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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0447484c-051a-4f5f-8ccb-4a5189241dd3 | adapointr-diverse-point-cloud-completion-with | 2301.04545 | null | https://arxiv.org/abs/2301.04545v1 | https://arxiv.org/pdf/2301.04545v1.pdf | AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers | In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion. By representing the point cloud as a set of unordered groups of points with posit... | ['Jie zhou', 'Jiwen Lu', 'Ziyi Wang', 'Yongming Rao', 'Xumin Yu'] | 2023-01-11 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-5.29513732e-02 -1.47175908e-01 -7.33688772e-02 -4.04131830e-01
-1.17564559e+00 -6.78034306e-01 4.90368396e-01 -7.60856345e-02
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2.68403664e-02 -1.09180713e+00 -1.20801604e+00 -4.20112491e-01
3.56400199e-02 5.50351024e-01 2.56539673e-01 -2.39221573... | [8.17960262298584, -3.528113842010498] |
a288e90f-d056-4441-8b16-c1adfe1a6912 | coffee-counterfactual-fairness-for | 2210.15500 | null | https://arxiv.org/abs/2210.15500v1 | https://arxiv.org/pdf/2210.15500v1.pdf | COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation | Personalized text generation has broad industrial applications, such as explanation generation for recommendations, conversational systems, etc. Personalized text generators are usually trained on user written text, e.g., reviews collected on e-commerce platforms. However, due to historical, social, or behavioral reaso... | ['Hongning Wang', 'Hamed Firooz', 'Jingzhou Liu', 'Maziar Sanjabi', 'Yi-Chia Wang', 'Qifan Wang', 'Shaoliang Nie', 'Nan Wang'] | 2022-10-14 | null | null | null | null | ['counterfactual-inference', 'explanation-generation'] | ['miscellaneous', 'natural-language-processing'] | [ 1.54436976e-01 6.39231503e-01 -5.97940087e-01 -5.80787838e-01
-4.80704606e-01 -4.24507618e-01 7.03198791e-01 -6.99780434e-02
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2.76770294e-01 3.96590889e-01 -7.48715043e-01 -5.27640760... | [9.502202033996582, 5.620974540710449] |
c1a01e37-60fc-4668-ad86-53004a1551ad | occinpflow-occlusion-inpainting-optical-flow | 2006.16637 | null | https://arxiv.org/abs/2006.16637v1 | https://arxiv.org/pdf/2006.16637v1.pdf | OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning | Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we pres... | ['Shuaicheng Liu', 'Kunming Luo', 'Chuan Wang', 'Jue Wang', 'Nianjin Ye'] | 2020-06-30 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-1.45273417e-01 -4.56056774e-01 -3.85081291e-01 -3.76847684e-01
-1.30786881e-01 -5.20260751e-01 2.77221352e-01 -5.11865735e-01
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9.28580090e-02 5.52603304e-02 2.15937749e-01 3.31095792... | [10.572612762451172, -1.4686411619186401] |
ea28c669-9791-41c3-8fc4-91f267f5c785 | anomaly-detection-in-3d-point-clouds-using | 2202.11660 | null | https://arxiv.org/abs/2202.11660v1 | https://arxiv.org/pdf/2202.11660v1.pdf | Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors | We present a new method for the unsupervised detection of geometric anomalies in high-resolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network o... | ['David Sattlegger', 'Paul Bergmann'] | 2022-02-23 | null | null | null | null | ['3d-anomaly-detection-and-segmentation', '3d-anomaly-detection'] | ['methodology', 'methodology'] | [ 1.28254607e-01 1.96862355e-01 3.64937425e-01 -4.93403226e-01
-7.38307476e-01 -3.64985734e-01 5.89484096e-01 4.75750774e-01
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-2.55505890e-01 6.82679176e-01 6.69288099e-01 -1.10806832... | [7.6424455642700195, 2.134782075881958] |
39a836de-02f0-409b-a766-8679cd7249ab | credible-remote-sensing-scene-classification | 2301.00622 | null | https://arxiv.org/abs/2301.00622v1 | https://arxiv.org/pdf/2301.00622v1.pdf | Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images | Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the pot... | ['Lijian Zhou', 'Jie Sun', 'Siyuan Hao', 'Qian Gao', 'Kun Zhao'] | 2023-01-02 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.62560728e-03 -1.51057482e-01 -2.51561373e-01 -3.54019284e-01
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2.77809829e-01 -1.00632999e-02 -3.82346690e-01 2.82712039... | [10.094260215759277, -1.5834299325942993] |
4681cce5-fd08-4481-a65a-b30ceff22874 | egpde-net-building-continuous-neural-networks | 2208.01913 | null | https://arxiv.org/abs/2208.01913v1 | https://arxiv.org/pdf/2208.01913v1.pdf | EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables | While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modelled with complex unknown partial differential... | ['John Y. Goulermas', 'Ping Guo', 'Rui Zhang', 'Kaizhu Huang', 'Xi Yang', 'Penglei Gao'] | 2022-08-03 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-6.98339520e-03 -3.58507663e-01 1.83921441e-01 1.14529990e-01
-5.12092233e-01 -4.34554666e-01 3.37609291e-01 -2.58638978e-01
-1.50331944e-01 8.70476186e-01 -3.15908432e-01 -3.81630003e-01
-4.70085889e-01 -4.84017551e-01 -7.40044713e-01 -9.09903169e-01
-4.96298790e-01 7.05925971e-02 -3.04226696e-01 -4.47144091... | [6.972207069396973, 3.0954368114471436] |
d693840c-4e63-49a0-9e38-4c198d294bb6 | word-level-representation-from-bytes-for | 2211.12677 | null | https://arxiv.org/abs/2211.12677v1 | https://arxiv.org/pdf/2211.12677v1.pdf | Word-Level Representation From Bytes For Language Modeling | Modern language models mostly take sub-words as input, a design that balances the trade-off between vocabulary size, number of parameters, and performance. However, sub-word tokenization still has disadvantages like not being robust to noise and difficult to generalize to new languages. Also, the current trend of scali... | ['Xipeng Qiu', 'Qipeng Guo', 'Chu-Tak Lee'] | 2022-11-23 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 5.34593873e-02 -2.34622329e-01 -5.43638229e-01 -3.29086006e-01
-9.95197177e-01 -6.84346199e-01 5.36946058e-01 3.23997736e-01
-1.11372638e+00 4.26002681e-01 3.21792066e-01 -9.66397822e-01
6.94634080e-01 -6.62030816e-01 -8.52206647e-01 -4.47781801e-01
4.01021652e-02 2.50571668e-01 2.31608585e-01 -5.61827347... | [10.78007698059082, 8.653162956237793] |
1e4a4c52-8a03-4ee8-b00d-a751580f82df | deepmesh-differentiable-iso-surface | 2106.11795 | null | https://arxiv.org/abs/2106.11795v2 | https://arxiv.org/pdf/2106.11795v2.pdf | DeepMesh: Differentiable Iso-Surface Extraction | Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable parameterization that is unlimited in resolution. Unfortunately... | ['Stephan R. Richter', 'Pascal Fua', 'Pierre Baque', 'Timur Bagautdinov', 'Artem Lukoianov', 'Edoardo Remelli', 'Benoit Guillard'] | 2021-06-20 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 1.73151359e-01 3.05511147e-01 4.01539266e-01 -3.25766146e-01
-8.04222047e-01 -6.31251335e-01 7.49920964e-01 7.38025531e-02
1.08186910e-02 6.87826216e-01 -2.36793488e-01 -2.14913189e-01
-1.54154956e-01 -1.21848083e+00 -9.93357480e-01 -5.35393953e-01
-1.65530935e-01 9.54865634e-01 1.42341673e-01 -4.95885164... | [8.69382095336914, -3.662421703338623] |
c673c36e-00c2-40d9-8434-d704382114b0 | you-do-not-need-additional-priors-or | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.pdf | You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement | Images captured in low-light conditions often suffer from significant quality degradation. Recent works have built a large variety of deep Retinex-based networks to enhance low-light images. The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-p... | ['Huadong Ma', 'Chuanming Wang', 'Xin Wang', 'Xiangyu Meng', 'Wenkai Zheng', 'Huiyuan Fu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['self-knowledge-distillation', 'image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.46247637e-01 -4.86619741e-01 1.55452639e-01 -4.81036872e-01
-5.44270933e-01 -2.50501513e-01 3.49178255e-01 -4.63090509e-01
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4.95469540e-01 -2.60151803e-01 -1.04702167e-01 -2.65417576... | [10.708074569702148, -2.517397403717041] |
eb46aeba-3b1b-4b59-b9c7-8f0382757c23 | collaboration-with-cellular-networks-for-rfi | 2210.17083 | null | https://arxiv.org/abs/2210.17083v1 | https://arxiv.org/pdf/2210.17083v1.pdf | Collaboration with Cellular Networks for RFI Cancellation at Radio Telescope | The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at the Radio Telescope without any active collaboration with the interfering source... | ['Aveek Dutta', 'Dola Saha', 'Maqsood Careem', 'Gregory Hellbourg', 'Shuvam Chakraborty'] | 2022-10-31 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 3.01947355e-01 -1.50289461e-01 4.03188318e-01 2.37908006e-01
-1.10401204e-02 -1.14709473e+00 6.19142771e-01 -6.22728169e-01
-4.99141030e-02 1.13616765e+00 2.08927989e-01 -7.66310513e-01
-9.39645648e-01 -7.48867810e-01 -2.55907744e-01 -7.18601227e-01
-4.85199124e-01 -2.66378261e-02 -2.08352476e-01 -2.24565536... | [6.3642120361328125, 1.234813928604126] |
4a90ccd5-bec8-443f-86ee-20e7d0e02232 | interval-type-2-fuzzy-neural-networks-for | 2302.10430 | null | https://arxiv.org/abs/2302.10430v1 | https://arxiv.org/pdf/2302.10430v1.pdf | Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification | Prediction of multi-dimensional labels plays an important role in machine learning problems. We found that the classical binary labels could not reflect the contents and their relationships in an instance. Hence, we propose a multi-label classification model based on interval type-2 fuzzy logic. In the proposed model, ... | ['Yiwen Wei', 'Feifei Li', 'Dayong Tian'] | 2023-02-21 | null | null | null | null | ['type'] | ['speech'] | [ 1.94072351e-02 4.32856828e-02 -4.13180977e-01 -1.09349692e+00
-4.89717752e-01 -6.08858645e-01 1.98334143e-01 4.44922775e-01
-2.01050311e-01 7.43734896e-01 -5.47816038e-01 -2.35861570e-01
-5.12998402e-01 -1.17203867e+00 -6.41855299e-01 -4.03669298e-01
3.84596847e-02 8.84574056e-01 -6.93499893e-02 -1.51173458... | [9.253998756408691, 4.127432823181152] |
9e57433b-6311-4fdc-b0b3-c210238154ca | large-language-models-can-self-improve | 2210.11610 | null | https://arxiv.org/abs/2210.11610v2 | https://arxiv.org/pdf/2210.11610v2.pdf | Large Language Models Can Self-Improve | Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-impro... | ['Jiawei Han', 'Hongkun Yu', 'Xuezhi Wang', 'Yuexin Wu', 'Le Hou', 'Shixiang Shane Gu', 'Jiaxin Huang'] | 2022-10-20 | null | null | null | null | ['gsm8k', 'arithmetic-reasoning', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning', 'reasoning'] | [-1.57092497e-01 5.13364911e-01 -7.13820234e-02 -7.65848458e-01
-1.32416177e+00 -7.15400577e-01 2.98955560e-01 -2.21527778e-02
-4.08368379e-01 8.88888299e-01 3.64134878e-01 -5.83447695e-01
7.59993941e-02 -8.11051071e-01 -9.78125095e-01 -8.08085501e-02
4.15896505e-01 8.42804551e-01 1.65837869e-01 -5.26235342... | [9.817837715148926, 7.4685587882995605] |
0bd1ad2d-7668-4ef4-8757-4de9607810d1 | type-theory-in-human-like-learning-and | 2210.01634 | null | https://arxiv.org/abs/2210.01634v1 | https://arxiv.org/pdf/2210.01634v1.pdf | Type theory in human-like learning and inference | Humans can generate reasonable answers to novel queries (Schulz, 2012): if I asked you what kind of food you want to eat for lunch, you would respond with a food, not a time. The thought that one would respond "After 4pm" to "What would you like to eat" is either a joke or a mistake, and seriously entertaining it as a ... | ['Tomer Ullman', 'Felix A. Sosa'] | 2022-10-04 | null | null | null | null | ['type'] | ['speech'] | [ 4.41032276e-02 7.32928336e-01 1.42069936e-01 -6.30746424e-01
-1.46306485e-01 -6.29146099e-01 8.27723324e-01 4.79134053e-01
-4.96259302e-01 8.50847423e-01 2.72650272e-01 -8.23647201e-01
-2.22677499e-01 -1.19044614e+00 -5.44745743e-01 -1.18251108e-01
4.53967124e-01 7.35598445e-01 2.28831157e-01 -4.19292510... | [9.455474853515625, 7.132212162017822] |
dc25239c-f333-417d-8f59-79c07613fa5c | generalizing-surgical-instruments | 2306.16285 | null | https://arxiv.org/abs/2306.16285v1 | https://arxiv.org/pdf/2306.16285v1.pdf | Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis | Despite their impressive performance in various surgical scene understanding tasks, deep learning-based methods are frequently hindered from deploying to real-world surgical applications for various causes. Particularly, data collection, annotation, and domain shift in-between sites and patients are the most common obs... | ['Hongliang Ren', 'Mengya Xu', 'Mobarakol Islam', 'An Wang'] | 2023-06-28 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.15162075e-01 2.12878913e-01 -3.32102597e-01 -1.93799019e-01
-8.88880491e-01 -5.90426147e-01 3.41530740e-01 2.08565500e-02
-4.12117630e-01 6.81091547e-01 -2.81674936e-02 -4.43456382e-01
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4.60457265e-01 4.09952372e-01 1.52142957e-01 -1.33908316... | [14.588363647460938, -2.248415470123291] |
a6890a78-0630-409d-b2f3-c2fd8b36fb31 | cell-detection-from-imperfect-annotation-by | 2107.09289 | null | https://arxiv.org/abs/2107.09289v2 | https://arxiv.org/pdf/2107.09289v2.pdf | Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification | Cell detection is an essential task in cell image analysis. Recent deep learning-based detection methods have achieved very promising results. In general, these methods require exhaustively annotating the cells in an entire image. If some of the cells are not annotated (imperfect annotation), the detection performance ... | ['Ryoma Bise', 'Kazuya Nishimura', 'Daiki Suehiro', 'Kazuma Fujii'] | 2021-07-20 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.00912881e-01 4.18878719e-02 -9.67453644e-02 -1.49022743e-01
-9.47670162e-01 -6.23320818e-01 3.35034847e-01 6.09501421e-01
-7.21227050e-01 1.17923152e+00 -4.33134019e-01 1.23850219e-02
8.11472535e-01 -6.61005378e-01 -7.12908506e-01 -1.16732311e+00
9.90887210e-02 8.33178639e-01 4.92913067e-01 4.80316937... | [14.687287330627441, -3.180307388305664] |
2321efa9-d7ea-46e8-8456-ee27689df25d | reading-between-the-lines-exploring-infilling | 2010.13944 | null | https://arxiv.org/abs/2010.13944v1 | https://arxiv.org/pdf/2010.13944v1.pdf | Reading Between the Lines: Exploring Infilling in Visual Narratives | Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence. In this regard, there is often crucial subtext that is derived from the surrounding contexts. The general seq2seq training methods render the models shorthanded while attem... | ['Alan Black', 'Ruo-Ping Dong', 'Khyathi Raghavi Chandu'] | 2020-10-26 | null | https://aclanthology.org/2020.emnlp-main.93 | https://aclanthology.org/2020.emnlp-main.93.pdf | emnlp-2020-11 | ['visual-storytelling'] | ['natural-language-processing'] | [ 7.22392440e-01 4.05029893e-01 1.21386461e-01 -5.60249507e-01
-1.12704802e+00 -8.77849519e-01 1.10553670e+00 -1.79664075e-01
-2.90091515e-01 1.08678591e+00 8.60274374e-01 -1.96930632e-01
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1.72150716e-01 4.93072450e-01 -1.59673601e-01 -1.56788886... | [11.164979934692383, 0.7061339020729065] |
ee2b381b-9a98-4c7f-b60c-ce3106bc4315 | neural-responding-machine-for-short-text | 1503.02364 | null | http://arxiv.org/abs/1503.02364v2 | http://arxiv.org/pdf/1503.02364v2.pdf | Neural Responding Machine for Short-Text Conversation | We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder
framework: it formalizes the generation of response as a decoding process based
on the latent representation of the input text, while both encoding and
decoding are re... | ['Hang Li', 'Zhengdong Lu', 'Lifeng Shang'] | 2015-03-09 | neural-responding-machine-for-short-text-1 | https://aclanthology.org/P15-1152 | https://aclanthology.org/P15-1152.pdf | ijcnlp-2015-7 | ['short-text-conversation'] | ['natural-language-processing'] | [ 5.84560633e-01 4.88011509e-01 3.30479145e-02 -7.12778926e-01
-1.09477699e+00 -2.88904488e-01 9.13942516e-01 -2.56534517e-01
-2.19219729e-01 7.08612859e-01 9.85706270e-01 -3.75410616e-01
3.59876156e-01 -8.95683408e-01 -5.19157290e-01 -1.48885399e-01
2.54924685e-01 1.09515440e+00 -4.35284913e-01 -9.14363086... | [12.575733184814453, 8.315216064453125] |
f42ec56a-f121-4e38-8010-1a9a46b70616 | dptdr-deep-prompt-tuning-for-dense-passage | 2208.11503 | null | https://arxiv.org/abs/2208.11503v1 | https://arxiv.org/pdf/2208.11503v1.pdf | DPTDR: Deep Prompt Tuning for Dense Passage Retrieval | Deep prompt tuning (DPT) has gained great success in most natural language processing~(NLP) tasks. However, it is not well-investigated in dense retrieval where fine-tuning~(FT) still dominates. When deploying multiple retrieval tasks using the same backbone model~(e.g., RoBERTa), FT-based methods are unfriendly in ter... | ['Ting Yao', 'Benyou Wang', 'Zhengyang Tang'] | 2022-08-24 | null | https://aclanthology.org/2022.coling-1.103 | https://aclanthology.org/2022.coling-1.103.pdf | coling-2022-10 | ['natural-questions', 'passage-retrieval'] | ['miscellaneous', 'natural-language-processing'] | [-6.97915554e-02 -4.57630008e-01 -2.27770939e-01 -1.58592537e-01
-1.24259698e+00 -7.40476727e-01 4.66620654e-01 9.85976425e-04
-7.37103939e-01 3.56114060e-01 -1.50769930e-02 -6.11686587e-01
-2.78133392e-01 -6.61857665e-01 -5.39105833e-01 -4.36333090e-01
2.50214458e-01 9.11113560e-01 2.12005764e-01 -5.46431005... | [11.408666610717773, 7.711244106292725] |
5b73757c-5471-488f-aaa4-c6b6d8497d0e | non-semantic-evaluation-of-image-forensics | 2105.02700 | null | https://arxiv.org/abs/2105.02700v1 | https://arxiv.org/pdf/2105.02700v1.pdf | Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database | With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image. Thus, the only forgery cues left are the specific alterations of one or several aspects of the image formation pipeline. This methodology creates automatically forged images ... | ['Jean-Michel Morel', 'Miguel Colom', 'Rafael Grompone', 'Marina Gardella', 'Tina Nikoukhah', 'Quentin Bammey'] | 2021-05-04 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 4.92637664e-01 -1.62147880e-02 3.99731606e-01 1.04410753e-01
-4.98040289e-01 -8.35976481e-01 7.27766633e-01 2.39068434e-01
-3.35282117e-01 3.17815930e-01 -2.21603513e-01 -5.03711760e-01
-5.17077707e-02 -6.17779016e-01 -7.87025630e-01 -3.09820235e-01
3.72298717e-01 8.35824311e-02 7.61739075e-01 3.14476155... | [12.419820785522461, 1.0226277112960815] |
a9dc5973-7900-4b8f-b228-581de190e561 | an-end-to-end-food-image-analysis-system | 2102.00645 | null | https://arxiv.org/abs/2102.00645v1 | https://arxiv.org/pdf/2102.00645v1.pdf | An End-to-End Food Image Analysis System | Modern deep learning techniques have enabled advances in image-based dietary assessment such as food recognition and food portion size estimation. Valuable information on the types of foods and the amount consumed are crucial for prevention of many chronic diseases. However, existing methods for automated image-based f... | ['Fengqing Zhu', 'Carol J. Boushey', 'Deborah A. Kerr', 'Janine L. Wright', 'Zeman Shao', 'Runyu Mao', 'Jiangpeng He'] | 2021-02-01 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 3.66011202e-01 -4.50929761e-01 -5.89922071e-01 -6.35015309e-01
-6.94361806e-01 -7.37734258e-01 -1.81792840e-01 8.94116580e-01
-1.58674359e-01 1.29060835e-01 1.43936992e-01 9.02470052e-02
4.12474751e-01 -1.15112758e+00 -1.07843387e+00 -6.40008688e-01
-2.02950388e-02 2.53737688e-01 -3.67714584e-01 2.06942812... | [11.561532020568848, 4.398895740509033] |
053ef02b-ff6d-4195-893b-2ebefb0b608e | learning-with-confident-examples-rank-pruning | 1705.01936 | null | http://arxiv.org/abs/1705.01936v3 | http://arxiv.org/pdf/1705.01936v3.pdf | Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels | Noisy PN learning is the problem of binary classification when training
examples may be mislabeled (flipped) uniformly with noise rate rho1 for
positive examples and rho0 for negative examples. We propose Rank Pruning (RP)
to solve noisy PN learning and the open problem of estimating the noise rates,
i.e. the fraction ... | ['Isaac L. Chuang', 'Curtis G. Northcutt', 'Tailin Wu'] | 2017-05-04 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 3.72138768e-01 3.45424742e-01 -3.66593927e-01 -4.43262726e-01
-1.42549014e+00 -7.54642725e-01 3.22243214e-01 6.43275306e-02
-6.09107018e-01 1.17977107e+00 -3.12318265e-01 -4.44762796e-01
-3.16440850e-01 -8.00222099e-01 -1.17729962e+00 -8.62741411e-01
9.05759037e-02 7.44600356e-01 5.25807478e-02 6.54025078... | [8.173715591430664, 4.011573791503906] |
ac6d0f96-2be1-40df-b987-afa12bf65f69 | localized-text-to-image-generation-for-free | 2306.14636 | null | https://arxiv.org/abs/2306.14636v1 | https://arxiv.org/pdf/2306.14636v1.pdf | Localized Text-to-Image Generation for Free via Cross Attention Control | Despite the tremendous success in text-to-image generative models, localized text-to-image generation (that is, generating objects or features at specific locations in an image while maintaining a consistent overall generation) still requires either explicit training or substantial additional inference time. In this wo... | ['J. Zico Kolter', 'Ruslan Salakhutdinov', 'Yutong He'] | 2023-06-26 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 6.53399944e-01 4.71385419e-01 2.99946796e-02 -5.20078957e-01
-1.28816676e+00 -6.70650482e-01 1.03283060e+00 -1.97466820e-01
-5.39625548e-02 5.16054690e-01 1.55236293e-02 -6.74087256e-02
2.36340538e-01 -9.86568570e-01 -1.11419046e+00 -4.12859708e-01
2.70167679e-01 9.60001647e-01 1.62035972e-01 -8.25165957... | [11.26921272277832, -0.1145544946193695] |
144ebc5e-83d8-4047-88c1-5915a97900dd | ccprompt-counterfactual-contrastive-prompt | 2211.05987 | null | https://arxiv.org/abs/2211.05987v1 | https://arxiv.org/pdf/2211.05987v1.pdf | CCPrompt: Counterfactual Contrastive Prompt-Tuning for Many-Class Classification | With the success of the prompt-tuning paradigm in Natural Language Processing (NLP), various prompt templates have been proposed to further stimulate specific knowledge for serving downstream tasks, e.g., machine translation, text generation, relation extraction, and so on. Existing prompt templates are mainly shared a... | ['Guodong Long', 'Jing Jiang', 'Tao Shen', 'Canran Xu', 'Yang Li'] | 2022-11-11 | null | null | null | null | ['relation-classification', 'entity-typing'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.01423842e-01 3.97313684e-01 -7.48370349e-01 -4.60996032e-01
-9.16270196e-01 -5.83878875e-01 1.11615813e+00 3.20881046e-02
-3.45518082e-01 1.19395375e+00 5.14903724e-01 -4.89083827e-01
-1.48970455e-01 -5.86081445e-01 -5.49165666e-01 -7.15101600e-01
1.10471025e-01 5.58879077e-01 -2.39748105e-01 -1.04909323... | [9.796875953674316, 8.54799747467041] |
3eb24548-9ce0-40c0-b8a3-84f371e46009 | cross-cultural-deception-detection | null | null | https://aclanthology.org/P14-2072 | https://aclanthology.org/P14-2072.pdf | Cross-cultural Deception Detection | null | ["Ver{\\'o}nica P{\\'e}rez-Rosas", 'Rada Mihalcea'] | 2014-06-01 | null | null | null | acl-2014-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.419890880584717, 3.7577121257781982] |
70e80982-dbc0-427e-93ce-5b14493a4dd8 | the-past-mistake-is-the-future-wisdom-error | null | null | https://openreview.net/forum?id=DW8WNS97jP5 | https://openreview.net/pdf?id=DW8WNS97jP5 | The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking | Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors, which are mainly caused by the phonological or visual similarity. Recently, pre-trained language models (PLMs) promote the progress of CSC task. However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task. PL... | ['Anonymous'] | 2021-11-16 | null | https://openreview.net/forum?id=cEA-p49sP8x | https://openreview.net/pdf?id=cEA-p49sP8x | acl-arr-september-2021-9 | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 2.21018642e-01 -3.63823503e-01 -3.84216616e-03 -5.78790382e-02
-4.57337111e-01 -1.87599942e-01 4.80034620e-01 2.12100863e-01
-5.76315165e-01 5.75887680e-01 5.41246891e-01 -2.62370110e-01
2.95256674e-01 -4.89734322e-01 -6.07931435e-01 -4.54519987e-01
6.38105035e-01 1.98427185e-01 5.76441526e-01 -1.25868499... | [10.942098617553711, 10.848289489746094] |
7f62c189-ab05-428d-87a1-068401e6383b | completeness-modeling-and-context-separation | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Completeness_Modeling_and_Context_Separation_for_Weakly_Supervised_Temporal_Action_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Completeness_Modeling_and_Context_Separation_for_Weakly_Supervised_Temporal_Action_CVPR_2019_paper.pdf | Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization | Temporal action localization is crucial for understanding untrimmed videos. In this work, we first identify two underexplored problems posed by the weak supervision for temporal action localization, namely action completeness modeling and action-context separation. Then by presenting a novel network architecture and it... | [' Yizhou Wang', ' Tingting Jiang', 'Daochang Liu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 6.24312758e-01 2.29326159e-01 -6.76142812e-01 -2.87598550e-01
-5.79797924e-01 -3.60529184e-01 6.23110056e-01 -3.91754389e-01
-4.51669961e-01 7.22523272e-01 4.95678037e-01 9.27278921e-02
5.72455861e-02 -9.45022926e-02 -8.05748403e-01 -7.94478536e-01
-4.00062442e-01 -9.65456963e-02 5.08819520e-01 1.32413328... | [8.425071716308594, 0.5744823217391968] |
f2e537d6-9332-4793-a90f-97da0a35599b | monocular-visual-odometry-for-an-unmanned-sea | 1707.04444 | null | http://arxiv.org/abs/1707.04444v2 | http://arxiv.org/pdf/1707.04444v2.pdf | Monocular Visual Odometry for an Unmanned Sea-Surface Vehicle | We tackle the problem of localizing an autonomous sea-surface vehicle in
river estuarine areas using monocular camera and angular velocity input from an
inertial sensor. Our method is challenged by two prominent drawbacks associated
with the environment, which are typically not present in standard visual
simultaneous l... | ['Phil Culverhouse', 'Robert Sutton', 'Riccardo Polvara', 'Sanjay Sharma', 'George Terzakis'] | 2017-07-14 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-7.30165914e-02 -2.05704551e-02 1.12117313e-01 -3.30913782e-01
-5.85154891e-01 -9.11494255e-01 5.37913263e-01 1.39571920e-01
-7.16168880e-01 9.81434941e-01 -2.80777514e-01 -2.02782899e-01
4.92163301e-02 -8.99251580e-01 -8.55828106e-01 -5.96460104e-01
-1.03208177e-01 6.90346837e-01 3.64520669e-01 -4.67246264... | [7.4404377937316895, -2.1543924808502197] |
4603c6be-8911-48d5-bf17-40568b0712b7 | an-efficient-multitask-neural-network-for | 2103.07615 | null | https://arxiv.org/abs/2103.07615v3 | https://arxiv.org/pdf/2103.07615v3.pdf | An Efficient Multitask Neural Network for Face Alignment, Head Pose Estimation and Face Tracking | While Convolutional Neural Networks (CNNs) have significantly boosted the performance of face related algorithms, maintaining accuracy and efficiency simultaneously in practical use remains challenging. The state-of-the-art methods employ deeper networks for better performance, which makes it less practical for mobile ... | ['Min Xu', 'Shuo Yang', 'Shiping Wen', 'Haimin Zhang', 'Jiahao Xia'] | 2021-03-13 | null | null | null | null | ['head-pose-estimation', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-5.50505400e-01 -2.85822928e-01 -1.51338622e-01 -6.26085937e-01
-1.83261767e-01 -3.97988223e-02 1.30174056e-01 -5.85406065e-01
-3.05463642e-01 1.93940103e-01 1.02467380e-01 7.52241388e-02
5.97074740e-02 -6.10765934e-01 -5.35884559e-01 -8.26311171e-01
9.01537985e-02 2.53213525e-01 1.73974156e-01 -1.35203227... | [13.443146705627441, 0.5156810283660889] |
a1023528-a30e-48d9-8b0c-fa779e8c5416 | re-improving-multi-hop-question-answering | null | null | http://rescience.github.io/bibliography/P_2021.html | https://zenodo.org/record/4834942/files/article.pdf | [Re] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings | Scope of Reproducibility
Our work consists of four parts:
1. Reproducing the results from [1].
2. Exploring the effect of various knowledge graph embedding models in the Knowledge Graph Embedding module.
3. Exploring the effect of various transformer models in the Question Embedding
module.
4. Verifying the importance... | ['Ashish Sardana', 'Jishnu Jaykumar P'] | 2021-01-31 | null | null | null | rc-2020 | ['knowledge-graph-embeddings', 'multi-hop-question-answering', 'knowledge-graph-embeddings', 'answer-selection'] | ['graphs', 'knowledge-base', 'methodology', 'natural-language-processing'] | [-3.00920814e-01 5.53046048e-01 1.37136966e-01 -1.06089145e-01
-1.14589155e+00 -9.11963820e-01 3.46872687e-01 4.84373748e-01
-4.29878682e-01 6.65372133e-01 2.01124877e-01 -8.41600537e-01
-5.88457346e-01 -7.45661736e-01 -8.05215061e-01 -3.32058966e-01
1.84141725e-01 5.84156036e-01 6.33741915e-01 -4.62604254... | [9.896696090698242, 8.046154975891113] |
f35700f9-a7de-4e04-896c-ab88aa3fb5c4 | weakly-supervised-indoor-localization-via | 2202.03239 | null | https://arxiv.org/abs/2202.03239v1 | https://arxiv.org/pdf/2202.03239v1.pdf | Weakly Supervised Indoor Localization via Manifold Matching | Inferring the location of a mobile device in an indoor setting is an open problem of utmost significance. A leading approach that does not require the deployment of expensive infrastructure is fingerprinting, where a classifier is trained to predict the location of a device based on its captured signal. The main caveat... | ['Ariel Jaffe', 'Ioannis G. Kevrekidis', 'Erez Peterfreund'] | 2022-02-07 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.31363738e-01 -1.38706472e-02 -4.63335440e-02 -4.57986236e-01
-8.42327416e-01 -8.19978535e-01 5.48519254e-01 1.89634413e-01
-3.21008563e-01 9.66754973e-01 -1.37582928e-01 -5.14241815e-01
-3.43612283e-01 -7.41975725e-01 -8.25752974e-01 -5.59227586e-01
-2.00068250e-01 2.45510951e-01 2.39409745e-01 1.78902641... | [6.407538414001465, 0.9161232113838196] |
54dc79b3-45c1-44e3-8f0f-4656d9444938 | wavelet-based-segmentation-on-the-sphere | 1609.06500 | null | https://arxiv.org/abs/1609.06500v2 | https://arxiv.org/pdf/1609.06500v2.pdf | Wavelet-Based Segmentation on the Sphere | Segmentation, a useful/powerful technique in pattern recognition, is the process of identifying object outlines within images. There are a number of efficient algorithms for segmentation in Euclidean space that depend on the variational approach and partial differential equation modelling. Wavelets have been used succe... | ['Xiaohao Cai', 'Jennifer Y. H. Chan', 'Jason D. McEwen', 'Christopher G. R. Wallis'] | 2016-09-21 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 2.34586373e-01 -1.18776307e-01 3.13437819e-01 -9.76779684e-02
-3.54788214e-01 -4.27230328e-01 3.31033587e-01 -2.30609383e-02
-4.69039857e-01 4.91023988e-01 -1.04057945e-01 -1.13843508e-01
-2.32737482e-01 -9.19257820e-01 -3.00795734e-01 -1.13403547e+00
2.90721357e-02 2.79729187e-01 5.77851832e-01 -3.46633613... | [11.619739532470703, -2.3888211250305176] |
375c6d32-5e72-45b0-af87-2b1499e8f2c1 | unitary-evolution-recurrent-neural-networks | 1511.06464 | null | http://arxiv.org/abs/1511.06464v4 | http://arxiv.org/pdf/1511.06464v4.pdf | Unitary Evolution Recurrent Neural Networks | Recurrent neural networks (RNNs) are notoriously difficult to train. When the
eigenvalues of the hidden to hidden weight matrix deviate from absolute value
1, optimization becomes difficult due to the well studied issue of vanishing
and exploding gradients, especially when trying to learn long-term
dependencies. To cir... | ['Yoshua Bengio', 'Martin Arjovsky', 'Amar Shah'] | 2015-11-20 | null | null | null | null | ['sequential-image-classification'] | ['computer-vision'] | [ 2.67661303e-01 1.65820658e-01 1.12183407e-01 -2.56692678e-01
-4.44676936e-01 -4.52838957e-01 3.09898913e-01 -2.23051876e-01
-7.98400879e-01 6.24574840e-01 1.92475930e-01 -6.68139935e-01
2.45898925e-02 -4.70501542e-01 -7.89279401e-01 -9.84395921e-01
-3.06156337e-01 5.45423388e-01 -2.54876047e-01 -4.33450758... | [7.85614538192749, 3.584315299987793] |
1b980507-2543-4135-830c-58937bf15356 | local-trend-inconsistency-a-prediction-driven | 1908.01146 | null | https://arxiv.org/abs/1908.01146v3 | https://arxiv.org/pdf/1908.01146v3.pdf | Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality | On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, t... | ['Stephen Jarvis', 'Carsten Maple', 'Yuhua Cui', 'Yi Su', 'Wentai Wu', 'Ligang He', 'Weiwei Lin'] | 2019-08-03 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 0.02817951 -0.6810697 0.10088519 -0.35414842 -0.30548394 -0.54783756
0.5005268 0.74169034 -0.2405337 0.34252796 -0.11618509 -0.29038447
-0.35239348 -0.77426726 -0.21672866 -0.6866133 -0.4590693 0.2033253
0.4997639 -0.02236547 0.4766048 0.7199758 -1.6166251 -0.21048321
1.0678381 1.5054196 -0.3... | [7.282581329345703, 2.799025774002075] |
e3ca8d50-c421-43fd-a327-7468ea12c402 | data-efficient-sequence-based-visual-place | 2302.13314 | null | https://arxiv.org/abs/2302.13314v1 | https://arxiv.org/pdf/2302.13314v1.pdf | Data-Efficient Sequence-Based Visual Place Recognition with Highly Compressed JPEG Images | Visual Place Recognition (VPR) is a fundamental task that allows a robotic platform to successfully localise itself in the environment. For decentralised VPR applications where the visual data has to be transmitted between several agents, the communication channel may restrict the localisation process when limited band... | ['Shoaib Ehsan', 'Klaus McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Mihnea-Alexandru Tomita'] | 2023-02-26 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 4.25201684e-01 -1.60631046e-01 -6.40506148e-02 1.07834078e-02
-5.31141102e-01 -5.68721056e-01 8.81908774e-01 4.12564516e-01
-1.10538685e+00 6.91734314e-01 -2.39946961e-01 -2.57981926e-01
-3.90206218e-01 -8.91187966e-01 -8.38337183e-01 -6.88587964e-01
-5.51454425e-01 3.92730296e-01 4.94899422e-01 -2.54808903... | [7.500892639160156, -1.8689601421356201] |
0bf9181b-1e0e-445d-b0e8-8c63f9184ee5 | one-shot-learning-for-deformable-medical | 1907.04641 | null | https://arxiv.org/abs/1907.04641v3 | https://arxiv.org/pdf/1907.04641v3.pdf | One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking | Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unsee... | ['Dimos Baltas', 'Tobias Fechter'] | 2019-07-10 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 1.63712770e-01 -9.11422670e-02 6.27108663e-02 -3.36726248e-01
-8.69467080e-01 -3.45335394e-01 7.91822016e-01 3.14206153e-01
-9.44790900e-01 4.43856150e-01 -1.26006141e-01 6.94871396e-02
-2.15695098e-01 -6.08017862e-01 -4.28471625e-01 -8.45451593e-01
-2.54720181e-01 9.03568089e-01 6.82010174e-01 -2.41358489... | [14.132004737854004, -2.6266515254974365] |
2f00df71-f4b8-4798-9ed1-e466cd338dbe | jointly-learning-visual-and-auditory-speech | 2212.06246 | null | https://arxiv.org/abs/2212.06246v2 | https://arxiv.org/pdf/2212.06246v2.pdf | Jointly Learning Visual and Auditory Speech Representations from Raw Data | We present RAVEn, a self-supervised multi-modal approach to jointly learn visual and auditory speech representations. Our pre-training objective involves encoding masked inputs, and then predicting contextualised targets generated by slowly-evolving momentum encoders. Driven by the inherent differences between video an... | ['Maja Pantic', 'Stavros Petridis', 'Rodrigo Mira', 'Pingchuan Ma', 'Alexandros Haliassos'] | 2022-12-12 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 2.26464465e-01 4.15260755e-02 -6.60249814e-02 -2.16161653e-01
-1.34870064e+00 -5.76505899e-01 7.90096223e-01 -2.13813379e-01
-6.23812377e-01 3.85408729e-01 7.07042456e-01 -2.71672875e-01
3.76128614e-01 -2.81831622e-01 -1.01090467e+00 -3.07905763e-01
-1.84632353e-02 2.92882740e-01 6.10541068e-02 -4.17629853... | [14.442608833312988, 5.041819095611572] |
50bb7bd6-3bc3-4195-87c3-d72565b4548b | finding-optimal-solutions-to-token-swapping | 1806.09487 | null | http://arxiv.org/abs/1806.09487v1 | http://arxiv.org/pdf/1806.09487v1.pdf | Finding Optimal Solutions to Token Swapping by Conflict-based Search and Reduction to SAT | We study practical approaches to solving the token swapping (TSWAP) problem
optimally in this short paper. In TSWAP, we are given an undirected graph with
colored vertices. A colored token is placed in each vertex. A pair of tokens
can be swapped between adjacent vertices. The goal is to perform a sequence of
swaps so ... | ['Pavel Surynek'] | 2018-06-25 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.87591428e-01 3.48227769e-01 -1.14982948e-01 -3.63539383e-02
-6.03521883e-01 -9.91017282e-01 3.75607789e-01 3.67873937e-01
-8.19232911e-02 1.11337948e+00 -5.32858014e-01 -4.90014553e-01
-4.32431191e-01 -1.03949869e+00 -5.62665343e-01 -6.40767217e-01
-7.74671853e-01 1.24971902e+00 1.01516259e+00 -3.90813082... | [4.980146884918213, 1.7973443269729614] |
8758ca72-1adb-42bc-8e22-f3100d307cf9 | multi-label-image-classification-via | 1809.05884 | null | http://arxiv.org/abs/1809.05884v2 | http://arxiv.org/pdf/1809.05884v2.pdf | Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection | Multi-label image classification is a fundamental but challenging task
towards general visual understanding. Existing methods found the region-level
cues (e.g., features from RoIs) can facilitate multi-label classification.
Nevertheless, such methods usually require laborious object-level annotations
(i.e., object labe... | ['Chunhong Pan', 'Lu Sheng', 'Yongcheng Liu', 'Shiming Xiang', 'Jing Shao', 'Junjie Yan'] | 2018-09-16 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.61487365e-01 -3.97904729e-03 -3.39566797e-01 -5.27882278e-01
-1.02702320e+00 -4.83138204e-01 4.19331878e-01 3.56173426e-01
-5.46406507e-01 4.20029938e-01 -2.66412854e-01 -1.74053848e-01
1.88646868e-01 -3.64281893e-01 -6.81542933e-01 -8.22187543e-01
4.67134923e-01 3.52252066e-01 3.49036723e-01 3.37589502... | [9.791651725769043, 3.9159138202667236] |
8cea29ae-c0eb-467b-b67f-b7175c647843 | explaining-graph-level-predictions-with | 2201.12380 | null | https://arxiv.org/abs/2201.12380v5 | https://arxiv.org/pdf/2201.12380v5.pdf | GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games | Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In t... | ['Neil Shah', 'Yozen Liu', 'Yizhou Sun', 'Shichang Zhang'] | 2022-01-28 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 4.38398093e-01 9.37074900e-01 -6.07927620e-01 -1.34727746e-01
-8.43304768e-02 -5.61850667e-01 6.73723757e-01 8.08009565e-01
9.22692716e-02 6.66742384e-01 3.05230469e-01 -7.36846447e-01
-5.51739156e-01 -9.83144760e-01 -6.57835364e-01 -5.30241847e-01
-1.07374340e-01 4.63097513e-01 4.04070271e-03 -5.92559338... | [7.746540069580078, 6.1985931396484375] |
d429f391-5433-419e-8d1f-f95e3b8e4151 | attack-named-entity-recognition-by-entity | 2305.05253 | null | https://arxiv.org/abs/2305.05253v1 | https://arxiv.org/pdf/2305.05253v1.pdf | Attack Named Entity Recognition by Entity Boundary Interference | Named Entity Recognition (NER) is a cornerstone NLP task while its robustness has been given little attention. This paper rethinks the principles of NER attacks derived from sentence classification, as they can easily violate the label consistency between the original and adversarial NER examples. This is due to the fi... | ['Hai Zhao', 'Hongqiu Wu', 'Yifei Yang'] | 2023-05-09 | null | null | null | null | ['sentence-classification', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.25088567e-01 2.01612487e-01 2.10248157e-01 -1.54007629e-01
-6.73127770e-01 -1.27259612e+00 5.02634823e-01 2.69719750e-01
-6.21533751e-01 8.44730735e-01 1.88048229e-01 -6.76573753e-01
2.57188141e-01 -8.26404691e-01 -6.92639768e-01 -5.17941296e-01
-2.66959183e-02 1.05339594e-01 3.10588926e-01 -3.46133322... | [6.097877502441406, 8.105792999267578] |
0b735729-999b-440d-bfd7-8e4b8d7e16d5 | stnet-spatial-and-temporal-feature-fusion | 2304.11422 | null | https://arxiv.org/abs/2304.11422v1 | https://arxiv.org/pdf/2304.11422v1.pdf | STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images | As an important task in remote sensing image analysis, remote sensing change detection (RSCD) aims to identify changes of interest in a region from spatially co-registered multi-temporal remote sensing images, so as to monitor the local development. Existing RSCD methods usually formulate RSCD as a binary classificatio... | ['Wei zhang', 'Tian Feng', 'Ziyan Zhao', 'Mengting Ma', 'Tingfeng Hong', 'Jiawei Yang', 'Xiaowen Ma'] | 2023-04-22 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.16884035e-01 -6.79742217e-01 1.28838316e-01 -4.77271348e-01
-6.13139868e-01 -3.39545578e-01 8.81191194e-01 6.72322959e-02
-2.85449833e-01 4.33367401e-01 3.04234356e-01 -1.19169429e-01
-4.09935862e-01 -1.14266372e+00 -5.70927441e-01 -8.79514813e-01
-2.57469863e-01 -4.21707422e-01 3.17020565e-01 -3.76076251... | [9.721839904785156, -1.3437902927398682] |
32a98bf8-c1a9-4e6c-8a06-d00cacc2d1a2 | safe-learning-based-optimal-motion-planning | 1805.09994 | null | http://arxiv.org/abs/1805.09994v2 | http://arxiv.org/pdf/1805.09994v2.pdf | Safe learning-based optimal motion planning for automated driving | This paper presents preliminary work on learning the search heuristic for the
optimal motion planning for automated driving in urban traffic. Previous work
considered search-based optimal motion planning framework (SBOMP) that utilized
numerical or model-based heuristics that did not consider dynamic obstacles.
Optimal... | ['Bakir Lacevic', 'Zlatan Ajanovic', 'Georg Stettinger', 'Daniel Watzenig', 'Martin Horn'] | 2018-05-25 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [-2.74729222e-01 1.96252599e-01 -7.09110856e-01 -2.02498585e-01
-5.16870081e-01 -1.44974053e-01 5.75366318e-01 1.10708252e-01
-6.22955322e-01 9.73348439e-01 -1.51599124e-01 -9.81265247e-01
-4.62235212e-01 -9.51886415e-01 -2.31398299e-01 -5.23897648e-01
-4.94178772e-01 8.48156869e-01 8.31810117e-01 -4.11292315... | [5.291443347930908, 1.4378246068954468] |
c75f2dd8-009a-4914-be68-e91b11b856d8 | a-low-memory-footprint-quantized-neural | 2205.12918 | null | https://arxiv.org/abs/2205.12918v1 | https://arxiv.org/pdf/2205.12918v1.pdf | A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps | Sparse active illumination enables precise time-of-flight depth sensing as it maximizes signal-to-noise ratio for low power budgets. However, depth completion is required to produce dense depth maps for 3D perception. We address this task with realistic illumination and sensor resolution constraints by simulating ToF d... | ['Pietro Zanuttigh', 'Fabien Cardinaux', 'Adriano Simonetto', 'Cynthia Ifeyinwa Ugwu', 'Gianluca Agresti', 'Valerio Cambareri', 'Xiaowen Jiang'] | 2022-05-25 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 6.25401378e-01 3.30883116e-01 1.11808874e-01 -5.38623035e-01
-1.01821387e+00 -2.52608716e-01 3.87841463e-01 3.47192377e-01
-7.51481354e-01 3.82086098e-01 1.41542152e-01 -7.67441615e-02
3.99682112e-02 -9.78788972e-01 -9.02759373e-01 -5.50054312e-01
-1.67947009e-01 1.69434398e-01 2.90622920e-01 -4.18247171... | [8.772995948791504, -2.63564133644104] |
76caf9fd-9ace-423b-9355-d27acac1b7b6 | knowledge-graph-representation-learning-using | null | null | https://aclanthology.org/2021.emnlp-main.750 | https://aclanthology.org/2021.emnlp-main.750.pdf | Knowledge Graph Representation Learning using Ordinary Differential Equations | Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space. The capability of KGEs in preserving graph characteristics including structural aspects and semantics, highly depends on the design of their sc... | ['Sahar Vahdati', 'Jens Lehmann', 'Mirza Mohtashim Alam', 'Franca Hoffmann', 'Chengjin Xu', 'Mojtaba Nayyeri'] | null | null | null | null | emnlp-2021-11 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.94204968e-01 6.13409579e-01 -1.57160997e-01 -3.40087116e-02
4.07737106e-01 -6.53458714e-01 5.20947337e-01 1.99813336e-01
5.94477318e-02 4.35963154e-01 9.01106447e-02 -3.18874985e-01
-4.16278690e-01 -1.39684176e+00 -9.35463071e-01 -6.61102235e-01
-3.48219812e-01 1.99552745e-01 1.15890980e-01 -3.79894465... | [8.564901351928711, 7.779609203338623] |
2a6aa7e5-4307-4c66-baba-3e2ab99019b2 | momentum-provably-improves-error-feedback | 2305.15155 | null | https://arxiv.org/abs/2305.15155v1 | https://arxiv.org/pdf/2305.15155v1.pdf | Momentum Provably Improves Error Feedback! | Due to the high communication overhead when training machine learning models in a distributed environment, modern algorithms invariably rely on lossy communication compression. However, when untreated, the errors caused by compression propagate, and can lead to severely unstable behavior, including exponential divergen... | ['Peter Richtárik', 'Alexander Tyurin', 'Ilyas Fatkhullin'] | 2023-05-24 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 2.04189286e-01 1.64724186e-01 -1.13037132e-01 1.42502829e-01
-8.63656580e-01 -4.96011496e-01 2.11827129e-01 3.82172108e-01
-4.78094727e-01 1.06660473e+00 1.67814195e-01 -3.69655579e-01
-4.84779835e-01 -6.13588274e-01 -1.12631416e+00 -8.76241505e-01
-1.28771737e-01 2.19193250e-01 -6.63314611e-02 -1.51371941... | [6.4275221824646, 4.767563343048096] |
5639f853-571a-4551-9ab5-0652aafa81f0 | cognitive-visual-commonsense-reasoning-using | 2107.01671 | null | https://arxiv.org/abs/2107.01671v3 | https://arxiv.org/pdf/2107.01671v3.pdf | Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory | Visual Commonsense Reasoning (VCR) predicts an answer with corresponding rationale, given a question-image input. VCR is a recently introduced visual scene understanding task with a wide range of applications, including visual question answering, automated vehicle systems, and clinical decision support. Previous approa... | ['Ji Zhang', 'Zhen Liu', 'Qiong Hu', 'Travers B. Child', 'Wenbin Zhang', 'Xin Huang', 'Xuejiao Tang'] | 2021-07-04 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.07530427e-01 2.40751117e-01 -2.70529062e-01 -3.98982704e-01
-4.20344621e-01 -4.05222714e-01 4.96230721e-01 3.41163099e-01
-9.16686803e-02 6.05162740e-01 4.84475553e-01 -8.56345832e-01
-1.07013479e-01 -8.03275585e-01 -5.50516665e-01 -1.35821208e-01
4.42463070e-01 2.28599668e-01 2.28543401e-01 -2.18471959... | [10.797627449035645, 1.7951998710632324] |
1ea60fc8-4dd0-4f02-a50c-9b4d86b5dd83 | generate-rank-a-multi-task-framework-for-math | 2109.03034 | null | https://arxiv.org/abs/2109.03034v1 | https://arxiv.org/pdf/2109.03034v1.pdf | Generate & Rank: A Multi-task Framework for Math Word Problems | Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes whil... | ['Qun Liu', 'Ming Zhang', 'Xin Jiang', 'Lifeng Shang', 'Lin Li', 'Yichun Yin', 'Jianhao Shen'] | 2021-09-07 | null | https://aclanthology.org/2021.findings-emnlp.195 | https://aclanthology.org/2021.findings-emnlp.195.pdf | findings-emnlp-2021-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.70698631e-01 -9.90918092e-03 2.02951819e-01 -4.87968117e-01
-1.30514169e+00 -7.38159180e-01 6.21510208e-01 1.68711785e-02
-3.58216345e-01 8.61283064e-01 1.68347239e-01 -2.69903570e-01
2.60623574e-01 -1.03946543e+00 -8.45689476e-01 -3.17660749e-01
3.47524732e-01 6.36172891e-01 -2.04024747e-01 -4.81725276... | [9.782225608825684, 7.483064651489258] |
8b391393-5c81-447f-bf5b-b8fa860bea8a | domain-generalized-stereo-matching-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chang_Domain_Generalized_Stereo_Matching_via_Hierarchical_Visual_Transformation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chang_Domain_Generalized_Stereo_Matching_via_Hierarchical_Visual_Transformation_CVPR_2023_paper.pdf | Domain Generalized Stereo Matching via Hierarchical Visual Transformation | Recently, deep Stereo Matching (SM) networks have shown impressive performance and attracted increasing attention in computer vision. However, existing deep SM networks are prone to learn dataset-dependent shortcuts, which fail to generalize well on unseen realistic datasets. This paper takes a step towards trainin... | ['Meng Wang', 'Tianzhu Zhang', 'Xun Yang', 'Tianyu Chang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 4.72539634e-01 -3.34986970e-02 -1.55798331e-01 -6.57312036e-01
-8.32023084e-01 -4.56336081e-01 5.07935584e-01 -4.63198751e-01
-1.47561669e-01 8.30716550e-01 1.32780716e-01 1.26033261e-01
-3.60283926e-02 -7.09253490e-01 -8.95411789e-01 -7.21488893e-01
6.32599533e-01 3.51038426e-01 3.51481229e-01 -2.29272828... | [8.774577140808105, -2.299265146255493] |
be503a61-0c6a-4d87-96d6-1a8029b3bf56 | pointclip-v2-adapting-clip-for-powerful-3d | 2211.11682 | null | https://arxiv.org/abs/2211.11682v1 | https://arxiv.org/pdf/2211.11682v1.pdf | PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning | Contrastive Language-Image Pre-training (CLIP) has shown promising open-world performance on 2D image tasks, while its transferred capacity on 3D point clouds, i.e., PointCLIP, is still far from satisfactory. In this work, we propose PointCLIP V2, a powerful 3D open-world learner, to fully unleash the potential of CLIP... | ['Peng Gao', 'Shanghang Zhang', 'Ziyao Zeng', 'Bowei He', 'Renrui Zhang', 'Xiangyang Zhu'] | 2022-11-21 | null | null | null | null | ['training-free-3d-point-cloud-classification', '3d-classification', 'zero-shot-transfer-3d-point-cloud', 'training-free-3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.81586668e-01 2.92724576e-02 -2.47059345e-01 -1.24340437e-01
-1.13123655e+00 -5.58114648e-01 4.65885490e-01 -1.23005293e-01
-1.43038407e-01 1.38955608e-01 -9.71783996e-02 -4.52457607e-01
3.86590511e-01 -8.64734888e-01 -1.04175460e+00 -4.16937530e-01
2.07754046e-01 4.43058819e-01 3.27158660e-01 -1.62686765... | [8.087719917297363, -3.309032917022705] |
66f594ba-405a-4300-91a4-928c81da4f5c | progressively-guided-alternate-refinement | 2008.07064 | null | https://arxiv.org/abs/2008.07064v1 | https://arxiv.org/pdf/2008.07064v1.pdf | Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection | In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alte... | ['Yun Fu', 'Shuhan Chen'] | 2020-08-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/618_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530511.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection', 'salient-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.02431756e-01 2.12761417e-01 -1.25357747e-01 -1.57011807e-01
-6.09725952e-01 -3.58998291e-02 2.44222909e-01 2.17941683e-02
-3.66582900e-01 4.86539930e-01 1.34810612e-01 -1.40599966e-01
2.37186074e-01 -7.82467484e-01 -7.65592694e-01 -6.91470325e-01
1.11192137e-01 -6.85547143e-02 9.92937207e-01 -1.15723602... | [9.617293357849121, -0.805276095867157] |
96399352-493c-4a2e-b132-c9e7bcc4337f | increasing-visual-awareness-in-multimodal | 2210.08478 | null | https://arxiv.org/abs/2210.08478v1 | https://arxiv.org/pdf/2210.08478v1.pdf | Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective | Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image. Despite the promising performance, MMT models still suffer the problem of input degradation: models focus more on textual information while visual information is generally overlooked. ... | ['Si Shen', 'Bojie Hu', 'Yicheng Zou', 'Tong Zhang', 'Baijun Ji'] | 2022-10-16 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.40171242e-01 -6.34184033e-02 -3.84477705e-01 -2.04037979e-01
-1.08746862e+00 -5.55559933e-01 7.82543838e-01 -2.23814771e-01
-1.38565511e-01 5.16410828e-01 4.72607493e-01 -4.75831240e-01
4.90203500e-01 -3.75332773e-01 -8.89688373e-01 -5.57515621e-01
4.93320137e-01 8.25794712e-02 -8.14418793e-02 -4.60790396... | [11.457159042358398, 1.4801956415176392] |
568a6413-318c-4861-9eca-24ab047b250a | an-interactively-reinforced-paradigm-for | 2305.09999 | null | https://arxiv.org/abs/2305.09999v1 | https://arxiv.org/pdf/2305.09999v1.pdf | An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection | This research focuses on the discovery and localization of hidden objects in the wild and serves unmanned systems. Through empirical analysis, infrared and visible image fusion (IVIF) enables hard-to-find objects apparent, whereas multimodal salient object detection (SOD) accurately delineates the precise spatial locat... | ['Xin Fan', 'Risheng Liu', 'JinYuan Liu', 'Di Wang'] | 2023-05-17 | null | null | null | null | ['infrared-and-visible-image-fusion', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 5.08258939e-01 -1.45670101e-01 1.24645740e-01 -1.50320128e-01
-6.27298295e-01 -3.25488895e-01 3.79176557e-01 -1.78591356e-01
-8.21198374e-02 4.04571205e-01 1.28528282e-01 -7.49214888e-02
-4.67082709e-01 -5.37522972e-01 -6.38581157e-01 -9.26341772e-01
5.35669550e-02 -4.60172504e-01 2.75825262e-01 -4.15083885... | [9.846918106079102, -0.8051373362541199] |
f00b5a46-5a7f-442e-b246-93a495370cd5 | openmix-reviving-known-knowledge-for | 2004.05551 | null | https://arxiv.org/abs/2004.05551v1 | https://arxiv.org/pdf/2004.05551v1.pdf | OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World | In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a model with labeled data, and then identify new classes in unlabeled data via unsupervised clustering. However, the labeled data that provide ess... | ['Linchao Zhu', 'Zhun Zhong', 'Shaozi Li', 'Yi Yang', 'Zhiming Luo', 'Nicu Sebe'] | 2020-04-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhong_OpenMix_Reviving_Known_Knowledge_for_Discovering_Novel_Visual_Categories_in_CVPR_2021_paper.pdf | cvpr-2021-1 | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.22862691e-01 2.96335965e-01 -6.91322029e-01 -5.82040429e-01
-7.67752528e-01 -7.71895885e-01 4.33370680e-01 2.55562570e-02
-5.11704497e-02 1.00612688e+00 -1.08283497e-01 8.88852254e-02
1.17451549e-01 -5.34606218e-01 -6.69001698e-01 -1.02353215e+00
4.28021848e-01 8.17454636e-01 1.67115211e-01 4.56180274... | [9.724126815795898, 2.9627552032470703] |
1055d80e-af1c-48de-8f72-fc173f8d0a3a | understanding-and-eliminating-spurious-modes | 2211.09767 | null | https://arxiv.org/abs/2211.09767v1 | https://arxiv.org/pdf/2211.09767v1.pdf | Understanding and eliminating spurious modes in variational Monte Carlo using collective variables | The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years. However, as we demonstrate in the context of the periodic Heisenberg spin chain, this approach can produce unreliable wave function approximations. One o... | ['Jonathan Weare', 'Timothy C. Berkelbach', 'Michael Lindsey', 'Robert J. Webber', 'huan zhang'] | 2022-11-11 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 3.96080792e-01 -1.56003788e-01 -1.55786201e-01 -1.02309942e-01
-7.17700541e-01 -2.76848227e-01 8.24510813e-01 1.67431623e-01
-6.08788311e-01 1.23858047e+00 2.03138459e-02 -3.71879190e-01
-1.50471479e-01 -8.07900548e-01 -6.51269317e-01 -1.32726407e+00
-1.20759316e-01 5.21480143e-01 -6.95461407e-02 -1.94937930... | [5.567676067352295, 4.917116641998291] |
da918006-5c73-46f7-8e77-b9cf1e88ce4e | the-short-text-matching-model-enhanced-with | 2304.03898 | null | https://arxiv.org/abs/2304.03898v1 | https://arxiv.org/pdf/2304.03898v1.pdf | The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning | In recent years, short Text Matching tasks have been widely applied in the fields ofadvertising search and recommendation. The difficulty lies in the lack of semantic information and word ambiguity caused by the short length of the text. Previous works have introduced complement sentences or knowledge bases to provide ... | ['Yanlong Du', 'Xiangzheng Liu', 'Shaohua Xu', 'Qiang Zhang', 'Hanjie Mai', 'Mengmeng Cui', 'Qiqiang Zhong'] | 2023-04-08 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 1.16726607e-02 -1.18458472e-01 -3.85634691e-01 -2.46918768e-01
-4.54709947e-01 -3.64085913e-01 6.87061489e-01 3.43838513e-01
-6.25253797e-01 3.89346570e-01 6.43587887e-01 -9.24261287e-02
-2.55507618e-01 -1.17513740e+00 -5.61748803e-01 -2.51249909e-01
7.65529156e-01 3.77510309e-01 5.48527896e-01 -5.09172261... | [10.95497989654541, 8.143447875976562] |
4a587095-6073-4f1e-8975-c71a108f3207 | tl-dr-out-of-context-adversarial-text | 2104.00782 | null | https://arxiv.org/abs/2104.00782v1 | https://arxiv.org/pdf/2104.00782v1.pdf | "TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation | This paper presents Out-of-Context Summarizer, a tool that takes arbitrary public news articles out of context by summarizing them to coherently fit either a liberal- or conservative-leaning agenda. The Out-of-Context Summarizer also suggests hashtag keywords to bolster the polarization of the summary, in case one is i... | ['Emma Pieroni', 'Filipo Sharevski', 'Peter Jachim'] | 2021-04-01 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 4.65212064e-03 7.76447773e-01 -4.96796280e-01 -1.97265759e-01
-1.48847640e+00 -1.11235380e+00 1.47157514e+00 6.73461258e-01
-4.57028240e-01 9.57526922e-01 8.95930111e-01 -9.88755345e-01
1.52319595e-01 -8.12212288e-01 -8.19345474e-01 -3.76489103e-01
4.13309515e-01 4.02613819e-01 2.24858135e-01 -5.70396960... | [8.348793983459473, 10.082615852355957] |
2720c66d-9113-43ed-9322-eacc1113efd6 | iiit-ar-13k-a-new-dataset-for-graphical | 2008.02569 | null | https://arxiv.org/abs/2008.02569v1 | https://arxiv.org/pdf/2008.02569v1.pdf | IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents | We introduce a new dataset for graphical object detection in business documents, more specifically annual reports. This dataset, IIIT-AR-13k, is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images... | ['Peter Lipps', 'C. V. Jawahar', 'Ajoy Mondal'] | 2020-08-06 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-3.35038938e-02 4.57152016e-02 -3.18849236e-01 -2.66653925e-01
-1.27530682e+00 -1.01601005e+00 8.31204772e-01 2.53227562e-01
2.70919681e-01 6.15712889e-02 4.23554108e-02 -6.44012272e-01
3.80112417e-02 -8.00967216e-01 -1.00112009e+00 -7.51899928e-02
-1.09560624e-01 3.55038106e-01 2.14663863e-01 -2.80826148... | [11.598943710327148, 2.8557870388031006] |
846eccad-e09d-4b92-8eb4-ed76aa1c515b | massive-mimo-relaying-with-linear-precoding | 1802.10255 | null | http://arxiv.org/abs/1802.10255v1 | http://arxiv.org/pdf/1802.10255v1.pdf | Massive MIMO relaying with linear precoding in correlated channels under limited feedback | In this paper we study on a massive MIMO relay system with linear precoding
under the conditions of imperfect channel state information at the transmitter
(CSIT) and per-user channel transmit correlation. In our system the
source-relay channels are massive multiple-input multiple-output (MIMO) ones
and the relay-destin... | [] | 2018-02-28 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 1.44506022e-01 5.95432758e-01 -5.78823239e-02 -1.03273131e-02
-5.72822750e-01 -3.91467899e-01 2.71001250e-01 -4.57475662e-01
-1.33074999e-01 1.01021779e+00 2.00318247e-01 -1.14028752e+00
-7.18817472e-01 -5.87050378e-01 -2.42662072e-01 -1.08422434e+00
-1.05685377e+00 -4.77464885e-01 -1.92420542e-01 -5.96877873... | [6.164609909057617, 1.425498604774475] |
07761246-553e-48cd-93d1-bc5e58e46056 | improved-texture-networks-maximizing-quality | 1701.02096 | null | http://arxiv.org/abs/1701.02096v2 | http://arxiv.org/pdf/1701.02096v2.pdf | Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis | The recent work of Gatys et al., who characterized the style of an image by
the statistics of convolutional neural network filters, ignited a renewed
interest in the texture generation and image stylization problems. While their
image generation technique uses a slow optimization process, recently several
authors have ... | ['Dmitry Ulyanov', 'Andrea Vedaldi', 'Victor Lempitsky'] | 2017-01-09 | improved-texture-networks-maximizing-quality-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ulyanov_Improved_Texture_Networks_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ulyanov_Improved_Texture_Networks_CVPR_2017_paper.pdf | cvpr-2017-7 | ['image-stylization'] | ['computer-vision'] | [ 5.28911829e-01 3.00884247e-01 6.76032901e-02 -2.87284374e-01
-5.23714125e-01 -5.32664716e-01 1.01267648e+00 -9.59892124e-02
-1.44207522e-01 8.31343949e-01 3.22143763e-01 5.46301976e-02
-2.05105618e-02 -1.02331042e+00 -9.13358688e-01 -9.42422509e-01
3.19791853e-01 1.36090755e-01 -1.02304511e-01 -4.14949656... | [11.578569412231445, -0.47214236855506897] |
68a9812a-ce2e-4619-a91b-c6b083cd7c73 | similarity-learning-on-an-explicit-polynomial | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Chen_Similarity_Learning_on_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Chen_Similarity_Learning_on_2015_CVPR_paper.pdf | Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification | In this paper, we address the person re-identification problem, discovering the correct matches for a probe person image from a set of gallery person images. We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched image... | ['Zejian yuan', 'Dapeng Chen', 'Jingdong Wang', 'Nanning Zheng', 'Gang Hua'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['patch-matching'] | ['computer-vision'] | [ 3.47731151e-02 -2.57704437e-01 8.36371779e-02 -5.11462152e-01
-8.17627788e-01 -6.32418931e-01 4.49811906e-01 1.73574969e-01
-3.86875391e-01 2.71496773e-01 1.19854271e-01 5.64531207e-01
-5.32875776e-01 -5.63660443e-01 -5.34079671e-01 -7.55005300e-01
-1.36902943e-01 3.39291930e-01 1.83143385e-03 -7.21964659... | [14.749180793762207, 1.0200077295303345] |
1a3d6fd3-1c69-4d2f-86c1-93090c500c8b | graph-theoretic-spatiotemporal-context | 1707.07815 | null | http://arxiv.org/abs/1707.07815v1 | http://arxiv.org/pdf/1707.07815v1.pdf | Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency Detection | As an important and challenging problem in computer vision, video saliency
detection is typically cast as a spatiotemporal context modeling problem over
consecutive frames. As a result, a key issue in video saliency detection is how
to effectively capture the intrinsical properties of atomic video structures as
well as... | ['Jun Xiao', 'Xi Li', 'Fangfang Wang', 'Fei Wu', 'Lina Wei'] | 2017-07-25 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 4.05955523e-01 -1.19741544e-01 -2.99445987e-01 -2.79747304e-02
-3.53885531e-01 -1.94524482e-01 4.63186949e-01 5.37636697e-01
-6.29307479e-02 4.66057569e-01 3.51555526e-01 6.53892010e-02
-3.93854558e-01 -3.42964679e-01 -7.97794342e-01 -6.05496287e-01
-2.51164913e-01 -4.80749041e-01 7.56794333e-01 -9.87544656... | [9.639599800109863, -0.34632959961891174] |
a43a2a9a-6f62-4b26-b0e2-6b97bf63cca4 | making-reconstruction-based-method-great | 2301.12048 | null | https://arxiv.org/abs/2301.12048v1 | https://arxiv.org/pdf/2301.12048v1.pdf | Making Reconstruction-based Method Great Again for Video Anomaly Detection | Anomaly detection in videos is a significant yet challenging problem. Previous approaches based on deep neural networks employ either reconstruction-based or prediction-based approaches. Nevertheless, existing reconstruction-based methods 1) rely on old-fashioned convolutional autoencoders and are poor at modeling temp... | ['Yun Fu', 'Xu Ma', 'Yi Xu', 'Yue Bai', 'Can Qin', 'Yizhou Wang'] | 2023-01-28 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-2.42831167e-02 -2.06469506e-01 4.12597917e-02 -1.50665835e-01
-2.67870218e-01 -1.39821656e-02 2.42881060e-01 -1.42211571e-01
-3.10715020e-01 4.19982582e-01 -7.58316666e-02 -2.64150381e-01
-2.99005974e-02 -6.66232884e-01 -1.02137876e+00 -8.38832557e-01
-2.46336207e-01 -3.69585454e-02 2.99537182e-01 -1.05783537... | [7.912792682647705, 1.5461821556091309] |
08b00c76-c484-43d0-a1cd-a3ad01e5e63a | a-taylor-based-sampling-scheme-for-machine | 2101.11105 | null | https://arxiv.org/abs/2101.11105v2 | https://arxiv.org/pdf/2101.11105v2.pdf | A Taylor Based Sampling Scheme for Machine Learning in Computational Physics | Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations. We take advantage of the ability to generate data using numerical simulations programs to train ML models better and achieve accuracy gain with no performance cost. We elaborate a new data sampling scheme based on Taylor ... | ['Pietro Congedo', 'David Lugato', 'Gaël Poëtte', 'Paul Novello'] | 2021-01-20 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-3.87925565e-01 -1.06160976e-01 -2.03021675e-01 -2.17344448e-01
-4.57813501e-01 1.23880811e-01 3.80204886e-01 -3.73937190e-01
-3.16874295e-01 1.40371382e+00 -5.40513456e-01 -8.75579655e-01
7.44641572e-02 -5.42892575e-01 -8.60963464e-01 -7.55723536e-01
-1.66547731e-01 4.26441967e-01 -1.72723874e-01 -2.44086847... | [6.514041900634766, 3.447796583175659] |
f02812b6-8843-44c1-9ea1-ee6b0cdeb6e3 | weakly-supervised-detection-of-baby-cry | 2304.10001 | null | https://arxiv.org/abs/2304.10001v1 | https://arxiv.org/pdf/2304.10001v1.pdf | Weakly Supervised Detection of Baby Cry | Detection of baby cries is an important part of baby monitoring and health care. Almost all existing methods use supervised SVM, CNN, or their varieties. In this work, we propose to use weakly supervised anomaly detection to detect a baby cry. In this weak supervision, we only need weak annotation if there is a cry in ... | ['Jingfeng Liu', 'Qi Yao', 'Weijun Tan'] | 2023-04-19 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 2.78881729e-01 2.49919415e-01 1.98699147e-01 -7.36220777e-01
-8.41458514e-02 -1.42182395e-01 -9.48900580e-02 8.01746070e-01
-4.06576723e-01 2.08276749e-01 1.19688705e-01 -1.87285528e-01
1.34809971e-01 -7.57252157e-01 -6.47381186e-01 -7.87640631e-01
-1.54656917e-01 1.47788376e-01 3.06128085e-01 -6.63757995... | [7.620370388031006, 2.3305089473724365] |
f803847b-5eb2-42b4-bca5-f6a752fb5550 | the-cognitive-load-of-observation-tasks-in-3d | 2302.12968 | null | https://arxiv.org/abs/2302.12968v2 | https://arxiv.org/pdf/2302.12968v2.pdf | The Cognitive Load of Observation Tasks in 3D Video is Lower Than That in 2D Video | We are exposed to more and more 3D videos, some for entertainment and some for scientific research. Some experiments using 3D video as a stimulus focus only on its visual effect. We studied the cognitive difference between 3D and 2D videos by analyzing EEG. This research adopts a 2 x 4 experimental design, including 2D... | ['Peiguang Jing', 'Han Wang', 'Yu Liu', 'Jinglin Sun', 'Yunpeng Yin'] | 2023-02-25 | null | null | null | null | ['experimental-design', 'eeg', 'eeg'] | ['methodology', 'methodology', 'time-series'] | [-2.97902107e-01 -4.48210210e-01 2.62799174e-01 -4.54305895e-02
2.92948276e-01 -4.90659624e-01 2.96367794e-01 -2.54596472e-01
-4.69149679e-01 4.89940077e-01 3.55964690e-01 8.19326490e-02
5.16177863e-02 -3.38969588e-01 -3.25930178e-01 -6.36253178e-01
-2.34578028e-01 -5.33862770e-01 3.59734297e-01 7.01133385... | [13.176981925964355, 3.3073008060455322] |
d0b14230-9e54-4904-93d0-72cb1a92138a | learning-with-rejection-for-abstractive-text | 2302.08531 | null | https://arxiv.org/abs/2302.08531v1 | https://arxiv.org/pdf/2302.08531v1.pdf | Learning with Rejection for Abstractive Text Summarization | State-of-the-art abstractive summarization systems frequently hallucinate content that is not supported by the source document, mainly due to noise in the training dataset. Existing methods opt to drop the noisy samples or tokens from the training set entirely, reducing the effective training set size and creating an a... | ['Jackie Chi Kit Cheung', 'Jingyi He', 'Yue Dong', 'Meng Cao'] | 2023-02-16 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 5.91948152e-01 5.25735021e-01 -3.05624157e-01 -2.33300075e-01
-1.23259556e+00 -5.60284257e-01 7.39100218e-01 6.63273513e-01
-5.14983892e-01 1.05335343e+00 1.00074255e+00 -3.15362141e-02
2.27497682e-01 -5.64477324e-01 -8.22653532e-01 -2.89580196e-01
3.55432332e-01 6.47274792e-01 -2.11117752e-02 5.38807288... | [12.181923866271973, 9.255243301391602] |
d979ba02-269a-45bb-8be1-060d23e69915 | skeleton-based-relational-reasoning-for-group | 2011.05653 | null | https://arxiv.org/abs/2011.05653v3 | https://arxiv.org/pdf/2011.05653v3.pdf | Skeleton-based Relational Reasoning for Group Activity Analysis | Research on group activity recognition mostly leans on the standard two-stream approach (RGB and Optical Flow) as their input features. Few have explored explicit pose information, with none using it directly to reason about the persons interactions. In this paper, we leverage the skeleton information to learn the inte... | ['Alex C. Kot', 'Jun Liu', 'Mauricio Perez'] | 2020-11-11 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [-6.60290616e-03 1.88356414e-01 -2.84888983e-01 -2.73905396e-01
-1.21759564e-01 -3.02902699e-01 8.73432040e-01 1.78385571e-01
-4.49508876e-01 6.56965673e-01 5.32283068e-01 4.11715537e-01
-4.48858023e-01 -6.73225820e-01 -5.82642078e-01 -5.71212113e-01
-4.03636515e-01 8.93536866e-01 3.35764498e-01 -2.47740835... | [8.09973430633545, 0.5160886645317078] |
da183260-d93f-4b53-838c-f3ab739cda28 | sssnet-semi-supervised-signed-network | 2110.06623 | null | https://arxiv.org/abs/2110.06623v3 | https://arxiv.org/pdf/2110.06623v3.pdf | SSSNET: Semi-Supervised Signed Network Clustering | Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited. In particular, most state-of-the-art methods generating node embeddings of signed networks focus on link sign prediction, and those that pertain to node clus... | ['Mihai Cucuringu', 'Songchao Wang', 'Gesine Reinert', 'Yixuan He'] | 2021-10-13 | null | null | null | null | ['cloud-removal', 'link-sign-prediction', 'stochastic-block-model'] | ['computer-vision', 'graphs', 'graphs'] | [ 2.03115821e-01 4.35054064e-01 -5.64864516e-01 -1.73789293e-01
1.75741628e-01 -4.65023398e-01 8.71342242e-01 4.34439749e-01
-1.42696455e-01 5.24398386e-01 6.95477128e-02 -3.89934748e-01
-6.74942553e-01 -1.07710063e+00 -3.22108358e-01 -9.89017248e-01
-3.75789136e-01 7.09963083e-01 2.47070014e-01 -4.44688618... | [7.122715473175049, 6.037972927093506] |
09a89da2-53ef-4cf4-82aa-6c8f6009175d | improving-reference-based-distinctive-image | 2306.14259 | null | https://arxiv.org/abs/2306.14259v1 | https://arxiv.org/pdf/2306.14259v1.pdf | Improving Reference-based Distinctive Image Captioning with Contrastive Rewards | Distinctive Image Captioning (DIC) -- generating distinctive captions that describe the unique details of a target image -- has received considerable attention over the last few years. A recent DIC method proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference im... | ['Long Chen', 'Yueting Zhuang', 'Jian Shao', 'Meng Cao', 'Dong Zhang', 'Jun Xiao', 'Yangjun Mao'] | 2023-06-25 | null | null | null | null | ['contrastive-learning', 'image-captioning', 'contrastive-learning', 'benchmarking', 'benchmarking'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous', 'robots'] | [ 3.71240646e-01 -1.85087174e-01 2.58330673e-01 -5.33276856e-01
-9.34873998e-01 -6.97921813e-01 8.43226314e-01 -5.76468587e-01
-1.10842638e-01 5.10832489e-01 2.32452706e-01 1.54947922e-01
3.85558069e-01 -3.38575721e-01 -8.59247804e-01 -6.45696878e-01
6.00747883e-01 3.39910358e-01 3.39706779e-01 -2.32018024... | [11.003145217895508, 0.8983159065246582] |
c4cf385b-188b-477e-b0ca-8306a79b3ede | etc-nlg-end-to-end-topic-conditioned-natural | 2008.10875 | null | https://arxiv.org/abs/2008.10875v3 | https://arxiv.org/pdf/2008.10875v3.pdf | ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation | Plug-and-play language models (PPLMs) enable topic-conditioned natural language generation by pairing large pre-trained generators with attribute models used to steer the predicted token distribution towards the selected topic. Despite their computational efficiency, PPLMs require large amounts of labeled texts to effe... | ['Gabriele Sarti', 'Ginevra Carbone'] | 2020-08-25 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 7.20051378e-02 7.16892660e-01 -1.64111823e-01 -3.74280959e-01
-1.51617265e+00 -6.04103625e-01 1.34501159e+00 -2.59883925e-02
-3.23431194e-01 1.00337863e+00 7.01344967e-01 -2.45447069e-01
2.85600334e-01 -7.83275604e-01 -5.28608561e-01 -4.17574465e-01
1.20793000e-01 1.08791924e+00 -2.72228062e-01 2.31253486... | [11.690462112426758, 8.935609817504883] |
246ea536-5a61-47a8-9f27-d8ee30cd99ea | cplanet-enhancing-image-geolocalization-by | 1808.02130 | null | http://arxiv.org/abs/1808.02130v1 | http://arxiv.org/pdf/1808.02130v1.pdf | CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps | Image geolocalization is the task of identifying the location depicted in a
photo based only on its visual information. This task is inherently challenging
since many photos have only few, possibly ambiguous cues to their geolocation.
Recent work has cast this task as a classification problem by partitioning the
earth ... | ['Tobias Weyand', 'Jack Sim', 'Bohyung Han', 'Paul Hongsuck Seo'] | 2018-08-06 | cplanet-enhancing-image-geolocalization-by-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Paul_Hongsuck_Seo_Enhancing_Image_Geolocalization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Paul_Hongsuck_Seo_Enhancing_Image_Geolocalization_ECCV_2018_paper.pdf | eccv-2018-9 | ['photo-geolocation-estimation'] | ['computer-vision'] | [ 4.45623584e-02 -1.34374589e-01 -3.28726202e-01 -3.54751378e-01
-7.68754959e-01 -9.27247763e-01 6.40675247e-01 5.82286358e-01
-4.08915102e-01 6.93861902e-01 1.09810814e-01 -2.45069310e-01
1.08287416e-01 -1.16838980e+00 -6.77394032e-01 -8.67298543e-01
-6.13100864e-02 5.02459288e-01 5.21139443e-01 4.09105867... | [7.675294876098633, -1.7612603902816772] |
809b0bbd-5638-4467-8832-efc0d49bee7b | molecular-activity-prediction-using-graph | 1907.01103 | null | https://arxiv.org/abs/1907.01103v2 | https://arxiv.org/pdf/1907.01103v2.pdf | Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph | Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features);... | ['Keisuke Yanagisawa', 'Ryota Ii', 'Yutaka Akiyama', 'Masahito Ohue'] | 2019-07-02 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-5.29918708e-02 -9.96545926e-02 -1.30009666e-01 -4.57867414e-01
-5.52962674e-03 -5.15046895e-01 3.31176609e-01 7.18423367e-01
-4.65145767e-01 7.51372993e-01 1.47381146e-02 -4.01999623e-01
-5.47046252e-02 -1.06889451e+00 -8.68589163e-01 -8.62175286e-01
-2.40217313e-01 8.15835446e-02 4.52288926e-01 -3.04001272... | [5.175868988037109, 5.8839335441589355] |
082e9031-9097-463a-9d0b-677d40b1bb5b | bi-directional-convlstm-u-net-with-densley | 1909.00166 | null | https://arxiv.org/abs/1909.00166v1 | https://arxiv.org/pdf/1909.00166v1.pdf | Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected co... | ['Maryam Asadi-Aghbolaghi', 'Mahmood Fathy', 'Reza Azad', 'Sergio Escalera'] | 2019-08-31 | bi-directional-convlstm-u-net-with-densley-1 | https://openaccess.thecvf.com/content_ICCVW_2019/html/VRMI/Azad_Bi-Directional_ConvLSTM_U-Net_with_Densley_Connected_Convolutions_ICCVW_2019_paper.html | https://openaccess.thecvf.com/content_ICCVW_2019/papers/VRMI/Azad_Bi-Directional_ConvLSTM_U-Net_with_Densley_Connected_Convolutions_ICCVW_2019_paper.pdf | in-proceedings-of-the-ieee-cvf-international | ['skin-lesion-segmentation', 'lung-nodule-segmentation'] | ['medical', 'medical'] | [ 3.38995725e-01 2.13158995e-01 -9.24125910e-02 -4.02502716e-01
-2.96944559e-01 5.61382547e-02 1.32204726e-01 -9.26592425e-02
-7.88407803e-01 5.34083009e-01 -3.44005600e-02 -6.37719393e-01
2.20857739e-01 -9.18148756e-01 -4.54005063e-01 -6.74390018e-01
1.97834402e-01 -3.09698850e-01 6.99680090e-01 -2.61215009... | [14.694619178771973, -2.6633517742156982] |
e2beac76-7e96-47d2-9a3c-b3906d50e038 | capacity-bounds-for-the-deeponet-method-of | 2205.11359 | null | https://arxiv.org/abs/2205.11359v1 | https://arxiv.org/pdf/2205.11359v1.pdf | Capacity Bounds for the DeepONet Method of Solving Differential Equations | In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems. A particularly active area in this theme has been "physics informed machine learning" [1] which focuses on using neural nets for numerically solving differential equations. Among all the pro... | ['Anirbit Mukherjee', 'Sayar Karmakar', 'Pulkit Gopalani'] | 2022-05-23 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.22501679e-01 2.49485001e-01 3.78916115e-02 -9.04544517e-02
-3.30331057e-01 -3.53338480e-01 5.36820173e-01 -5.61294928e-02
-4.19733107e-01 9.84123111e-01 -4.24852610e-01 -4.22856450e-01
-4.16964114e-01 -7.70689189e-01 -8.62011194e-01 -1.19387543e+00
-3.38156909e-01 5.14618814e-01 1.94618925e-01 -3.97135884... | [6.4177045822143555, 3.6408300399780273] |
6ef767f0-e4af-41fc-9289-58582be91fc3 | less-data-is-more-why-small-data-holds-the | 1907.10424 | null | https://arxiv.org/abs/1907.10424v1 | https://arxiv.org/pdf/1907.10424v1.pdf | Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence | The claims that big data holds the key to enterprise successes and that Artificial Intelligence is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry. However, while these claims may indeed capture some truth, they have also been massively overs... | ['Jacopo Tagliabue', 'Andrea Polonioli', 'Ciro Greco'] | 2019-07-22 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.27137855e-01 6.28154814e-01 -3.01765231e-03 -4.98639703e-01
-4.25668418e-01 -6.70145929e-01 7.93956757e-01 2.22731024e-01
-5.86293757e-01 4.94124591e-01 4.72629040e-01 -4.18357998e-01
-4.59491670e-01 -7.70890892e-01 -4.37609464e-01 -5.22367358e-01
3.30985993e-01 7.17277110e-01 -2.81905293e-01 -5.59054375... | [9.0072021484375, 6.3140177726745605] |
06f6a273-b8c8-4086-99d7-2b550141d468 | predictive-spectral-analysis-using-an-end-to | null | null | https://www.researchgate.net/publication/343677373_Predictive_spectral_analysis_using_an_end-to-end_deep_model_from_hyperspectral_images_for_high-throughput_plant_phenotyping | https://www.researchgate.net/publication/343677373_Predictive_spectral_analysis_using_an_end-to-end_deep_model_from_hyperspectral_images_for_high-throughput_plant_phenotyping | Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping | . | ['Jian Jin', 'Libo Zhang', 'Liangju Wang', 'Dongdong Ma', 'Tanzeel Rehman'] | 2020-10-01 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [-1.10906266e-01 3.38844448e-01 -7.15696812e-01 -5.60718954e-01
-2.77354211e-01 -9.53714073e-01 4.44827318e-01 -7.60408580e-01
-4.20112014e-01 1.25710678e+00 9.47987214e-02 -7.03111470e-01
-1.98643133e-01 -8.21348190e-01 -5.60620844e-01 -8.90468895e-01
-8.29958439e-01 6.89341724e-01 1.49948433e-01 -7.01835871... | [-7.293264865875244, 3.693053960800171] |
de87f816-0925-408f-800d-22e4c8118a6b | sam-for-poultry-science | 2305.10254 | null | https://arxiv.org/abs/2305.10254v1 | https://arxiv.org/pdf/2305.10254v1.pdf | SAM for Poultry Science | In recent years, the agricultural industry has witnessed significant advancements in artificial intelligence (AI), particularly with the development of large-scale foundational models. Among these foundation models, the Segment Anything Model (SAM), introduced by Meta AI Research, stands out as a groundbreaking solutio... | ['Lilong Chai', 'Tianming Liu', 'Changying Li', 'Guoyu Lu', 'Jin Sun', 'Sachin Subedi', 'Ramesh Bist', 'Zihao Wu', 'Haixing Dai', 'Xiao Yang'] | 2023-05-17 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 1.35235280e-01 -3.87864262e-01 -9.72563773e-02 -3.52659672e-01
3.93068194e-01 -4.71133143e-01 -1.43435270e-01 4.75883186e-01
-4.19153214e-01 -3.92711200e-02 -6.90542161e-01 -2.28049621e-01
-2.76359797e-01 -5.61263561e-01 -5.39186835e-01 -8.02784562e-01
-1.61024615e-01 4.33824092e-01 3.97127450e-01 -5.51607490... | [7.87324857711792, -0.991664707660675] |
f43a4c77-02b1-43f1-9e14-37dd78b3a947 | iris-recognition-under-biologically | 1809.00182 | null | http://arxiv.org/abs/1809.00182v1 | http://arxiv.org/pdf/1809.00182v1.pdf | Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes | This paper presents the most comprehensive analysis of iris recognition
reliability in the occurrence of various biological processes happening
naturally and pathologically in the human body, including aging, illnesses, and
post-mortem changes to date. Insightful conclusions are offered in relation to
all three of thes... | ['Piotr Maciejewicz', 'Adam Czajka', 'Mateusz Trokielewicz'] | 2018-09-01 | null | null | null | null | ['pupil-dilation'] | ['computer-vision'] | [ 4.31823701e-01 -2.58486092e-01 -1.85719490e-01 -7.62639567e-02
2.73346841e-01 -2.01877415e-01 3.23657960e-01 -1.20619163e-02
-3.82257015e-01 7.48702347e-01 2.66580403e-01 -5.19160271e-01
-2.89177209e-01 -2.78967172e-01 -2.55191773e-01 -9.60535109e-01
5.70056476e-02 -5.43409884e-02 -3.76525044e-01 1.14118926... | [3.749099016189575, -3.626408338546753] |
4c469424-fd76-4ed7-8ccd-e2313fc7d2c0 | weighted-self-distillation-for-chinese-word | null | null | https://aclanthology.org/2022.findings-acl.139 | https://aclanthology.org/2022.findings-acl.139.pdf | Weighted self Distillation for Chinese word segmentation | Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese Word Segmentation (CWS). However, these methods rely heavily on such additional information mentioned above and focus less on the model itself. We thus propose a novel neural framework, named Weighted self Distillation fo... | ['Jialei Zhang', 'Zhong Ming', 'Shubin Cai', 'Rian He'] | null | null | null | null | findings-acl-2022-5 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.36049011e-01 -3.05218667e-01 -3.97671849e-01 -4.13992822e-01
-7.94263363e-01 -5.62480390e-01 3.49538684e-01 -8.26480761e-02
-1.03509009e+00 6.95788264e-01 8.22625682e-02 -8.02693486e-01
1.09457038e-01 -6.50810242e-01 -4.48311567e-01 -6.91063106e-01
4.63702947e-01 2.78299093e-01 4.57156867e-01 -1.41060889... | [10.062912940979004, 10.13814640045166] |
8a180353-057d-4c6d-9edf-bdb5fc0d4f66 | a-corpus-and-model-integrating-multiword | null | null | https://aclanthology.info/papers/N15-1177/n15-1177 | https://www.aclweb.org/anthology/N15-1177 | A Corpus and Model Integrating Multiword Expressions and Supersenses | null | ['Nathan Schneider', 'Noah A. Smith'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['miscellaneous'] | ['miscellaneous'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.53920316696167, 15.869207382202148] |
a4448e48-42f8-4929-b786-7321c0741b9d | gatehub-gated-history-unit-with-background-1 | 2206.04668 | null | https://arxiv.org/abs/2206.04668v1 | https://arxiv.org/pdf/2206.04668v1.pdf | GateHUB: Gated History Unit with Background Suppression for Online Action Detection | Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and has to solely rely on the history, i.e., the frames observed so far, to make predictions. It is therefore important to accentuate parts of t... | ['Mei Chen', 'Yu Kong', 'Ye Yu', 'Gaurav Mittal', 'Junwen Chen'] | 2022-06-09 | gatehub-gated-history-unit-with-background | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.pdf | cvpr-2022-1 | ['online-action-detection'] | ['computer-vision'] | [ 4.38972652e-01 -1.30078346e-01 -3.84263635e-01 -8.73974338e-02
-4.60193008e-01 -1.69932082e-01 3.79887253e-01 -3.93356942e-02
-3.74812186e-01 6.95870042e-01 6.48157954e-01 -5.62316701e-02
2.10303590e-01 -6.28796458e-01 -5.23026228e-01 -7.21765101e-01
-3.05933684e-01 -2.52657622e-01 9.52751696e-01 -2.61115879... | [8.38829517364502, 0.3673955798149109] |
f53c9acd-233a-4042-8f44-d4cb5a020221 | did-you-miss-it-automatic-lung-nodule | 1910.03986 | null | https://arxiv.org/abs/1910.03986v1 | https://arxiv.org/pdf/1910.03986v1.pdf | Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists' screening performance | Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, search lung nodules is a high complexity task, which affects the success of screening programs. Whilst computer-aided detection systems can be used as second observer... | ['Aurélio Campilho', 'António Cunha', 'Eduardo Negrão', 'João Rebelo', 'Teresa Araújo', 'João Pedrosa', 'Margarida Morgado', 'Isabel Ramos', 'Guilherme Aresta', 'Filipe Alves', 'Carlos Ferreira'] | 2019-10-09 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.60011742e-02 5.30079722e-01 -1.28155753e-01 -4.63517271e-02
-7.58213043e-01 -5.09118974e-01 -2.46191382e-01 4.20795023e-01
-8.06895137e-01 3.40664059e-01 -2.61184007e-01 -7.17275858e-01
3.91611122e-02 -2.81219244e-01 -3.20354670e-01 -7.68604577e-01
3.41972083e-01 5.00853777e-01 7.61503637e-01 5.64379752... | [15.346799850463867, -2.1738269329071045] |
ddd1a886-c8e9-404e-bf1f-c31a7d5bb535 | multi-label-thoracic-disease-image | 2007.10859 | null | https://arxiv.org/abs/2007.10859v1 | https://arxiv.org/pdf/2007.10859v1.pdf | Multi-label Thoracic Disease Image Classification with Cross-Attention Networks | Automated disease classification of radiology images has been emerging as a promising technique to support clinical diagnosis and treatment planning. Unlike generic image classification tasks, a real-world radiology image classification task is significantly more challenging as it is far more expensive to collect the t... | ['Hu Wang', 'Steven C. H. Hoi', 'Congbo Ma'] | 2020-07-21 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 4.32616413e-01 3.12395781e-01 -4.11389261e-01 -4.63797510e-01
-1.22096694e+00 9.03238431e-02 1.06089547e-01 4.18045908e-01
-5.81156790e-01 7.27246284e-01 1.53881207e-01 -3.34503859e-01
-3.30180407e-01 -6.22717679e-01 -4.04726535e-01 -8.95188689e-01
2.39093393e-01 5.82324862e-01 4.89393063e-02 1.62436187... | [14.912049293518066, -2.2070555686950684] |
f57cbcb0-e0cc-4cf1-a651-bcd4ddcc8f28 | a-bit-stream-feature-based-energy-estimator | 2212.05609 | null | https://arxiv.org/abs/2212.05609v1 | https://arxiv.org/pdf/2212.05609v1.pdf | A Bit Stream Feature-Based Energy Estimator for HEVC Software Encoding | The total energy consumption of today's video coding systems is globally significant and emphasizes the need for sustainable video coder applications. To develop such sustainable video coders, the knowledge of the energy consumption of state-of-the-art video coders is necessary. For that purpose, we need a dedicated se... | ['Christian Herglotz', 'André Kaup', 'Geetha Ramasubbu'] | 2022-12-11 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 2.45010898e-01 -2.49748185e-01 -1.57810360e-01 -1.64475203e-01
-3.67385864e-01 -4.56735313e-01 1.30482316e-01 1.88879594e-01
-1.52179539e-01 3.07842314e-01 -2.88150430e-01 -2.87294209e-01
3.73198688e-02 -7.08914697e-01 -3.49704385e-01 -7.93370008e-01
-2.38800377e-01 -1.20521054e-01 3.77182394e-01 -1.19461156... | [11.331352233886719, -1.6328939199447632] |
293ecfe7-3e4e-44cb-b441-bbe483a25b5e | efficient-partitioning-method-of-large-scale | 2306.12857 | null | https://arxiv.org/abs/2306.12857v2 | https://arxiv.org/pdf/2306.12857v2.pdf | Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints | The storage, management, and application of massive spatio-temporal data are widely applied in various practical scenarios, including public safety. However, due to the unique spatio-temporal distribution characteristics of re-al-world data, most existing methods have limitations in terms of the spatio-temporal proximi... | ['Zeli Guan', 'Zhe Xue', 'Yawen Li', 'Jie Gao'] | 2023-06-22 | null | null | null | null | ['graph-partitioning', 'management'] | ['graphs', 'miscellaneous'] | [-7.06173956e-01 -5.60976863e-01 -2.58194476e-01 1.27087414e-01
-4.03251290e-01 -1.77327678e-01 2.72838436e-02 7.35553861e-01
-2.43239164e-01 7.19460964e-01 1.64475694e-01 -5.27388632e-01
-8.58775020e-01 -1.39829993e+00 -2.48972088e-01 -7.98373163e-01
-3.77890110e-01 4.85499024e-01 1.13664281e+00 -1.38389453... | [7.246235370635986, 4.8075642585754395] |
0c4d5710-ae2c-4600-9862-ed119ac356d0 | clsebert-contrastive-learning-for-syntax | 2108.04556 | null | https://arxiv.org/abs/2108.04556v3 | https://arxiv.org/pdf/2108.04556v3.pdf | SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation | Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence. Recently, many pre-trained language models for source code (e.g., CuBERT and CodeBERT) have been proposed to model the context of ... | ['Xin Jiang', 'Yao Wan', 'Fei Mi', 'Li Li', 'Jin Liu', 'Hao Wu', 'Xiao Liu', 'Pingyi Zhou', 'Yasheng Wang', 'Xin Wang'] | 2021-08-10 | null | null | null | null | ['type-prediction', 'code-translation', 'code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-code', 'computer-vision'] | [-3.60397138e-02 -1.09205313e-01 -6.70505702e-01 -3.11025172e-01
-4.55351681e-01 -5.34552276e-01 4.71147209e-01 4.90331918e-01
8.17454234e-02 -2.00079471e-01 3.80521595e-01 -5.69774568e-01
1.97670192e-01 -6.84472740e-01 -6.90021515e-01 -2.55638957e-01
-5.19372709e-02 -9.48299170e-02 4.68848571e-02 -1.67660460... | [7.509591579437256, 8.013595581054688] |
bba320ac-b60b-45a3-8e41-054c20b55521 | sg-vad-stochastic-gates-based-speech-activity | 2210.16022 | null | https://arxiv.org/abs/2210.16022v1 | https://arxiv.org/pdf/2210.16022v1.pdf | SG-VAD: Stochastic Gates Based Speech Activity Detection | We propose a novel voice activity detection (VAD) model in a low-resource environment. Our key idea is to model VAD as a denoising task, and construct a network that is designed to identify nuisance features for a speech classification task. We train the model to simultaneously identify irrelevant features while predic... | ['Ofir Lindenbaum', 'Jonathan Svirsky'] | 2022-10-28 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-2.29223877e-01 -2.64234185e-01 -2.18059257e-01 -3.15766722e-01
-1.02396429e+00 -5.85639656e-01 4.65319127e-01 -3.25144231e-01
-4.26717162e-01 3.69916469e-01 4.87965882e-01 -6.70315206e-01
2.63735175e-01 -1.52905017e-01 -1.89182058e-01 -5.48808455e-01
-1.62728131e-01 3.91019508e-02 1.11012071e-01 6.08536303... | [14.482985496520996, 6.277797698974609] |
2cb2aec2-ec7a-49e6-802b-05f6289e40e4 | look-evolve-and-mold-learning-3d-shape | 2103.04789 | null | https://arxiv.org/abs/2103.04789v3 | https://arxiv.org/pdf/2103.04789v3.pdf | Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data | Inferring the stereo structure of objects in the real world is a challenging yet practical task. To equip deep models with this ability usually requires abundant 3D supervision which is hard to acquire. It is promising that we can simply benefit from synthetic data, where pairwise ground-truth is easy to access. Nevert... | ['Yi Yang', 'Keyang Luo', 'Yawei Luo', 'Qianyu Feng'] | 2021-03-08 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-2.17441795e-03 1.12615101e-01 8.24681744e-02 -4.44159180e-01
-6.01396799e-01 -7.50029862e-01 6.75249696e-01 -4.29873049e-01
4.22083028e-02 4.80537921e-01 1.66347235e-01 2.16509495e-02
8.61524884e-03 -7.03165650e-01 -1.15083182e+00 -6.26423359e-01
4.34624076e-01 5.17725170e-01 3.05518627e-01 -1.66355610... | [8.499224662780762, -3.1139025688171387] |
f249ea81-9cd9-4e90-895f-8952761f906f | learning-symmetric-rules-with-satnet | 2206.13998 | null | https://arxiv.org/abs/2206.13998v2 | https://arxiv.org/pdf/2206.13998v2.pdf | Learning Symmetric Rules with SATNet | SATNet is a differentiable constraint solver with a custom backpropagation algorithm, which can be used as a layer in a deep-learning system. It is a promising proposal for bridging deep learning and logical reasoning. In fact, SATNet has been successfully applied to learn, among others, the rules of a complex logical ... | ['Hongseok Yang', 'Eun-Gyeol Oh', 'Sangho Lim'] | 2022-06-28 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 5.59780225e-02 5.03198922e-01 -2.23215565e-01 -5.49447834e-01
-1.05657347e-01 -7.64084280e-01 2.39902273e-01 -3.46236944e-01
8.95538181e-02 1.01020896e+00 -1.61112741e-01 -7.29534984e-01
-6.23273790e-01 -1.08202386e+00 -1.14629376e+00 -6.40550375e-01
-3.77141312e-02 8.86406600e-01 3.10246110e-01 -3.67303550... | [8.983356475830078, 7.088293075561523] |
5b661b4a-9956-4df2-a744-668b6eddc7a5 | s-volsdf-sparse-multi-view-stereo | 2303.17712 | null | https://arxiv.org/abs/2303.17712v1 | https://arxiv.org/pdf/2303.17712v1.pdf | S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces | Neural rendering of implicit surfaces performs well in 3D vision applications. However, it requires dense input views as supervision. When only sparse input images are available, output quality drops significantly due to the shape-radiance ambiguity problem. We note that this ambiguity can be constrained when a 3D poin... | ['Dimitris Samaras', 'Alexandros Graikos', 'HaoYu Wu'] | 2023-03-30 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 2.71970898e-01 6.38281479e-02 2.46247366e-01 -3.69193107e-01
-9.51053619e-01 -3.97391856e-01 4.82083887e-01 -3.74541283e-01
-3.54041457e-02 3.99105877e-01 1.34225339e-01 -2.41098166e-01
2.72543907e-01 -8.11733246e-01 -8.26828539e-01 -6.19573593e-01
3.00504744e-01 4.10859406e-01 1.92415997e-01 2.75339577... | [9.133755683898926, -3.1266632080078125] |
dffea8e6-5988-4969-89e4-2cd3b351a18c | locate-and-beamform-two-dimensional-locating | 2305.10821 | null | https://arxiv.org/abs/2305.10821v3 | https://arxiv.org/pdf/2305.10821v3.pdf | Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation | Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from cl... | ['Fei Wang', 'Chengyun Deng', 'Jianwu Dang', 'Gaoyan Zhang', 'Longbiao Wang', 'Haoran Yin', 'Nan Li', 'Honglong Wang', 'Meng Ge', 'Yanjie Fu'] | 2023-05-18 | null | null | null | null | ['speech-separation'] | ['speech'] | [-1.55978248e-01 -4.57754999e-01 2.54169255e-02 -2.40998432e-01
-1.26680100e+00 -6.09163702e-01 2.53140926e-01 -5.61249375e-01
-1.85213223e-01 5.98022699e-01 9.08543110e-01 -2.57645041e-01
-2.09996119e-01 -8.60756561e-02 -3.34520131e-01 -8.75767529e-01
-7.30527192e-02 -2.62769014e-02 -4.10105661e-02 1.04834788... | [14.935098648071289, 5.86012601852417] |
155e9562-cbed-4236-9588-17b4a63bd08e | open-vocabulary-phrase-detection | 1811.07212 | null | https://arxiv.org/abs/1811.07212v3 | https://arxiv.org/pdf/1811.07212v3.pdf | Revisiting Image-Language Networks for Open-ended Phrase Detection | Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image. In this paper we address a more realistic version of the natural language grounding task where we must both identify whether the phrase is relevant to an image and localize... | ['Kevin J. Shih', 'Yichen Li', 'Svetlana Lazebnik', 'Kate Saenko', 'Stan Sclaroff', 'Ke Xu', 'Bryan A. Plummer'] | 2018-11-17 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.28263959e-01 4.01984453e-01 3.69331576e-02 -2.37320602e-01
-7.72961617e-01 -8.02453160e-01 7.85971522e-01 4.48636502e-01
-7.91105568e-01 4.34386194e-01 2.02334076e-01 -4.97306198e-01
3.19739874e-03 -6.27890170e-01 -9.20054972e-01 -4.75298643e-01
-6.61673620e-02 2.58165956e-01 3.87279272e-01 -3.79443347... | [10.501020431518555, 1.5446211099624634] |
2930ad94-7dd6-4cb1-8998-2e892c1a64cd | text-based-person-search-with-limited-data | 2110.10807 | null | https://arxiv.org/abs/2110.10807v1 | https://arxiv.org/pdf/2110.10807v1.pdf | Text-Based Person Search with Limited Data | Text-based person search (TBPS) aims at retrieving a target person from an image gallery with a descriptive text query. Solving such a fine-grained cross-modal retrieval task is challenging, which is further hampered by the lack of large-scale datasets. In this paper, we present a framework with two novel components to... | ['Tao Xiang', 'Li Zhang', 'Sen He', 'Xiao Han'] | 2021-10-20 | null | null | null | null | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [ 3.03128269e-02 -4.84511942e-01 -1.85825184e-01 -2.50973493e-01
-1.49100173e+00 -5.00164211e-01 8.42879713e-01 -1.95322514e-01
-6.26552701e-01 7.06570745e-01 3.86121780e-01 4.07637149e-01
-4.32901978e-01 -4.06816125e-01 -5.52237749e-01 -6.23293221e-01
3.78784090e-01 9.90003884e-01 7.62537047e-02 -1.74428731... | [14.686552047729492, 0.8561645150184631] |
0624ed0d-285d-4e45-b476-f2ccfe2bfc9c | less-confusion-more-transferable-minimum | 1912.03699 | null | https://arxiv.org/abs/1912.03699v3 | https://arxiv.org/pdf/1912.03699v3.pdf | Minimum Class Confusion for Versatile Domain Adaptation | There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific scenario, and may underperform for scenarios th... | ['Jian-Min Wang', 'Ying Jin', 'Mingsheng Long', 'Ximei Wang'] | 2019-12-08 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3769_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660460.pdf | eccv-2020-8 | ['multi-target-domain-adaptation', 'partial-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 1.83988422e-01 -5.78115024e-02 -3.49500537e-01 -3.30301017e-01
-7.84924030e-01 -1.11794126e+00 6.21632218e-01 -1.68735221e-01
-3.92994523e-01 8.77996862e-01 8.17967486e-03 -3.91371101e-01
-1.35625705e-01 -5.51736414e-01 -4.61314470e-01 -7.28111207e-01
2.58808166e-01 7.01631904e-01 3.97573948e-01 -5.21857500... | [10.166153907775879, 3.042670488357544] |
0a58729e-9769-4805-9037-7a259d0e0f5d | selectaugment-hierarchical-deterministic | 2112.02862 | null | https://arxiv.org/abs/2112.02862v1 | https://arxiv.org/pdf/2112.02862v1.pdf | SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation | Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks. However, in most cases, data augmentation is randomly performed for each training sample with a certain probability, which might incur content destruction and visual ambiguities. To eliminate this, in this paper, we prop... | ['Zhibo Chen', 'Wenjun Zeng', 'Xin Li', 'Zhizheng Zhang', 'Shiqi Lin'] | 2021-12-06 | null | null | null | null | ['fine-grained-image-recognition', 'hierarchical-reinforcement-learning'] | ['computer-vision', 'methodology'] | [ 4.69137281e-01 -1.81069925e-01 -3.07488024e-01 -2.57344276e-01
-3.21610808e-01 -3.52999747e-01 4.54095304e-01 9.69570428e-02
-5.63868761e-01 7.51098394e-01 4.19130027e-02 -2.65862614e-01
8.49727318e-02 -8.05263519e-01 -6.05896950e-01 -1.09633052e+00
5.06413221e-01 4.20491129e-01 -9.95166525e-02 1.16254441... | [9.417231559753418, 2.4398491382598877] |
c2098ece-ddd2-4c96-89f4-e7d8625cd12f | viewpoint-estimation-insights-model-1 | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Gilad_Divon_Viewpoint_Estimation_-_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Gilad_Divon_Viewpoint_Estimation_-_ECCV_2018_paper.pdf | Viewpoint Estimation---Insights & Model | This paper addresses the problem of viewpoint estimation of an object in a given image. It presents five key insights and a CNN that is based on them. The network's major properties are as follows. (i) The architecture jointly solves detection, classification, and viewpoint estimation. (ii) New types of data are added... | ['Ayellet Tal', 'Gilad Divon'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['viewpoint-estimation'] | ['computer-vision'] | [ 1.24246754e-01 7.18641654e-02 7.30281547e-02 -4.37759042e-01
-6.19939864e-01 -4.14924979e-01 4.83750463e-01 -3.00176591e-01
-3.10716510e-01 2.84467965e-01 7.15485737e-02 -3.76846194e-02
1.36168659e-01 -5.98715901e-01 -6.31175935e-01 -6.10376537e-01
1.92339290e-02 3.50983471e-01 4.90570933e-01 -1.45763904... | [7.986939907073975, -2.446171760559082] |
ae79a8a0-b711-434a-97ee-8028d73e093e | unifyspeech-a-unified-framework-for-zero-shot | 2301.03801 | null | https://arxiv.org/abs/2301.03801v1 | https://arxiv.org/pdf/2301.03801v1.pdf | UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion | Text-to-speech (TTS) and voice conversion (VC) are two different tasks both aiming at generating high quality speaking voice according to different input modality. Due to their similarity, this paper proposes UnifySpeech, which brings TTS and VC into a unified framework for the first time. The model is based on the ass... | ['JianHua Tao', 'Zhengqi Wen', 'Jiangyan Yi', 'Ruibo Fu', 'Tao Wang', 'Haogeng Liu'] | 2023-01-10 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [-9.40423924e-03 3.07588190e-01 -3.41673672e-01 -2.86067367e-01
-8.01304936e-01 -5.10312021e-01 6.93966687e-01 -2.73076892e-01
-9.14564207e-02 5.90636909e-01 8.05375516e-01 -3.59580398e-01
2.92486966e-01 -3.96968395e-01 -2.45212808e-01 -5.43589711e-01
6.12436116e-01 3.00320655e-01 2.26859972e-01 -4.37761188... | [14.936166763305664, 6.5243072509765625] |
906bd83f-96c4-4a57-b2e7-596f098e50db | leveraging-causal-inference-for-explainable | 2205.13342 | null | https://arxiv.org/abs/2205.13342v2 | https://arxiv.org/pdf/2205.13342v2.pdf | Leveraging Causal Inference for Explainable Automatic Program Repair | Deep learning models have made significant progress in automatic program repair. However, the black-box nature of these methods has restricted their practical applications. To address this challenge, this paper presents an interpretable approach for program repair based on sequence-to-sequence models with causal infere... | ['Jing Xiao', 'Zhenhou Hong', 'Xiaoyang Qu', 'Zhitao Zhu', 'Shijing Si', 'Jianzong Wang'] | 2022-05-26 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 3.67764771e-01 7.06656456e-01 -5.77823579e-01 -3.94436151e-01
-5.85849404e-01 -4.80147630e-01 3.51624846e-01 4.13943261e-01
4.73405004e-01 4.50204283e-01 5.26392460e-01 -9.25479650e-01
-1.20953232e-01 -1.12006581e+00 -9.99156058e-01 -7.65117481e-02
-2.58235693e-01 -3.81038152e-02 1.26528040e-01 -4.39733826... | [7.705283164978027, 7.733797073364258] |
fe082401-eb84-4f17-9b64-13c7a41578f7 | a-new-angle-on-l2-regularization | 1806.11186 | null | http://arxiv.org/abs/1806.11186v1 | http://arxiv.org/pdf/1806.11186v1.pdf | A New Angle on L2 Regularization | Imagine two high-dimensional clusters and a hyperplane separating them.
Consider in particular the angle between: the direction joining the two
clusters' centroids and the normal to the hyperplane. In linear classification,
this angle depends on the level of L2 regularization used. Can you explain why? | ['Lewis D. Griffin', 'Thomas Tanay'] | 2018-06-28 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-4.62137908e-01 4.39221382e-01 -4.77749050e-01 -4.77361411e-01
-2.98090190e-01 -5.05908251e-01 4.42938209e-01 4.32259887e-01
-4.04059470e-01 2.00089738e-01 2.66904116e-01 -3.75372887e-01
-2.03891113e-01 -4.18266356e-01 -3.68350744e-01 -8.73830318e-01
-4.45274889e-01 5.27618349e-01 2.22581830e-02 2.83295866... | [7.719199180603027, 4.326310634613037] |
b0138485-550f-4643-a365-11ca8a792b3c | scalable-algorithms-for-physics-informed | 2205.08332 | null | https://arxiv.org/abs/2205.08332v1 | https://arxiv.org/pdf/2205.08332v1.pdf | Scalable algorithms for physics-informed neural and graph networks | Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available ... | ['George Em Karniadakis', 'Nathaniel Trask', 'Mengjia Xu', 'Khemraj Shukla'] | 2022-05-16 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 9.95163321e-02 3.56255397e-02 2.82469422e-01 9.18870270e-02
-2.64145792e-01 -2.30339199e-01 5.10713637e-01 1.20524295e-01
-2.93241650e-01 1.11722875e+00 -3.34527582e-01 -3.78407687e-01
-6.86820865e-01 -1.05590475e+00 -9.26121771e-01 -1.11537445e+00
-5.49169481e-01 8.35522711e-01 -2.65200399e-02 -5.66051364... | [6.40374755859375, 3.4858145713806152] |
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