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e688f15c-f7d8-4992-ad52-ccfc8ab04c8b | a-deterministic-algorithm-for-bridging | 1811.05721 | null | http://arxiv.org/abs/1811.05721v1 | http://arxiv.org/pdf/1811.05721v1.pdf | A Deterministic Algorithm for Bridging Anaphora Resolution | Previous work on bridging anaphora resolution (Poesio et al., 2004; Hou et
al., 2013b) use syntactic preposition patterns to calculate word relatedness.
However, such patterns only consider NPs' head nouns and hence do not fully
capture the semantics of NPs. Recently, Hou (2018) created word embeddings
(embeddings_PP) ... | ['Yufang Hou'] | 2018-11-14 | a-deterministic-algorithm-for-bridging-1 | https://aclanthology.org/D18-1219 | https://aclanthology.org/D18-1219.pdf | emnlp-2018-10 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [-3.02764863e-01 4.90342915e-01 -4.83291209e-01 -1.76276043e-01
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-5.45906797e-02 9.08517540e-01 4.28098202e-01 -6.37936652... | [9.676392555236816, 9.249931335449219] |
f8f8b45c-f6ec-4da8-b798-ff75530b3fe1 | dict-tts-learning-to-pronounce-with-prior | 2206.02147 | null | https://arxiv.org/abs/2206.02147v2 | https://arxiv.org/pdf/2206.02147v2.pdf | Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech | Polyphone disambiguation aims to capture accurate pronunciation knowledge from natural text sequences for reliable Text-to-speech (TTS) systems. However, previous approaches require substantial annotated training data and additional efforts from language experts, making it difficult to extend high-quality neural TTS sy... | ['Zhenhui Ye', 'Jinglin Liu', 'Yi Ren', 'Qian Yang', 'Zhou Zhao', 'Su Zhe', 'Ziyue Jiang'] | 2022-06-05 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 1.45951351e-02 -1.34414315e-01 -2.65455961e-01 -4.24241215e-01
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6.57544434e-01 5.96521139e-01 -9.06313211e-03 -3.96506131... | [14.635180473327637, 6.824821472167969] |
c0ad4733-fd42-49fe-abd6-ffa054870b44 | xbnet-an-extremely-boosted-neural-network | 2106.05239 | null | https://arxiv.org/abs/2106.05239v3 | https://arxiv.org/pdf/2106.05239v3.pdf | XBNet : An Extremely Boosted Neural Network | Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio. However, it has been observed that their performance is not up to the mark in tabular data; hence tree-based models are preferred in such scenarios. A popular model for tabular data is boosted trees, a hi... | ['Tushar Sarkar'] | 2021-06-09 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'diabetes-prediction', 'classification'] | ['knowledge-base', 'medical', 'medical', 'methodology'] | [-3.67543548e-02 1.76569402e-01 -2.99302310e-01 -4.85409141e-01
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-1.13248840e-01 7.22118795e-01 -3.55715603e-02 -4.03603941... | [8.623873710632324, 4.0223565101623535] |
9680281a-2c92-4f16-baa8-b77239d6d686 | taspm-targeted-sequential-pattern-mining | 2202.13202 | null | https://arxiv.org/abs/2202.13202v1 | https://arxiv.org/pdf/2202.13202v1.pdf | TaSPM: Targeted Sequential Pattern Mining | Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number ... | ['Philip S. Yu', 'Wensheng Gan', 'Gengsen Huang'] | 2022-02-26 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 6.59720421e-01 -6.48385584e-01 -3.25757980e-01 -1.62037119e-01
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-7.57257640e-02 4.28389072e-01 1.14338005e+00 -4.08467948... | [8.31763744354248, 6.297606945037842] |
01dbde5e-1703-424c-b74b-f391ff7f55b1 | pandagpt-one-model-to-instruction-follow-them | 2305.16355 | null | https://arxiv.org/abs/2305.16355v1 | https://arxiv.org/pdf/2305.16355v1.pdf | PandaGPT: One Model To Instruction-Follow Them All | We present PandaGPT, an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities. Our pilot experiments show that PandaGPT can perform complex tasks such as detailed image description generation, writing stories inspired by videos, and answering questions about audios. More ... | ['Deng Cai', 'Yan Wang', 'Jialu Xu', 'Huayang Li', 'Tian Lan', 'Yixuan Su'] | 2023-05-25 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.45850834e-02 1.37594774e-01 9.80392322e-02 -1.14825383e-01
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2.35480934e-01 1.97983056e-01 -1.19987629e-01 -9.87142697... | [10.606285095214844, 1.2927532196044922] |
a3d71a48-776a-41c6-985c-b8efba9633da | a-spatio-temporal-identity-verification | 2111.00228 | null | https://arxiv.org/abs/2111.00228v2 | https://arxiv.org/pdf/2111.00228v2.pdf | whu-nercms at trecvid2021:instance search task | We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the automatic and interactive tasks of Instance Search (INS). For the automatic task, the retrieval target is divided into two parts, person retrieval, and action retr... | ['Jun Chen', 'Dongshu Xu', 'Shishi Wen', 'Ji Huang', 'Yue Zhang', 'Ankang Lu', 'Yanrui Niu', 'Zhongyuan Wang', 'Baojin Huang', 'Chao Liang', 'Jingyao Yang'] | 2021-10-30 | null | null | null | null | ['person-retrieval', 'instance-search'] | ['computer-vision', 'computer-vision'] | [ 2.26885289e-01 -3.51565987e-01 -1.54038474e-01 -5.48569918e-01
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1.83564410e-01 6.08867347e-01 5.55706620e-01 6.64302409... | [14.640499114990234, 0.8229175209999084] |
37470063-1e87-4124-a3bd-65232b82261c | multi-scale-alignment-and-spatial-roi-module | 2207.01345 | null | https://arxiv.org/abs/2207.01345v1 | https://arxiv.org/pdf/2207.01345v1.pdf | Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis | Coronavirus Disease 2019 (COVID-19) has spread globally and become a health crisis faced by humanity since first reported. Radiology imaging technologies such as computer tomography (CT) and chest X-ray imaging (CXR) are effective tools for diagnosing COVID-19. However, in CT and CXR images, the infected area occupies ... | ['Arcot Sowmya', 'Dadong Wang', 'Hongyan Xu'] | 2022-07-04 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.49762729e-01 -5.75787961e-01 1.63975686e-01 -9.83992070e-02
-4.43650991e-01 -4.37374800e-01 1.58477142e-01 3.31618458e-01
-6.97564542e-01 5.15457153e-01 4.87108417e-02 -4.26390946e-01
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-1.00142799e-01 2.31725380e-01 4.41115588e-01 1.45974532... | [15.54178237915039, -1.7393099069595337] |
9a28b92f-f300-45b7-91ce-17dbd904f2b6 | interpolating-item-and-user-fairness-in | 2306.10050 | null | https://arxiv.org/abs/2306.10050v1 | https://arxiv.org/pdf/2306.10050v1.pdf | Interpolating Item and User Fairness in Recommendation Systems | Online platforms employ recommendation systems to enhance customer engagement and drive revenue. However, in a multi-sided platform where the platform interacts with diverse stakeholders such as sellers (items) and customers (users), each with their own desired outcomes, finding an appropriate middle ground becomes a c... | ['Djallel Bouneffouf', 'Negin Golrezaei', 'Jason Cheuk Nam Liang', 'Qinyi Chen'] | 2023-06-12 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-2.74393737e-01 4.55874130e-02 -5.10721743e-01 -3.77220899e-01
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-1.05287895e-01 1.73817545e-01 -4.43742454e-01 -3.51764530... | [9.575563430786133, 5.579000473022461] |
3df00d9d-5cb1-46cc-aaac-9af428476833 | focused-proofreading-efficiently-extracting | 1409.1199 | null | http://arxiv.org/abs/1409.1199v1 | http://arxiv.org/pdf/1409.1199v1.pdf | Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images | Identifying complex neural circuitry from electron microscopic (EM) images
may help unlock the mysteries of the brain. However, identifying this circuitry
requires time-consuming, manual tracing (proofreading) due to the size and
intricacy of these image datasets, thus limiting state-of-the-art analysis to
very small b... | ['Stephen M. Plaza'] | 2014-09-03 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 3.04562569e-01 1.41967326e-01 4.72597867e-01 -2.88406350e-02
-5.77225983e-01 -7.96712458e-01 3.47500771e-01 2.50382721e-01
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3.01847756e-01 4.79951352e-01 5.73716819e-01 6.90825060... | [14.320436477661133, -3.141075611114502] |
8cb8aafa-1281-409d-9c44-1d82c4b75d50 | adaptive-network-combination-for-single-image | 2204.01505 | null | https://arxiv.org/abs/2204.01505v1 | https://arxiv.org/pdf/2204.01505v1.pdf | Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective | Recently, multiple synthetic and real-world datasets have been built to facilitate the training of deep single image reflection removal (SIRR) models. Meanwhile, diverse testing sets are also provided with different types of reflection and scenes. However, the non-negligible domain gaps between training and testing set... | ['Lei Zhang', 'WangMeng Zuo', 'Zifei Yan', 'Jianan Pan', 'Ming Liu'] | 2022-04-04 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 6.04261577e-01 -1.59627512e-01 3.25397760e-01 -5.55705607e-01
-9.21776235e-01 -1.67637676e-01 4.47950780e-01 -3.52124035e-01
-2.46497840e-01 6.23187542e-01 -1.18403807e-01 -1.46389022e-01
-8.99410099e-02 -9.10688877e-01 -6.65044248e-01 -9.04378831e-01
2.40710601e-01 2.90794730e-01 2.98348367e-01 -3.49848896... | [10.921873092651367, -2.681542158126831] |
f7bb2320-5f89-4c09-9122-7233e9142c86 | set-generation-networks-for-end-to-end | null | null | https://aclanthology.org/2021.emnlp-main.760 | https://aclanthology.org/2021.emnlp-main.760.pdf | Set Generation Networks for End-to-End Knowledge Base Population | The task of knowledge base population (KBP) aims to discover facts about entities from texts and expand a knowledge base with these facts. Previous studies shape end-to-end KBP as a machine translation task, which is required to convert unordered fact into a sequence according to a pre-specified order. However, the fac... | ['Wei Bi', 'Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Chenhao Wang', 'Dianbo Sui'] | null | null | null | null | emnlp-2021-11 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 3.57535332e-01 7.41927803e-01 -8.83132890e-02 -4.77144748e-01
-7.83715427e-01 -5.98857999e-01 4.53220606e-01 1.59136146e-01
-3.51040393e-01 1.28146863e+00 4.57050025e-01 -3.42345625e-01
-8.84660855e-02 -1.32049143e+00 -1.35232818e+00 -3.45956743e-01
2.73333758e-01 8.32633138e-01 4.25681099e-02 -5.49661934... | [9.814414978027344, 8.489052772521973] |
d93833ec-75d0-42c1-a1f6-746474b19f38 | blind-source-extraction-based-on-multi | 2005.07976 | null | https://arxiv.org/abs/2005.07976v2 | https://arxiv.org/pdf/2005.07976v2.pdf | Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data Augmentation | Extracting the desired speech from a mixture is a meaningful and challenging task. The end-to-end DNN-based methods, though attractive, face the problem of generalization. In this paper, we explore a sequential approach for target speech extraction by combining blind source separation (BSS) with the x-vector based spea... | ['Jing Lu', 'Kai Chen', 'Lele Liao', 'Zhaoyi Gu'] | 2020-05-16 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 1.17223851e-01 -3.54451627e-01 2.65283823e-01 -2.29348525e-01
-8.76286149e-01 -4.82672930e-01 5.48268139e-01 -6.41044915e-01
-2.41406396e-01 7.24856436e-01 5.54969311e-01 -3.13113570e-01
-1.87786028e-01 -3.07311974e-02 -3.94972086e-01 -1.01535165e+00
2.03854933e-01 1.14788890e-01 -4.03897107e-01 -1.56999603... | [15.068680763244629, 5.760958194732666] |
7640a3c9-c052-46c5-a9a8-73e551ff658e | non-deep-networks-1 | 2110.07641 | null | https://arxiv.org/abs/2110.07641v1 | https://arxiv.org/pdf/2110.07641v1.pdf | Non-deep Networks | Depth is the hallmark of deep neural networks. But more depth means more sequential computation and higher latency. This begs the question -- is it possible to build high-performing "non-deep" neural networks? We show that it is. To do so, we use parallel subnetworks instead of stacking one layer after another. This he... | ['Vladlen Koltun', 'Jia Deng', 'Alexey Bochkovskiy', 'Ankit Goyal'] | 2021-10-14 | non-deep-networks | https://openreview.net/forum?id=Xg47v73CDaj | https://openreview.net/pdf?id=Xg47v73CDaj | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.61480442e-01 1.32277906e-01 1.58765689e-01 -5.47589362e-01
-3.77306074e-01 -4.53486234e-01 2.96698641e-02 -2.54327387e-01
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-2.09677383e-01 2.72826433e-01 6.98539674e-01 -6.81604594... | [8.618408203125, 2.951463460922241] |
5b291ff6-ca7a-4183-8eed-d59e9a877032 | unveiling-the-two-faced-truth-disentangling | 2306.03002 | null | https://arxiv.org/abs/2306.03002v1 | https://arxiv.org/pdf/2306.03002v1.pdf | Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection | Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability ... | ['Jaime S. Cardoso', 'Ana F. Sequeira', 'Naser Damer', 'Tiago Gonçalves', 'Pedro C. Neto', 'Eduarda Caldeira'] | 2023-06-05 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 2.77286232e-01 2.76400298e-01 1.34643286e-01 -5.49460709e-01
-2.28109106e-01 -7.06246793e-01 7.39829004e-01 1.35946080e-01
-2.37271711e-01 6.06781602e-01 -2.02806950e-01 -3.89970124e-01
-2.85823405e-01 -5.60213804e-01 -4.25010502e-01 -7.73890257e-01
7.76092783e-02 8.44575167e-01 -1.67906746e-01 -3.02327216... | [13.011161804199219, 1.0850123167037964] |
a3fb0083-1c05-489a-ab80-ccec1485d10c | decision-trees-for-decision-making-under-the | 2003.00360 | null | https://arxiv.org/abs/2003.00360v2 | https://arxiv.org/pdf/2003.00360v2.pdf | Decision Trees for Decision-Making under the Predict-then-Optimize Framework | We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A ... | ['Jason Cheuk Nam Liang', 'Ryan McNellis', 'Adam N. Elmachtoub'] | 2020-02-29 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6150-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6150-Paper.pdf | icml-2020-1 | ['parameter-prediction'] | ['miscellaneous'] | [ 3.37868690e-01 4.11885262e-01 -5.04608572e-01 -8.77402306e-01
-7.40508258e-01 -3.88459116e-01 2.00050190e-01 2.96085894e-01
-4.11756575e-01 6.62664413e-01 -3.96149121e-02 -8.09332371e-01
-5.74255228e-01 -9.53815162e-01 -6.18361592e-01 -6.64477587e-01
-9.87933129e-02 7.68888533e-01 -7.27450475e-02 1.42156882... | [8.091716766357422, 4.159327507019043] |
31a7595e-4b37-4d20-9603-d4de5ff59043 | fair-information-spread-on-social-networks | 2305.08791 | null | https://arxiv.org/abs/2305.08791v1 | https://arxiv.org/pdf/2305.08791v1.pdf | Fair Information Spread on Social Networks with Community Structure | Information spread through social networks is ubiquitous. Influence maximiza- tion (IM) algorithms aim to identify individuals who will generate the greatest spread through the social network if provided with information, and have been largely devel- oped with marketing in mind. In social networks with community struct... | ['Ji Zhu', 'Elizaveta Levina', 'Octavio Mesner'] | 2023-05-15 | null | null | null | null | ['community-detection', 'marketing'] | ['graphs', 'miscellaneous'] | [ 3.06977004e-01 4.25922453e-01 -6.82159662e-01 1.24100782e-01
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-6.24041378e-01 6.99114382e-01 1.87775925e-01 -3.30313236... | [6.871538162231445, 5.338607311248779] |
18af1368-70ef-4d51-87bd-93b6880f1fb9 | face-recognition-in-the-age-of-clip-billion | 2301.07315 | null | https://arxiv.org/abs/2301.07315v1 | https://arxiv.org/pdf/2301.07315v1.pdf | Face Recognition in the age of CLIP & Billion image datasets | CLIP (Contrastive Language-Image Pre-training) models developed by OpenAI have achieved outstanding results on various image recognition and retrieval tasks, displaying strong zero-shot performance. This means that they are able to perform effectively on tasks for which they have not been explicitly trained. Inspired b... | ['Shrey Jain', 'Aaditya Bhat'] | 2023-01-18 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.56688914e-02 -2.59570777e-01 -3.06265652e-01 1.97290182e-02
-7.48501062e-01 -5.11941195e-01 7.54802227e-01 -1.09911695e-01
-3.98331195e-01 4.69492346e-01 9.67207178e-02 -1.94338083e-01
-1.79473832e-01 -6.82899296e-01 -7.06309140e-01 -5.27871966e-01
-1.13204323e-01 3.67002964e-01 8.64269491e-03 -3.55897874... | [12.770979881286621, 1.0708577632904053] |
18edeaa6-9e6f-4ccf-bb8d-2108610c9cdc | frustratingly-easy-transferability-estimation | 2106.09362 | null | https://arxiv.org/abs/2106.09362v4 | https://arxiv.org/pdf/2106.09362v4.pdf | Frustratingly Easy Transferability Estimation | Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer. Existing estimation algorithms either require intensive training on target tasks or have diffi... | ['Junzhou Huang', 'Qiang Yang', 'Yu Rong', 'Ying WEI', 'Long-Kai Huang'] | 2021-06-17 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.19349343e-01 2.73524314e-01 -2.13548124e-01 -4.06116188e-01
-8.29965115e-01 -3.58861893e-01 7.15569675e-01 2.15839684e-01
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-1.67261094e-01 -7.05494404e-01 -8.35760772e-01 -5.90489268e-01
-3.22937757e-01 2.90078253e-01 1.09763175e-01 7.22034425... | [9.631421089172363, 3.02311635017395] |
393bde1c-2df8-4435-9a87-7656e058fd72 | generating-3d-bio-printable-patches-using | 2203.03814 | null | https://arxiv.org/abs/2203.03814v1 | https://arxiv.org/pdf/2203.03814v1.pdf | Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers | We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surgery to treat diabetic foot ulcers (DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D image capturing, sem... | ['Taebin Lim', 'Seungyeob Han', 'Hyewon Son', 'Seunghwan Lee', 'Han Joo Chae'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chae_Generating_3D_Bio-Printable_Patches_Using_Wound_Segmentation_and_Reconstruction_To_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chae_Generating_3D_Bio-Printable_Patches_Using_Wound_Segmentation_and_Reconstruction_To_CVPR_2022_paper.pdf | cvpr-2022-1 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 5.44590592e-01 3.15377772e-01 7.48632252e-02 4.24020253e-02
-8.33278298e-01 -4.74653065e-01 -1.08591788e-01 3.36333185e-01
6.89740255e-02 4.70994711e-01 2.44421795e-01 -7.43050337e-01
1.09879172e-03 -9.38757002e-01 -5.67325771e-01 3.58692044e-03
2.99479216e-02 8.57866406e-01 4.23922896e-01 -3.19491953... | [12.898773193359375, -2.813842296600342] |
88fd2c82-f7a2-4817-b7cc-2281dbcb05c1 | sketchbetween-video-to-video-synthesis-for | 2209.00185 | null | https://arxiv.org/abs/2209.00185v1 | https://arxiv.org/pdf/2209.00185v1.pdf | SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches | 2D animation is a common factor in game development, used for characters, effects and background art. It involves work that takes both skill and time, but parts of which are repetitive and tedious. Automated animation approaches exist, but are designed without animators in mind. The focus is heavily on real-life video,... | ['Matthew Guzdial', 'Dagmar Lukka Loftsdóttir'] | 2022-09-01 | null | null | null | null | ['video-to-video-synthesis'] | ['computer-vision'] | [ 5.44775352e-02 1.10590737e-02 5.04120179e-02 2.47190714e-01
-1.47530232e-02 -6.91325963e-01 8.70866418e-01 -3.11786830e-01
-3.87302190e-02 3.13812464e-01 1.68768261e-02 -3.22881460e-01
1.10654451e-01 -7.55644441e-01 -6.94283247e-01 -1.94553420e-01
-2.85787612e-01 4.91790295e-01 4.99104202e-01 -5.26089668... | [11.021347045898438, -0.5605491995811462] |
7a82e064-5ce0-4c6e-9fec-56bf9d57947d | learning-semantic-representations-for-1 | null | null | https://aclanthology.org/D15-1164 | https://aclanthology.org/D15-1164.pdf | Learning Semantic Representations for Nonterminals in Hierarchical Phrase-Based Translation | null | ['Xing Wang', 'Deyi Xiong', 'Min Zhang'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['learning-semantic-representations'] | ['methodology'] | [-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... | [-7.266462802886963, 3.744016170501709] |
2ba86980-6b6d-474b-a70e-110eae160dc2 | vararray-meets-t-sot-advancing-the-state-of | 2209.04974 | null | https://arxiv.org/abs/2209.04974v2 | https://arxiv.org/pdf/2209.04974v2.pdf | VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition | This paper presents a novel streaming automatic speech recognition (ASR) framework for multi-talker overlapping speech captured by a distant microphone array with an arbitrary geometry. Our framework, named t-SOT-VA, capitalizes on independently developed two recent technologies; array-geometry-agnostic continuous spee... | ['Takuya Yoshioka', 'Jinyu Li', 'Zhuo Chen', 'Xiaofei Wang', 'Jian Wu', 'Naoyuki Kanda'] | 2022-09-12 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.37931007e-01 -2.51264274e-01 6.69643521e-01 -4.35042948e-01
-1.84404624e+00 -5.89423954e-01 1.76829129e-01 -2.83309937e-01
-6.48256838e-02 2.18478039e-01 4.12778765e-01 -5.47523677e-01
1.96101721e-02 -7.77659267e-02 -6.10872149e-01 -1.00434899e+00
9.30522084e-02 2.13679776e-01 4.83165830e-02 -1.94284841... | [14.77794075012207, 6.095434665679932] |
ee1a1016-9e32-444b-934a-f8d4d0327dd7 | continuous-3d-label-stereo-matching-using | 1603.08328 | null | http://arxiv.org/abs/1603.08328v3 | http://arxiv.org/pdf/1603.08328v3.pdf | Continuous 3D Label Stereo Matching using Local Expansion Moves | We present an accurate stereo matching method using local expansion moves
based on graph cuts. This new move-making scheme is used to efficiently infer
per-pixel 3D plane labels on a pairwise Markov random field (MRF) that
effectively combines recently proposed slanted patch matching and curvature
regularization terms.... | ['Yasuyuki Matsushita', 'Tatsunori Taniai', 'Takeshi Naemura', 'Yoichi Sato'] | 2016-03-28 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 2.65405476e-01 6.66501746e-02 -4.10899609e-01 -3.98169249e-01
-1.31188595e+00 -3.48919779e-01 3.18553418e-01 2.41010740e-01
-3.68052274e-01 6.05113983e-01 1.40727043e-01 -1.43361121e-01
-1.05147801e-01 -9.10331070e-01 -1.00337148e+00 -5.58932483e-01
-1.12922154e-01 8.08684647e-01 9.06897902e-01 -1.89412415... | [8.937685012817383, -2.4124374389648438] |
c41952f6-63ec-4d71-a4d5-f1c31b8b4619 | incremental-predictive-process-monitoring-how | 1804.03967 | null | http://arxiv.org/abs/1804.03967v1 | http://arxiv.org/pdf/1804.03967v1.pdf | Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments | A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive ... | ['Fabrizio Maria Maggi', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Williams Rizzi', 'Cosimo Damiano Persia'] | 2018-04-11 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 6.08367741e-01 4.94043194e-02 -2.10646316e-02 -9.88419577e-02
-1.10324249e-01 -3.24342608e-01 8.40969622e-01 5.85731864e-01
-2.07120746e-01 6.51716948e-01 -4.98769321e-02 -2.83574134e-01
-4.38013405e-01 -9.44980383e-01 -1.14108182e-01 -4.72690314e-01
-6.66356325e-01 8.95198405e-01 7.36055255e-01 2.12897927... | [8.587271690368652, 6.007868766784668] |
e2ffbc50-8f82-4872-8723-03a0987d834d | dualnet-locate-then-detect-effective-payload | 2010.12171 | null | https://arxiv.org/abs/2010.12171v1 | https://arxiv.org/pdf/2010.12171v1.pdf | DualNet: Locate Then Detect Effective Payload with Deep Attention Network | Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years. Howeve... | ['Hui Guo', 'Peilun Wu', 'Shiyi Yang'] | 2020-10-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 4.28836159e-02 -7.92763233e-01 -5.99101111e-02 -6.83208406e-02
1.72552496e-01 -3.02284092e-01 6.46566570e-01 2.20383629e-01
-6.64344370e-01 5.20902097e-01 -4.80405927e-01 -5.71799040e-01
-4.29859757e-01 -9.66275811e-01 8.39301385e-03 -4.88789201e-01
-1.91589281e-01 2.64648646e-01 7.27082193e-01 -2.41001874... | [5.239187717437744, 7.1727190017700195] |
0de56492-fc1f-4185-bd9c-3437029323c0 | bjtu-wechat-s-systems-for-the-wmt22-chat | 2211.15009 | null | https://arxiv.org/abs/2211.15009v1 | https://arxiv.org/pdf/2211.15009v1.pdf | BJTU-WeChat's Systems for the WMT22 Chat Translation Task | This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we utilize the pre-training-then-fine-tuning paradigm. In the first pre-training stage, w... | ['Jie zhou', 'Yufeng Chen', 'Jinan Xu', 'Fandong Meng', 'Yunlong Liang'] | 2022-11-28 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.05193086e-01 -1.79793268e-01 5.91411367e-02 -4.52163517e-01
-1.43807530e+00 -6.22463942e-01 7.73820221e-01 -3.34822029e-01
-6.23838603e-01 1.21333385e+00 5.45194447e-01 -4.58102763e-01
2.49632820e-01 -4.91228044e-01 -6.18839383e-01 -3.04866821e-01
5.76135397e-01 8.36095273e-01 -2.50118300e-02 -6.01293981... | [14.343335151672363, 7.293102264404297] |
26c614ab-45b8-4a21-922c-89d59160ddf7 | machine-learning-approach-of-automatic | null | null | https://ieeexplore.ieee.org/abstract/document/8822896 | https://ieeexplore.ieee.org/abstract/document/8822896 | Machine learning approach of automatic identification and counting of blood cells | A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment’s and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a ... | ['Mohammad Tariqul Islam', 'Mohammad Mahmudul Alam'] | 2019-09-05 | null | null | null | healthcare-technology-letters-iet-2019-9 | ['blood-cell-count', 'cbc-test', 'blood-cell-detection'] | ['computer-vision', 'computer-vision', 'medical'] | [-2.85869271e-01 -1.78321660e-01 1.93232462e-01 5.08937202e-02
-8.55425596e-02 -3.92728746e-01 2.84559518e-01 8.78212452e-01
-8.65863204e-01 8.19182217e-01 -3.32588017e-01 -1.84972033e-01
3.14700246e-01 -1.03054380e+00 1.45440295e-01 -8.71471882e-01
1.77409887e-01 1.09892368e+00 6.29656240e-02 3.05198640... | [14.858492851257324, -3.1367743015289307] |
7fe292be-509a-4c4e-8c04-855827d2a8da | template-free-articulated-neural-point-clouds | 2305.19065 | null | https://arxiv.org/abs/2305.19065v1 | https://arxiv.org/pdf/2305.19065v1.pdf | Template-free Articulated Neural Point Clouds for Reposable View Synthesis | Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformation fields makes reanimation of the captured object poses challenging. Moreover, the state of the art dynamic models are often limited by lo... | ['Petr Kellnhofer', 'Elmar Eisemann', 'Lukas Uzolas'] | 2023-05-30 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.38598168e-01 -2.01795414e-01 7.91849494e-02 -1.18795894e-01
-8.74446809e-01 -7.99642086e-01 6.01040244e-01 -6.82001770e-01
2.08002254e-01 4.19806600e-01 9.66393948e-02 5.68358563e-02
-2.45621398e-01 -5.39473236e-01 -9.96949077e-01 -7.33340621e-01
2.69292325e-01 3.54050994e-01 1.88237712e-01 -9.28254873... | [8.894805908203125, -2.819120168685913] |
8fb27434-a903-456d-8110-b3b1d442a644 | research-on-dynamic-target-detection-and | 1912.01992 | null | https://arxiv.org/abs/1912.01992v1 | https://arxiv.org/pdf/1912.01992v1.pdf | Research on dynamic target detection and tracking system of hexapod robot | Dynamic target detection and target tracking are hot issues in the field of image. In order to explore its application value in the field of mobile robot, a dynamic target detection and tracking system is designed based on hexapod robot. Firstly, the dynamic target detection method is introduced with region merging and... | ['Dexin Wang'] | 2019-12-04 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [-2.56055653e-01 -3.44280362e-01 -1.77050438e-02 2.42387906e-01
2.62253672e-01 -3.27315927e-01 1.73615739e-01 -4.15039569e-01
-6.55255973e-01 1.46433199e-02 -4.61300582e-01 -2.43256483e-02
-6.06554188e-02 -6.83892608e-01 1.87109003e-03 -1.09064770e+00
2.48949468e-01 4.94102091e-01 1.06664097e+00 -1.35662109... | [6.915713787078857, -1.9407333135604858] |
ecb96aa6-15b7-4365-bbb5-6851ffa9fc45 | zero-resource-cross-domain-named-entity | 2002.05923 | null | https://arxiv.org/abs/2002.05923v2 | https://arxiv.org/pdf/2002.05923v2.pdf | Zero-Resource Cross-Domain Named Entity Recognition | Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-resource target domains is not only expensive but also time-consuming. Hence, we propose a cross-domain NER model that does not use any exter... | ['Genta Indra Winata', 'Pascale Fung', 'Zihan Liu'] | 2020-02-14 | zero-resource-cross-domain-named-entity-1 | https://aclanthology.org/2020.repl4nlp-1.1 | https://aclanthology.org/2020.repl4nlp-1.1.pdf | ws-2020-7 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [-9.51099768e-02 -2.46423498e-01 -3.76872480e-01 -3.83741170e-01
-1.17629921e+00 -9.44208086e-01 6.05990708e-01 -5.64953722e-02
-8.96664679e-01 1.09797585e+00 4.75679012e-03 -2.11248174e-01
4.54754651e-01 -6.28913403e-01 -6.37825966e-01 -1.71906546e-01
3.58168781e-01 7.20487356e-01 5.06470084e-01 -8.06938484... | [9.801044464111328, 9.534828186035156] |
4980fcfd-c926-4152-ad6d-6aeba51229cf | leveraged-weighted-loss-for-partial-label-1 | 2106.05731 | null | https://arxiv.org/abs/2106.05731v1 | https://arxiv.org/pdf/2106.05731v1.pdf | Leveraged Weighted Loss for Partial Label Learning | As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true. Despite many methodology studies on learning from partial labels, there still lacks theoretical understandings of their risk cons... | ['Zhouchen Lin', 'Yisen Wang', 'Jiabin Liu', 'Hanyuan Hang', 'Jingyi Cui', 'Hongwei Wen'] | 2021-06-10 | leveraged-weighted-loss-for-partial-label | https://openreview.net/forum?id=DHkGKg2fJay | https://openreview.net/pdf?id=DHkGKg2fJay | null | ['partial-label-learning'] | ['methodology'] | [ 1.77919045e-01 4.85030621e-01 -7.06622005e-01 -7.64198780e-01
-1.00431848e+00 -5.19693136e-01 3.67176890e-01 5.09729028e-01
-4.12599832e-01 8.36256087e-01 -2.68034071e-01 -2.64081627e-01
-4.58312690e-01 -5.36814392e-01 -7.12689102e-01 -9.12387729e-01
-4.26242650e-02 3.97958666e-01 1.27182424e-01 4.72305894... | [9.16380500793457, 4.167608737945557] |
41e07ea9-7d59-4b1f-9754-f63230ff8493 | relation-dependent-contrastive-learning-with | 2211.12266 | null | https://arxiv.org/abs/2211.12266v1 | https://arxiv.org/pdf/2211.12266v1.pdf | Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction | Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relation prediction have received increasing attention, which can predict relation for unseen entities based on the extracted subgraph surroundin... | ['Haifeng Hu', 'Sijie Mai', 'Jianfeng Wu'] | 2022-11-22 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [ 1.01910323e-01 7.55197585e-01 -5.01368105e-01 -3.91639441e-01
-2.80559957e-01 -2.39395916e-01 5.50604582e-01 3.59247267e-01
-1.68226898e-01 7.51746356e-01 2.26574495e-01 -2.52834946e-01
-4.50933278e-01 -1.20609415e+00 -7.69084156e-01 -4.95628983e-01
-2.78013319e-01 7.52883434e-01 2.81809598e-01 -4.19702321... | [8.954375267028809, 8.119715690612793] |
86933b98-e14d-423e-8665-bf4c15afe133 | explainable-inference-on-sequential-data-via | null | null | https://www.ijcai.org/Proceedings/2020/278 | https://www.ijcai.org/Proceedings/2020/0278.pdf | Explainable Inference on Sequential Data via Memory-Tracking | In this paper we present a novel mechanism to
get explanations that allow to better understand
network predictions when dealing with sequential
data. Specifically, we adopt memory-based networks — Differential Neural Computers — to exploit their capability of storing data in memory and
reusing it for inference. By ... | ['Daniele Nardi', 'Roberto Capobianco', 'Biagio La Rosa'] | 2020-07-11 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 5.78942895e-01 5.20113349e-01 -1.24179069e-02 -1.24907844e-01
8.35814849e-02 -5.25831580e-01 6.25943184e-01 4.63258594e-01
-4.16112483e-01 9.64743376e-01 1.81173742e-01 -4.12840813e-01
-3.74878764e-01 -1.18551803e+00 -7.10028708e-01 -6.28104210e-01
-1.62765607e-01 7.05118001e-01 4.18456346e-01 -3.51304710... | [8.251848220825195, 3.23345947265625] |
19b0b821-79d2-499c-bae2-b17570782046 | optimizing-industrial-hvac-systems-with | 2209.08112 | null | https://arxiv.org/abs/2209.08112v1 | https://arxiv.org/pdf/2209.08112v1.pdf | Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning | Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A major challenge in industrial control involves learning behaviors that are feasible in the real world due to machinery constraints. For exa... | ['Jerry Luo', 'Cosmin Paduraru', 'Yuri Chervonyi', 'Octavian Voicu', 'Praneet Dutta', 'William Wong'] | 2022-09-16 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.42154986e-01 2.94457793e-01 -4.85549539e-01 -7.61359259e-02
-3.86308163e-01 -7.49065638e-01 3.63146245e-01 3.48848939e-01
-2.64405906e-01 1.24781525e+00 -2.99139142e-01 -4.50168461e-01
-4.85220194e-01 -8.91312540e-01 -5.34790397e-01 -7.72781491e-01
-3.10741633e-01 5.13474464e-01 -1.44901901e-01 -3.11203718... | [4.688526630401611, 2.161872386932373] |
e85ee8a0-98fb-4108-a593-bde5bf573286 | retinal-oct-disease-classification-with | 1904.00790 | null | http://arxiv.org/abs/1904.00790v1 | http://arxiv.org/pdf/1904.00790v1.pdf | Retinal OCT disease classification with variational autoencoder regularization | According to the World Health Organization, 285 million people worldwide live
with visual impairment. The most commonly used imaging technique for diagnosis
in ophthalmology is optical coherence tomography (OCT). However, analysis of
retinal OCT requires trained ophthalmologists and time, making a comprehensive
early d... | ['Lüder A. Kahrs', 'Max-Heinrich Laves', 'Tobias Ortmaier', 'Sontje Ihler'] | 2019-03-23 | null | null | null | null | ['retinal-oct-disease-classification'] | ['computer-vision'] | [-2.90723771e-01 4.39408869e-02 1.04155652e-01 -1.65405840e-01
-2.41911218e-01 -1.07563384e-01 -6.81173801e-02 -4.40771170e-02
-6.96229458e-01 9.05884266e-01 2.02035442e-01 -2.90795743e-01
-7.16826618e-02 -5.86865425e-01 -4.42360103e-01 -6.95098579e-01
3.77688408e-01 4.99958515e-01 1.72330618e-01 2.02573866... | [15.812399864196777, -3.9625892639160156] |
42ddb20d-2921-45f7-a4ac-704327796544 | what-matters-in-unsupervised-optical-flow | 2006.04902 | null | https://arxiv.org/abs/2006.04902v2 | https://arxiv.org/pdf/2006.04902v2.pdf | What Matters in Unsupervised Optical Flow | We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is most effective. Alongside this investigation we construct a number of novel improvements to unsupervised flow models, such as cost volume no... | ['Anelia Angelova', 'Ariel Gordon', 'Rico Jonschkowski', 'Jonathan T. Barron', 'Austin Stone', 'Kurt Konolige'] | 2020-06-08 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3651_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470545.pdf | eccv-2020-8 | ['occlusion-handling'] | ['computer-vision'] | [-8.00337922e-03 -2.12233469e-01 -2.44596407e-01 -2.20714018e-01
1.38012007e-01 -4.62585121e-01 8.51341069e-01 -2.74904985e-02
-5.18879533e-01 1.01544404e+00 6.30024016e-01 -4.21008877e-02
-3.23567212e-01 -5.06026447e-01 -4.05497253e-01 -4.30743277e-01
-2.50529617e-01 3.70510489e-01 3.06037575e-01 3.50885987... | [8.793191909790039, -1.788657546043396] |
4fa3bb2f-3d4c-4c45-ae11-890aeff34fd0 | generating-medically-accurate-summaries-of | 2305.05982 | null | https://arxiv.org/abs/2305.05982v1 | https://arxiv.org/pdf/2305.05982v1.pdf | Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models | A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogu... | ['Anitha Kannan', 'Elliot Schumacher', 'Varun Nair'] | 2023-05-10 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 6.60277069e-01 6.59416497e-01 -2.35745177e-01 -6.76999152e-01
-1.47664475e+00 -5.14227688e-01 4.98319536e-01 1.18392026e+00
-1.98505044e-01 9.18992162e-01 1.24148309e+00 -2.87153989e-01
-2.22191930e-01 -3.04893047e-01 -2.50872541e-02 -3.43858689e-01
3.26896384e-02 9.37679350e-01 -1.13364495e-01 -2.22740695... | [12.260376930236816, 8.83348274230957] |
90e01783-b7ed-4d0d-94d2-325bd70f0fb6 | deep-video-harmonization-with-color-mapping | 2205.00687 | null | https://arxiv.org/abs/2205.00687v1 | https://arxiv.org/pdf/2205.00687v1.pdf | Deep Video Harmonization with Color Mapping Consistency | Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the... | ['Liqing Zhang', 'Wenyan Cong', 'Li Niu', 'Shengyuan Huang', 'Xinyuan Lu'] | 2022-05-02 | null | null | null | null | ['video-harmonization'] | ['computer-vision'] | [-1.25330716e-01 -3.90125126e-01 -1.19017377e-01 3.77273038e-02
-4.52706546e-01 -5.30912519e-01 3.74540120e-01 -3.14428687e-01
-1.70510367e-01 7.42186964e-01 2.64193267e-01 -4.73166853e-02
1.45943940e-01 -6.70369208e-01 -7.84360468e-01 -7.20907569e-01
4.26028848e-01 -3.20899844e-01 4.59612221e-01 -1.17924541... | [11.190349578857422, -1.1719237565994263] |
d5045fe7-80e2-4cd2-8689-7af9a8e80308 | dynamic-named-entity-recognition | 2302.10314 | null | https://arxiv.org/abs/2302.10314v1 | https://arxiv.org/pdf/2302.10314v1.pdf | Dynamic Named Entity Recognition | Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text. So far,NER still approaches entity typing as a task of classification into universal classes (e.g. date, person, or location). Recent advances innatural language processing focus on architectures... | ['Aurélien Baelde', 'Siwar Jendoubi', 'Vincent Guigue', 'Laure Soulier', 'Tristan Luiggi'] | 2023-02-16 | null | null | null | null | ['memorization', 'entity-typing'] | ['natural-language-processing', 'natural-language-processing'] | [-1.32262230e-01 -8.36181045e-02 -1.00898571e-01 -4.25677299e-01
-5.68935275e-01 -8.34260523e-01 9.22127903e-01 6.89125836e-01
-1.29656661e+00 1.04355335e+00 3.54065835e-01 -3.43575656e-01
2.29121417e-01 -9.00089145e-01 -5.84896743e-01 -1.80358380e-01
4.52449769e-02 5.62267303e-01 2.67095000e-01 -9.87389311... | [9.62482738494873, 9.487841606140137] |
a76f4ca3-8255-4747-a17a-64fd6c3224cf | extractive-summarization-as-text-matching | 2004.08795 | null | https://arxiv.org/abs/2004.08795v1 | https://arxiv.org/pdf/2004.08795v1.pdf | Extractive Summarization as Text Matching | This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we formulate the extractive summarization task as a semantic text matching p... | ['Xuanjing Huang', 'PengFei Liu', 'Yiran Chen', 'Xipeng Qiu', 'Danqing Wang', 'Ming Zhong'] | 2020-04-19 | extractive-summarization-as-text-matching-1 | https://aclanthology.org/2020.acl-main.552 | https://aclanthology.org/2020.acl-main.552.pdf | acl-2020-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.12216508e-01 5.00254154e-01 -1.65940911e-01 -4.17513371e-01
-8.80266905e-01 -5.11596739e-01 7.98502445e-01 4.47381616e-01
-3.95789534e-01 6.44298851e-01 1.23596036e+00 1.21857770e-01
-1.38091102e-01 -8.40952814e-01 -6.53676808e-01 -1.93366036e-01
2.61541396e-01 1.76317573e-01 -1.08696438e-01 -4.86903191... | [12.358423233032227, 9.398187637329102] |
ae8281c9-1ef7-43e6-8f9a-6c14f2934698 | hierarchical-multiresolution-feature-and | 2306.02143 | null | https://arxiv.org/abs/2306.02143v1 | https://arxiv.org/pdf/2306.02143v1.pdf | Hierarchical Multiresolution Feature- and Prior-based Graphs for Classification | To incorporate spatial (neighborhood) and bidirectional hierarchical relationships as well as features and priors of the samples into their classification, we formulated the classification problem on three variants of multiresolution neighborhood graphs and the graph of a hierarchical conditional random field. Each of ... | ['Faezeh Fallah'] | 2023-06-03 | null | null | null | null | ['edge-detection', 'outlier-detection'] | ['computer-vision', 'methodology'] | [ 2.07196310e-01 -2.95970100e-03 2.28804667e-02 -5.08125007e-01
-3.59071761e-01 -4.18667495e-01 7.90372849e-01 2.93388724e-01
-2.55026042e-01 9.45087314e-01 -3.07786446e-02 -2.70600498e-01
-6.95199192e-01 -1.30315495e+00 -5.15877068e-01 -9.56434190e-01
-3.67463440e-01 3.34447920e-01 8.24928403e-01 1.00089014... | [7.621306896209717, 4.567877769470215] |
45532aee-7baa-4496-bd3a-ee2116441b0b | learning-with-noisy-labels-by-efficient-1 | 2111.14932 | null | https://arxiv.org/abs/2111.14932v2 | https://arxiv.org/pdf/2111.14932v2.pdf | Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection | Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label miscorrection, res... | ['Buru Chang', 'Joonyoung Yi', 'Kwanghee Choi', 'Seong Min Kye'] | 2021-11-29 | learning-with-noisy-labels-by-efficient | https://openreview.net/forum?id=g1D7SfQKbg | https://openreview.net/pdf?id=g1D7SfQKbg | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.66090208e-01 1.33031741e-01 -1.90217271e-01 -6.60835564e-01
-1.22465897e+00 -4.39585328e-01 4.09829140e-01 5.71635365e-01
-7.27808833e-01 6.16728723e-01 -5.37395850e-02 -9.76965427e-02
7.85795227e-02 -4.62045729e-01 -7.36540794e-01 -9.30111289e-01
4.91762727e-01 2.30045035e-01 9.83980075e-02 1.05398804... | [9.360489845275879, 3.9904983043670654] |
49540e4f-5f02-4664-9a94-344602047dad | reasoning-circuits-few-shot-multihop-question | 2211.08466 | null | https://arxiv.org/abs/2211.08466v1 | https://arxiv.org/pdf/2211.08466v1.pdf | Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales | Multi-hop Question Generation is the task of generating questions which require the reader to reason over and combine information spread across multiple passages using several reasoning steps. Chain-of-thought rationale generation has been shown to improve performance on multi-step reasoning tasks and make model predic... | ['Anna Rumshisky', 'Saurabh Kulshreshtha'] | 2022-11-15 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.44365561e-01 9.13861334e-01 1.51348159e-01 -4.33424860e-01
-1.75792098e+00 -6.75279915e-01 9.97122586e-01 5.02346754e-01
-2.08935514e-01 9.92364347e-01 6.56202018e-01 -3.90352428e-01
-7.27886185e-02 -6.62895501e-01 -7.68498778e-01 -9.40395519e-02
4.97695237e-01 9.02761996e-01 2.75397569e-01 -4.70984310... | [11.355720520019531, 8.182151794433594] |
13cd1500-0a00-4c0a-8a7d-db588707135d | reconvat-a-semi-supervised-automatic-music | 2107.04954 | null | https://arxiv.org/abs/2107.04954v2 | https://arxiv.org/pdf/2107.04954v2.pdf | ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data | Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings from diverse musical genres that are not presented in the labelled training data. In this paper, we propose a semi-supervised framework, Re... | ['Li Su', 'Dorien Herremans', 'Kin Wai Cheuk'] | 2021-07-11 | null | null | null | null | ['music-transcription'] | ['music'] | [ 6.86093807e-01 -9.77015197e-02 2.37580277e-02 -1.19148307e-01
-1.18086743e+00 -9.98317838e-01 2.46195406e-01 -1.76940158e-01
-2.34894797e-01 7.75613129e-01 1.62100911e-01 2.32179929e-02
-3.29445675e-02 -4.72635210e-01 -8.92130435e-01 -5.66952765e-01
6.09330609e-02 4.88310128e-01 -1.42737314e-01 -2.16007471... | [15.721281051635742, 5.339897155761719] |
e688a58a-8422-4e45-ba26-097f6e6a7cea | muller-multilayer-laplacian-resizer-for | 2304.02859 | null | https://arxiv.org/abs/2304.02859v1 | https://arxiv.org/pdf/2304.02859v1.pdf | MULLER: Multilayer Laplacian Resizer for Vision | Image resizing operation is a fundamental preprocessing module in modern computer vision. Throughout the deep learning revolution, researchers have overlooked the potential of alternative resizing methods beyond the commonly used resizers that are readily available, such as nearest-neighbors, bilinear, and bicubic. The... | ['Hossein Talebi', 'Peyman Milanfar', 'Zhengzhong Tu'] | 2023-04-06 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 5.15756644e-02 9.87617648e-04 -1.68001339e-01 -1.93786383e-01
-7.63090611e-01 -3.78053427e-01 3.57135594e-01 -2.83604026e-01
-7.15359509e-01 4.79939789e-01 1.88649818e-02 -3.82876664e-01
2.99900830e-01 -5.72700500e-01 -1.06893158e+00 -7.38307834e-01
5.09114802e-01 -1.61838070e-01 3.68947238e-01 -9.92139578... | [10.94921588897705, -1.7900110483169556] |
9c3d9247-c796-4990-8558-2c3012f69fc9 | ingram-inductive-knowledge-graph-embedding | 2305.19987 | null | https://arxiv.org/abs/2305.19987v2 | https://arxiv.org/pdf/2305.19987v2.pdf | InGram: Inductive Knowledge Graph Embedding via Relation Graphs | Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assume that all entities can be new, they do not allow new relations to appear at inference time. This r... | ['Joyce Jiyoung Whang', 'Chanyoung Chung', 'Jaejun Lee'] | 2023-05-31 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding', 'inductive-knowledge-graph-completion', 'knowledge-graph-completion', 'knowledge-graphs', 'entity-embeddings'] | ['graphs', 'graphs', 'knowledge-base', 'knowledge-base', 'knowledge-base', 'methodology'] | [ 3.42092067e-02 8.87694836e-01 -5.64652085e-01 -3.62585336e-01
3.84784043e-02 -5.18883467e-01 5.58772326e-01 6.97951138e-01
-2.62797028e-01 9.31690812e-01 2.75158048e-01 -4.36114043e-01
-4.02278453e-01 -1.49851584e+00 -9.56985116e-01 -2.96601504e-01
-5.04154027e-01 9.19603348e-01 9.66456831e-02 -1.18643187... | [8.862540245056152, 8.0051851272583] |
1760168d-2d13-41d5-a819-d97447ad3e2b | align-perturb-and-decouple-toward-better | 2305.18714 | null | https://arxiv.org/abs/2305.18714v1 | https://arxiv.org/pdf/2305.18714v1.pdf | Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection | Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information i... | ['Wenbing Zhu', 'Chengjie Wang', 'Yabiao Wang', 'Mingmin Chi', 'Ming Xie', 'Yuxi Li', 'Supeng Wang'] | 2023-05-30 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.54528153e-01 -3.60253662e-01 -4.44722641e-03 -4.92233664e-01
-7.68578529e-01 -5.11913419e-01 7.97164500e-01 5.88076711e-02
-2.19297320e-01 4.18381870e-01 5.65511942e-01 3.42363268e-02
-3.57847139e-02 -8.11338902e-01 -5.97656369e-01 -8.73098195e-01
-2.67898768e-01 -1.90114558e-01 4.33325917e-01 -4.30871755... | [9.683207511901855, -1.2590593099594116] |
643971c0-ac9d-44f6-849d-546ef492cef4 | a-unified-view-of-deep-learning-for-reaction | 2306.15890 | null | https://arxiv.org/abs/2306.15890v1 | https://arxiv.org/pdf/2306.15890v1.pdf | A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges | Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these problems, and some have achieved initial success. In this surve... | ['Irwin King', 'Yang Yu', 'Peilin Zhao', 'Ziqiao Meng'] | 2023-06-28 | null | null | null | null | ['drug-discovery', 'retrosynthesis'] | ['medical', 'medical'] | [ 2.33620003e-01 -1.72337964e-01 -7.32656837e-01 -5.00797778e-02
-4.28964227e-01 -7.32759953e-01 8.52214873e-01 5.43078363e-01
-3.02414298e-01 8.68668199e-01 9.93523002e-02 -6.72480881e-01
4.35351208e-03 -6.43324375e-01 -3.94891858e-01 -1.00030005e+00
-3.66075486e-02 2.97492146e-01 -6.77294433e-02 -1.67309374... | [4.576940059661865, 6.065827369689941] |
51a86fec-3217-4bc6-b423-aa6f63b94fb6 | study-of-lexical-aspect-in-the-french-medical | null | null | https://aclanthology.org/W19-1907 | https://aclanthology.org/W19-1907.pdf | Study of lexical aspect in the French medical language. Development of a lexical resource | This paper details the development of a linguistic resource designed to improve temporal information extraction systems and to integrate aspectual values. After a brief review of recent works in temporal information extraction for the medical area, we discuss the linguistic notion of aspect and how it got a place in th... | ["C{\\'e}drick Fairon", 'Agathe Pierson'] | 2019-06-01 | null | null | null | ws-2019-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 6.41147420e-02 5.81541538e-01 -9.51820016e-01 -5.79414546e-01
-4.00125772e-01 -3.37168992e-01 6.67298257e-01 1.00432599e+00
-6.65530920e-01 1.18993425e+00 7.03268886e-01 -4.23550844e-01
-6.78117573e-01 -9.53094184e-01 -2.84207892e-03 -4.19428468e-01
-3.75161439e-01 7.09254801e-01 2.15315521e-01 -1.66377053... | [8.580159187316895, 8.991235733032227] |
9a299693-c7ff-42ac-a249-cfd93c32d7fc | fastano-fast-anomaly-detection-via-spatio | 2106.08613 | null | https://arxiv.org/abs/2106.08613v4 | https://arxiv.org/pdf/2106.08613v4.pdf | FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation | Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approach is one of the most studied methods to detect anomalies by predicting frames that include abnormal events in the test set after learning w... | ['Sangyoun Lee', 'Minhyeok Lee', 'MyeongAh Cho', 'Chaewon Park'] | 2021-06-16 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 3.28721374e-01 -2.78247416e-01 -8.92768241e-03 -6.31394088e-02
-3.02034076e-02 -2.04972923e-01 6.33262813e-01 2.48133376e-01
-2.46376902e-01 4.66887325e-01 -4.46460181e-04 -2.25280434e-01
1.27586082e-01 -7.15829611e-01 -7.06080794e-01 -7.00392485e-01
-4.42830652e-01 5.67329228e-02 5.55304110e-01 7.57689327... | [7.873534679412842, 1.5441735982894897] |
164e10c0-0cbb-463d-84a3-6388a30368e3 | stapi-an-automatic-scraper-for-extracting | null | null | https://aclanthology.org/2022.lrec-1.371 | https://aclanthology.org/2022.lrec-1.371.pdf | STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents | Formal documents often are organized into sections of text, each with a title, and extracting this structure remains an under-explored aspect of natural language processing. This iterative title-text structure is valuable data for building models for headline generation and section title generation, but there is no cor... | ['Prasenjit Mitra', 'Shomir Wilson', 'Nan Zhang'] | null | null | null | null | lrec-2022-6 | ['headline-generation'] | ['natural-language-processing'] | [ 6.93955004e-01 1.90496817e-01 -6.94237292e-01 -2.07116857e-01
-1.03367651e+00 -1.09693646e+00 9.37926352e-01 5.24874032e-01
-1.75011531e-01 7.12055326e-01 5.58760583e-01 -6.39963269e-01
-1.95715055e-01 -6.00575864e-01 -6.10111177e-01 -1.46617919e-01
3.97119433e-01 7.76139021e-01 4.74438012e-01 -5.03489515... | [11.304261207580566, 8.663585662841797] |
820529b0-3541-4567-8da1-0f2223bd6275 | amharic-text-clustering-using-encyclopedic | 2105.00809 | null | https://arxiv.org/abs/2105.00809v2 | https://arxiv.org/pdf/2105.00809v2.pdf | Amharic Text Clustering Using Encyclopedic Knowledge with Neural Word Embedding | In this digital era, almost in every discipline people are using automated systems that generate information represented in document format in different natural languages. As a result, there is a growing interest towards better solutions for finding, organizing and analyzing these documents. In this paper, we propose a... | ['Yeregal Assabie', 'Dessalew Yohannes'] | 2021-03-31 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-4.0925592e-01 -1.5475189e-02 7.5328231e-02 -1.1590752e-01
-1.0483571e-01 -5.6034452e-01 8.1049562e-01 7.8108662e-01
-7.9917794e-01 3.3971110e-01 7.3323363e-01 7.4942581e-02
-3.7570456e-01 -1.0317972e+00 1.4203332e-02 -6.2472707e-01
2.3404171e-01 4.5415682e-01 9.3941696e-02 -3.1632152e-01
8.5100514e-01... | [10.314980506896973, 8.580270767211914] |
970b3835-fe6f-49e1-bed8-e05c7db81596 | language-models-are-causal-knowledge | 2304.03754 | null | https://arxiv.org/abs/2304.03754v1 | https://arxiv.org/pdf/2304.03754v1.pdf | Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering | Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. Existing question synthesis methods pre-trained question generation (QG) systems on reading comprehension datasets with text descriptions as inputs. However, QG models only learn to ask asso... | ['Shih-Fu Chang', 'Winston H. Hsu', 'Xudong Lin', 'Yulei Niu', 'Hung-Ting Su'] | 2023-04-07 | null | null | null | null | ['video-question-answering', 'reading-comprehension', 'question-generation'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 2.23620296e-01 4.20705140e-01 -1.79571077e-01 -3.19270432e-01
-1.13528228e+00 -5.81450462e-01 6.60349369e-01 9.76136550e-02
8.17802548e-02 1.03598082e+00 7.99521625e-01 -5.90788007e-01
-4.36029255e-01 -1.01232326e+00 -1.10475159e+00 -2.16504291e-01
7.54612163e-02 2.11594835e-01 4.17629659e-01 -3.62779796... | [10.425924301147461, 1.0854419469833374] |
deff59f2-77c7-4c55-b5cc-c27dec2315b9 | fault-tolerant-fpga-implementation-on | 2304.08165 | null | https://arxiv.org/abs/2304.08165v1 | https://arxiv.org/pdf/2304.08165v1.pdf | Fault Tolerant FPGA Implementation on Redundancy Techniques and ECG Denoising | As more the communications and signal process we use in the today life the more we intend to develop more reliable devices which gives fewer errors due to transient fault, So we use a technique called 5-modular redundancy to generate fewer errors. 5-Modular redundancy is an approach to increasing the reliability of har... | ['Sakthivel SM', 'Sathvik Reddy O'] | 2023-04-17 | null | null | null | null | ['ecg-denoising'] | ['medical'] | [ 1.42132670e-01 -2.41796702e-01 3.73729795e-01 -1.54023930e-01
4.40910101e-01 -5.14592409e-01 4.98659685e-02 4.76992399e-01
-2.15190932e-01 8.04391444e-01 -1.47286728e-01 -2.29507312e-01
-1.57752231e-01 -7.65187919e-01 -2.95558184e-01 -2.50438720e-01
-2.05532476e-01 -2.22108752e-01 6.18542373e-01 -5.32262087... | [7.922689437866211, 2.6324403285980225] |
cc2e8045-a95a-4b54-bf6f-1d7fa73ebc1d | speaker-clustering-in-textual-dialogue-with | null | null | https://openreview.net/forum?id=s-5K23UWff7 | https://openreview.net/pdf?id=s-5K23UWff7 | Speaker Clustering in Textual Dialogue with Utterance Correlation and Cross-corpus Dialogue Act Supervision | We propose a textual dialogue speaker clustering model, which groups the utterances of a multi-party dialogue without speaker annotations, so that the real speakers are identical inside each cluster. We find that, even without knowing the speakers, the interactions between utterances are still implied in the text. Such... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['cross-corpus', 'dialogue-act-classification'] | ['computer-vision', 'natural-language-processing'] | [ 1.12971686e-01 6.62800252e-01 4.12881672e-02 -1.03501248e+00
-7.67450452e-01 -6.81216776e-01 9.92171526e-01 2.95324158e-02
-1.78418476e-02 3.96809578e-01 9.59682524e-01 1.00416675e-01
1.80697501e-01 -1.76403508e-01 -1.27268657e-01 -7.35337853e-01
-1.33425087e-01 1.11270213e+00 -1.11355633e-01 -5.34997344... | [12.675891876220703, 7.739925861358643] |
a57216ac-fe99-4324-917e-51d7fc836712 | painterly-image-harmonization-in-dual-domains | 2212.08846 | null | https://arxiv.org/abs/2212.08846v4 | https://arxiv.org/pdf/2212.08846v4.pdf | Painterly Image Harmonization in Dual Domains | Image harmonization aims to produce visually harmonious composite images by adjusting the foreground appearance to be compatible with the background. When the composite image has photographic foreground and painterly background, the task is called painterly image harmonization. There are only few works on this task, wh... | ['Li Niu', 'Yan Hong', 'Junyan Cao'] | 2022-12-17 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.33476818e-01 -2.33144000e-01 9.53015238e-02 8.24377015e-02
-5.61995804e-01 -5.52959681e-01 4.37954575e-01 -4.90584135e-01
-1.58661306e-02 6.45530462e-01 4.55090255e-02 5.00101708e-02
2.42077604e-01 -1.01838267e+00 -7.58665025e-01 -9.59375560e-01
4.41260725e-01 -1.33492783e-01 1.70164630e-01 -4.40595657... | [11.243170738220215, -1.1750848293304443] |
25e930e6-bad8-4ea3-a3af-3a3291241af3 | autoqnn-an-end-to-end-framework-for | 2304.03782 | null | https://arxiv.org/abs/2304.03782v1 | https://arxiv.org/pdf/2304.03782v1.pdf | AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks | Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automa... | ['Tao Li', 'Chenkun Du', 'Deng Qian', 'Surong Dai', 'Ye Lu', 'Cheng Gong'] | 2023-04-07 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.56949788e-01 -2.74511546e-01 -6.87541068e-01 -4.56109852e-01
-9.03365552e-01 -3.56004387e-01 3.25867385e-01 -1.65939242e-01
-7.18529999e-01 6.85375571e-01 -2.36798838e-01 -6.37113929e-01
-1.58979788e-01 -8.71964157e-01 -9.83646631e-01 -6.93888664e-01
1.74912736e-01 5.27323604e-01 2.78555214e-01 -9.31401923... | [8.637969970703125, 3.0399389266967773] |
fa953f7a-5003-4ce5-a90a-cdf627b29ec2 | persistent-anti-muslim-bias-in-large-language | 2101.05783 | null | https://arxiv.org/abs/2101.05783v2 | https://arxiv.org/pdf/2101.05783v2.pdf | Persistent Anti-Muslim Bias in Large Language Models | It has been observed that large-scale language models capture undesirable societal biases, e.g. relating to race and gender; yet religious bias has been relatively unexplored. We demonstrate that GPT-3, a state-of-the-art contextual language model, captures persistent Muslim-violence bias. We probe GPT-3 in various way... | ['James Zou', 'Maheen Farooqi', 'Abubakar Abid'] | 2021-01-14 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [-1.81965027e-02 6.63898230e-01 -3.78488809e-01 -2.69343048e-01
-5.69970787e-01 -8.09649348e-01 9.36422110e-01 5.01363099e-01
-3.01422119e-01 8.99235666e-01 1.22989690e+00 -3.66187304e-01
1.02825716e-01 -9.63729382e-01 -5.18036962e-01 -2.39182547e-01
3.97545904e-01 6.05389118e-01 -4.59326893e-01 -8.59310389... | [9.2589750289917, 10.202986717224121] |
6e4aeb9a-88cd-4285-b901-c4dd68c6b90f | 3d-human-pose-estimation-via-intuitive | 2303.18246 | null | https://arxiv.org/abs/2303.18246v2 | https://arxiv.org/pdf/2303.18246v2.pdf | 3D Human Pose Estimation via Intuitive Physics | Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unrealistic proxy bodies, a... | ['Dimitrios Tzionas', 'Michael J. Black', 'Omid Taheri', 'Chun-Hao P. Huang', 'Lea Müller', 'Shashank Tripathi'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tripathi_3D_Human_Pose_Estimation_via_Intuitive_Physics_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tripathi_3D_Human_Pose_Estimation_via_Intuitive_Physics_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation'] | ['computer-vision'] | [-2.99081534e-01 2.52172202e-01 8.50124285e-02 1.72742717e-02
-1.45486176e-01 -4.58192617e-01 5.10496795e-01 -8.41734372e-03
-1.32239982e-01 7.97411561e-01 1.01893701e-01 1.41549706e-01
-1.59371980e-02 -5.70695877e-01 -1.14556110e+00 -3.27155441e-01
-2.49891356e-01 7.93047845e-01 2.53832400e-01 -3.33702207... | [7.116305828094482, -1.1527976989746094] |
3f4737df-4a09-415d-9f10-0b32940fead4 | single-stage-multi-human-parsing-via-point | 2304.11356 | null | https://arxiv.org/abs/2304.11356v1 | https://arxiv.org/pdf/2304.11356v1.pdf | Single-stage Multi-human Parsing via Point Sets and Center-based Offsets | This work studies the multi-human parsing problem. Existing methods, either following top-down or bottom-up two-stage paradigms, usually involve expensive computational costs. We instead present a high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem in... | ['Jian Zhao', 'Junliang Xing', 'Lei Jin', 'Jiaming Chu'] | 2023-04-22 | null | null | null | null | ['multi-human-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 2.70325512e-01 5.38619995e-01 -1.51664698e-02 -5.48489869e-01
-1.02794659e+00 -4.70169693e-01 1.83598578e-01 4.69591767e-02
-3.86255771e-01 3.53776187e-01 -1.56674981e-01 7.17531983e-03
2.33845055e-01 -7.09215641e-01 -9.55542386e-01 -5.41631997e-01
1.81124732e-01 8.04541230e-01 6.40755415e-01 -6.48827553... | [8.609846115112305, -0.0028932930435985327] |
199d5419-a28e-4dcd-b6f6-9b694975a123 | online-anomaly-detection-in-surveillance | 2010.07110 | null | https://arxiv.org/abs/2010.07110v1 | https://arxiv.org/pdf/2010.07110v1.pdf | Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate | Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of recent methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, online decision making is ... | ['Yasin Yilmaz', 'Keval Doshi'] | 2020-10-10 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 5.28151505e-02 -3.69426221e-01 -1.45301104e-01 -3.71351331e-01
-4.97422069e-01 -2.85171628e-01 2.17508703e-01 1.36582732e-01
-3.52080762e-01 2.38648191e-01 -2.98519224e-01 -4.00204778e-01
-1.31543025e-01 -5.49427509e-01 -7.35538840e-01 -8.66336882e-01
-4.90189910e-01 1.05006769e-01 3.73218626e-01 1.12244263... | [7.866522789001465, 1.549054503440857] |
07ac3024-ae71-4e7e-b5d6-18f99010c205 | terrestrial-laser-interferometers | 2103.01740 | null | https://arxiv.org/abs/2103.01740v1 | https://arxiv.org/pdf/2103.01740v1.pdf | Terrestrial Laser Interferometers | Terrestrial laser interferometers for gravitational-wave detection made the landmark first detection of gravitational waves in 2015. We provide an overview of the history of how these laser interferometers prevailed as the most promising technology in the search for gravitational waves. We describe their working princi... | ['Jo van den Brand', 'Hartmut Grote', 'Katherine L Dooley'] | 2021-03-02 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.06891561e-01 -4.90613461e-01 1.98411047e-01 -5.63127883e-02
-2.73808062e-01 -6.52615190e-01 5.93294144e-01 -1.04408085e+00
-3.99873644e-01 6.30886137e-01 1.09016828e-01 -4.28715348e-01
-9.45912972e-02 -1.05647874e+00 1.03559403e-03 -6.70860231e-01
-5.27164102e-01 5.71473122e-01 6.35859489e-01 -6.49172366... | [7.554080009460449, 3.102977752685547] |
d5be0b99-9eab-488a-b60b-ab895ae984b6 | mot16-a-benchmark-for-multi-object-tracking | 1603.00831 | null | http://arxiv.org/abs/1603.00831v2 | http://arxiv.org/pdf/1603.00831v2.pdf | MOT16: A Benchmark for Multi-Object Tracking | Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for reseach.
Recently, a new benchmark for Multiple Object Tr... | ['Laura Leal-Taixe', 'Anton Milan', 'Stefan Roth', 'Konrad Schindler', 'Ian Reid'] | 2016-03-02 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-2.32315406e-01 -4.33747768e-01 -2.55163938e-01 -1.18481293e-01
-5.75266659e-01 -6.76436543e-01 6.81540251e-01 3.95007968e-01
-7.76882112e-01 9.40478325e-01 -3.03309355e-02 1.48810849e-01
1.01421140e-02 -3.91588658e-01 -5.90812981e-01 -6.50067747e-01
-9.37974229e-02 6.66383386e-01 9.14799929e-01 4.54031825... | [6.359598636627197, -2.009089469909668] |
9fad9e55-34d8-46fc-8987-3059317c3c79 | moc-gan-mixing-objects-and-captions-to | 2106.03128 | null | https://arxiv.org/abs/2106.03128v1 | https://arxiv.org/pdf/2106.03128v1.pdf | MOC-GAN: Mixing Objects and Captions to Generate Realistic Images | Generating images with conditional descriptions gains increasing interests in recent years. However, existing conditional inputs are suffering from either unstructured forms (captions) or limited information and expensive labeling (scene graphs). For a targeted scene, the core items, objects, are usually definite while... | ['Yikang Li', 'Tao Ma'] | 2021-06-06 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [ 4.14306402e-01 3.53267550e-01 -1.35639578e-01 -5.85954309e-01
-6.63526595e-01 -3.25007468e-01 8.26258898e-01 -3.94994944e-01
1.12745576e-01 7.57551491e-01 3.83955508e-01 2.09232062e-01
1.67851835e-01 -9.00161207e-01 -1.16319621e+00 -8.22738945e-01
5.53300977e-01 5.78938305e-01 7.42734596e-02 -1.81221649... | [10.870280265808105, 0.8913512825965881] |
db4065fa-c0b6-4f81-afbd-e82baa38d79d | icassp-2021-acoustic-echo-cancellation | 2009.04972 | null | https://arxiv.org/abs/2009.04972v2 | https://arxiv.org/pdf/2009.04972v2.pdf | ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework | The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report reasonable performance on synthet... | ['Ross Cutler', 'Kusha Sridhar', 'Sebastian Braun', 'Hannes Gamper', 'Sriram Srinivasan', 'Robert Aichner', 'Ando Saabas', 'Tanel Parnamaa'] | 2020-09-10 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.84620430e-02 -5.52739501e-01 8.37351501e-01 -3.06871623e-01
-1.23212934e+00 -5.22115648e-01 2.69694507e-01 6.45450577e-02
-4.06667352e-01 4.38738197e-01 5.28306067e-01 -2.95783073e-01
5.94601082e-03 -1.53561220e-01 -4.45490241e-01 -6.19181216e-01
-4.24376309e-01 -1.57622620e-01 2.92798728e-01 -3.29961807... | [14.986717224121094, 5.916009426116943] |
20b6888e-bc69-41d4-ae15-e6b45968d380 | bayesian-optimization-with-conformal-coverage | 2210.12496 | null | https://arxiv.org/abs/2210.12496v3 | https://arxiv.org/pdf/2210.12496v3.pdf | Bayesian Optimization with Conformal Prediction Sets | Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization. Bayesian optimization selects decisions (i.e. objective function queries) with maximal expected utility with respect to the posterior... | ['Andrew Gordon Wilson', 'Wesley Maddox', 'Samuel Stanton'] | 2022-10-22 | null | null | null | null | ['decision-making-under-uncertainty', 'prediction-intervals', 'decision-making-under-uncertainty'] | ['medical', 'miscellaneous', 'reasoning'] | [ 1.60181180e-01 5.14821231e-01 -9.02751982e-01 -5.73749483e-01
-1.54537034e+00 -9.57157969e-01 3.92856389e-01 3.35678130e-01
-4.19698805e-01 1.11451566e+00 4.25163925e-01 -6.89331055e-01
-8.01929295e-01 -8.19969356e-01 -7.29123592e-01 -4.82179105e-01
2.31395513e-02 1.02763546e+00 -2.38920316e-01 4.55214798... | [4.526650428771973, 3.24007511138916] |
9d235b16-61a7-448b-90a3-4590fb015036 | enhancing-the-protein-tertiary-structure | 2306.01824 | null | https://arxiv.org/abs/2306.01824v1 | https://arxiv.org/pdf/2306.01824v1.pdf | Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation | The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alig... | ['Siqi Sun', 'Yu Li', 'Tao Shen', 'Jiayang Chen', 'Le Zhang'] | 2023-06-02 | null | null | null | null | ['multiple-sequence-alignment', 'protein-structure-prediction', 'protein-folding'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [ 4.20085728e-01 4.15477343e-02 -3.18046324e-02 -4.64566380e-01
-1.00366735e+00 -7.61569738e-01 -2.49715019e-02 3.94442946e-01
-1.09660096e-01 1.31504774e+00 2.23266780e-01 -4.89287019e-01
3.42231810e-01 -5.69771826e-01 -1.13268864e+00 -7.37548709e-01
-4.02823910e-02 4.30186540e-01 8.28262642e-02 -3.63970608... | [4.70175838470459, 5.616098403930664] |
3dba634d-aa8f-42c1-84f5-df70a8237286 | learning-discriminative-feature-with-crf-for | 2008.01270 | null | https://arxiv.org/abs/2008.01270v1 | https://arxiv.org/pdf/2008.01270v1.pdf | Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation | In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task. To capture the inherent correlation among video frames, we learn discriminative features (D-features) from the input images that reveal feature distribution from a glob... | ['Jiaxiang Shang', 'Long Quan', 'Lei Zhou', 'Haoan Feng', 'Mingmin Zhen', 'Tian Fang', 'Shiwei Li'] | 2020-08-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5794_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720443.pdf | eccv-2020-8 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.94508666e-01 -2.58972943e-01 -5.32606244e-01 -4.54530269e-01
-5.53671181e-01 -2.98889726e-01 6.58276081e-01 -3.72746795e-01
-2.59187192e-01 5.54813981e-01 1.05768412e-01 1.32084772e-01
-7.87521526e-02 -3.94978166e-01 -9.88389611e-01 -6.74307108e-01
-1.50135338e-01 8.16126466e-02 7.78207362e-01 1.39946193... | [9.331472396850586, -0.23569414019584656] |
8d27e6be-c87c-4289-ab74-ff446bdde96c | exploiting-diffusion-prior-for-real-world | 2305.07015 | null | https://arxiv.org/abs/2305.07015v2 | https://arxiv.org/pdf/2305.07015v2.pdf | Exploiting Diffusion Prior for Real-World Image Super-Resolution | We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the gen... | ['Chen Change Loy', 'Kelvin C. K. Chan', 'Shangchen Zhou', 'Zongsheng Yue', 'Jianyi Wang'] | 2023-05-11 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.77807409e-01 -9.89102721e-02 -9.86004993e-02 -4.16994318e-02
-9.23116922e-01 -4.45915073e-01 7.01384783e-01 -3.79232645e-01
-1.66039422e-01 6.89618886e-01 7.62609363e-01 1.15205854e-01
-1.59472123e-01 -7.82223284e-01 -6.49990022e-01 -5.66546917e-01
1.36967286e-01 1.05294669e-02 2.30671540e-01 -5.37926592... | [11.311005592346191, -1.9554007053375244] |
68cc3944-e51c-4e24-8269-f563af8020c5 | anomaly-detection-in-video-via-self | 2011.07491 | null | https://arxiv.org/abs/2011.07491v3 | https://arxiv.org/pdf/2011.07491v3.pdf | Anomaly Detection in Video via Self-Supervised and Multi-Task Learning | Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at ... | ['Mubarak Shah', 'Marius Popescu', 'Fahad Shahbaz Khan', 'Radu Tudor Ionescu', 'Antonio Barbalau', 'Mariana-Iuliana Georgescu'] | 2020-11-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Georgescu_Anomaly_Detection_in_Video_via_Self-Supervised_and_Multi-Task_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.96369016e-01 -1.60539642e-01 1.61000028e-01 -2.72654533e-01
-8.66093934e-01 -2.68836975e-01 5.96386015e-01 4.96347845e-01
-5.36145151e-01 2.74473488e-01 -1.10959418e-01 -2.03123555e-01
2.52481282e-01 -3.91650945e-01 -1.06524980e+00 -6.03536069e-01
-3.51894110e-01 3.94225955e-01 7.93019712e-01 2.91307848... | [7.840611934661865, 1.6341747045516968] |
f25bc6fa-8be8-4784-ab86-78da2577491b | visual-re-ranking-with-natural-language | 1810.12738 | null | http://arxiv.org/abs/1810.12738v1 | http://arxiv.org/pdf/1810.12738v1.pdf | Visual Re-ranking with Natural Language Understanding for Text Spotting | Many scene text recognition approaches are based on purely visual information
and ignore the semantic relation between scene and text. In this paper, we
tackle this problem from natural language processing perspective to fill the
gap between language and vision. We propose a post-processing approach to
improve scene te... | ['Lluís Padró', 'Francesc Moreno-Noguer', 'Ahmed Sabir'] | 2018-10-29 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 3.58764082e-01 -3.42826009e-01 2.29729518e-01 -4.18522567e-01
-2.88972497e-01 -4.11497504e-01 1.04853487e+00 5.93578517e-01
-9.31133628e-01 1.98771387e-01 5.01131296e-01 -1.54660657e-01
2.49266148e-01 -8.01491261e-01 -5.09787202e-01 -2.26059183e-01
6.35232329e-01 4.37042862e-01 4.05036032e-01 -9.94682834... | [11.764827728271484, 2.237490653991699] |
3d0f5fba-81de-48eb-bcb5-49b7e7847075 | a-tvsnet-aggregated-two-view-stereo-network | 2003.00711 | null | https://arxiv.org/abs/2003.00711v1 | https://arxiv.org/pdf/2003.00711v1.pdf | A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation | We propose a learning-based network for depth map estimation from multi-view stereo (MVS) images. Our proposed network consists of three sub-networks: 1) a base network for initial depth map estimation from an unstructured stereo image pair, 2) a novel refinement network that leverages both photometric and geometric in... | ['Sizhang Dai', 'Weibing Huang'] | 2020-03-02 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.49624655e-01 5.85356094e-02 1.43722609e-01 -4.48609859e-01
-8.91140401e-01 -4.03288275e-01 4.44867462e-01 -9.41468701e-02
-3.70741159e-01 6.56664193e-01 4.18750048e-01 4.52621467e-02
-6.39401318e-04 -1.00566065e+00 -6.95204318e-01 -6.22929037e-01
3.15587938e-01 4.44546759e-01 7.64051676e-01 -1.48482814... | [8.918947219848633, -2.612142324447632] |
b523757f-2cc2-494e-b20a-f4f876424ad1 | multi-granularity-contrastive-knowledge | null | null | https://openreview.net/forum?id=Xf7cE59PJuP | https://openreview.net/pdf?id=Xf7cE59PJuP | Multi-Granularity Contrastive Knowledge Distillation for Multimodal Named Entity Recognition | It is very valuable to recognize named entities from short and informal multimodal posts in this age of information explosion. Despite existing methods success in multi-modal named entity recognition (MNER), they rely on the well aligned text and image pairs, while a lot of noises exist in the datasets. And the represe... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['multi-modal-named-entity-recognition'] | ['natural-language-processing'] | [ 1.80049688e-01 -8.51074047e-03 -1.13197885e-01 -3.72260928e-01
-1.03899896e+00 -4.86111164e-01 7.28159785e-01 1.06231887e-02
-5.36769509e-01 7.36872673e-01 5.51973641e-01 2.66659290e-01
-4.00712378e-02 -5.14136255e-01 -9.08181667e-01 -5.91088533e-01
5.84894359e-01 2.85148323e-01 -3.24708619e-03 3.67873646... | [10.786239624023438, 1.4174575805664062] |
056db6f4-a854-46be-b23d-38308a14ddb5 | building-robust-machine-learning-models-for | 2208.10784 | null | https://arxiv.org/abs/2208.10784v1 | https://arxiv.org/pdf/2208.10784v1.pdf | Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity | Shear viscosity, though being a fundamental property of all liquids, is computationally expensive to estimate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a rel... | ['Sundaram Balasubramanian', 'Sudarshan Behera', 'Shivanand K. Veesam', 'Nikhil V. S. Avula'] | 2022-08-23 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-6.21282123e-03 -2.17930868e-01 -7.16477111e-02 -3.92748207e-01
-8.73233140e-01 -5.32701910e-01 5.56243777e-01 4.69870806e-01
-5.91133356e-01 1.29296291e+00 -3.86964798e-01 -3.74984682e-01
-2.99844086e-01 -8.17984939e-01 -6.16593897e-01 -1.11334026e+00
-5.12501895e-02 6.24659419e-01 2.02737018e-01 2.41047591... | [6.192411422729492, 3.679269790649414] |
d21d87e3-de7d-486c-9adc-3e0d6a3b268d | extending-compositional-attention-networks | 2210.01191 | null | https://arxiv.org/abs/2210.01191v1 | https://arxiv.org/pdf/2210.01191v1.pdf | Extending Compositional Attention Networks for Social Reasoning in Videos | We propose a novel deep architecture for the task of reasoning about social interactions in videos. We leverage the multi-step reasoning capabilities of Compositional Attention Networks (MAC), and propose a multimodal extension (MAC-X). MAC-X is based on a recurrent cell that performs iterative mid-level fusion of inpu... | ['Alexandros Potamianos', 'Georgios Paraskevopoulos', 'Christina Sartzetaki'] | 2022-10-03 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 4.23405111e-01 1.84906557e-01 2.73627639e-01 -4.53117937e-01
-9.78162646e-01 -3.32028508e-01 7.51707673e-01 2.05263682e-02
-5.76231182e-01 3.11351091e-01 6.46933794e-01 -3.87558579e-01
-4.67046201e-02 -3.50724548e-01 -8.54477167e-01 -2.52169400e-01
1.31462306e-01 -7.52472505e-02 2.44097501e-01 -1.64439783... | [10.42888069152832, 1.0941272974014282] |
b6a68ea2-f559-45ae-8ee2-6d1624aa24bf | x2face-a-network-for-controlling-face-1 | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.pdf | X2Face: A network for controlling face generation using images, audio, and pose codes | The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. We make the following three contributions. First, we introduce a network, X2... | ['Andrew Zisserman', 'Olivia Wiles', 'A. Sophia Koepke'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['talking-head-generation'] | ['computer-vision'] | [ 7.26649284e-01 6.10063851e-01 2.02662334e-01 -7.34156072e-01
-3.81190926e-01 -5.66524386e-01 7.90009856e-01 -6.62437499e-01
-1.99558496e-01 6.33537471e-01 5.29516041e-02 2.74341047e-01
4.99537557e-01 -6.24782205e-01 -1.26049495e+00 -7.25391865e-01
5.50032444e-02 2.96155840e-01 9.65434238e-02 -3.45154941... | [13.08745002746582, -0.35870662331581116] |
9473cf4b-984b-4006-a99b-50c1def9829d | image-processing-methods-for-coronal-hole | 2201.01380 | null | https://arxiv.org/abs/2201.01380v1 | https://arxiv.org/pdf/2201.01380v1.pdf | Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification | The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the... | ['C. N. Arge', 'M. S. Pattichis', 'V. Jatla'] | 2022-01-04 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.56747416e-01 -1.22305505e-01 2.94993937e-01 -3.48697126e-01
-7.76602149e-01 -5.23488581e-01 6.72947884e-01 4.29752588e-01
-1.36410728e-01 7.10947156e-01 -3.56189102e-01 -5.15736043e-01
-3.61738950e-01 -1.10090852e+00 -5.00798523e-01 -7.18789220e-01
2.07123011e-01 9.76371408e-01 6.96619391e-01 -2.00705498... | [9.303457260131836, -1.6414005756378174] |
dfcc3f0a-86d9-45d0-bbaf-f0c2490c210f | 25-years-of-criticality-in-neuroscience | 1903.05129 | null | http://arxiv.org/abs/1903.05129v1 | http://arxiv.org/pdf/1903.05129v1.pdf | 25 years of criticality in neuroscience -- established results, open controversies, novel concepts | Twenty-five years ago, Dunkelmann and Radons (1994) proposed that neural
networks should self-organize to a critical state. In models, criticality
offers a number of computational advantages. Thus this hypothesis, and in
particular the experimental work by Beggs and Plenz (2003), has triggered an
avalanche of research,... | [] | 2019-03-12 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.35652253e-01 1.05147779e-01 -2.58206666e-01 -2.69584566e-01
6.18020184e-02 -6.56313241e-01 6.94136798e-01 1.65992469e-01
-6.48727536e-01 9.46914315e-01 -5.49451485e-02 -6.80906177e-01
-7.04230487e-01 -3.79645675e-01 -3.68960798e-01 -6.58600807e-01
-2.37758070e-01 2.47023389e-01 5.20908654e-01 -4.96209003... | [8.08639907836914, 3.381420135498047] |
299eace6-d49e-4f5d-9455-1f06910a28cc | a-video-summarization-method-using-temporal | 2109.12581 | null | https://arxiv.org/abs/2109.12581v4 | https://arxiv.org/pdf/2109.12581v4.pdf | A Stacking Ensemble Approach for Supervised Video Summarization | Video summarization methods are usually classified into shot-level or frame-level methods, which are individually used in a general way. This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a stacking ensemble approach is proposed for supervised video summarization.... | ['Guoqiang Zhang', 'Shenghui Zhao', 'Yubo An'] | 2021-09-26 | null | null | null | null | ['supervised-video-summarization'] | ['computer-vision'] | [ 4.83961046e-01 -1.21284217e-01 -3.30246031e-01 -2.61222929e-01
-9.18823242e-01 1.93254650e-02 5.89043915e-01 1.10492535e-01
-1.86539620e-01 8.30773592e-01 5.39457262e-01 2.78131187e-01
3.43883298e-02 -3.78835201e-01 -6.25246644e-01 -1.04051840e+00
5.81657290e-02 -2.20455125e-01 6.11292005e-01 2.38016367... | [10.358960151672363, 0.41821005940437317] |
ec254f65-f0d7-4169-a70a-da9905f63b27 | open-set-semi-supervised-object-detection | 2208.13722 | null | https://arxiv.org/abs/2208.13722v1 | https://arxiv.org/pdf/2208.13722v1.pdf | Open-Set Semi-Supervised Object Detection | Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector. However, thus far these methods have assumed that the unlabeled data does not contain out-of-distribution (OOD) classes, which is unrealistic with larger-scale unlabeled dat... | ['Zsolt Kira', 'Zijian He', 'Peter Vajda', 'Junjiao Tian', 'Xiaoliang Dai', 'Chih-Yao Ma', 'Yen-Cheng Liu'] | 2022-08-29 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [-2.46536564e-02 2.49379665e-01 -2.82415003e-01 -2.76811957e-01
-5.12215137e-01 -5.04623830e-01 4.71188992e-01 -3.24013941e-02
-2.54613101e-01 3.59852344e-01 -8.09943527e-02 -7.39892945e-02
2.69782811e-01 -4.52683866e-01 -8.07312250e-01 -6.15253747e-01
1.37459591e-01 5.68982005e-01 8.48690033e-01 9.37819034... | [9.272408485412598, 1.3872967958450317] |
e1049783-2e66-4ab6-b373-678d3a464c93 | robust-event-stream-pattern-tracking-based-on | 1803.06490 | null | http://arxiv.org/abs/1803.06490v1 | http://arxiv.org/pdf/1803.06490v1.pdf | Robust event-stream pattern tracking based on correlative filter | Object tracking based on retina-inspired and event-based dynamic vision
sensor (DVS) is challenging for the noise events, rapid change of event-stream
shape, chaos of complex background textures, and occlusion. To address these
challenges, this paper presents a robust event-stream pattern tracking method
based on corre... | ['Luping Shi', 'Hongmin Li'] | 2018-03-17 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [-8.86213183e-02 -9.08774912e-01 3.05196673e-01 -2.85655651e-02
2.14619949e-01 -2.72105753e-01 6.75149202e-01 5.48516288e-02
-5.21540821e-01 4.17430490e-01 -9.38648358e-03 2.81092077e-01
-8.03865939e-02 -7.12685168e-01 -7.87447333e-01 -7.04214334e-01
-2.52350539e-01 -1.19288497e-01 1.14988816e+00 -7.29859844... | [8.596421241760254, -1.2518936395645142] |
f262baf4-b6f7-4b0b-b722-866ec98e2701 | a-cross-modal-distillation-network-for-person | 1810.11641 | null | https://arxiv.org/abs/1810.11641v3 | https://arxiv.org/pdf/1810.11641v3.pdf | Cross-Modal Distillation for RGB-Depth Person Re-Identification | Person re-identification is a key challenge for surveillance across multiple sensors. Prompted by the advent of powerful deep learning models for visual recognition, and inexpensive RGB-D cameras and sensor-rich mobile robotic platforms, e.g. self-driving vehicles, we investigate the relatively unexplored problem of cr... | ['Frank Hafner', 'Amran Bhuiyan', 'Julian F. P. Kooij', 'Eric Granger'] | 2018-10-27 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [ 1.55468419e-01 -3.63280326e-01 2.19292432e-01 -3.80566806e-01
-6.90039635e-01 -5.05176067e-01 8.16573977e-01 -1.64144024e-01
-7.75581062e-01 4.65654492e-01 1.12820953e-01 3.02367121e-01
-4.18261588e-02 -4.43082660e-01 -6.65010035e-01 -8.54531825e-01
2.17930302e-01 3.64837557e-01 -1.14634678e-01 -1.72193646... | [14.604158401489258, 0.9408527612686157] |
6ddb3392-6b1d-4714-8023-8f02bd17ba8c | exploring-conditional-text-generation-for | 2110.02334 | null | https://arxiv.org/abs/2110.02334v2 | https://arxiv.org/pdf/2110.02334v2.pdf | Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair. In this article, we propose transforming ABSA into an abst... | ['Thamar Solorio', 'Nedim Lipka', 'Franck Dernoncourt', 'Siva Uday Sampreeth Chebolu'] | 2021-10-05 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 5.80972254e-01 3.53339821e-01 -1.22105077e-01 -7.18207002e-01
-1.22331297e+00 -6.34949923e-01 1.02266049e+00 3.89600128e-01
-2.92971972e-02 9.71369267e-01 7.11594701e-01 -5.94867051e-01
5.43495953e-01 -1.03596663e+00 -6.30029678e-01 -4.11739171e-01
5.02471626e-01 6.30905151e-01 -1.88756019e-01 -6.25458717... | [11.520869255065918, 6.823875904083252] |
de094260-23b2-4e48-b24c-2e2c5d340a59 | topic-guided-abstractive-multi-document | 2110.11207 | null | https://arxiv.org/abs/2110.11207v1 | https://arxiv.org/pdf/2110.11207v1.pdf | Topic-Guided Abstractive Multi-Document Summarization | A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-s... | ['Le Hu', 'Peng Cui'] | 2021-10-21 | null | https://aclanthology.org/2021.findings-emnlp.126 | https://aclanthology.org/2021.findings-emnlp.126.pdf | findings-emnlp-2021-11 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 1.26343086e-01 3.97791386e-01 -4.22766805e-01 -3.20686907e-01
-1.11274624e+00 -3.13937664e-01 7.79083550e-01 3.61262798e-01
1.01342224e-01 7.66832769e-01 1.14657712e+00 1.28324345e-01
-7.66268373e-02 -8.48741114e-01 -7.27665544e-01 -6.49742782e-01
2.44898811e-01 7.17181385e-01 3.02416205e-01 -2.07116142... | [12.585346221923828, 9.493476867675781] |
2e6d4eef-953f-443d-a8bb-a40581518f29 | decoupling-makes-weakly-supervised-local | 2201.02861 | null | https://arxiv.org/abs/2201.02861v2 | https://arxiv.org/pdf/2201.02861v2.pdf | Decoupling Makes Weakly Supervised Local Feature Better | Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supervision cannot distinguish the losses caused by the detection and description steps, directly conducting weakly supervised learning within a... | ['Longguang Wang', 'Yulan Guo', 'Kai Xu', 'Qing Ran', 'Li Liu', 'Kunhong Li'] | 2022-01-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Decoupling_Makes_Weakly_Supervised_Local_Feature_Better_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Decoupling_Makes_Weakly_Supervised_Local_Feature_Better_CVPR_2022_paper.pdf | cvpr-2022-1 | ['camera-localization', 'image-matching'] | ['computer-vision', 'computer-vision'] | [-1.25039602e-02 -1.41424417e-01 -5.26064634e-01 -5.90586603e-01
-1.55247319e+00 -8.81572723e-01 8.14584374e-01 1.76880166e-01
-5.62035978e-01 3.46114159e-01 1.82175010e-01 2.72741139e-01
-2.42281309e-03 -2.91356653e-01 -7.75205255e-01 -7.05315948e-01
2.24299893e-01 5.20239472e-01 4.26941216e-01 1.95911184... | [8.008402824401855, -2.1625423431396484] |
4c13294f-87c3-40fa-9db9-480bd61dce73 | commonsense-knowledge-augmented-pretrained | null | null | https://openreview.net/forum?id=51c-7iGxPri | https://openreview.net/pdf?id=51c-7iGxPri | Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification | Commonsense knowledge can be leveraged for identifying causal relations in text. In this work, we convert triples in ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, to natural language text and continually pretrain a BERT pretrained language model. We evaluate the resulting model on answering commons... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [ 2.24131271e-01 5.69966376e-01 -6.02106988e-01 -3.21173817e-01
-4.52500433e-01 -5.16303420e-01 1.09839332e+00 4.61675793e-01
-3.03547710e-01 1.07284784e+00 8.88869047e-01 -4.64275718e-01
-2.82247633e-01 -1.16022122e+00 -8.83303285e-01 2.26839408e-01
1.38278887e-01 8.86894286e-01 4.93685216e-01 -7.98445344... | [10.029989242553711, 8.075284957885742] |
114f667f-5d60-4b6c-bc14-37441e18e315 | is-chatgpt-a-biomedical-expert-exploring-the | 2306.16108 | null | https://arxiv.org/abs/2306.16108v1 | https://arxiv.org/pdf/2306.16108v1.pdf | Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance of Current GPT Models in Biomedical Tasks | We assessed the performance of commercial Large Language Models (LLMs) GPT-3.5-Turbo and GPT-4 on tasks from the 2023 BioASQ challenge. In Task 11b Phase B, which is focused on answer generation, both models demonstrated competitive abilities with leading systems. Remarkably, they achieved this with simple zero-shot le... | ['Udo Kruschwitz', 'Samy Ateia'] | 2023-06-28 | null | null | null | null | ['retrieval', 'answer-generation'] | ['methodology', 'natural-language-processing'] | [-1.69032782e-01 -1.06629334e-01 -2.31843516e-01 1.93703756e-01
-1.65079808e+00 -4.77425426e-01 8.88325930e-01 3.44042718e-01
-7.23004341e-01 8.06702793e-01 3.05741459e-01 -3.79736930e-01
-3.65918338e-01 -5.56807995e-01 -4.88385975e-01 -1.29366621e-01
-3.27646255e-01 1.14032769e+00 5.05950212e-01 -8.67106974... | [11.454195976257324, 7.901678085327148] |
c4a4d557-56a6-4d37-af63-f10ad9fa36d5 | latent-correlation-based-multiview-learning | 2106.07115 | null | https://arxiv.org/abs/2106.07115v3 | https://arxiv.org/pdf/2106.07115v3.pdf | Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective | Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA]... | ['Songtao Lu', 'Weiran Wang', 'Xiao Fu', 'Qi Lyu'] | 2021-06-14 | understanding-latent-correlation-based | https://openreview.net/forum?id=5FUq05QRc5b | https://openreview.net/pdf?id=5FUq05QRc5b | iclr-2022-4 | ['multiview-learning'] | ['computer-vision'] | [ 4.48979549e-02 1.92578688e-01 -3.42172235e-01 -2.34113887e-01
-6.75739408e-01 -6.14413619e-01 7.52717853e-01 -4.62028056e-01
2.17143625e-01 4.58730757e-01 4.20381486e-01 2.70322055e-01
-4.75728214e-01 -3.85906935e-01 -7.63301790e-01 -1.13333642e+00
1.71257313e-02 1.02071553e-01 -7.43587375e-01 4.49068025... | [8.350378036499023, 4.555517196655273] |
2cb6c71a-6b3a-4c3b-b0a4-9941c96395d6 | continual-mixed-language-pre-training-for | 2105.03953 | null | https://arxiv.org/abs/2105.03953v1 | https://arxiv.org/pdf/2105.03953v1.pdf | Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation | The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems. Fine-tuning a multilingual pre-trained model (e.g., mBART (Liu et al., 2020)) on the translation task is a good approach for low-resource languages; however, its performance will be greatly limited... | ['Pascale Fung', 'Genta Indra Winata', 'Zihan Liu'] | 2021-05-09 | null | https://aclanthology.org/2021.findings-acl.239 | https://aclanthology.org/2021.findings-acl.239.pdf | findings-acl-2021-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-1.40796766e-01 -3.42133760e-01 -3.50950897e-01 -2.86442846e-01
-1.50775373e+00 -8.97395134e-01 7.07930267e-01 -6.00432515e-01
-5.27596295e-01 1.14954245e+00 2.23811269e-01 -7.82567561e-01
6.91658139e-01 -4.81788486e-01 -1.00544536e+00 -4.03615773e-01
6.50281072e-01 7.56205797e-01 -2.46514007e-01 -6.01268291... | [11.637646675109863, 10.27037525177002] |
2969ea36-6c7e-46fe-b6c4-ed0fefd3892e | topics-as-entity-clusters-entity-based-topics | 2301.02458 | null | https://arxiv.org/abs/2301.02458v1 | https://arxiv.org/pdf/2301.02458v1.pdf | Topics as Entity Clusters: Entity-based Topics from Language Models and Graph Neural Networks | Topic models aim to reveal the latent structure behind a corpus, typically conducted over a bag-of-words representation of documents. In the context of topic modeling, most vocabulary is either irrelevant for uncovering underlying topics or contains strong relationships with relevant concepts, impacting the interpretab... | ['Tri Kurniawan Wijaya', 'Steven Derby', 'Manuel V. Loureiro'] | 2023-01-06 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-4.31928426e-01 4.99324292e-01 -5.90778530e-01 -1.19426996e-01
-3.69885117e-01 -5.37950695e-01 1.10468674e+00 8.74399841e-01
-2.30383158e-01 2.21169695e-01 8.28515291e-01 -2.57694095e-01
-3.55187446e-01 -1.22331607e+00 -5.04256427e-01 -4.11570787e-01
-3.97994995e-01 5.65037489e-01 1.98451594e-01 -2.19650224... | [10.419486045837402, 7.005243301391602] |
710435d3-d4c8-49fe-97a4-24dea6c1ddc1 | mapping-instructions-to-actions-in-3d | 1809.00786 | null | http://arxiv.org/abs/1809.00786v2 | http://arxiv.org/pdf/1809.00786v2.pdf | Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction | We propose to decompose instruction execution to goal prediction and action
generation. We design a model that maps raw visual observations to goals using
LINGUNET, a language-conditioned image generation network, and then generates
the actions required to complete them. Our model is trained from demonstration
only wit... | ['Andrew Bennett', 'Dipendra Misra', 'Valts Blukis', 'Yoav Artzi', 'Max Shatkhin', 'Eyvind Niklasson'] | 2018-09-04 | mapping-instructions-to-actions-in-3d-1 | https://aclanthology.org/D18-1287 | https://aclanthology.org/D18-1287.pdf | emnlp-2018-10 | ['action-generation'] | ['computer-vision'] | [ 3.17172974e-01 3.15341085e-01 -2.41262734e-01 -4.17190582e-01
-6.39042258e-01 -3.71895462e-01 1.05071819e+00 -2.85078049e-01
-5.20766795e-01 9.01372015e-01 5.44238985e-01 -7.83772767e-01
5.22653461e-01 -6.31020546e-01 -1.15215647e+00 -3.04119557e-01
-2.53814697e-01 4.42311168e-01 1.77170392e-02 -2.58871168... | [4.381521701812744, 0.8763519525527954] |
c7ba0156-33ed-4585-9ca8-89e47513ba96 | atco2-corpus-a-large-scale-dataset-for | 2211.04054 | null | https://arxiv.org/abs/2211.04054v2 | https://arxiv.org/pdf/2211.04054v2.pdf | ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications | Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dial... | ['Dietrich Klakow', 'Khalid Choukri', 'Petr Motlicek', 'Alexander Blatt', 'Jan Černocký', 'Allan Tart', 'Pavel Kolčárek', 'Claudia Cevenini', 'Iuliia Nigmatulina', 'Seyyed Saeed Sarfjoo', 'Amrutha Prasad', 'Mickael Rigault', 'Martin Kocour', 'Igor Szöke', 'Karel Veselý', 'Juan Zuluaga-Gomez'] | 2022-11-08 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 1.64280191e-01 3.57512623e-01 4.17972952e-02 -4.63412076e-01
-1.06811249e+00 -8.88979912e-01 5.47707140e-01 -7.95706138e-02
-3.36537480e-01 6.60937309e-01 6.28359079e-01 -7.62400508e-01
-1.71904340e-02 -3.37902635e-01 3.49805132e-02 -2.74723858e-01
1.23486910e-02 7.19481826e-01 3.21443751e-02 -5.81979275... | [14.234077453613281, 6.902078151702881] |
abd96b13-8a4a-4366-87a4-2834eb97bb8c | spi-gcn-a-simple-permutation-invariant-graph | null | null | https://hal.archives-ouvertes.fr/hal-02093451/ | https://hal.archives-ouvertes.fr/hal-02093451/document | SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network | A wide range of machine learning problems involve handling graph-structured data. Existing machine learning approaches for graphs, however, often imply computing expensive graph similarity measures, preprocessing input graphs, or explicitly ordering graph nodes. In this work, we present a novel and simple convolutional... | ['Jean-Claude Crivello', 'Nataliya Sokolovska', 'Asma Atamna'] | 2019-04-08 | null | null | null | hal-archives-ouvertes-2019-4 | ['graph-similarity'] | ['graphs'] | [ 2.72612125e-01 2.82836348e-01 -1.92620769e-01 -3.22079122e-01
-1.49640515e-01 -3.51836324e-01 3.48085910e-01 8.92809510e-01
-3.44153166e-01 4.89250749e-01 -3.60501736e-01 -8.84804666e-01
-1.27756909e-01 -1.52554035e+00 -8.63356769e-01 -5.56122959e-01
-4.77340788e-01 6.82261527e-01 3.17654699e-01 -1.28271088... | [6.905698776245117, 6.204734802246094] |
595d83e6-aa44-4bb3-b3b2-df7d32806e2e | novel-pipeline-for-diagnosing-acute | 2307.04014 | null | https://arxiv.org/abs/2307.04014v2 | https://arxiv.org/pdf/2307.04014v2.pdf | Novel Pipeline for Diagnosing Acute Lymphoblastic Leukemia Sensitive to Related Biomarkers | Acute Lymphoblastic Leukemia (ALL) is one of the most common types of childhood blood cancer. The quick start of the treatment process is critical to saving the patient's life, and for this reason, early diagnosis of this disease is essential. Examining the blood smear images of these patients is one of the methods use... | ['Mohammad Hossein Rohban', 'Ali Sharifi-Zarchi', 'Amirhossein Askari-Farsangi'] | 2023-07-08 | null | null | null | null | ['multiple-instance-learning', 'specificity'] | ['methodology', 'natural-language-processing'] | [ 1.41547099e-01 2.36382633e-01 -2.00108394e-01 -3.31588507e-01
-8.14591527e-01 -2.72746295e-01 3.26970309e-01 7.75470853e-01
-5.76948404e-01 6.79115236e-01 -3.06448996e-01 -4.57033575e-01
-1.22926161e-01 -1.00521123e+00 -3.61845493e-01 -8.50761294e-01
1.66076854e-01 9.42623258e-01 3.46126735e-01 1.37822777... | [15.0772066116333, -2.9741296768188477] |
04c2c551-138a-4a6b-b615-2e76eb0c8b83 | advance-prediction-of-ventricular | 1811.12938 | null | http://arxiv.org/abs/1811.12938v1 | http://arxiv.org/pdf/1811.12938v1.pdf | Advance Prediction of Ventricular Tachyarrhythmias using Patient Metadata and Multi-Task Networks | We describe a novel neural network architecture for the prediction of
ventricular tachyarrhythmias. The model receives input features that capture
the change in RR intervals and ectopic beats, along with features based on
heart rate variability and frequency analysis. Patient age is also included as
a trainable embeddi... | ['Marek Sirendi', 'Marek Rei', 'Joshua Oppenheimer'] | 2018-11-30 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-4.55415025e-02 2.46959507e-01 -3.29941899e-01 -5.29723287e-01
-6.25521779e-01 -3.52386117e-01 -4.76616845e-02 2.27930501e-01
-5.04466832e-01 9.73461688e-01 1.62091419e-01 -5.66874504e-01
-2.76930243e-01 -4.88414645e-01 -1.42329544e-01 -6.19496703e-01
-6.79747045e-01 5.68019509e-01 -5.69240391e-01 1.47494629... | [14.374743461608887, 3.3461363315582275] |
5cccbd21-bbae-48ff-ad6a-79544782e4f9 | mkis-net-a-light-weight-multi-kernel-network | 2210.08168 | null | https://arxiv.org/abs/2210.08168v1 | https://arxiv.org/pdf/2210.08168v1.pdf | MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation | Image segmentation is an important task in medical imaging. It constitutes the backbone of a wide variety of clinical diagnostic methods, treatments, and computer-aided surgeries. In this paper, we propose a multi-kernel image segmentation net (MKIS-Net), which uses multiple kernels to create an efficient receptive fie... | ['Erik Meijering', 'Antonio Robles-Kelly', 'Muhammad Arsalan', 'Tariq M. Khan'] | 2022-10-15 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 2.80090332e-01 1.41725704e-01 -2.43972182e-01 -3.63323629e-01
-5.01800597e-01 -2.29479909e-01 1.08277850e-01 9.40894559e-02
-7.59856164e-01 3.94192308e-01 -8.81534144e-02 -3.99000406e-01
-1.81506440e-01 -4.70498711e-01 -3.78570706e-01 -8.21017146e-01
2.47798771e-01 1.94513679e-01 7.77576685e-01 9.38633233... | [14.685514450073242, -2.593090534210205] |
cadb981f-1541-45a1-833b-b5d14cef92b4 | temporal-knowledge-propagation-for-image-to | 1908.03885 | null | https://arxiv.org/abs/1908.03885v3 | https://arxiv.org/pdf/1908.03885v3.pdf | Temporal Knowledge Propagation for Image-to-Video Person Re-identification | In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image an... | ['Bingpeng Ma', 'Xilin Chen', 'Xinqian Gu', 'Shiguang Shan', 'Hong Chang'] | 2019-08-11 | temporal-knowledge-propagation-for-image-to-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Gu_Temporal_Knowledge_Propagation_for_Image-to-Video_Person_Re-Identification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Gu_Temporal_Knowledge_Propagation_for_Image-to-Video_Person_Re-Identification_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-to-video-person-re-identification'] | ['computer-vision'] | [-7.10272416e-02 -5.18138468e-01 -3.51355463e-01 -3.78172964e-01
-5.44529080e-01 -4.86359864e-01 4.60083187e-01 -4.46692109e-01
-3.99364144e-01 4.44582611e-01 1.50166333e-01 3.80863845e-01
-2.02606067e-01 -4.44813520e-01 -6.99578822e-01 -6.62955225e-01
-7.16381073e-02 4.45020087e-02 1.44935620e-03 2.22204506... | [14.643102645874023, 1.000072717666626] |
112f8e92-0174-4d5f-8da5-25c80cbbfc79 | zero-shot-cross-lingual-conversational-1 | null | null | https://openreview.net/forum?id=Qb6XuSBODsW | https://openreview.net/pdf?id=Qb6XuSBODsW | Zero-shot Cross-lingual Conversational Semantic Role Labeling | While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.85955778e-01 4.38942075e-01 -2.79953778e-01 -7.34488308e-01
-1.39475250e+00 -7.99522281e-01 7.96336710e-01 -4.94245179e-02
-4.62658972e-01 1.03881276e+00 1.09404671e+00 -4.72660214e-01
3.02778155e-01 -4.55221474e-01 -3.40263337e-01 -2.96798348e-01
1.39929458e-01 7.02326119e-01 -5.66116124e-02 -9.09700572... | [12.400540351867676, 8.112032890319824] |
fc489ab6-8fe1-4b24-8e7f-f9dfa3015ee8 | cgof-controllable-3d-face-synthesis-with | 2211.13251 | null | https://arxiv.org/abs/2211.13251v1 | https://arxiv.org/pdf/2211.13251v1.pdf | CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields | Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, previous methods focus o... | ['Hongsheng Li', 'Quan Wang', 'Zhaoyang Huang', 'Ning Zhang', 'Shangzhe Wu', 'Keqiang Sun'] | 2022-11-23 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 2.70208687e-01 4.34267223e-01 1.97667077e-01 -2.79995322e-01
-2.87777424e-01 -3.80091757e-01 8.70931506e-01 -6.14605188e-01
2.92407334e-01 6.68818533e-01 1.04492173e-01 3.65604460e-01
-3.50107066e-02 -1.12619972e+00 -9.15133119e-01 -8.69928896e-01
3.71858895e-01 6.50551260e-01 -2.04125747e-01 -1.33584559... | [12.630338668823242, -0.3666478991508484] |
fe696415-ab5e-4432-8755-30dbfdea7b90 | enhancing-feature-invariance-with-learned | 2002.01642 | null | https://arxiv.org/abs/2002.01642v4 | https://arxiv.org/pdf/2002.01642v4.pdf | Learning Test-time Augmentation for Content-based Image Retrieval | Off-the-shelf convolutional neural network features achieve outstanding results in many image retrieval tasks. However, their invariance to target data is pre-defined by the network architecture and training data. Existing image retrieval approaches require fine-tuning or modification of pre-trained networks to adapt t... | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Osman Tursun'] | 2020-02-05 | null | null | null | null | ['trademark-retrieval', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 1.39618158e-01 -6.84556007e-01 -3.85111183e-01 -7.46015489e-01
-1.02996504e+00 -7.35930443e-01 7.19061017e-01 -1.23624668e-01
-9.05356407e-01 4.32300329e-01 6.55259192e-02 -1.44790777e-03
-4.69340771e-01 -6.54185295e-01 -8.32513213e-01 -6.87131703e-01
-2.06433728e-01 4.61598516e-01 7.39205554e-02 -2.02876925... | [10.64837646484375, 0.728018045425415] |
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