements. We validate our design with extensive experiments on the realistic PlotQA dataset, outperforming previous approaches by a large margin, while showing competitive performance on FigureQA. Our model is particularly well suited for realistic questions with out-of-vocabulary answers that require regression. | ['Dani Lischinski', 'Rami Ben-Ari', 'Matan Levy'] | 2021-11-29 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 1.91445649e-01 -2.19476366e-04 2.98658669e-01 -2.37024099e-01
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8f4cb7eb-9b31-4d9b-9951-cf2894be5786 | a-comprehensive-and-versatile-multimodal-deep | 2303.16412 | null | https://arxiv.org/abs/2303.16412v1 | https://arxiv.org/pdf/2303.16412v1.pdf | A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials | We present a multimodal deep learning (MDL) framework for predicting physical properties of a 10-dimensional acrylic polymer composite material by merging physical attributes and chemical data. Our MDL model comprises four modules, including three generative deep learning models for material structure characterization and a fourth model for property prediction. Our approach handles an 18-dimensional complexity, with 10 compositional inputs and 8 property outputs, successfully predicting 913,680 property data points across 114,210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. We propose a framework to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data is available. This study advances future research on different materials and the development of more sophisticated models, drawing us closer to the ultimate goal of predicting all properties of all materials. | ['Kenji Hata', 'Yasuaki Miki', 'Shun Muroga'] | 2023-03-29 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 4.08140928e-01 -1.99680269e-01 -2.14481145e-01 -9.86383632e-02
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3ec29dff-61af-4a33-bbb9-fcc93d26739c | self-supervised-pre-training-and-contrastive | 2009.08043 | null | https://arxiv.org/abs/2009.08043v2 | https://arxiv.org/pdf/2009.08043v2.pdf | Self-supervised pre-training and contrastive representation learning for multiple-choice video QA | Video Question Answering (Video QA) requires fine-grained understanding of both video and language modalities to answer the given questions. In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in the main stage as an auxiliary learning. In the self-supervised pre-training stage, we transform the original problem format of predicting the correct answer into the one that predicts the relevant question to provide a model with broader contextual inputs without any further dataset or annotation. For contrastive learning in the main stage, we add a masking noise to the input corresponding to the ground-truth answer, and consider the original input of the ground-truth answer as a positive sample, while treating the rest as negative samples. By mapping the positive sample closer to the masked input, we show that the model performance is improved. We further employ locally aligned attention to focus more effectively on the video frames that are particularly relevant to the given corresponding subtitle sentences. We evaluate our proposed model on highly competitive benchmark datasets related to multiple-choice video QA: TVQA, TVQA+, and DramaQA. Experimental results show that our model achieves state-of-the-art performance on all datasets. We also validate our approaches through further analyses. | ['Seohyeong Jeong', 'Seonhoon Kim', 'Eunbyul Kim', 'Nojun Kwak', 'Inho Kang'] | 2020-09-17 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 5.96944451e-01 -1.50541598e-02 -1.48492306e-01 -5.72456777e-01
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2556eefb-8901-4428-b2d5-6d1800d66348 | controlflag-a-self-supervised-idiosyncratic | 2011.03616 | null | https://arxiv.org/abs/2011.03616v5 | https://arxiv.org/pdf/2011.03616v5.pdf | ControlFlag: A Self-Supervised Idiosyncratic Pattern Detection System for Software Control Structures | Software debugging has been shown to utilize upwards of half of developers' time. Yet, machine programming (MP), the field concerned with the automation of software (and hardware) development, has recently made strides in both research and production-quality automated debugging systems. In this paper we present ControlFlag, a self-supervised MP system that aims to improve debugging by attempting to detect idiosyncratic pattern violations in software control structures. ControlFlag also suggests possible corrections in the event an anomalous pattern is detected. We present ControlFlag's design and provide an experimental evaluation and analysis of its efficacy in identifying potential programming errors in production-quality software. As a first concrete evidence towards improving software quality, ControlFlag has already found an anomaly in CURL that has been acknowledged and fixed by its developers. We also discuss future extensions of ControlFlag. | ['Justin Gottschlich', 'Niranjan Hasabnis'] | 2020-11-06 | null | null | null | null | ['programming-error-detection'] | ['computer-code'] | [-1.23593777e-01 3.51155937e-01 -2.63736963e-01 -3.46380979e-01
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80ba6dd1-60e4-4fc9-97da-2590265dd21d | can-llm-already-serve-as-a-database-interface | 2305.03111 | null | https://arxiv.org/abs/2305.03111v2 | https://arxiv.org/pdf/2305.03111v2.pdf | Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs | Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this task. However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus on database schema with few rows of database contents leaving the gap between academic study and real-world applications. To mitigate this gap, we present Bird, a big benchmark for large-scale database grounded in text-to-SQL tasks, containing 12,751 pairs of text-to-SQL data and 95 databases with a total size of 33.4 GB, spanning 37 professional domains. Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases. To solve these problems, text-to-SQL models must feature database value comprehension in addition to semantic parsing. The experimental results demonstrate the significance of database values in generating accurate text-to-SQLs for big databases. Furthermore, even the most effective text-to-SQL models, i.e. ChatGPT, only achieves 40.08% in execution accuracy, which is still far from the human result of 92.96%, proving that challenges still stand. Besides, we also provide an efficiency analysis to offer insights into generating text-to-efficient-SQLs that are beneficial to industries. We believe that BIRD will contribute to advancing real-world applications of text-to-SQL research. The leaderboard and source code are available: https://bird-bench.github.io/. | ['Reynold Cheng', 'Guoliang Li', 'Chenhao Ma', 'Xuanhe Zhou', 'Yongbin Li', 'Fei Huang', 'Kevin C. C. Chang', 'Nan Huo', 'Ruiying Geng', 'Rongyu Cao', 'Bowen Qin', 'Bailin Wang', 'Bowen Li', 'Jiaxi Yang', 'Binhua Li', 'Ge Qu', 'Binyuan Hui', 'Jinyang Li'] | 2023-05-04 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-3.26743990e-01 8.66530836e-02 -2.32494831e-01 -7.14773476e-01
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94525ccf-5bc7-412a-a59e-3e681037223e | fedselect-customized-selection-of-parameters | 2306.13264 | null | https://arxiv.org/abs/2306.13264v2 | https://arxiv.org/pdf/2306.13264v2.pdf | FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning | Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task. Existing methods for such personalization either prune a global model or fine-tune a global model on a local client distribution. However, these existing methods either personalize at the expense of retaining important global knowledge, or predetermine network layers for fine-tuning, resulting in suboptimal storage of global knowledge within client models. Enlightened by the lottery ticket hypothesis, we first introduce a hypothesis for finding optimal client subnetworks to locally fine-tune while leaving the rest of the parameters frozen. We then propose a novel FL framework, FedSelect, using this procedure that directly personalizes both client subnetwork structure and parameters, via the simultaneous discovery of optimal parameters for personalization and the rest of parameters for global aggregation during training. We show that this method achieves promising results on CIFAR-10. | ['Andy Zhou', 'Ron Arel', 'Chengjun Lu', 'John Won', 'Rishub Tamirisa'] | 2023-06-23 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.95310110e-01 7.39184320e-02 -3.45980197e-01 -7.73826957e-01
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8ae2d849-939a-482c-8d46-e6b4d8373358 | wildly-unsupervised-domain-adaptation-and-its | null | null | https://openreview.net/forum?id=rkl2s34twS | https://openreview.net/pdf?id=rkl2s34twS | Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution | In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is hard to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA). We show that WUDA ruins all UDA methods if taking no care of label noise in SD, and to this end, we propose a Butterfly framework, a powerful and efficient solution to WUDA. Butterfly maintains four models (e.g., deep networks) simultaneously, where two take care of all adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and SD-to-TD-distributional) and then the other two can focus on classification in TD. As a consequence, Butterfly possesses all the conceptually necessary components for solving WUDA. Experiments demonstrate that under WUDA, Butterfly significantly outperforms existing baseline methods. | ['Masashi Sugiyama', 'Guangquan Zhang', 'Gang Niu', 'Bo Han', 'Jie Lu', 'Feng Liu'] | 2019-09-25 | null | null | null | null | ['wildly-unsupervised-domain-adaptation'] | ['computer-vision'] | [ 6.39769882e-02 9.98705328e-02 -4.88653183e-02 -5.65127969e-01
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16dbf83a-234b-4ed0-92a2-9dbe87511e98 | revisiting-the-prepositional-phrase | 2102.00924 | null | https://arxiv.org/abs/2102.00924v2 | https://arxiv.org/pdf/2102.00924v2.pdf | Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge | We revisit the challenging problem of resolving prepositional-phrase (PP) attachment ambiguity. To date, proposed solutions are either rule-based, where explicit grammar rules direct how to resolve ambiguities; or statistical, where the decision is learned from a corpus of labeled examples. We argue that explicit commonsense knowledge bases can provide an essential ingredient for making good attachment decisions. We implemented a module, named Patch-Comm, that can be used by a variety of conventional parsers, to make attachment decisions. Where the commonsense KB does not provide direct answers, we fall back on a more general system that infers "out-of-knowledge-base" assertions in a manner similar to the way some NLP systems handle out-of-vocabulary words. Our results suggest that the commonsense knowledge-based approach can provide the best of both worlds, integrating rule-based and statistical techniques. As the field is increasingly coming to recognize the importance of explainability in AI, a commonsense approach can enable NLP developers to better understand the behavior of systems, and facilitate natural dialogues with end users. | ['Peter Chin', 'Henry Lieberman', 'Yida Xin'] | 2021-02-01 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 3.41683105e-02 6.58385217e-01 -2.59556353e-01 -6.94472194e-01
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38e285e6-fb0e-40bf-bf58-b2934fc994cd | enhancing-crowd-flow-prediction-in-various | 2203.07372 | null | https://arxiv.org/abs/2203.07372v1 | https://arxiv.org/pdf/2203.07372v1.pdf | Enhancing crowd flow prediction in various spatial and temporal granularities | Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such technologies, the last years have witnessed a significant growth of human mobility studies, motivated by their importance in a wide range of applications, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet's reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments. | ['Luca Pappalardo', 'Massimiliano Luca', 'Marco Cardia'] | 2022-03-12 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.69039798e-01 1.27243415e-01 -2.03510493e-01 -1.39324471e-01
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a5d69cf3-6c96-4941-8830-cd3805d5912c | recent-advances-in-text-to-sql-a-survey-of | 2208.10099 | null | https://arxiv.org/abs/2208.10099v1 | https://arxiv.org/pdf/2208.10099v1.pdf | Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect | Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text-to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope that this survey can serve as quick access to existing work and motivate future research. | ['Yue Zhang', 'Yulong Chen', 'Naihao Deng'] | 2022-08-22 | null | https://aclanthology.org/2022.coling-1.190 | https://aclanthology.org/2022.coling-1.190.pdf | coling-2022-10 | ['text-to-sql'] | ['computer-code'] | [ 1.73614979e-01 -3.00382078e-02 -3.70224237e-01 -1.07663453e+00
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6eac1cde-754a-4222-8f5c-95025c019aa6 | measuring-cross-lingual-transferability-of | 2305.08800 | null | https://arxiv.org/abs/2305.08800v1 | https://arxiv.org/pdf/2305.08800v1.pdf | Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification | Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability. However, current methods do not measure cross-lingual transferability well, hindering the understanding of multilingual Transformers. In this paper, we propose IGap, a cross-lingual transferability metric for multilingual Transformers on sentence classification tasks. IGap takes training error into consideration, and can also estimate transferability without end-task data. Experimental results show that IGap outperforms baseline metrics for transferability measuring and transfer direction ranking. Besides, we conduct extensive systematic experiments where we compare transferability among various multilingual Transformers, fine-tuning algorithms, and transfer directions. More importantly, our results reveal three findings about cross-lingual transfer, which helps us to better understand multilingual Transformers. | ['Xian-Ling Mao', 'Heyan Huang', 'Zewen Chi'] | 2023-05-15 | null | null | null | null | ['sentence-classification', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-3.64691406e-01 -2.83408165e-01 -4.31942463e-01 -4.01598781e-01
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b0103a02-05c3-41bd-85e3-de6f70e835af | the-role-of-object-centric-representations | 2304.07091 | null | https://arxiv.org/abs/2304.07091v1 | https://arxiv.org/pdf/2304.07091v1.pdf | The role of object-centric representations, guided attention, and external memory on generalizing visual relations | Visual reasoning is a long-term goal of vision research. In the last decade, several works have attempted to apply deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of the generalization of the relations learned. In recent years, several innovations in DNNs have been developed in order to enable learning abstract relation from images. In this work, we systematically evaluate a series of DNNs that integrate mechanism such as slot attention, recurrently guided attention, and external memory, in the simplest possible visual reasoning task: deciding whether two objects are the same or different. We found that, although some models performed better than others in generalizing the same-different relation to specific types of images, no model was able to generalize this relation across the board. We conclude that abstract visual reasoning remains largely an unresolved challenge for DNNs. | ['Jeffrey S. Bowers', 'Guillermo Puebla'] | 2023-04-14 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 1.37562051e-01 1.95689008e-01 1.41304076e-01 -4.60040957e-01
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97cf9c98-7961-4ae5-8b2d-7b6d7bddab3a | pare-part-attention-regressor-for-3d-human | 2104.08527 | null | https://arxiv.org/abs/2104.08527v2 | https://arxiv.org/pdf/2104.08527v2.pdf | PARE: Part Attention Regressor for 3D Human Body Estimation | Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable. To address this, we introduce a soft attention mechanism, called the Part Attention REgressor (PARE), that learns to predict body-part-guided attention masks. We observe that state-of-the-art methods rely on global feature representations, making them sensitive to even small occlusions. In contrast, PARE's part-guided attention mechanism overcomes these issues by exploiting information about the visibility of individual body parts while leveraging information from neighboring body-parts to predict occluded parts. We show qualitatively that PARE learns sensible attention masks, and quantitative evaluation confirms that PARE achieves more accurate and robust reconstruction results than existing approaches on both occlusion-specific and standard benchmarks. The code and data are available for research purposes at {\small \url{https://pare.is.tue.mpg.de/}} | ['Michael J. Black', 'Otmar Hilliges', 'Chun-Hao P. Huang', 'Muhammed Kocabas'] | 2021-04-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-pose-and-shape-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.95395847e-02 5.12249410e-01 -3.98397028e-01 -3.04224133e-01
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a4127027-6f1c-497d-a011-a9f7d754f49f | retrognn-approximating-retrosynthesis-by | 2011.13042 | null | https://arxiv.org/abs/2011.13042v1 | https://arxiv.org/pdf/2011.13042v1.pdf | RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design | De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compounds. We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process. We evaluate our method on a benchmark involving searching for drug-like molecules with antibiotic properties. Compared to enumerating over five million existing molecules from the ZINC database, our approach finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable. Importantly, our deep neural network can successfully filter out hard to synthesize molecules while achieving a $10^5$ times speed-up over using the retrosynthesis planning software. | ['Marwin H. S. Segler', 'Yoshua Bengio', 'Paweł Włodarczyk-Pruszyński', 'Stanisław Jastrzębski', 'Maksym Korablyov', 'Cheng-Hao Liu'] | 2020-11-25 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.03717506e-01 4.64298904e-01 -2.40710720e-01 -1.10256732e-01
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f81634ab-9257-4279-8646-c19d17422dff | multi-view-dynamic-facial-action-unit | 1704.07863 | null | http://arxiv.org/abs/1704.07863v2 | http://arxiv.org/pdf/1704.07863v2.pdf | Multi-View Dynamic Facial Action Unit Detection | We propose a novel convolutional neural network approach to address the
fine-grained recognition problem of multi-view dynamic facial action unit
detection. We leverage recent gains in large-scale object recognition by
formulating the task of predicting the presence or absence of a specific action
unit in a still image of a human face as holistic classification. We then
explore the design space of our approach by considering both shared and
independent representations for separate action units, and also different CNN
architectures for combining color and motion information. We then move to the
novel setup of the FERA 2017 Challenge, in which we propose a multi-view
extension of our approach that operates by first predicting the viewpoint from
which the video was taken, and then evaluating an ensemble of action unit
detectors that were trained for that specific viewpoint. Our approach is
holistic, efficient, and modular, since new action units can be easily included
in the overall system. Our approach significantly outperforms the baseline of
the FERA 2017 Challenge, with an absolute improvement of 14% on the F1-metric.
Additionally, it compares favorably against the winner of the FERA 2017
challenge. Code source is available at https://github.com/BCV-Uniandes/AUNets. | ['Andres Romero', 'Juan Leon', 'Pablo Arbelaez'] | 2017-04-25 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.12941819e-01 -1.19397543e-01 -1.41401276e-01 -3.64254147e-01
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35c986eb-9d01-47ea-8eba-3a3136aff237 | beqi-revitalize-the-senegalese-wolof-language | 2305.08518 | null | https://arxiv.org/abs/2305.08518v1 | https://arxiv.org/pdf/2305.08518v1.pdf | Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector | The progress of Natural Language Processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. This is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction. | ['Moussa Diallo', 'Derguene Mbaye'] | 2023-05-15 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 3.57634395e-01 -1.08588077e-01 2.24377096e-01 -3.16795677e-01
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20034757-381a-415d-8f92-30adcf187e31 | adapting-to-skew-imputing-spatiotemporal | 2301.04233 | null | https://arxiv.org/abs/2301.04233v1 | https://arxiv.org/pdf/2301.04233v1.pdf | Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking | We adapt image inpainting techniques to impute large, irregular missing regions in urban settings characterized by sparsity, variance in both space and time, and anomalous events. Missing regions in urban data can be caused by sensor or software failures, data quality issues, interference from weather events, incomplete data collection, or varying data use regulations; any missing data can render the entire dataset unusable for downstream applications. To ensure coverage and utility, we adapt computer vision techniques for image inpainting to operate on 3D histograms (2D space + 1D time) commonly used for data exchange in urban settings. Adapting these techniques to the spatiotemporal setting requires handling skew: urban data tend to follow population density patterns (small dense regions surrounded by large sparse areas); these patterns can dominate the learning process and fool the model into ignoring local or transient effects. To combat skew, we 1) train simultaneously in space and time, and 2) focus attention on dense regions by biasing the masks used for training to the skew in the data. We evaluate the core model and these two extensions using the NYC taxi data and the NYC bikeshare data, simulating different conditions for missing data. We show that the core model is effective qualitatively and quantitatively, and that biased masking during training reduces error in a variety of scenarios. We also articulate a tradeoff in varying the number of timesteps per training sample: too few timesteps and the model ignores transient events; too many timesteps and the model is slow to train with limited performance gain. | ['Bill Howe', 'Bin Han'] | 2023-01-10 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.69615698e-01 -1.38137445e-01 -1.96142599e-01 -2.69366592e-01
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a9800fbc-1da1-42ca-90bd-5e65e259ab6a | multistream-neural-architectures-for-cued | 2204.04965 | null | https://arxiv.org/abs/2204.04965v1 | https://arxiv.org/pdf/2204.04965v1.pdf | Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding | This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understand spoken language with the help of hand gestures that can uniquely identify the uttered phonemes in complement to lipreading. The proposed approach is based on a pre-trained hand and lips tracker used for visual feature extraction and a phonetic decoder based on a multistream recurrent neural network trained with connectionist temporal classification loss and combined with a pronunciation lexicon. The proposed system is evaluated on an updated version of the French CS dataset CSF18 for which the phonetic transcription has been manually checked and corrected. With a decoding accuracy at the phonetic level of 70.88%, the proposed system outperforms our previous CNN-HMM decoder and competes with more complex baselines. | ['Thomas Hueber', 'Denis Beautemps', 'Sanjana Sankar'] | 2022-04-11 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.82736367e-01 2.21542820e-01 -2.18430441e-02 -5.11008874e-02
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54ced8ac-6032-4fa3-be59-d358ce6ae757 | language-acquisition-neutral-change-and | null | null | https://aclanthology.org/2022.lchange-1.2 | https://aclanthology.org/2022.lchange-1.2.pdf | Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers | Languages around the world employ classifier systems as a method of semantic organization and categorization. These systems are rife with variability, violability, and ambiguity, and are prone to constant change over time. We explicitly model change in classifier systems as the population-level outcome of child language acquisition over time in order to shed light on the factors that drive change to classifier systems. Our research consists of two parts: a contrastive corpus study of Cantonese and Mandarin child-directed speech to determine the role that ambiguity and homophony avoidance may play in classifier learning and change followed by a series of population-level learning simulations of an abstract classifier system. We find that acquisition without reference to ambiguity avoidance is sufficient to drive broad trends in classifier change and suggest an additional role for adults and discourse factors in classifier death. | ['Jordan Kodner', 'Aniket Kali'] | null | null | null | null | lchange-acl-2022-5 | ['language-acquisition'] | ['natural-language-processing'] | [ 2.02308893e-01 1.72188386e-01 -2.16163874e-01 -6.76450074e-01
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b55ad3e0-52e9-44a3-b1c1-f85418d17ddf | a-platform-independent-user-friendly | null | null | https://aclanthology.org/L12-1541 | https://aclanthology.org/L12-1541.pdf | A platform-independent user-friendly dictionary from Italian to LIS | The Lack of written representation for Italian Sign Language (LIS) makes it difficult to do perform tasks like looking up a new word in a dictionary. Most of the paper dictionaries show LIS signs in drawings or pictures. It's not a simple proposition to understand the meaning of sign from paper dictionaries unless one already knows the meanings. This paper presents the LIS dictionary which provides the facility to translate Italian text into sign language. LIS signs are shown as video animations performed by a virtual character. The LIS dictionary provides the integration with MultiWordNet database. The integration with MultiWordNet allows a rich extension with the meanings and senses of the words existing in MultiWordNet. The dictionary allows users to acquire information about lemmas, synonyms and synsets in the Sign Language (SL). The application is platform independent and can be used on any operating system. The results of input lemmas are displayed in groups of grammatical categories. | ['Umar Shoaib', 'Nadeem Ahmad', 'Paolo Prinetto', 'Gabriele Tiotto'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['sign-language-translation'] | ['computer-vision'] | [-1.09997272e-01 -3.78095418e-01 -2.43435010e-01 -2.28613436e-01
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48677434-3c9f-45b3-90de-e7ec74ff7c13 | fine-grained-activities-of-people-worldwide | 2207.05182 | null | https://arxiv.org/abs/2207.05182v2 | https://arxiv.org/pdf/2207.05182v2.pdf | Fine-grained Activities of People Worldwide | Every day, humans perform many closely related activities that involve subtle discriminative motions, such as putting on a shirt vs. putting on a jacket, or shaking hands vs. giving a high five. Activity recognition by ethical visual AI could provide insights into our patterns of daily life, however existing activity recognition datasets do not capture the massive diversity of these human activities around the world. To address this limitation, we introduce Collector, a free mobile app to record video while simultaneously annotating objects and activities of consented subjects. This new data collection platform was used to curate the Consented Activities of People (CAP) dataset, the first large-scale, fine-grained activity dataset of people worldwide. The CAP dataset contains 1.45M video clips of 512 fine grained activity labels of daily life, collected by 780 subjects in 33 countries. We provide activity classification and activity detection benchmarks for this dataset, and analyze baseline results to gain insight into how people around with world perform common activities. The dataset, benchmarks, evaluation tools, public leaderboards and mobile apps are available for use at visym.github.io/cap. | ['Gil Ettinger', 'Zhongheng Li', 'Greg Castanon', 'Jeffrey Byrne'] | 2022-07-11 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 2.77247995e-01 -5.08892059e-01 -5.93469381e-01 -1.91081509e-01
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c591b8b8-2345-4b93-8b3f-6081d4b8033d | creating-multi-level-skill-hierarchies-in | 2306.09980 | null | https://arxiv.org/abs/2306.09980v1 | https://arxiv.org/pdf/2306.09980v1.pdf | Creating Multi-Level Skill Hierarchies in Reinforcement Learning | What is a useful skill hierarchy for an autonomous agent? We propose an answer based on the graphical structure of an agent's interaction with its environment. Our approach uses hierarchical graph partitioning to expose the structure of the graph at varying timescales, producing a skill hierarchy with multiple levels of abstraction. At each level of the hierarchy, skills move the agent between regions of the state space that are well connected within themselves but weakly connected to each other. We illustrate the utility of the proposed skill hierarchy in a wide variety of domains in the context of reinforcement learning. | ['Özgür Şimşek', 'Joshua B. Evans'] | 2023-06-16 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.76179442e-02 4.85756814e-01 5.39810248e-02 1.53098315e-01
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30ea93a0-51af-4fb5-ae45-982d908e3788 | model-blind-video-denoising-via-frame-to | 1811.12766 | null | https://arxiv.org/abs/1811.12766v3 | https://arxiv.org/pdf/1811.12766v3.pdf | Model-blind Video Denoising Via Frame-to-frame Training | Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pre-trained AWGN denoising network to the video with a novel frame-to-frame training strategy. Our denoiser can be used without knowledge of the origin of the video or burst and the post processing steps applied from the camera sensor. The on-line process only requires a couple of frames before achieving visually-pleasing results for a wide range of perturbations. It nonetheless reaches state of the art performance for standard Gaussian noise, and can be used off-line with still better performance. | ['Gabriele Facciolo', 'Jean-Michel Morel', 'Thibaud Ehret', 'Pablo Arias', 'Axel Davy'] | 2018-11-30 | model-blind-video-denoising-via-frame-to-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ehret_Model-Blind_Video_Denoising_via_Frame-To-Frame_Training_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ehret_Model-Blind_Video_Denoising_via_Frame-To-Frame_Training_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-denoising'] | ['computer-vision'] | [ 4.77896541e-01 -4.25003290e-01 5.06773591e-01 8.20676796e-03
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8aca1d13-d32c-4e1c-84cd-9bc8c089a932 | convolutional-gated-recurrent-neural-network | 1702.07787 | null | http://arxiv.org/abs/1702.07787v1 | http://arxiv.org/pdf/1702.07787v1.pdf | Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging | Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network (CNN) to extract robust features from mel-filter banks (MFBs),
spectrograms or even raw waveforms for audio tagging. Gated recurrent unit
(GRU) based recurrent neural networks (RNNs) are then cascaded to model the
long-term temporal structure of the audio signal. To complement the input
information, an auxiliary CNN is designed to learn on the spatial features of
stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging)
of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE
2016) challenge. Compared with our recent DNN-based method, the proposed
structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the
development set. The spatial features can further reduce the EER to 0.10. The
performance of the end-to-end learning on raw waveforms is also comparable.
Finally, on the evaluation set, we get the state-of-the-art performance with
0.12 EER while the performance of the best existing system is 0.15 EER. | ['Wenwu Wang', 'Yong Xu', 'Qiang Huang', 'Mark D. Plumbley', 'Qiuqiang Kong'] | 2017-02-24 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.22296908e-01 -2.71567672e-01 4.26908284e-01 -2.81603038e-01
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73099a49-0abd-4f5f-9ac3-899df12530b5 | integrating-knowledge-graph-embeddings-to | null | null | https://aclanthology.org/2020.crac-1.7 | https://aclanthology.org/2020.crac-1.7.pdf | Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution | Lexical semantics and world knowledge are crucial for interpreting bridging anaphora. Yet, existing computational methods for acquiring and injecting this type of information into bridging resolution systems suffer important limitations. Based on explicit querying of external knowledge bases, earlier approaches are computationally expensive (hence, hardly scalable) and they map the data to be processed into high-dimensional spaces (careful handling of the curse of dimensionality and overfitting has to be in order). In this work, we take a different and principled approach which naturally addresses these issues. Specifically, we convert the external knowledge source (in this case, WordNet) into a graph, and learn embeddings of the graph nodes of low dimension to capture the crucial features of the graph topology and, at the same time, rich semantic information. Once properly identified from the mention text spans, these low dimensional graph node embeddings are combined with distributional text-based embeddings to provide enhanced mention representations. We illustrate the effectiveness of our approach by evaluating it on commonly used datasets, namely ISNotes and BASHI. Our enhanced mention representations yield significant accuracy improvements on both datasets when compared to different standalone text-based mention representations. | ['Liva Ralaivola', 'Pascal Denis', 'Onkar Pandit'] | null | null | null | null | coling-crac-2020-12 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'bridging-anaphora-resolution'] | ['graphs', 'methodology', 'natural-language-processing'] | [-8.39240029e-02 4.66292799e-01 -5.69342494e-01 -1.63161904e-01
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4b7f82dc-6a6a-452b-a8cf-6ea1f17c6b87 | text-editing-as-imitation-game | 2210.12276 | null | https://arxiv.org/abs/2210.12276v1 | https://arxiv.org/pdf/2210.12276v1.pdf | Text Editing as Imitation Game | Text editing, such as grammatical error correction, arises naturally from imperfect textual data. Recent works frame text editing as a multi-round sequence tagging task, where operations -- such as insertion and substitution -- are represented as a sequence of tags. While achieving good results, this encoding is limited in flexibility as all actions are bound to token-level tags. In this work, we reformulate text editing as an imitation game using behavioral cloning. Specifically, we convert conventional sequence-to-sequence data into state-to-action demonstrations, where the action space can be as flexible as needed. Instead of generating the actions one at a time, we introduce a dual decoders structure to parallel the decoding while retaining the dependencies between action tokens, coupled with trajectory augmentation to alleviate the distribution shift that imitation learning often suffers. In experiments on a suite of Arithmetic Equation benchmarks, our model consistently outperforms the autoregressive baselines in terms of performance, efficiency, and robustness. We hope our findings will shed light on future studies in reinforcement learning applying sequence-level action generation to natural language processing. | ['Zhouhan Lin', 'Jie Fu', 'Yewen Pu', 'Longtao Huang', 'Bo Yuan', 'Bin Tang', 'Ning Shi'] | 2022-10-21 | null | null | null | null | ['action-generation', 'grammatical-error-correction'] | ['computer-vision', 'natural-language-processing'] | [ 7.12167561e-01 2.31791556e-01 -1.94002420e-01 -4.32443887e-01
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b9fb10fd-2a62-4f4b-8502-baa0e0d874f5 | reducing-odd-generation-from-neural-headline | null | null | https://aclanthology.org/Y18-1034 | https://aclanthology.org/Y18-1034.pdf | Reducing Odd Generation from Neural Headline Generation | null | ['Masaaki Nagata', 'Kentaro Inui', 'Naoaki Okazaki', 'Jun Suzuki', 'Sho Takase', 'Shun Kiyono'] | null | null | null | null | paclic-2018-12 | ['headline-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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95461392-b2f2-4d79-81f1-4561ec705d20 | latent-space-explorations-of-singing-voice | 2103.07197 | null | https://arxiv.org/abs/2103.07197v1 | https://arxiv.org/pdf/2103.07197v1.pdf | Latent Space Explorations of Singing Voice Synthesis using DDSP | Machine learning based singing voice models require large datasets and lengthy training times. In this work we present a lightweight architecture, based on the Differentiable Digital Signal Processing (DDSP) library, that is able to output song-like utterances conditioned only on pitch and amplitude, after twelve hours of training using small datasets of unprocessed audio. The results are promising, as both the melody and the singer's voice are recognizable. In addition, we present two zero-configuration tools to train new models and experiment with them. Currently we are exploring the latent space representation, which is included in the DDSP library, but not in the original DDSP examples. Our results indicate that the latent space improves both the identification of the singer as well as the comprehension of the lyrics. Our code is available at https://github.com/juanalonso/DDSP-singing-experiments with links to the zero-configuration notebooks, and our sound examples are at https://juanalonso.github.io/DDSP-singing-experiments/ . | ['Cumhur Erkut', 'Juan Alonso'] | 2021-03-12 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.31125256e-01 -1.08858697e-01 2.77050048e-01 -6.73637912e-02
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d82a4379-8570-4a19-9ef9-f175cf0b6744 | service-composition-scenarios-for-task | null | null | https://aclanthology.org/L12-1477 | https://aclanthology.org/L12-1477.pdf | Service Composition Scenarios for Task-Oriented Translation | Due to instant availability and low cost, machine translation is becoming popular. Machine translation mediated communication plays a more and more important role in international collaboration. However, machine translators cannot guarantee high quality translation. In a multilingual communication task, many in-domain resources, for example domain dictionaries, are needed to promote translation quality. This raises the problem of how to help communication task designers provide higher quality translation systems, systems that can take advantage of various in-domain resources. The Language Grid, a service-oriented collective intelligent platform, allows in-domain resources to be wrapped into language services. For task-oriented translation, we propose service composition scenarios for the composition of different language services, where various in-domain resources are utilized effectively. We design the architecture, provide a script language as the interface for the task designer, which is easy for describing the composition scenario, and make a case study of a Japanese-English campus orientation task. Based on the case study, we analyze the increase in translation quality possible and the usage of in-domain resources. The results demonstrate a clear improvement in translation accuracy when the in-domain resources are used. | ['Chunqi Shi', 'Donghui Lin', 'Toru Ishida'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['service-composition'] | ['miscellaneous'] | [-3.12615216e-01 -1.64518714e-01 -5.25842488e-01 -3.38045061e-01
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4203eed5-240c-4d7d-9e54-7b71069d5e07 | on-numerical-integration-in-neural-ordinary | 2206.07335 | null | https://arxiv.org/abs/2206.07335v1 | https://arxiv.org/pdf/2206.07335v1.pdf | On Numerical Integration in Neural Ordinary Differential Equations | The combination of ordinary differential equations and neural networks, i.e., neural ordinary differential equations (Neural ODE), has been widely studied from various angles. However, deciphering the numerical integration in Neural ODE is still an open challenge, as many researches demonstrated that numerical integration significantly affects the performance of the model. In this paper, we propose the inverse modified differential equations (IMDE) to clarify the influence of numerical integration on training Neural ODE models. IMDE is determined by the learning task and the employed ODE solver. It is shown that training a Neural ODE model actually returns a close approximation of the IMDE, rather than the true ODE. With the help of IMDE, we deduce that (i) the discrepancy between the learned model and the true ODE is bounded by the sum of discretization error and learning loss; (ii) Neural ODE using non-symplectic numerical integration fail to learn conservation laws theoretically. Several experiments are performed to numerically verify our theoretical analysis. | ['Yifa Tang', 'Beibei Zhu', 'Pengzhan Jin', 'Aiqing Zhu'] | 2022-06-15 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.60988891e-01 4.11826335e-02 1.63863018e-01 8.02532807e-02
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8e650738-88d2-45d4-8731-446aa693dad3 | knowledge-guided-deep-reinforcement-learning | 2004.08068 | null | https://arxiv.org/abs/2004.08068v1 | https://arxiv.org/pdf/2004.08068v1.pdf | Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation | Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods. | ['Xianzhi Wang', 'Wenjie Zhang', 'Lina Yao', 'Xiaocong Chen', 'Wei Liu', 'Chaoran Huang'] | 2020-04-17 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-3.04119557e-01 -1.06864557e-01 -7.23747075e-01 -3.16910297e-01
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24e064f9-f672-414d-862f-e15e64a4bcc9 | semantic-answer-type-prediction-using-bert | 2109.06714 | null | https://arxiv.org/abs/2109.06714v1 | https://arxiv.org/pdf/2109.06714v1.pdf | Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020 | This paper summarizes our participation in the SMART Task of the ISWC 2020 Challenge. A particular question we are interested in answering is how well neural methods, and specifically transformer models, such as BERT, perform on the answer type prediction task compared to traditional approaches. Our main finding is that coarse-grained answer types can be identified effectively with standard text classification methods, with over 95% accuracy, and BERT can bring only marginal improvements. For fine-grained type detection, on the other hand, BERT clearly outperforms previous retrieval-based approaches. | ['Krisztian Balog', 'Vinay Setty'] | 2021-09-14 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-1.70894843e-02 2.33392522e-01 -4.27815408e-01 -4.25380111e-01
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dd82b7ed-868d-41a5-961d-7aeb1de85f09 | texture-aware-video-frame-interpolation | 2102.13520 | null | https://arxiv.org/abs/2102.13520v1 | https://arxiv.org/pdf/2102.13520v1.pdf | Texture-aware Video Frame Interpolation | Temporal interpolation has the potential to be a powerful tool for video compression. Existing methods for frame interpolation do not discriminate between video textures and generally invoke a single general model capable of interpolating a wide range of video content. However, past work on video texture analysis and synthesis has shown that different textures exhibit vastly different motion characteristics and they can be divided into three classes (static, dynamic continuous and dynamic discrete). In this work, we study the impact of video textures on video frame interpolation, and propose a novel framework where, given an interpolation algorithm, separate models are trained on different textures. Our study shows that video texture has significant impact on the performance of frame interpolation models and it is beneficial to have separate models specifically adapted to these texture classes, instead of training a single model that tries to learn generic motion. Our results demonstrate that models fine-tuned using our framework achieve, on average, a 0.3dB gain in PSNR on the test set used. | ['David Bull', 'Duolikun Danier'] | 2021-02-26 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.63959193e-01 -5.45699358e-01 -3.16505343e-01 -3.17766875e-01
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fd78c0d8-89cb-4fea-8719-e2db441125cf | query-guided-end-to-end-person-search | 1905.01203 | null | https://arxiv.org/abs/1905.01203v1 | https://arxiv.org/pdf/1905.01203v1.pdf | Query-guided End-to-End Person Search | Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii. the person search should leverage the query image extensively (e.g. emphasizing unique query patterns). However, so far, no prior art realizes this. We introduce a novel query-guided end-to-end person search network (QEEPS) to address both aspects. We leverage a most recent joint detector and re-identification work, OIM [37]. We extend this with i. a query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global context from both the query and gallery images, ii. a query-guided region proposal network (QRPN) to produce query-relevant proposals, and iii. a query-guided similarity subnetwork (QSimNet), to learn a query-guided reidentification score. QEEPS is the first end-to-end query-guided detection and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46] datasets, we outperform the previous state-of-the-art by a large margin. | ['Federico Tombari', 'Fabio Galasso', 'Sikandar Amin', 'Bharti Munjal'] | 2019-05-03 | query-guided-end-to-end-person-search-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.pdf | cvpr-2019-6 | ['person-search'] | ['computer-vision'] | [ 3.49933021e-02 -2.27619410e-01 -1.14675239e-02 -2.83791244e-01
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41af1fad-c1bc-4753-9f21-44e3ba77ce49 | adversarial-retriever-ranker-for-dense-text | 2110.03611 | null | https://arxiv.org/abs/2110.03611v5 | https://arxiv.org/pdf/2110.03611v5.pdf | Adversarial Retriever-Ranker for dense text retrieval | Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained term-wise interactions. This results in a sub-optimal recall performance. Second, their model training highly relies on a negative sampling technique to build up the negative documents in their contrastive losses. To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. The two models are jointly optimized according to a minimax adversarial objective: the retriever learns to retrieve negative documents to cheat the ranker, while the ranker learns to rank a collection of candidates including both the ground-truth and the retrieved ones, as well as providing progressive direct feedback to the dual-encoder retriever. Through this adversarial game, the retriever gradually produces harder negative documents to train a better ranker, whereas the cross-encoder ranker provides progressive feedback to improve retriever. We evaluate AR2 on three benchmarks. Experimental results show that AR2 consistently and significantly outperforms existing dense retriever methods and achieves new state-of-the-art results on all of them. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%). Code and models are available at https://github.com/microsoft/AR2. | ['Weizhu Chen', 'Nan Duan', 'Jiancheng Lv', 'Yelong Shen', 'Yeyun Gong', 'Hang Zhang'] | 2021-10-07 | adversarial-retriever-ranker-for-dense-text-1 | https://openreview.net/forum?id=MR7XubKUFB | https://openreview.net/pdf?id=MR7XubKUFB | iclr-2022-4 | ['triviaqa'] | ['miscellaneous'] | [-3.76517326e-02 -2.90601134e-01 -1.63741782e-01 -2.40103766e-01
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a6c9dbca-2879-44ae-80f6-7651fbc9887b | an-interpretable-probabilistic-autoregressive | 2204.09640 | null | https://arxiv.org/abs/2204.09640v3 | https://arxiv.org/pdf/2204.09640v3.pdf | Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting | Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error, combining the explainability, scalability, and "white-box-like" prediction behavior of both models. Notably, the PARNN model provides uncertainty quantification through prediction intervals, setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. By effectively capturing the complexities present in time series data, it outperforms existing methods in terms of accuracy and reliability. The ability to quantify uncertainty through prediction intervals further enhances the model's usefulness in decision-making processes. | ['Abdenour Hadid', 'Tanujit Chakraborty', 'Uttam Kumar', 'Madhurima Panja'] | 2022-04-01 | null | null | null | null | ['epidemiology', 'prediction-intervals'] | ['medical', 'miscellaneous'] | [-3.95905197e-01 -4.74238724e-01 -2.16088042e-01 -3.25618684e-01
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ac8ecb8c-8432-4078-b6e3-4c49eb0ea8e6 | clustering-based-identification-of-precursors | 2306.16291 | null | https://arxiv.org/abs/2306.16291v1 | https://arxiv.org/pdf/2306.16291v1.pdf | Clustering-based Identification of Precursors of Extreme Events in Chaotic Systems | Abrupt and rapid high-amplitude changes in a dynamical system's states known as extreme event appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description and prediction is of great importance. However, because of their chaotic nature, their modelling represents a great challenge up to this day. The applicability of a data-driven modularity-based clustering technique to identify precursors of rare and extreme events in chaotic systems is here explored. The proposed identification framework based on clustering of system states, probability transition matrices and state space tessellation was developed and tested on two different chaotic systems that exhibit extreme events: the Moehliss-Faisst-Eckhardt model of self-sustained turbulence and the 2D Kolmogorov flow. Both exhibit extreme events in the form of bursts in kinetic energy and dissipation. It is shown that the proposed framework provides a way to identify pathways towards extreme events and predict their occurrence from a probabilistic standpoint. The clustering algorithm correctly identifies the precursor states leading to extreme events and allows for a statistical description of the system's states and its precursors to extreme events. | ['Nguyen Anh Khoa Doan', 'Urszula Golyska'] | 2023-06-20 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.55517989e-01 -4.87579912e-01 6.18032515e-01 1.63385093e-01
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e1d69a8d-4781-4e57-9048-0843fff856a3 | on-the-perception-of-difficulty-differences | 2304.09803 | null | https://arxiv.org/abs/2304.09803v1 | https://arxiv.org/pdf/2304.09803v1.pdf | On the Perception of Difficulty: Differences between Humans and AI | With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances.These estimations are crucial to evaluate each agent's capabilities and, thus, required to facilitate effective collaboration. So far, research in the field of human-AI interaction estimates the perceived difficulty of humans and AI independently from each other. However, the effective interaction of human and AI agents depends on metrics that accurately reflect each agent's perceived difficulty in achieving valuable outcomes. Research to date has not yet adequately examined the differences in the perceived difficulty of humans and AI. Thus, this work reviews recent research on the perceived difficulty in human-AI interaction and contributing factors to consistently compare each agent's perceived difficulty, e.g., creating the same prerequisites. Furthermore, we present an experimental design to thoroughly examine the perceived difficulty of both agents and contribute to a better understanding of the design of such systems. | ['Niklas Kühl', 'Michael Vössing', 'Joshua Holstein', 'Philipp Spitzer'] | 2023-04-19 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-6.87393770e-02 2.86994189e-01 1.05736054e-01 -9.26611871e-02
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5aa5ea2d-c8c4-45ba-9279-05ebef63c70a | standardized-max-logits-a-simple-yet | 2107.11264 | null | https://arxiv.org/abs/2107.11264v4 | https://arxiv.org/pdf/2107.11264v4.pdf | Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation | Identifying unexpected objects on roads in semantic segmentation (e.g., identifying dogs on roads) is crucial in safety-critical applications. Existing approaches use images of unexpected objects from external datasets or require additional training (e.g., retraining segmentation networks or training an extra network), which necessitate a non-trivial amount of labor intensity or lengthy inference time. One possible alternative is to use prediction scores of a pre-trained network such as the max logits (i.e., maximum values among classes before the final softmax layer) for detecting such objects. However, the distribution of max logits of each predicted class is significantly different from each other, which degrades the performance of identifying unexpected objects in urban-scene segmentation. To address this issue, we propose a simple yet effective approach that standardizes the max logits in order to align the different distributions and reflect the relative meanings of max logits within each predicted class. Moreover, we consider the local regions from two different perspectives based on the intuition that neighboring pixels share similar semantic information. In contrast to previous approaches, our method does not utilize any external datasets or require additional training, which makes our method widely applicable to existing pre-trained segmentation models. Such a straightforward approach achieves a new state-of-the-art performance on the publicly available Fishyscapes Lost & Found leaderboard with a large margin. Our code is publicly available at this $\href{https://github.com/shjung13/Standardized-max-logits}{link}$. | ['Jaegul Choo', 'Sungha Choi', 'Daehoon Gwak', 'Jungsoo Lee', 'Sanghun Jung'] | 2021-07-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jung_Standardized_Max_Logits_A_Simple_yet_Effective_Approach_for_Identifying_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jung_Standardized_Max_Logits_A_Simple_yet_Effective_Approach_for_Identifying_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-segmentation'] | ['computer-vision'] | [ 2.01745033e-01 2.34438568e-01 -2.33070105e-01 -6.66247487e-01
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ee4f4078-0ec7-48d0-9612-ff8eb6a0f84a | leveraging-redundancy-in-multiple-audio | 2303.00692 | null | https://arxiv.org/abs/2303.00692v1 | https://arxiv.org/pdf/2303.00692v1.pdf | Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition | To achieve robust far-field automatic speech recognition (ASR), existing techniques typically employ an acoustic front end (AFE) cascaded with a neural transducer (NT) ASR model. The AFE output, however, could be unreliable, as the beamforming output in AFE is steered to a wrong direction. A promising way to address this issue is to exploit the microphone signals before the beamforming stage and after the acoustic echo cancellation (post-AEC) in AFE. We argue that both, post-AEC and AFE outputs, are complementary and it is possible to leverage the redundancy between these signals to compensate for potential AFE processing errors. We present two fusion networks to explore this redundancy and aggregate these multi-channel (MC) signals: (1) Frequency-LSTM based, and (2) Convolutional Neural Network based fusion networks. We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion. Our experimental results on commercial virtual assistant tasks demonstrate that using the AFE output and two post-AEC signals with fusion networks offers up to 25.9% word error rate (WER) relative improvement over the model using the AFE output only, at the cost of <= 2% parameter increase. | ['Roland Maas', 'Brian King', 'Athanasios Mouchtaris', 'Maurizio Omologo', 'Harish Mallidi', 'Martin Radfar', 'Rupak Vignesh Swaminathan', 'Anastasios Alexandridis', 'Feng-Ju Chang'] | 2023-03-01 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.84398270e-01 1.51799947e-01 6.24853134e-01 -3.77703965e-01
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8.48983645e-01 -5.27633071e-01 -6.20917618e-01 -1.81702986e-01] | [14.843742370605469, 6.047713279724121] |
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