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0cc47a98-cf1f-4bdb-a86e-1f17e8ce01fc | synthesizing-affective-neurophysiological | 2306.03112 | null | https://arxiv.org/abs/2306.03112v1 | https://arxiv.org/pdf/2306.03112v1.pdf | Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper | The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of... | ['Mark Billinghurst', 'Gonzalo Maso Talou', 'Vanessa Tang', 'Alireza F. Nia'] | 2023-06-05 | null | null | null | null | ['eeg', 'emotional-intelligence', 'eeg'] | ['methodology', 'natural-language-processing', 'time-series'] | [ 3.00412357e-01 -2.63978153e-01 2.81376511e-01 -5.89267313e-01
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-3.21439147e-01 9.10521865e-01 8.98443684e-02 3.32236320e-01
-3.62270772e-01 -3.16638440e-01 -1.78588420e-01 -8.73240113e-01
-5.49794957e-02 3.84157263e-02 -9.63683426e-01 -2.26164192... | [13.19323444366455, 3.419835329055786] |
4373669e-cb0e-4d1b-b539-ae009bf2f8fe | semimultipose-a-semi-supervised-multi-animal | 2204.07072 | null | https://arxiv.org/abs/2204.07072v1 | https://arxiv.org/pdf/2204.07072v1.pdf | SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework | Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology. Advanced approaches have been proposed to support multi-animal estimation and achieve state-of-the-art performance. However, these models rarely exploit unlabeled data during training even though real wor... | ['Anqi Wu', 'Liam Paninski', 'Andres Bendesky', 'Christoph Gebhardt', 'Ari Blau'] | 2022-04-14 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.09244630e-01 -2.73931593e-01 -3.44583213e-01 -6.95517004e-01
-6.60693347e-01 -4.60088104e-01 1.02003366e-01 -1.88800409e-01
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-3.91455710e-01 6.64932609e-01 1.78174958e-01 7.51486942... | [7.604117393493652, -0.9115826487541199] |
90f54964-a243-46e6-a7d5-3e6a05398bb7 | coarse-to-fine-person-re-identification-with | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.pdf | Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck | Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis. However, due to the large intra-class variation of a person across cameras, e.g., occlusions, illuminations, viewpoints, and p... | ['Yongcheng Zhou', 'Wenxi Liu', 'Yuzhen Niu', 'Yueming Gao', 'Anguo Zhang'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['miscellaneous'] | ['miscellaneous'] | [ 1.08119287e-02 -7.41216004e-01 2.21513584e-01 -4.16951150e-01
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3.02260727e-01 1.47273391e-01 2.65544653e-01 9.79756266... | [14.723139762878418, 0.9724113941192627] |
43ce75b1-0ab0-4801-b463-9bb4288688c9 | contrastive-training-improves-zero-shot | 2210.05613 | null | https://arxiv.org/abs/2210.05613v1 | https://arxiv.org/pdf/2210.05613v1.pdf | Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents | We investigate semi-structured document classification in a zero-shot setting. Classification of semi-structured documents is more challenging than that of standard unstructured documents, as positional, layout, and style information play a vital role in interpreting such documents. The standard classification setting ... | ['Miguel Ballesteros', 'Sunil Mallya', 'Graham Horwood', 'Shuai Wang', 'Yogarshi Vyas', 'Muhammad Khalifa'] | 2022-10-11 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 6.57755792e-01 -3.58116813e-02 2.68810112e-02 -8.35943401e-01
-7.43729532e-01 -7.48875380e-01 8.64496112e-01 4.19183671e-01
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1.08272575e-01 7.73786545e-01 3.55981916e-01 -1.66859180... | [10.004424095153809, 3.4123926162719727] |
71fda749-5205-4114-b3e0-4da16586b5e8 | implicit-distributional-reinforcement | 2007.06159 | null | https://arxiv.org/abs/2007.06159v2 | https://arxiv.org/pdf/2007.06159v2.pdf | Implicit Distributional Reinforcement Learning | To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution. We adopt ... | ['Mingyuan Zhou', 'Zhendong Wang', 'Yuguang Yue'] | 2020-07-13 | null | http://proceedings.neurips.cc/paper/2020/hash/4f20f7f5d2e7a1b640ebc8244428558c-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/4f20f7f5d2e7a1b640ebc8244428558c-Paper.pdf | neurips-2020-12 | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.36326838e-01 3.55221927e-01 -3.85105282e-01 -9.50819701e-02
-7.52450764e-01 -6.55257344e-01 8.74272168e-01 -5.37182130e-02
-8.31183434e-01 1.15169311e+00 4.50871855e-01 -2.30668902e-01
-1.17651664e-01 -7.36782193e-01 -8.40540469e-01 -1.03067172e+00
-1.14965297e-01 7.40425169e-01 -1.00147754e-01 -2.01797128... | [4.062713623046875, 2.4839680194854736] |
2acc3816-c59a-4ac8-bb10-d04f802ca026 | partial-label-learning-with-self-guided | 1902.03045 | null | http://arxiv.org/abs/1902.03045v1 | http://arxiv.org/pdf/1902.03045v1.pdf | Partial Label Learning with Self-Guided Retraining | Partial label learning deals with the problem where each training instance is
assigned a set of candidate labels, only one of which is correct. This paper
provides the first attempt to leverage the idea of self-training for dealing
with partially labeled examples. Specifically, we propose a unified formulation
with pro... | ['Lei Feng', 'Bo An'] | 2019-02-08 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.77940351e-01 7.32872188e-01 -8.21834624e-01 -7.76679039e-01
-1.19778025e+00 -5.03543735e-01 2.67020762e-01 1.74819767e-01
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3.95339668e-01 6.66686952e-01 -1.81665599e-01 5.15839398... | [9.414383888244629, 4.036522388458252] |
4266c56d-93f0-489c-a066-a8da4f17b04c | pruning-meets-low-rank-parameter-efficient | 2305.18403 | null | https://arxiv.org/abs/2305.18403v2 | https://arxiv.org/pdf/2305.18403v2.pdf | Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning | Large pre-trained models (LPMs), such as LLaMA and ViT-G, have shown exceptional performance across various tasks. Although parameter-efficient fine-tuning (PEFT) has emerged to cheaply fine-tune these large models on downstream tasks, their deployment is still hindered by the vast model scale and computational costs. ... | ['Hao Chen', 'Bohan Zhuang', 'Xinyi Yu', 'Linlin Ou', 'Zhen Yang', 'Chunhua Shen', 'Mingyang Zhang'] | 2023-05-28 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.58352822e-01 -1.99555367e-01 -3.67242515e-01 -3.80088449e-01
-1.05047262e+00 -3.60534459e-01 5.40029228e-01 -5.62904701e-02
-6.63764238e-01 7.14781821e-01 1.31770834e-01 -3.53966326e-01
-3.31261933e-01 -4.22988534e-01 -7.33082116e-01 -6.53905392e-01
3.94684374e-02 3.69397640e-01 2.48826474e-01 -3.21584120... | [8.779716491699219, 3.5550107955932617] |
d17a8d9b-097d-463a-bd62-8238c961dcbf | population-wise-labeling-of-sulcal-graphs | 2301.13532 | null | https://arxiv.org/abs/2301.13532v1 | https://arxiv.org/pdf/2301.13532v1.pdf | Population-wise Labeling of Sulcal Graphs using Multi-graph Matching | Population-wise matching of the cortical fold is necessary to identify biomarkers of neurological or psychiatric disorders. The difficulty comes from the massive interindividual variations in the morphology and spatial organization of the folds. This task is challenging at both methodological and conceptual levels. In ... | ['Guillaume Auzias', 'S. Takerkart', 'François-Xavier Dupé', 'Rohit Yadav'] | 2023-01-31 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 2.18218207e-01 1.59053907e-01 2.74962187e-01 -2.14015618e-01
-6.23307645e-01 -5.45652807e-01 5.83543539e-01 5.46916306e-01
-2.66242743e-01 5.18622696e-01 -1.33864969e-01 1.78766057e-01
-3.12191546e-01 -8.06482553e-01 -6.07306719e-01 -5.49317658e-01
-3.79097164e-02 6.35234058e-01 4.09537703e-01 -2.62423694... | [14.046255111694336, -2.4460673332214355] |
39b952e1-9cca-4cfb-92c2-be8213b00985 | an-accurate-iris-segmentation-framework-under | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zhao_An_Accurate_Iris_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zhao_An_Accurate_Iris_ICCV_2015_paper.pdf | An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model | This paper proposes a novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children... | ['Kumar Ajay', 'Zijing Zhao'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['iris-segmentation'] | ['medical'] | [ 3.89448553e-01 -4.25791919e-01 -2.95999497e-01 -4.30298001e-01
-6.31231844e-01 -3.64974976e-01 1.21246822e-01 -1.48585305e-01
-1.70907438e-01 4.47542340e-01 5.56396358e-02 -2.20669121e-01
-3.92904222e-01 -3.19783241e-01 -3.95713836e-01 -1.05773020e+00
2.37584785e-01 -3.86785269e-02 -2.63168871e-01 2.28953928... | [3.7464797496795654, -3.629788637161255] |
81b2b474-751a-440b-b326-d3878610d88a | spatiotemporal-implicit-neural-representation | 2301.00127 | null | https://arxiv.org/abs/2301.00127v2 | https://arxiv.org/pdf/2301.00127v2.pdf | Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction | Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, ... | ['Hongjiang Wei', 'Yuyao Zhang', 'Zhiyong Zhang', 'Qing Wu', 'Ruimin Feng', 'Jie Feng'] | 2022-12-31 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.75803560e-01 -1.62255272e-01 -8.03219303e-02 -4.79966193e-01
-7.64347136e-01 -6.87177386e-03 1.59663320e-01 -2.11511627e-02
-5.03175020e-01 5.93124926e-01 3.56675714e-01 -1.03640109e-01
-5.12425661e-01 -5.12364626e-01 -6.92189395e-01 -1.03906024e+00
-3.41964453e-01 2.00172886e-01 6.89974949e-02 -1.03326768... | [13.48510456085205, -2.4202301502227783] |
7f797c9f-81ca-4694-b633-89bb28c3fbb3 | describing-language-variation-in-the | null | null | https://aclanthology.org/2022.digitam-1.4 | https://aclanthology.org/2022.digitam-1.4.pdf | Describing Language Variation in the Colophons of Armenian Manuscripts | The colophons of Armenian manuscripts constitute a large textual corpus spanning a millennium of written culture. These texts are highly diverse and rich in terms of linguistic variation. This poses a challenge to NLP tools, especially considering the fact that linguistic resources designed or suited for Armenian are s... | ['Emmanuel Van Elverdinghe', 'Bastien Kindt'] | null | null | null | null | digitam-lrec-2022-6 | ['lemmatization', 'culture'] | ['natural-language-processing', 'speech'] | [-1.51880115e-01 -3.21473092e-01 6.40408322e-02 -1.25283882e-01
-4.10374582e-01 -1.09125757e+00 1.02313137e+00 5.79107881e-01
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1.76794812e-01 6.82051837e-01 -2.81655993e-02 -6.17515266... | [10.319713592529297, 10.268806457519531] |
4f3e50bc-1de9-467d-9826-0ffd9d80144f | improving-model-understanding-and-trust-with | 2206.02790 | null | https://arxiv.org/abs/2206.02790v1 | https://arxiv.org/pdf/2206.02790v1.pdf | Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence | In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showing confidence scores in human-agent interaction systems can help build trust between humans and AI systems. However, most existing research o... | ['Liz Sonenberg', 'Ronal Singh', 'Tim Miller', 'Thao Le'] | 2022-06-06 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-2.87079275e-01 1.01243770e+00 -2.54413158e-01 -5.45683861e-01
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-4.04351167e-02 -6.94373250e-01 -6.19021475e-01 2.10003108e-02
-2.94441968e-01 4.30646926e-01 -9.72498134e-02 -1.39582813... | [8.79911994934082, 5.925322532653809] |
b634392f-2a72-4691-a3e0-a221d26017fb | on-consistency-in-graph-neural-network | 2205.13733 | null | https://arxiv.org/abs/2205.13733v2 | https://arxiv.org/pdf/2205.13733v2.pdf | Towards Faithful and Consistent Explanations for Graph Neural Networks | Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. Though various algorithms are proposed, most... | ['Suhang Wang', 'Xiang Zhang', 'Dongsheng Luo', 'Tianxiang Zhao'] | 2022-05-27 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 4.60527062e-01 9.06515062e-01 -5.83355606e-01 -3.84837478e-01
1.52685329e-01 -5.47076464e-01 6.39669538e-01 3.55908513e-01
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-4.46712643e-01 -8.34596157e-01 -9.94636893e-01 -5.72795212e-01
-5.60113303e-02 -1.10426605e-01 2.80398726e-02 -1.16765864... | [8.324769973754883, 5.94536828994751] |
cf7bd50f-a193-4c2c-902d-1267cde08150 | rst-modnet-real-time-spatio-temporal-moving | 1912.00438 | null | https://arxiv.org/abs/1912.00438v1 | https://arxiv.org/pdf/1912.00438v1.pdf | RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving | Moving Object Detection (MOD) is a critical task for autonomous vehicles as moving objects represent higher collision risk than static ones. The trajectory of the ego-vehicle is planned based on the future states of detected moving objects. It is quite challenging as the ego-motion has to be modelled and compensated to... | ['Senthil Yogamani', 'Hazem Rashed', 'Mohamed Ramzy', 'Ahmad El Sallab'] | 2019-12-01 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [-1.67027131e-01 -2.18873620e-01 6.89996406e-03 -3.40665460e-01
-3.11332822e-01 -4.28607523e-01 6.52600408e-01 -2.22760051e-01
-9.76522088e-01 2.73583323e-01 -2.12681610e-02 -3.02675039e-01
2.89033711e-01 -6.30184054e-01 -8.42347085e-01 -5.39779603e-01
-2.47785062e-01 3.70328963e-01 9.46891248e-01 -2.38804176... | [8.149144172668457, -1.4163340330123901] |
9c602d26-89b7-491f-a896-70d74707d942 | 3dsam-adapter-holistic-adaptation-of-sam-from | 2306.13465 | null | https://arxiv.org/abs/2306.13465v1 | https://arxiv.org/pdf/2306.13465v1.pdf | 3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation | Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and not stable, especially when dealing with tumor segmentation tasks that inv... | ['Qi Dou', 'Pheng-Ann Heng', 'Jingyang Zhang', 'Zhao Wang', 'Jinpeng Li', 'Wenao Ma', 'Yuan Zhong', 'Shizhan Gong'] | 2023-06-23 | null | null | null | null | ['tumor-segmentation', 'medical-image-segmentation'] | ['computer-vision', 'medical'] | [ 2.82952964e-01 2.62196988e-01 -3.31878811e-01 -3.69495064e-01
-9.06446755e-01 -6.09111965e-01 1.85464248e-01 1.48385271e-01
-4.69257176e-01 3.35487038e-01 -7.62404501e-02 -6.64862752e-01
2.14768678e-01 -7.01736271e-01 -6.09372497e-01 -6.17171943e-01
3.74689773e-02 6.21826351e-01 5.85466623e-01 -1.46436557... | [14.678135871887207, -2.5143747329711914] |
3a1a302f-4534-4f9c-bf7f-7e53cf432be4 | losh-long-short-text-joint-prediction-network | 2306.08736 | null | https://arxiv.org/abs/2306.08736v1 | https://arxiv.org/pdf/2306.08736v1.pdf | LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation | Referring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains sophisticated descriptions of the instance's appearance, actions, and relations with others. It is therefore rather difficult for an RVOS model to cap... | ['Zijie Yue', 'Miaojing Shi', 'Linfeng Yuan'] | 2023-06-14 | null | null | null | null | ['referring-expression-segmentation', 'referring-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.20213220e-01 2.87015438e-01 -2.82645851e-01 -5.39761066e-01
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2.45136350e-01 5.63869596e-01 7.52416074e-01 -6.33960441... | [9.633481979370117, 0.5527945756912231] |
6394fe1e-83d2-4cd6-9607-392875b27a05 | uofl-at-semeval-2016-task-4-multi-domain | null | null | https://aclanthology.org/S16-1024 | https://aclanthology.org/S16-1024.pdf | UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification | null | ['Adel Elmaghraby', 'Omar Abdelwahab'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.290785312652588, 3.691213607788086] |
aea05bf2-bb68-460d-8c20-9623bd0fd5e6 | neural-software-analysis | 2011.07986 | null | https://arxiv.org/abs/2011.07986v2 | https://arxiv.org/pdf/2011.07986v2.pdf | Neural Software Analysis | Many software development problems can be addressed by program analysis tools, which traditionally are based on precise, logical reasoning and heuristics to ensure that the tools are practical. Recent work has shown tremendous success through an alternative way of creating developer tools, which we call neural software... | ['Satish Chandra', 'Michael Pradel'] | 2020-11-16 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.75224945e-01 3.60826135e-01 -2.45970517e-01 -3.65469992e-01
-2.40136579e-01 -4.94418651e-01 -4.60182838e-02 5.34286559e-01
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-3.96313779e-02 -7.78731883e-01 -7.01994538e-01 -6.71038777e-02
-1.00269832e-01 7.42773265e-02 1.52539417e-01 -1.70729637... | [7.672738552093506, 7.706411838531494] |
0ef54f9f-9229-4358-8a54-930935af16d3 | verifai-verified-generative-ai | 2307.02796 | null | https://arxiv.org/abs/2307.02796v1 | https://arxiv.org/pdf/2307.02796v1.pdf | VerifAI: Verified Generative AI | Generative AI has made significant strides, yet concerns about the accuracy and reliability of its outputs continue to grow. Such inaccuracies can have serious consequences such as inaccurate decision-making, the spread of false information, privacy violations, legal liabilities, and more. Although efforts to address t... | ['Lei Cao', 'Ju Fan', 'Chenyu Yang', 'Nan Tang'] | 2023-07-06 | null | null | null | null | ['knowledge-graphs', 'misinformation', 'management', 'decision-making'] | ['knowledge-base', 'miscellaneous', 'miscellaneous', 'reasoning'] | [ 3.49537134e-01 6.50842369e-01 3.37389484e-02 -3.83731723e-01
-7.75208056e-01 -9.21601176e-01 6.15441442e-01 4.66833860e-01
-8.73525888e-02 8.38501811e-01 4.62026983e-01 -4.82731551e-01
-1.02833256e-01 -1.11795723e+00 -8.29611957e-01 -4.69419241e-01
3.64489287e-01 3.33550662e-01 -2.42383808e-01 2.14420930... | [8.850357055664062, 6.112376689910889] |
b8bef4a4-4c86-40e9-9d6d-64c8603a8877 | lung-nodules-detection-and-segmentation-using | 1907.07676 | null | https://arxiv.org/abs/1907.07676v1 | https://arxiv.org/pdf/1907.07676v1.pdf | Lung Nodules Detection and Segmentation Using 3D Mask-RCNN | Accurate assessment of Lung nodules is a time consuming and error prone ingredient of the radiologist interpretation work. Automating 3D volume detection and segmentation can improve workflow as well as patient care. Previous works have focused either on detecting lung nodules from a full CT scan or on segmenting them ... | ['Guy Engelhard', 'Evi Kopelowitz'] | 2019-07-17 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.93835035e-01 4.49099123e-01 -9.94535610e-02 -1.67536363e-01
-7.65583038e-01 -6.89651370e-01 2.64072955e-01 6.30985349e-02
-2.96891004e-01 -4.20860127e-02 -3.24195586e-02 -8.35412741e-01
1.21904649e-01 -6.59664750e-01 -3.37062001e-01 -2.90348321e-01
-2.51982007e-02 1.19972360e+00 1.12341177e+00 1.93646222... | [15.381422996520996, -2.151587963104248] |
687936fa-6a91-44bf-b693-f9c7966f8984 | learning-semantic-aligned-feature | 2112.06714 | null | https://arxiv.org/abs/2112.06714v1 | https://arxiv.org/pdf/2112.06714v1.pdf | Learning Semantic-Aligned Feature Representation for Text-based Person Search | Text-based person search aims to retrieve images of a certain pedestrian by a textual description. The key challenge of this task is to eliminate the inter-modality gap and achieve the feature alignment across modalities. In this paper, we propose a semantic-aligned embedding method for text-based person search, in whi... | ['Min Zhang', 'Min Cao', 'Shiping Li'] | 2021-12-13 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.60054520e-01 -4.21890289e-01 -1.14709459e-01 -6.49215877e-01
-1.05019403e+00 -2.77230740e-01 9.41032887e-01 -1.90530092e-01
-6.54951036e-01 3.49680126e-01 6.65642679e-01 3.57352883e-01
-3.53052139e-01 -4.68974531e-01 -5.77526033e-01 -6.45292699e-01
3.42881769e-01 2.86971748e-01 1.48371309e-01 -7.29651004... | [14.629586219787598, 0.8663081526756287] |
daf4c657-44ae-4f03-8f5d-c482ea9a3a39 | seeing-glass-joint-point-cloud-and-depth | 2110.00087 | null | https://arxiv.org/abs/2110.00087v1 | https://arxiv.org/pdf/2110.00087v1.pdf | Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects | The basis of many object manipulation algorithms is RGB-D input. Yet, commodity RGB-D sensors can only provide distorted depth maps for a wide range of transparent objects due light refraction and absorption. To tackle the perception challenges posed by transparent objects, we propose TranspareNet, a joint point cloud ... | ['Animesh Garg', 'Florian Shkurti', 'Alàn Aspuru-Guzik', 'Sagi Eppel', 'Yi Ru Wang', 'Haoping Xu'] | 2021-09-30 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.39268920e-01 -7.13556781e-02 5.79860151e-01 -3.94632638e-01
-4.16215599e-01 -8.14236224e-01 3.06577682e-01 -2.52804458e-01
-1.95897236e-01 3.00575286e-01 5.67897176e-03 -4.21801619e-02
1.31487370e-01 -7.74430275e-01 -4.20124024e-01 -3.86830062e-01
2.19544813e-01 7.00675428e-01 6.16454422e-01 -2.03237496... | [7.021670341491699, -1.9960604906082153] |
13a28cbc-c03a-45a4-990a-e15e7ba690d7 | hexatagging-projective-dependency-parsing-as | 2306.05477 | null | https://arxiv.org/abs/2306.05477v1 | https://arxiv.org/pdf/2306.05477v1.pdf | Hexatagging: Projective Dependency Parsing as Tagging | We introduce a novel dependency parser, the hexatagger, that constructs dependency trees by tagging the words in a sentence with elements from a finite set of possible tags. In contrast to many approaches to dependency parsing, our approach is fully parallelizable at training time, i.e., the structure-building actions ... | ['Ryan Cotterell', 'Tianyu Liu', 'Afra Amini'] | 2023-06-08 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-1.80592418e-01 6.58484578e-01 -2.53153116e-01 -7.95697927e-01
-1.41693056e+00 -8.70324135e-01 1.87268451e-01 3.26452136e-01
-4.82169122e-01 7.43194997e-01 2.81277776e-01 -7.74253070e-01
3.39669824e-01 -7.17271388e-01 -8.02421987e-01 -5.23292780e-01
-2.51964420e-01 7.73156881e-01 4.58426088e-01 -9.99291167... | [10.338915824890137, 9.66922378540039] |
83ac2a42-a015-4b6c-9125-18b98fcda352 | sparse-recovery-via-bootstrapping | null | null | https://openreview.net/forum?id=BUCHknhWq8D | https://openreview.net/pdf?id=BUCHknhWq8D | Sparse Recovery via Bootstrapping: Collaborative or Independent? | Sparse regression problems have traditionally been solved using all available measurements simultaneously. However, this approach fails in challenging scenarios such as when the noise level is high or there are missing data / adversarial samples.
We propose JOBS (Joint-Sparse Optimization via Bootstrap Samples) -- a \... | ['Anonymous'] | 2021-01-01 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.85411656e-01 -1.03677614e-02 -2.58675069e-01 -5.16069651e-01
-1.53197336e+00 -2.30469123e-01 1.17521271e-01 -3.71603698e-01
-2.88533330e-01 9.51146841e-01 1.01980507e-01 1.06548681e-03
-1.90805316e-01 -4.07714784e-01 -9.62077677e-01 -1.14464235e+00
-3.10609527e-02 3.83702964e-01 -3.17267865e-01 5.91134243... | [7.076717853546143, 4.481830596923828] |
99e5a717-9008-44f3-820f-e82ff53dfa4e | revisiting-facial-key-point-detection-an | 2205.07121 | null | https://arxiv.org/abs/2205.07121v1 | https://arxiv.org/pdf/2205.07121v1.pdf | Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks | Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eyes center, the eye's inner and outer corners, the mouth center, and the nose tip from which human emotions... | ['Sabeesh Ethiraj', 'Bharath Kumar Bolla', 'Prathima Dileep'] | 2022-05-14 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [-5.68937004e-01 3.18940103e-01 -3.10241520e-01 -6.07855082e-01
-8.14859495e-02 -1.87276810e-01 4.58990812e-01 -1.86895877e-01
-4.57773477e-01 5.11776507e-01 4.90236543e-02 -8.89996439e-02
-1.09655559e-01 -5.33200145e-01 -4.51969147e-01 -5.10576427e-01
3.62900607e-02 1.43675908e-01 -5.04221201e-01 -9.63351205... | [13.444416999816895, 1.302703857421875] |
d467b19e-df2a-4e99-9488-6ae765170586 | entity-projection-via-machine-translation-for | 1909.05356 | null | https://arxiv.org/abs/1909.05356v2 | https://arxiv.org/pdf/1909.05356v2.pdf | Entity Projection via Machine Translation for Cross-Lingual NER | Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. Motivated by this fact, we leverage machine translation to improve annotation-projection approaches to cross-lingual named entity recognition... | ['Bhargavi Paranjape', 'Alankar Jain', 'Zachary C. Lipton'] | 2019-08-31 | entity-projection-via-machine-translation-for-1 | https://aclanthology.org/D19-1100 | https://aclanthology.org/D19-1100.pdf | ijcnlp-2019-11 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.65203869e-01 -1.79656103e-01 -5.58047831e-01 -4.70173657e-01
-1.76044679e+00 -1.16782463e+00 8.45756114e-01 8.46798122e-02
-5.46288729e-01 9.51816738e-01 5.80804288e-01 -6.04641616e-01
4.70632941e-01 -5.82056463e-01 -6.76605701e-01 -1.60108775e-01
4.12903637e-01 1.01057100e+00 -2.21570387e-01 -1.21022522... | [10.662985801696777, 9.974372863769531] |
effebeaa-0216-4221-b04d-e68ac9b62364 | contour-and-centreline-tracking-of-vessels | 1707.03710 | null | http://arxiv.org/abs/1707.03710v1 | http://arxiv.org/pdf/1707.03710v1.pdf | Contour and Centreline Tracking of Vessels from Angiograms using the Classical Image Processing Techniques | This article deals with the problem of vessel edge and centerline detection
using classical image processing techniques due to their simpleness and
easiness to be implemented. The method is divided into four steps: the vessel
enhancement which implies a non-linear filtering proposed by Frangi, the
thresholding using Ot... | ['Tache Irina Andra'] | 2017-06-13 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.47033572e-01 -9.71110910e-02 1.61497295e-01 5.48377559e-02
1.31402433e-01 -4.36245888e-01 2.55795598e-01 6.20149672e-01
-7.92416334e-01 7.06683636e-01 -4.12185900e-02 -5.60291946e-01
-1.05495468e-01 -6.56667233e-01 2.46927530e-01 -6.17893696e-01
-2.90136397e-01 3.14519584e-01 7.06418335e-01 1.20682292... | [14.727148056030273, -2.9061691761016846] |
fe573c28-5cbe-430a-8b95-251bfb2973de | multi-agent-reinforcement-learning-2 | 2305.06446 | null | https://arxiv.org/abs/2305.06446v3 | https://arxiv.org/pdf/2305.06446v3.pdf | Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation | We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server. We propose a provably efficient algorithm based on value iteration that enable asynchronous communication while ensuring the advantage of cooperat... | ['Quanquan Gu', 'Tianhao Wang', 'Jiafan He', 'Yifei Min'] | 2023-05-10 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.74726784e-01 7.21637249e-01 2.28655055e-01 -1.14024915e-01
-1.02989233e+00 -5.30727029e-01 8.51015653e-03 6.40421689e-01
-1.12595069e+00 9.73252952e-01 -3.15834045e-01 -4.10896957e-01
-6.24769330e-01 -1.13362360e+00 -6.56455457e-01 -1.05705631e+00
-7.54521787e-01 7.20377028e-01 4.84933816e-02 -1.51379794... | [4.356756687164307, 2.8810746669769287] |
4a92ca55-31a7-4448-8b9b-326730b22049 | weakly-supervised-image-segmentation-beyond | 2301.12053 | null | https://arxiv.org/abs/2301.12053v1 | https://arxiv.org/pdf/2301.12053v1.pdf | Weakly Supervised Image Segmentation Beyond Tight Bounding Box Annotations | Weakly supervised image segmentation approaches in the literature usually achieve high segmentation performance using tight bounding box supervision and decrease the performance greatly when supervised by loose bounding boxes. However, compared with loose bounding box, it is much more difficult to acquire tight boundin... | ['Bin Xia', 'Juan Wang'] | 2023-01-28 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.37688720e-01 2.49949083e-01 -4.76259232e-01 -4.03432459e-01
-1.01473224e+00 -4.78766084e-01 1.45960137e-01 4.30115134e-01
-5.37282944e-01 8.38725626e-01 -2.32246920e-01 -1.06372833e-01
-2.12921754e-01 -6.97528899e-01 -1.07070017e+00 -1.08734739e+00
1.66727439e-01 7.08658934e-01 5.00382185e-01 1.36759087... | [9.580742835998535, 0.3040638566017151] |
3e1b3731-cf26-4679-a114-6efb54245d47 | symmetry-and-group-in-attribute-object | 2004.00587 | null | https://arxiv.org/abs/2004.00587v1 | https://arxiv.org/pdf/2004.00587v1.pdf | Symmetry and Group in Attribute-Object Compositions | Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee the rationality. In this pap... | ['Yong-Lu Li', 'Yue Xu', 'Xiaohan Mao', 'Cewu Lu'] | 2020-04-01 | symmetry-and-group-in-attribute-object-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Symmetry_and_Group_in_Attribute-Object_Compositions_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Symmetry_and_Group_in_Attribute-Object_Compositions_CVPR_2020_paper.pdf | cvpr-2020-6 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.56169736e-01 -7.38727525e-02 -8.41849819e-02 -6.53205514e-01
-1.20625488e-01 -4.51292634e-01 6.65101290e-01 -3.09925467e-01
-6.12531267e-02 2.08932504e-01 2.04512537e-01 1.13315634e-01
-1.90594286e-01 -1.04804564e+00 -6.62132144e-01 -8.94905210e-01
4.44579512e-01 4.09708649e-01 1.80951998e-01 -2.60430396... | [10.114606857299805, 2.350795269012451] |
b02db73a-fc1c-466e-bd05-e0e546942c12 | privacy-inference-empowered-stealthy-backdoor | 2306.08011 | null | https://arxiv.org/abs/2306.08011v1 | https://arxiv.org/pdf/2306.08011v1.pdf | Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios | Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one hand, the effectiveness of backdoor attacks on FL may drop significantly under non-IID scenarios. On the other hand, malicious clients may ste... | ['Longfei Zheng', 'Jun Wu', 'Gaolei Li', 'Haochen Mei'] | 2023-06-13 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-9.11098197e-02 -2.60810554e-01 -2.38787889e-01 -1.98224932e-01
-4.27533865e-01 -1.03528106e+00 5.30404031e-01 -5.05433261e-01
-2.46116724e-02 5.90026617e-01 4.09686305e-02 -5.17466187e-01
-3.25476490e-02 -1.02395916e+00 -6.97804630e-01 -1.03574347e+00
3.36695351e-02 -8.24829265e-02 7.54254907e-02 -1.96113110... | [5.8331403732299805, 7.117053985595703] |
6f73e663-17f0-4f39-830d-038f3fed5c10 | improved-dual-correlation-reduction-network | 2202.12533 | null | https://arxiv.org/abs/2202.12533v1 | https://arxiv.org/pdf/2202.12533v1.pdf | Improved Dual Correlation Reduction Network | Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observed that the existing methods suffer from the representation collapse problem and easily tend to encode samples with ... | ['Xihong Yang', 'Wenxuan Tu', 'Xinwang Liu', 'Sihang Zhou', 'Yue Liu'] | 2022-02-25 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-3.21721107e-01 -7.55683929e-02 -4.95199300e-02 -2.18623862e-01
-2.02879369e-01 -4.93643910e-01 3.33667547e-01 2.58536004e-02
7.51867518e-03 1.22069776e-01 2.09390074e-01 2.02612206e-01
-5.87915480e-01 -8.12837481e-01 -3.04280847e-01 -1.23814189e+00
-8.84543434e-02 2.95569181e-01 -4.26885039e-02 2.51957744... | [7.452425956726074, 5.963438510894775] |
f4c52355-079a-4e33-aecc-7b26eba1fd47 | a-sentinel-2-multi-year-multi-country | 2204.00951 | null | https://arxiv.org/abs/2204.00951v2 | https://arxiv.org/pdf/2204.00951v2.pdf | A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning | In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing co... | ['Ioannis Papoutsis', 'Dimitrios Zografakis', 'Maria Sdraka', 'Dimitrios Sykas'] | 2022-04-02 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 7.22299963e-02 -1.46485537e-01 -4.03865486e-01 -2.08977208e-01
-4.55600947e-01 -1.14683688e+00 6.93329453e-01 9.33113813e-01
-3.03206146e-01 8.37611675e-01 -2.70063188e-02 -5.40047407e-01
-2.55874872e-01 -1.50528371e+00 -7.97551334e-01 -8.70224476e-01
-5.34741640e-01 1.90959826e-01 -3.72034907e-01 -4.91826892... | [9.417177200317383, -1.6168004274368286] |
75cd0bf1-b088-444e-b6d3-577c08020e72 | ergodic-limits-relaxations-and-geometric | 2109.04526 | null | https://arxiv.org/abs/2109.04526v1 | https://arxiv.org/pdf/2109.04526v1.pdf | Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings | Random walk based node embedding algorithms learn vector representations of nodes by optimizing an objective function of node embedding vectors and skip-bigram statistics computed from random walks on the network. They have been applied to many supervised learning problems such as link prediction and node classificatio... | ['Prakash Ishwar', 'Daniel Sussman', 'Christy Lin'] | 2021-09-09 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.36502420e-02 7.07225025e-01 -5.03396332e-01 -1.10940330e-01
-2.96036184e-01 -4.95426238e-01 5.24591863e-01 1.62119791e-01
-2.59893328e-01 2.38092020e-01 2.27962762e-01 -4.69836712e-01
-8.05027664e-01 -9.56589699e-01 -6.06202543e-01 -1.17741597e+00
-8.76076698e-01 8.79509687e-01 2.16829926e-02 -5.21409772... | [7.080700397491455, 5.864130020141602] |
997f4e71-010b-43f2-989f-1d82baf16b6e | which-spurious-correlations-impact-reasoning | 2306.12146 | null | https://arxiv.org/abs/2306.12146v1 | https://arxiv.org/pdf/2306.12146v1.pdf | Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals | We present a human-in-the-loop dashboard tailored to diagnosing potential spurious features that NLI models rely on for predictions. The dashboard enables users to generate diverse and challenging examples by drawing inspiration from GPT-3 suggestions. Additionally, users can receive feedback from a trained NLI model o... | ['Mennatallah El-Assady', 'Afra Amini', 'Robin Chan'] | 2023-06-21 | null | null | null | null | ['logical-fallacies'] | ['miscellaneous'] | [ 3.14471662e-01 7.10958183e-01 -2.98272908e-01 -6.99762523e-01
-6.44717097e-01 -7.46369123e-01 5.82493365e-01 7.53835365e-02
2.12592199e-01 1.00081193e+00 2.96944737e-01 -4.64413732e-01
-2.34680712e-01 -6.71366215e-01 -8.40635836e-01 2.40535349e-01
3.43135029e-01 6.71254277e-01 -1.33974031e-01 1.44890314... | [10.613188743591309, 8.12588882446289] |
0c5e7683-9ac5-4533-bfe1-166d09b39012 | constraining-linear-chain-crfs-to-regular | 2106.07306 | null | https://arxiv.org/abs/2106.07306v5 | https://arxiv.org/pdf/2106.07306v5.pdf | Constraining Linear-chain CRFs to Regular Languages | A major challenge in structured prediction is to represent the interdependencies within output structures. When outputs are structured as sequences, linear-chain conditional random fields (CRFs) are a widely used model class which can learn \textit{local} dependencies in the output. However, the CRF's Markov assumption... | ['Sebastian Padó', 'Roman Klinger', 'Sean Papay'] | 2021-06-14 | constraining-linear-chain-crfs-to-regular-1 | https://openreview.net/forum?id=jbrgwbv8nD | https://openreview.net/pdf?id=jbrgwbv8nD | iclr-2022-4 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.49723387e-01 5.47805727e-01 -5.48697472e-01 -9.22475994e-01
-5.99759102e-01 -1.02210855e+00 5.07642388e-01 7.49182925e-02
-3.69713932e-01 9.81672943e-01 3.92173856e-01 -7.16174364e-01
1.25636876e-01 -7.35630751e-01 -1.03062952e+00 -5.93703449e-01
1.45389978e-02 7.62460113e-01 1.18147261e-01 3.21685191... | [10.4258451461792, 9.431923866271973] |
ca00e7ba-258a-4490-b440-25f030294780 | predict-then-propagate-graph-neural-networks | 1810.05997 | null | https://arxiv.org/abs/1810.05997v6 | https://arxiv.org/pdf/1810.05997v6.pdf | Predict then Propagate: Graph Neural Networks meet Personalized PageRank | Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationshi... | ['Johannes Gasteiger', 'Stephan Günnemann', 'Aleksandar Bojchevski'] | 2018-10-14 | predict-then-propagate-graph-neural-networks-1 | https://openreview.net/forum?id=H1gL-2A9Ym | https://openreview.net/pdf?id=H1gL-2A9Ym | iclr-2019-5 | ['node-classification-on-non-homophilic'] | ['graphs'] | [ 3.50710094e-01 6.26007318e-01 -6.78378224e-01 -5.83790123e-01
-5.75781524e-01 -3.06809574e-01 6.47416353e-01 5.79759121e-01
-3.37271482e-01 7.48297453e-01 2.53736526e-02 -7.43145466e-01
-2.63724923e-01 -1.02537143e+00 -7.38724470e-01 -4.88866001e-01
-4.99713808e-01 6.92780375e-01 8.08879852e-01 -1.44286349... | [6.98975133895874, 6.189652919769287] |
1be5b5f2-06af-49fb-88d8-c9c4893f089f | a-call-for-standardization-and-validation-of | 2306.00539 | null | https://arxiv.org/abs/2306.00539v1 | https://arxiv.org/pdf/2306.00539v1.pdf | A Call for Standardization and Validation of Text Style Transfer Evaluation | Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a meta-analysis on human and automated TST evaluation and experimentation that thoroughly examines existing literature in the field. The meta-analysis reveals a substantial standardization gap in human and automated evaluation. In... | ['Sophie Fellenz', 'Marius Kloft', 'Mayank Nagda', 'Phil Ostheimer'] | 2023-06-01 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 3.28656465e-01 8.36883187e-02 -2.42573872e-01 -2.99865305e-01
-8.27830017e-01 -8.83351743e-01 5.87733924e-01 2.60968208e-01
-3.67290258e-01 5.31243861e-01 2.32467324e-01 -5.61479568e-01
-1.41338110e-01 -1.92912877e-01 -3.48710716e-01 7.52886459e-02
4.51337487e-01 3.96364152e-01 2.46001840e-01 -2.70832717... | [11.447616577148438, 9.58362865447998] |
b1403c41-2721-4208-9431-3ba405409ec4 | semi-supervised-multimodal-representation | 2306.15711 | null | https://arxiv.org/abs/2306.15711v1 | https://arxiv.org/pdf/2306.15711v1.pdf | Semi-supervised Multimodal Representation Learning through a Global Workspace | Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations, or to translate signals from one domain to another (as in image captioning, or text-to-image generation). However, current approaches mainly rely on brute-force s... | ['Rufin VanRullen', 'Léopold Maytié', 'Benjamin Devillers'] | 2023-06-27 | null | null | null | null | ['image-generation', 'image-captioning', 'transfer-learning'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 8.88910651e-01 2.79961467e-01 -9.87534150e-02 -4.22702223e-01
-9.01577532e-01 -9.26948667e-01 1.17305183e+00 -1.03062704e-01
-3.95338714e-01 5.98525941e-01 2.36163273e-01 -1.48336872e-01
2.75474042e-02 -3.67884696e-01 -1.06551874e+00 -6.68023467e-01
7.75239095e-02 3.50922614e-01 -1.46235749e-01 -9.62139741... | [11.065253257751465, 1.4168344736099243] |
6121fa7a-a76c-4f41-808c-3644b1d02ece | t-gap-learning-to-walk-across-time-for | 2012.10595 | null | https://arxiv.org/abs/2012.10595v1 | https://arxiv.org/pdf/2012.10595v1.pdf | T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion | Temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge, as opposed to static knowledge graphs. Naturally, automatic TKG completion has drawn much research interests for a more realistic modeling of relational reasoning. However, most of the existing mod-els for TKG completion e... | ['U Kang', 'Jinhong Jung', 'JaeHun Jung'] | 2020-12-19 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-3.33483577e-01 4.85355079e-01 -6.79234505e-01 -3.65017176e-01
-4.95400757e-01 -6.78644598e-01 6.44630671e-01 7.19625533e-01
-2.89974332e-01 4.67648208e-01 7.23693669e-01 -5.09414077e-01
-4.10718322e-01 -1.25909078e+00 -9.92455661e-01 -9.34222788e-02
-6.47078812e-01 7.51906693e-01 5.30220687e-01 -1.74416572... | [8.572199821472168, 7.914571285247803] |
c810d120-5654-42b4-8087-8a26bbcf830b | semi-supervised-image-to-image-translation | 1901.08212 | null | http://arxiv.org/abs/1901.08212v1 | http://arxiv.org/pdf/1901.08212v1.pdf | Semi-Supervised Image-to-Image Translation | Image-to-image translation is a long-established and a difficult problem in
computer vision. In this paper we propose an adversarial based model for
image-to-image translation. The regular deep neural-network based methods
perform the task of image-to-image translation by comparing gram matrices and
using image segment... | ['Manan Oza', 'Sudhir Bagul', 'Himanshu Vaghela'] | 2019-01-24 | null | null | null | null | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 5.12243509e-01 4.32746142e-01 3.24227244e-01 -3.62880617e-01
-5.19207060e-01 -6.69403613e-01 1.05036402e+00 -3.35291237e-01
-4.82754976e-01 7.82468200e-01 4.37357975e-03 -2.00734302e-01
2.13833973e-01 -1.00292480e+00 -1.11498177e+00 -7.52538621e-01
4.32321012e-01 6.35784447e-01 1.90727666e-01 -3.30519468... | [11.687090873718262, -0.3814775347709656] |
7633bc7c-cc89-4841-abd7-9883deaae6cd | versatile-multi-modal-pre-training-for-human | 2203.13815 | null | https://arxiv.org/abs/2203.13815v1 | https://arxiv.org/pdf/2203.13815v1.pdf | Versatile Multi-Modal Pre-Training for Human-Centric Perception | Human-centric perception plays a vital role in vision and graphics. But their data annotations are prohibitively expensive. Therefore, it is desirable to have a versatile pre-train model that serves as a foundation for data-efficient downstream tasks transfer. To this end, we propose the Human-Centric Multi-Modal Contr... | ['Ziwei Liu', 'Zhongang Cai', 'Liang Pan', 'Fangzhou Hong'] | 2022-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.pdf | cvpr-2022-1 | ['human-parsing'] | ['computer-vision'] | [ 8.26950371e-02 5.01662195e-02 -2.71233618e-01 -4.73820597e-01
-1.16399431e+00 -3.86507154e-01 5.99031210e-01 -4.71269004e-02
-5.59863746e-01 4.42336887e-01 5.00360131e-01 1.60027713e-01
-2.56648138e-02 -5.53973258e-01 -1.01242709e+00 -6.13621712e-01
2.86823422e-01 6.69613183e-01 2.68078953e-01 -1.82184070... | [8.012614250183105, -0.43323829770088196] |
166093b9-7be1-41fd-94b5-69fb1bb17765 | dynamic-object-removal-for-effective-slam | 2303.10923 | null | https://arxiv.org/abs/2303.10923v1 | https://arxiv.org/pdf/2303.10923v1.pdf | Dynamic Object Removal for Effective Slam | This research paper focuses on the problem of dynamic objects and their impact on effective motion planning and localization. The paper proposes a two-step process to address this challenge, which involves finding the dynamic objects in the scene using a Flow-based method and then using a deep Video inpainting algorith... | ['Raj Kolamuri', 'Abhishek Bamotra', 'Phani Krishna Uppala'] | 2023-03-20 | null | null | null | null | ['video-inpainting', 'motion-planning'] | ['computer-vision', 'robots'] | [ 2.07476653e-02 -2.52257049e-01 1.04733273e-01 -1.50430098e-01
-2.48926222e-01 -5.05995929e-01 7.15034366e-01 -1.00492397e-02
-7.84955859e-01 8.42609227e-01 -1.33702753e-03 -6.77210018e-02
-3.16248268e-01 -6.08277142e-01 -5.27526319e-01 -5.90896368e-01
-2.88873821e-01 7.40106225e-01 5.63327014e-01 -2.65948474... | [7.293731212615967, -2.106858015060425] |
1529f223-346d-4091-ba7e-0137ed4a3085 | video-text-pre-training-with-learned-regions | 2112.01194 | null | https://arxiv.org/abs/2112.01194v2 | https://arxiv.org/pdf/2112.01194v2.pdf | Video-Text Pre-training with Learned Regions | Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information. State-of-the-art approaches extract visual features from raw pixels in an end-to-end fashion. However, these methods operate at frame-level directly ... | ['Jinhui Tang', 'Guanyu Cai', 'Xudong Lin', 'Alex Jinpeng Wang', 'Yixiao Ge', 'Mike Zheng Shou', 'Rui Yan'] | 2021-12-02 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 2.94274539e-02 -2.83585340e-01 -3.01737458e-01 -3.54735792e-01
-9.48476255e-01 -5.89654803e-01 7.80313194e-01 2.84319162e-01
-6.29562438e-01 3.19924444e-01 2.85565972e-01 -1.44752949e-01
2.11995512e-01 -5.87828755e-01 -9.47608173e-01 -6.04453325e-01
6.99536800e-02 2.97739118e-01 4.42208290e-01 1.09540656... | [10.067136764526367, 0.913196325302124] |
ae11741d-70f6-4480-be8c-5162adcdce53 | learning-modulated-loss-for-rotated-object | 1911.08299 | null | https://arxiv.org/abs/1911.08299v3 | https://arxiv.org/pdf/1911.08299v3.pdf | Learning Modulated Loss for Rotated Object Detection | Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) to describe the rotated bounding box and l1-loss as the loss function. In this paper, we argue that the aforementioned integration can cause training instability and performance degenerati... | ['Yue Guo', 'Xue Yang', 'Silong Peng', 'Junchi Yan', 'Wen Qian'] | 2019-11-19 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 8.94939601e-02 -1.54919758e-01 -2.62102693e-01 -2.18703225e-01
-6.21627867e-01 -3.56539607e-01 3.41317117e-01 -2.79416800e-01
-4.34439272e-01 5.37636697e-01 -1.61160544e-01 -2.46302158e-01
-4.73578155e-01 -3.34035695e-01 -6.23246610e-01 -8.44211757e-01
-2.26393938e-01 -2.22124428e-01 2.37379789e-01 -4.09775287... | [8.710329055786133, -0.8182957172393799] |
a40665c0-f866-4789-a4ef-41c2d4896886 | beyond-farthest-point-sampling-in-point-wise | 2107.04291 | null | https://arxiv.org/abs/2107.04291v3 | https://arxiv.org/pdf/2107.04291v3.pdf | Task-Aware Sampling Layer for Point-Wise Analysis | Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strategy for point-wise analysis tasks. Unlike the widely used sampling technique, Farthest Point Sampling (FPS), we propose to learn sampling an... | ['Shuguang Cui', 'Xiaoguang Han', 'Chongyang Ma', 'Haibin Huang', 'Lichang Chen', 'Yiqun Lin'] | 2021-07-09 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 7.64549002e-02 -6.32120669e-02 -2.44668230e-01 -4.81926054e-01
-1.18466473e+00 -4.17025924e-01 4.67550784e-01 3.70558411e-01
-2.06771359e-01 4.27749306e-01 -3.14294875e-01 -2.12816492e-01
-1.90196365e-01 -9.16762650e-01 -1.19178224e+00 -4.49056178e-01
-9.46641862e-02 8.65101814e-01 5.79570949e-01 2.07150891... | [8.038382530212402, -3.4402241706848145] |
53e86f43-d43a-4069-9cd2-81aa3efa72e9 | real-time-optical-flow-for-vehicular | 2112.10591 | null | https://arxiv.org/abs/2112.10591v1 | https://arxiv.org/pdf/2112.10591v1.pdf | Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras | Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional framebased sensors show their limits with blur and over- or underexposed pixels. Thanks to the... | ['Franck Davoine', 'Julien Moreau', 'Vincent Brebion'] | 2021-12-20 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 4.16673094e-01 -6.16136611e-01 3.07002902e-01 4.99013737e-02
-1.27122357e-01 -3.38497430e-01 6.36772931e-01 -3.36721204e-02
-9.66427743e-01 9.29669917e-01 1.03335828e-01 3.30174923e-01
-3.98473293e-02 -6.66719079e-01 -7.00539649e-01 -7.13568151e-01
-5.23343608e-02 -1.43955514e-01 6.56539857e-01 1.45809308... | [8.6663179397583, -1.2876839637756348] |
be108769-f303-480c-8134-80b345b1bbc9 | multi-scale-self-calibrated-network-for-image | 2104.08838 | null | https://arxiv.org/abs/2104.08838v1 | https://arxiv.org/pdf/2104.08838v1.pdf | Multi-scale Self-calibrated Network for Image Light Source Transfer | Image light source transfer (LLST), as the most challenging task in the domain of image relighting, has attracted extensive attention in recent years. In the latest research, LLST is decomposed three sub-tasks: scene reconversion, shadow estimation, and image re-rendering, which provides a new paradigm for image religh... | ['Yuntao Wu', 'Yanduo Zhang', 'Tao Lu', 'Yuanzhi Wang'] | 2021-04-18 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 6.93204701e-01 -4.79792982e-01 1.59294903e-01 -3.92107219e-01
-2.70362586e-01 -2.97506899e-01 5.00368059e-01 -2.19050050e-01
-1.74040094e-01 6.79572940e-01 3.57531786e-01 7.62965307e-02
2.79180318e-01 -7.13289142e-01 -7.96408892e-01 -9.16179717e-01
8.64864290e-01 -1.48534670e-01 5.31744719e-01 -3.10948133... | [10.530145645141602, -2.35139799118042] |
312c5b8f-7da1-499c-9520-5f992ba4d29a | in-or-out-fixing-imagenet-out-of-distribution | 2306.00826 | null | https://arxiv.org/abs/2306.00826v1 | https://arxiv.org/pdf/2306.00826v1.pdf | In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation | Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to the in-distribution task. The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being tested on a small range of test OOD datasets. We find that most of the currently used test OOD datasets, ... | ['Matthias Hein', 'Maximilian Müller', 'Julian Bitterwolf'] | 2023-06-01 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 8.44161306e-03 -1.04377598e-01 -6.59242272e-02 -4.41912115e-01
-6.78974211e-01 -8.71292233e-01 6.88657999e-01 -2.30687037e-02
-1.56544209e-01 4.07812327e-01 -2.07476914e-01 -6.04809165e-01
3.76341343e-02 -5.86624324e-01 -8.47645581e-01 -3.89861047e-01
-2.28579223e-01 6.14633024e-01 3.02079350e-01 2.20957994... | [9.360740661621094, 2.7460591793060303] |
479474ae-661f-48cd-912e-7a20ed5418ba | your-day-in-your-pocket-complex-activity | 2301.06993 | null | https://arxiv.org/abs/2301.06993v1 | https://arxiv.org/pdf/2301.06993v1.pdf | Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers | Human Activity Recognition (HAR) enables context-aware user experiences where mobile apps can alter content and interactions depending on user activities. Hence, smartphones have become valuable for HAR as they allow large, and diversified data collection. Although previous work in HAR managed to detect simple activiti... | ['Daniel Gatica-Perez', 'Lakmal Meegahapola', 'Emma Bouton--Bessac'] | 2023-01-17 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.80009472e-01 -1.94792897e-01 -5.70442915e-01 -1.39530122e-01
-3.45538020e-01 -3.17946583e-01 5.60681999e-01 2.99035639e-01
-3.92323583e-01 7.04072475e-01 7.81623662e-01 -2.47834176e-01
-1.43341199e-01 -7.71262884e-01 -3.01262408e-01 -6.31221473e-01
-2.01714337e-01 2.10391685e-01 -4.97747138e-02 -1.57007858... | [7.330082416534424, 0.7140392661094666] |
d0a70a11-6b84-4871-8841-8f7e0c902993 | robust-regression-for-safe-exploration-in | 1906.05819 | null | https://arxiv.org/abs/1906.05819v2 | https://arxiv.org/pdf/1906.05819v2.pdf | Robust Regression for Safe Exploration in Control | We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn to achieve a challenging control goal (e.g., an agile maneuver close to a boundary). A central challenge in this setting is how to quantify... | ['Yisong Yue', 'Soon-Jo Chung', 'Anima Anandkumar', 'Anqi Liu', 'Guanya Shi'] | 2019-06-13 | null | https://openreview.net/forum?id=PdxyQilsUrG | https://openreview.net/pdf?id=PdxyQilsUrG | l4dc-2020-6 | ['safe-exploration'] | ['robots'] | [ 3.70870590e-01 5.06792963e-01 -3.51210207e-01 1.83611467e-01
-1.25464082e+00 -6.41469777e-01 3.73978734e-01 2.82205582e-01
-2.12756604e-01 1.04708719e+00 -1.62669316e-01 -5.36852717e-01
-6.67397022e-01 -6.06886148e-01 -1.03443062e+00 -1.03408921e+00
-4.67629731e-01 4.73964006e-01 -1.15523972e-01 7.30709732... | [4.683619976043701, 2.217881917953491] |
d4ed177a-816b-4057-b90e-daa5b2dbd47c | joint-feature-learning-and-relation-modeling | 2203.11991 | null | https://arxiv.org/abs/2203.11991v4 | https://arxiv.org/pdf/2203.11991v4.pdf | Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework | The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability. To tackle the above issue, we propose a nove... | ['Xilin Chen', 'Shiguang Shan', 'Bingpeng Ma', 'Hong Chang', 'Botao Ye'] | 2022-03-22 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-1.97867632e-01 -4.83943224e-01 -6.36312842e-01 -1.89267308e-01
-7.88785934e-01 -4.06578243e-01 6.86340690e-01 -9.16675478e-02
-4.15674865e-01 2.91286469e-01 -1.07545994e-01 -6.02127239e-02
-2.68646255e-02 -4.36802387e-01 -4.42088306e-01 -8.48834455e-01
-2.24206373e-02 1.34836793e-01 9.79291260e-01 7.71390349... | [6.306602478027344, -2.134387969970703] |
9cf6582d-c026-4dbd-8af1-d4af5d481d76 | a-simple-language-model-for-task-oriented | 2005.00796 | null | https://arxiv.org/abs/2005.00796v4 | https://arxiv.org/pdf/2005.00796v4.pdf | A Simple Language Model for Task-Oriented Dialogue | Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response. While such decomposition might suggest a dedicated model for each sub-task, we find a simple, unified approach leads to state-of-the-art performance on the MultiWOZ dataset. SimpleTOD is a... | ['Chien-Sheng Wu', 'Ehsan Hosseini-Asl', 'Richard Socher', 'Semih Yavuz', 'Bryan McCann'] | 2020-05-02 | null | http://proceedings.neurips.cc/paper/2020/hash/e946209592563be0f01c844ab2170f0c-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e946209592563be0f01c844ab2170f0c-Paper.pdf | neurips-2020-12 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [ 2.49448583e-01 7.82426715e-01 -3.72434527e-01 -4.41693008e-01
-1.29635978e+00 -7.57999718e-01 1.07836115e+00 1.31905779e-01
-3.00053775e-01 1.01590610e+00 8.33168805e-01 -2.43805960e-01
1.32677957e-01 -4.08018768e-01 -2.69502938e-01 -2.26893276e-01
1.56013861e-01 8.04024935e-01 2.67906696e-01 -5.83917737... | [12.768430709838867, 8.057605743408203] |
d4b62751-87e1-4c8e-b6cd-0321e05f2356 | layoutreader-pre-training-of-text-and-layout | 2108.11591 | null | https://arxiv.org/abs/2108.11591v2 | https://arxiv.org/pdf/2108.11591v2.pdf | LayoutReader: Pre-training of Text and Layout for Reading Order Detection | Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded ... | ['Furu Wei', 'Jingbo Shang', 'Lei Cui', 'Yiheng Xu', 'Zilong Wang'] | 2021-08-26 | null | https://aclanthology.org/2021.emnlp-main.389 | https://aclanthology.org/2021.emnlp-main.389.pdf | emnlp-2021-11 | ['document-layout-analysis'] | ['computer-vision'] | [ 3.59322548e-01 -2.96602428e-01 -5.14022633e-02 -3.22077215e-01
-7.15715587e-01 -1.10837662e+00 7.36363769e-01 3.40729982e-01
-2.30690375e-01 4.61350009e-02 4.46917146e-01 -8.46775651e-01
6.66609854e-02 -7.70594954e-01 -1.13983810e+00 -1.55938655e-01
3.61129582e-01 3.34444314e-01 7.71388486e-02 -6.24380484... | [11.57256031036377, 2.4432485103607178] |
e7e93717-d2df-4c2a-bc26-0393a79e7160 | mammut-a-simple-architecture-for-joint | 2303.16839 | null | https://arxiv.org/abs/2303.16839v2 | https://arxiv.org/pdf/2303.16839v2.pdf | MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks | The development of language models have moved from encoder-decoder to decoder-only designs. In addition, the common knowledge has it that the two most popular multimodal tasks, the generative and contrastive tasks, tend to conflict with one another, are hard to accommodate in one architecture, and further need complex ... | ['Anelia Angelova', 'Claire Cui', 'Zhifeng Chen', 'Andrew Dai', 'Luowei Zhou', 'Abhijit Ogale', 'Wei Li', 'Ben Caine', 'Xiyang Luo', 'Dahun Kim', 'AJ Piergiovanni', 'Weicheng Kuo'] | 2023-03-29 | null | null | null | null | ['open-vocabulary-object-detection', 'video-captioning', 'video-question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.30794394e-01 1.71429873e-01 6.94136471e-02 -2.39232853e-01
-1.17272091e+00 -5.60034633e-01 1.04698431e+00 -3.46708506e-01
-7.03686297e-01 4.91363525e-01 2.84988314e-01 -3.92443568e-01
2.10341886e-01 -3.06029290e-01 -9.76396441e-01 -6.26299679e-01
2.81575590e-01 7.15899885e-01 3.01383197e-01 -3.05726409... | [10.924966812133789, 1.5475414991378784] |
0ab4a0a2-af2c-4835-b2ac-97ec28b8f4de | benchmarking-shadow-removal-for-facial | 2111.13790 | null | https://arxiv.org/abs/2111.13790v1 | https://arxiv.org/pdf/2111.13790v1.pdf | Benchmarking Shadow Removal for Facial Landmark Detection and Beyond | Facial landmark detection is a very fundamental and significant vision task with many important applications. In practice, facial landmark detection can be affected by a lot of natural degradations. One of the most common and important degradations is the shadow caused by light source blocking. While many advanced shad... | ['Song Wang', 'Yang Liu', 'Wei Feng', 'Hongkai Yu', 'Felix Juefei-Xu', 'Qing Guo', 'Lan Fu'] | 2021-11-27 | null | null | null | null | ['shadow-removal', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision'] | [ 5.35033047e-01 -2.36674041e-01 2.37222716e-01 -1.78171977e-01
-1.90083653e-01 -5.20073235e-01 5.14652014e-01 -3.89668822e-01
-9.11781862e-02 7.49826372e-01 -1.82185415e-02 -1.53384164e-01
2.04747796e-01 -5.03935993e-01 -5.26013255e-01 -1.22179663e+00
2.35864848e-01 -2.39031658e-01 6.46387041e-01 -2.70862162... | [10.870951652526855, -4.09283971786499] |
12f429a0-fed4-49a9-a3a1-85c01d490963 | application-of-machine-learning-in-1 | 2112.01998 | null | https://arxiv.org/abs/2112.01998v1 | https://arxiv.org/pdf/2112.01998v1.pdf | Application of Machine Learning in understanding plant virus pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus interplay and management | Inclusion of high throughput technologies in the field of biology has generated massive amounts of biological data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the tas... | ['Supriya Chakraborty', 'Hariprasad Kodamana', 'Srija Chakraborty', 'Dibyendu Ghosh'] | 2021-12-03 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 3.20351452e-01 -3.38952720e-01 -1.70790985e-01 -6.57263473e-02
1.40961662e-01 -5.09768009e-01 3.69129509e-01 6.37209415e-01
-5.91066480e-02 6.90866709e-01 -4.67049122e-01 -6.00744545e-01
1.21101309e-02 -9.94792700e-01 -5.67814112e-01 -1.07712173e+00
-1.04915528e-02 7.38131762e-01 -2.16806028e-02 -3.32439929... | [5.398075103759766, 5.479242324829102] |
57b26eba-2450-439b-a0e4-25b314ae0d97 | recurrent-neural-network-language-model | 1611.00196 | null | http://arxiv.org/abs/1611.00196v1 | http://arxiv.org/pdf/1611.00196v1.pdf | Recurrent Neural Network Language Model Adaptation Derived Document Vector | In many natural language processing (NLP) tasks, a document is commonly
modeled as a bag of words using the term frequency-inverse document frequency
(TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature
vector is that it ignores word orders that carry syntactic and semantic
relationships among t... | ['Brian Kan Wing Mak', 'Wei Li'] | 2016-11-01 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 5.28612174e-02 -3.88333142e-01 -5.38216531e-01 -5.42589664e-01
-3.67857933e-01 -5.17656922e-01 8.40256929e-01 1.70291960e-01
-7.47822046e-01 7.35585749e-01 6.90276325e-01 -4.50877160e-01
-2.82960415e-01 -6.73641562e-01 -4.44360346e-01 -6.97244048e-01
-6.62173480e-02 4.66617703e-01 1.75351590e-01 -1.64090604... | [10.854142189025879, 8.334634780883789] |
a3a6ae62-b330-45a0-8410-8a2b195157d1 | crnns-for-urban-sound-tagging-with | 2008.10413 | null | https://arxiv.org/abs/2008.10413v2 | https://arxiv.org/pdf/2008.10413v2.pdf | CRNNs for Urban Sound Tagging with spatiotemporal context | This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge. This task focuses on hierarchical multilabel urban sound tagging with spatiotemporal context. The code is available on our GitHub repository at https://github.com/multitel-ai/urban-sound-tagging. | ['Nicolas Riche', 'Augustin Arnault'] | 2020-08-24 | null | null | null | null | ['audio-tagging', 'environmental-sound-classification'] | ['audio', 'audio'] | [-5.83848834e-01 4.23307978e-02 -2.02433676e-01 -3.83704543e-01
-1.42372096e+00 -7.26863265e-01 6.87623799e-01 1.34528831e-01
-6.81586862e-01 6.36201382e-01 7.79748499e-01 -3.18503976e-01
4.47632819e-01 -5.78431726e-01 -4.04953003e-01 -1.77793652e-01
-3.99328947e-01 3.07522327e-01 3.79508436e-01 -1.37586683... | [15.209370613098145, 5.099123001098633] |
76c1df46-1d92-4e4d-ace5-75fba43db19c | language-resources-to-support-language | null | null | https://aclanthology.org/2022.lrec-1.58 | https://aclanthology.org/2022.lrec-1.58.pdf | Language Resources to Support Language Diversity – the ELRA Achievements | This article highlights ELRA’s latest achievements in the field of Language Resources (LRs) identification, sharing and production. It also reports on ELRA’s involvement in several national and international projects, as well as in the organization of events for the support of LRs and related Language Technologies, inc... | ['Hélène Mazo', 'Khalid Choukri', 'Victoria Arranz', 'Valérie Mapelli'] | null | null | null | null | lrec-2022-6 | ['de-identification'] | ['natural-language-processing'] | [-2.71152020e-01 1.90770835e-01 -4.50935155e-01 -4.21939716e-02
-1.16688716e+00 -7.00940490e-01 9.82451200e-01 5.86076617e-01
-8.35441053e-01 6.93305612e-01 7.90787578e-01 -6.87332690e-01
1.25699684e-01 -6.15327835e-01 -3.43356840e-02 -3.77825461e-02
3.26132238e-01 8.66781890e-01 -9.74375457e-02 -5.75631142... | [10.550886154174805, 10.219650268554688] |
9749629a-9f47-42ce-979f-1e936a501981 | how-to-train-pointgoal-navigation-agents-on-a | 2012.06117 | null | https://arxiv.org/abs/2012.06117v1 | https://arxiv.org/pdf/2012.06117v1.pdf | How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget | PointGoal navigation has seen significant recent interest and progress, spurred on by the Habitat platform and associated challenge. In this paper, we study PointGoal navigation under both a sample budget (75 million frames) and a compute budget (1 GPU for 1 day). We conduct an extensive set of experiments, cumulativel... | ['Dhruv Batra', 'Irfan Essa', 'Erik Wijmans'] | 2020-12-11 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-1.83745205e-01 4.05171402e-02 2.59848405e-03 -2.78405905e-01
-8.47347677e-01 -7.09679723e-01 7.12171078e-01 2.36360326e-01
-9.46677864e-01 5.81014156e-01 3.75626534e-01 -4.14917350e-01
2.64471948e-01 -8.31064045e-01 -8.89405072e-01 -3.57617229e-01
-7.21286297e-01 2.37502933e-01 4.53107655e-01 -6.20342433... | [4.528714179992676, 0.8366382122039795] |
35e4bd91-2c65-4db4-bf06-0916faecbace | span-detection-for-aspect-based-sentiment | 2110.07833 | null | https://arxiv.org/abs/2110.07833v1 | https://arxiv.org/pdf/2110.07833v1.pdf | Span Detection for Aspect-Based Sentiment Analysis in Vietnamese | Aspect-based sentiment analysis plays an essential role in natural language processing and artificial intelligence. Recently, researchers only focused on aspect detection and sentiment classification but ignoring the sub-task of detecting user opinion span, which has enormous potential in practical applications. In thi... | ['Kiet Van Nguyen', 'Duc-Vu Nguyen', 'Phuc Huynh Pham', 'Luong Luc Phan', 'Sieu Khai Huynh', 'Kim Thi-Thanh Nguyen'] | 2021-10-15 | null | null | null | null | ['vietnamese-aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-7.90031627e-02 -3.46838415e-01 -7.15603307e-02 -3.59521389e-01
-9.25595522e-01 -4.73219723e-01 8.62795562e-02 4.69954461e-01
-6.34674013e-01 4.67570812e-01 4.27820355e-01 -3.15985292e-01
6.99762940e-01 -7.13689327e-01 -1.84639812e-01 -4.13469166e-01
2.61758029e-01 7.81223476e-02 -1.73271939e-01 -4.39425558... | [11.373903274536133, 6.741175651550293] |
a875ad95-998f-4c44-89cc-3e9a12204b88 | bifsmnv2-pushing-binary-neural-networks-for | 2211.06987 | null | https://arxiv.org/abs/2211.06987v2 | https://arxiv.org/pdf/2211.06987v2.pdf | BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance | Deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications while suffering expensive computation and storage. Therefore, network compression technologies like binarization are studied to deploy KWS models on edge. In this paper, we present a strong yet efficient binary... | ['Xianglong Liu', 'Jie Luo', 'Jiakai Wang', 'Zejun Ma', 'Yang Zhang', 'Xiaoyang Li', 'Yifu Ding', 'Xudong Ma', 'Haotong Qin'] | 2022-11-13 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.70927867e-01 -2.51611114e-01 -8.91143441e-01 -3.88613492e-01
-5.45450687e-01 3.50578129e-02 8.40780735e-02 -4.85323481e-02
-6.80593550e-01 4.14190054e-01 2.27025673e-02 -1.09837377e+00
-8.10880661e-02 -9.80342805e-01 -1.11080873e+00 -5.94826758e-01
1.27527595e-01 -1.28599986e-01 3.71728867e-01 -3.80422056... | [8.577640533447266, 3.043286085128784] |
b301923b-0ef4-4e86-83c7-74f109169898 | pt2pc-learning-to-generate-3d-point-cloud | 2003.08624 | null | https://arxiv.org/abs/2003.08624v2 | https://arxiv.org/pdf/2003.08624v2.pdf | PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions | 3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation. In order to learn such a conditional shape ... | ['Xinchen Yan', 'Leonidas J. Guibas', 'Kaichun Mo', 'He Wang'] | 2020-03-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/32_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510681.pdf | eccv-2020-8 | ['3d-shape-generation'] | ['computer-vision'] | [ 3.58784229e-01 2.80728430e-01 2.26375446e-01 -5.04994452e-01
-6.22490466e-01 -5.83523810e-01 8.79568398e-01 -1.57659069e-01
6.07641578e-01 2.93999940e-01 -5.63870370e-03 -3.37552577e-01
1.81267131e-02 -1.19322491e+00 -9.29170907e-01 -5.15241027e-01
2.02937737e-01 8.56290340e-01 2.10151598e-02 -5.62878177... | [8.813520431518555, -3.6378421783447266] |
947f25de-3dbf-4266-8489-f5dd346538dc | weather2k-a-multivariate-spatio-temporal | 2302.10493 | null | https://arxiv.org/abs/2302.10493v1 | https://arxiv.org/pdf/2302.10493v1.pdf | Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations | Weather forecasting is one of the cornerstones of meteorological work. In this paper, we present a new benchmark dataset named Weather2K, which aims to make up for the deficiencies of existing weather forecasting datasets in terms of real-time, reliability, and diversity, as well as the key bottleneck of data quality. ... | ['Ziheng Yang', 'Bin Zhang', 'Gaozhen Nie', 'Ming Wu', 'Yutong Xiong', 'Xun Zhu'] | 2023-02-21 | null | null | null | null | ['weather-forecasting', 'spatio-temporal-forecasting'] | ['miscellaneous', 'time-series'] | [-6.57933891e-01 -4.28214163e-01 6.29725084e-02 -5.56491613e-01
6.73401728e-02 -6.13094270e-01 6.40425622e-01 1.07311174e-01
-1.14767395e-01 7.64815450e-01 3.92525107e-01 -6.89736664e-01
-3.97868663e-01 -1.36403918e+00 -4.54968959e-01 -8.20401251e-01
-7.08264709e-01 -3.28917392e-02 -1.11928936e-02 -7.54856586... | [6.652734279632568, 2.763509511947632] |
496107a5-212f-4cda-a0b5-a1878e9b27fb | graph-mining-for-cybersecurity-a-survey | 2304.00485 | null | https://arxiv.org/abs/2304.00485v1 | https://arxiv.org/pdf/2304.00485v1.pdf | Graph Mining for Cybersecurity: A Survey | The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hard... | ['Junping Du', 'Yanfang Ye', 'Qi Li', 'Yong Fang', 'Chuan Shi', 'Cheng Yang', 'Bo Yan'] | 2023-04-02 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 7.30424747e-02 -3.69006582e-02 -3.79997939e-01 2.60378987e-01
1.60160735e-01 -1.07416415e+00 6.72817409e-01 9.00786400e-01
5.80848530e-02 3.10393393e-01 -3.33701819e-01 -1.04999948e+00
-4.13615167e-01 -1.20439851e+00 -8.91493782e-02 -1.59972414e-01
-6.84360385e-01 1.68347135e-01 3.67500305e-01 -4.32942122... | [6.24870491027832, 7.236874580383301] |
62edfa7c-dce7-4096-b90c-16ce9fdcfc9a | aesthetic-image-captioning-from-weakly | 1908.11310 | null | https://arxiv.org/abs/1908.11310v1 | https://arxiv.org/pdf/1908.11310v1.pdf | Aesthetic Image Captioning From Weakly-Labelled Photographs | Aesthetic image captioning (AIC) refers to the multi-modal task of generating critical textual feedbacks for photographs. While in natural image captioning (NIC), deep models are trained in an end-to-end manner using large curated datasets such as MS-COCO, no such large-scale, clean dataset exists for AIC. Towards this... | ['Koustav Ghosal', 'Aljosa Smolic', 'Aakanksha Rana'] | 2019-08-29 | null | null | null | null | ['aesthetic-image-captioning'] | ['computer-vision'] | [ 4.11733955e-01 5.19187152e-01 2.01637536e-01 -4.08021867e-01
-1.48254001e+00 -6.49258137e-01 5.75845122e-01 1.67675242e-01
-2.05153778e-01 5.79608917e-01 5.76358795e-01 6.88357651e-02
3.00702602e-01 -3.27848941e-01 -1.12716961e+00 -5.09191513e-01
2.55003572e-01 2.65630484e-01 -4.26737487e-01 -5.33265173... | [11.016257286071777, 1.0503814220428467] |
10d2ba19-460c-4581-9d7b-4119863df9cd | structure-aware-face-clustering-on-a-large | 2103.13225 | null | https://arxiv.org/abs/2103.13225v2 | https://arxiv.org/pdf/2103.13225v2.pdf | Structure-Aware Face Clustering on a Large-Scale Graph with $\bf{10^{7}}$ Nodes | Face clustering is a promising method for annotating unlabeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer f... | ['Jie zhou', 'Jiwen Lu', 'Dalong Du', 'Guan Huang', 'Zheng Zhu', 'Wanhua Li', 'Shuai Shen'] | 2021-03-24 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-8.16053823e-02 2.46150807e-01 -3.45690936e-01 -6.29603505e-01
-8.61872315e-01 -3.26573551e-01 3.20515752e-01 -1.88671723e-01
-8.59927014e-02 4.42296743e-01 -2.35609468e-02 -1.59673989e-01
-1.81884497e-01 -7.87057042e-01 -6.19743586e-01 -7.72770762e-01
-9.10222083e-02 6.49381816e-01 1.71572492e-01 1.64561570... | [13.458288192749023, 1.003105640411377] |
e6502583-cf5d-4ddf-be3f-631766a15e53 | irb-nlp-at-semeval-2022-task-1-exploring-the | 2205.06840 | null | https://arxiv.org/abs/2205.06840v1 | https://arxiv.org/pdf/2205.06840v1.pdf | IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations | What is the relation between a word and its description, or a word and its embedding? Both descriptions and embeddings are semantic representations of words. But, what information from the original word remains in these representations? Or more importantly, which information about a word do these two representations sh... | ['Ivan Grubišić', 'Damir Korenčić'] | 2022-05-13 | null | https://aclanthology.org/2022.semeval-1.5 | https://aclanthology.org/2022.semeval-1.5.pdf | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 3.88632715e-02 9.04209092e-02 -4.52660143e-01 -4.09014970e-01
-1.35365874e-01 -5.63702524e-01 9.23007071e-01 5.66174805e-01
-6.46979690e-01 2.51432061e-01 9.73728240e-01 -4.32659000e-01
-1.65211305e-01 -8.82521212e-01 -2.07569063e-01 -3.42180401e-01
8.00827984e-03 4.55309272e-01 -3.02943766e-01 -6.09294593... | [10.400944709777832, 8.846542358398438] |
4ef70061-9b9a-4a80-828f-c7b3ded19081 | no-one-left-behind-real-world-federated-class | 2302.00903 | null | https://arxiv.org/abs/2302.00903v1 | https://arxiv.org/pdf/2302.00903v1.pdf | No One Left Behind: Real-World Federated Class-Incremental Learning | Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most existing models unreasonably assume that data categories of FL framework are known and fxed in advance. It renders the global model to signifcantly degrade recognition perform... | ['Dengxin Dai', 'Bernt Schiele', 'Yulun Zhang', 'Gan Sun', 'Yang Cong', 'Jiahua Dong'] | 2023-02-02 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-2.79937357e-01 -5.77672720e-02 -2.84519196e-01 -5.90172231e-01
-5.16471863e-01 -6.17586732e-01 3.23495418e-01 -2.48730853e-01
-5.02658665e-01 9.87249553e-01 5.93278818e-02 -1.12967044e-01
-4.78209108e-02 -8.32593679e-01 -7.93728709e-01 -1.03910470e+00
2.22278282e-01 5.73903978e-01 3.15773100e-01 1.81713089... | [5.855233192443848, 6.330670356750488] |
dcff982f-1b50-4873-addb-1d00a62f134f | restore-anything-pipeline-segment-anything | 2305.13093 | null | https://arxiv.org/abs/2305.13093v2 | https://arxiv.org/pdf/2305.13093v2.pdf | Restore Anything Pipeline: Segment Anything Meets Image Restoration | Recent image restoration methods have produced significant advancements using deep learning. However, existing methods tend to treat the whole image as a single entity, failing to account for the distinct objects in the image that exhibit individual texture properties. Existing methods also typically generate a single ... | ['Christian Holz', 'Jiaxi Jiang'] | 2023-05-22 | null | null | null | null | ['deblurring', 'jpeg-artifact-removal'] | ['computer-vision', 'computer-vision'] | [ 4.66883272e-01 -5.05647838e-01 2.51654863e-01 -2.97389746e-01
-9.35533047e-01 -4.86211032e-01 3.53175312e-01 -1.18274957e-01
-3.59528661e-02 1.94855452e-01 3.07046682e-01 -3.63757908e-01
1.11308441e-01 -7.16170609e-01 -7.62914419e-01 -7.78644800e-01
2.31002524e-01 8.65480956e-03 1.85174540e-01 -3.00270438... | [11.202908515930176, -2.1902220249176025] |
c682d72f-f765-4a00-b117-d9e1adcdc041 | tarvis-a-unified-approach-for-target-based | 2301.02657 | null | https://arxiv.org/abs/2301.02657v2 | https://arxiv.org/pdf/2301.02657v2.pdf | TarViS: A Unified Approach for Target-based Video Segmentation | The general domain of video segmentation is currently fragmented into different tasks spanning multiple benchmarks. Despite rapid progress in the state-of-the-art, current methods are overwhelmingly task-specific and cannot conceptually generalize to other tasks. Inspired by recent approaches with multi-task capability... | ['Bastian Leibe', 'Deva Ramanan', 'Jonathon Luiten', 'Alexander Hermans', 'Ali Athar'] | 2023-01-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Athar_TarViS_A_Unified_Approach_for_Target-Based_Video_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Athar_TarViS_A_Unified_Approach_for_Target-Based_Video_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation', 'video-instance-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.42311913e-01 -2.47047648e-01 -5.07892549e-01 -3.86833489e-01
-1.20803607e+00 -7.34663367e-01 5.18346965e-01 -5.24225891e-01
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640fc451-9a44-47af-bd6c-bc45177f4397 | focal-visual-text-attention-for-visual | 1806.01873 | null | https://arxiv.org/abs/1806.01873v2 | https://arxiv.org/pdf/1806.01873v2.pdf | Focal Visual-Text Attention for Visual Question Answering | Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photos, we have to look at whole collections with sequences of photos or videos... | ['Li-Jia Li', 'Junwei Liang', 'Alexander Hauptmann', 'Lu Jiang', 'Liangliang Cao'] | 2018-06-05 | focal-visual-text-attention-for-visual-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Liang_Focal_Visual-Text_Attention_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Liang_Focal_Visual-Text_Attention_CVPR_2018_paper.pdf | cvpr-2018-6 | ['memex-question-answering'] | ['natural-language-processing'] | [ 1.58332005e-01 -2.06042528e-01 1.14849649e-01 -4.98151094e-01
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3.34418714e-01 5.52868485e-01 5.44222713e-01 -3.32143635... | [10.597115516662598, 1.2876787185668945] |
72b5cfd5-7d65-4a89-ac96-663052f23915 | depth-based-6dof-object-pose-estimation-using | 2303.02133 | null | https://arxiv.org/abs/2303.02133v2 | https://arxiv.org/pdf/2303.02133v2.pdf | Depth-based 6DoF Object Pose Estimation using Swin Transformer | Accurately estimating the 6D pose of objects is crucial for many applications, such as robotic grasping, autonomous driving, and augmented reality. However, this task becomes more challenging in poor lighting conditions or when dealing with textureless objects. To address this issue, depth images are becoming an increa... | ['Ioannis Stamos', 'Zhujun Li'] | 2023-03-03 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'robots'] | [ 1.01073757e-01 -2.45885894e-01 -1.82317406e-01 -4.69784707e-01
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2.10853606e-01 8.44480097e-01 3.47131670e-01 4.55772951... | [7.511162281036377, -2.6066370010375977] |
233183bd-08b4-406e-ac59-682fa577f5c6 | customics-a-versatile-deep-learning-based | 2209.05485 | null | https://arxiv.org/abs/2209.05485v1 | https://arxiv.org/pdf/2209.05485v1.pdf | CustOmics: A versatile deep-learning based strategy for multi-omics integration | Recent advances in high-throughput sequencing technologies have enabled the extraction of multiple features that depict patient samples at diverse and complementary molecular levels. The generation of such data has led to new challenges in computational biology regarding the integration of high-dimensional and heteroge... | ['Paul-Henry Cournède', 'Stefan Michiels', 'Yoann Pradat', 'Hakim Benkirane'] | 2022-09-12 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.20282339e-02 -2.80886501e-01 8.90112966e-02 -2.59117037e-01
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-7.64908716e-02 6.84330046e-01 -4.06648487e-01 -1.39209837... | [5.968252658843994, 5.656187534332275] |
34583b2a-9fa9-452b-81a3-ec2ace1dabf6 | fusemodnet-real-time-camera-and-lidar-based | 1910.05395 | null | https://arxiv.org/abs/1910.05395v3 | https://arxiv.org/pdf/1910.05395v3.pdf | FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving | Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned attending to the future states of the moving elements of the scene. Motion can be perceived using temporal information such as optical flow... | ['Ganesh Sistu', 'Mohamed Ramzy', 'Hazem Rashed', 'Senthil Yogamani', 'Victor Vaquero', 'Ahmad El Sallab'] | 2019-10-11 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [-5.42270280e-02 -5.53790212e-01 8.79695490e-02 -2.65346825e-01
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-1.38365045e-01 1.68426648e-01 6.70963347e-01 -1.14302449... | [8.12402629852295, -1.5170823335647583] |
9c93ae77-4d7d-4da1-99fe-3baf782b915a | asking-questions-the-human-way-scalable | 2002.00748 | null | https://arxiv.org/abs/2002.00748v2 | https://arxiv.org/pdf/2002.00748v2.pdf | Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus | The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffec... | ['Yancheng He', 'Di Niu', 'Haolan Chen', 'Haojie Wei', 'Bang Liu'] | 2020-01-27 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 1.73688188e-01 7.69509077e-01 3.51879686e-01 -4.11696255e-01
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6.18132830e-01 9.54809070e-01 1.10559076e-01 -7.70981312... | [11.545605659484863, 8.153206825256348] |
8410401a-a95d-4db4-a6a1-c4f2ff1cc29f | an-extensive-empirical-evaluation-of | null | null | https://aclanthology.org/E17-1048 | https://aclanthology.org/E17-1048.pdf | An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages | This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarely- and unseen words gracefully. We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological ... | ['Guenter Neumann', 'Josef van Genabith', 'Georg Heigold'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.90504420e-02 -2.51407772e-01 -8.42913147e-03 -3.20455879e-01
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75dffd6e-f9ca-444b-a8a8-7a1eb4d1d146 | business-entity-matching-with-siamese-graph | 2105.03701 | null | https://arxiv.org/abs/2105.03701v1 | https://arxiv.org/pdf/2105.03701v1.pdf | Business Entity Matching with Siamese Graph Convolutional Networks | Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent developments in machine learning and in particular deep learning have opened the way to more general and efficient solutions to da... | ['Anton Zorin', 'Christoph Miksovic', 'Paolo Scotton', 'Katsiaryna Mirylenka', 'Mattia Atzeni', 'Evgeny Krivosheev'] | 2021-05-08 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-4.56850469e-01 2.73323417e-01 -6.53449714e-01 -1.76949888e-01
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cf23de7e-b4a6-4587-a44b-179ec2cf385a | docstruct-a-multimodal-method-to-extract | 2010.11685 | null | https://arxiv.org/abs/2010.11685v1 | https://arxiv.org/pdf/2010.11685v1.pdf | DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding | Form understanding depends on both textual contents and organizational structure. Although modern OCR performs well, it is still challenging to realize general form understanding because forms are commonly used and of various formats. The table detection and handcrafted features in previous works cannot apply to all fo... | ['Ding Liang', 'Xuebo Liu', 'Mingjie Zhan', 'Zilong Wang'] | 2020-10-15 | null | https://aclanthology.org/2020.findings-emnlp.80 | https://aclanthology.org/2020.findings-emnlp.80.pdf | findings-of-the-association-for-computational | ['table-detection'] | ['miscellaneous'] | [ 3.58026892e-01 -3.86440575e-01 -2.59877086e-01 -4.09077555e-01
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2.31326416e-01 1.35063827e-01 2.78299332e-01 -3.36185515... | [11.535667419433594, 2.4460361003875732] |
e42dd7f6-d2a1-4881-8fbf-b9e7ab520e19 | a-comparative-study-of-pre-trained-encoders | null | null | https://openreview.net/forum?id=tJPQtbiO6jv | https://openreview.net/pdf?id=tJPQtbiO6jv | A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition | Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data. However, their performance in low-resource scenarios, where such data is not available, remain... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 7.45608360e-02 1.70370508e-02 -1.34229332e-01 -4.40484554e-01
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-3.42740417e-02 6.04831219e-01 2.92932183e-01 -4.57931936... | [9.656146049499512, 9.313446998596191] |
e4a89db1-01e7-42b6-939e-761e778850d0 | a-holistic-approach-to-cross-channel-image | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Nam_A_Holistic_Approach_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Nam_A_Holistic_Approach_CVPR_2016_paper.pdf | A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising | Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different col... | ['Youngbae Hwang', 'Seonghyeon Nam', 'Seon Joo Kim', 'Yasuyuki Matsushita'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['tone-mapping'] | ['computer-vision'] | [ 3.14552546e-01 -6.49897099e-01 6.96803987e-01 -1.79089069e-01
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3.71019602e-01 4.96791229e-02 5.76814890e-01 -1.07510544... | [11.259976387023926, -2.4548513889312744] |
c5262aac-f6f6-4bd7-a04e-dce3e8a110ab | linear-algebra-with-transformers | null | null | https://openreview.net/forum?id=L2a_bcarHcF | https://openreview.net/pdf?id=L2a_bcarHcF | Linear algebra with transformers | Most applications of transformers to mathematics, from integration to theorem proving, focus on symbolic computation. In this paper, we show that transformers can be trained to perform numerical calculations with high accuracy. We consider problems of linear algebra: matrix transposition, addition, multiplication, eig... | ['Francois Charton'] | 2021-09-29 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.67436707e-02 -1.83376357e-01 -2.18292370e-01 -1.03604168e-01
-8.97654653e-01 -7.68843770e-01 5.81478000e-01 -1.50131018e-04
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-4.44781750e-01 6.93223357e-01 -1.63233593e-01 -5.62193513... | [9.140740394592285, 7.1045451164245605] |
98f6765e-5953-41ab-93cb-667a4ccb16bc | user-assisted-video-reflection-removal | 2009.03281 | null | https://arxiv.org/abs/2009.03281v1 | https://arxiv.org/pdf/2009.03281v1.pdf | User-assisted Video Reflection Removal | Reflections in videos are obstructions that often occur when videos are taken behind reflective surfaces like glass. These reflections reduce the quality of such videos, lead to information loss and degrade the accuracy of many computer vision algorithms. A video containing reflections is a combination of background an... | ['Mohamed Hefeeda', 'Mohamed Elgharib', 'Amgad Ahmed', 'Suhong Kim'] | 2020-09-07 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 6.85694158e-01 -2.90806919e-01 2.63234615e-01 1.53535634e-01
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1.25713721e-01 -5.36379814e-01 5.85579276e-01 -4.93405797... | [10.389802932739258, -2.6144344806671143] |
f72c8a03-94d3-4d90-b56e-31ff6b27aec7 | chain-of-thought-prompt-distillation-for | 2306.14122 | null | https://arxiv.org/abs/2306.14122v2 | https://arxiv.org/pdf/2306.14122v2.pdf | Chain-of-Thought Prompt Distillation for Multimodal Named Entity and Multimodal Relation Extraction | Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) necessitate the fundamental reasoning capacity for intricate linguistic and multimodal comprehension. In this study, we explore distilling the reasoning ability of large language models (LLMs) into a more compact student model by genera... | ['Yujian Feng', 'Feng Chen'] | 2023-06-25 | null | null | null | null | ['domain-generalization', 'relation-extraction'] | ['methodology', 'natural-language-processing'] | [ 6.45941019e-01 4.81148064e-01 1.02589980e-01 -5.75902283e-01
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2.53008664e-01 4.88240093e-01 -2.02349767e-01 -3.71478766... | [10.990202903747559, 8.132355690002441] |
55d3535a-0ba3-44a0-9a0c-eb9321a64aab | sinddm-a-single-image-denoising-diffusion | 2211.16582 | null | https://arxiv.org/abs/2211.16582v3 | https://arxiv.org/pdf/2211.16582v3.pdf | SinDDM: A Single Image Denoising Diffusion Model | Denoising diffusion models (DDMs) have led to staggering performance leaps in image generation, editing and restoration. However, existing DDMs use very large datasets for training. Here, we introduce a framework for training a DDM on a single image. Our method, which we coin SinDDM, learns the internal statistics of t... | ['Tomer Michaeli', 'Matan Kleiner', 'Shahar Yadin', 'Vladimir Kulikov'] | 2022-11-29 | null | null | null | null | ['text-guided-image-editing', 'single-image-generation', 'text-guided-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.09294218e-01 -8.51728916e-02 2.82413155e-01 -1.84203476e-01
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5.16457558e-01 3.80186051e-01 -9.97304246e-02 -3.43246609... | [11.465901374816895, -0.44852444529533386] |
231507f0-240a-4c27-a9c1-3c5ae840d9bc | constrained-causal-bayesian-optimization | 2305.20011 | null | https://arxiv.org/abs/2305.20011v1 | https://arxiv.org/pdf/2305.20011v1.pdf | Constrained Causal Bayesian Optimization | We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restric... | ['Silvia Chiappa', 'Ira Ktena', 'Alan Malek', 'Virginia Aglietti'] | 2023-05-31 | null | null | null | null | ['gaussian-processes', 'bayesian-optimization'] | ['methodology', 'methodology'] | [ 5.67255974e-01 4.83951062e-01 -6.64471030e-01 -2.09087417e-01
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-6.79197609e-01 8.13777268e-01 -2.55618058e-02 5.52340508... | [7.79578161239624, 5.303860664367676] |
709c051c-5309-4e30-b281-565b2465e789 | knowledge-graph-for-nlg-in-the-context-of | 2307.01548 | null | https://arxiv.org/abs/2307.01548v1 | https://arxiv.org/pdf/2307.01548v1.pdf | Knowledge Graph for NLG in the context of conversational agents | The use of knowledge graphs (KGs) enhances the accuracy and comprehensiveness of the responses provided by a conversational agent. While generating answers during conversations consists in generating text from these KGs, it is still regarded as a challenging task that has gained significant attention in recent years. I... | ['Christophe Cruz', 'Massinissa Atmani', 'Hussam Ghanem'] | 2023-07-04 | null | null | null | null | ['knowledge-graphs', 'text-generation', 'kg-to-text'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 2.08723232e-01 8.48166108e-01 2.58731954e-02 -4.41462249e-01
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1.00373410e-01 1.04319477e+00 -4.89678413e-01 -6.95218801... | [12.440619468688965, 8.353795051574707] |
3abd8811-c624-4e30-95f3-6dc2ddbe57ed | cross-lingual-information-retrieval-and | null | null | https://aclanthology.org/R13-1063 | https://aclanthology.org/R13-1063.pdf | Cross-Lingual Information Retrieval and Semantic Interoperability for Cultural Heritage Repositories | null | ['Maria Pia di Buono', 'Federica Marano', 'Johanna Monti', 'Mario Monteleone'] | 2013-09-01 | cross-lingual-information-retrieval-and-1 | https://aclanthology.org/R13-1063 | https://aclanthology.org/R13-1063.pdf | ranlp-2013-9 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.383035659790039, 3.59415340423584] |
fcd905a5-086e-458f-a02d-a65bed1dcc84 | cdnet-a-cascaded-decoupling-architecture-for | null | null | https://openreview.net/forum?id=DmKu5T2gEqc | https://openreview.net/pdf?id=DmKu5T2gEqc | CDNet: A cascaded decoupling architecture for video prediction | Video prediction is an essential task in the computer vision community, helping to solve many downstream vision tasks by predicting and modeling future motion dynamics and appearance. In the deterministic video prediction task, current methods mainly employ variants of stacked Recurrent Neural Networks (RNN) to capture... | ['Mingtao Pei', 'Chuanqi Zang'] | 2021-09-29 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 4.53069881e-02 -2.55623937e-01 -2.69794196e-01 -2.35547498e-01
4.34590541e-02 -1.50065482e-01 5.75438201e-01 -7.34415412e-01
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1.05951414e-01 -4.13907021e-01 -7.85693467e-01 -7.90579855e-01
1.64852422e-02 -2.04284966e-01 5.53527594e-01 -1.09469846... | [8.802618026733398, 0.17750431597232819] |
680e6e60-7e69-4054-8ebc-8eb149182f92 | detecting-and-classifying-lesions-in | 1707.08401 | null | http://arxiv.org/abs/1707.08401v3 | http://arxiv.org/pdf/1707.08401v3.pdf | Detecting and classifying lesions in mammograms with Deep Learning | In the last two decades Computer Aided Diagnostics (CAD) systems were
developed to help radiologists analyze screening mammograms. The benefits of
current CAD technologies appear to be contradictory and they should be improved
to be ultimately considered useful. Since 2012 deep convolutional neural
networks (CNN) have ... | ['István Csabai', 'Péter Pollner', 'Zsuzsa Unger', 'Anna Horváth', 'Dezső Ribli'] | 2017-07-26 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.84988379e-01 4.24813271e-01 -4.54175562e-01 -5.40739477e-01
-8.96254241e-01 -1.79883078e-01 4.70446587e-01 4.24366057e-01
-6.02683783e-01 3.28245342e-01 -2.52118111e-01 -7.93251157e-01
-1.35330409e-01 -7.77555585e-01 -4.54939961e-01 -6.31565809e-01
-1.74746796e-01 7.23444462e-01 5.32867253e-01 -1.84555262... | [15.252618789672852, -2.5111420154571533] |
41a4d4d5-ae1e-41ec-8903-84db8bec9577 | speaker-and-age-invariant-training-for-child | 2210.10231 | null | https://arxiv.org/abs/2210.10231v2 | https://arxiv.org/pdf/2210.10231v2.pdf | Speaker- and Age-Invariant Training for Child Acoustic Modeling Using Adversarial Multi-Task Learning | One of the major challenges in acoustic modelling of child speech is the rapid changes that occur in the children's articulators as they grow up, their differing growth rates and the subsequent high variability in the same age group. These high acoustic variations along with the scarcity of child speech corpora have im... | ['Julien Epps', 'Beena Ahmed', 'Mostafa Shahin'] | 2022-10-19 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.56619537e-01 2.79664367e-01 3.73830914e-01 -4.87806171e-01
-9.00366724e-01 -2.93377250e-01 2.43327558e-01 -8.62943456e-02
-3.77432913e-01 3.90117854e-01 1.71333641e-01 -1.38369100e-02
4.36421074e-02 -5.16326129e-01 -5.36812901e-01 -7.54637837e-01
7.55270496e-02 5.09920299e-01 2.18068808e-01 -6.73502460... | [14.437618255615234, 6.480188846588135] |
e510dc5c-07cc-4bbc-ac36-45a1a0351f10 | cqr-sql-conversational-question-reformulation | 2205.07686 | null | https://arxiv.org/abs/2205.07686v3 | https://arxiv.org/pdf/2205.07686v3.pdf | CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers | Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependenc... | ['Yunbo Cao', 'Zhoujun Li', 'Zhao Yan', 'Qian-Wen Zhang', 'Linzheng Chai', 'Dongling Xiao'] | 2022-05-16 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.26320392e-01 4.24934775e-01 -7.54231885e-02 -9.54117298e-01
-1.43478823e+00 -8.52009833e-01 5.17077208e-01 5.76097667e-01
-1.22952741e-02 2.85445601e-01 7.63318419e-01 -7.96309769e-01
1.05231598e-01 -1.20538366e+00 -9.13080513e-01 3.49841356e-01
4.13298607e-01 7.52345622e-01 5.04599094e-01 -6.65209293... | [10.019991874694824, 7.851653575897217] |
4cd97e9f-53d2-495d-a4ad-ae83d5ecac85 | strictly-breadth-first-amr-parsing | 2211.03922 | null | https://arxiv.org/abs/2211.03922v1 | https://arxiv.org/pdf/2211.03922v1.pdf | Strictly Breadth-First AMR Parsing | AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. We focus on the breadth-first strategy of this task, which was proposed recently and achieved better performance than other strategies. However, current models under this strategy only \emph{encourage} the model to produce the AMR grap... | ['Daniel Gildea', 'Chen Yu'] | 2022-11-08 | null | null | null | null | ['amr-parsing'] | ['natural-language-processing'] | [ 5.59224784e-01 9.44862187e-01 2.63899192e-02 -7.22220540e-01
-6.33642137e-01 -7.07275391e-01 4.71996069e-01 1.81856577e-03
-6.36932701e-02 5.03773212e-01 2.90448368e-01 -6.44201875e-01
2.63034016e-01 -1.21500444e+00 -6.33919120e-01 8.38371590e-02
3.33987266e-01 5.80868542e-01 3.89057875e-01 -4.02613461... | [10.441776275634766, 9.312012672424316] |
0a779e47-26b1-47a4-b10f-dd8cc269cfba | stochastic-video-prediction-with-structure | 2203.10528 | null | https://arxiv.org/abs/2203.10528v2 | https://arxiv.org/pdf/2203.10528v2.pdf | Stochastic Video Prediction with Structure and Motion | While stochastic video prediction models enable future prediction under uncertainty, they mostly fail to model the complex dynamics of real-world scenes. For example, they cannot provide reliable predictions for scenes with a moving camera and independently moving foreground objects in driving scenarios. The existing m... | ['Fatma Güney', 'Sadra Safadoust', 'Adil Kaan Akan'] | 2022-03-20 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 1.11967467e-01 -1.97966680e-01 -2.13869676e-01 -5.26831925e-01
-7.21436962e-02 -5.37766099e-01 9.19998467e-01 -3.05505633e-01
-9.49868094e-03 5.43217182e-01 4.23731774e-01 -1.79979235e-01
1.82630673e-01 -6.65735543e-01 -9.59849060e-01 -8.06779146e-01
-1.96097761e-01 4.90402192e-01 7.42688954e-01 -1.35394245... | [8.487957000732422, 0.12461459636688232] |
61721afc-5a94-4631-8b49-abcb94abcb8d | elaborative-rehearsal-for-zero-shot-action | 2108.02833 | null | https://arxiv.org/abs/2108.02833v2 | https://arxiv.org/pdf/2108.02833v2.pdf | Elaborative Rehearsal for Zero-shot Action Recognition | The growing number of action classes has posed a new challenge for video understanding, making Zero-Shot Action Recognition (ZSAR) a thriving direction. The ZSAR task aims to recognize target (unseen) actions without training examples by leveraging semantic representations to bridge seen and unseen actions. However, du... | ['Dong Huang', 'ShiZhe Chen'] | 2021-08-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_Elaborative_Rehearsal_for_Zero-Shot_Action_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_Elaborative_Rehearsal_for_Zero-Shot_Action_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.27291632e-01 -1.18975371e-01 -4.50203031e-01 -3.29063714e-01
-7.37349331e-01 -3.69797647e-01 7.76946723e-01 -5.88944033e-02
-4.60056394e-01 4.70489293e-01 8.25756431e-01 2.25164339e-01
1.41058536e-02 -4.37496036e-01 -8.08801830e-01 -4.93871778e-01
-2.24572551e-02 2.82624394e-01 4.77005303e-01 -1.87710822... | [8.678227424621582, 0.8118352293968201] |
a3e6babf-3c18-4cc4-acfa-baacdad87d62 | a-financial-service-chatbot-based-on-deep | 2003.04987 | null | https://arxiv.org/abs/2003.04987v1 | https://arxiv.org/pdf/2003.04987v1.pdf | A Financial Service Chatbot based on Deep Bidirectional Transformers | We develop a chatbot using Deep Bidirectional Transformer models (BERT) to handle client questions in financial investment customer service. The bot can recognize 381 intents, and decides when to say "I don't know" and escalates irrelevant/uncertain questions to human operators. Our main novel contribution is the discu... | ['Yuxin Chen', 'Hussain Zaidi', 'Shi Yu'] | 2020-02-17 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [-3.58005315e-02 4.02369052e-01 2.37259731e-01 -5.67608356e-01
-9.86875057e-01 -7.68291235e-01 2.85380006e-01 -1.97988600e-01
-4.38441873e-01 8.53534758e-01 5.66360131e-02 -5.08248389e-01
-2.69519717e-01 -6.48777783e-01 -2.31644213e-01 -5.05072653e-01
4.21469092e-01 1.13909388e+00 2.35892922e-01 -5.69157004... | [12.658655166625977, 7.721740245819092] |
5e48eca7-a333-4b45-ba0c-24c5ac379dc9 | kernel-based-distributed-q-learning-a | 2302.10434 | null | https://arxiv.org/abs/2302.10434v1 | https://arxiv.org/pdf/2302.10434v1.pdf | Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes | In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases, such as cancer, diabetes, and mental disease, have been collected to facilitate medical diagnosis. Modeling the dynamic properties of EHRs related to chronic diseases can be efficiently done using dynamic treatment regimes (... | ['Shao-Bo Lin', 'Shaojie Tang', 'Yao Wang', 'Di Wang'] | 2023-02-21 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-4.64563631e-02 1.30524859e-01 -6.11457825e-01 -1.60760835e-01
-1.15994155e+00 -1.69782862e-01 -6.34294078e-02 4.97774839e-01
-3.27757150e-01 1.07743895e+00 2.08854303e-01 -5.06617904e-01
-6.27631605e-01 -9.96533096e-01 -6.29614711e-01 -7.10423589e-01
-1.73895106e-01 5.33165216e-01 -3.55457842e-01 3.13310735... | [4.073030948638916, 2.8453516960144043] |
3fb12a04-4f7f-4996-9e54-17cbf01973ed | leveraging-pre-trained-audioldm-for-sound | 2303.03857 | null | https://arxiv.org/abs/2303.03857v2 | https://arxiv.org/pdf/2303.03857v2.pdf | Leveraging Pre-trained AudioLDM for Text to Sound Generation: A Benchmark Study | Deep neural networks have recently achieved breakthroughs in sound generation with text prompts. Despite their promising performance, current text-to-sound generation models face issues on small-scale datasets (e.g., overfitting), significantly limiting their performance. In this paper, we investigate the use of pre-tr... | ['Wenwu Wang', 'Mark D. Plumbley', 'Xubo Liu', 'Jinhua Liang', 'Haohe Liu', 'Yi Yuan'] | 2023-03-07 | null | null | null | null | ['audio-generation'] | ['audio'] | [-2.36733574e-02 -2.46242285e-01 1.61068022e-01 -1.05101146e-01
-1.11693990e+00 -4.91453528e-01 6.12187326e-01 -2.80955344e-01
-1.27926961e-01 7.33565807e-01 5.27924836e-01 -2.12675884e-01
1.95571765e-01 -9.31132555e-01 -5.93205869e-01 -4.64023262e-01
1.83948442e-01 3.69974285e-01 6.39555231e-02 -2.62798160... | [15.364167213439941, 6.119099140167236] |
3b68622c-a3c0-4532-8945-365e11360f12 | implementation-of-robust-face-recognition | 1811.07339 | null | http://arxiv.org/abs/1811.07339v1 | http://arxiv.org/pdf/1811.07339v1.pdf | Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN | The way to accurately and effectively identify people has always been an
interesting topic in research and industry. With the rapid development of
artificial intelligence in recent years, facial recognition gains lots of
attention due to prompting the development of emerging identification methods.
Compared to traditio... | ['Yang Li', 'Sangwhan Cha'] | 2018-11-18 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-1.14277415e-02 -7.61548340e-01 -1.11378189e-02 -5.86453080e-01
2.93608844e-01 -1.51490092e-01 3.31866205e-01 -2.10535392e-01
-5.70657969e-01 3.20482582e-01 -3.32251877e-01 2.18883492e-02
-2.62462258e-01 -1.15234256e+00 -1.11974783e-01 -7.30580866e-01
1.75573677e-01 2.81450778e-01 -8.82848352e-02 5.10436893... | [13.264286994934082, 0.9022314548492432] |
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