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16198b6c-e326-49b5-9d61-aaedec5fc486 | isometric-transformation-invariant-and | 2005.06316 | null | https://arxiv.org/abs/2005.06316v4 | https://arxiv.org/pdf/2005.06316v4.pdf | Isometric Transformation Invariant and Equivariant Graph Convolutional Networks | Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as physical simulations. A crucial requirement for applying graphs in Euclidean spaces to physical simulations is lear... | ['Toshiaki Hishinuma', 'Yu Ihara', 'Naoto Mitsume', 'Masanobu Horie', 'Naoki Morita'] | 2020-05-13 | null | https://openreview.net/forum?id=FX0vR39SJ5q | https://openreview.net/pdf?id=FX0vR39SJ5q | iclr-2021-1 | ['physical-simulations'] | ['miscellaneous'] | [-7.58561352e-03 -7.21811503e-02 2.18190312e-01 -3.83705437e-01
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-2.09395856e-01 6.05530620e-01 2.74559498e-01 -3.89483780... | [6.841278076171875, 6.071304798126221] |
40ae1eca-6755-4eee-9f51-4defff252633 | does-the-explanation-satisfy-your-needs-a | 2211.05667 | null | https://arxiv.org/abs/2211.05667v2 | https://arxiv.org/pdf/2211.05667v2.pdf | What Makes a Good Explanation?: A Harmonized View of Properties of Explanations | Interpretability provides a means for humans to verify aspects of machine learning (ML) models and empower human+ML teaming in situations where the task cannot be fully automated. Different contexts require explanations with different properties. For example, the kind of explanation required to determine if an early ca... | ['Finale Doshi-Velez', 'Weiwei Pan', 'Marton Havasi', 'Varshini Subhash', 'Zixi Chen'] | 2022-11-10 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 5.24756849e-01 5.96396208e-01 -4.86687779e-01 -8.07740033e-01
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3.48451465e-01 6.59854352e-01 -3.65351796e-01 2.52768219... | [8.802586555480957, 5.907437801361084] |
38b9bf0c-08db-4c47-9914-4b7024f7a257 | sample-crop-track-self-supervised-mobile-3d | 2209.10471 | null | https://arxiv.org/abs/2209.10471v1 | https://arxiv.org/pdf/2209.10471v1.pdf | Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR | Deep learning has led to great progress in the detection of mobile (i.e. movement-capable) objects in urban driving scenes in recent years. Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self-supervised methods to avoid th... | ['Niki Trigoni', 'Andrew Markham', 'Kaichen Zhou', 'Madhu Vankadari', 'Stuart Golodetz', 'Sangyun Shin'] | 2022-09-21 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 1.35122731e-01 -1.61068030e-02 -3.75447184e-01 -4.46158499e-01
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b91a4f33-0a01-4c46-9804-3bd23a6c01c6 | forward-and-inverse-approximation-theory-for | 2305.18478 | null | https://arxiv.org/abs/2305.18478v1 | https://arxiv.org/pdf/2305.18478v1.pdf | Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks | We present a theoretical analysis of the approximation properties of convolutional architectures when applied to the modeling of temporal sequences. Specifically, we prove an approximation rate estimate (Jackson-type result) and an inverse approximation theorem (Bernstein-type result), which together provide a comprehe... | ['Qianxiao Li', 'Haotian Jiang'] | 2023-05-29 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 8.52679014e-02 6.29117712e-02 -2.33538643e-01 -1.74824908e-01
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-7.33783424e-01 7.51348510e-02 4.55647767e-01 -4.16782379... | [7.666269779205322, 3.5000860691070557] |
49806351-763c-4a04-9f5b-777d0aabdf5e | video-object-segmentation-in-panoptic-wild | 2305.04470 | null | https://arxiv.org/abs/2305.04470v2 | https://arxiv.org/pdf/2305.04470v2.pdf | Video Object Segmentation in Panoptic Wild Scenes | In this paper, we introduce semi-supervised video object segmentation (VOS) to panoptic wild scenes and present a large-scale benchmark as well as a baseline method for it. Previous benchmarks for VOS with sparse annotations are not sufficient to train or evaluate a model that needs to process all possible objects in r... | ['Yi Yang', 'Zongxin Yang', 'Yuanyou Xu'] | 2023-05-08 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.02499267e-02 -5.78708649e-01 -4.67208982e-01 -2.32120335e-01
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-8.79190862e-02 6.26784503e-01 8.91872108e-01 -1.07502356... | [9.289186477661133, 0.10923925787210464] |
efded397-3645-4c39-9973-1ed1fc0700c3 | class-interference-regularization | 2009.02396 | null | https://arxiv.org/abs/2009.02396v1 | https://arxiv.org/pdf/2009.02396v1.pdf | Class Interference Regularization | Contrastive losses yield state-of-the-art performance for person re-identification, face verification and few shot learning. They have recently outperformed the cross-entropy loss on classification at the ImageNet scale and outperformed all self-supervision prior results by a large margin (SimCLR). Simple and effective... | ['Sikandar Amin', 'Fabio Galasso', 'Bharti Munjal'] | 2020-09-04 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.74398944e-02 -8.12158734e-02 -1.55041501e-01 -2.70783573e-01
-5.77544749e-01 -1.67627886e-01 7.95095086e-01 7.64103830e-02
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-1.45262182e-01 6.50375783e-01 -5.02835289e-02 -2.02733099... | [14.74284553527832, 1.051376223564148] |
f9a14fb2-0828-44f7-b158-8ff274bb6f32 | few-shot-referring-relationships-in-videos | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kumar_Few-Shot_Referring_Relationships_in_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kumar_Few-Shot_Referring_Relationships_in_Videos_CVPR_2023_paper.pdf | Few-Shot Referring Relationships in Videos | Interpreting visual relationships is a core aspect of comprehensive video understanding. Given a query visual relationship as <subject, predicate, object> and a test video, our objective is to localize the subject and object that are connected via the predicate. Given modern visio-lingual understanding capabilities... | ['Anand Mishra', 'Yogesh Kumar'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 2.08376661e-01 -1.10772841e-01 -3.98620099e-01 -2.98471540e-01
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-2.20632300e-01 3.54147136e-01 5.09814203e-01 1.50619626... | [9.620452880859375, 0.7579352259635925] |
541e6242-1e9e-42c4-9d24-2d4c49aa1468 | augmenting-control-over-exploration-space-in | 2306.14705 | null | https://arxiv.org/abs/2306.14705v1 | https://arxiv.org/pdf/2306.14705v1.pdf | Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies | This study introduces the P5 model - a foundational method that utilizes reinforcement learning (RL) to augment control, effectiveness, and scalability in molecular dynamics simulations (MD). Our innovative strategy optimizes the sampling of target polymer chain conformations, marking an efficiency improvement of over ... | ['Gregory Rutledge', 'Neil Malur', 'Luis Martinez', 'Andrew Emmel', 'Paloma Gonzalez-Rojas'] | 2023-06-26 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.91712368e-01 -8.33464414e-02 -6.94699585e-01 3.01345527e-01
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-3.85906965e-01 5.42989194e-01 -3.49710211e-02 -3.28598261... | [4.888193607330322, 5.540378093719482] |
9fee7939-d2a0-475b-ba0b-f6e54efb3a77 | impact-of-feature-selection-on-micro-text | 1708.08123 | null | http://arxiv.org/abs/1708.08123v1 | http://arxiv.org/pdf/1708.08123v1.pdf | Impact of Feature Selection on Micro-Text Classification | Social media datasets, especially Twitter tweets, are popular in the field of
text classification. Tweets are a valuable source of micro-text (sometimes
referred to as "micro-blogs"), and have been studied in domains such as
sentiment analysis, recommendation systems, spam detection, clustering, among
others. Tweets of... | ['Ankit Vadehra', 'Gordon V. Cormack', 'Maura R. Grossman'] | 2017-08-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 7.56783336e-02 -2.28548422e-01 -3.74784470e-01 -5.45838833e-01
-5.52643836e-01 -6.04413569e-01 7.51749516e-01 1.36023211e+00
-1.00122094e+00 4.54843491e-01 5.26916265e-01 -4.40523416e-01
1.43237367e-01 -1.11643422e+00 -2.25492433e-01 -6.49288476e-01
3.17146406e-02 1.18298009e-01 1.35552391e-01 -3.39747161... | [10.798110961914062, 7.16273832321167] |
bd43d382-7758-4480-973e-394556b6314c | unsupervised-hyperalignment-for-multilingual | 1811.01124 | null | https://arxiv.org/abs/1811.01124v3 | https://arxiv.org/pdf/1811.01124v3.pdf | Unsupervised Hyperalignment for Multilingual Word Embeddings | We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages ... | ['Marco Cuturi', 'Edouard Grave', 'Armand Joulin', 'Jean Alaux'] | 2018-11-02 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [ 3.72669280e-01 6.79876655e-02 -5.22075593e-01 -3.59894127e-01
-1.22044885e+00 -9.59940493e-01 7.43924320e-01 -3.83546464e-02
-6.74564838e-01 9.36650455e-01 4.55128610e-01 -5.96925855e-01
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4.16496933e-01 7.40708292e-01 -1.49376601e-01 -5.38291574... | [11.169116020202637, 10.145537376403809] |
c0568466-bb17-4cd1-9a45-8b9fb65891a9 | enriched-long-term-recurrent-convolutional | 1805.08417 | null | http://arxiv.org/abs/1805.08417v1 | http://arxiv.org/pdf/1805.08417v1.pdf | Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition | Facial micro-expression (ME) recognition has posed a huge challenge to
researchers for its subtlety in motion and limited databases. Recently,
handcrafted techniques have achieved superior performance in micro-expression
recognition but at the cost of domain specificity and cumbersome parametric
tunings. In this paper,... | ['Raphael C. -W. Phan', 'Weiyao Lin', 'Huai-Qian Khor', 'John See'] | 2018-05-22 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.99378401e-01 -2.16117740e-01 -9.64071453e-02 -7.89011240e-01
-5.80096543e-01 -1.65615216e-01 6.25525594e-01 -4.66600567e-01
-4.41595703e-01 5.59243023e-01 2.40848795e-01 -5.68752848e-02
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-4.06498015e-01 -3.10465902e-01 -3.50338906e-01 -2.76739895... | [13.595952987670898, 1.777076005935669] |
db4cb614-1ae2-4edc-a2f1-33baf0bfc52f | unsupervised-inference-of-data-driven | 2210.09559 | null | https://arxiv.org/abs/2210.09559v1 | https://arxiv.org/pdf/2210.09559v1.pdf | Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder | With a growing need for robust and general discourse structures in many downstream tasks and real-world applications, the current lack of high-quality, high-quantity discourse trees poses a severe shortcoming. In order the alleviate this limitation, we propose a new strategy to generate tree structures in a task-agnost... | ['Giuseppe Carenini', 'Patrick Huber'] | 2022-10-18 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 6.00597799e-01 7.98862875e-01 -6.22063987e-02 -4.30020481e-01
-9.11518395e-01 -6.75948143e-01 7.59377658e-01 2.78568000e-01
-1.69159457e-01 1.18244231e+00 6.59775138e-01 -5.22712648e-01
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2.30302736e-01 4.66956109e-01 3.00299317e-01 -1.77940696... | [10.811488151550293, 9.334900856018066] |
51fe91f0-84f6-4189-89f8-ccf81496fb16 | real-time-universal-and-robust-adversarial | 2003.02301 | null | https://arxiv.org/abs/2003.02301v2 | https://arxiv.org/pdf/2003.02301v2.pdf | Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems | As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services. In this paper, we propose the first real-time, universal, and robust adversarial attack against the state-... | ['Zhuohang Li', 'Yi Xie', 'Yingying Chen', 'Cong Shi', 'Bo Yuan', 'Jian Liu'] | 2020-03-04 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 1.88062936e-01 -1.19411074e-01 4.36762482e-01 -2.48738259e-01
-1.11282694e+00 -8.08816135e-01 3.97010863e-01 -3.90451312e-01
-1.91452250e-01 2.81247467e-01 2.30217457e-01 -7.42170990e-01
1.75688416e-01 -4.44099307e-01 -6.39495730e-01 -8.69493961e-01
-2.09456369e-01 3.78723145e-02 1.05314940e-01 -1.05303667... | [14.022130966186523, 5.843821048736572] |
d6fb0af8-713f-4c78-b38e-da0247a34ec7 | molecular-representation-learning-by | null | null | https://github.com/PaddlePaddle/PaddleHelix/blob/dev/competition/ogbg_molhiv/Molecule_Representation_Learning_by_Leveraging_Chemical_Information.pdf | https://github.com/PaddlePaddle/PaddleHelix/blob/dev/competition/ogbg_molhiv/Molecule_Representation_Learning_by_Leveraging_Chemical_Information.pdf | Molecular Representation Learning by Leveraging Chemical Information | Molecular property prediction is of great importance in AI drug design due to its high experimental efficiency compared with biological experiments. As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as... | ['Fan Wang', 'Shikun Feng', 'Xiaomin Fang', 'Jieqiong Lei', 'Zhengjie Huang', 'Lihang Liu', 'Shanzhuo Zhang', 'Weibin Li'] | 2021-03-15 | null | null | null | na-2021-3 | ['graph-property-prediction'] | ['graphs'] | [ 3.99985313e-01 4.29374762e-02 -9.46271420e-01 -1.39752731e-01
-7.65895322e-02 -4.61808383e-01 3.69500160e-01 9.86388505e-01
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-1.48116380e-01 3.88865829e-01 2.17013329e-01 -2.73765206... | [5.209540367126465, 5.878997802734375] |
50704ecd-4b98-4248-88f8-e8562e12c12c | a-physics-informed-feature-engineering | 2003.01878 | null | https://arxiv.org/abs/2003.01878v3 | https://arxiv.org/pdf/2003.01878v3.pdf | Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration | Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poor... | ['Behnam Amin-ahmadi', 'Othmane Benafan', 'Sen Liu', 'Branden B. Kappes', 'Xiaoli Zhang', 'Aaron P. Stebner'] | 2020-03-04 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.94275528e-01 -2.55009979e-01 -2.17352241e-01 -3.34134877e-01
-8.50404441e-01 -1.91048980e-01 6.17651343e-01 3.63453746e-01
7.74273928e-03 5.89886427e-01 -7.17249587e-02 -1.86437890e-01
-8.49321663e-01 -6.59655392e-01 -6.62536204e-01 -1.01448476e+00
1.24557428e-01 9.29326713e-01 -6.07752055e-02 -6.20223463... | [5.282450199127197, 5.266504287719727] |
056ffa2e-6eba-45df-a5c1-2009aafbbd59 | centered-self-attention-layers | 2306.01610 | null | https://arxiv.org/abs/2306.01610v1 | https://arxiv.org/pdf/2306.01610v1.pdf | Centered Self-Attention Layers | The self-attention mechanism in transformers and the message-passing mechanism in graph neural networks are repeatedly applied within deep learning architectures. We show that this application inevitably leads to oversmoothing, i.e., to similar representations at the deeper layers for different tokens in transformers a... | ['Lior Wolf', 'Tomer Galanti', 'Ameen Ali'] | 2023-06-02 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 1.48390442e-01 7.97535658e-01 1.47415310e-01 -4.05024618e-01
-4.95346695e-01 -7.62638688e-01 7.26615071e-01 2.94765174e-01
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2.75759041e-01 -7.45372951e-01 -9.63295162e-01 -5.38063645e-01
-3.62668559e-02 5.11262715e-01 5.92940450e-01 -1.08985849... | [6.887908458709717, 6.120258331298828] |
16b8afbf-9f4f-4b5f-be2f-70d3c97cb77e | mapping-research-topics-in-software-testing-a | 2109.04086 | null | https://arxiv.org/abs/2109.04086v4 | https://arxiv.org/pdf/2109.04086v4.pdf | Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades | Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have evolved. Method: We apply co-word analysis to map the topology of testing research as a network wher... | ['Ehsan Mohammadi', 'Gregory Gay', 'Alireza Salahirad'] | 2021-09-09 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.08754799e-01 -3.82063836e-02 -7.15243876e-01 1.50203943e-01
-4.13037419e-01 -9.47969139e-01 2.42204934e-01 3.62059236e-01
1.92672566e-01 3.60505879e-01 3.23249549e-01 -9.66883421e-01
-7.46335745e-01 -5.63694179e-01 -8.81541431e-01 9.11390111e-02
-1.66154623e-01 2.35329643e-01 1.21562935e-01 -4.41072211... | [7.690410137176514, 7.848615646362305] |
f69de2d8-b6df-4e27-8dc9-725d244b38d2 | cerec-a-corpus-for-entity-resolution-in-email | 2105.10606 | null | https://arxiv.org/abs/2105.10606v2 | https://arxiv.org/pdf/2105.10606v2.pdf | CEREC: A Corpus for Entity Resolution in Email Conversations | We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 60,383 entity coreference chains. The annotation is carried out as a two-step process with minimal manual effort. Exper... | ['Dan I. Moldovan', 'Parag Pravin Dakle'] | 2021-05-21 | null | https://aclanthology.org/2020.coling-main.30 | https://aclanthology.org/2020.coling-main.30.pdf | coling-2020-8 | ['entity-resolution'] | ['natural-language-processing'] | [ 3.17056865e-01 7.56456792e-01 -2.41941363e-01 -4.45149869e-01
-1.49822342e+00 -7.44397581e-01 9.66995001e-01 3.64027739e-01
-8.47285390e-01 9.71074045e-01 8.96989465e-01 -3.92191172e-01
-1.97242424e-02 -1.41685933e-01 -3.09120238e-01 -2.83610225e-01
1.57344621e-02 9.99679923e-01 2.35545039e-01 -2.58072138... | [9.390941619873047, 9.371644020080566] |
0e011554-9f2d-4419-a8ee-ab52e857cb92 | facial-action-unit-detection-using-3d-facial | 2005.08343 | null | https://arxiv.org/abs/2005.08343v1 | https://arxiv.org/pdf/2005.08343v1.pdf | Facial Action Unit Detection using 3D Facial Landmarks | In this paper, we propose to detect facial action units (AU) using 3D facial landmarks. Specifically, we train a 2D convolutional neural network (CNN) on 3D facial landmarks, tracked using a shape index-based statistical shape model, for binary and multi-class AU detection. We show that the proposed approach is able to... | ['Shaun Canavan', 'Saurabh Hinduja'] | 2020-05-17 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [-2.20776960e-01 1.01397663e-01 7.15228692e-02 -2.63131201e-01
-6.43519044e-01 -3.15094590e-01 5.76562822e-01 -7.55040199e-02
-5.37704468e-01 -2.09851451e-02 -8.49128589e-02 5.03621757e-01
4.61416155e-01 -5.37260413e-01 -7.70543516e-01 -4.93394315e-01
-3.21748734e-01 1.66928262e-01 -1.33093134e-01 -2.51625568... | [13.552587509155273, 1.5417859554290771] |
e64340f2-06c2-40b1-821d-1031041bd547 | dense-recurrent-neural-networks-for-scene | 1801.06831 | null | http://arxiv.org/abs/1801.06831v1 | http://arxiv.org/pdf/1801.06831v1.pdf | Dense Recurrent Neural Networks for Scene Labeling | Recently recurrent neural networks (RNNs) have demonstrated the ability to
improve scene labeling through capturing long-range dependencies among image
units. In this paper, we propose dense RNNs for scene labeling by exploring
various long-range semantic dependencies among image units. In comparison with
existing RNN ... | ['Heng Fan', 'Haibin Ling'] | 2018-01-21 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 3.00135791e-01 1.33538857e-01 -3.14157397e-01 -6.67170584e-01
-3.73145677e-02 -2.53434420e-01 5.12711406e-01 -1.65504292e-01
-6.16302848e-01 4.54410911e-01 8.33905339e-01 -7.54161403e-02
-1.10908616e-02 -9.39087152e-01 -8.43211949e-01 -4.57985252e-01
1.53008789e-01 2.50356287e-01 2.31758222e-01 -2.33392254... | [9.570086479187012, 0.4655357003211975] |
67800219-3cd1-4382-bcc9-df028fd043e9 | backdoor-learning-on-sequence-to-sequence | 2305.02424 | null | https://arxiv.org/abs/2305.02424v1 | https://arxiv.org/pdf/2305.02424v1.pdf | Backdoor Learning on Sequence to Sequence Models | Backdoor learning has become an emerging research area towards building a trustworthy machine learning system. While a lot of works have studied the hidden danger of backdoor attacks in image or text classification, there is a limited understanding of the model's robustness on backdoor attacks when the output space is ... | ['Heng Huang', 'Minhao Cheng', 'Lichang Chen'] | 2023-05-03 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 5.98338187e-01 -1.34480044e-01 -5.41225851e-01 2.21688628e-01
-1.04146445e+00 -1.37735248e+00 7.03159392e-01 6.85994029e-02
8.25982988e-02 5.56122839e-01 -1.06665902e-01 -1.16586006e+00
6.48765802e-01 -5.54185748e-01 -1.13995886e+00 -7.30713904e-01
-1.71691477e-01 -2.21119702e-01 2.72962004e-01 -1.07184060... | [5.886063575744629, 7.807079315185547] |
59bb943b-82e8-45bb-b245-8ae40db323e8 | automated-grain-boundary-gb-segmentation-and | 2305.07790 | null | https://arxiv.org/abs/2305.07790v1 | https://arxiv.org/pdf/2305.07790v1.pdf | Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy | Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such a... | ['Keerti S Kappagantula', 'Alan L Schemer-Kohrn', 'Ram Devanathan', 'Madison Wenzlick', 'Marissa Masden', 'Jing Wang', 'M. F. N. Taufique', 'Shoieb Ahmed Chowdhury'] | 2023-05-12 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 4.23477590e-01 1.20948406e-03 4.23867851e-01 -5.40296972e-01
-7.82310426e-01 -4.07193631e-01 2.79402286e-01 3.41919422e-01
-6.30911648e-01 4.76309270e-01 -5.14939308e-01 -4.60748732e-01
-6.89671785e-02 -1.04964316e+00 -7.53392220e-01 -8.65917265e-01
2.62428969e-01 9.13313925e-01 3.04282069e-01 -7.36722425... | [14.173625946044922, -2.87034273147583] |
632a298a-31a2-4c44-bf2c-a1f3ff4d9994 | speech-gesture-generation-from-the-trimodal | 2009.02119 | null | https://arxiv.org/abs/2009.02119v1 | https://arxiv.org/pdf/2009.02119v1.pdf | Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity | For human-like agents, including virtual avatars and social robots, making proper gestures while speaking is crucial in human--agent interaction. Co-speech gestures enhance interaction experiences and make the agents look alive. However, it is difficult to generate human-like gestures due to the lack of understanding o... | ['Joo-Haeng Lee', 'Youngwoo Yoon', 'Jaehong Kim', 'Minsu Jang', 'Jaeyeon Lee', 'Geehyuk Lee', 'Bok Cha'] | 2020-09-04 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 1.94300681e-01 1.74728304e-01 6.88567460e-02 -3.81438643e-01
-6.70113742e-01 -7.71391273e-01 1.09635615e+00 -7.81123698e-01
-2.51572371e-01 4.89113420e-01 5.22042215e-01 -2.40622014e-02
3.59279662e-01 -4.04980272e-01 -5.20846486e-01 -8.45650196e-01
3.73392133e-04 5.25801361e-01 -1.60655200e-01 -4.39357072... | [5.6110944747924805, -0.10605797171592712] |
cc6672f4-93c7-4ff8-b4c9-73d4cb4066ac | deep-tiling-texture-tile-synthesis-using-a | 2103.07992 | null | https://arxiv.org/abs/2103.07992v1 | https://arxiv.org/pdf/2103.07992v1.pdf | Deep Tiling: Texture Tile Synthesis Using a Deep Learning Approach | Texturing is a fundamental process in computer graphics. Texture is leveraged to enhance the visualization outcome for a 3D scene. In many cases a texture image cannot cover a large 3D model surface because of its small resolution. Conventional techniques like repeating, mirror repeating or clamp to edge do not yield v... | ['Ioannis Fudos', 'Vasilis Toulatzis'] | 2021-03-14 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.95020717e-01 1.28670827e-01 4.14017230e-01 2.15206861e-01
-2.71617323e-01 -1.80457816e-01 7.04321742e-01 -3.74586657e-02
1.53885916e-01 7.53417134e-01 4.45889235e-02 -3.86495918e-01
1.43303707e-01 -1.20589638e+00 -6.98352277e-01 -7.51282215e-01
3.10593367e-01 4.21863973e-01 4.41905022e-01 -2.96648145... | [9.601944923400879, -3.032437324523926] |
524b9d2c-b46e-48b1-96e2-8dec9b6d2d86 | icsde-an-indicator-for-constrained-multi | 2305.18734 | null | https://arxiv.org/abs/2305.18734v1 | https://arxiv.org/pdf/2305.18734v1.pdf | IcSDE+ -- An Indicator for Constrained Multi-Objective Optimization | The effectiveness of Constrained Multi-Objective Evolutionary Algorithms (CMOEAs) depends on their ability to reach the different feasible regions during evolution, by exploiting the information present in infeasible solutions, in addition to optimizing the several conflicting objectives. Over the years, researchers ha... | ['Sri Srinivasa Raju M', 'Rammohan Mallipeddi', 'Oladayo S. Ajani'] | 2023-05-30 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 8.75674114e-02 -4.99264449e-01 -1.15823954e-01 1.95461467e-01
-2.12443426e-01 -4.29102480e-01 -2.63051331e-01 8.20712969e-02
-3.78944039e-01 1.24335361e+00 -8.92439783e-02 -5.64251877e-02
-1.04181385e+00 -7.24891305e-01 -2.94222683e-01 -8.95394325e-01
-2.59546816e-01 6.69153392e-01 1.58726513e-01 -2.84943223... | [5.709583759307861, 3.5072433948516846] |
549ef9d1-c5df-433c-82ab-647e3561eb82 | taploss-a-temporal-acoustic-parameter-loss | 2302.08088 | null | https://arxiv.org/abs/2302.08088v1 | https://arxiv.org/pdf/2302.08088v1.pdf | TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement | Speech enhancement models have greatly progressed in recent years, but still show limits in perceptual quality of their speech outputs. We propose an objective for perceptual quality based on temporal acoustic parameters. These are fundamental speech features that play an essential role in various applications, includi... | ['Bhiksha Raj', 'Shinji Watanabe', 'Anurag Kumar', 'Muqiao Yang', 'David Bick', 'Shuo Han', 'Joseph Konan', 'Yunyang Zeng'] | 2023-02-16 | null | null | null | null | ['speaker-recognition', 'speech-enhancement'] | ['speech', 'speech'] | [ 2.31856272e-01 -2.33660400e-01 3.19286793e-01 -4.66917336e-01
-1.30049253e+00 -4.70944256e-01 6.74103737e-01 2.92486280e-01
-4.80818272e-01 2.78553605e-01 8.24063659e-01 -2.98678786e-01
-4.17620361e-01 -1.66613489e-01 -1.69221789e-01 -7.45311260e-01
-4.31852341e-01 -4.82753813e-01 1.47044033e-01 -4.84066516... | [15.014670372009277, 5.951858997344971] |
f43b4f50-fb02-4987-885e-897c464dddbc | synthesizer-rethinking-self-attention-in | 2005.00743 | null | https://arxiv.org/abs/2005.00743v3 | https://arxiv.org/pdf/2005.00743v3.pdf | Synthesizer: Rethinking Self-Attention in Transformer Models | The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importance and contribution of the dot product-based self-attention mechanism on the performance of Transformer models. Via extensive experiments, w... | ['Che Zheng', 'Da-Cheng Juan', 'Zhe Zhao', 'Yi Tay', 'Donald Metzler', 'Dara Bahri'] | 2020-05-02 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.37282455e-01 6.83181807e-02 -6.44302294e-02 -2.49690846e-01
-1.06134462e+00 -6.88234448e-01 8.33973229e-01 -2.50200391e-01
-3.63023102e-01 6.25071287e-01 3.73224407e-01 -8.27736914e-01
2.34895751e-01 -7.18116999e-01 -1.14260507e+00 -4.43212450e-01
1.82842836e-01 7.18844056e-01 -1.58023059e-01 -5.33714771... | [10.875831604003906, 7.643625736236572] |
489d5bdc-e886-424e-bbef-3b20330b7eff | aligning-bag-of-regions-for-open-vocabulary | 2302.13996 | null | https://arxiv.org/abs/2302.13996v1 | https://arxiv.org/pdf/2302.13996v1.pdf | Aligning Bag of Regions for Open-Vocabulary Object Detection | Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic concepts. However, existing open-vocabulary object detectors only align region embeddings individually with the corresponding features extra... | ['Chen Change Loy', 'Wentao Liu', 'Sheng Jin', 'Wenwei Zhang', 'Size Wu'] | 2023-02-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Aligning_Bag_of_Regions_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Aligning_Bag_of_Regions_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [-8.59761983e-02 -4.03070226e-02 -3.56502324e-01 -4.66129154e-01
-6.37824237e-01 -5.59054613e-01 7.82293499e-01 1.95915222e-01
-6.24264896e-01 2.01483607e-01 3.86272311e-01 2.38659792e-02
5.45242012e-01 -6.28250897e-01 -9.86944258e-01 -7.25240469e-01
3.52812558e-01 8.65960121e-02 3.90046686e-01 1.77423973... | [9.982748031616211, 1.6044068336486816] |
44036254-2e97-494b-95ad-bc1fe5f24ce6 | patternrank-leveraging-pretrained-language | 2210.05245 | null | https://arxiv.org/abs/2210.05245v2 | https://arxiv.org/pdf/2210.05245v2.pdf | PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction | Keyphrase extraction is the process of automatically selecting a small set of most relevant phrases from a given text. Supervised keyphrase extraction approaches need large amounts of labeled training data and perform poorly outside the domain of the training data. In this paper, we present PatternRank, which leverages... | ['Florian Matthes', 'Simon Klimek', 'Tim Schopf'] | 2022-10-11 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.65592298e-01 1.46760372e-02 -6.75725937e-01 7.22026676e-02
-1.27536297e+00 -1.18699634e+00 1.05570173e+00 8.70242953e-01
-7.92836666e-01 7.08751440e-01 5.45845747e-01 -3.58953476e-01
-2.52181720e-02 -8.18832099e-01 -5.11430383e-01 -2.42689893e-01
-1.35964364e-01 5.86370826e-01 5.91902256e-01 -1.37173429... | [12.244593620300293, 8.911954879760742] |
e78f2609-34a6-4e95-acf1-f3f483a0c77c | inconsistency-aware-uncertainty-estimation | 2110.08762 | null | https://arxiv.org/abs/2110.08762v1 | https://arxiv.org/pdf/2110.08762v1.pdf | Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation | In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation... | ['Yang Gao', 'Lei Qi', 'Qian Yu', 'Yefeng Zheng', 'Jiwen Lu', 'Tong Ling', 'Jian Zhang', 'Yinghuan Shi'] | 2021-10-17 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.96113694e-01 7.57381380e-01 -1.29191488e-01 -6.66868210e-01
-9.58373964e-01 -4.46360856e-01 2.85366386e-01 4.46289629e-01
-4.44965303e-01 9.24832463e-01 -1.86125651e-01 -4.18969125e-01
-7.73942769e-02 -5.76172352e-01 -8.20188582e-01 -7.71008670e-01
-7.12284297e-02 8.35428238e-01 4.79876906e-01 4.37607199... | [14.621150016784668, -2.134460687637329] |
887d75e2-4c4a-4e32-a630-af2260e3ea7a | iterative-knowledge-exchange-between-deep | 2012.07123 | null | https://arxiv.org/abs/2012.07123v1 | https://arxiv.org/pdf/2012.07123v1.pdf | Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos | We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: a space-time graph that discovers objects in videos and a deep network that learns powerful object features. The system uses an iterative knowledge exchange policy. A novel spectral s... | ['Marius Leordeanu', 'Adina Magda Florea', 'Emanuela Haller'] | 2020-12-13 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 1.32988736e-01 3.06742281e-01 -2.71987736e-01 -2.11462304e-01
-2.61996895e-01 -5.47394216e-01 2.22165748e-01 -1.60380051e-01
-5.33236504e-01 3.79274666e-01 -2.00892985e-01 1.02584779e-01
-3.95799696e-01 -5.73554754e-01 -9.86111343e-01 -9.24366832e-01
-5.53274810e-01 5.28760612e-01 5.52494526e-01 3.11827779... | [9.108854293823242, -0.24289478361606598] |
79f6491d-3d40-4177-ba21-7cf1ab01c0a6 | building-goal-oriented-dialogue-systems-with | 2111.11576 | null | https://arxiv.org/abs/2111.11576v1 | https://arxiv.org/pdf/2111.11576v1.pdf | Building Goal-Oriented Dialogue Systems with Situated Visual Context | Most popular goal-oriented dialogue agents are capable of understanding the conversational context. However, with the surge of virtual assistants with screen, the next generation of agents are required to also understand screen context in order to provide a proper interactive experience, and better understand users' go... | ['Tagyoung Chung', 'Shuyang Gao', 'Emre Barut', 'Arijit Biswas', 'Jan Jezabek', 'Sanchit Agarwal'] | 2021-11-22 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-6.62105456e-02 9.57057104e-02 2.50393689e-01 -6.18796527e-01
-3.99478495e-01 -9.81429279e-01 8.40713322e-01 2.23166212e-01
-3.20573866e-01 5.33761561e-01 3.32927436e-01 -3.46608192e-01
1.69068590e-01 -6.60975635e-01 -3.92856508e-01 -2.13811010e-01
2.47258008e-01 6.32342458e-01 1.34172723e-01 -5.60548306... | [10.990988731384277, 1.2674751281738281] |
c13c0f14-5ce9-47d5-a232-e4a5f1982901 | object-augmented-rgb-d-slam-for-wide | 2108.02522 | null | https://arxiv.org/abs/2108.02522v1 | https://arxiv.org/pdf/2108.02522v1.pdf | Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation | We propose a novel object-augmented RGB-D SLAM system that is capable of constructing a consistent object map and performing relocalisation based on centroids of objects in the map. The approach aims to overcome the view dependence of appearance-based relocalisation methods using point features or images. During the ma... | ['Andrew Calway', 'Xingrui Yang', 'Yuhang Ming'] | 2021-08-05 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [ 2.78934449e-01 1.38723310e-02 2.29152054e-01 -6.53521955e-01
-6.69551849e-01 -4.93931115e-01 7.57821441e-01 2.35834956e-01
-6.63834393e-01 5.17070830e-01 -1.52679428e-01 3.08426738e-01
-2.05516055e-01 -7.78181434e-01 -8.55465233e-01 -4.70942914e-01
-3.23837847e-02 1.16739523e+00 7.88730145e-01 -1.07326142... | [7.390466213226318, -2.3389484882354736] |
39d7a801-43e2-43ef-9956-12be8c6109c6 | robust-graph-learning-from-noisy-data | 1812.06673 | null | http://arxiv.org/abs/1812.06673v1 | http://arxiv.org/pdf/1812.06673v1.pdf | Robust Graph Learning from Noisy Data | Learning graphs from data automatically has shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often
corrupted, which may cause the learned graph to be inexact or unreliable. In
this paper, we propose a novel robust graph learning scheme to learn reliable
graphs from r... | ['Haiqi Pan', 'Zenglin Xu', 'Zhao Kang', 'Steven C. H. Hoi'] | 2018-12-17 | null | null | null | null | ['shadow-removal', 'video-background-subtraction', 'imagedocument-clustering', 'image-shadow-removal'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.26903760e-01 -2.22090259e-01 -1.26458360e-02 -2.05837324e-01
-8.62582803e-01 -3.64279062e-01 4.41274583e-01 1.63908258e-01
-5.22104912e-02 4.33898777e-01 1.95578769e-01 1.58590525e-01
-3.66015881e-01 -4.85861808e-01 -7.19841301e-01 -1.28130257e+00
1.11329347e-01 4.07049716e-01 1.61273420e-01 1.36829108... | [7.9842329025268555, 4.5394368171691895] |
3dbf81d5-d1f7-4453-9e2f-7b7390b0940b | bi-pointflownet-bidirectional-learning-for | 2207.07522 | null | https://arxiv.org/abs/2207.07522v1 | https://arxiv.org/pdf/2207.07522v1.pdf | Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation | Scene flow estimation, which extracts point-wise motion between scenes, is becoming a crucial task in many computer vision tasks. However, all of the existing estimation methods utilize only the unidirectional features, restricting the accuracy and generality. This paper presents a novel scene flow estimation architect... | ['Jong Hwan Ko', 'Wencan Cheng'] | 2022-07-15 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-3.61118525e-01 -7.25099802e-01 -4.04926777e-01 -3.07880849e-01
7.87030719e-03 -4.48521584e-01 6.36959732e-01 -4.15568024e-01
-4.20040816e-01 6.87197506e-01 5.91679931e-01 -1.71765178e-01
2.88965758e-02 -7.68253028e-01 -3.62256378e-01 -6.38614774e-01
-1.96533889e-01 -3.84500474e-01 5.78347802e-01 -5.56881726... | [8.69857120513916, -1.821067214012146] |
9d0e9268-195c-4fc2-ae6c-a3cf040bbdcd | seeing-what-you-miss-vision-language-pre | 2211.13437 | null | https://arxiv.org/abs/2211.13437v2 | https://arxiv.org/pdf/2211.13437v2.pdf | Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning | Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed f... | ['Wei Liu', 'Yujiu Yang', 'Hongfa Wang', 'Wenzhe Zhao', 'Chengfei Cai', 'Weijie Kong', 'Jie Jiang', 'RongCheng Tu', 'Yatai Ji'] | 2022-11-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 2.73925543e-01 -6.97868690e-02 -4.36409801e-01 -4.34423298e-01
-9.12212372e-01 -1.49971068e-01 9.32574809e-01 1.99513100e-02
-3.51532906e-01 3.48804206e-01 4.48773921e-01 -4.97954860e-02
1.69055790e-01 -3.86539549e-01 -8.47301126e-01 -7.68038750e-01
6.75418675e-01 2.82354891e-01 2.12562084e-01 -1.27594933... | [10.782820701599121, 1.3792363405227661] |
57ba0ae8-b732-41b2-9e55-1033ed0b81ee | underwater-robotics-semantic-parser-assistant | 2301.12134 | null | https://arxiv.org/abs/2301.12134v1 | https://arxiv.org/pdf/2301.12134v1.pdf | Underwater Robotics Semantic Parser Assistant | Semantic parsing is a means of taking natural language and putting it in a form that a computer can understand. There has been a multitude of approaches that take natural language utterances and form them into lambda calculus expressions -- mathematical functions to describe logic. Here, we experiment with a sequence t... | ['Jake Imyak', 'Cedric McGuire', 'Parth Parekh'] | 2023-01-28 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 2.11477369e-01 6.03756368e-01 -6.67415559e-02 -7.06364334e-01
-3.78950924e-01 -9.07917142e-01 5.28058946e-01 -4.19326499e-02
-1.59157410e-01 6.03819013e-01 -6.04052059e-02 -1.07895696e+00
1.17983572e-01 -1.19949806e+00 -7.98693061e-01 1.83283523e-01
5.13778217e-02 4.21480089e-01 6.41137719e-01 -5.28652668... | [8.90965747833252, 7.186615467071533] |
55196b6b-09f3-4566-8abd-f9700f8411b9 | parsing-line-segments-of-floor-plan-images | 2303.03851 | null | https://arxiv.org/abs/2303.03851v1 | https://arxiv.org/pdf/2303.03851v1.pdf | Parsing Line Segments of Floor Plan Images Using Graph Neural Networks | In this paper, we present a GNN-based Line Segment Parser (GLSP), which uses a junction heatmap to predict line segments' endpoints, and graph neural networks to extract line segments and their categories. Different from previous floor plan recognition methods, which rely on semantic segmentation, our proposed method i... | ['Cihui Pan', 'Mingxiang Chen'] | 2023-03-07 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 3.66715848e-01 3.45400274e-01 -1.48764312e-01 -5.70612669e-01
-4.49943036e-01 -7.30581224e-01 3.48827362e-01 4.73173589e-01
-3.82282399e-02 3.54434431e-01 1.73415542e-01 -5.72328210e-01
1.35225236e-01 -1.34256339e+00 -5.97795367e-01 6.69883192e-02
-2.13357180e-01 5.84256470e-01 3.43409002e-01 -3.24982136... | [8.384824752807617, -1.6990293264389038] |
c088f82a-5b2e-4929-a3db-c447ce06de52 | goal-a-challenging-knowledge-grounded-video | 2303.14655 | null | https://arxiv.org/abs/2303.14655v1 | https://arxiv.org/pdf/2303.14655v1.pdf | GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation | Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i.e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such ... | ['Yu Xu', 'Hui Liu', 'Weidong Guo', 'Jie Tang', 'Juanzi Li', 'Lei Hou', 'Bin Xu', 'Yuxiao Dong', 'Xiaozhi Wang', 'Xinyu Guan', 'Yifan Xu', 'Kunyu Gao', 'Teng Tu', 'Jifan Yu', 'Ji Qi'] | 2023-03-26 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 2.66745239e-01 1.13827378e-01 -1.94444746e-01 -3.69120538e-01
-1.03071582e+00 -6.19649172e-01 6.01236165e-01 -1.94504559e-01
-1.30379528e-01 1.11796093e+00 7.95066953e-01 4.19889204e-02
-7.46079385e-02 -2.82880574e-01 -9.79892313e-01 -3.76790017e-01
-3.76464538e-02 3.11799198e-01 2.57509232e-01 -3.26735049... | [10.486340522766113, 0.8389461040496826] |
7601bc8c-bc7c-42dc-8c69-0a20a270b96c | towards-solving-text-based-games-by-producing | 1812.00855 | null | http://arxiv.org/abs/1812.00855v1 | http://arxiv.org/pdf/1812.00855v1.pdf | Towards Solving Text-based Games by Producing Adaptive Action Spaces | To solve a text-based game, an agent needs to formulate valid text commands
for a given context and find the ones that lead to success. Recent attempts at
solving text-based games with deep reinforcement learning have focused on the
latter, i.e., learning to act optimally when valid actions are known in
advance. In thi... | ['Layla El Asri', 'Marc-Alexandre Côté', 'Xingdi Yuan', 'Ruo Yu Tao'] | 2018-12-03 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 1.59666225e-01 4.78801504e-02 2.04992667e-01 -2.98016459e-01
-7.46683836e-01 -5.38370430e-01 6.53126180e-01 -6.58437461e-02
-7.38169312e-01 1.05565679e+00 1.16388716e-01 -2.90946275e-01
-1.18476026e-01 -1.23839700e+00 -6.64243698e-01 -4.83824551e-01
2.17279240e-01 1.12331927e+00 2.10924506e-01 -6.26758039... | [3.7598073482513428, 1.516864538192749] |
b3948b90-342e-4ce5-a6ce-24c008e96d5c | clare-conservative-model-based-reward | 2302.04782 | null | https://arxiv.org/abs/2302.04782v2 | https://arxiv.org/pdf/2302.04782v2.pdf | CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning | This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift. Leveraging both expert data and ... | ['Junshan Zhang', 'Ju Ren', 'Sen Lin', 'Zhaofeng Zhang', 'Wei Shao', 'Guanbo Wang', 'Sheng Yue'] | 2023-02-09 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.53424044e-02 3.57651085e-01 -5.05587101e-01 1.29427537e-01
-7.08672166e-01 -6.12167060e-01 3.35101157e-01 -1.00605085e-01
-7.69206703e-01 1.07748830e+00 2.06603184e-02 -4.09931272e-01
-5.14145195e-01 -2.95751899e-01 -1.03416288e+00 -8.46032858e-01
-2.97358483e-01 3.30105782e-01 -2.19426960e-01 -3.17530513... | [4.1310715675354, 2.3046841621398926] |
be76c717-5d4c-43b6-ba6b-f35094a7f77e | real-time-speech-frequency-bandwidth | 2010.10677 | null | https://arxiv.org/abs/2010.10677v2 | https://arxiv.org/pdf/2010.10677v2.pdf | Real-time Speech Frequency Bandwidth Extension | In this paper we propose a lightweight model for frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz while restoring the high frequency content to a level almost indistinguishable from the 16kHz ground truth. The model architecture is based on SEANet (Sound EnhAncement ... | [] | 2020-10-21 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 2.51139313e-01 4.09109980e-01 2.68119961e-01 -2.02489749e-01
-8.95675182e-01 -4.59743381e-01 2.73216695e-01 -4.00569260e-01
-5.69383442e-01 4.39859688e-01 5.67446411e-01 -5.64364910e-01
1.30842119e-01 -8.22126091e-01 -7.54197299e-01 -6.99593961e-01
-4.47803527e-01 -2.09793955e-01 5.01822650e-01 -3.30791444... | [15.2560396194458, 5.879628658294678] |
768bd3ad-a358-4d26-b34a-a921d79cd35b | hardware-aware-graph-neural-network-automated | 2303.10875 | null | https://arxiv.org/abs/2303.10875v2 | https://arxiv.org/pdf/2303.10875v2.pdf | Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms | Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application sce... | ['Chunming Hu', 'Weisheng Zhao', 'Tong Qiao', 'Yumeng Shi', 'Yingjie Qi', 'Jianlei Yang', 'Ao Zhou'] | 2023-03-20 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-2.80751109e-01 -4.01997745e-01 -4.27803308e-01 7.94759840e-02
6.29421845e-02 -2.39782885e-01 -3.90498862e-02 -4.90774028e-02
-3.67136449e-01 2.58728147e-01 -3.92378956e-01 -7.78815448e-01
-3.34995866e-01 -1.06600845e+00 -5.28512061e-01 -3.17959428e-01
-7.72322640e-02 3.01157773e-01 1.42804474e-01 -1.81619644... | [7.107876300811768, 5.518571376800537] |
99626ab6-39ff-4097-a5a6-989d65dff4c4 | contrastive-learning-of-general-purpose-audio | 2010.10915 | null | https://arxiv.org/abs/2010.10915v1 | https://arxiv.org/pdf/2010.10915v1.pdf | Contrastive Learning of General-Purpose Audio Representations | We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from... | ['Neil Zeghidour', 'David Grangier', 'Aaqib Saeed'] | 2020-10-21 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 4.23474073e-01 -1.32306829e-01 1.64521173e-01 -5.44993043e-01
-1.14371514e+00 -7.22092450e-01 3.82164717e-01 2.71726429e-01
-4.55364525e-01 1.41812906e-01 5.54407716e-01 2.61702649e-02
1.65856462e-02 -4.92844015e-01 -8.71167183e-01 -2.99628764e-01
-5.80742836e-01 3.94762665e-01 1.00066520e-01 -1.52521282... | [15.34662914276123, 5.135128974914551] |
2efece1f-ec04-4995-a3f1-c9ce9eb07f61 | unsupervised-dual-cascade-learning-with | 1811.00436 | null | http://arxiv.org/abs/1811.00436v1 | http://arxiv.org/pdf/1811.00436v1.pdf | Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-based Extractive Summarization | We propose Dual-CES -- a novel unsupervised, query-focused, multi-document
extractive summarizer. Dual-CES is designed to better handle the tradeoff
between saliency and focus in summarization. To this end, Dual-CES employs a
two-step dual-cascade optimization approach with saliency-based pseudo-feedback
distillation. ... | ['Haggai Roitman', 'Doron Cohen', 'David Konopnicki', 'Odellia Boni', 'Guy Feigenblat'] | 2018-11-01 | null | null | null | null | ['query-based-extractive-summarization'] | ['natural-language-processing'] | [-3.18762264e-03 7.31563941e-02 -5.22455037e-01 -2.42740780e-01
-1.50328898e+00 -3.73179525e-01 7.49923468e-01 6.63533628e-01
-3.98311049e-01 6.04842424e-01 9.76293623e-01 9.51815024e-02
3.66286263e-02 -2.66634643e-01 -5.00890195e-01 -1.31398991e-01
1.33486599e-01 5.97332895e-01 2.98520386e-01 -5.82757950... | [12.451024055480957, 9.436189651489258] |
2b642b98-63fd-4fea-90cf-6f613291c807 | comparative-study-and-optimization-of-feature | 1208.6335 | null | https://arxiv.org/abs/1208.6335v2 | https://arxiv.org/pdf/1208.6335v2.pdf | Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval | The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors includ... | ['Ravdeep Johar', 'Aman Chadha', 'Sushmit Mallik'] | 2012-08-30 | null | null | null | null | ['content-based-image-retrieval', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 1.73632622e-01 -6.33507669e-01 -1.88439429e-01 -1.96398929e-01
-8.71524811e-01 -7.94655442e-01 6.55372798e-01 7.02513456e-01
-5.71072400e-01 3.39798361e-01 -5.70431612e-02 1.91946983e-01
-6.92320526e-01 -8.03272784e-01 -5.55095486e-02 -7.52270997e-01
-4.77592275e-03 3.25833201e-01 5.26756465e-01 -3.70995402... | [10.766709327697754, 0.09267441183328629] |
f1ac4b2f-1d3f-4262-934b-a7274e6e7908 | deep-texture-and-structure-aware-filtering | 1712.02893 | null | http://arxiv.org/abs/1712.02893v2 | http://arxiv.org/pdf/1712.02893v2.pdf | Deep Texture and Structure Aware Filtering Network for Image Smoothing | Image smoothing is a fundamental task in computer vision, that aims to retain
salient structures and remove insignificant textures. In this paper, we aim to
address the fundamental shortcomings of existing image smoothing methods, which
cannot properly distinguish textures and structures with similar low-level
appearan... | ['Nick Barnes', 'ShaoDi You', 'Kaiyue Lu'] | 2017-12-07 | deep-texture-and-structure-aware-filtering-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Kaiyue_Lu_Deep_Texture_and_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Kaiyue_Lu_Deep_Texture_and_ECCV_2018_paper.pdf | eccv-2018-9 | ['image-smoothing'] | ['computer-vision'] | [ 7.71984398e-01 2.18936607e-01 1.84599414e-01 -4.19694483e-01
-2.87359208e-01 -1.27596125e-01 7.51058757e-01 1.53953522e-01
1.34114558e-02 3.67800564e-01 3.42022479e-01 -8.99441657e-04
-2.06277310e-03 -9.86616910e-01 -6.53892994e-01 -1.03031719e+00
2.26214454e-01 -6.16245084e-02 6.09492123e-01 -2.42679626... | [10.89677906036377, -1.4455939531326294] |
4bddfb93-98ac-4fad-8474-14afabe5278b | language-model-cascades | 2207.10342 | null | https://arxiv.org/abs/2207.10342v2 | https://arxiv.org/pdf/2207.10342v2.pdf | Language Model Cascades | Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities. These compositions are probabilistic models, and may be expressed in the language of graphical models with rando... | ['Charles Sutton', 'Kevin Murphy', 'Jascha Sohl-Dickstein', 'Rif A. Saurous', 'Henryk Michalewski', 'Yuhuai Wu', 'Raphael Gontijo Lopes', 'David Bieber', 'Jacob Austin', 'Aitor Lewkowycz', 'Winnie Xu', 'David Dohan'] | 2022-07-21 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.25567600e-01 -4.16844748e-02 -5.42791724e-01 -1.19215488e-01
-6.74122810e-01 -8.79544139e-01 7.05312729e-01 -2.47340128e-02
2.08378360e-01 6.06973767e-01 -1.57247156e-01 -9.02449906e-01
-4.05550331e-01 -1.21960557e+00 -6.76136672e-01 -1.59240395e-01
-2.20665127e-01 6.69001281e-01 6.25350296e-01 -1.37821913... | [8.47161865234375, 6.867863178253174] |
df377099-0a5e-4148-9aa1-1d6144e3cd13 | the-npu-aslp-system-for-audio-visual-speech | 2303.06341 | null | https://arxiv.org/abs/2303.06341v1 | https://arxiv.org/pdf/2303.06341v1.pdf | The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge | This paper describes our NPU-ASLP system for the Audio-Visual Diarization and Recognition (AVDR) task in the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. Specifically, the weighted prediction error (WPE) and guided source separation (GSS) techniques are used to reduce reverberation and generat... | ['Peikun Chen', 'Ao Zhang', 'Bingshen Mu', 'He Wang', 'Pengcheng Guo'] | 2023-03-11 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 2.04079881e-01 -7.20832497e-02 2.89046258e-01 -2.16607735e-01
-1.56918800e+00 -3.45740348e-01 6.97732687e-01 -2.01284438e-01
-2.20909640e-01 3.23985219e-01 7.43297279e-01 -2.99557328e-01
2.08304301e-01 8.19893647e-03 -5.94394982e-01 -8.36693883e-01
2.09388986e-01 -9.64339525e-02 -5.12012169e-02 -1.07161656... | [14.641359329223633, 5.618353366851807] |
0b0a7ccb-e3ac-4853-9ba8-7a19abbeb657 | precise-and-generalized-robustness | 2306.06747 | null | https://arxiv.org/abs/2306.06747v1 | https://arxiv.org/pdf/2306.06747v1.pdf | Precise and Generalized Robustness Certification for Neural Networks | The objective of neural network (NN) robustness certification is to determine if a NN changes its predictions when mutations are made to its inputs. While most certification research studies pixel-level or a few geometrical-level and blurring operations over images, this paper proposes a novel framework, GCERT, which c... | ['Zhendong Su', 'Shuai Wang', 'Yuanyuan Yuan'] | 2023-06-11 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 3.46193731e-01 -3.03890884e-01 1.43636823e-01 -3.48278761e-01
-2.28336334e-01 -9.95750368e-01 6.46168411e-01 -5.77433884e-01
-2.00442716e-01 3.00492018e-01 -1.63968787e-01 -5.22521436e-01
-4.03863072e-01 -8.07047069e-01 -1.10246861e+00 -7.55980015e-01
3.15073460e-01 -4.26241338e-01 2.32640952e-01 -3.69029999... | [5.471975803375244, 7.972486972808838] |
78400780-2c03-421c-aeff-a2753ff1c36b | temporal-aware-mixed-attention-based | 2305.18234 | null | https://arxiv.org/abs/2305.18234v1 | https://arxiv.org/pdf/2305.18234v1.pdf | Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition | Emotion recognition plays a crucial role in human-computer interaction, and electroencephalography (EEG) is advantageous for reflecting human emotional states. In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information. The model is inspired by neuroscience r... | ['Dong Ming', 'Yulin Sun', 'Dong Huang', 'Xiaopeng Si'] | 2023-05-18 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-1.34192511e-01 -5.62361293e-02 -4.60530557e-02 -2.47457653e-01
-3.27631295e-01 3.82064208e-02 1.43224865e-01 -2.28013411e-01
-5.71029067e-01 8.81366849e-01 1.98795378e-01 4.05639380e-01
-4.07630438e-03 -1.80901915e-01 -4.72122699e-01 -5.86847246e-01
-5.38008332e-01 -3.19329441e-01 -4.03896928e-01 -2.15448543... | [13.174604415893555, 3.520907402038574] |
15f1c45d-8902-4b49-ad5b-2ef329194a5e | using-orthophoto-for-building-boundary | 1905.09150 | null | https://arxiv.org/abs/1905.09150v1 | https://arxiv.org/pdf/1905.09150v1.pdf | Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model | Nowadays dense stereo matching has become one of the dominant tools in 3D reconstruction of urban regions for its low cost and high flexibility in generating dense 3D points. However, state-of-the-art stereo matching algorithms usually apply a semi-global matching (SGM) strategy. This strategy normally assumes the surf... | ['Rong-Jun Qin', 'Xiaohu Lu', 'Xu Huang'] | 2019-05-22 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 5.13923585e-01 9.02778208e-02 3.89360726e-01 -4.07560587e-01
-3.43519509e-01 -7.57331327e-02 5.81396103e-01 2.51093566e-01
-8.71748626e-02 7.82458246e-01 -1.35809064e-01 -4.15503643e-02
-1.37231603e-01 -1.35423362e+00 -4.32436705e-01 -5.50388515e-01
2.61715323e-01 6.91393733e-01 6.87600434e-01 -3.95069331... | [8.406064987182617, -2.610987901687622] |
01b66095-7c7b-4189-900a-50c95069548b | meta-hallucinator-towards-few-shot-cross | 2305.06978 | null | https://arxiv.org/abs/2305.06978v1 | https://arxiv.org/pdf/2305.06978v1.pdf | Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation | Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achieved promising cross-modality medical image segmentation by transferring knowledge from a label-rich source domain to an unlabeled target do... | ['S. Kevin Zhou', 'Cuntai Guan', 'Zeng Zeng', 'Fangcheng Zhou', 'Ziyuan Zhao'] | 2023-05-11 | null | null | null | null | ['cardiac-segmentation', 'unsupervised-domain-adaptation'] | ['medical', 'methodology'] | [ 5.06095409e-01 3.13568085e-01 -4.10562754e-01 -3.16892892e-01
-1.35495865e+00 -2.34797120e-01 3.16363752e-01 -5.76661378e-02
-2.14888811e-01 7.90968418e-01 2.69654006e-01 3.73613164e-02
1.15323298e-01 -5.48638344e-01 -5.89410126e-01 -8.57304573e-01
5.10719359e-01 5.96784532e-01 1.27236918e-01 -1.90152466... | [14.61273193359375, -1.967191219329834] |
27a03d97-f619-405f-8d89-bf038cb1a1f3 | a-transformer-based-feature-segmentation-and | 2201.09206 | null | https://arxiv.org/abs/2201.09206v1 | https://arxiv.org/pdf/2201.09206v1.pdf | A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization | Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., unmanned aerial vehicle (UAV) and satellite. The most difficult challenges are the position shift and the uncertainty of distance and scale. Existing methods are mainly aimed at digging for more comprehensive fine-gr... | ['Enhui Zheng', 'Jiedong Zhuang', 'Jianhong Hu', 'Ming Dai'] | 2022-01-23 | null | null | null | null | ['drone-navigation', 'drone-view-target-localization'] | ['computer-vision', 'computer-vision'] | [-2.54644781e-01 -4.90570903e-01 6.31593242e-02 -3.92501801e-01
-6.21890783e-01 -9.56234753e-01 3.53923470e-01 -1.28678143e-01
-2.46164963e-01 4.24928576e-01 1.73357464e-02 -2.11100895e-02
-1.65523127e-01 -9.55876052e-01 -4.97708738e-01 -7.28160262e-01
1.74716279e-01 1.61288053e-01 5.43673813e-01 -4.42982793... | [7.7866621017456055, -1.9335157871246338] |
d3faa150-5bce-444c-a60a-e3caf923becb | intra-and-inter-action-understanding-via | 2005.10229 | null | https://arxiv.org/abs/2005.10229v1 | https://arxiv.org/pdf/2005.10229v1.pdf | Intra- and Inter-Action Understanding via Temporal Action Parsing | Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their i... | ['Dahua Lin', 'Dian Shao', 'Bo Dai', 'Yue Zhao'] | 2020-05-20 | intra-and-inter-action-understanding-via-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Shao_Intra-_and_Inter-Action_Understanding_via_Temporal_Action_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shao_Intra-_and_Inter-Action_Understanding_via_Temporal_Action_Parsing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['action-understanding', 'action-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 3.21058124e-01 8.58191699e-02 -5.48087180e-01 -4.83343422e-01
-2.05683812e-01 -4.30062383e-01 6.89444304e-01 9.53114331e-02
-9.77143198e-02 4.75629866e-01 6.42795682e-01 1.13149509e-01
-1.58395842e-01 -7.19234169e-01 -8.95083964e-01 -5.49096823e-01
-4.41362530e-01 2.46231437e-01 6.22516513e-01 -1.17507629... | [8.309096336364746, 0.5773602724075317] |
84a12f9b-15d4-44b4-b749-9350c37f00aa | seeing-without-looking-analysis-pipeline-for | 2204.14110 | null | https://arxiv.org/abs/2204.14110v1 | https://arxiv.org/pdf/2204.14110v1.pdf | Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets | The online sharing and viewing of Child Sexual Abuse Material (CSAM) are growing fast, such that human experts can no longer handle the manual inspection. However, the automatic classification of CSAM is a challenging field of research, largely due to the inaccessibility of target data that is - and should forever be -... | ['Jefersson A. dos Santos', 'Sandra Avila', 'João Macedo', 'Camila Laranjeira'] | 2022-04-29 | null | null | null | null | ['pornography-detection'] | ['computer-vision'] | [ 5.27022421e-01 2.86703020e-01 -2.54345745e-01 -7.18795538e-01
-9.24596608e-01 -9.80570138e-01 3.15261573e-01 8.25396419e-01
-3.14541519e-01 3.58373582e-01 2.32275948e-01 -1.52480245e-01
-1.99762970e-01 -7.07237840e-01 -6.38208807e-01 -7.15212107e-01
-4.08170044e-01 2.24672243e-01 7.42817968e-02 1.20768055... | [12.870583534240723, 1.0257683992385864] |
794f3bae-a674-499d-97ca-a9219de9b495 | h-analysis-and-data-parallel-physics-informed | 2302.08835 | null | https://arxiv.org/abs/2302.08835v1 | https://arxiv.org/pdf/2302.08835v1.pdf | $h$-analysis and data-parallel physics-informed neural networks | We explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics processing units (GPUs) architectures. In order to develop scale-robust PIML models for sophisticated applications (e.g., involving complex and hi... | ['Gonzalo A. Ruz', 'Paul Escapil-Inchauspé'] | 2023-02-17 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 7.39865974e-02 -5.89909740e-02 8.88303816e-02 -3.76952052e-01
-6.92561448e-01 -3.37551683e-01 6.55328333e-01 -5.75415511e-03
-7.12786555e-01 8.34344208e-01 -6.70750737e-01 -4.68734980e-01
-4.29157317e-01 -7.30344355e-01 -1.00178564e+00 -1.13559163e+00
-5.61326563e-01 8.37695658e-01 2.03003556e-01 -2.16951862... | [6.4012956619262695, 3.628180980682373] |
bbdfad5e-4d0a-4eac-908f-3b34d4ed7a2e | encouraging-divergent-thinking-in-large | 2305.19118 | null | https://arxiv.org/abs/2305.19118v1 | https://arxiv.org/pdf/2305.19118v1.pdf | Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate | Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy is self... | ['Shuming Shi', 'Zhaopeng Tu', 'Yujiu Yang', 'Rui Wang', 'Yan Wang', 'Xing Wang', 'Wenxiang Jiao', 'Zhiwei He', 'Tian Liang'] | 2023-05-30 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [-1.22838646e-01 5.89993954e-01 -2.67022476e-02 -2.88975567e-01
-6.25799954e-01 -6.64435446e-01 9.28760111e-01 2.34348655e-01
-4.01216596e-01 7.34083474e-01 3.53684336e-01 -6.96910381e-01
1.13050915e-01 -8.26591969e-01 -5.29363453e-01 -5.19665658e-01
3.01726252e-01 7.79245794e-01 3.98002239e-03 -6.90382421... | [9.603625297546387, 7.381516933441162] |
4c659cdd-bac0-4255-9808-22756fb038a1 | decoupled-spatial-temporal-transformer-for | 2104.06637 | null | https://arxiv.org/abs/2104.06637v1 | https://arxiv.org/pdf/2104.06637v1.pdf | Decoupled Spatial-Temporal Transformer for Video Inpainting | Video inpainting aims to fill the given spatiotemporal holes with realistic appearance but is still a challenging task even with prosperous deep learning approaches. Recent works introduce the promising Transformer architecture into deep video inpainting and achieve better performance. However, it still suffers from sy... | ['Hongsheng Li', 'Jifeng Dai', 'Xiaogang Wang', 'Wenxiu Sun', 'Lewei Lu', 'Xiaoyu Shi', 'Yangyi Huang', 'Hanming Deng', 'Rui Liu'] | 2021-04-14 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 2.80243009e-02 -3.08504313e-01 -2.10877955e-01 2.56709987e-03
-6.19205475e-01 -8.78005698e-02 4.42438602e-01 -2.28808314e-01
-1.83787182e-01 7.47715652e-01 3.74842376e-01 1.72486439e-01
-2.16624495e-02 -7.08588481e-01 -1.07846987e+00 -8.45445931e-01
2.88584650e-01 -1.01102181e-01 6.54726803e-01 -1.75437942... | [10.869129180908203, -1.3348575830459595] |
66d978a7-20bc-48d4-a18c-f8f9181852b0 | neuro-symbolic-approaches-for-context-aware | 2306.05058 | null | https://arxiv.org/abs/2306.05058v1 | https://arxiv.org/pdf/2306.05058v1.pdf | Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition | Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information... | ['Claudio Bettini', 'Gabriele Civitarese', 'Luca Arrotta'] | 2023-06-08 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 0.24119183 0.16610295 -0.5668742 -0.51644737 -0.25298545 -0.3161402
0.50821394 0.02179015 -0.6594799 0.9752991 -0.13416228 -0.19056088
-0.4767167 -0.7748696 -0.77746755 -0.40373668 0.17179866 0.5984843
0.18930385 -0.09326394 -0.2359384 0.5294418 -1.7524023 0.22156788
0.88845116 1.3251237 -0.01... | [7.808737277984619, 0.9772263169288635] |
eb1253c6-922b-477f-ad01-d2264263f1be | a-structured-span-selector | 2205.03977 | null | https://arxiv.org/abs/2205.03977v2 | https://arxiv.org/pdf/2205.03977v2.pdf | A Structured Span Selector | Many natural language processing tasks, e.g., coreference resolution and semantic role labeling, require selecting text spans and making decisions about them. A typical approach to such tasks is to score all possible spans and greedily select spans for task-specific downstream processing. This approach, however, does n... | ['Mrinmaya Sachan', 'Ryan Cotterell', 'Yuchen Eleanor Jiang', 'Tianyu Liu'] | 2022-05-08 | null | https://aclanthology.org/2022.naacl-main.189 | https://aclanthology.org/2022.naacl-main.189.pdf | naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.5699050e-01 6.2005937e-01 -6.4461058e-01 -6.1335194e-01
-1.1220137e+00 -8.5002315e-01 3.9281285e-01 6.0116422e-01
-5.3844756e-01 9.7011989e-01 8.7378234e-01 -4.1056314e-01
-2.1336296e-01 -7.4760723e-01 -4.2218128e-01 -2.6778615e-01
1.0162680e-01 8.9137125e-01 5.2195334e-01 -2.7521592e-01
5.7996869e-01... | [10.1627197265625, 9.27225399017334] |
e0595421-3fc7-4ab7-9d0a-2e1e3cd16c9c | principled-and-efficient-motif-finding-for | 2302.04599 | null | https://arxiv.org/abs/2302.04599v3 | https://arxiv.org/pdf/2302.04599v3.pdf | Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models | Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. It consists in automatically learning a logical theory from data. The basis for structure learning is mining repeating patterns in the data, known as structural motifs. Finding these patterns reduc... | ['Efthymia Tsamoura', 'Dominic Phillips', 'Jonathan Feldstein'] | 2023-02-09 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.59875560e-01 2.65509486e-01 -4.05831695e-01 -3.12862754e-01
-5.92062056e-01 -4.57436383e-01 4.44943905e-01 6.54566526e-01
-2.49392390e-01 4.96919900e-01 4.09690179e-02 -5.75799108e-01
-7.14669526e-01 -9.17170763e-01 -1.07775104e+00 -6.11570358e-01
-5.15002608e-01 9.97304738e-01 5.67677319e-01 9.17823464... | [8.777297973632812, 6.781511306762695] |
0f04ad35-759c-447a-a05d-bde8284dcac1 | generalizability-of-machine-learning-models | 2202.01337 | null | https://arxiv.org/abs/2202.01337v2 | https://arxiv.org/pdf/2202.01337v2.pdf | Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls | Purpose: Despite the potential of machine learning models, the lack of generalizability has hindered their widespread adoption in clinical practice. We investigate three methodological pitfalls: (1) violation of independence assumption, (2) model evaluation with an inappropriate performance indicator or baseline for co... | ['Reza Forghani', 'Alan Spatz', 'Caroline Reinhold', 'Rajiv Gupta', 'Katie Ovens', 'Farhad Maleki'] | 2022-02-01 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 4.48928088e-01 6.93102777e-02 -4.25181329e-01 -2.92391181e-01
-9.77444530e-01 -3.03291380e-01 4.26880240e-01 5.34367025e-01
-7.18672574e-01 9.33421135e-01 9.27492604e-02 -8.84444058e-01
-3.95927012e-01 -5.42666376e-01 -4.08050895e-01 -9.69974399e-01
3.77309732e-02 4.63593006e-01 1.04352117e-01 5.42412102... | [15.168002128601074, -2.8172976970672607] |
f415d219-37f7-4808-807d-93b5059b9c4f | gpr-net-geometric-prototypical-network-for | 2304.06007 | null | https://arxiv.org/abs/2304.06007v1 | https://arxiv.org/pdf/2304.06007v1.pdf | GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot Learning | In the realm of 3D-computer vision applications, point cloud few-shot learning plays a critical role. However, it poses an arduous challenge due to the sparsity, irregularity, and unordered nature of the data. Current methods rely on complex local geometric extraction techniques such as convolution, graph, and attentio... | ['Dena Bazazian', 'Tejas Anvekar'] | 2023-04-12 | null | null | null | null | ['metric-learning', 'few-shot-3d-point-cloud-classification', 'gpr', 'metric-learning', 'gpr'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [-2.71195590e-01 -3.84658456e-01 1.00028470e-01 -2.29406103e-01
-9.08118069e-01 -2.74004668e-01 5.27014077e-01 1.04972132e-01
-1.65884048e-01 2.36563981e-01 -2.92201370e-01 -1.00764900e-01
-4.33593184e-01 -1.03833747e+00 -7.84990191e-01 -6.25813425e-01
-1.72436520e-01 5.96563339e-01 3.91492933e-01 -2.54858226... | [8.01235580444336, -3.395920753479004] |
0ed61d08-5921-40b5-9915-8d80be4e69ab | a-large-corpus-of-product-reviews-in | null | null | https://aclanthology.org/L14-1354 | https://aclanthology.org/L14-1354.pdf | A Large Corpus of Product Reviews in Portuguese: Tackling Out-Of-Vocabulary Words | Web 2.0 has allowed a never imagined communication boom. With the widespread use of computational and mobile devices, anyone, in practically any language, may post comments in the web. As such, formal language is not necessarily used. In fact, in these communicative situations, language is marked by the absence of more... | ['ra', "S Alu{\\'\\i}sio", 'Maria das Gra{\\c{c}}as Volpe Nunes', 'Lucas Avan{\\c{c}}o', 'Thiago Pardo', 'Nathan Hartmann', 'Magali Duran', 'Pedro Balage'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['lexical-normalization'] | ['natural-language-processing'] | [ 1.21482136e-02 6.94106519e-02 6.75350875e-02 -1.31368294e-01
-2.18156248e-01 -8.28554392e-01 8.06202650e-01 8.61478209e-01
-5.03462195e-01 7.54951060e-01 3.00720602e-01 -5.99214733e-01
1.25263527e-01 -6.09233975e-01 1.43791452e-01 -4.41128105e-01
4.03755575e-01 2.80412316e-01 1.55167446e-01 -6.54254079... | [11.024356842041016, 7.100668907165527] |
3f16e847-c9b7-4861-a31f-26443ae88990 | at-bert-adversarial-training-bert-for-acronym | 2101.03700 | null | https://arxiv.org/abs/2101.03700v2 | https://arxiv.org/pdf/2101.03700v2.pdf | AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21 | Acronym identification focuses on finding the acronyms and the phrases that have been abbreviated, which is crucial for scientific document understanding tasks. However, the limited size of manually annotated datasets hinders further improvement for the problem. Recent breakthroughs of language models pre-trained on la... | ['Jiayu Tang', 'Weilin Wu', 'Guanxiong Zeng', 'Qiwei Zhong', 'Yang Zhang', 'Wangli Lin', 'Danqing Zhu'] | 2021-01-11 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 3.90898198e-01 1.60760930e-04 -1.67715251e-01 -3.77468377e-01
-7.50945091e-01 -7.76808023e-01 7.89428473e-01 -7.04657612e-03
-3.61954331e-01 9.45116580e-01 2.30582014e-01 -4.31124955e-01
-1.74097985e-01 -7.38213718e-01 -8.26480150e-01 -6.18805468e-01
5.38960159e-01 5.46905100e-01 -3.33130389e-01 -1.55606031... | [10.011470794677734, 8.566057205200195] |
0af80438-25b2-43d3-a127-6d3b2e689fa0 | sparse-in-space-and-time-audio-visual | 2210.07055 | null | https://arxiv.org/abs/2210.07055v1 | https://arxiv.org/pdf/2210.07055v1.pdf | Sparse in Space and Time: Audio-visual Synchronisation with Trainable Selectors | The objective of this paper is audio-visual synchronisation of general videos 'in the wild'. For such videos, the events that may be harnessed for synchronisation cues may be spatially small and may occur only infrequently during a many seconds-long video clip, i.e. the synchronisation signal is 'sparse in space and ti... | ['Andrew Zisserman', 'Esa Rahtu', 'Weidi Xie', 'Vladimir Iashin'] | 2022-10-13 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 2.73642033e-01 1.30494162e-01 7.84547776e-02 1.51125550e-01
-8.60391319e-01 -3.98828000e-01 7.46922433e-01 -1.80217057e-01
5.23524582e-02 4.42859292e-01 6.19848251e-01 1.35002837e-01
-1.65472195e-01 -7.35242292e-02 -9.15439010e-01 -6.99299574e-01
-4.75107908e-01 4.63117920e-02 2.93299943e-01 -2.42248271... | [14.98043155670166, 5.116672992706299] |
e9737010-034c-4595-a767-9ec73c5e2ae5 | vip-cnn-visual-phrase-guided-convolutional | 1702.07191 | null | http://arxiv.org/abs/1702.07191v2 | http://arxiv.org/pdf/1702.07191v2.pdf | ViP-CNN: Visual Phrase Guided Convolutional Neural Network | As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. pers... | ["Xiao'ou Tang", 'Yikang Li', 'Wanli Ouyang', 'Xiaogang Wang'] | 2017-02-23 | vip-cnn-visual-phrase-guided-convolutional-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_ViP-CNN_Visual_Phrase_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_ViP-CNN_Visual_Phrase_CVPR_2017_paper.pdf | cvpr-2017-7 | ['visual-relationship-detection'] | ['computer-vision'] | [ 3.54354471e-01 1.09267242e-01 -3.26061368e-01 -3.82660091e-01
-4.09846395e-01 -1.85051173e-01 7.04760492e-01 1.39309049e-01
-3.69894266e-01 4.29654181e-01 1.29680768e-01 -1.98584080e-01
2.29601227e-02 -6.15998983e-01 -1.05461192e+00 -4.13285762e-01
-9.19195078e-03 3.48112255e-01 2.75612295e-01 -1.60064101... | [10.373311996459961, 1.5671747922897339] |
23963f3c-4368-4fb4-ad3a-22a076e57091 | online-illumination-invariant-moving-object | 1808.01066 | null | http://arxiv.org/abs/1808.01066v1 | http://arxiv.org/pdf/1808.01066v1.pdf | Online Illumination Invariant Moving Object Detection by Generative Neural Network | Moving object detection (MOD) is a significant problem in computer vision
that has many real world applications. Different categories of methods have
been proposed to solve MOD. One of the challenges is to separate moving objects
from illumination changes and shadows that are present in most real world
videos. State-of... | ['Nilanjan Ray', 'Moein Shakeri', 'Fateme Bahri'] | 2018-08-03 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 4.00808424e-01 -7.63469160e-01 5.90007231e-02 -4.62689400e-01
-3.59169513e-01 -3.36746424e-01 4.24574494e-01 -5.48554301e-01
-5.01969635e-01 3.18520993e-01 -3.15627754e-01 -1.18543148e-01
2.56271124e-01 -4.66854602e-01 -7.10867822e-01 -1.03541887e+00
1.08361848e-01 2.51666009e-01 8.62095058e-01 1.40837029... | [8.996256828308105, -0.6542775630950928] |
df7a856c-ff33-4fcd-907b-3fd3538b263c | gravitational-dimensionality-reduction-using | 2211.01369 | null | https://arxiv.org/abs/2211.01369v1 | https://arxiv.org/pdf/2211.01369v1.pdf | Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity | Due to the effectiveness of using machine learning in physics, it has been widely received increased attention in the literature. However, the notion of applying physics in machine learning has not been given much awareness to. This work is a hybrid of physics and machine learning where concepts of physics are used in ... | ['Smriti Sharma', 'Benyamin Ghojogh'] | 2022-10-30 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-3.21471542e-01 -2.48290692e-02 2.01237991e-01 -3.08757931e-01
2.47305512e-01 -4.78455812e-01 7.65045404e-01 -3.30222607e-01
-3.29335213e-01 4.28765267e-01 3.60407010e-02 -3.15630943e-01
-5.87944090e-01 -1.10089362e+00 -2.48571157e-01 -1.15939069e+00
-2.48186648e-01 4.73685056e-01 3.93794924e-01 -4.30486798... | [7.849867343902588, 4.152456283569336] |
b36e71e3-9929-4f79-a2f8-c54f241f51df | an-industry-4-0-example-real-time-quality | 2206.05818 | null | https://arxiv.org/abs/2206.05818v1 | https://arxiv.org/pdf/2206.05818v1.pdf | An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data | Insufficient steel quality in mass production can cause extremely costly damage to tooling, production downtimes and low quality products. Automatic, fast and cheap strategies to estimate essential material properties for quality control, risk mitigation and the prediction of faults are highly desirable. In this work w... | ['Kerstin Bunte', 'Nick Goet', 'Kevin Koster', 'Michiel Straat'] | 2022-06-12 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.65296936e-01 2.93856651e-01 2.49338999e-01 -1.80737510e-01
-5.56219339e-01 -3.47468168e-01 1.20040737e-01 3.97968113e-01
2.34548658e-01 5.67382216e-01 -6.29828811e-01 -1.69385806e-01
-6.02633834e-01 -8.61506343e-01 -7.46517241e-01 -6.70630097e-01
-6.50850460e-02 6.62840843e-01 6.38421714e-01 -9.19146836... | [6.793285369873047, 2.3606481552124023] |
4b811d15-fb00-4e6d-adea-12fad4822e5b | matcha-enhancing-visual-language-pretraining | 2212.09662 | null | https://arxiv.org/abs/2212.09662v2 | https://arxiv.org/pdf/2212.09662v2.pdf | MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering | Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data. We propose MatCha (Math reasoning and Chart derendering pretraining) to enhance visual language models' capabilities in jointly modeling cha... | ['Julian Martin Eisenschlos', 'Nigel Collier', 'Yasemin Altun', 'Mandar Joshi', 'Kenton Lee', 'Chenxi Pang', 'Syrine Krichene', 'Francesco Piccinno', 'Fangyu Liu'] | 2022-12-19 | null | null | null | null | ['chart-question-answering', 'chart-question-answering', 'data-summarization'] | ['computer-code', 'computer-vision', 'miscellaneous'] | [-1.74780875e-01 1.07720951e-02 -4.00320701e-02 -2.80364215e-01
-5.93711793e-01 -8.09794664e-01 1.12209189e+00 3.22100222e-01
-1.18681416e-01 1.17620319e-01 3.02649021e-01 -1.08978033e+00
5.15125036e-01 -5.85204303e-01 -1.05550182e+00 1.10383913e-01
1.79323718e-01 3.05833489e-01 5.68108261e-02 -7.59039894... | [11.109640121459961, 2.0356361865997314] |
ac48f33e-0040-4aeb-8603-c8196f75f355 | readability-based-sentence-ranking-for | 1603.06009 | null | http://arxiv.org/abs/1603.06009v1 | http://arxiv.org/pdf/1603.06009v1.pdf | Readability-based Sentence Ranking for Evaluating Text Simplification | We propose a new method for evaluating the readability of simplified
sentences through pair-wise ranking. The validity of the method is established
through in-corpus and cross-corpus evaluation experiments. The approach
correctly identifies the ranking of simplified and unsimplified sentences in
terms of their reading ... | ['Sowmya Vajjala', 'Detmar Meurers'] | 2016-03-18 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 2.48266563e-01 2.42655948e-01 -1.25461206e-01 -4.35588151e-01
-1.13750827e+00 -7.22920418e-01 7.05968082e-01 8.20496917e-01
-6.66488290e-01 5.87489724e-01 8.67689371e-01 -2.62516916e-01
-2.83213884e-01 -5.27773559e-01 -4.01574582e-01 -2.11773198e-02
4.72431362e-01 2.07679868e-01 -1.07308790e-01 -5.49998283... | [11.168195724487305, 10.06638240814209] |
016f462a-4c17-4fab-8fae-0412aca7a196 | combining-lexical-features-and-a-supervised | 1710.08451 | null | http://arxiv.org/abs/1710.08451v1 | http://arxiv.org/pdf/1710.08451v1.pdf | Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis | The importance of building sentiment analysis tools for Arabic social media
has been recognized during the past couple of years, especially with the rapid
increase in the number of Arabic social media users. One of the main
difficulties in tackling this problem is that text within social media is
mostly colloquial, wit... | ['Muhammad Hammad', 'Talaat Khalil', 'Samhaa R. El-Beltagy', 'Amal Halaby'] | 2017-10-23 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.95485681e-01 -4.84311283e-02 4.68249381e-01 -4.56619084e-01
-2.75673658e-01 -6.68271899e-01 6.85022056e-01 8.68167877e-01
-5.57565272e-01 5.01866043e-01 3.20444375e-01 -1.16428733e-01
-1.71622723e-01 -8.71047735e-01 4.93721366e-02 -3.68937701e-01
1.94042213e-02 2.45332614e-01 9.49511230e-02 -1.27747369... | [11.06828498840332, 6.928679466247559] |
e68804c1-0002-4788-999c-40e4af7aaf5d | survey-on-sparse-coded-features-for-content | 1402.4888 | null | https://arxiv.org/abs/1402.4888v1 | https://arxiv.org/pdf/1402.4888v1.pdf | Survey on Sparse Coded Features for Content Based Face Image Retrieval | Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari... | ['D. Johnvictor', 'G. Selvavinayagam'] | 2014-02-20 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 1.91454709e-01 -5.35071433e-01 -5.61753213e-01 -5.74740529e-01
-6.03914797e-01 -2.57764071e-01 3.54394644e-01 -7.69390464e-02
-3.52215797e-01 4.89002436e-01 3.97362530e-01 5.88570654e-01
-6.07967257e-01 -8.88411283e-01 5.25243245e-02 -5.90415359e-01
-1.49206802e-01 5.81341684e-02 2.73749560e-01 -2.32004777... | [11.184035301208496, 0.36685362458229065] |
1166a47a-8705-4cb3-8454-fb3cb73f4cad | z-gmot-zero-shot-generic-multiple-object | 2305.17648 | null | https://arxiv.org/abs/2305.17648v1 | https://arxiv.org/pdf/2305.17648v1.pdf | Z-GMOT: Zero-shot Generic Multiple Object Tracking | Despite the significant progress made in recent years, Multi-Object Tracking (MOT) approaches still suffer from several limitations, including their reliance on prior knowledge of tracking targets, which necessitates the costly annotation of large labeled datasets. As a result, existing MOT methods are limited to a sma... | ['Ngan Hoang Le', 'Donald Adjeroh', 'Khoa Luu', 'Thinh Phan', 'Pha Nguyen', 'Anh Duy Le Dinh', 'Tien-Phat Nguyen', 'Kim Hoang Tran'] | 2023-05-28 | null | null | null | null | ['object-tracking', 'multiple-object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.72218858e-02 -3.71640384e-01 -1.42591327e-01 -1.78395793e-01
-7.42856145e-01 -6.74520075e-01 4.96206075e-01 -2.11343896e-02
-5.97880602e-01 6.33916020e-01 -3.51458192e-01 5.47416359e-02
-7.10228235e-02 -5.58382690e-01 -9.20422673e-01 -6.94384038e-01
5.18128835e-02 6.43021703e-01 1.11874044e+00 -9.95998159... | [6.375926494598389, -2.02769136428833] |
a7193f7b-35c2-4007-99be-953377302844 | polarmot-how-far-can-geometric-relations-take | 2208.01957 | null | https://arxiv.org/abs/2208.01957v1 | https://arxiv.org/pdf/2208.01957v1.pdf | PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking? | Most (3D) multi-object tracking methods rely on appearance-based cues for data association. By contrast, we investigate how far we can get by only encoding geometric relationships between objects in 3D space as cues for data-driven data association. We encode 3D detections as nodes in a graph, where spatial and tempora... | ['Laura Leal-Taixé', 'Aljoša Ošep', 'Guillem Brasó', 'Aleksandr Kim'] | 2022-08-03 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-7.97247067e-02 -2.72383213e-01 -2.53425628e-01 -1.19106174e-01
-1.36745945e-01 -8.97887588e-01 8.66484642e-01 2.46193171e-01
-1.38062954e-01 2.42532268e-01 1.40205294e-01 -2.30920330e-01
-5.08056402e-01 -6.66756213e-01 -8.39299440e-01 -4.97187555e-01
-6.03293717e-01 5.84268272e-01 3.54117692e-01 -9.62487981... | [6.516005516052246, -2.123605728149414] |
1165ec91-7c41-45bb-a895-92d1082e016a | conditional-random-field-and-deep-feature | 1711.04483 | null | http://arxiv.org/abs/1711.04483v2 | http://arxiv.org/pdf/1711.04483v2.pdf | Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation | Image segmentation is considered to be one of the critical tasks in
hyperspectral remote sensing image processing. Recently, convolutional neural
network (CNN) has established itself as a powerful model in segmentation and
classification by demonstrating excellent performances. The use of a graphical
model such as a co... | ['Alan Wee-Chung Liew', 'Jun Zhou', 'Yongsheng Gao', 'Xiuping Jia', 'Jocelyn Chanussot', 'Fahim Irfan Alam'] | 2017-11-13 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 6.02065980e-01 -3.03561926e-01 1.80548191e-01 -5.82378566e-01
-6.95498765e-01 -4.60131824e-01 4.31423157e-01 -3.05539444e-02
-4.81575280e-01 6.91346586e-01 -2.19876423e-01 -2.71536291e-01
-3.93104255e-01 -1.06160593e+00 -6.14859879e-01 -9.70131993e-01
2.69118901e-02 3.40409189e-01 4.70829941e-02 1.22863092... | [9.870851516723633, -1.7479420900344849] |
1e038714-299a-4976-91f4-62897dba9636 | joint-goal-segmentation-and-goal-success | null | null | https://aclanthology.org/2022.coling-1.41 | https://aclanthology.org/2022.coling-1.41.pdf | Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations | To evaluate the performance of a multi-domain goal-oriented Dialogue System (DS), it is important to understand what the users’ goals are for the conversations and whether those goals are successfully achieved. The success rate of goals directly correlates with user satisfaction and perceived usefulness of the DS. In t... | ['Chenlei Guo', 'Tuan-Hung Pham', 'Yu Zhang', 'Xiaohu Liu', 'Bin Guo', 'Benjamin Yao', 'Meiguo Wang'] | null | null | null | null | coling-2022-10 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 5.49605303e-02 7.19061792e-01 -9.76017267e-02 -5.22507727e-01
-1.13107979e+00 -5.77562571e-01 8.03475082e-01 3.02484810e-01
-4.97893184e-01 9.79691565e-01 6.45629287e-01 -1.22954704e-01
3.05132251e-02 -5.67053139e-01 2.63322473e-01 3.83350626e-02
1.35575473e-01 8.95691037e-01 2.22643375e-01 -7.98707664... | [12.865876197814941, 8.029955863952637] |
c659d5cf-1f36-4a57-845e-a7a142942382 | exploring-the-use-of-an-unsupervised | 2008.03615 | null | https://arxiv.org/abs/2008.03615v1 | https://arxiv.org/pdf/2008.03615v1.pdf | Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification | In this paper, we propose a novel way of addressing text-dependent automatic speaker verification (TD-ASV) by using a shared-encoder with task-specific decoders. An autoregressive predictive coding (APC) encoder is pre-trained in an unsupervised manner using both out-of-domain (LibriSpeech, VoxCeleb) and in-domain (Dee... | ['Ruchao Fan', 'Vijay Ravi', 'Amber Afshan', 'Abeer Alwan', 'Huanhua Lu'] | 2020-08-08 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 2.19889820e-01 6.72042668e-02 -5.96868433e-02 -9.15957928e-01
-1.72737062e+00 -7.20266104e-01 7.32781887e-01 -3.34154636e-01
-4.45047438e-01 3.83905768e-01 4.91912097e-01 -5.14564216e-01
4.50917095e-01 5.60925622e-03 -6.09718978e-01 -7.86538899e-01
3.71518224e-01 7.09253669e-01 -1.86076224e-01 -1.05121419... | [14.358796119689941, 6.249532699584961] |
11ab5378-57ae-4bc2-83ed-8b8a6179c688 | heterogeneous-trajectory-forecasting-via-risk | 2211.00848 | null | https://arxiv.org/abs/2211.00848v2 | https://arxiv.org/pdf/2211.00848v2.pdf | Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning | Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints. In this work, we propose a risk and scene graph learn... | ['Jianru Xue', 'Hongkai Yu', 'Pu Zhang', 'Chen Zhu', 'Jianwu Fang'] | 2022-11-02 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-1.81071103e-01 3.11615348e-01 -1.08287513e-01 -4.48171824e-01
-4.44501638e-01 -4.45319384e-01 9.64304149e-01 8.78004432e-02
-4.08078097e-02 4.51347023e-01 3.73723984e-01 -5.10063112e-01
-5.37137926e-01 -1.37210274e+00 -6.12506211e-01 -4.94401783e-01
-3.35575670e-01 9.53787804e-01 7.33421326e-01 -4.92899626... | [5.946789741516113, 0.8864409327507019] |
d51949b4-5aa4-4b45-8192-69e62b353c7a | a-collaborative-transfer-learning-framework | 2306.16425 | null | https://arxiv.org/abs/2306.16425v1 | https://arxiv.org/pdf/2306.16425v1.pdf | A Collaborative Transfer Learning Framework for Cross-domain Recommendation | In the recommendation systems, there are multiple business domains to meet the diverse interests and needs of users, and the click-through rate(CTR) of each domain can be quite different, which leads to the demand for CTR prediction modeling for different business domains. The industry solution is to use domain-specifi... | ['Dong Wang', 'Xingxing Wang', 'Bo Zhang', 'Pengye Zhang', 'Wei zhang'] | 2023-06-26 | null | null | null | null | ['transfer-learning', 'click-through-rate-prediction'] | ['miscellaneous', 'miscellaneous'] | [-1.82027921e-01 -4.16762859e-01 -5.17236888e-01 -2.81092227e-01
-2.46539071e-01 -4.22849029e-01 2.45178297e-01 -2.08604187e-01
-1.97453678e-01 6.40666366e-01 -6.34143651e-02 -8.05946812e-02
-2.64334291e-01 -1.04679668e+00 -5.95540583e-01 -7.12021887e-01
2.11594209e-01 4.65499640e-01 4.50223565e-01 -6.01316631... | [10.088403701782227, 5.277833938598633] |
a0de62f4-c930-4632-b2d5-0044c4b2fb92 | ddlp-unsupervised-object-centric-video | 2306.05957 | null | https://arxiv.org/abs/2306.05957v1 | https://arxiv.org/pdf/2306.05957v1.pdf | DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles | We propose a new object-centric video prediction algorithm based on the deep latent particle (DLP) representation. In comparison to existing slot- or patch-based representations, DLPs model the scene using a set of keypoints with learned parameters for properties such as position and size, and are both efficient and in... | ['Aviv Tamar', 'Tal Daniel'] | 2023-06-09 | null | null | null | null | ['video-generation', 'video-prediction', 'unconditional-video-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.02566683e-01 4.91465256e-03 -3.57402802e-01 1.02546372e-01
-4.58467454e-01 -3.72770190e-01 8.68879616e-01 -9.49445143e-02
1.07356958e-01 5.61385751e-01 6.64804637e-01 1.17632717e-01
8.68814290e-02 -8.46248686e-01 -1.28800178e+00 -8.58673930e-01
-2.37379998e-01 6.98588192e-01 6.09205246e-01 2.47743964... | [10.57587718963623, -0.4247100353240967] |
4a27b212-affd-4907-9563-7bce5afc63c0 | deep-fully-connected-networks-for-video | 1603.04930 | null | http://arxiv.org/abs/1603.04930v2 | http://arxiv.org/pdf/1603.04930v2.pdf | Deep Fully-Connected Networks for Video Compressive Sensing | In this work we present a deep learning framework for video compressive
sensing. The proposed formulation enables recovery of video frames in a few
seconds at significantly improved reconstruction quality compared to previous
approaches. Our investigation starts by learning a linear mapping between video
sequences and ... | ['Michael Iliadis', 'Aggelos K. Katsaggelos', 'Leonidas Spinoulas'] | 2016-03-16 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 1.71407446e-01 3.36835235e-02 -2.11891383e-01 -1.16399683e-01
-8.12219024e-01 -3.00381929e-01 4.79495525e-01 -3.84619713e-01
-1.45126790e-01 6.22149706e-01 4.62323248e-01 -3.65996301e-01
-1.79312468e-01 -3.69408637e-01 -1.02574730e+00 -5.25472522e-01
-5.81954539e-01 -2.68121630e-01 -5.78647070e-02 9.54175666... | [11.164856910705566, -2.0739054679870605] |
237b5544-c24b-4296-ad9a-3342705bed6d | dynamic-loss-balancing-and-sequential | 2211.04165 | null | https://arxiv.org/abs/2211.04165v1 | https://arxiv.org/pdf/2211.04165v1.pdf | Dynamic loss balancing and sequential enhancement for road-safety assessment and traffic scene classification | Road-safety inspection is an indispensable instrument for reducing road-accident fatalities contributed to road infrastructure. Recent work formalizes road-safety assessment in terms of carefully selected risk factors that are also known as road-safety attributes. In current practice, these attributes are manually anno... | ['Siniša Šegvić', 'Marko Ševrović', 'Marin Kačan'] | 2022-11-08 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.35211778e-01 -1.25765115e-01 -4.49937820e-01 -7.72603095e-01
-1.28460741e+00 -3.30203176e-01 6.03085399e-01 -4.59502749e-02
-7.72777498e-01 6.09337449e-01 3.50962579e-01 -5.62493861e-01
-3.15661937e-01 -1.06104374e+00 -1.09077001e+00 -4.88220781e-01
-5.17340265e-02 1.18407495e-01 4.09872562e-01 -1.49371475... | [6.687402725219727, 0.6077577471733093] |
9a0876f2-362e-41e0-924f-46a48e5ced4c | effective-transfer-of-pretrained-large-visual | 2306.16186 | null | https://arxiv.org/abs/2306.16186v1 | https://arxiv.org/pdf/2306.16186v1.pdf | Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection | Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existi... | ['Ying Qu', 'Jinpiao Liao', 'Zuofeng Zhong', 'Wai Keung Wong', 'Zhewei Chen'] | 2023-06-28 | null | null | null | null | ['few-shot-learning', 'benchmarking', 'benchmarking'] | ['methodology', 'miscellaneous', 'robots'] | [ 6.15812182e-01 -2.37676337e-01 -2.46997878e-01 -3.07100415e-01
-5.94923139e-01 -5.37881076e-01 6.09998554e-02 1.25993297e-01
-2.73453984e-02 3.90504271e-01 5.22822365e-02 1.99195463e-02
-1.97203666e-01 -7.61713564e-01 -6.29503489e-01 -5.42473614e-01
1.93795994e-01 7.73421004e-02 2.72931725e-01 -3.21990848... | [10.23140811920166, -0.16905878484249115] |
2019ffc2-37c6-4dcb-a240-b8dfcbde62cb | selective-transfer-machine-for-personalized | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Chu_Selective_Transfer_Machine_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Chu_Selective_Transfer_Machine_2013_CVPR_paper.pdf | Selective Transfer Machine for Personalized Facial Action Unit Detection | Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth ver... | ['Fernando de la Torre', 'Wen-Sheng Chu', 'Jeffery F. Cohn'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.40325058e-01 -1.61926568e-01 -2.75560200e-01 -8.19924712e-01
-5.30397177e-01 -5.45024872e-01 6.10323012e-01 -2.95326561e-01
-3.71628463e-01 8.25299263e-01 1.07519254e-01 3.83929312e-01
3.69381234e-02 -6.09018385e-01 -3.75169724e-01 -8.83966029e-01
-7.69679025e-02 2.49353305e-01 -6.51429817e-02 -2.55966187... | [13.56940746307373, 1.6920770406723022] |
9cfd4256-0fa4-405c-99c8-cb5f5ece757a | viewsynth-learning-local-features-from-depth | 1911.10248 | null | https://arxiv.org/abs/1911.10248v4 | https://arxiv.org/pdf/1911.10248v4.pdf | ViewSynth: Learning Local Features from Depth using View Synthesis | The rapid development of inexpensive commodity depth sensors has made keypoint detection and matching in the depth image modality an important problem in computer vision. Despite great improvements in recent RGB local feature learning methods, adapting them directly in the depth modality leads to unsatisfactory perform... | ['Jan-Michael Frahm', 'Kuan-Chuan Peng', 'Peri Akiva', 'Rajat Vikram Singh', 'Spondon Kundu', 'Jisan Mahmud'] | 2019-11-22 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 1.65956885e-01 -1.15910545e-01 -1.72633976e-01 -5.50563574e-01
-8.66516471e-01 -6.43952310e-01 7.15592265e-01 -1.96715862e-01
-3.15365791e-01 6.38596341e-02 6.07775524e-02 9.00724679e-02
-9.74160284e-02 -7.68712223e-01 -7.96982586e-01 -6.94816709e-01
4.81632531e-01 2.54684061e-01 2.91672111e-01 4.24973257... | [7.948269844055176, -2.7145605087280273] |
67e6b046-e89e-4a58-b014-cce51bb627b2 | evaluation-of-a-region-proposal-architecture | 2106.11797 | null | https://arxiv.org/abs/2106.11797v1 | https://arxiv.org/pdf/2106.11797v1.pdf | Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis | Automatically recognizing the layout of handwritten documents is an important step towards useful extraction of information from those documents. The most common application is to feed downstream applications such as automatic text recognition and keyword spotting; however, the recognition of the layout also helps to e... | ['Enrique Vidal', 'Lorenzo Quirós'] | 2021-06-22 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 6.57373846e-01 -3.90516490e-01 -1.48661062e-01 -1.93649247e-01
-4.27053392e-01 -8.71046484e-01 6.80947244e-01 5.85911274e-01
-2.59473264e-01 3.64584953e-01 1.89467873e-02 -4.13608670e-01
-3.88070941e-01 -5.06679475e-01 -2.80872285e-01 -5.95786393e-01
2.40013435e-01 3.85400087e-01 2.82236248e-01 -4.09339443... | [11.771750450134277, 2.6640100479125977] |
dc836120-b862-4a2b-aeb2-e817f062c44d | contrastive-deep-graph-clustering-with | 2212.03559 | null | https://arxiv.org/abs/2212.03559v1 | https://arxiv.org/pdf/2212.03559v1.pdf | Contrastive Deep Graph Clustering with Learnable Augmentation | Graph contrastive learning is an important method for deep graph clustering. The existing methods first generate the graph views with stochastic augmentations and then train the network with a cross-view consistency principle. Although good performance has been achieved, we observe that the existing augmentation method... | ['En Zhu', 'Xinwang Liu', 'Siwei Wang', 'Sihang Zhou', 'Yue Liu', 'Xihong Yang'] | 2022-12-07 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.74758479e-02 1.20079301e-01 -2.16179624e-01 -3.08474988e-01
-6.56059265e-01 -5.70465446e-01 5.41968346e-01 -2.31367245e-01
1.05431668e-01 3.05826634e-01 1.31233707e-01 1.17486775e-01
-9.24167037e-02 -7.83020377e-01 -6.28311753e-01 -1.12850344e+00
1.54217646e-01 6.36105955e-01 -7.81323537e-02 1.08690802... | [7.486689567565918, 5.947271347045898] |
ad033e16-0556-43ed-a001-0786d5d4592a | hierarchical-semantic-aggregation-for | 2012.02733 | null | https://arxiv.org/abs/2012.02733v2 | https://arxiv.org/pdf/2012.02733v2.pdf | Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning | Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations as an individual class and tries to distinguish them from all other images, has been verified effective for representation learning. However... | ['Qi Tian', 'Hongkai Xiong', 'Lingxi Xie', 'Hao Li', 'Xiaopeng Zhang', 'Haohang Xu'] | 2020-12-04 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.56318760e-01 2.19911098e-01 -3.80345106e-01 -2.71873176e-01
-1.06349969e+00 -5.15612245e-01 6.80289507e-01 3.22820425e-01
-3.67031068e-01 5.97009003e-01 -3.70254405e-02 1.16529599e-01
-1.50562659e-01 -7.07703590e-01 -1.01571500e+00 -6.84606135e-01
-3.47475708e-02 3.22747409e-01 2.93320060e-01 -6.51794076... | [9.480894088745117, 2.436263084411621] |
7f8216ac-b0bb-4f3c-912e-35e0c10015c2 | semantic-visual-simultaneous-localization-and | 2209.06428 | null | https://arxiv.org/abs/2209.06428v1 | https://arxiv.org/pdf/2209.06428v1.pdf | Semantic Visual Simultaneous Localization and Mapping: A Survey | Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous pub... | ['ShengYong Chen', 'Ruyu Liu', 'Qiyi Tong', 'Jialing Liu', 'Jianhua Zhang', 'Kaiqi Chen'] | 2022-09-14 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.12051964e-01 -3.15692008e-01 -1.62590355e-01 -5.66727102e-01
-3.47858638e-01 -4.90595818e-01 7.03982711e-01 1.02560930e-01
-5.39794743e-01 6.50049567e-01 -1.49332657e-01 -6.18026517e-02
-2.00505078e-01 -8.87609065e-01 -3.50626022e-01 -4.16381359e-01
1.24780640e-01 5.54504633e-01 5.95453680e-01 -3.57003361... | [7.3114824295043945, -2.1383025646209717] |
59cc45c8-dad5-4b18-94c8-16a4699e7c12 | inside-outside-net-detecting-objects-in | 1512.04143 | null | http://arxiv.org/abs/1512.04143v1 | http://arxiv.org/pdf/1512.04143v1.pdf | Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | It is well known that contextual and multi-scale representations are
important for accurate visual recognition. In this paper we present the
Inside-Outside Net (ION), an object detector that exploits information both
inside and outside the region of interest. Contextual information outside the
region of interest is int... | ['Kavita Bala', 'C. Lawrence Zitnick', 'Sean Bell', 'Ross Girshick'] | 2015-12-14 | inside-outside-net-detecting-objects-in-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Bell_Inside-Outside_Net_Detecting_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Bell_Inside-Outside_Net_Detecting_CVPR_2016_paper.pdf | cvpr-2016-6 | ['small-object-detection'] | ['computer-vision'] | [ 1.81360021e-01 -2.39918530e-01 -2.29726568e-01 -2.68727094e-01
-9.98295546e-01 -7.31671274e-01 5.35732865e-01 2.34776020e-01
-7.97122955e-01 6.69919401e-02 1.86996497e-02 -1.00946695e-01
2.99641401e-01 -4.31851983e-01 -1.11585951e+00 -4.18732405e-01
-4.23178747e-02 -2.58767545e-01 6.92133307e-01 -1.40131220... | [9.049690246582031, 0.4735760986804962] |
d6c099f5-92dd-408f-aef8-245f35a983e8 | immune-system-approaches-to-intrusion | 1305.7144 | null | http://arxiv.org/abs/1305.7144v1 | http://arxiv.org/pdf/1305.7144v1.pdf | Immune System Approaches to Intrusion Detection - A Review (ICARIS) | The use of artificial immune systems in intrusion detection is an appealing
concept for two reasons. Firstly, the human immune system provides the human
body with a high level of protection from invading pathogens, in a robust,
self-organised and distributed manner. Secondly, current techniques used in
computer securit... | ['Jamie Twycross', 'Julie Greensmith', 'Uwe Aickelin'] | 2013-05-30 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.72115707e-01 -3.51292491e-01 2.72676554e-02 1.20800614e-01
6.32812381e-01 -4.65275168e-01 6.50465608e-01 5.95999956e-01
-5.45691431e-01 8.40626180e-01 -3.01347822e-01 -3.23505580e-01
-1.42128587e-01 -9.65437889e-01 1.06636882e-01 -7.51894653e-01
-4.29101199e-01 5.21547794e-01 5.65869331e-01 -6.69994295... | [5.70191764831543, 4.102051258087158] |
22dbae09-21fe-4a74-bfb2-610ace161146 | multi-robot-coordination-and-layout-design | 2305.06436 | null | https://arxiv.org/abs/2305.06436v2 | https://arxiv.org/pdf/2305.06436v2.pdf | Multi-Robot Coordination and Layout Design for Automated Warehousing | With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throu... | ['Jiaoyang Li', 'Stefanos Nikolaidis', 'Varun Bhatt', 'Matthew C. Fontaine', 'Yulun Zhang'] | 2023-05-10 | null | null | null | null | ['layout-design', 'multi-agent-path-finding'] | ['computer-vision', 'playing-games'] | [-4.92613018e-01 2.08596244e-01 3.09634864e-01 -2.12872684e-01
-4.05702651e-01 -9.96393800e-01 1.44733325e-01 8.53361964e-01
-1.09123558e-01 9.21545446e-01 3.10607366e-02 -4.02095914e-01
-5.55239439e-01 -1.36885667e+00 -7.10890770e-01 -4.61884648e-01
-3.14605564e-01 1.19875526e+00 3.77333343e-01 -6.79618299... | [4.955246925354004, 1.7139307260513306] |
1a61ccf6-45a0-49b1-b64b-e48b19bcfa63 | tractable-data-enriched-distributionally | 2207.03286 | null | https://arxiv.org/abs/2207.03286v1 | https://arxiv.org/pdf/2207.03286v1.pdf | Tractable Data Enriched Distributionally Robust Chance-Constrained CVR | This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable energy not only brings clean power but also challenges th... | ['Zhaoyu Wang', 'Yi Guo', 'Fankun Bu', 'Qianzhi Zhang'] | 2022-07-07 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-3.29692394e-01 -4.92240906e-01 5.69038186e-03 -8.95105228e-02
-9.82877672e-01 -9.81864035e-01 3.18043798e-01 2.85860777e-01
3.70868474e-01 1.39569998e+00 2.44856589e-02 -4.36969936e-01
-8.02891970e-01 -9.71800327e-01 -3.86262387e-01 -1.13884139e+00
-1.89728945e-01 5.00691533e-01 -4.05717194e-01 2.24341303... | [5.707006931304932, 2.608750104904175] |
21126e35-812d-4115-b5c8-dbb7472267f1 | abn-agent-aware-boundary-networks-for | 2203.08942 | null | https://arxiv.org/abs/2203.08942v1 | https://arxiv.org/pdf/2203.08942v1.pdf | ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation | Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding. Despite the great achievement in TAPG, most existing works ignore the human perception of interaction betwe... | ['Ngan Le', 'Akihiro Sugimoto', 'Minh-Triet Tran', 'Sang Truong', 'Kashu Yamazaki', 'Khoa Vo'] | 2022-03-16 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 5.29624373e-02 -2.99178988e-01 -1.40115857e-01 5.37351705e-02
-2.74740577e-01 -4.36484605e-01 8.39689493e-01 -3.25454742e-01
-4.33040053e-01 5.65201938e-01 3.69032085e-01 3.54833975e-02
-1.74068157e-02 -8.22654784e-01 -6.52217507e-01 -7.78975546e-01
-5.60498536e-01 1.52879328e-01 6.95108891e-01 -1.97061390... | [8.347282409667969, 0.5789557695388794] |
15964460-759f-4d70-b600-0fc74bc1196d | conditional-permutation-invariant-flows | 2206.09021 | null | https://arxiv.org/abs/2206.09021v1 | https://arxiv.org/pdf/2206.09021v1.pdf | Conditional Permutation Invariant Flows | We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation equivariant dynamics. These dynamics are driven by a learnable per-set-element term and pairwise interactions, both parametrized by deep neu... | ['Frank Wood', 'Trevor Campbell', 'Jonathan Wilder Lavington', 'Setareh Dabiri', 'Justice Sefas', 'Yunpeng Liu', 'Vasileios Lioutas', 'Matthew Niedoba', 'Adam Ścibior', 'Berend Zwartsenberg'] | 2022-06-17 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.45248514e-01 1.45157456e-01 9.44507271e-02 -4.41190660e-01
-7.45234549e-01 -8.21762800e-01 1.23149872e+00 -4.89386320e-01
-2.32343048e-01 9.09691453e-01 2.07313448e-01 -7.60865808e-02
-4.04738098e-01 -9.47056949e-01 -1.17081189e+00 -8.98603439e-01
-4.05314416e-01 1.02142143e+00 1.72530577e-01 5.92816398... | [9.043949127197266, -3.2916462421417236] |
bd19fb7c-8040-4e31-8a5b-08969f96b00f | howkgpt-investigating-the-detection-of | 2305.18226 | null | https://arxiv.org/abs/2305.18226v2 | https://arxiv.org/pdf/2305.18226v2.pdf | HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis | As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns arise over their potential to compromise academic integrity. The education sector currently tussles with distinguishing student-authored homework assignments from AI-generated ones. This paper addresses the challenge by introduci... | ['Michail Maniatakos', 'Yasir Zaki', 'Talal Rahwan', 'Manaar Alam', 'Christoforos Vasilatos'] | 2023-05-26 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 1.50866911e-01 3.19523245e-01 -1.70867741e-01 -1.69828370e-01
-9.44606662e-01 -7.86930919e-01 8.48369241e-01 6.92470253e-01
-4.11622792e-01 6.40217304e-01 5.44780195e-01 -7.21844375e-01
-4.89812762e-01 -7.17641950e-01 -2.91344762e-01 -4.32324320e-01
5.25159895e-01 3.73363703e-01 -1.50658116e-01 1.66525040... | [11.135890007019043, 9.179248809814453] |
fbd044ca-e4c3-431d-a929-95bf2aa54a7e | unified-keypoint-based-action-recognition | 2303.15270 | null | https://arxiv.org/abs/2303.15270v1 | https://arxiv.org/pdf/2303.15270v1.pdf | Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling | This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition; skeleton detection and tracking errors, poor variety of the targeted actions, as well as person-wise and frame-wise action recognition. A point cloud deep-learning paradigm is introduced to the action r... | ['Taiki Sekii', 'Fumiaki Sato', 'Ryo Hachiuma'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hachiuma_Unified_Keypoint-Based_Action_Recognition_Framework_via_Structured_Keypoint_Pooling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hachiuma_Unified_Keypoint-Based_Action_Recognition_Framework_via_Structured_Keypoint_Pooling_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-classification', 'weakly-supervised-temporal-action', 'action-localization', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.72187763e-01 -2.35619202e-01 -4.49510396e-01 -1.12513162e-01
-5.46868742e-01 -1.68606251e-01 5.95816016e-01 -2.88554102e-01
-5.86121559e-01 3.35581332e-01 2.21238136e-01 4.09052819e-01
-2.19565090e-02 -6.45734310e-01 -8.14824939e-01 -8.68294477e-01
-3.45285162e-02 1.43340036e-01 7.50598073e-01 3.93975899... | [7.901422500610352, 0.40543219447135925] |
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