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36d21c35-bf94-40f6-a517-8432b41e9351 | phocnet-a-deep-convolutional-neural-network | 1604.00187 | null | http://arxiv.org/abs/1604.00187v3 | http://arxiv.org/pdf/1604.00187v3.pdf | PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents | In recent years, deep convolutional neural networks have achieved state of
the art performance in various computer vision task such as classification,
detection or segmentation. Due to their outstanding performance, CNNs are more
and more used in the field of document image analysis as well. In this work, we
present a ... | ['Sebastian Sudholt', 'Gernot A. Fink'] | 2016-04-01 | null | null | null | null | ['word-spotting-in-handwritten-documents'] | ['computer-vision'] | [ 3.07471603e-01 -3.46254528e-01 -2.62862056e-01 -2.13830560e-01
-4.37282056e-01 -3.75932425e-01 8.11360180e-01 5.36079288e-01
-6.71762764e-01 4.38752919e-01 -1.65227830e-01 -4.17832136e-01
9.05265808e-02 -8.30305696e-01 -5.95406532e-01 -4.22617525e-01
2.95260221e-01 2.60892838e-01 2.63754517e-01 -1.05162628... | [11.463109016418457, 2.6212246417999268] |
7f9dcc03-35c7-4408-9037-d7decbf9a513 | sentiment-analysis-of-arabic-tweets-using | null | null | http://www.ijcis.info/Vol13N1/Vol13N1PP9-14.pdf | http://www.ijcis.info/Vol13N1/Vol13N1PP9-14.pdf | Sentiment Analysis of Arabic Tweets Using Semantic Resources | Sentiment analysis has grown to be one of the most active research areas in natural language processing
and text mining. Many researchers have investigated sentiment analysis and opinion mining from different
classification approaches. However, limited research is conducted on Arabic sentiment analysis as compared to... | ['Lamia Al-Horaibi', 'Muhammad Badruddin Khan'] | 2017-01-30 | null | null | null | international-journal-of-computing | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.38358911e-02 -5.92853576e-02 1.71973005e-01 -7.18853951e-01
1.37719601e-01 -6.18064880e-01 6.94105208e-01 6.87726140e-01
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-2.78137941e-02 -1.06814492e+00 8.29532221e-02 -4.35216457e-01
3.03418010e-01 4.69580442e-01 1.57425910e-01 -1.22403884... | [11.04418659210205, 6.9142584800720215] |
48759338-99e6-46b4-89fa-8a643bc57cae | exact-set-valued-estimation-using-constrained | 2304.04826 | null | https://arxiv.org/abs/2304.04826v1 | https://arxiv.org/pdf/2304.04826v1.pdf | Exact Set-valued Estimation using Constrained Convex Generators for uncertain Linear Systems | Set-valued state estimation when in the presence of uncertainties in the model have been addressed in the literature essentially following three main approaches: i) interval arithmetic of the uncertain dynamics with the estimates; ii) factorizing the uncertainty into matrices with unity rank; and, iii) performing the c... | ['Daniel Silvestre'] | 2023-04-10 | null | null | null | null | ['unity'] | ['computer-vision'] | [-7.50415623e-02 5.84480584e-01 2.22148195e-01 3.57099473e-02
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-3.04254681e-01 6.11420214e-01 -1.58553377e-01 -4.33954656... | [5.295392036437988, 2.3864738941192627] |
8b54a991-3790-4d35-ab54-9949e5ca83ba | toward-asymptotic-optimality-sequential | 2302.09810 | null | https://arxiv.org/abs/2302.09810v1 | https://arxiv.org/pdf/2302.09810v1.pdf | Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification | Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series. Conventional SDRE algorithms can fail to estimate DRs precisely due to the internal overnormalization problem, which prevents the DR-based sequential algorithm, Sequential Probability R... | ['Hitoshi Imaoka', 'Kazuyuki Sakurai', 'Taiki Miyagawa', 'Akinori F. Ebihara'] | 2023-02-20 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-2.89464384e-01 -4.25898850e-01 -3.52412879e-01 -4.36558425e-01
-1.24614477e+00 -2.79243082e-01 2.49230236e-01 -6.46070167e-02
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-3.33961010e-01 -4.60105449e-01 -1.52318582e-01 -8.12394440e-01
-1.21774815e-01 6.49112284e-01 2.89042622e-01 3.46709102... | [8.107417106628418, 3.8782920837402344] |
12309e86-bfb8-4245-8ae7-6be595b0bd94 | texture-generation-using-dual-domain-feature | 2203.06901 | null | https://arxiv.org/abs/2203.06901v1 | https://arxiv.org/pdf/2203.06901v1.pdf | Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations | We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model ... | ['Songhwai Oh', 'Jungchan Cho', 'Seunggyu Chang'] | 2022-03-14 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.40564290e-02 1.02760084e-01 1.78066298e-01 -1.96844146e-01
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5.58733821e-01 7.61864364e-01 8.14758763e-02 -8.59707147... | [9.366791725158691, -3.131375789642334] |
51665a30-3cdb-4b44-89a0-c4450ad912fd | diplomat-a-dialogue-dataset-for-situated | 2306.09030 | null | https://arxiv.org/abs/2306.09030v2 | https://arxiv.org/pdf/2306.09030v2.pdf | DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning | Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations and is essential for the development of communicative social agents. In this paper, we introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning an... | ['Song-Chun Zhu', 'Zilong Zheng', 'Hengli Li'] | 2023-06-15 | null | null | null | null | ['conversational-question-answering', 'question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.13007173e-01 6.78247690e-01 8.89603794e-02 -4.02220160e-01
-4.76184368e-01 -4.87749130e-01 1.12961638e+00 -1.27777100e-01
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4.28562105e-01 7.24106312e-01 -1.53376818e-01 -7.75476217... | [12.532151222229004, 7.991625785827637] |
c3144124-2f1c-499e-b55e-a27757746f50 | modelling-context-and-syntactical-features | null | null | https://aclanthology.org/2020.acl-main.293 | https://aclanthology.org/2020.acl-main.293.pdf | Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis | The aspect-based sentiment analysis (ABSA) consists of two conceptual tasks, namely an aspect extraction and an aspect sentiment classification. Rather than considering the tasks separately, we build an end-to-end ABSA solution. Previous works in ABSA tasks did not fully leverage the importance of syntactical informati... | ['Philip O. Ogunbona', 'Minh Hieu Phan'] | 2020-07-01 | null | null | null | acl-2020-6 | ['aspect-extraction'] | ['natural-language-processing'] | [-3.88657711e-02 3.62053752e-01 -9.24779698e-02 -7.49990225e-01
-6.87741220e-01 -6.15633011e-01 8.49145055e-01 5.44188321e-01
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1.37748480e-01 2.71317422e-01 -1.21304274e-01 -4.15733367... | [11.433107376098633, 6.715325355529785] |
51fc57ef-90c2-42bb-970b-93b13ae946e3 | yes-this-way-learning-to-ground-referring | 2305.12880 | null | https://arxiv.org/abs/2305.12880v1 | https://arxiv.org/pdf/2305.12880v1.pdf | Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers | The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally. In this paper, we present an initial study that evaluates intra-episodic feedback given in a collaborative setting. We use a referential language game as ... | ['David Schlangen', 'Sherzod Hakimov', 'Philipp Sadler'] | 2023-05-22 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 4.40009713e-01 7.07953990e-01 -6.92387596e-02 -3.66344959e-01
-5.39162397e-01 -6.87694252e-01 1.06304443e+00 1.75557300e-01
-6.62776768e-01 7.97682226e-01 1.52490482e-01 -3.03422868e-01
-1.56029865e-01 -5.49147010e-01 -8.04050922e-01 -5.41379392e-01
-3.81015837e-01 6.29925013e-01 4.04259294e-01 -4.22845662... | [4.0790300369262695, 1.490466594696045] |
def7f9f8-eb2e-4026-bac5-6a8b5c12f951 | self-knowledge-distillation-via-dropout | 2208.05642 | null | https://arxiv.org/abs/2208.05642v1 | https://arxiv.org/pdf/2208.05642v1.pdf | Self-Knowledge Distillation via Dropout | To boost the performance, deep neural networks require deeper or wider network structures that involve massive computational and memory costs. To alleviate this issue, the self-knowledge distillation method regularizes the model by distilling the internal knowledge of the model itself. Conventional self-knowledge disti... | ['Myungjoo Kang', 'Hyun Seo', 'Yeachan Park', 'Hyoje Lee'] | 2022-08-11 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-1.23971559e-01 -7.20577091e-02 -1.52156036e-02 -4.87018973e-01
-5.13565361e-01 -5.50223053e-01 2.75685310e-01 -6.75203279e-02
-6.64032578e-01 8.99817050e-01 -4.13633406e-01 -1.60646468e-01
1.04985364e-01 -8.15439105e-01 -1.14392686e+00 -9.86543059e-01
5.52818179e-01 8.47876891e-02 6.09358370e-01 8.86766706... | [9.469125747680664, 3.445453643798828] |
a9f5ee33-2cb6-4c87-a9ab-ffc9f8f635db | inter-subject-deep-transfer-learning-for | 2103.05351 | null | https://arxiv.org/abs/2103.05351v1 | https://arxiv.org/pdf/2103.05351v1.pdf | Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding | Convolutional neural networks (CNNs) have become a powerful technique to decode EEG and have become the benchmark for motor imagery EEG Brain-Computer-Interface (BCI) decoding. However, it is still challenging to train CNNs on multiple subjects' EEG without decreasing individual performance. This is known as the negati... | ['A. Aldo Faisal', 'Pablo Ortega', 'Xiaoxi Wei'] | 2021-03-09 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.00528109e-01 1.88340386e-03 3.10751706e-01 -4.10140216e-01
-8.30084324e-01 -3.56209934e-01 3.36623281e-01 -4.95999575e-01
-6.67493641e-01 9.73875403e-01 -4.50354293e-02 -1.44607201e-01
-2.39045337e-01 -4.05916035e-01 -9.99768496e-01 -8.64636362e-01
-6.20142445e-02 2.68095523e-01 -4.97598909e-02 -1.37363940... | [13.080970764160156, 3.4226105213165283] |
2fd37f6f-4e1d-46ec-9a84-fa33d8edf457 | rankcse-unsupervised-representation-learning | null | null | https://openreview.net/forum?id=y_sZyxuuFh3 | https://openreview.net/pdf?id=y_sZyxuuFh3 | RankCSE: Unsupervised Representation Learning via Learning to Rank | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones... | ['Anonymous'] | 2022-11-14 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 6.57461107e-01 -2.22953379e-01 -1.08911484e-01 -8.71165931e-01
-1.00193810e+00 -4.70202833e-01 7.20346630e-01 8.41473639e-01
-7.68173277e-01 5.50876439e-01 7.53084421e-01 -6.35770187e-02
-3.15298915e-01 -6.16415620e-01 -3.40468675e-01 -6.04074836e-01
3.15735638e-01 4.24586982e-01 3.52000266e-01 -5.05083084... | [11.030416488647461, 8.576904296875] |
d82a5e03-224d-4b91-ad5e-a84301ac4e53 | weakly-supervised-anomaly-detection-a-survey | 2302.04549 | null | https://arxiv.org/abs/2302.04549v1 | https://arxiv.org/pdf/2302.04549v1.pdf | Weakly Supervised Anomaly Detection: A Survey | Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in ... | ['Yue Zhao', 'Philip S. Yu', 'Xiangnan He', 'Hailiang Huang', 'Songqiao Han', 'Xiyang Hu', 'Ao Zheng', 'Chaochuan Hou', 'Minqi Jiang'] | 2023-02-09 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.21687219e-01 -3.62779796e-02 -4.97274607e-01 -4.23990041e-01
-3.67505878e-01 -4.98728216e-01 5.93914211e-01 5.80372453e-01
-2.16058940e-01 4.93777812e-01 -3.62207741e-02 -2.97516286e-01
4.06981334e-02 -4.08770740e-01 -5.00204921e-01 -4.03379828e-01
-1.62025511e-01 2.35225245e-01 1.09236874e-01 8.52631181... | [7.77650260925293, 1.947541356086731] |
5f7d8d4c-a842-4b35-a1f5-1ea26c07a21f | the-carbon-footprint-of-machine-learning | 2204.05149 | null | https://arxiv.org/abs/2204.05149v1 | https://arxiv.org/pdf/2204.05149v1.pdf | The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink | Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML energy use (across research, development, and production) held steady... | ['Jeff Dean', 'Maud Texier', 'David So', 'Daniel Rothchild', 'Lluis-Miquel Munguia', 'Chen Liang', 'Quoc Le', 'Urs Hölzle', 'Joseph Gonzalez', 'David Patterson'] | 2022-04-11 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.60745841e-02 1.69224724e-01 -6.35383666e-01 -3.63350809e-02
-5.83471179e-01 -7.12060571e-01 6.73160553e-01 1.48316324e-01
-5.46499431e-01 7.32316732e-01 7.10558668e-02 -1.05778062e+00
1.61379859e-01 -8.23301733e-01 -1.02781141e+00 -2.97400326e-01
5.61588228e-01 -9.64736044e-02 -3.99617672e-01 5.10444462... | [8.450190544128418, 3.3697052001953125] |
42833e0d-29f0-49f0-b5be-41a610833d33 | practical-real-video-denoising-with-realistic | 2208.11803 | null | https://arxiv.org/abs/2208.11803v3 | https://arxiv.org/pdf/2208.11803v3.pdf | Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising | Video denoising aims at removing noise from videos to recover clean ones. Some existing works show that optical flow can help the denoising by exploiting the additional spatial-temporal clues from nearby frames. However, the flow estimation itself is also sensitive to noise, and can be unusable under large noise levels... | ['Luc van Gool', 'Radu Timofte', 'Kai Zhang', 'Yulun Zhang', 'Jingyun Liang', 'Qin Wang', 'JieZhang Cao'] | 2022-08-25 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.92710492e-02 -4.59169209e-01 2.30040282e-01 -1.68211594e-01
-6.10524595e-01 -4.54413384e-01 3.41027647e-01 -3.61108899e-01
-3.23299766e-01 7.91731656e-01 4.65809792e-01 1.41409654e-02
1.58088971e-02 -9.20798659e-01 -5.38049877e-01 -8.89079273e-01
2.64210301e-03 -2.16927499e-01 4.99969840e-01 -3.81940991... | [11.262986183166504, -2.0638787746429443] |
0540809a-3632-4e90-87c1-9a0b2c009d67 | state-of-the-art-in-open-set-iris | 2208.10564 | null | https://arxiv.org/abs/2208.10564v1 | https://arxiv.org/pdf/2208.10564v1.pdf | State Of The Art In Open-Set Iris Presentation Attack Detection | Research in presentation attack detection (PAD) for iris recognition has largely moved beyond evaluation in "closed-set" scenarios, to emphasize ability to generalize to presentation attack types not present in the training data. This paper offers several contributions to understand and extend the state-of-the-art in o... | ['Adam Czajka', 'Kevin Bowyer', 'Lucas Parzianello', 'Jeremy Speth', 'Aidan Boyd'] | 2022-08-22 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 4.61166412e-01 -3.91970098e-01 -3.14069688e-01 1.51968701e-02
-1.10767639e+00 -8.38341057e-01 5.02190113e-01 -4.87288088e-02
-2.03464106e-01 3.84541094e-01 2.55493879e-01 -6.73105896e-01
-6.02148056e-01 -1.56681195e-01 -3.66169095e-01 -7.41266012e-01
-2.64494270e-01 5.46752393e-01 -2.52493918e-01 -3.72645885... | [3.737504482269287, -3.6358189582824707] |
70cc7a53-0548-4b33-947c-9bb85e8aa01e | can-sam-count-anything-an-empirical-study-on | 2304.10817 | null | https://arxiv.org/abs/2304.10817v1 | https://arxiv.org/pdf/2304.10817v1.pdf | Can SAM Count Anything? An Empirical Study on SAM Counting | Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few... | ['Qinnan Shangguan', 'Xiaopeng Hong', 'Zhiheng Ma'] | 2023-04-21 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.27630159e-01 -1.10280298e-01 -2.15323299e-01 -4.05077457e-01
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1.71143100e-01 -4.30756092e-01 -5.26450872e-01 -4.33139831e-01
1.46319523e-01 1.09770811e+00 5.42843759e-01 3.78296189... | [9.038473129272461, 0.5926404595375061] |
a2549aa1-3753-445c-b4e8-34878dbe715b | analyzing-different-expert-opined-strategies | 2307.02254 | null | https://arxiv.org/abs/2307.02254v1 | https://arxiv.org/pdf/2307.02254v1.pdf | Analyzing Different Expert-Opined Strategies to Enhance the Effect on the Goal of a Multi-Attribute Decision-Making System Using a Concept of Effort Propagation and Application in Enhancement of High School Students' Performance | In many real-world multi-attribute decision-making (MADM) problems, mining the inter-relationships and possible hierarchical structures among the factors are considered to be one of the primary tasks. But, besides that, one major task is to determine an optimal strategy to work on the factors to enhance the effect on t... | ['Adrijit Goswami', 'Suvojit Dhara'] | 2023-07-05 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.33988762e-01 3.40879798e-01 -1.93800539e-01 -3.71453553e-01
-2.58001804e-01 -2.98882008e-01 2.29195744e-01 7.23473370e-01
-5.49226999e-01 9.39548969e-01 1.49198011e-01 -5.04658878e-01
-1.26185405e+00 -9.62630332e-01 -1.30090982e-01 -5.69899738e-01
4.32066202e-01 7.51290202e-01 1.63716733e-01 -3.15926284... | [8.78353500366211, 5.8791823387146] |
7f0816f5-571b-4d05-b48f-72177bc74a42 | semlinker-a-modular-and-open-source-framework | null | null | https://aclanthology.org/L16-1085 | https://aclanthology.org/L16-1085.pdf | SemLinker, a Modular and Open Source Framework for Named Entity Discovery and Linking | This paper presents SemLinker, an open source system that discovers named entities, connects them to a reference knowledge base, and clusters them semantically. SemLinker relies on several modules that perform surface form generation, mutual disambiguation, entity clustering, and make use of two annotation engines. Sem... | ['Ludovic Jean-Louis', 'Marie-Jean Meurs', 'Eric Charton', 'Hayda Almeida'] | 2016-05-01 | semlinker-a-modular-and-open-source-framework-1 | https://aclanthology.org/L16-1085 | https://aclanthology.org/L16-1085.pdf | lrec-2016-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.64785486e-01 7.76544392e-01 -3.59260917e-01 -3.24380118e-03
-7.15698659e-01 -1.02048993e+00 7.08671033e-01 9.17116702e-01
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2.78516510e-03 1.03276205e+00 5.54898441e-01 -3.06087017... | [9.421154975891113, 8.835136413574219] |
2e0e4088-4a82-4bbf-a864-4ad28218aa07 | systematic-generalization-on-gscan-what-is | 2109.12243 | null | https://arxiv.org/abs/2109.12243v1 | https://arxiv.org/pdf/2109.12243v1.pdf | Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? | We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of the original benchmark can be solved by commonly used methods in multi-modal research. We find that a general-purpose Transformer-based mod... | ['Fei Sha', 'Peter Shaw', 'BoWen Zhang', 'Hexiang Hu', 'Linlu Qiu'] | 2021-09-25 | null | https://aclanthology.org/2021.emnlp-main.166 | https://aclanthology.org/2021.emnlp-main.166.pdf | emnlp-2021-11 | ['systematic-generalization'] | ['reasoning'] | [ 2.02930406e-01 2.65786797e-01 -5.93372062e-02 -4.37613100e-01
-1.04534066e+00 -8.90528262e-01 6.97689354e-01 3.09629608e-02
-1.90769419e-01 5.08234918e-01 6.62215352e-01 -5.58130980e-01
-1.43883646e-01 -6.06698871e-01 -9.84800279e-01 -2.72886485e-01
1.16919324e-01 4.34883267e-01 9.41983834e-02 -7.33319044... | [10.514466285705566, 1.9496873617172241] |
27bc4b52-094c-4c9a-b01d-c16ba7394a4c | out-of-the-box-reasoning-with-graph | 1811.00538 | null | http://arxiv.org/abs/1811.00538v1 | http://arxiv.org/pdf/1811.00538v1.pdf | Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering | Accurately answering a question about a given image requires combining
observations with general knowledge. While this is effortless for humans,
reasoning with general knowledge remains an algorithmic challenge. To advance
research in this direction a novel `fact-based' visual question answering
(FVQA) task has been in... | ['Alexander G. Schwing', 'Svetlana Lazebnik', 'Medhini Narasimhan'] | 2018-11-01 | out-of-the-box-reasoning-with-graph-1 | http://papers.nips.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering | http://papers.nips.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering.pdf | neurips-2018-12 | ['factual-visual-question-answering'] | ['computer-vision'] | [ 2.58887231e-01 6.57417119e-01 1.53664008e-01 -4.73538011e-01
-1.00718343e+00 -7.36831963e-01 7.10783541e-01 7.67375231e-01
-2.75727570e-01 6.92594409e-01 6.26026168e-02 -5.36736012e-01
5.17423749e-02 -9.55648601e-01 -9.58787382e-01 -3.99189174e-01
1.42534211e-01 6.52408183e-01 5.88431954e-01 -8.18692967... | [10.760616302490234, 1.8061845302581787] |
db64597d-1d7b-4d09-9966-0ad25ad5f536 | workshop-on-autonomous-driving-at-cvpr-2021 | 2108.04230 | null | https://arxiv.org/abs/2108.04230v1 | https://arxiv.org/pdf/2108.04230v1.pdf | Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge | In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YOLOX. On the Argoverse-HD dataset, our system achieves 41.0 streaming AP, which surpassed second place by 7.8/6.1 on detection-only track/ful... | ['Jian Sun', 'Xuming He', 'Zeming Li', 'Zheng Ge', 'Songtao Liu', 'Lin Song', 'Songyang Zhang'] | 2021-07-27 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-7.39379048e-01 -1.05414279e-01 -1.85615569e-01 -3.76750827e-01
-6.88772023e-01 -3.93663853e-01 1.71749696e-01 -6.73520640e-02
-7.44088769e-01 5.14647365e-01 -4.01316702e-01 -2.93516338e-01
3.87902796e-01 -7.40871668e-01 -1.14817464e+00 -5.12420177e-01
-2.47495472e-01 3.47713441e-01 7.42078900e-01 -2.36245040... | [8.50391674041748, -0.38364261388778687] |
2600da23-f268-493e-9646-d84e853d8d4a | learning-deep-graph-matching-with-channel | null | null | https://openreview.net/forum?id=rJgBd2NYPH | https://openreview.net/pdf?id=rJgBd2NYPH | Learning deep graph matching with channel-independent embedding and Hungarian attention | Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete. Until very recently, deep graph matching methods start to resort to deep networks to achieve unprecedented matching accuracy. Along this direction, this paper makes two c... | ['Runzhong Wang', 'Junchi Yan', 'Baoxin Li', 'Tianshu Yu'] | 2020-01-01 | null | null | null | iclr-2020-1 | ['hard-attention'] | ['methodology'] | [ 1.34969428e-01 5.23909807e-01 -4.02290314e-01 -2.40623206e-01
-6.59157336e-01 -3.74024928e-01 5.33696353e-01 4.79815513e-01
-3.90890926e-01 4.06076163e-01 -3.02670430e-02 -2.67012894e-01
-2.10268006e-01 -1.23253679e+00 -9.55784857e-01 -5.69265842e-01
-1.11039594e-01 6.11475289e-01 1.68817177e-01 -2.13182032... | [7.098380088806152, 6.3503737449646] |
51f9265a-4eab-4db9-bc34-cb49bdb486bb | flow-guided-recurrent-neural-encoder-for | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Flow_Guided_Recurrent_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Flow_Guided_Recurrent_CVPR_2018_paper.pdf | Flow Guided Recurrent Neural Encoder for Video Salient Object Detection | Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks. However, extending state-of-the-art saliency detectors from image to video is challenging. The performance of salient object detection suffers from object or camera motion and the dramatic change of the appea... | ['Liang Lin', 'Keze Wang', 'Tianhao Wei', 'Guanbin Li', 'Yuan Xie'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.68958288e-01 -2.58577853e-01 -3.42692852e-01 -8.31588954e-02
-3.99162889e-01 -4.26548161e-02 4.44832742e-01 -3.44132364e-01
-2.41536215e-01 7.08658993e-01 5.12557924e-01 2.02859238e-01
2.02266142e-01 -2.94030756e-01 -9.37842011e-01 -6.10997260e-01
-2.18395054e-01 -4.83370572e-01 1.12082791e+00 -4.35012579... | [9.707315444946289, -0.3270321786403656] |
fdcd56d9-133f-4d77-a546-87316e188e6c | on-the-role-of-morphological-information-for | 2302.00407 | null | https://arxiv.org/abs/2302.00407v1 | https://arxiv.org/pdf/2302.00407v1.pdf | On the Role of Morphological Information for Contextual Lemmatization | Lemmatization is a Natural Language Processing (NLP) task which consists of producing, from a given inflected word, its canonical form or lemma. Lemmatization is one of the basic tasks that facilitate downstream NLP applications, and is of particular importance for high-inflected languages. Given that the process to ob... | ['Rodrigo Agerri', 'Olia Toporkov'] | 2023-02-01 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [ 1.34981006e-01 6.58881143e-02 -6.10987544e-02 -2.40141705e-01
-5.58874547e-01 -1.18476784e+00 5.33947110e-01 7.34011889e-01
-8.02594900e-01 6.92058146e-01 5.56480050e-01 -1.06121337e+00
-6.89862221e-02 -9.51606452e-01 -4.98335034e-01 -4.28176939e-01
1.58454791e-01 5.23500443e-01 9.50046778e-02 -4.42551047... | [10.44703197479248, 9.981082916259766] |
3cea09f4-a62b-4dff-86db-e18cb0c8d172 | representation-matters-the-game-of-chess | 2304.14918 | null | https://arxiv.org/abs/2304.14918v1 | https://arxiv.org/pdf/2304.14918v1.pdf | Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers | While transformers have gained the reputation as the "Swiss army knife of AI", no one has challenged them to master the game of chess, one of the classical AI benchmarks. Simply using vision transformers (ViTs) within AlphaZero does not master the game of chess, mainly because ViTs are too slow. Even making them more e... | ['Kristian Kersting', 'Jannis Blüml', 'Johannes Czech'] | 2023-04-28 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-2.31320754e-01 3.38038169e-02 2.66180843e-01 2.32900903e-02
-2.41286904e-01 -8.44365001e-01 5.31703770e-01 -1.02367334e-01
-8.60730171e-01 7.11943626e-01 -2.34527677e-01 -8.87574196e-01
-1.14659235e-01 -8.45695972e-01 -5.85624635e-01 -3.56206864e-01
1.97305396e-01 5.24114072e-01 8.22864294e-01 -1.03349698... | [3.444587469100952, 1.432051658630371] |
45ec418a-266e-4a51-989f-382c70cbdb36 | few-shot-keypoint-detection-with-uncertainty | 2112.06183 | null | https://arxiv.org/abs/2112.06183v3 | https://arxiv.org/pdf/2112.06183v3.pdf | Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species | Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their heatmaps, tailored to specific body parts, cannot recognize novel keypoints (keypoints not labelled for training) on unseen species. We rais... | ['Piotr Koniusz', 'Changsheng Lu'] | 2021-12-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-4.30705771e-03 -1.43963113e-01 -2.83174157e-01 -8.38093907e-02
-1.13114786e+00 -8.36050212e-01 9.03997779e-01 4.31839645e-01
-4.20902342e-01 5.04394293e-01 -1.96095631e-01 4.75050211e-01
-3.48505020e-01 -3.32603335e-01 -1.00508106e+00 -6.85218573e-01
-1.79549828e-01 6.39642298e-01 8.19838226e-01 9.83368754... | [7.819828987121582, -2.172847270965576] |
2da6765b-d547-400f-ae86-1345bbeec6f4 | effective-parallel-corpus-mining-using | 1807.11906 | null | http://arxiv.org/abs/1807.11906v2 | http://arxiv.org/pdf/1807.11906v2.pdf | Effective Parallel Corpus Mining using Bilingual Sentence Embeddings | This paper presents an effective approach for parallel corpus mining using
bilingual sentence embeddings. Our embedding models are trained to produce
similar representations exclusively for bilingual sentence pairs that are
translations of each other. This is achieved using a novel training method that
introduces hard ... | ['Yun-Hsuan Sung', 'Keith Stevens', 'Yinfei Yang', 'Qinlan Shen', 'Brian Strope', 'Daniel Cer', 'Mandy Guo', 'Heming Ge', 'Ray Kurzweil', 'Noah Constant', 'Gustavo Hernandez Abrego'] | 2018-07-31 | effective-parallel-corpus-mining-using-1 | https://aclanthology.org/W18-6317 | https://aclanthology.org/W18-6317.pdf | ws-2018-10 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 2.49892309e-01 3.53144407e-01 -2.65875936e-01 -2.53910094e-01
-1.34881926e+00 -7.25534797e-01 1.01425958e+00 5.51740110e-01
-9.18708444e-01 7.55759001e-01 6.27018392e-01 -7.40596354e-01
1.93047211e-01 -7.84820378e-01 -8.05178583e-01 -1.06276438e-01
2.72191972e-01 9.12184477e-01 -2.11235687e-01 -6.82362139... | [11.178882598876953, 10.105116844177246] |
fa8679d0-d7f8-4497-9a6f-44500c70947f | deep-sequence-learning-for-video-anticipation | 2010.04368 | null | https://arxiv.org/abs/2010.04368v1 | https://arxiv.org/pdf/2010.04368v1.pdf | Deep Sequence Learning for Video Anticipation: From Discrete and Deterministic to Continuous and Stochastic | Video anticipation is the task of predicting one/multiple future representation(s) given limited, partial observation. This is a challenging task due to the fact that given limited observation, the future representation can be highly ambiguous. Based on the nature of the task, video anticipation can be considered from ... | ['Sadegh Aliakbarian'] | 2020-10-09 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 7.72935987e-01 2.51907617e-01 -1.29373997e-01 -3.36876094e-01
-3.00243467e-01 -4.26139265e-01 9.81444895e-01 1.08485095e-01
1.17470212e-01 5.87154150e-01 6.68560445e-01 -5.74026257e-02
-1.66361645e-01 -3.71709913e-01 -5.43241858e-01 -7.19431341e-01
-2.10190386e-01 2.21447319e-01 1.22837745e-01 7.87883066... | [8.058835983276367, 0.2620854079723358] |
3d56acee-8f69-4c09-a47c-2c6a0846293e | symmetric-shape-preserving-autoencoder-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.pdf | Symmetric Shape-Preserving Autoencoder for Unsupervised Real Scene Point Cloud Completion | Unsupervised completion of real scene objects is of vital importance but still remains extremely challenging in preserving input shapes, predicting accurate results, and adapting to multi-category data. To solve these problems, we propose in this paper an Unsupervised Symmetric Shape-Preserving Autoencoding Network... | ['Yanwen Guo', 'Chongjun Wang', 'Jie Guo', 'Pengxiao Guo', 'Yinuo Chen', 'Changfeng Ma'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 2.23082289e-01 1.46500006e-01 1.58222198e-01 -5.31604886e-01
-6.08315587e-01 -5.84545016e-01 7.22725749e-01 -1.84872746e-01
2.40235757e-02 2.75078326e-01 1.71831116e-01 2.33246192e-01
-3.13988209e-01 -9.56022561e-01 -9.37306404e-01 -7.22776532e-01
2.44293734e-01 1.15756035e+00 2.01511845e-01 1.29639646... | [8.325148582458496, -3.4077160358428955] |
b4821170-2966-482d-b6e1-68b5d8c7178a | multilingual-text-classification-for | 2112.01705 | null | https://arxiv.org/abs/2112.01705v1 | https://arxiv.org/pdf/2112.01705v1.pdf | Multilingual Text Classification for Dravidian Languages | As the fourth largest language family in the world, the Dravidian languages have become a research hotspot in natural language processing (NLP). Although the Dravidian languages contain a large number of languages, there are relatively few public available resources. Besides, text classification task, as a basic task o... | ['Lianxi Wang', 'Shengyi Jiang', 'Kanoksak Wattanachote', 'Nankai Lin', 'Xiaotian Lin'] | 2021-12-03 | null | null | null | null | ['multilingual-text-classification'] | ['miscellaneous'] | [-1.72513232e-01 -2.39678085e-01 -1.75680354e-01 -1.72438905e-01
-5.87750614e-01 -4.94819760e-01 5.71835637e-01 1.33148447e-01
-6.74893618e-01 7.66754270e-01 1.85101852e-01 -4.57259744e-01
2.24616721e-01 -9.55969214e-01 -3.13199967e-01 -6.41341686e-01
4.52897191e-01 5.21245182e-01 -1.41713664e-01 -5.78885078... | [10.550395011901855, 9.865342140197754] |
c414d32d-f9d7-4e4e-804f-c27ace6d8456 | statistical-qos-provisioning-analysis-and | 2302.10092 | null | https://arxiv.org/abs/2302.10092v4 | https://arxiv.org/pdf/2302.10092v4.pdf | Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective | In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging a... | ['Chenwu Zhang', 'Langtian Qin', 'Chang Wen Chen', 'Hancheng Lu', 'Yuang Chen'] | 2023-02-20 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-3.84754948e-02 8.02139789e-02 -4.91694301e-01 6.11187741e-02
-7.47212350e-01 -3.38579476e-01 -1.99229464e-01 -9.89213884e-02
-2.47398645e-01 1.27295363e+00 -1.98607400e-01 -8.50023568e-01
-6.28123105e-01 -6.35604203e-01 -3.14609647e-01 -1.20449424e+00
-6.51081920e-01 -4.00422186e-01 -4.15599525e-01 5.20228408... | [6.114058494567871, 1.463743805885315] |
7d0de619-07a0-41df-b4a1-d0b48be7710e | potential-convolution-embedding-point-clouds | 2104.01754 | null | https://arxiv.org/abs/2104.01754v1 | https://arxiv.org/pdf/2104.01754v1.pdf | Potential Convolution: Embedding Point Clouds into Potential Fields | Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications, such as shape classification, scene segmentation and so on. However, they still suffer from some drawbacks. For continuous kernels, the ina... | ['Kai Xu', 'Yao Duan', 'Duo Li', 'Jun Li', 'Haowen Deng', 'Dengsheng Chen'] | 2021-04-05 | null | null | null | null | ['3d-shape-retrieval', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [-7.99161419e-02 -4.51103896e-01 4.87369709e-02 -4.65429306e-01
-3.94518763e-01 -5.20958126e-01 4.47009623e-01 3.10351461e-01
-3.96820098e-01 3.19283664e-01 -3.24467689e-01 -4.25348908e-01
-1.44884363e-01 -1.09599888e+00 -6.80875838e-01 -7.93050110e-01
-9.16347876e-02 1.62226766e-01 5.60545206e-01 -1.04847867... | [7.973772048950195, -3.587324619293213] |
409c3dae-1297-488b-b613-4b338d22b3d4 | end-to-end-multihop-retrieval-for | 2106.00200 | null | https://arxiv.org/abs/2106.00200v2 | https://arxiv.org/pdf/2106.00200v2.pdf | Iterative Hierarchical Attention for Answering Complex Questions over Long Documents | We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop question-answering (QA) systems, at each step, DocHopper uses a query $q$ to attend to information from a document, combines this ``retrieved'' in... | ['Ruslan Salakhutdinov', 'William W. Cohen', 'Haitian Sun'] | 2021-06-01 | iterative-hierarchical-attention-for | https://openreview.net/forum?id=EVqFdCB5PfV | https://openreview.net/pdf?id=EVqFdCB5PfV | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 7.36687332e-02 3.72768104e-01 2.58900404e-01 -2.43241146e-01
-1.69220531e+00 -9.00917649e-01 3.18758577e-01 4.98428732e-01
-6.22962654e-01 6.28798604e-01 3.84461939e-01 -6.28973246e-01
-5.24569452e-01 -1.18242550e+00 -9.95831251e-01 -2.80393094e-01
1.47878779e-02 1.14781857e+00 7.51821935e-01 -5.48406839... | [11.192288398742676, 7.8630900382995605] |
3683418c-bacc-4a8e-94cc-34e2c16ed463 | micron-bert-bert-based-facial-micro | 2304.03195 | null | https://arxiv.org/abs/2304.03195v1 | https://arxiv.org/pdf/2304.03195v1.pdf | Micron-BERT: BERT-based Facial Micro-Expression Recognition | Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief period, i.e., 0.25 to 0.5 seconds. Recent advances in pre-training deep Bidirectional Transformers (BERT) have significantly improved self-sup... | ['Khoa Luu', 'Han-Seok Seo', 'Susan Gauch', 'Xin Li', 'Chi Nhan Duong', 'Xuan-Bac Nguyen'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['micro-expression-recognition'] | ['computer-vision'] | [-8.04113373e-02 -1.96074829e-01 -9.92534161e-02 -6.41068578e-01
-6.94649220e-01 -1.15207456e-01 1.46095142e-01 -4.48348284e-01
-3.56136441e-01 5.96879900e-01 -1.48957267e-01 2.33689666e-01
4.78174418e-01 -3.88751358e-01 -7.57055879e-01 -8.35003376e-01
9.16621909e-02 1.74177240e-03 -2.82292604e-01 -4.44896311... | [13.619549751281738, 1.7189652919769287] |
efeb9085-a65e-4ab9-a372-f424f9d20f17 | wide-and-deep-volumetric-residual-networks | 1710.01217 | null | http://arxiv.org/abs/1710.01217v1 | http://arxiv.org/pdf/1710.01217v1.pdf | Wide and deep volumetric residual networks for volumetric image classification | 3D shape models that directly classify objects from 3D information have
become more widely implementable. Current state of the art models rely on deep
convolutional and inception models that are resource intensive. Residual neural
networks have been demonstrated to be easier to optimize and do not suffer from
vanishing... | ['Eric Oermann', 'Samuel Cho', 'Anthony Costa', 'Marcus Badgeley', 'Varun Arvind'] | 2017-09-18 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-2.88749874e-01 8.02241713e-02 -1.25149578e-01 -5.25367260e-01
-1.86121941e-01 -5.78828156e-01 5.98681331e-01 -3.10098290e-01
-1.98556140e-01 3.93783189e-02 -1.70016028e-02 -6.80225551e-01
-7.24469647e-02 -8.31405342e-01 -7.94705391e-01 -1.94805786e-01
-2.57383645e-01 6.80452704e-01 2.08796144e-01 -2.45204400... | [8.139060020446777, -3.7552883625030518] |
bb1e0b88-e01c-4410-8616-0581f495d2a7 | structural-rnn-deep-learning-on-spatio | 1511.05298 | null | http://arxiv.org/abs/1511.05298v3 | http://arxiv.org/pdf/1511.05298v3.pdf | Structural-RNN: Deep Learning on Spatio-Temporal Graphs | Deep Recurrent Neural Network architectures, though remarkably capable at
modeling sequences, lack an intuitive high-level spatio-temporal structure.
That is while many problems in computer vision inherently have an underlying
high-level structure and can benefit from it. Spatio-temporal graphs are a
popular tool for i... | ['Ashutosh Saxena', 'Silvio Savarese', 'Amir R. Zamir', 'Ashesh Jain'] | 2015-11-17 | structural-rnn-deep-learning-on-spatio-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Jain_Structural-RNN_Deep_Learning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Jain_Structural-RNN_Deep_Learning_CVPR_2016_paper.pdf | cvpr-2016-6 | ['human-pose-forecasting'] | ['computer-vision'] | [ 3.88127744e-01 7.69136772e-02 -1.83659762e-01 -3.61693144e-01
-2.07431883e-01 -4.91544038e-01 8.24926138e-01 -1.23233281e-01
-2.10241809e-01 3.90837282e-01 1.55635387e-01 -6.15458369e-01
-3.89615983e-01 -6.58806741e-01 -9.82033670e-01 -8.95427227e-01
-3.87080967e-01 3.34475309e-01 2.26986334e-01 -4.40324664... | [8.648189544677734, 0.411617249250412] |
9c663ded-310b-4599-be62-a2c8a8ad0705 | un-reasonable-allure-of-ante-hoc | 2306.02312 | null | https://arxiv.org/abs/2306.02312v2 | https://arxiv.org/pdf/2306.02312v2.pdf | (Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility | Ante-hoc interpretability has become the holy grail of explainable artificial intelligence for high-stakes domains such as healthcare; however, this notion is elusive, lacks a widely-accepted definition and depends on the operational context. It can refer to predictive models whose structure adheres to domain-specific ... | ['Julia E. Vogt', 'Kacper Sokol'] | 2023-06-04 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 6.31734133e-01 1.22078812e+00 -3.88432443e-01 -6.86538100e-01
-3.17980528e-01 -7.01231182e-01 7.45297492e-01 3.67595106e-01
-2.21592620e-01 6.97521985e-01 3.69145274e-01 -9.92232621e-01
-9.40686464e-01 -2.71255910e-01 -3.48448068e-01 -3.74752969e-01
2.50470072e-01 7.58238196e-01 -4.60525721e-01 1.46462126... | [8.76712417602539, 5.90104866027832] |
99801714-71c0-419b-a702-002d92b433e7 | facial-movement-synergies-and-action-unit | 2008.08791 | null | https://arxiv.org/abs/2008.08791v1 | https://arxiv.org/pdf/2008.08791v1.pdf | Facial movement synergies and Action Unit detection from distal wearable Electromyography and Computer Vision | Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalizes on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction ... | ['Saho Ayabe-Kanamura', 'Monica Perusquia-Hernandez', 'Felix Dollack', 'Kenji Suzuki', 'Chun Kwang Tan', 'Shushi Namba'] | 2020-08-20 | null | null | null | null | ['action-unit-detection', 'electromyography-emg'] | ['computer-vision', 'medical'] | [ 2.43343472e-01 2.10736364e-01 -2.56553918e-01 1.04552798e-01
-8.81986439e-01 -5.42908907e-01 2.50489339e-02 -5.91993630e-01
-2.74709046e-01 4.50243413e-01 1.94164723e-01 2.83121407e-01
-2.73686886e-01 -4.02891599e-02 -4.93933111e-01 -9.06854153e-01
-2.28366271e-01 2.08790526e-01 -4.38822746e-01 7.30472654... | [6.872739791870117, 0.21440060436725616] |
2aa4b68f-300d-4a45-8653-31dcb4403749 | poem-polarization-of-embeddings-for-domain | 2305.13046 | null | https://arxiv.org/abs/2305.13046v1 | https://arxiv.org/pdf/2305.13046v1.pdf | POEM: Polarization of Embeddings for Domain-Invariant Representations | Handling out-of-distribution samples is a long-lasting challenge for deep visual models. In particular, domain generalization (DG) is one of the most relevant tasks that aims to train a model with a generalization capability on novel domains. Most existing DG approaches share the same philosophy to minimize the discrep... | ['Sung Whan Yoon', 'Sang-Yeong Jo'] | 2023-05-22 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.79166034e-01 -1.69994414e-01 -4.38276589e-01 -5.44829011e-01
-2.21855327e-01 -6.97642982e-01 7.95222938e-01 7.39755407e-02
-1.43131822e-01 6.75729454e-01 3.38518530e-01 2.57903002e-02
-3.89237016e-01 -8.25854659e-01 -5.31943083e-01 -7.27710664e-01
-3.69893853e-03 3.05456012e-01 3.85582209e-01 -4.29706454... | [10.329095840454102, 3.041821002960205] |
780810c6-cf8c-4512-9328-4af7f866b944 | generalizable-low-resource-activity | 2306.04641 | null | https://arxiv.org/abs/2306.04641v2 | https://arxiv.org/pdf/2306.04641v2.pdf | Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning | Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a major bottleneck for training a generalizable HAR model, which assists customization and optimization of online web applications. However, it... | ['Yiqiang Chen', 'Xing Xie', 'Yongchun Zhu', 'Wang Lu', 'Shuo Ma', 'Jindong Wang', 'Xin Qin'] | 2023-05-25 | null | null | null | null | ['human-activity-recognition', 'time-series-classification', 'human-activity-recognition'] | ['computer-vision', 'time-series', 'time-series'] | [-8.48033279e-02 -5.66210806e-01 -3.32858801e-01 -2.64858216e-01
-4.38217372e-01 -3.62214446e-01 1.87705532e-01 -2.23461539e-01
-2.70119518e-01 6.78698778e-01 4.26048607e-01 3.49735111e-01
-2.36183539e-01 -6.36157632e-01 -5.68150759e-01 -8.81068766e-01
3.12953927e-02 1.57026425e-01 -3.92068066e-02 6.43069819... | [7.919923782348633, 0.9191197752952576] |
ee25c0b6-d79c-4815-ac2c-461c44b1d209 | multispectral-fusion-for-object-detection | 2009.12664 | null | https://arxiv.org/abs/2009.12664v1 | https://arxiv.org/pdf/2009.12664v1.pdf | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks | Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a ne... | ['Sébastien Lefevre', 'Heng Zhang', 'Elisa Fromont', 'Bruno Avignon'] | 2020-09-26 | null | null | null | null | ['multispectral-object-detection'] | ['computer-vision'] | [ 4.88003731e-01 -8.84326100e-01 4.20894653e-01 -3.00855070e-01
-6.19204462e-01 -8.96791697e-01 6.49543166e-01 2.76507605e-02
-4.13797170e-01 4.98772264e-01 -2.94325531e-01 -2.76280850e-01
-4.33882385e-01 -8.02336931e-01 -5.02149940e-01 -9.86544728e-01
1.42104566e-01 -2.88777590e-01 2.61434764e-01 -3.02585840... | [10.11235523223877, -1.7519246339797974] |
6b7b99d7-48d5-4544-a5a4-c7111730bb2a | deepedge-a-multi-scale-bifurcated-deep | 1412.1123 | null | http://arxiv.org/abs/1412.1123v3 | http://arxiv.org/pdf/1412.1123v3.pdf | DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection | Contour detection has been a fundamental component in many image segmentation
and object detection systems. Most previous work utilizes low-level features
such as texture or saliency to detect contours and then use them as cues for a
higher-level task such as object detection. However, we claim that recognizing
objects... | ['Lorenzo Torresani', 'Gedas Bertasius', 'Jianbo Shi'] | 2014-12-02 | deepedge-a-multi-scale-bifurcated-deep-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Bertasius_DeepEdge_A_Multi-Scale_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Bertasius_DeepEdge_A_Multi-Scale_2015_CVPR_paper.pdf | cvpr-2015-6 | ['contour-detection'] | ['computer-vision'] | [ 5.98988652e-01 2.85077184e-01 -1.70535848e-01 -4.23504919e-01
-8.12690079e-01 -7.28464663e-01 4.79415178e-01 5.03490686e-01
-6.93863630e-01 1.09710619e-01 -2.73957163e-01 -3.15367967e-01
4.43124145e-01 -1.00367820e+00 -7.22668409e-01 -6.30880713e-01
-1.60897285e-01 4.23129082e-01 1.09664524e+00 1.42188873... | [9.467556953430176, 0.2752316892147064] |
fdea7e24-915a-4bed-a657-c8c9b073d7aa | finetuning-for-sarcasm-detection-with-a | 2212.12213 | null | https://arxiv.org/abs/2212.12213v1 | https://arxiv.org/pdf/2212.12213v1.pdf | Finetuning for Sarcasm Detection with a Pruned Dataset | Sarcasm is a form of irony that involves saying or writing something that is opposite or opposite to what one really means, often in a humorous or mocking way. It is often used to mock or mock someone or something, or to be humorous or amusing. Sarcasm is usually conveyed through tone of voice, facial expressions, or o... | ['Sanjana Dulam', 'Priyank Bhandia', 'Ishita Goyal'] | 2022-12-23 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [ 3.69906798e-02 2.82471120e-01 -1.82151452e-01 -3.23327422e-01
-1.12365007e-01 -6.33313298e-01 9.68214333e-01 7.59647340e-02
-3.56885403e-01 6.47088110e-01 7.89027333e-01 2.03098021e-02
4.62842762e-01 -4.06146824e-01 -1.19521022e-01 -3.36475253e-01
6.08566701e-01 4.60576385e-01 -1.74011111e-01 -6.40490830... | [9.057194709777832, 10.778542518615723] |
6f2f8cfa-9ebc-4a48-a3a0-5ab112cd966f | free-lunch-for-surgical-video-understanding | 2205.09292 | null | https://arxiv.org/abs/2205.09292v2 | https://arxiv.org/pdf/2205.09292v2.pdf | Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions | Self-supervised learning has witnessed great progress in vision and NLP; recently, it also attracted much attention to various medical imaging modalities such as X-ray, CT, and MRI. Existing methods mostly focus on building new pretext self-supervision tasks such as reconstruction, orientation, and masking identificati... | ['Xiaomeng Li', 'Ziwei Liu', 'Xinpeng Ding'] | 2022-05-19 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 3.61860752e-01 2.45099530e-01 -8.34041297e-01 -5.23515284e-01
-9.59852993e-01 -3.48117024e-01 2.69268364e-01 1.50986210e-01
-3.87548715e-01 6.18227124e-01 5.06316960e-01 -3.11797768e-01
4.05103825e-02 -5.15835226e-01 -8.66504371e-01 -8.51410151e-01
2.21884832e-01 2.69291401e-01 4.92442632e-03 5.94600625... | [14.31570816040039, -2.9327642917633057] |
89e84eed-354f-4724-9d49-ed32c0cbd540 | content-explorer-recommending-novel-entities | null | null | https://aclanthology.org/D18-1374 | https://aclanthology.org/D18-1374.pdf | Content Explorer: Recommending Novel Entities for a Document Writer | Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivit... | ['Richard Zens', 'Michal Lukasik'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['extreme-multi-label-classification'] | ['methodology'] | [ 9.77198854e-02 -1.25653565e-01 -4.75452453e-01 -5.25358081e-01
-8.22108507e-01 -6.16112769e-01 7.45658636e-01 2.77692586e-01
-5.39591074e-01 5.59298158e-01 3.85099165e-02 -4.23606873e-01
-3.38239521e-01 -6.39771104e-01 -4.60909933e-01 -5.44961214e-01
4.84556377e-01 7.93715656e-01 -3.98289859e-02 -1.00805517... | [10.159745216369629, 7.94735860824585] |
b05445fa-6bf2-4238-afd0-b6cee75711ce | leveraging-advantages-of-interactive-and-non | 2111.01992 | null | https://arxiv.org/abs/2111.01992v1 | https://arxiv.org/pdf/2111.01992v1.pdf | Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval | Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively. From the retrieval accuracy and computational efficiency perspectives, each model has its... | ['Haibo Zhang', 'Dayiheng Liu', 'Tianchi Bi', 'Xiaoyu Lv', 'Baosong Yang', 'Linlong Xu'] | 2021-11-03 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.87711942e-01 -4.41718578e-01 -6.47140622e-01 -5.92905320e-02
-1.33013892e+00 -8.68159831e-01 1.16527903e+00 3.38742107e-01
-9.44465280e-01 3.29801261e-01 3.26306045e-01 -2.89354950e-01
-2.70166129e-01 -5.70647299e-01 -4.74401414e-01 -3.57617766e-01
7.42683783e-02 4.32893008e-01 1.34672299e-01 -6.44010544... | [11.294699668884277, 9.73925495147705] |
e20a8673-62e1-43ad-a4f3-32c9d8b18f9d | neurologic-decoding-un-supervised-neural-text | 2010.12884 | null | https://arxiv.org/abs/2010.12884v2 | https://arxiv.org/pdf/2010.12884v2.pdf | NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn ... | ['Yejin Choi', 'Chandra Bhagavatula', 'Ronan Le Bras', 'Rowan Zellers', 'Peter West', 'Ximing Lu'] | 2020-10-24 | null | https://aclanthology.org/2021.naacl-main.339 | https://aclanthology.org/2021.naacl-main.339.pdf | naacl-2021-4 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 6.03078485e-01 5.66038609e-01 -5.41150451e-01 -4.71590817e-01
-8.68371427e-01 -6.92712784e-01 8.18566561e-01 -1.80826709e-01
-2.58623838e-01 1.25006580e+00 4.55389351e-01 -6.59677684e-01
1.83130890e-01 -1.13816512e+00 -9.82136309e-01 -2.00703427e-01
3.38002533e-01 1.03216636e+00 -6.70248717e-02 -3.64783913... | [11.421944618225098, 9.011964797973633] |
9d234ba5-1ecd-4b06-8534-2508233e9381 | towards-active-learning-for-action-spotting | 2304.04220 | null | https://arxiv.org/abs/2304.04220v1 | https://arxiv.org/pdf/2304.04220v1.pdf | Towards Active Learning for Action Spotting in Association Football Videos | Association football is a complex and dynamic sport, with numerous actions occurring simultaneously in each game. Analyzing football videos is challenging and requires identifying subtle and diverse spatio-temporal patterns. Despite recent advances in computer vision, current algorithms still face significant challenge... | ['Marc Van Droogenbroeck', 'Bernard Ghanem', 'Kerry Peek', 'Andreas Serner', 'Johsan Billingham', 'Julia Georgieva', 'Anthony Cioppa', 'Silvio Giancola'] | 2023-04-09 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 3.48308891e-01 -1.87668800e-01 -7.93477237e-01 -1.93762705e-01
-9.19579327e-01 -6.30838871e-01 2.99714416e-01 2.77612537e-01
-9.25340176e-01 5.99755347e-01 3.80456448e-01 1.69986069e-01
-2.28582144e-01 -5.44809937e-01 -6.81598365e-01 -5.67678630e-01
-5.88634372e-01 5.05148113e-01 9.77796376e-01 -1.24165609... | [8.094093322753906, 0.30513760447502136] |
6affd99d-aabe-40e0-a3ad-1e93892a252b | a-paired-sparse-representation-model-for | 1910.02192 | null | https://arxiv.org/abs/1910.02192v1 | https://arxiv.org/pdf/1910.02192v1.pdf | A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample | Sparse representation-based classification (SRC) has been shown to achieve a high level of accuracy in face recognition (FR). However, matching faces captured in unconstrained video against a gallery with a single reference facial still per individual typically yields low accuracy. For improved robustness to intra-clas... | ['Fania Mokhayeri', 'Eric Granger'] | 2019-10-05 | null | null | null | null | ['robust-face-recognition', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 4.31103587e-01 -1.81829274e-01 -2.70961616e-02 -5.26764512e-01
-9.08593357e-01 -4.46466237e-01 5.34843624e-01 -6.39901280e-01
9.30881798e-02 7.13689983e-01 -4.98097856e-03 7.02064633e-01
1.64810047e-02 -3.26906532e-01 -7.80068517e-01 -9.13921893e-01
2.44393319e-01 4.05041456e-01 -3.03121805e-01 -2.26029292... | [12.882552146911621, 0.33724281191825867] |
25cc882c-67f9-4f03-a682-7cb2640ee7b6 | polar-shapelets | astro-ph/0408445 | null | https://arxiv.org/abs/astro-ph/0408445v3 | https://arxiv.org/pdf/astro-ph/0408445v3.pdf | Polar Shapelets | The shapelets method for image analysis is based upon the decomposition of localised objects into a series of orthogonal components with convenient mathematical properties. We extend the "Cartesian shapelet" formalism from earlier work, and construct "polar shapelet" basis functions that separate an image into componen... | ['Alexandre Refregier', 'Richard Massey'] | 2004-08-24 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 2.78305332e-03 -3.43840241e-01 2.80926049e-01 -9.88184884e-02
-3.48667920e-01 -9.35254335e-01 9.20828462e-01 -2.96748161e-01
-2.02112138e-01 3.79798353e-01 -1.64822564e-01 -3.01995963e-01
-5.48595548e-01 -6.95695162e-01 -7.82484189e-02 -1.01604533e+00
6.11914210e-02 5.61176479e-01 3.61891776e-01 -1.61894917... | [11.566454887390137, -2.362438440322876] |
8b06e557-5a8a-4461-a4b8-93eb1fdc3a37 | complementing-gpt-3-with-few-shot-sequence-to | 2305.14202 | null | https://arxiv.org/abs/2305.14202v1 | https://arxiv.org/pdf/2305.14202v1.pdf | Complementing GPT-3 with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata | As the largest knowledge base, Wikidata is a massive source of knowledge, complementing large language models with well-structured data. In this paper, we present WikiWebQuestions, a high-quality knowledge base question answering benchmark for Wikidata. This new benchmark uses real-world human data with SPARQL annotati... | ['Monica S. Lam', 'Sina J. Semnani', 'Meng-Hsi Wu', 'Theo Culhane', 'Silei Xu'] | 2023-05-23 | null | null | null | null | ['knowledge-base-question-answering', 'semantic-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-3.33928347e-01 6.15844190e-01 -8.79173577e-02 -2.53286600e-01
-1.32864070e+00 -9.36159849e-01 2.55464077e-01 2.21567988e-01
-5.73694885e-01 1.07397687e+00 3.33101600e-01 -2.00225636e-01
-5.31240880e-01 -1.29647517e+00 -9.85047936e-01 4.00437564e-02
4.26076651e-01 1.17171955e+00 8.58305335e-01 -4.97683555... | [10.392068862915039, 7.942034721374512] |
808e20fd-e2bf-421c-9c28-6ac581bc22f2 | deep-learning-meets-liveness-detection-recent | 2112.14796 | null | https://arxiv.org/abs/2112.14796v1 | https://arxiv.org/pdf/2112.14796v1.pdf | Deep Learning meets Liveness Detection: Recent Advancements and Challenges | Facial biometrics has been recently received tremendous attention as a convenient replacement for traditional authentication systems. Consequently, detecting malicious attempts has found great significance, leading to extensive studies in face anti-spoofing~(FAS),i.e., face presentation attack detection. Deep feature l... | ['Mohammad Akbari', 'Kooshan Hashemifard', 'Marzieh Oghbaie', 'Arian Sabaghi'] | 2021-12-29 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.15100265e-01 -5.49970210e-01 -2.80283362e-01 -3.74847651e-01
-2.26205632e-01 -3.79401058e-01 8.20257604e-01 -1.63910404e-01
-1.35634631e-01 4.02750373e-01 -1.65457740e-01 -1.10442616e-01
-8.03121179e-02 -8.27801526e-01 -1.07245058e-01 -9.62850749e-01
-8.17780867e-02 -1.63411900e-01 -1.06327549e-01 -3.23774040... | [13.040496826171875, 1.1555067300796509] |
1d24d511-84b0-46ea-8abe-dc41ead0515a | cross-domain-data-integration-for-named | 2110.08228 | null | https://arxiv.org/abs/2110.08228v1 | https://arxiv.org/pdf/2110.08228v1.pdf | Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text | Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the u... | ['Christopher Ré', 'Xiao Ling', 'Megan Leszczynski', 'Sen Wu', 'Laurel Orr', 'Maya Varma'] | 2021-10-15 | null | https://aclanthology.org/2021.findings-emnlp.388 | https://aclanthology.org/2021.findings-emnlp.388.pdf | findings-emnlp-2021-11 | ['data-integration', 'entity-disambiguation'] | ['knowledge-base', 'natural-language-processing'] | [ 6.47096410e-02 4.49357331e-01 -5.42354882e-01 -2.47783318e-01
-1.10115111e+00 -4.13072079e-01 3.52326334e-01 7.32349098e-01
-8.67386520e-01 1.31544626e+00 4.71556574e-01 -1.25942692e-01
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1.14298992e-01 7.75911570e-01 2.81708062e-01 -2.87609816... | [8.650890350341797, 8.807275772094727] |
9325ea8e-d29a-4cd4-bf7a-b6121402cc76 | heterogeneous-directed-hypergraph-neural | 2305.04228 | null | https://arxiv.org/abs/2305.04228v2 | https://arxiv.org/pdf/2305.04228v2.pdf | Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification | Code classification is a difficult issue in program understanding and automatic coding. Due to the elusive syntax and complicated semantics in programs, most existing studies use techniques based on abstract syntax tree (AST) and graph neural network (GNN) to create code representations for code classification. These t... | ['Liang Dou', 'Tiancheng Jin', 'Guang Yang'] | 2023-05-07 | null | null | null | null | ['code-classification'] | ['computer-code'] | [-3.13503265e-01 2.41378307e-01 -3.38524401e-01 -3.96932751e-01
4.24340338e-01 -5.06986260e-01 1.35128126e-01 7.64216661e-01
2.78875679e-01 2.44854465e-01 3.22675824e-01 -6.45849526e-01
-2.30472967e-01 -1.26862407e+00 -6.00735903e-01 -2.21555814e-01
-5.29761791e-01 7.77243450e-02 2.27942556e-01 -1.43834949... | [7.47296667098999, 7.85239839553833] |
fb33758d-bae4-4f53-9f51-c3bfcd4f40e4 | the-benefits-of-being-distributional-small | 2305.15703 | null | https://arxiv.org/abs/2305.15703v2 | https://arxiv.org/pdf/2305.15703v2.pdf | The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning | While distributional reinforcement learning (RL) has demonstrated empirical success, the question of when and why it is beneficial has remained unanswered. In this work, we provide one explanation for the benefits of distributional RL through the lens of small-loss bounds, which scale with the instance-dependent optima... | ['Wen Sun', 'Nathan Kallus', 'Runzhe Wu', 'Kevin Zhou', 'Kaiwen Wang'] | 2023-05-25 | null | null | null | null | ['distributional-reinforcement-learning', 'multi-armed-bandits', 'offline-rl'] | ['methodology', 'miscellaneous', 'playing-games'] | [-1.15223840e-01 3.34077418e-01 -9.03558791e-01 -2.87022263e-01
-1.60271144e+00 -6.76765561e-01 1.96656398e-02 2.74740428e-01
-4.05993074e-01 1.15293097e+00 1.38485879e-01 -5.87040961e-01
-6.01665318e-01 -6.59613550e-01 -1.33104563e+00 -1.02170753e+00
-2.90007710e-01 8.43116522e-01 -2.52377748e-01 1.95290327... | [4.3904523849487305, 3.0033984184265137] |
b668d612-f7f5-4425-ad02-a9619311b866 | robust-3d-shape-classification-via-non-local | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.pdf | Robust 3D Shape Classification via Non-Local Graph Attention Network | We introduce a non-local graph attention network (NLGAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN).... | ['Ligang Liu', 'Zhong Li', 'Shengwei Qin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-retrieval'] | ['computer-vision'] | [-2.47782767e-01 -1.99963525e-02 -1.66504025e-01 -1.15713306e-01
-3.25357795e-01 -3.32984507e-01 4.27451283e-01 1.72167621e-03
1.21996000e-01 1.08572833e-01 1.81933746e-01 6.20086268e-02
-4.34775680e-01 -1.14206624e+00 -7.04639971e-01 -8.08237076e-01
-1.25988305e-01 3.70918840e-01 1.67866275e-01 -2.59042650... | [7.949936866760254, -3.5882396697998047] |
b9df6778-ba10-4c1c-8279-9954aa4a9a45 | object-to-scene-learning-to-transfer-object | 2108.00399 | null | https://arxiv.org/abs/2108.00399v1 | https://arxiv.org/pdf/2108.00399v1.pdf | Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition | Accurate perception of the surrounding scene is helpful for robots to make reasonable judgments and behaviours. Therefore, developing effective scene representation and recognition methods are of significant importance in robotics. Currently, a large body of research focuses on developing novel auxiliary features and n... | ['Yangsheng Xu', 'Tin Lun Lam', 'Ajmal Mian', 'Liguang Zhou', 'Bo Miao'] | 2021-08-01 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.42288524e-01 -6.35171905e-02 7.78782442e-02 -6.90984666e-01
-1.44131541e-01 -1.46225378e-01 5.82256019e-01 4.22463343e-02
-3.75054508e-01 4.32664633e-01 -1.61192119e-01 2.94167902e-02
-4.71907526e-01 -9.40283537e-01 -8.54903340e-01 -6.23775840e-01
2.64132112e-01 1.68623090e-01 3.67513239e-01 -1.27176091... | [9.28484058380127, -0.8261705636978149] |
a73f223b-864a-4517-9b2d-e0e98ff88a65 | a-survey-on-deep-learning-based-architectures | 1912.10230 | null | https://arxiv.org/abs/1912.10230v5 | https://arxiv.org/pdf/1912.10230v5.pdf | A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images | Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in c... | ['Irem Ulku', 'Erdem Akagunduz'] | 2019-12-21 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 5.91665566e-01 4.66807157e-01 -3.78107168e-02 -4.70415175e-01
-4.89250422e-01 -7.88434625e-01 5.46942055e-01 9.97692496e-02
-5.97846150e-01 2.06634939e-01 -1.99595183e-01 -1.99592352e-01
-2.47296646e-01 -7.18477547e-01 -3.91355157e-01 -6.80005014e-01
-7.73552209e-02 6.67674720e-01 5.15634060e-01 -1.57043263... | [9.6470365524292, 0.3493928909301758] |
7d64dcc7-d73a-4157-a340-8837e0060653 | emergence-of-maps-in-the-memories-of-blind | 2301.13261 | null | https://arxiv.org/abs/2301.13261v1 | https://arxiv.org/pdf/2301.13261v1.pdf | Emergence of Maps in the Memories of Blind Navigation Agents | Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surp... | ['Dhruv Batra', 'Ari S. Morcos', 'Stefan Lee', 'Irfan Essa', 'Manolis Savva', 'Erik Wijmans'] | 2023-01-30 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [ 1.49293497e-01 4.00449276e-01 3.99621725e-01 2.00889513e-01
2.24900588e-01 -7.54604280e-01 7.34168291e-01 -2.45230898e-01
-6.52155817e-01 7.70944595e-01 3.21732521e-01 -5.94294190e-01
-3.96509469e-01 -9.07778680e-01 -5.53781807e-01 -9.02942955e-01
-4.92595285e-01 3.13047975e-01 3.33683223e-01 -8.57914746... | [4.354980945587158, 1.1202305555343628] |
aeddd706-842a-43f3-a14f-df98a14d04e0 | importance-filtering-with-risk-models-for | 2303.06935 | null | https://arxiv.org/abs/2303.06935v1 | https://arxiv.org/pdf/2303.06935v1.pdf | Importance Filtering with Risk Models for Complex Driving Situations | Self-driving cars face complex driving situations with a large amount of agents when moving in crowded cities. However, some of the agents are actually not influencing the behavior of the self-driving car. Filtering out unimportant agents would inherently simplify the behavior or motion planning task for the system. Th... | ['Julian Eggert', 'Malte Probst', 'Benedict Flade', 'Raphael Wenzel', 'Tim Puphal'] | 2023-03-13 | null | null | null | null | ['self-driving-cars', 'motion-planning'] | ['computer-vision', 'robots'] | [-4.29188579e-01 1.59460664e-01 -1.21068314e-01 -4.67993140e-01
-1.07759297e-01 -1.24690041e-01 6.13303661e-01 -1.77268878e-01
-6.89307153e-01 7.31960893e-01 3.52435797e-01 -3.29364002e-01
-2.24392712e-01 -1.03419244e+00 -1.38926238e-01 -7.48640656e-01
-1.38671711e-01 5.38473904e-01 8.77635419e-01 -6.64033175... | [5.760205268859863, 0.9444959163665771] |
2190aad3-da5b-470f-826d-896be5291a86 | flow-plugin-network-for-conditional | 2110.04081 | null | https://arxiv.org/abs/2110.04081v1 | https://arxiv.org/pdf/2110.04081v1.pdf | Flow Plugin Network for conditional generation | Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud generation. However, by default, we cannot control its sampling process, i.e., we cannot generate a sample with a specific set of attributes. The... | ['Maciej Zięba', 'Michał Koperski', 'Patryk Wielopolski'] | 2021-10-07 | null | null | null | null | ['conditional-image-generation', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 7.57651106e-02 3.05481493e-01 -1.86484959e-02 -3.44520569e-01
-3.26164230e-03 -4.18138862e-01 8.48745584e-01 -6.41256332e-01
9.57513377e-02 9.19350326e-01 -9.10448506e-02 -2.53637910e-01
2.77985513e-01 -1.43414235e+00 -5.81986189e-01 -5.45305014e-01
2.65975535e-01 6.98299646e-01 5.22370264e-02 3.43050361... | [8.977559089660645, -3.5940258502960205] |
846e1225-4fa7-4b39-bf40-852acfb1bd0b | multi-step-retriever-reader-interaction-for-1 | 1905.05733 | null | https://arxiv.org/abs/1905.05733v1 | https://arxiv.org/pdf/1905.05733v1.pdf | Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering | This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only requiring access to the token-level hidden representations of the reader. The retriever us... | ['Manzil Zaheer', 'Shehzaad Dhuliawala', 'Andrew McCallum', 'Rajarshi Das'] | 2019-05-14 | multi-step-retriever-reader-interaction-for | https://openreview.net/forum?id=HkfPSh05K7 | https://openreview.net/pdf?id=HkfPSh05K7 | iclr-2019-5 | ['triviaqa'] | ['miscellaneous'] | [-1.04996204e-01 6.99866533e-01 -7.34459981e-02 -3.62253666e-01
-1.77071357e+00 -9.17082548e-01 6.35071218e-01 4.28006858e-01
-5.59560478e-01 6.50863588e-01 7.52890170e-01 -4.93923843e-01
-3.50080550e-01 -1.02932000e+00 -7.79014945e-01 -2.77055085e-01
2.83173442e-01 1.37922907e+00 7.03153014e-01 -6.75424099... | [11.224189758300781, 7.9764556884765625] |
34da9303-81a7-4f9b-a5a4-ff0fefbf7847 | the-multi-modal-universe-of-fast-fashion-the | 2204.06972 | null | https://arxiv.org/abs/2204.06972v2 | https://arxiv.org/pdf/2204.06972v2.pdf | The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark | We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famou... | ['Marco Cristani', 'Berniero Scarpa', 'Matteo Denitto', 'Christian Joppi', 'Geri Skenderi'] | 2022-04-14 | null | null | null | null | ['short-observation-new-product-sales'] | ['time-series'] | [-1.93422794e-01 -2.82596022e-01 -2.12374583e-01 -4.39722359e-01
-4.72439677e-01 -6.07587099e-01 7.92309046e-01 1.43490568e-01
-2.37996072e-01 4.15860206e-01 -3.27620618e-02 2.38554955e-01
-3.54907773e-02 -9.42825317e-01 -9.24938798e-01 -7.30976641e-01
-1.38798892e-01 6.93344414e-01 -3.04527193e-01 -6.90298557... | [7.156383037567139, 2.8455698490142822] |
bcf4f771-e4f4-405d-9c64-e26d51d4c6a1 | n-ary-constituent-tree-parsing-with-recursive | null | null | https://aclanthology.org/2021.acl-long.205/ | https://aclanthology.org/2021.acl-long.205.pdf | N-ary Constituent Tree Parsing with Recursive Semi-Markov Model | In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label ins... | ['Zeqi Tan', 'Jinlong Li', 'Xin Xin'] | 2021-07-26 | null | https://aclanthology.org/2021.acl-long.205 | https://aclanthology.org/2021.acl-long.205.pdf | acl-2021-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.70053923e-01 5.04350364e-01 -3.65124047e-01 -4.20705706e-01
-4.13790405e-01 -3.98074061e-01 2.05090210e-01 4.00327206e-01
-7.74574280e-02 6.25618160e-01 1.97750609e-02 -8.08067143e-01
-1.28451770e-03 -1.01851761e+00 -5.73726118e-01 -7.84677088e-01
-4.74780053e-02 3.62496644e-01 7.45236576e-01 -9.07360911... | [10.245637893676758, 9.608823776245117] |
85b7eca6-0079-407c-bf99-0c596008b7dd | machine-learning-based-source-code | 1703.07638 | null | http://arxiv.org/abs/1703.07638v1 | http://arxiv.org/pdf/1703.07638v1.pdf | Machine Learning Based Source Code Classification Using Syntax Oriented Features | As of today the programming language of the vast majority of the published
source code is manually specified or programmatically assigned based on the
sole file extension. In this paper we show that the source code programming
language identification task can be fully automated using machine learning
techniques. We fir... | ['Shaul Zevin', 'Catherine Holzem'] | 2017-03-04 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 2.30224401e-01 -3.48507501e-02 -5.57270586e-01 -4.85463291e-01
-6.22909606e-01 -9.48159873e-01 1.77003741e-01 4.54238385e-01
-1.95672691e-01 2.44441494e-01 -2.14975953e-01 -7.23291576e-01
1.70843929e-01 -7.45864272e-01 -4.14844275e-01 -7.51995891e-02
-2.12597009e-02 3.08143020e-01 2.68704087e-01 1.18474975... | [7.669430732727051, 7.835238933563232] |
9179cf8c-df00-4cce-a8a1-52d2da643435 | on-the-applicability-of-synthetic-data-for | 2104.02815 | null | https://arxiv.org/abs/2104.02815v1 | https://arxiv.org/pdf/2104.02815v1.pdf | On the Applicability of Synthetic Data for Face Recognition | Face verification has come into increasing focus in various applications including the European Entry/Exit System, which integrates face recognition mechanisms. At the same time, the rapid advancement of biometric authentication requires extensive performance tests in order to inhibit the discriminatory treatment of tr... | ['Christoph Busch', 'Kiran Raja', 'Raghavendra Ramachandra', 'Marcel Grimmer', 'Haoyu Zhang'] | 2021-04-06 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.04599702e-01 -6.96366951e-02 3.69512767e-01 -4.42409188e-01
-5.42876542e-01 -4.74862963e-01 6.22427821e-01 -2.33753696e-01
-4.23784524e-01 7.07080662e-01 -2.74898171e-01 -3.20518374e-01
-4.73075002e-01 -6.85056329e-01 -2.05313489e-01 -9.13918495e-01
3.15719657e-02 3.20586830e-01 -7.44885206e-01 -1.75378293... | [13.038721084594727, 0.8751640915870667] |
bffb1966-f426-4e71-9372-629cb3066128 | mando-multi-level-heterogeneous-graph | 2208.13252 | null | https://arxiv.org/abs/2208.13252v2 | https://arxiv.org/pdf/2208.13252v2.pdf | MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities | Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing te... | ['Lingxiao Jiang', 'Thanh-Nam Doan', 'Daniel Kudendo', 'Zahra Ahmadi', 'Chunyao Xie', 'Nhat-Minh Nguyen', 'Hoang H. Nguyen'] | 2022-08-28 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-3.52063298e-01 3.45236510e-01 -5.26381135e-01 -8.05464238e-02
-4.25809354e-01 -1.02030218e+00 3.66645187e-01 5.39814174e-01
4.76998717e-01 -6.75065368e-02 4.44170743e-01 -7.89929688e-01
-2.40480788e-02 -1.24200165e+00 -6.14535749e-01 -2.79014647e-01
-7.22296834e-01 1.99288696e-01 5.94246507e-01 -5.26195526... | [7.094398021697998, 7.703649520874023] |
87f4c1d2-ec4e-421e-9aee-b04d95907c80 | exponentially-weighted-l-2-regularization | 2007.01208 | null | https://arxiv.org/abs/2007.01208v1 | https://arxiv.org/pdf/2007.01208v1.pdf | Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model | In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules, but they cannot effectively describe the behavior within local regions defined by the antecedent parts. In this article, a theoretical and practical design method... | ['Shanzhen Lu', 'Sung-Kwun Oh', 'Congcong Zhang', 'Witold Pedrycz', 'Zunwei Fu'] | 2020-07-02 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.48507535e-01 -1.02071956e-01 -2.17754751e-01 -8.10876638e-02
-7.59261772e-02 -1.63203269e-01 2.10650135e-02 1.19850829e-01
-1.80801928e-01 5.66797078e-01 -2.05073595e-01 -1.15207441e-01
-7.10190892e-01 -9.08809602e-01 -2.52949327e-01 -1.02223420e+00
2.75112629e-01 -1.80110130e-02 4.81472790e-01 -4.39393848... | [7.66286039352417, 4.344898700714111] |
aa27d72b-1a42-4ca3-b098-5a25f7ad4433 | forecasting-action-through-contact | 2102.00649 | null | https://arxiv.org/abs/2102.00649v1 | https://arxiv.org/pdf/2102.00649v1.pdf | Forecasting Action through Contact Representations from First Person Video | Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and... | ['Yiannis Aloimonos', 'Cornelia Fermuller', 'Michael Maynord', 'Chinmaya Devaraj', 'Eadom Dessalene'] | 2021-02-01 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.30266404e-01 4.17634517e-01 -1.65143922e-01 -3.44067514e-01
-5.54229617e-02 -6.03120327e-01 9.58045900e-01 1.43453762e-01
-1.61422431e-01 -2.75231972e-02 8.47619593e-01 1.20139480e-01
-2.51592040e-01 -4.86957371e-01 -6.01793289e-01 -1.40695781e-01
-3.72042567e-01 6.33983672e-01 4.80533689e-01 -2.42826343... | [8.044692039489746, 0.5417651534080505] |
2b5acfaf-e3c3-43e5-9337-9870e4e1662e | multi-task-neural-processes-1 | 2111.05820 | null | https://arxiv.org/abs/2111.05820v2 | https://arxiv.org/pdf/2111.05820v2.pdf | Multi-Task Neural Processes | Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can encode contextual data in the network's function space, they offer a new way to model task relatedness in multi-task learning. To study its pote... | ['Ling Shao', 'Marcel Worring', 'XianTong Zhen', 'Jiayi Shen'] | 2021-11-10 | multi-task-neural-processes | https://openreview.net/forum?id=wfRZkDvxOqj | https://openreview.net/pdf?id=wfRZkDvxOqj | null | ['brain-image-segmentation'] | ['medical'] | [ 6.80897892e-01 -4.54665758e-02 -1.64150670e-01 -4.51234907e-01
-7.07927585e-01 -3.65176558e-01 6.89885974e-01 -1.61305785e-01
-5.70138931e-01 8.80666792e-01 1.16107792e-01 1.14215970e-01
-6.43649101e-01 -6.54042304e-01 -9.07885015e-01 -9.40021813e-01
1.62075981e-01 6.29871964e-01 2.11765364e-01 1.78504989... | [9.299196243286133, 3.571328639984131] |
150e7d16-087b-4406-918a-e5cb29025fb9 | synthetic-speech-detection-using-meta | 2201.09470 | null | https://arxiv.org/abs/2201.09470v1 | https://arxiv.org/pdf/2201.09470v1.pdf | Synthetic speech detection using meta-learning with prototypical loss | Recent works on speech spoofing countermeasures still lack generalization ability to unseen spoofing attacks. This is one of the key issues of ASVspoof challenges especially with the rapid development of diverse and high-quality spoofing algorithms. In this work, we address the generalizability of spoofing detection by... | ['Sunil Kumar Kopparapu', 'Ashish Panda', 'Aditya Raikar', 'Monisankha Pal'] | 2022-01-24 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 3.07710350e-01 -3.07136238e-01 -4.14483905e-01 7.69120008e-02
-6.44980729e-01 -4.34884548e-01 7.24698305e-01 2.32030466e-01
-4.59727824e-01 4.57697064e-01 3.85334253e-01 -9.69548285e-01
-9.32652950e-02 -5.67803621e-01 -8.26981246e-01 -4.51815844e-01
-1.44809872e-01 2.67503887e-01 3.44428271e-01 -6.40304506... | [14.064783096313477, 5.845643520355225] |
73fb6f6f-c74a-44a5-b7cf-c972c51a7864 | bridging-few-shot-learning-and-adaptation-new | 2105.11804 | null | https://arxiv.org/abs/2105.11804v2 | https://arxiv.org/pdf/2105.11804v2.pdf | Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift | Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes encountered at test-time, they only require a support set composed of a few labelled samples. FSL benchmarks commonly assume that those querie... | ['Céline Hudelot', 'Antoine Toubhans', 'Myriam Tami', 'Victor Bouvier', 'Etienne Bennequin'] | 2021-05-25 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.49747145e-01 -1.81310043e-01 -4.66623813e-01 -6.45110428e-01
-9.19971228e-01 -8.26567352e-01 8.06636393e-01 1.24671549e-01
-4.35425997e-01 8.96100163e-01 -3.22884647e-03 -1.19877020e-02
-3.18446785e-01 -8.08638513e-01 -9.29496109e-01 -6.31183684e-01
-4.42697741e-02 7.21256495e-01 5.75988591e-01 -4.30561364... | [9.87329387664795, 3.0098423957824707] |
798686f7-d06f-4b80-bd3e-c62129da008f | the-multivariate-community-hawkes-model-for | 2205.00639 | null | https://arxiv.org/abs/2205.00639v2 | https://arxiv.org/pdf/2205.00639v2.pdf | The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks | The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents... | ['Kevin S. Xu', 'Subhadeep Paul', 'Zhipeng Huang', 'Lingfei Zhao', 'Hadeel Soliman'] | 2022-05-02 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-1.50305508e-02 1.51254330e-02 -9.18279961e-02 -8.55358392e-02
1.48284689e-01 -4.53305095e-01 9.50845003e-01 1.35537609e-01
1.38432890e-01 7.40695834e-01 1.67261027e-02 -4.94590908e-01
-4.60568368e-01 -1.15146947e+00 -5.76276839e-01 -9.07892287e-01
-7.20836043e-01 1.05116391e+00 6.72585011e-01 -1.10249877... | [6.974259853363037, 5.2930073738098145] |
8a52c398-1f70-4d6c-b92e-09a280fbe4e4 | binary-segmentation-of-seismic-facies-using | 2012.03675 | null | https://arxiv.org/abs/2012.03675v1 | https://arxiv.org/pdf/2012.03675v1.pdf | Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks | The interpretation of seismic data is vital for characterizing sediments' shape in areas of geological study. In seismic interpretation, deep learning becomes useful for reducing the dependence on handcrafted facies segmentation geometry and the time required to study geological areas. This work presents a Deep Neural ... | ['Ariane da Silveira', 'Felipe Zeiser', 'Sandro Rigo', 'Gabriel Ramos', 'Gefersom Lima'] | 2020-11-15 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [-2.64633209e-01 2.21757308e-01 2.90903598e-01 -6.14753544e-01
-7.40110695e-01 -5.31146049e-01 2.63164878e-01 1.67649865e-01
-5.69160879e-01 6.21597469e-01 2.30460152e-01 -6.25680923e-01
2.00326182e-02 -1.36037290e+00 -7.81227171e-01 -7.00642645e-01
-6.38068318e-01 7.39128530e-01 5.91208339e-01 -4.72655803... | [7.137646675109863, 2.1773884296417236] |
06b9d68f-39c0-46ec-8676-baebd9ce1abf | rethinking-complex-valued-deep-neural | 2301.04320 | null | https://arxiv.org/abs/2301.04320v1 | https://arxiv.org/pdf/2301.04320v1.pdf | Rethinking complex-valued deep neural networks for monaural speech enhancement | Despite multiple efforts made towards adopting complex-valued deep neural networks (DNNs), it remains an open question whether complex-valued DNNs are generally more effective than real-valued DNNs for monaural speech enhancement. This work is devoted to presenting a critical assessment by systematically examining comp... | ['Daniel Wong', 'Anurag Kumar', 'Buye Xu', 'Ke Tan', 'Haibin Wu'] | 2023-01-11 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 3.88317406e-01 1.98007300e-01 2.20071435e-01 -2.47363985e-01
-6.21992111e-01 -2.04344362e-01 4.89287704e-01 -2.28422388e-01
-7.32170582e-01 6.80414975e-01 5.21005690e-01 -7.10335016e-01
9.94519070e-02 -6.52744353e-01 -5.93087018e-01 -7.42260039e-01
-2.51545042e-01 -3.57975721e-01 6.97403178e-02 -6.55937314... | [14.927382469177246, 5.931375503540039] |
d69c20c2-d527-4afd-8491-1969ef6d73b8 | modeformer-modality-preserving-embedding-for | 2303.11551 | null | https://arxiv.org/abs/2303.11551v1 | https://arxiv.org/pdf/2303.11551v1.pdf | ModEFormer: Modality-Preserving Embedding for Audio-Video Synchronization using Transformers | Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which inde... | ['WonDong Jang', 'Rohun Tripathi', 'Akash Gupta'] | 2023-03-21 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 1.12908237e-01 -3.31027150e-01 -4.49198075e-02 -3.03373307e-01
-1.24634600e+00 -6.76859915e-01 1.36262178e-01 8.10647234e-02
-2.31483400e-01 2.17934951e-01 2.21822992e-01 -1.77300259e-01
1.36617169e-01 -3.17543298e-01 -7.41427958e-01 -5.29213190e-01
-3.39250475e-01 -1.82769418e-01 4.59196746e-01 -1.60360858... | [14.844554901123047, 5.662452697753906] |
80a72632-47f0-4302-8850-e6fc1ade47b2 | domain-randomization-for-object-counting | 2202.08670 | null | https://arxiv.org/abs/2202.08670v1 | https://arxiv.org/pdf/2202.08670v1.pdf | Domain Randomization for Object Counting | Recently, the use of synthetic datasets based on game engines has been shown to improve the performance of several tasks in computer vision. However, these datasets are typically only appropriate for the specific domains depicted in computer games, such as urban scenes involving vehicles and people. In this paper, we p... | ["Noel E. O'Connor", 'Diego Ortego', 'Kevin McGuinness', 'Enric Moreu'] | 2022-02-17 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.18111409e-01 -1.63429111e-01 3.28054756e-01 -1.42765343e-01
-3.65446121e-01 -9.26412404e-01 9.77251947e-01 -9.96120423e-02
-5.80418169e-01 7.02896416e-01 -2.62995809e-01 -1.94666505e-01
3.72453034e-01 -9.41758037e-01 -7.08313942e-01 -3.00684363e-01
3.61193836e-01 7.03948498e-01 7.69469500e-01 -1.22385710... | [8.496335983276367, -1.0808643102645874] |
169692e6-6af1-48e2-9d7e-2922b7acb557 | sl-cyclegan-blind-motion-deblurring-in-cycles | 2111.04026 | null | https://arxiv.org/abs/2111.04026v1 | https://arxiv.org/pdf/2111.04026v1.pdf | SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning | In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN. For the first time in blind motion deblurring, we propose a sparse ResNet-block as a combination of sparse convolution layers and a trainable spa... | ['Fang Fang', 'Li-Yun Wang', 'Ali Syed Saqlain'] | 2021-11-07 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 3.63003939e-01 -1.83245718e-01 -2.92495131e-01 1.78383335e-01
-9.37712729e-01 -5.98163247e-01 5.73448956e-01 -1.22182691e+00
-8.92182663e-02 8.60081136e-01 8.94407094e-01 -3.64630461e-01
5.63562572e-01 -3.80303502e-01 -1.12540174e+00 -9.68200147e-01
2.00077027e-01 -1.79569572e-01 4.87051457e-02 6.23848215... | [11.4435453414917, -2.382512331008911] |
bb4b00a2-3151-4b55-9e95-e94a108f5cff | jointly-extracting-explicit-and-implicit | null | null | https://aclanthology.org/2021.naacl-main.453 | https://aclanthology.org/2021.naacl-main.453.pdf | Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network | Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowl... | ['Yongfeng Huang', 'Changran Hu', 'Yunqi Zhang', 'Yubo Chen'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['implicit-relations'] | ['natural-language-processing'] | [-1.14383869e-01 5.22573233e-01 -7.17674732e-01 -3.73819083e-01
6.53594136e-02 -4.10009652e-01 2.81765759e-01 5.50192535e-01
5.98323941e-02 9.15162027e-01 2.10324571e-01 -3.10976624e-01
-5.27663887e-01 -1.66029394e+00 -7.32954443e-01 -9.66603458e-02
-1.43091857e-01 5.27497530e-01 6.56437755e-01 -2.51419336... | [9.019426345825195, 8.047045707702637] |
960332b3-ac7c-445a-a9f8-5abbaa7a6e99 | on-hyperparameter-search-in-cluster-ensembles | 1803.11008 | null | http://arxiv.org/abs/1803.11008v1 | http://arxiv.org/pdf/1803.11008v1.pdf | On Hyperparameter Search in Cluster Ensembles | Quality assessments of models in unsupervised learning and clustering
verification in particular have been a long-standing problem in the machine
learning research. The lack of robust and universally applicable cluster
validity scores often makes the algorithm selection and hyperparameter
evaluation a tough guess. In t... | ['Ralf Banisch', 'Luzie Helfmann', 'Mattes Mollenhauer', 'Johannes von Lindheim'] | 2018-03-29 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 5.93726849e-03 -1.22548193e-01 4.06492919e-01 -4.76548463e-01
-8.13974321e-01 -8.30724597e-01 5.05733311e-01 3.34558427e-01
-3.40136170e-01 6.56879067e-01 -1.13352500e-02 -4.38290574e-02
-7.86395252e-01 -4.36499208e-01 -1.24823645e-01 -1.51264048e+00
-2.97535751e-02 7.77942657e-01 4.19684462e-02 2.35425338... | [7.612945079803467, 4.527587413787842] |
b0e46f4c-afe6-4800-9d4a-8b35fb32f2b4 | star-ris-assisted-privacy-protection-in | 2306.12675 | null | https://arxiv.org/abs/2306.12675v1 | https://arxiv.org/pdf/2306.12675v1.pdf | STAR-RIS-Assisted Privacy Protection in Semantic Communication System | Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances c... | ['Zehui Xiong', 'Yuping Zhao', 'Pengxin Guan', 'Wanting Yang', 'Yiru Wang'] | 2023-06-22 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 5.78417599e-01 6.72580063e-01 6.95020556e-01 -2.12664112e-01
-6.08170509e-01 -7.92634249e-01 2.47021139e-01 -1.09542392e-01
-3.25744182e-01 7.59909809e-01 1.61706388e-01 -3.02011520e-01
-3.72353911e-01 -7.73595035e-01 -7.16329873e-01 -8.39374959e-01
-3.63387652e-02 -1.06149800e-02 -6.16048351e-02 -1.66636780... | [5.88025426864624, 6.626924991607666] |
0c258566-ec8e-4dbc-808a-eff7f01ee95f | big-data-and-cross-document-coreference | 1311.3987 | null | http://arxiv.org/abs/1311.3987v1 | http://arxiv.org/pdf/1311.3987v1.pdf | Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities | Information Extraction (IE) is the task of automatically extracting
structured information from unstructured/semi-structured machine-readable
documents. Among various IE tasks, extracting actionable intelligence from
ever-increasing amount of data depends critically upon Cross-Document
Coreference Resolution (CDCR) - t... | ['Seyed-Mehdi-Reza Beheshti', 'Seung Hwan Ryu', 'Boualem Benatallah', 'Wei Wang', 'Srikumar Venugopal'] | 2013-11-14 | null | null | null | null | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 3.22931468e-01 6.96002185e-01 -1.93618491e-01 -2.71994382e-01
-1.29689372e+00 -8.60506415e-01 7.64102519e-01 6.10670030e-01
-5.91298997e-01 1.23043764e+00 8.72717500e-01 -8.62793252e-02
-5.72417140e-01 -5.36049843e-01 -4.90944564e-01 -2.74470657e-01
-1.80292249e-01 1.05596018e+00 7.08675385e-02 -2.28140309... | [9.364861488342285, 9.107513427734375] |
dde0829f-ebf9-4f16-ba51-a38557255f26 | mo-padgan-generating-diverse-designs-with | 2007.04790 | null | https://arxiv.org/abs/2007.04790v1 | https://arxiv.org/pdf/2007.04790v1.pdf | MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement | Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing ... | ['Wei Chen', 'Faez Ahmed'] | 2020-07-07 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-1.84592739e-01 -4.66954038e-02 -2.88774610e-01 -1.45992488e-02
-5.77634871e-01 -4.97706443e-01 2.88154364e-01 -4.79961842e-01
5.49741805e-01 1.07189715e+00 3.10194761e-01 -3.50866139e-01
-5.02631247e-01 -9.41563904e-01 -6.05892360e-01 -4.47095960e-01
-4.39760350e-02 4.39177185e-01 -3.13978970e-01 -2.77894497... | [5.821075439453125, 3.3088138103485107] |
7c662050-7ea2-4942-b737-2dfa0e244ce6 | music-instrument-classification-reprogrammed | 2211.08379 | null | https://arxiv.org/abs/2211.08379v1 | https://arxiv.org/pdf/2211.08379v1.pdf | Music Instrument Classification Reprogrammed | The performance of approaches to Music Instrument Classification, a popular task in Music Information Retrieval, is often impacted and limited by the lack of availability of annotated data for training. We propose to address this issue with "reprogramming," a technique that utilizes pre-trained deep and complex neural ... | ['Alexander Lerch', 'Hsin-Hung Chen'] | 2022-11-15 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 5.16824901e-01 -1.36757150e-01 -1.40209883e-01 -2.56108772e-02
-8.10806572e-01 -1.01765263e+00 4.02087361e-01 -2.69007653e-01
-4.33611274e-01 5.02232552e-01 1.97367936e-01 -1.11315940e-02
-2.46815830e-01 -3.27443570e-01 -6.51689589e-01 -3.77098948e-01
3.10486466e-01 4.01002765e-01 -2.75921971e-01 -3.37199122... | [15.763580322265625, 5.268509387969971] |
bbb98908-b4c9-496d-b28b-f23518f5f835 | comparative-benchmarking-of-causal-discovery | 1708.06246 | null | http://arxiv.org/abs/1708.06246v2 | http://arxiv.org/pdf/1708.06246v2.pdf | Comparative Benchmarking of Causal Discovery Techniques | In this paper we present a comprehensive view of prominent causal discovery
algorithms, categorized into two main categories (1) assuming acyclic and no
latent variables, and (2) allowing both cycles and latent variables, along with
experimental results comparing them from three perspectives: (a) structural
accuracy, (... | ['Garima Gupta', 'Vartika Tewari', 'Gautam Shroff', 'Karamjit Singh'] | 2017-08-18 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.85362148e-01 4.19615537e-01 -7.14357793e-01 -4.27115500e-01
-2.08865970e-01 -3.39423895e-01 1.21358621e+00 2.10270405e-01
-7.36156330e-02 1.50150478e+00 6.44949734e-01 -8.12527835e-01
-8.00532997e-01 -1.08083403e+00 -7.72163987e-01 -5.61267495e-01
-6.07992411e-01 6.87992871e-01 1.55381337e-01 3.45223367... | [8.018698692321777, 5.398641109466553] |
9db523b5-5d51-4b74-b766-b7ce8f6782e8 | high-frequency-residual-learning-for-multi | 1905.02649 | null | https://arxiv.org/abs/1905.02649v1 | https://arxiv.org/pdf/1905.02649v1.pdf | High Frequency Residual Learning for Multi-Scale Image Classification | We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. The architecture utilizes two networks: a low resolution network to efficiently approximate low frequency components and a high resolution ne... | ['Jian-Feng Wang', 'Bowen Cheng', 'Rong Xiao', 'Lei Zhang', 'Thomas Huang'] | 2019-05-07 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 1.94140822e-01 3.25491391e-02 -1.83584824e-01 -9.87028256e-02
-7.17844486e-01 -8.06272700e-02 3.23441535e-01 -5.71943045e-01
-6.62614405e-01 6.42506897e-01 -1.07804865e-01 -1.28476202e-01
-8.67246091e-02 -7.73350060e-01 -6.71893477e-01 -4.53922868e-01
-3.58659655e-01 1.08489938e-01 6.55857682e-01 -2.42854193... | [9.258988380432129, 1.545785903930664] |
3635cf96-9147-43cf-8ba7-904534ac68ce | multi-task-pre-training-for-plug-and-play-1 | null | null | https://openreview.net/forum?id=46-q5-S-mEF | https://openreview.net/pdf?id=46-q5-S-mEF | Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System | Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-1.93645041e-02 5.58311641e-01 6.63457513e-02 -6.20223820e-01
-1.06473625e+00 -7.81190813e-01 1.11118889e+00 -2.18787733e-02
-5.38547218e-01 9.46459413e-01 8.46700370e-01 -1.17406659e-01
6.58843994e-01 -2.02379286e-01 1.73407242e-01 -1.00010835e-01
4.26491708e-01 1.32185400e+00 1.84387416e-01 -7.80995727... | [12.742323875427246, 8.08378791809082] |
b63d23b1-01c3-426e-8ac9-de720d9497ff | adaptive-sequence-submodularity | 1902.05981 | null | https://arxiv.org/abs/1902.05981v2 | https://arxiv.org/pdf/1902.05981v2.pdf | Adaptive Sequence Submodularity | In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e.g., guiding an agent through a series of states). Not only do sequences already pose a dauntingly large search space, but... | ['Andreas Krause', 'Amin Karbasi', 'Moran Feldman', 'Marko Mitrovic', 'Ehsan Kazemi'] | 2019-02-15 | adaptive-sequence-submodularity-1 | http://papers.nips.cc/paper/8776-adaptive-sequence-submodularity | http://papers.nips.cc/paper/8776-adaptive-sequence-submodularity.pdf | neurips-2019-12 | ['product-recommendation'] | ['miscellaneous'] | [ 1.71882316e-01 4.35872003e-02 -5.70080876e-01 -2.43434638e-01
-2.90338248e-01 -9.98908699e-01 2.14485943e-01 2.03229263e-01
-6.56332612e-01 8.35639477e-01 1.04978561e-01 -6.19311929e-01
-3.59056950e-01 -7.53602684e-01 -6.02991641e-01 -4.78040427e-01
-2.43796036e-01 7.61249006e-01 -5.08420803e-02 1.65837128... | [4.631370544433594, 3.2744929790496826] |
20065493-6f52-4803-81fe-901ea56820f8 | integrating-whole-context-to-sequence-to | 1912.01777 | null | https://arxiv.org/abs/1912.01777v2 | https://arxiv.org/pdf/1912.01777v2.pdf | Integrating Knowledge into End-to-End Speech Recognition from External Text-Only Data | Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained with speech-text paired data. It is challenging to incorporate external text-only data into AED models. Another issue of the AED model is t... | ['Jian-Hua Tao', 'Zhengkun Tian', 'Zhengqi Wen', 'Jiangyan Yi', 'Ye Bai', 'Shuai Zhang'] | 2019-12-04 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 1.85093299e-01 1.52164519e-01 -2.23714352e-01 -4.70038086e-01
-9.75012124e-01 -1.40599638e-01 4.50035989e-01 -6.20417744e-02
-4.92983073e-01 6.08534336e-01 5.54175913e-01 -5.51296055e-01
3.38226229e-01 -4.39887792e-01 -6.80518925e-01 -6.48838758e-01
6.76249743e-01 1.84302583e-01 1.67480320e-01 3.18533182... | [14.464595794677734, 6.989711284637451] |
f3805c50-7b5d-469c-ba0c-0b8e65902fa8 | local-class-specific-and-global-image-level | 1912.12215 | null | https://arxiv.org/abs/1912.12215v3 | https://arxiv.org/pdf/1912.12215v3.pdf | Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation | In this paper, we address the task of semantic-guided scene generation. One open challenge in scene generation is the difficulty of the generation of small objects and detailed local texture, which has been widely observed in global image-level generation methods. To tackle this issue, in this work we consider learning... | ['Nicu Sebe', 'Philip H. S. Torr', 'Yan Yan', 'Hao Tang', 'Dan Xu'] | 2019-12-27 | local-class-specific-and-global-image-level-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-generation'] | ['computer-vision'] | [ 5.39243937e-01 1.87682018e-01 6.30938709e-02 -4.02596742e-01
-9.26538646e-01 -1.30730405e-01 8.03063095e-01 -2.49619082e-01
4.31779176e-02 7.75935471e-01 3.05357724e-01 2.13233262e-01
1.25946417e-01 -1.20462084e+00 -7.85686374e-01 -1.16276884e+00
4.20872509e-01 1.87980175e-01 3.55183721e-01 -2.89156765... | [11.513790130615234, -0.683214545249939] |
23118726-97df-417c-9f78-d5586a66c3e5 | transformers-and-ensemble-methods-a-solution | 2303.09823 | null | https://arxiv.org/abs/2303.09823v1 | https://arxiv.org/pdf/2303.09823v1.pdf | Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages | This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their combination using 2 ensemble approaches. The best results on the training set, in a five-fold cr... | ['Wajdi Zaghouani', 'Paolo Rosso', 'Imene Bensalem', 'Angel Felipe Magnossão de Paula'] | 2023-03-17 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.00484261e-01 2.79468417e-01 3.13335776e-01 -1.92537084e-01
-9.26330566e-01 -6.92824244e-01 1.04649603e+00 -5.41646034e-02
-5.09941161e-01 9.00033593e-01 2.74201244e-01 -2.23053873e-01
1.14460774e-01 -9.91787836e-02 2.49913968e-02 -7.65863955e-01
2.32956156e-01 3.72179210e-01 4.29499418e-01 -2.10826129... | [8.900127410888672, 10.601318359375] |
a1be54df-b6d0-49d1-973d-d46c062bb77b | satellite-image-semantic-segmentation | 2110.05812 | null | https://arxiv.org/abs/2110.05812v1 | https://arxiv.org/pdf/2110.05812v1.pdf | Satellite Image Semantic Segmentation | In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building, and road). We rely on Swin Transformer architecture and build the dataset from IGN open data. We report quantitative and qualitative segment... | ['Benoît Martinez', 'Christian Wolf', 'Killian Oechslin', 'Eric Guérin'] | 2021-10-12 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 4.51554239e-01 2.06268877e-01 -1.55685753e-01 -5.32773137e-01
-1.80774301e-01 -5.70569515e-01 6.75323665e-01 -2.54927307e-01
-1.64931327e-01 9.24839377e-01 1.44385442e-01 -4.61449474e-01
-1.88135937e-01 -1.24913049e+00 -3.83742094e-01 -4.73286718e-01
-5.24504423e-01 7.42743015e-01 6.26440227e-01 -1.45072058... | [9.14718246459961, -1.6089978218078613] |
af82f0ad-d97e-4abf-aff4-bd12a9f3a6f8 | tvr-a-large-scale-dataset-for-video-subtitle | 2001.09099 | null | https://arxiv.org/abs/2001.09099v2 | https://arxiv.org/pdf/2001.09099v2.pdf | TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval | We introduce TV show Retrieval (TVR), a new multimodal retrieval dataset. TVR requires systems to understand both videos and their associated subtitle (dialogue) texts, making it more realistic. The dataset contains 109K queries collected on 21.8K videos from 6 TV shows of diverse genres, where each query is associated... | ['Tamara L. Berg', 'Licheng Yu', 'Mohit Bansal', 'Jie Lei'] | 2020-01-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3768_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660443.pdf | eccv-2020-8 | ['moment-retrieval'] | ['computer-vision'] | [ 1.36498675e-01 -3.52002978e-01 -3.27207386e-01 -5.41734278e-01
-1.61801684e+00 -1.12108636e+00 8.61827731e-01 5.28714107e-03
-3.29427928e-01 2.09336162e-01 5.58440328e-01 1.53134555e-01
1.25671387e-01 -1.83826089e-01 -9.53592420e-01 -6.90515995e-01
-3.05110723e-01 3.94082606e-01 2.58729041e-01 -2.86822200... | [10.26692008972168, 0.8680185079574585] |
b9e13f1a-ae05-439e-bca9-12a3aef28036 | compressive-sensing-with-tensorized | 2303.06235 | null | https://arxiv.org/abs/2303.06235v1 | https://arxiv.org/pdf/2303.06235v1.pdf | Compressive Sensing with Tensorized Autoencoder | Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example, same object with different lighting, background, or pose. Furthermore, in many cases, parts of images can be corrupted by noise or missing... | ['M. Salman Asif', 'Rakib Hyder'] | 2023-03-10 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 3.03791106e-01 -9.29660723e-03 1.34351268e-01 -2.13060439e-01
-5.55626869e-01 -4.31806237e-01 3.92152518e-01 -5.68452418e-01
-1.17510892e-02 6.15195274e-01 6.18318319e-01 1.60393029e-01
-1.12772569e-01 -7.20609069e-01 -1.07284260e+00 -9.52514172e-01
5.44989169e-01 3.26084435e-01 -5.76625645e-01 -9.96532142... | [11.342315673828125, -2.0990407466888428] |
e3a82885-41f5-49c1-aa93-0c00307c31f5 | generating-questions-from-wikidata-triples | null | null | https://aclanthology.org/2022.lrec-1.29 | https://aclanthology.org/2022.lrec-1.29.pdf | Generating Questions from Wikidata Triples | Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To handle rare entities and generalize to unseen properties, previous work on KBQG resorted to extensive, ... | ['Claire Gardent', 'Thiago castro Ferreira', 'Kelvin Han'] | null | null | null | null | lrec-2022-6 | ['knowledge-base-question-answering', 'question-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.18288526e-02 9.57428038e-01 1.67808443e-01 -9.47158486e-02
-1.33836436e+00 -8.88270020e-01 5.57442605e-01 7.05838084e-01
-3.16232324e-01 1.48300278e+00 3.89384419e-01 -3.42389703e-01
-3.48121524e-01 -1.48316967e+00 -1.04110670e+00 1.56435836e-02
2.29848504e-01 9.52438712e-01 8.94048274e-01 -7.55139291... | [10.551464080810547, 7.93468713760376] |
ae3bfac0-7f8c-48ab-89dd-0f6fafb917a1 | extreme-multi-label-classification-with-label | null | null | https://aclanthology.org/2022.ecnlp-1.16 | https://aclanthology.org/2022.ecnlp-1.16.pdf | Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction | Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicalization of extracted values. Furthermore, since values needed for actual services is static in many att... | ['Keiji Shinzato', 'Yandi Xia', 'Wei-Te Chen'] | null | null | null | null | ecnlp-acl-2022-5 | ['extreme-multi-label-classification', 'attribute-value-extraction'] | ['methodology', 'natural-language-processing'] | [ 3.85639048e-03 -1.86355591e-01 -6.63347244e-01 -7.65239596e-01
-5.68257689e-01 -9.37259555e-01 -6.28597513e-02 3.76344413e-01
-3.55935723e-01 7.40615129e-01 -2.45010898e-01 -3.10934722e-01
-8.41427147e-02 -1.10508382e+00 -4.30200100e-01 -6.38122439e-01
9.19616893e-02 4.50814784e-01 -1.46835357e-01 -6.38161600... | [9.901693344116211, 6.1671366691589355] |
23dce76e-6bce-4502-a312-79ea10d9f315 | robust-face-recognition-by-constrained-part | 1501.04717 | null | http://arxiv.org/abs/1501.04717v1 | http://arxiv.org/pdf/1501.04717v1.pdf | Robust Face Recognition by Constrained Part-based Alignment | Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a f... | ['Tsung-Han Chan', 'Yueming Wang', 'Kui Jia', 'Gang Pan', 'Yi Ma', 'Yuting Zhang'] | 2015-01-20 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.43321347e-01 -1.20781362e-01 -1.48236742e-02 -8.00947905e-01
-5.18488407e-01 -2.60484546e-01 5.73050976e-01 -5.70311546e-01
2.10968435e-01 1.93027735e-01 -3.22830170e-01 2.84462776e-02
-2.15316400e-01 -4.08437371e-01 -8.97287726e-01 -9.47035909e-01
1.31345347e-01 6.23225093e-01 -1.90536916e-01 7.29146451... | [13.140708923339844, 0.3966658115386963] |
88277b2c-2be8-4a7d-b062-48747d20fc2c | understanding-art-through-multi-modal | 1904.10615 | null | http://arxiv.org/abs/1904.10615v1 | http://arxiv.org/pdf/1904.10615v1.pdf | Understanding Art through Multi-Modal Retrieval in Paintings | In computer vision, visual arts are often studied from a purely aesthetics
perspective, mostly by analysing the visual appearance of an artistic
reproduction to infer its style, its author, or its representative features. In
this work, however, we explore art from both a visual and a language
perspective. Our aim is to... | ['Yuta Nakashima', 'Noa Garcia', 'Benjamin Renoust'] | 2019-04-24 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 4.17171210e-01 -9.48835835e-02 3.06423232e-02 -7.97843412e-02
-2.22454980e-01 -1.09430599e+00 1.20351779e+00 3.90948534e-01
1.88217551e-01 1.59509674e-01 5.61032593e-01 -4.25615385e-02
-3.36166650e-01 -4.85270739e-01 -3.53816867e-01 -3.96473795e-01
5.02390504e-01 4.05277520e-01 -1.76275566e-01 -1.34858921... | [11.329524993896484, 0.3122360110282898] |
ba51d198-0808-4f30-96c1-475ea2a59a4c | lower-bounds-and-accelerated-algorithms-in | 2305.07612 | null | https://arxiv.org/abs/2305.07612v1 | https://arxiv.org/pdf/2305.07612v1.pdf | Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression | Communication compression is an essential strategy for alleviating communication overhead by reducing the volume of information exchanged between computing nodes in large-scale distributed stochastic optimization. Although numerous algorithms with convergence guarantees have been obtained, the optimal performance limit... | ['Kun Yuan', 'Wotao Yin', 'Yiming Chen', 'Xinmeng Huang', 'Yutong He'] | 2023-05-12 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.25776321e-01 -8.29237625e-02 -2.45313793e-01 -2.76227891e-01
-9.42885756e-01 -5.44402003e-01 -8.49126056e-02 3.28152865e-01
-4.15348381e-01 8.87777746e-01 3.28103632e-01 -3.55883718e-01
-5.68061948e-01 -8.53507400e-01 -7.50630081e-01 -1.09553754e+00
-5.54543555e-01 7.09902465e-01 -9.27620530e-02 -1.44511983... | [6.354779243469238, 4.896623611450195] |
1b1922fb-7762-4132-b6a8-1d412453e17a | stepnet-spatial-temporal-part-aware-network | 2212.12857 | null | https://arxiv.org/abs/2212.12857v1 | https://arxiv.org/pdf/2212.12857v1.pdf | StepNet: Spatial-temporal Part-aware Network for Sign Language Recognition | Sign language recognition (SLR) aims to overcome the communication barrier for the people with deafness or the people with hard hearing. Most existing approaches can be typically divided into two lines, i.e., Skeleton-based and RGB-based methods, but both the two lines of methods have their limitations. RGB-based appro... | ['Yi Yang', 'Zhedong Zheng', 'Xiaolong Shen'] | 2022-12-25 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.89170551e-02 -3.25910181e-01 -4.28266287e-01 -4.50721115e-01
-6.60906255e-01 9.69088748e-02 2.53005534e-01 -6.19315386e-01
-5.11729479e-01 5.84870279e-01 5.41310608e-01 2.71277335e-02
-1.89270899e-01 -6.78799450e-01 -2.84444392e-01 -8.56328487e-01
7.46263424e-03 -1.51563168e-01 3.20573926e-01 -3.26171637... | [9.195001602172852, -6.476389408111572] |
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