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f646edb7-d0c1-433d-b6ad-060b99ef946a | zhou-qiaoli-a-divide-and-conquer-strategy-for | null | null | https://aclanthology.org/S12-1072 | https://aclanthology.org/S12-1072.pdf | Zhou qiaoli: A divide-and-conquer strategy for semantic dependency parsing | null | ['Guiping Zhang', 'Fei Liu', 'Qiaoli Zhou', 'Ling Zhang', 'Dongfeng Cai'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.280211448669434, 3.6812686920166016] |
fa4f48e4-6908-4a21-b3d1-2a81b4eb6fc1 | trace-encoding-in-process-mining-a-survey-and | 2301.02167 | null | https://arxiv.org/abs/2301.02167v1 | https://arxiv.org/pdf/2301.02167v1.pdf | Trace Encoding in Process Mining: a survey and benchmarking | Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usually performed as preprocessing steps and are responsible for transforming complex information into a numerical feature space. Most papers cho... | ['Gabriel M. Tavares', 'Rafael S. Oyamada', 'Paolo Ceravolo', 'Sylvio Barbon Jr.'] | 2023-01-05 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 6.97032332e-01 -4.29570898e-02 -1.55481592e-01 -2.71757215e-01
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-2.22819835e-01 7.05403805e-01 3.19368660e-01 2.87202805... | [8.59028434753418, 6.01471471786499] |
63b033d9-556e-410d-b145-a2df0f0fcb93 | stop-words-for-processing-software | 2303.10439 | null | https://arxiv.org/abs/2303.10439v2 | https://arxiv.org/pdf/2303.10439v2.pdf | Stop Words for Processing Software Engineering Documents: Do they Matter? | Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of sto... | ['Christoph Treude', 'Chetan Arora', 'Yaohou Fan'] | 2023-03-18 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 2.15494066e-01 -6.86822459e-02 -2.38492742e-01 -1.09943278e-01
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2.96903670e-01 8.24231580e-02 3.48675013e-01 -1.48124084... | [7.8046488761901855, 7.9750189781188965] |
fa1937a2-41a6-407f-a8d2-5de528c7f918 | personalized-ppg-normalization-based-on | 2202.11465 | null | https://arxiv.org/abs/2202.11465v1 | https://arxiv.org/pdf/2202.11465v1.pdf | Personalized PPG Normalization based on Subject Heartbeat in Resting State Condition | Physiological responses are nowadays widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of... | ['Stefania Bandini', 'Marta Giltri', 'Alessandra Grossi', 'Francesca Gasparini'] | 2022-02-23 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 5.70033967e-01 -5.20121567e-02 -8.28944817e-02 -4.09081250e-01
-1.01014577e-01 -3.43021870e-01 3.46760184e-01 2.61873245e-01
-6.75115287e-01 8.52094173e-01 1.04157545e-01 4.34190482e-01
-2.73477584e-01 -4.24790293e-01 1.18958140e-02 -9.23734009e-01
2.53817458e-02 -1.42701089e-01 -3.79563481e-01 -4.42136414... | [13.678200721740723, 3.0299031734466553] |
87575c6b-e0af-4ba5-8383-e69031522d84 | target-specific-and-selective-drug-design-for | 2004.01215 | null | https://arxiv.org/abs/2004.01215v2 | https://arxiv.org/pdf/2004.01215v2.pdf | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models | The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selecti... | ['Jannis Born', 'Matteo Manica', 'Aleksandra Mojsilovic', 'Payel Das', 'Kar Wai Lim', 'Inkit Padhi', 'Hendrik Strobelt', 'Benjamin Hoover', 'Teodoro Laino', 'Samuel C. Hoffman', 'Vijil Chenthamarakshan'] | 2020-04-02 | null | http://proceedings.neurips.cc/paper/2020/hash/2d16ad1968844a4300e9a490588ff9f8-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/2d16ad1968844a4300e9a490588ff9f8-Paper.pdf | neurips-2020-12 | ['retrosynthesis'] | ['medical'] | [ 2.18051255e-01 -1.74045980e-01 -2.91020811e-01 -1.56314194e-01
-8.12680900e-01 -1.03448522e+00 2.87322104e-01 5.03217340e-01
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-2.57232964e-01 7.63392866e-01 -1.74491733e-01 -2.07835451... | [4.874614715576172, 5.611928939819336] |
94c1eb46-75cf-4f5f-9e9b-3e0c8163ed4e | cross-lingual-vision-language-navigation-1 | 1910.11301 | null | https://arxiv.org/abs/1910.11301v3 | https://arxiv.org/pdf/1910.11301v3.pdf | Cross-Lingual Vision-Language Navigation | Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics. But the dominant language is English, according to previous studies on vision-language navigation (VLN). To go beyond English and serve people speaking different languages, we collect ... | ['Lei LI', 'William Yang Wang', 'An Yan', 'Xin Eric Wang', 'Jiangtao Feng'] | 2019-10-24 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [-1.19095601e-01 1.29769564e-01 -1.71061009e-01 -4.49690759e-01
-3.74321312e-01 -4.80223149e-01 7.41014242e-01 -5.31314731e-01
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6.91253841e-01 -8.22248638e-01 -7.40341067e-01 -4.60633963e-01
2.41575778e-01 6.38309240e-01 2.08887890e-01 -8.98550451... | [4.447103500366211, 0.5526334047317505] |
49cad5d7-803b-4868-a4dd-b9e06157d330 | a-comparative-study-on-crime-in-denver-city | 2001.02802 | null | https://arxiv.org/abs/2001.02802v1 | https://arxiv.org/pdf/2001.02802v1.pdf | A Comparative Study on Crime in Denver City Based on Machine Learning and Data Mining | To ensure the security of the general mass, crime prevention is one of the most higher priorities for any government. An accurate crime prediction model can help the government, law enforcement to prevent violence, detect the criminals in advance, allocate the government resources, and recognize problems causing crimes... | ['Md. Aminur Rab Ratul'] | 2020-01-09 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-2.20781595e-01 -4.05740321e-01 -2.47195482e-01 -2.81674057e-01
-2.75980949e-01 -3.21229994e-01 2.91979402e-01 5.41031837e-01
-4.53142315e-01 7.88866341e-01 2.33092591e-01 -7.66128838e-01
-5.42819858e-01 -1.13626456e+00 6.60791025e-02 -6.03766263e-01
-1.58579350e-01 1.71690017e-01 1.10502794e-01 -1.53922662... | [6.792037487030029, 1.9769750833511353] |
38587b3f-b47b-4d62-844e-ace927af5d39 | deconstruct-to-reconstruct-a-configurable | 2011.00483 | null | https://arxiv.org/abs/2011.00483v1 | https://arxiv.org/pdf/2011.00483v1.pdf | Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems | Many automatic evaluation metrics have been proposed to score the overall quality of a response in open-domain dialogue. Generally, the overall quality is comprised of various aspects, such as relevancy, specificity, and empathy, and the importance of each aspect differs according to the task. For instance, specificity... | ['Akiko Aizawa', 'Yang Zhao', 'Vitou Phy'] | 2020-11-01 | null | https://aclanthology.org/2020.coling-main.368 | https://aclanthology.org/2020.coling-main.368.pdf | coling-2020-8 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-4.64066267e-01 6.99776709e-02 -1.21528029e-01 -7.01787055e-01
-2.40405932e-01 -6.98165357e-01 5.32998681e-01 3.78447950e-01
-4.66265500e-01 5.82539976e-01 4.20678318e-01 1.09544672e-01
-4.77174640e-01 -6.36084318e-01 4.11636442e-01 -2.72971153e-01
5.86975574e-01 3.50150377e-01 2.28331864e-01 -6.75231993... | [12.823145866394043, 8.208946228027344] |
afbf454a-077e-4b3d-aeb3-a8aa2bf18d06 | a-stable-multi-scale-kernel-for-topological | 1412.6821 | null | http://arxiv.org/abs/1412.6821v1 | http://arxiv.org/pdf/1412.6821v1.pdf | A Stable Multi-Scale Kernel for Topological Machine Learning | Topological data analysis offers a rich source of valuable information to
study vision problems. Yet, so far we lack a theoretically sound connection to
popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In
this work, we establish such a connection by designing a multi-scale kernel for
persist... | ['Ulrich Bauer', 'Stefan Huber', 'Roland Kwitt', 'Jan Reininghaus'] | 2014-12-21 | a-stable-multi-scale-kernel-for-topological-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Reininghaus_A_Stable_Multi-Scale_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Reininghaus_A_Stable_Multi-Scale_2015_CVPR_paper.pdf | cvpr-2015-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 6.79224804e-02 -3.93241763e-01 -2.17309535e-01 -2.07122833e-01
-2.89880008e-01 -5.77242017e-01 6.88210011e-01 6.20060742e-01
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-7.42825866e-01 -7.45121300e-01 -3.69322091e-01 -1.14891911e+00
-4.49786752e-01 1.47442505e-01 7.85430253e-01 -1.74369335... | [7.5692596435546875, 4.073818206787109] |
92254e34-4685-4f9c-93b5-8575b40276c6 | mitigating-motion-sickness-with-optimization | 2301.07977 | null | https://arxiv.org/abs/2301.07977v1 | https://arxiv.org/pdf/2301.07977v1.pdf | Mitigating Motion Sickness with Optimization-based Motion Planning | The acceptance of automated driving is under the potential threat of motion sickness. It hinders the passengers' willingness to perform secondary activities. In order to mitigate motion sickness in automated vehicles, we propose an optimization-based motion planning algorithm that minimizes the distribution of accelera... | ['Tamas Keviczky', 'Barys Shyrokau', 'Yanggu Zheng'] | 2023-01-19 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 5.09258062e-02 3.50034714e-01 -1.09920576e-01 -7.32366368e-02
-7.21422553e-01 -3.30373198e-01 5.30185461e-01 1.84388682e-01
-7.59965599e-01 6.61650836e-01 8.41099396e-02 -7.23667979e-01
-3.27720851e-01 -6.44833624e-01 -5.80287457e-01 -7.94723213e-01
-4.67404835e-02 -4.95251939e-02 3.62011105e-01 -3.94338638... | [5.510324478149414, 1.4764595031738281] |
3095dc3f-c7ef-4e53-8492-12f8e7c55cf2 | natural-language-descriptions-of-human | null | null | https://aclanthology.org/W16-3205 | https://aclanthology.org/W16-3205.pdf | Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis | null | ['Yoshihiko Gotoh', 'Nouf Alharbi'] | 2016-08-01 | null | null | null | ws-2016-8 | ['video-description'] | ['computer-vision'] | [-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... | [-7.390260696411133, 3.621987819671631] |
0941f7e6-f4da-43c4-8cb8-3b6b325d965f | t-star-lite-a-fast-time-risk-optimal-motion | 2008.13048 | null | https://arxiv.org/abs/2008.13048v1 | https://arxiv.org/pdf/2008.13048v1.pdf | T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles | In this paper, we develop a new algorithm, called T$^{\star}$-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T$^{\star}$-Lite algorithm is a significantly faster version of the previously developed T$^{\star}$ algorithm. T$^{\star}$-Lite uses the novel time-risk co... | ['Zongyuan Shen', 'Thomas A. Wettergren', 'James P. Wilson', 'Shalabh Gupta'] | 2020-08-29 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [-1.47797152e-01 1.63026094e-01 -2.47713909e-01 8.62318352e-02
-6.81040525e-01 -3.10793161e-01 4.16416556e-01 -1.07466713e-01
-6.68448746e-01 9.70879614e-01 -6.43176317e-01 -9.62681592e-01
-7.49385536e-01 -1.12730408e+00 -5.53978205e-01 -7.70984411e-01
-4.88329083e-01 4.69719738e-01 4.83584374e-01 -6.85534179... | [5.1613593101501465, 1.6341394186019897] |
2d67de18-3b13-4c1f-8dfe-c98b87c2671e | modality-aware-contrastive-instance-learning | 2207.05500 | null | https://arxiv.org/abs/2207.05500v1 | https://arxiv.org/pdf/2207.05500v1.pdf | Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection | Weakly-supervised audio-visual violence detection aims to distinguish snippets containing multimodal violence events with video-level labels. Many prior works perform audio-visual integration and interaction in an early or intermediate manner, yet overlooking the modality heterogeneousness over the weakly-supervised se... | ['Yuejie Zhang', 'Rui Feng', 'Ying Cheng', 'Jinyu Liu', 'Jiashuo Yu'] | 2022-07-12 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 1.64120257e-01 -1.32569417e-01 -2.84899026e-01 -1.78015843e-01
-1.04541230e+00 -6.62009954e-01 8.01382780e-01 1.48636132e-01
-2.92468160e-01 4.24778908e-01 3.78574580e-01 -8.95786658e-02
-4.77445684e-03 -5.15000939e-01 -6.88326955e-01 -1.02378619e+00
-2.13223472e-02 1.70097023e-01 -1.39083723e-02 -4.61690538... | [13.467679023742676, 4.733566761016846] |
7e244342-1fcd-434a-b78c-840ff4d52867 | inverse-cooking-recipe-generation-from-food | 1812.06164 | null | https://arxiv.org/abs/1812.06164v2 | https://arxiv.org/pdf/1812.06164v2.pdf | Inverse Cooking: Recipe Generation from Food Images | People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recip... | ['Xavier Giro-i-Nieto', 'Michal Drozdzal', 'Amaia Salvador', 'Adriana Romero'] | 2018-12-14 | inverse-cooking-recipe-generation-from-food-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Salvador_Inverse_Cooking_Recipe_Generation_From_Food_Images_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Salvador_Inverse_Cooking_Recipe_Generation_From_Food_Images_CVPR_2019_paper.pdf | cvpr-2019-6 | ['recipe-generation'] | ['miscellaneous'] | [ 2.74971873e-01 3.05023137e-02 -2.48808190e-02 -4.84715372e-01
-7.12076902e-01 -9.07771766e-01 6.29050732e-01 5.88304043e-01
-1.56050637e-01 2.40197703e-01 7.70693600e-01 -1.48169650e-02
2.85444170e-01 -9.93038356e-01 -1.28721178e+00 -3.82543057e-01
2.32525975e-01 3.63597929e-01 -1.43182978e-01 -2.98071742... | [11.519472122192383, 4.47749137878418] |
23e4824a-b7b4-4eb9-8a16-b3efac88e5d2 | multi-task-learning-in-histo-pathology-for | 2005.08645 | null | https://arxiv.org/abs/2005.08645v1 | https://arxiv.org/pdf/2005.08645v1.pdf | Multi-Task Learning in Histo-pathology for Widely Generalizable Model | In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification, one of the most prevalent cancers in the developing world, to multi-tissue nuclei instance segmentation and classification. | ['Navid Alemi Kooohbanani', 'Jevgenij Gamper', 'Nasir Rajpoot'] | 2020-05-09 | null | null | null | null | ['oral-cancer-classification'] | ['medical'] | [ 2.98272848e-01 2.89094836e-01 -3.59829009e-01 -2.77089793e-02
-1.73116410e+00 -7.26235732e-02 2.76490211e-01 5.99411666e-01
-6.78225100e-01 7.58695066e-01 1.65900096e-01 -3.43843669e-01
-1.43357918e-01 -3.30666929e-01 -3.47156465e-01 -1.02988684e+00
1.98068723e-01 8.74591172e-01 3.65491390e-01 -9.52135101... | [15.046255111694336, -3.000272035598755] |
44b8d0a5-0b12-44f0-b5dd-ae39815e0f27 | multi-scale-attention-for-audio-question | 2305.17993 | null | https://arxiv.org/abs/2305.17993v1 | https://arxiv.org/pdf/2305.17993v1.pdf | Multi-Scale Attention for Audio Question Answering | Audio question answering (AQA), acting as a widely used proxy task to explore scene understanding, has got more attention. The AQA is challenging for it requires comprehensive temporal reasoning from different scales' events of an audio scene. However, existing methods mostly extend the structures of visual question an... | ['Di Hu', 'Yixin Xu', 'Guangyao Li'] | 2023-05-29 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 3.19054127e-02 -4.56551075e-01 2.65235782e-01 -4.59311217e-01
-1.16674173e+00 -5.18985391e-01 3.89783263e-01 3.65289897e-01
-1.97062165e-01 2.05086693e-01 6.15464330e-01 -2.26248220e-01
-3.51564944e-01 -6.12416506e-01 -4.98396993e-01 -4.95475531e-01
-1.39174476e-01 -1.44288805e-03 5.85143983e-01 -2.34793708... | [10.524393081665039, 1.1094987392425537] |
7a38c970-c7dc-468e-8158-3e5d50dd9fd9 | graphreg-dynamical-point-cloud-registration | 2302.01109 | null | https://arxiv.org/abs/2302.01109v1 | https://arxiv.org/pdf/2302.01109v1.pdf | GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing | This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid... | ['Huang Tiejun', 'Yan Dong-Ming', 'Jia Xiaohong', 'Ma Lei', 'Zhao Mingyang'] | 2023-02-02 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 2.42988259e-01 -6.04964733e-01 1.37099147e-01 -2.73914188e-01
-6.87970877e-01 -3.72005492e-01 6.67493045e-01 2.73852021e-01
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-4.71254498e-01 -9.03455436e-01 -7.34144449e-01 -7.96710610e-01
-3.64837833e-02 7.03801870e-01 4.60188866e-01 -1.70768425... | [7.6797075271606445, -2.9339849948883057] |
e504413d-39f6-42c4-a4c1-f54b5f073a5e | 2dpass-2d-priors-assisted-semantic | 2207.04397 | null | https://arxiv.org/abs/2207.04397v3 | https://arxiv.org/pdf/2207.04397v3.pdf | 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds | As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion. However, fusion-based approaches require paired data, i.e., LiDAR point clouds and camera images with strict point-to-pix... | ['Zhen Li', 'Shenghui Cui', 'Ruimao Zhang', 'Chao Zheng', 'Chaoda Zheng', 'Jiantao Gao', 'Xu Yan'] | 2022-07-10 | null | null | null | null | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.61936253e-01 4.83294465e-02 -3.74516875e-01 -5.92086911e-01
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8.71485472e-02 -8.57472718e-01 -1.26813006e+00 -5.83665133e-01
5.96923053e-01 7.50462115e-01 6.23555720e-01 -2.94840723... | [8.290787696838379, -2.718316078186035] |
0aa448a5-b016-43ba-bfee-9deea9bf8486 | 2305-14814 | 2305.14814 | null | https://arxiv.org/abs/2305.14814v1 | https://arxiv.org/pdf/2305.14814v1.pdf | What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding | We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power. Existing analyses relate this notion to the graph isomorphism problem, which is mostly relevant for graphs of small sizes, or studied graph classification or regression tasks, while pr... | ['Samuel Vaiter', 'Nicolas Keriven'] | 2023-05-24 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 2.98877120e-01 5.35616457e-01 -1.77987233e-01 -1.04588099e-01
1.04534425e-01 -6.46408856e-01 4.44035709e-01 3.87271106e-01
-3.18769753e-01 7.26254821e-01 -3.00737798e-01 -6.04856074e-01
-4.59159762e-01 -1.11945641e+00 -1.00017154e+00 -9.15288150e-01
-9.80322361e-01 2.60890931e-01 2.65511394e-01 -7.81290650... | [6.830629348754883, 6.112415790557861] |
a5775c4e-08db-4a0e-8a3f-982cffffaec7 | swin-transformer-hierarchical-vision | 2103.14030 | null | https://arxiv.org/abs/2103.14030v2 | https://arxiv.org/pdf/2103.14030v2.pdf | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows | This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high r... | ['Baining Guo', 'Stephen Lin', 'Zheng Zhang', 'Yixuan Wei', 'Han Hu', 'Yue Cao', 'Yutong Lin', 'Ze Liu'] | 2021-03-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper.pdf | iccv-2021-1 | ['thermal-image-segmentation', 'real-time-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.82372183e-01 1.57639908e-03 -4.85054813e-02 -3.58138800e-01
-7.29461253e-01 -6.15851343e-01 6.42376363e-01 -2.69251287e-01
-7.77675867e-01 3.67500484e-01 -2.36155838e-01 -3.73217285e-01
1.51218578e-01 -6.09723985e-01 -8.61212671e-01 -5.43475509e-01
2.19778359e-01 2.48686418e-01 6.85864091e-01 -6.12739548... | [9.465435981750488, 1.0924276113510132] |
8e40ee2f-3042-4a1f-a677-9acb0dc60165 | distinguishable-speaker-anonymization-based | 2211.03038 | null | https://arxiv.org/abs/2211.03038v1 | https://arxiv.org/pdf/2211.03038v1.pdf | Distinguishable Speaker Anonymization based on Formant and Fundamental Frequency Scaling | Speech data on the Internet are proliferating exponentially because of the emergence of social media, and the sharing of such personal data raises obvious security and privacy concerns. One solution to mitigate these concerns involves concealing speaker identities before sharing speech data, also referred to as speaker... | ['Jie Liu', 'Namin Wang', 'Lei Xie', 'Pengcheng Guo', 'Yi Lei', 'Qing Wang', 'Jixun Yao'] | 2022-11-06 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.37877733e-01 2.72567123e-01 1.27920330e-01 -4.51628149e-01
-7.01691568e-01 -8.65385175e-01 4.17593986e-01 -6.60303831e-02
-2.02512965e-01 4.76860046e-01 6.65176392e-01 -3.63979489e-01
2.03077659e-01 -4.11320686e-01 -3.79822612e-01 -7.49261498e-01
1.27187788e-01 -3.62989217e-01 -7.82843456e-02 5.54486997... | [13.994216918945312, 5.85652494430542] |
06165e74-895d-4a8d-b5e2-6e85f470d64b | learning-a-discriminative-feature-network-for | 1804.09337 | null | http://arxiv.org/abs/1804.09337v1 | http://arxiv.org/pdf/1804.09337v1.pdf | Learning a Discriminative Feature Network for Semantic Segmentation | Most existing methods of semantic segmentation still suffer from two aspects
of challenges: intra-class inconsistency and inter-class indistinction. To
tackle these two problems, we propose a Discriminative Feature Network (DFN),
which contains two sub-networks: Smooth Network and Border Network.
Specifically, to handl... | ['Jingbo Wang', 'Changqian Yu', 'Nong Sang', 'Gang Yu', 'Chao Peng', 'Changxin Gao'] | 2018-04-25 | learning-a-discriminative-feature-network-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Learning_a_Discriminative_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Learning_a_Discriminative_CVPR_2018_paper.pdf | cvpr-2018-6 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.25620455e-01 -5.06215729e-02 -1.76082537e-01 -6.70672417e-01
-7.01956928e-01 -4.04532880e-01 3.79977971e-01 -3.02523881e-01
-4.93220150e-01 4.14060324e-01 -2.56354120e-02 -1.88957229e-02
2.76150201e-02 -6.79562747e-01 -6.10266447e-01 -4.79493022e-01
2.83212751e-01 -3.62688070e-03 8.51694465e-01 2.51476150... | [9.54922103881836, 0.1640278846025467] |
713a3986-52ee-4065-aa63-9a870494aad0 | variational-disentangled-graph-auto-encoders | 2306.11315 | null | https://arxiv.org/abs/2306.11315v1 | https://arxiv.org/pdf/2306.11315v1.pdf | Variational Disentangled Graph Auto-Encoders for Link Prediction | With the explosion of graph-structured data, link prediction has emerged as an increasingly important task. Embedding methods for link prediction utilize neural networks to generate node embeddings, which are subsequently employed to predict links between nodes. However, the existing embedding methods typically take a ... | ['Dali Chen', 'Shuang Li', 'Xiaojuan Zhang', 'Jun Fu'] | 2023-06-20 | null | null | null | null | ['link-prediction', 'disentanglement'] | ['graphs', 'methodology'] | [ 2.64224820e-02 4.08800036e-01 -8.14530671e-01 2.18138602e-02
-3.42043079e-02 -5.43197989e-01 8.03587496e-01 5.69841824e-02
2.95200378e-01 6.77269220e-01 5.53679347e-01 -4.38200742e-01
-3.73643547e-01 -1.05821145e+00 -5.45829177e-01 -6.45728648e-01
-5.07532537e-01 2.78066158e-01 7.09235435e-03 -1.56325519... | [7.26071310043335, 6.199878215789795] |
5740b52c-8bd0-4b7b-aafa-247b37698075 | a-practical-guide-to-cnns-and-fisher-vectors | 1508.02496 | null | http://arxiv.org/abs/1508.02496v3 | http://arxiv.org/pdf/1508.02496v3.pdf | A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval | With deep learning becoming the dominant approach in computer vision, the use
of representations extracted from Convolutional Neural Nets (CNNs) is quickly
gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global
image descriptors for image instance retrieval. While the good performance of
CNNs for im... | ['Olivier Morère', 'Antoine Veillard', 'Jie Lin', 'Hanlin Goh', 'Vijay Chandrasekhar'] | 2015-08-11 | null | null | null | null | ['image-instance-retrieval'] | ['computer-vision'] | [-1.28400236e-01 -5.40913165e-01 -1.87114164e-01 -3.69872600e-01
-7.06086874e-01 -7.30786443e-01 9.48283553e-01 3.83996665e-01
-6.18169069e-01 2.04461113e-01 -3.17738280e-02 9.07998011e-02
-5.70864499e-01 -8.37378383e-01 -5.94707072e-01 -7.03839064e-01
-1.02706268e-01 2.36886710e-01 4.84824657e-01 -4.81591254... | [10.55742359161377, 0.4003359377384186] |
9af76571-e913-4fb2-927c-07e7840fc679 | deep-burst-super-resolution | 2101.10997 | null | https://arxiv.org/abs/2101.10997v2 | https://arxiv.org/pdf/2101.10997v2.pdf | Deep Burst Super-Resolution | While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high frequency details. In contrast, multi-frame super-resolution (MFSR) offers the possibility of reconstructing rich details by combining signal i... | ['Radu Timofte', 'Luc van Gool', 'Martin Danelljan', 'Goutam Bhat'] | 2021-01-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Bhat_Deep_Burst_Super-Resolution_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Bhat_Deep_Burst_Super-Resolution_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multi-frame-super-resolution', 'burst-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 5.34737468e-01 -3.21385860e-01 4.51990031e-02 -3.30484629e-01
-1.12767017e+00 -8.93464983e-02 5.17614961e-01 -3.78335804e-01
-3.99301291e-01 9.34747934e-01 4.13374692e-01 3.47299933e-01
-7.27879778e-02 -6.32481694e-01 -8.59556973e-01 -8.23806465e-01
1.67078108e-01 -1.71282619e-01 1.75900340e-01 -2.23343879... | [10.914033889770508, -2.002272367477417] |
c92bbcb7-4441-47da-ab76-f474806de74d | egocentric-activity-recognition-and | 2105.09544 | null | https://arxiv.org/abs/2105.09544v3 | https://arxiv.org/pdf/2105.09544v3.pdf | Egocentric Activity Recognition and Localization on a 3D Map | Given a video captured from a first person perspective and the environment context of where the video is recorded, can we recognize what the person is doing and identify where the action occurs in the 3D space? We address this challenging problem of jointly recognizing and localizing actions of a mobile user on a known... | ['Chao Li', 'James M. Rehg', 'Kristen Grauman', 'Yin Li', 'Kiran Somasundaram', 'Lingni Ma', 'Miao Liu'] | 2021-05-20 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 2.18409926e-01 -1.66241065e-01 -2.21765965e-01 -4.14793789e-01
-4.93314385e-01 -6.27666831e-01 6.47267878e-01 -4.46703494e-01
-1.90616429e-01 1.67959750e-01 9.94964659e-01 3.16231936e-01
2.34727368e-01 -4.83277053e-01 -8.51070225e-01 -4.88605171e-01
4.46932809e-03 4.92704272e-01 7.04836026e-02 4.12497729... | [8.135749816894531, 0.40318143367767334] |
6ab7d7fa-74d1-44b3-90f1-57f0b50710ac | vpit-real-time-embedded-single-object-3d | 2206.02619 | null | https://arxiv.org/abs/2206.02619v1 | https://arxiv.org/pdf/2206.02619v1.pdf | VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images | In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the first method that uses voxel pseudo images for 3D SOT. The input point cloud is structured by pillar-based voxelization, and the resulting pseudo image is used as an input to a ... | ['Alexandros Iosifidis', 'Anastasios Tefas', 'Nikolaos Passalis', 'Paraskevi Nousi', 'Illia Oleksiienko'] | 2022-06-06 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-2.68960688e-02 -3.84793997e-01 -2.04511061e-01 2.00664163e-01
-4.14702922e-01 -6.89061761e-01 4.57213491e-01 -9.42320153e-02
-6.56826138e-01 3.91220003e-01 -7.02835619e-01 -2.28549838e-01
1.36480227e-01 -5.58737099e-01 -8.24187279e-01 -6.79947138e-01
-9.90235880e-02 9.91273046e-01 1.31873834e+00 6.68748468... | [6.886017322540283, -2.280820369720459] |
d7543189-cff6-435a-bd60-6ba9b3be7197 | lets-lie-together-co-presence-effects-on | null | null | https://aclanthology.org/W12-0409 | https://aclanthology.org/W12-0409.pdf | Let's Lie Together: Co-Presence Effects on Children's Deceptive Skills | null | ['Marc Swerts'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3394060134887695, 3.8966827392578125] |
92272d20-56c1-476b-88b5-356c4969b92d | temporal-patterns-in-insulin-needs-for-type-1 | 2211.07393 | null | https://arxiv.org/abs/2211.07393v2 | https://arxiv.org/pdf/2211.07393v2.pdf | Temporal patterns in insulin needs for Type 1 diabetes | Type 1 Diabetes (T1D) is a chronic condition where the body produces little or no insulin, a hormone required for the cells to use blood glucose (BG) for energy and to regulate BG levels in the body. Finding the right insulin dose and time remains a complex, challenging and as yet unsolved control task. In this study, ... | ['Zahraa S. Abdallah', 'Isabella Degen'] | 2022-11-14 | null | null | null | null | ['type'] | ['speech'] | [ 3.77312660e-01 -4.86404866e-01 -6.74176216e-01 -3.87514681e-01
2.70406842e-01 -7.62619019e-01 9.80290994e-02 8.55809093e-01
9.16186199e-02 5.51231265e-01 6.20282948e-01 -3.23976070e-01
-3.65791261e-01 -7.69154966e-01 -4.29048747e-01 -6.43856645e-01
-5.29893637e-01 6.01498306e-01 -2.88534850e-01 -1.76035896... | [8.123228073120117, 5.58479642868042] |
7e0e4e70-19c9-46e1-b6b1-1d610c04c686 | rtm3d-real-time-monocular-3d-detection-from | 2001.03343 | null | https://arxiv.org/abs/2001.03343v1 | https://arxiv.org/pdf/2001.03343v1.pdf | RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving | In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatic... | ['PengFei Liu', 'Feidao Cao', 'Peixuan Li', 'Huaici Zhao'] | 2020-01-10 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1054_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480647.pdf | eccv-2020-8 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-4.03429925e-01 -3.35398793e-01 -3.83688807e-01 -3.22715975e-02
-2.34691426e-01 -5.65157771e-01 3.58081430e-01 -3.87702316e-01
-3.76213908e-01 -2.42824033e-02 -9.97923985e-02 -3.42098087e-01
4.50892746e-01 -5.41145146e-01 -7.88518906e-01 -4.36347604e-01
2.51660734e-01 4.70577776e-01 8.15007389e-01 1.28956530... | [7.794622898101807, -2.475839138031006] |
bd4e9d48-5ea2-47f7-8fe2-f2431dca25c3 | geometry-and-convergence-of-natural-policy | 2211.02105 | null | https://arxiv.org/abs/2211.02105v1 | https://arxiv.org/pdf/2211.02105v1.pdf | Geometry and convergence of natural policy gradient methods | We study the convergence of several natural policy gradient (NPG) methods in infinite-horizon discounted Markov decision processes with regular policy parametrizations. For a variety of NPGs and reward functions we show that the trajectories in state-action space are solutions of gradient flows with respect to Hessian ... | ['Guido Montúfar', 'Johannes Müller'] | 2022-11-03 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.84624249e-01 4.88474041e-01 -4.17786688e-01 -1.12361044e-01
-7.75612473e-01 -7.43060172e-01 5.69182575e-01 2.28799298e-01
-8.37556660e-01 1.18490970e+00 3.80740136e-01 -4.73745197e-01
-3.95928800e-01 -5.02439439e-01 -7.38935828e-01 -8.87436807e-01
-5.59929788e-01 3.93562406e-01 -9.45862308e-02 -4.09245402... | [4.269306182861328, 2.625560760498047] |
dec27325-789d-450f-8236-4d019f2801b4 | ccnet-criss-cross-attention-for-semantic | 1811.11721 | null | https://arxiv.org/abs/1811.11721v2 | https://arxiv.org/pdf/1811.11721v2.pdf | CCNet: Criss-Cross Attention for Semantic Segmentation | Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests ... | ['Wenyu Liu', 'Humphrey Shi', 'Yunchao Wei', 'Xinggang Wang', 'Thomas S. Huang', 'Lichao Huang', 'Zilong Huang'] | 2018-11-28 | ccnet-criss-cross-attention-for-semantic-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Huang_CCNet_Criss-Cross_Attention_for_Semantic_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_CCNet_Criss-Cross_Attention_for_Semantic_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['thermal-image-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 1.68978378e-01 -6.03388511e-02 -3.02089393e-01 -3.85801166e-01
-9.14540648e-01 -2.57918715e-01 2.59053290e-01 1.66885510e-01
-5.41582048e-01 5.01180649e-01 -5.55516742e-02 -2.09860161e-01
4.65533212e-02 -6.59051538e-01 -9.34688985e-01 -8.25063288e-01
4.20401424e-01 6.51638396e-03 6.53931856e-01 9.02806595... | [9.440918922424316, -0.19131898880004883] |
ceeecc1d-194a-406c-8170-2b1c30d0e99c | weakly-aligned-feature-fusion-for-multimodal | 2204.09848 | null | https://arxiv.org/abs/2204.09848v1 | https://arxiv.org/pdf/2204.09848v1.pdf | Weakly Aligned Feature Fusion for Multimodal Object Detection | To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned, making one object has different positions in diffe... | ['Hong Qiao', 'Zhen Lei', 'Xu Yang', 'Zhan Song', 'Xiangyu Zhu', 'Zhiyong Liu', 'Lu Zhang'] | 2022-04-21 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 2.03781322e-01 -4.62861210e-01 -5.07704094e-02 -4.96856809e-01
-6.48412108e-01 -5.29467106e-01 2.69718409e-01 5.35880588e-02
-3.75596732e-01 2.95260131e-01 4.08777855e-02 1.44772321e-01
-2.02543885e-01 -4.23180044e-01 -6.11693740e-01 -1.04271626e+00
4.38106924e-01 -4.40482125e-02 4.83414829e-01 -1.95640363... | [9.711146354675293, -0.9855990409851074] |
540b8fb8-0ca2-457e-9b23-ebe7a7ec9edd | structured-3d-features-for-reconstructing | 2212.06820 | null | https://arxiv.org/abs/2212.06820v3 | https://arxiv.org/pdf/2212.06820v3.pdf | Structured 3D Features for Reconstructing Controllable Avatars | We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface. The 3D points have associated semantics and can move freely in 3D space. This allows for optimal coverage of th... | ['Cristian Sminchisescu', 'Andrei Zanfir', 'Eduard Gabriel Bazavan', 'Thiemo Alldieck', 'Mihai Zanfir', 'Enric Corona'] | 2022-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Corona_Structured_3D_Features_for_Reconstructing_Controllable_Avatars_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Corona_Structured_3D_Features_for_Reconstructing_Controllable_Avatars_CVPR_2023_paper.pdf | cvpr-2023-1 | ['virtual-try-on'] | ['computer-vision'] | [ 1.13094352e-01 2.51362681e-01 2.45988771e-01 -2.44902253e-01
-2.41424695e-01 -4.68644768e-01 5.70571423e-01 -3.90137643e-01
8.08348060e-02 4.58897650e-01 3.70218515e-01 3.11665922e-01
4.01514024e-01 -6.91502869e-01 -9.34012413e-01 -3.08749855e-01
3.06456268e-01 7.72014380e-01 4.81964052e-02 -1.22050978... | [7.2329583168029785, -1.3026989698410034] |
2dd4899c-8102-40ee-95cb-e3c64deacc76 | chinese-idiom-paraphrasing | 2204.07555 | null | https://arxiv.org/abs/2204.07555v2 | https://arxiv.org/pdf/2204.07555v2.pdf | Chinese Idiom Paraphrasing | Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chinese Idioms are hard to be understood by children and non-native speakers. This study proposes a novel task, denoted as Chinese Idiom Paraph... | ['Xindong Wu', 'Yi Zhu', 'Yunhao Yuan', 'Yun Li', 'Chaowei Zhang', 'Yang Li', 'Jipeng Qiang'] | 2022-04-15 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.59656614e-01 -1.54502302e-01 -2.99593806e-01 -3.46295029e-01
-3.73080641e-01 -8.04364860e-01 4.71264660e-01 -3.09318602e-01
-2.60161757e-01 6.59582973e-01 7.44817674e-01 -3.69325906e-01
2.92740524e-01 -6.45681679e-01 -2.30049163e-01 -3.52384269e-01
6.27711236e-01 6.69482768e-01 -3.45765688e-02 -5.49299657... | [11.038776397705078, 9.282607078552246] |
6b2877a0-5c7b-4984-b5e4-af6e32110eff | i-can-t-believe-there-s-no-images-learning | 2211.09778 | null | https://arxiv.org/abs/2211.09778v3 | https://arxiv.org/pdf/2211.09778v3.pdf | I Can't Believe There's No Images! Learning Visual Tasks Using only Language Data | Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such as natural language processing. In this paper, we ask whether it is possible to learn those skills from textual data and t... | ['Aniruddha Kembhavi', 'Christopher Clark', 'Sophia Gu'] | 2022-11-17 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 5.09654999e-01 3.91921461e-01 1.68486256e-02 -4.06971723e-01
-7.39900827e-01 -8.98945451e-01 1.10958445e+00 1.25246376e-01
-7.72467017e-01 4.43041265e-01 4.43017632e-01 -6.56693697e-01
3.28101486e-01 -4.86269325e-01 -1.09073865e+00 -1.32736892e-01
4.39552069e-01 4.23753560e-01 1.41789809e-01 -1.74675167... | [10.834157943725586, 1.7077449560165405] |
0ec043df-99d8-4840-bf20-07a5dfb7c8ab | perceive-interact-predict-learning-dynamic | 2212.02181 | null | https://arxiv.org/abs/2212.02181v1 | https://arxiv.org/pdf/2212.02181v1.pdf | Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction | Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving. In this work, we propose PIP, the first end-to-end Transformer-based framework which jointly and interactively performs online mapping, object detection and motion prediction. PIP le... | ['Chang Huang', 'Wenyu Liu', 'Qian Zhang', 'Helong Zhou', 'Jiajie Chen', 'Tianheng Cheng', 'Bencheng Liao', 'Xinggang Wang', 'Shaoyu Chen', 'Bo Jiang'] | 2022-12-05 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-1.59387484e-01 2.07460150e-01 -2.96228141e-01 -5.70325911e-01
-6.86966538e-01 -4.91850674e-01 9.53153133e-01 2.60623395e-01
-3.91066521e-01 3.57909262e-01 2.98612326e-01 -5.14627285e-02
-1.43579900e-01 -9.93831694e-01 -6.14403963e-01 -3.31559807e-01
-3.93688560e-01 8.07841718e-01 1.11021602e+00 -4.38994527... | [5.870194435119629, 0.7817896604537964] |
e7b7adfe-4bf3-4d93-bd9f-e17b35911136 | bayesian-variable-selection-in-a-million | 2208.01180 | null | https://arxiv.org/abs/2208.01180v2 | https://arxiv.org/pdf/2208.01180v2.pdf | Bayesian Variable Selection in a Million Dimensions | Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. However, wider adoption of Bayesian variable selection has been hampered by computational challenges, especially in difficult regimes with a larg... | ['Martin Jankowiak'] | 2022-08-02 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 5.57042480e-01 -3.13459814e-01 -2.68454820e-01 -2.33199939e-01
-6.27406836e-01 -6.40985668e-01 4.54842448e-01 3.70253384e-01
-5.89964151e-01 1.19259143e+00 -2.83556908e-01 -5.02799094e-01
-2.83364892e-01 -9.37578738e-01 -6.38565361e-01 -1.01967645e+00
-2.22222596e-01 6.99842572e-01 1.18321672e-01 1.65274099... | [7.325560092926025, 4.564911842346191] |
0497ec47-d349-48e2-99c8-73ec0e0cebf1 | ambernet-a-compact-end-to-end-model-for | 2210.15781 | null | https://arxiv.org/abs/2210.15781v1 | https://arxiv.org/pdf/2210.15781v1.pdf | AmberNet: A Compact End-to-End Model for Spoken Language Identification | We present AmberNet, a compact end-to-end neural network for Spoken Language Identification. AmberNet consists of 1D depth-wise separable convolutions and Squeeze-and-Excitation layers with global context, followed by statistics pooling and linear layers. AmberNet achieves performance similar to state-of-the-art(SOTA) ... | ['Boris Ginsburg', 'Jagadeesh Balam', 'Nithin Rao Koluguri', 'Fei Jia'] | 2022-10-27 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-2.57109672e-01 -1.55030936e-01 2.40460739e-01 -8.26855898e-01
-9.44909632e-01 -6.35986984e-01 4.13146436e-01 -1.33015528e-01
-8.14963400e-01 1.14558034e-01 2.87209660e-01 -3.80667001e-01
3.24347258e-01 -3.15125018e-01 -4.73623961e-01 -5.23083746e-01
-3.39208603e-01 5.25605500e-01 4.15219329e-02 -1.64182171... | [14.290417671203613, 6.4580278396606445] |
3b1d799e-e34a-47ff-ba9e-7c40ee3f3b18 | epidemic-control-on-a-large-scale-agent-based | 2304.04475 | null | https://arxiv.org/abs/2304.04475v1 | https://arxiv.org/pdf/2304.04475v1.pdf | Epidemic Control on a Large-Scale-Agent-Based Epidemiology Model using Deep Deterministic Policy Gradient | To mitigate the impact of the pandemic, several measures include lockdowns, rapid vaccination programs, school closures, and economic stimulus. These interventions can have positive or unintended negative consequences. Current research to model and determine an optimal intervention automatically through round-tripping ... | ['Janani Venugopalan', 'Harshal Hayatnagarkar', 'Jayanta Kshirsagar', 'Gaurav Deshkar'] | 2023-04-10 | null | null | null | null | ['epidemiology'] | ['medical'] | [-1.47561818e-01 1.42909244e-01 -5.81012309e-01 1.38460055e-01
-2.30958968e-01 -2.88608372e-01 5.51162243e-01 7.49881923e-01
-7.65705884e-01 1.10887170e+00 6.26926303e-01 -7.93084502e-01
-3.84314805e-01 -1.07632923e+00 -5.37095249e-01 -4.38838422e-01
-6.03827178e-01 9.57989514e-01 -3.59970033e-02 -3.93522233... | [6.006906986236572, 4.378661632537842] |
5e145b2d-4fa0-4201-8b1e-a9ba7fb6d0ca | hyperparameter-optimization-in-black-box | null | null | https://www.cs.princeton.edu/~fheide/proxyopt | https://www.cs.princeton.edu/~fheide/ProxyOpt.pdf | Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies | Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicated hardware blocks, or as proprietary software modules on programmable hardware. The configuration p... | ['Felix Heide', 'Jean-François Lalonde', 'Derek Nowrouzezahrai', 'Karl St. Arnaud', 'Fahim Mannan', 'Yuting Yang', 'Felix Yu', 'Ethan Tseng'] | 2019-04-01 | null | null | null | siggraph-2019-2019-4 | ['image-quality-estimation'] | ['computer-vision'] | [ 3.95948499e-01 -3.42966557e-01 -8.51691365e-02 -5.18050194e-01
-9.73567545e-01 -8.38893890e-01 1.67343348e-01 -1.57272846e-01
-6.40666723e-01 -1.47009417e-01 -2.26048186e-01 -5.89081049e-01
2.53446959e-03 -2.67347366e-01 -8.73265922e-01 -7.37148702e-01
-8.11390281e-02 2.33236864e-01 1.33685008e-01 -3.10389176... | [10.44153118133545, -1.9796040058135986] |
52c5c5e8-d8d7-4ca1-bae3-f35bacc4d01a | representation-learning-with-function-call | 2205.06918 | null | https://arxiv.org/abs/2205.06918v3 | https://arxiv.org/pdf/2205.06918v3.pdf | Representation learning with function call graph transformations for malware open set recognition | Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should cl... | ['Philip K. Chan', 'Jingyun Jia'] | 2022-05-13 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.91009241e-01 -3.70094895e-01 -3.92979562e-01 -2.31989041e-01
-5.11201084e-01 -6.26885772e-01 4.52740759e-01 1.99618444e-01
-1.10689022e-01 6.01970673e-01 -3.79544437e-01 -7.25311100e-01
-9.31658968e-02 -8.39379072e-01 -4.58644658e-01 -5.85114717e-01
-2.16207325e-01 4.19326693e-01 1.68302104e-01 -2.86084354... | [14.34919261932373, 9.598189353942871] |
319c6e61-e166-4687-82c6-8f35e6a0cb38 | only-train-once-mr-fingerprinting-for | 2206.04383 | null | https://arxiv.org/abs/2206.04383v1 | https://arxiv.org/pdf/2206.04383v1.pdf | Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification | Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF) is a novel quantitative imaging technique that simultaneously measures several tissue parameters of semisolid macromolecule and free bulk water. In this study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that estimates the ... | ['HyunWook Park', 'Hye-Young Heo', 'Beomgu Kang'] | 2022-06-09 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.56365490e-01 -1.97365195e-01 -1.66859403e-01 -3.21921170e-01
-7.24624097e-01 -2.35381871e-01 4.28130955e-01 5.34203202e-02
-6.42895937e-01 6.96246862e-01 6.47077709e-02 -2.10899189e-01
-4.39758539e-01 -3.39841008e-01 -4.39085662e-01 -1.12937748e+00
-3.54242951e-01 6.57720327e-01 3.59425068e-01 2.28959113... | [13.519079208374023, -2.4051969051361084] |
f1644114-bbdc-435e-a5b6-e9a658e445e1 | twin-identification-over-viewpoint-change-a | 2207.05316 | null | https://arxiv.org/abs/2207.05316v1 | https://arxiv.org/pdf/2207.05316v1.pdf | Twin identification over viewpoint change: A deep convolutional neural network surpasses humans | Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participant... | ["Alice J. O'Toole", 'Carlos D. Castillo', 'Jacqueline G. Cavazos', 'Ying Hu', 'Vivekjyoti Banerjee', 'Virginia E. Strehle', 'Connor J. Parde'] | 2022-07-12 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 1.88853279e-01 -2.42125943e-01 2.61107177e-01 -7.43142247e-01
-2.42691383e-01 -8.46583664e-01 7.61262059e-01 -3.19964178e-02
-5.67471862e-01 1.28110170e-01 1.99214322e-03 6.28252327e-02
-5.06105535e-02 -5.75189948e-01 -2.44266808e-01 -3.64852011e-01
-1.45537704e-01 3.68597507e-01 -5.09198666e-01 -3.91929522... | [12.988640785217285, 1.203847885131836] |
f5a844bb-d70f-4298-9293-2e5de6db1bb6 | multi-class-model-fitting-by-energy | 1706.00827 | null | http://arxiv.org/abs/1706.00827v2 | http://arxiv.org/pdf/1706.00827v2.pdf | Multi-Class Model Fitting by Energy Minimization and Mode-Seeking | We propose a general formulation, called Multi-X, for multi-class
multi-instance model fitting - the problem of interpreting the input data as a
mixture of noisy observations originating from multiple instances of multiple
classes. We extend the commonly used alpha-expansion-based technique with a new
move in the label... | ['Jiri Matas', 'Daniel Barath'] | 2017-06-02 | multi-class-model-fitting-by-energy-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.pdf | eccv-2018-9 | ['motion-detection'] | ['computer-vision'] | [ 2.23182589e-01 -7.80595988e-02 -1.41465440e-01 -3.04326743e-01
-1.40008295e+00 -4.77136225e-01 3.84648472e-01 2.85080463e-01
-9.01453122e-02 3.83987069e-01 -3.60580236e-01 4.38320339e-02
-4.24641550e-01 -9.80530530e-02 -9.44689929e-01 -1.03836477e+00
1.41573489e-01 1.02040458e+00 3.43889236e-01 4.84511763... | [8.000880241394043, -2.6807377338409424] |
2b35bce5-0592-4634-bfca-7db874c81d87 | searching-for-efficient-multi-scale | 1809.04184 | null | http://arxiv.org/abs/1809.04184v1 | http://arxiv.org/pdf/1809.04184v1.pdf | Searching for Efficient Multi-Scale Architectures for Dense Image Prediction | The design of neural network architectures is an important component for
achieving state-of-the-art performance with machine learning systems across a
broad array of tasks. Much work has endeavored to design and build
architectures automatically through clever construction of a search space
paired with simple learning ... | ['Liang-Chieh Chen', 'Yukun Zhu', 'Jonathon Shlens', 'Florian Schroff', 'Maxwell D. Collins', 'Barret Zoph', 'Hartwig Adam', 'George Papandreou'] | 2018-09-11 | searching-for-efficient-multi-scale-1 | http://papers.nips.cc/paper/8087-searching-for-efficient-multi-scale-architectures-for-dense-image-prediction | http://papers.nips.cc/paper/8087-searching-for-efficient-multi-scale-architectures-for-dense-image-prediction.pdf | neurips-2018-12 | ['street-scene-parsing'] | ['computer-vision'] | [ 5.29579282e-01 3.36133838e-01 -7.56266043e-02 -6.14671707e-01
-9.35241461e-01 -3.01338196e-01 5.49598932e-01 -1.75545007e-01
-6.61720932e-01 3.25302124e-01 -8.33482146e-02 -2.92412728e-01
-8.13245401e-03 -7.55036652e-01 -8.73141170e-01 -3.01340431e-01
6.83331564e-02 9.23220992e-01 3.88223022e-01 -1.05880484... | [9.640812873840332, 0.8921961188316345] |
4213fefe-6de6-4f53-a35b-30be6b9c1617 | time-series-anomaly-detection-based-on | 2303.17802 | null | https://arxiv.org/abs/2303.17802v2 | https://arxiv.org/pdf/2303.17802v2.pdf | Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces | This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA). The key idea is to monitor slight temporal variations of the difference subspace between two signal subspaces corresponding to the past and present ti... | ['Kazuhiro Fukui', 'Atsuto Maki', 'Naoya Sogi', 'Takumi Kanai'] | 2023-03-31 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 1.48339465e-01 -5.98597944e-01 1.70216650e-01 -8.86322260e-02
-2.01960847e-01 -7.29497373e-01 4.69223529e-01 2.10275471e-01
-3.31421802e-03 3.92159857e-02 2.64585525e-01 -1.98404536e-01
-2.54347116e-01 -5.66895366e-01 -2.86663920e-01 -5.89232087e-01
-6.76456213e-01 -5.21036565e-01 3.89454305e-01 -6.16176784... | [7.245658874511719, 3.01836895942688] |
8caaaacc-3504-4f8e-ab17-7eb28e3055fc | counterfactual-collaborative-reasoning | 2307.00165 | null | https://arxiv.org/abs/2307.00165v1 | https://arxiv.org/pdf/2307.00165v1.pdf | Counterfactual Collaborative Reasoning | Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and ex... | ['Yongfeng Zhang', 'Hao Wang', 'Yingqiang Ge', 'Juntao Tan', 'Max Xiong', 'Shuyuan Xu', 'Zelong Li', 'Jianchao Ji'] | 2023-06-30 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 6.09431043e-02 7.36681819e-01 -5.43907821e-01 -4.42045510e-01
6.26322106e-02 -2.83139825e-01 6.89141154e-01 -2.48582706e-01
-2.29421586e-01 1.08018219e+00 6.50663197e-01 -8.38817775e-01
-3.27773780e-01 -9.56363440e-01 -6.57848001e-01 -2.64067709e-01
1.95770785e-01 1.62579075e-01 -4.51624632e-01 -4.49035496... | [9.384330749511719, 5.697742938995361] |
45c2a052-1826-40e2-84da-c529fa649120 | diffbfr-bootstrapping-diffusion-model-towards | 2305.04517 | null | https://arxiv.org/abs/2305.04517v1 | https://arxiv.org/pdf/2305.04517v1.pdf | DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration | Blind face restoration (BFR) is important while challenging. Prior works prefer to exploit GAN-based frameworks to tackle this task due to the balance of quality and efficiency. However, these methods suffer from poor stability and adaptability to long-tail distribution, failing to simultaneously retain source identity... | ['Xuecheng Nie', 'Tiande Guo', 'Bonan Li', 'ZiCheng Zhang', 'Congying Han', 'Xinmin Qiu'] | 2023-05-08 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 5.38487554e-01 1.18396692e-01 2.09477171e-01 -1.16115876e-01
-7.63513684e-01 -2.88057774e-01 5.06959856e-01 -6.55523896e-01
-1.95213541e-01 8.57661903e-01 2.14911014e-01 -9.12560374e-02
-1.19558960e-01 -9.83783960e-01 -8.85862529e-01 -1.23709667e+00
5.30267537e-01 2.56175362e-02 3.34576070e-02 -9.26663056... | [11.684853553771973, -1.7530957460403442] |
46e4b8c9-1327-4aeb-9ff5-ca76d88a49f7 | delivering-speaking-style-in-low-resource | 2211.08857 | null | https://arxiv.org/abs/2211.08857v2 | https://arxiv.org/pdf/2211.08857v2.pdf | Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints | Conveying the linguistic content and maintaining the source speech's speaking style, such as intonation and emotion, is essential in voice conversion (VC). However, in a low-resource situation, where only limited utterances from the target speaker are accessible, existing VC methods are hard to meet this requirement an... | ['Yuping Wang', 'Qiao Tian', 'Yuanzhe Chen', 'Lei Xie', 'Xinsheng Wang', 'Zhichao Wang'] | 2022-11-16 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [-1.82356425e-02 -3.07069182e-01 -2.39971548e-01 -3.53324920e-01
-5.99960923e-01 -2.67766476e-01 1.70036957e-01 -2.63862044e-01
-1.55840456e-01 5.56913793e-01 4.29346383e-01 -2.21714661e-01
1.11665845e-01 -3.71996939e-01 -2.36463457e-01 -6.90314233e-01
6.57112777e-01 -2.39724725e-01 -1.71637133e-01 -9.47655141... | [14.909741401672363, 6.450470447540283] |
4699bcac-3887-4f96-8881-44a546a63066 | unified-named-entity-recognition-as-word-word | 2112.10070 | null | https://arxiv.org/abs/2112.10070v1 | https://arxiv.org/pdf/2112.10070v1.pdf | Unified Named Entity Recognition as Word-Word Relation Classification | So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Cu... | ['Fei Li', 'Donghong Ji', 'Chong Teng', 'Meishan Zhang', 'Shengqiong Wu', 'Jiang Liu', 'Hao Fei', 'Jingye Li'] | 2021-12-19 | null | null | null | null | ['relation-classification', 'nested-named-entity-recognition', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-7.69518986e-02 -9.06748250e-02 -1.29607946e-01 -2.37334758e-01
-9.21515703e-01 -5.81913650e-01 4.03659672e-01 2.45608553e-01
-7.56313384e-01 7.98219621e-01 4.84610945e-01 -6.91656530e-01
-9.06798765e-02 -8.98608088e-01 -4.57446069e-01 -5.63387513e-01
7.39943981e-02 4.46193278e-01 1.95275515e-01 -3.08929741... | [9.641348838806152, 9.532496452331543] |
8733f9a3-55e6-4170-b52a-8944cb1cccef | transition-based-semantic-dependency-parsing | 2005.13344 | null | https://arxiv.org/abs/2005.13344v2 | https://arxiv.org/pdf/2005.13344v2.pdf | Transition-based Semantic Dependency Parsing with Pointer Networks | Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task. In order to further test the capabilities of these powerful neural networks on a harder NLP problem, w... | ['Carlos Gómez-Rodríguez', 'Daniel Fernández-González'] | 2020-05-27 | null | null | null | null | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 3.99235114e-02 5.85881174e-01 -2.86657333e-01 -4.08523589e-01
-6.85620248e-01 -6.96904242e-01 5.01270771e-01 3.75909120e-01
-7.00357735e-01 5.60096323e-01 5.40368855e-01 -9.79407787e-01
-1.42619731e-02 -9.94154453e-01 -6.84537470e-01 -2.10141182e-01
-3.94773096e-01 9.22443390e-01 5.41187465e-01 -2.69886255... | [10.326864242553711, 9.586577415466309] |
dd3d8cfc-481f-4d83-9644-a19972608ec4 | pixel-wise-orthogonal-decomposition-for-color | 1407.0010 | null | http://arxiv.org/abs/1407.0010v2 | http://arxiv.org/pdf/1407.0010v2.pdf | Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image | In this paper, we propose a novel, effective and fast method to obtain a
color illumination invariant and shadow-free image from a single outdoor image.
Different from state-of-the-art methods for shadow-free image that either need
shadow detection or statistical learning, we set up a linear equation set for
each pixel... | ['Zhi Han', 'Jiandong Tian', 'Yandong Tang', 'Liangqiong Qu'] | 2014-06-30 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 5.53790987e-01 -6.23718917e-01 2.85628974e-01 -3.83707583e-01
-9.52993184e-02 -4.40662712e-01 1.62255064e-01 -7.82328665e-01
-1.76695675e-01 7.32751667e-01 -1.75657541e-01 -3.27698618e-01
-5.07981926e-02 -6.97004914e-01 -4.77708012e-01 -1.16281939e+00
2.76211470e-01 -1.58691645e-01 5.97417533e-01 -2.98444837... | [10.501219749450684, -2.77299427986145] |
e25c608d-3968-472d-88ef-5908201d8715 | partial-domain-adaptation-using-selective | 2101.02275 | null | https://arxiv.org/abs/2101.02275v1 | https://arxiv.org/pdf/2101.02275v1.pdf | Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation | The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most real-world scenarios where data collection and their annotation involve huge cost. Various domain adaptation techniques exist in literature that bridge th... | ['Hemanth Venkateswara', 'Baoxin Li', 'Arunabha Sen', 'Riti Paul', 'Sandipan Choudhuri'] | 2021-01-06 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.02356112e-01 2.65256405e-01 -3.57361287e-01 -8.17905366e-01
-5.36037385e-01 -5.70238590e-01 3.39851201e-01 3.18143666e-01
-3.96731317e-01 7.34033287e-01 -2.24609852e-01 2.36783892e-01
9.37871709e-02 -5.94420671e-01 -7.22022474e-01 -8.74471903e-01
3.35191071e-01 6.35225296e-01 2.55555451e-01 1.27990335... | [10.324519157409668, 3.117658853530884] |
3931924f-3d0f-4d16-a6da-baf81cfd749d | neural-joint-entropy-estimation | 2012.11197 | null | https://arxiv.org/abs/2012.11197v1 | https://arxiv.org/pdf/2012.11197v1.pdf | Neural Joint Entropy Estimation | Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields. This problem has many applications in various domains, including machine learning, statistics and data compression. Over the years, a variety of estimation schemes have been suggested. However, despit... | ['Irad Ben-Gal', 'Amichai Painsky', 'Yuval Shalev'] | 2020-12-21 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.40538377e-01 4.70616333e-02 -1.89175487e-01 -5.91971099e-01
-4.90551621e-01 -1.76838428e-01 3.58237118e-01 3.94145131e-01
-6.86678886e-01 1.33378291e+00 -1.99261695e-01 -1.94670752e-01
-5.39422810e-01 -8.70762765e-01 -4.89775062e-01 -5.82157850e-01
-4.84697759e-01 2.69576520e-01 5.49083240e-02 5.45504689... | [7.636730670928955, 3.7512238025665283] |
53b5869f-dc3b-4cd1-8f7d-c89a664dcdfb | fcl-gan-a-lightweight-and-real-time-baseline | 2204.07820 | null | https://arxiv.org/abs/2204.07820v2 | https://arxiv.org/pdf/2204.07820v2.pdf | FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring | Blind image deblurring (BID) remains a challenging and significant task. Benefiting from the strong fitting ability of deep learning, paired data-driven supervised BID method has obtained great progress. However, paired data are usually synthesized by hand, and the realistic blurs are more complex than synthetic ones, ... | ['Meng Wang', 'Yi Yang', 'Mingliang Xu', 'Richang Hong', 'Zhao Zhang', 'Suiyi Zhao'] | 2022-04-16 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.46442205e-01 -4.60312992e-01 -9.71047431e-02 -2.14457706e-01
-5.42846382e-01 -4.29682434e-01 6.35726392e-01 -8.98231924e-01
-1.31472781e-01 8.84937108e-01 4.13026482e-01 6.83009764e-03
-2.85950303e-01 -4.70452517e-01 -5.41993678e-01 -9.89605546e-01
4.24988300e-01 -1.79863274e-01 2.02547893e-01 -1.30839646... | [11.422733306884766, -2.608238458633423] |
2c32c64b-a033-48c0-a132-e78fc33a8910 | patch-based-progressive-3d-point-set | 1811.11286 | null | http://arxiv.org/abs/1811.11286v3 | http://arxiv.org/pdf/1811.11286v3.pdf | Patch-based Progressive 3D Point Set Upsampling | We present a detail-driven deep neural network for point set upsampling. A
high-resolution point set is essential for point-based rendering and surface
reconstruction. Inspired by the recent success of neural image super-resolution
techniques, we progressively train a cascade of patch-based upsampling networks
on diffe... | ['Olga Sorkine-Hornung', 'Daniel Cohen-Or', 'Shihao Wu', 'Hui Huang', 'Wang Yifan'] | 2018-11-27 | patch-based-progressive-3d-point-set-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yifan_Patch-Based_Progressive_3D_Point_Set_Upsampling_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yifan_Patch-Based_Progressive_3D_Point_Set_Upsampling_CVPR_2019_paper.pdf | cvpr-2019-6 | ['point-cloud-super-resolution', 'point-set-upsampling'] | ['computer-vision', 'computer-vision'] | [ 4.77932245e-01 1.28881857e-01 3.07037354e-01 -2.86562592e-01
-1.22432959e+00 -1.89369656e-02 6.50084496e-01 -3.04941297e-01
1.67461634e-01 6.88259065e-01 2.53040433e-01 5.31431427e-03
-9.59759951e-02 -1.16054296e+00 -1.11060059e+00 -1.14405632e-01
-1.39562666e-01 2.99238205e-01 3.10037404e-01 -5.58411837... | [9.181977272033691, -3.2107176780700684] |
373e651a-4ce9-412b-b87f-2df4d3844118 | provably-learning-nash-policies-in | 2306.07749 | null | https://arxiv.org/abs/2306.07749v1 | https://arxiv.org/pdf/2306.07749v1.pdf | Provably Learning Nash Policies in Constrained Markov Potential Games | Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective. In many real-world instances, the agents may not only want to optimize their objectives, but also ensure safe behavior. For example, in traffic routing, each car (a... | ['Andreas Krause', 'Niao He', 'Giorgia Ramponi', 'Pragnya Alatur'] | 2023-06-13 | null | null | null | null | ['multi-agent-reinforcement-learning', 'safe-exploration'] | ['methodology', 'robots'] | [-2.30163574e-01 5.07813811e-01 -6.30566001e-01 3.77242237e-01
-1.17638946e+00 -7.72209466e-01 3.39537114e-01 2.31556684e-01
-5.95127523e-01 1.42274296e+00 -1.18020765e-01 -7.37866938e-01
-4.81070101e-01 -9.70305264e-01 -7.97678351e-01 -9.82800066e-01
-5.14707386e-01 9.57107842e-01 4.00221273e-02 -4.17394012... | [4.217959880828857, 2.5429840087890625] |
66532f5b-b961-4656-9eee-22c0369626a7 | evaluating-neural-morphological-taggers-for | 2005.10893 | null | https://arxiv.org/abs/2005.10893v1 | https://arxiv.org/pdf/2005.10893v1.pdf | Evaluating Neural Morphological Taggers for Sanskrit | Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that expl... | ['Amrith Krishna', 'Ashim Gupta', 'Oliver Hellwig', 'Pawan Goyal'] | 2020-05-21 | evaluating-neural-morphological-taggers-for-1 | https://aclanthology.org/2020.sigmorphon-1.23 | https://aclanthology.org/2020.sigmorphon-1.23.pdf | ws-2020-7 | ['morphological-tagging'] | ['natural-language-processing'] | [ 4.11014199e-01 2.08055556e-01 -1.44860089e-01 -5.43822050e-01
-4.50624287e-01 -1.05626166e+00 6.58228874e-01 4.11970496e-01
-6.94775403e-01 8.74740362e-01 3.93947542e-01 -7.07440317e-01
1.60671279e-01 -4.43134248e-01 -2.58386672e-01 -5.10231435e-01
7.07755387e-02 7.94478714e-01 3.86118203e-01 -3.10629666... | [10.327393531799316, 10.023092269897461] |
e900110f-854d-4525-a324-0621fd63e9ee | density-based-feasibility-learning-with | 2307.01317 | null | https://arxiv.org/abs/2307.01317v2 | https://arxiv.org/pdf/2307.01317v2.pdf | Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly | Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.e. whether they are feasible or not, to circumvent potential efficiency degradation. Previous works need both feasible and infeasible examples during training. However, the infeasible ones ar... | ['Rudolph Triebel', 'Stephan Günnemann', 'Maximilian Durner', 'Ismael Rodríguez', 'Matan Atad', 'Jianxiang Feng'] | 2023-07-03 | null | null | null | null | ['ood-detection'] | ['computer-vision'] | [ 1.82619944e-01 1.08067408e-01 -6.62699997e-01 -2.49011174e-01
-6.74786806e-01 -9.10969973e-01 2.37792164e-01 7.17309117e-02
1.10874966e-01 6.57531261e-01 6.01291656e-03 -6.44460320e-01
-1.29692793e-01 -7.25438774e-01 -1.05473912e+00 -4.28233206e-01
-1.15369096e-01 6.59212410e-01 -7.64162764e-02 1.05085284... | [4.996531009674072, 2.7051491737365723] |
e99aac64-0728-44b5-89d7-f3a98efa30c0 | didn-t-see-that-coming-a-survey-on-non-verbal | 2203.02480 | null | https://arxiv.org/abs/2203.02480v1 | https://arxiv.org/pdf/2203.02480v1.pdf | Didn't see that coming: a survey on non-verbal social human behavior forecasting | Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for mu... | ['Cristina Palmero', 'Isabelle Guyon', 'Wei-Wei Tu', 'Zhen Xu', 'Sergio Escalera', 'Johnny Núñez', 'German Barquero'] | 2022-03-04 | null | null | null | null | ['human-behavior-forecasting'] | ['time-series'] | [ 2.48163402e-01 4.21683639e-01 -1.12820745e-01 -3.25311542e-01
-2.10542828e-01 -3.06315631e-01 9.55676138e-01 -1.26787603e-01
-4.81643647e-01 8.54806602e-01 6.36722863e-01 4.69459802e-01
-2.96381056e-01 -2.95805365e-01 -3.85434814e-02 -1.03960037e+00
-6.96656525e-01 4.71733510e-01 2.88310111e-01 -6.19218230... | [7.184111595153809, 0.0874871015548706] |
74e11a3d-4c4e-47aa-9cc1-86eb2486374f | robust-boosting-forests-with-richer-deep | 2210.16451 | null | https://arxiv.org/abs/2210.16451v1 | https://arxiv.org/pdf/2210.16451v1.pdf | Robust Boosting Forests with Richer Deep Feature Hierarchy | We propose a robust variant of boosting forest to the various adversarial defense methods, and apply it to enhance the robustness of the deep neural network. We retain the deep network architecture, weights, and middle layer features, then install gradient boosting forest to select the features from each layer of the d... | ['Jianqiao Wangni'] | 2022-10-29 | null | null | null | null | ['adversarial-defense', 'face-model'] | ['adversarial', 'computer-vision'] | [ 8.24313909e-02 3.81987751e-01 1.06678233e-01 -5.25977135e-01
-2.38759592e-01 -7.23132432e-01 4.11970645e-01 -6.46331489e-01
-1.77175879e-01 7.26628244e-01 -2.77406834e-02 -6.91075206e-01
1.24842837e-01 -1.00937736e+00 -8.09137881e-01 -1.07823002e+00
-3.95060778e-01 -6.97216466e-02 1.46828289e-03 -2.69604385... | [5.524381160736084, 7.902141571044922] |
291661e7-233e-4765-bcd9-474a9d719d0a | twitter-sentiment-analysis | 1509.04219 | null | http://arxiv.org/abs/1509.04219v1 | http://arxiv.org/pdf/1509.04219v1.pdf | Twitter Sentiment Analysis | This project addresses the problem of sentiment analysis in twitter; that is
classifying tweets according to the sentiment expressed in them: positive,
negative or neutral. Twitter is an online micro-blogging and social-networking
platform which allows users to write short status updates of maximum length 140
character... | ['Afroze Ibrahim Baqapuri'] | 2015-09-14 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.63326725e-01 1.68717146e-01 -6.74372256e-01 -4.27140534e-01
-2.32365765e-02 -7.89345086e-01 9.24389124e-01 8.58506739e-01
-4.99314874e-01 6.48511410e-01 4.73388672e-01 -5.05967736e-01
7.67049909e-01 -1.12545276e+00 -9.12081636e-03 -3.66688818e-01
1.43110633e-01 4.25828457e-01 1.03926703e-01 -9.42597449... | [10.818168640136719, 6.934776782989502] |
5e5b4938-ad83-4d94-a4dd-e1a0608c5f61 | generative-adversarial-networks-for-dental | 2307.02019 | null | https://arxiv.org/abs/2307.02019v1 | https://arxiv.org/pdf/2307.02019v1.pdf | Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging | Objectives: This research introduces a novel area-preserving Generative Adversarial Networks (GAN) inversion technique for effectively de-identifying dental patient images. This innovative method addresses privacy concerns while preserving key dental features, thereby generating valuable resources for dental education ... | ['Eugene Loh', 'Kelvin Weng Chiong Foong', 'Wilson Weixun Lu', 'Mingchuan Tian'] | 2023-07-05 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 4.26657379e-01 6.63877785e-01 -1.75961941e-01 -3.68778229e-01
-1.31044960e+00 -3.35955799e-01 6.31020367e-02 -1.48544744e-01
-5.02752438e-02 4.14192408e-01 2.63870448e-01 -2.82817602e-01
2.82626357e-02 -8.22726488e-01 -4.61767346e-01 -1.04370224e+00
2.29124948e-01 3.53394836e-01 -3.86976600e-01 5.29203145... | [13.906986236572266, -1.7960929870605469] |
16ee1030-fd08-4a3f-b9f9-f7a5d9edd788 | 3d-multi-object-tracking-based-on-uncertainty | 2303.01786 | null | https://arxiv.org/abs/2303.01786v1 | https://arxiv.org/pdf/2303.01786v1.pdf | 3D Multi-Object Tracking Based on Uncertainty-Guided Data Association | In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage. Namely, the inherent uncertainties existing in tracks and detections are overlooked. In this work, we d... | ['Xiyang Wang', 'Chunyun Fu', 'JiaWei He'] | 2023-03-03 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.57178092e-01 -6.38773024e-01 -6.36194646e-02 -6.81387782e-02
-5.20912111e-01 -7.50596523e-01 6.00844204e-01 2.49127746e-01
-3.20147902e-01 7.51432598e-01 -2.15017781e-01 -1.24032609e-01
-4.28560078e-01 -7.38803804e-01 -5.71734071e-01 -8.52730393e-01
3.15438434e-02 4.76699978e-01 5.97561240e-01 3.03817511... | [6.545280456542969, -2.0419468879699707] |
9937a907-790e-4cfc-9ccc-c10e525847f1 | face-evolve-a-high-performance-face | 2107.08621 | null | https://arxiv.org/abs/2107.08621v4 | https://arxiv.org/pdf/2107.08621v4.pdf | Face.evoLVe: A High-Performance Face Recognition Library | In this paper, we develop face.evoLVe -- a comprehensive library that collects and implements a wide range of popular deep learning-based methods for face recognition. First of all, face.evoLVe is composed of key components that cover the full process of face analytics, including face alignment, data processing, variou... | ['Jian Zhao', 'Haoyi Xiong', 'Pengfei Zhang', 'Qingzhong Wang'] | 2021-07-19 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-5.00837624e-01 -2.87978500e-01 6.41083391e-03 -4.89153713e-01
-6.04762375e-01 -4.48722363e-01 3.36709023e-01 -3.38684857e-01
-1.72329426e-01 2.59695023e-01 -6.39524683e-02 -1.90372586e-01
4.87076864e-02 -5.66064954e-01 -5.85516989e-01 -6.43070281e-01
-2.00964019e-01 6.43096983e-01 -3.19662631e-01 -3.05459321... | [13.295279502868652, 0.7324897646903992] |
968ef101-bbc4-4b7c-9a7c-0d937b4380a6 | stage-conscious-attention-network-scan-a | 2112.02278 | null | https://arxiv.org/abs/2112.02278v1 | https://arxiv.org/pdf/2112.02278v1.pdf | Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation | In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve unseen tasks with few expert demonstrations becomes a popular research direction. The following capabilities are essential in robotics applications: (1) Behaving in compound tasks that contain multiple stages. (2) Retrieving knowledge from fe... | ['Winston H. Hsu', 'Yi-Ting Chen', 'Hung-Ting Su', 'Chi-Ming Chung', 'Jia-Fong Yeh'] | 2021-12-04 | null | null | null | null | ['few-shot-imitation-learning'] | ['methodology'] | [-2.80587710e-02 4.72921915e-02 5.57037108e-02 -2.65327469e-02
-4.25521702e-01 -5.45840263e-01 5.18537819e-01 -8.16005826e-01
-5.82554460e-01 8.59608114e-01 -1.08224489e-01 -1.61556918e-02
-1.50115803e-01 -4.73877266e-02 -9.43534195e-01 -8.19538772e-01
-1.63780183e-01 6.07946694e-01 4.08666044e-01 -1.77765548... | [4.33360481262207, 1.1033991575241089] |
8969a0ac-bc9e-4fd4-8a40-36a7f765f956 | imperfect-segmentation-labels-how-much-do | 1806.04618 | null | http://arxiv.org/abs/1806.04618v3 | http://arxiv.org/pdf/1806.04618v3.pdf | Imperfect Segmentation Labels: How Much Do They Matter? | Labeled datasets for semantic segmentation are imperfect, especially in
medical imaging where borders are often subtle or ill-defined. Little work has
been done to analyze the effect that label errors have on the performance of
segmentation methodologies. Here we present a large-scale study of model
performance in the ... | ['Joshua Dean', 'Nikolaos Papanikolopoulos', 'Nicholas Heller'] | 2018-06-12 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 2.12820679e-01 3.02723527e-01 -1.31882995e-01 -6.11630321e-01
-1.17161071e+00 -7.23767340e-01 2.03397229e-01 3.45025510e-01
-3.38852733e-01 8.26334894e-01 1.79314151e-01 -5.30500710e-01
1.74987733e-01 -3.34050566e-01 -8.48141193e-01 -6.99972272e-01
-2.13148177e-01 7.05242157e-01 2.88655609e-01 2.64598906... | [14.735795974731445, -2.2806904315948486] |
62a7a060-835f-4e65-aaac-674b933b2707 | skillqg-learning-to-generate-question-for | 2305.04737 | null | https://arxiv.org/abs/2305.04737v1 | https://arxiv.org/pdf/2305.04737v1.pdf | SkillQG: Learning to Generate Question for Reading Comprehension Assessment | We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Existing question generation systems widely differentiate questions by $\textit{literal}$ information such as question words and answer types ... | ['Lingfei Wu', 'Siliang Tang', 'Bang Liu', 'Xiaoqiang Wang'] | 2023-05-08 | null | null | null | null | ['reading-comprehension', 'question-generation', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 6.15677714e-01 5.31538785e-01 3.07326615e-01 -6.28866196e-01
-1.17318976e+00 -8.82268131e-01 4.99743193e-01 3.58732164e-01
-1.79088920e-01 5.86993039e-01 5.63981652e-01 -7.32599318e-01
-3.47547561e-01 -1.36914015e+00 -7.38528907e-01 -1.24852359e-02
4.56296712e-01 6.10383272e-01 3.22172672e-01 -8.37636590... | [11.397351264953613, 8.103897094726562] |
938bdf9e-c3da-465a-9b42-9f73961b0152 | improving-aspect-extraction-based-on-rules | null | null | https://openreview.net/forum?id=20Lgo1A-aSh | https://openreview.net/pdf?id=20Lgo1A-aSh | Improving Aspect Extraction based on Rules through Deep Syntax-Semantics Communication | Recent studies show integrating language resources which consist of lexical resources, syntactic resources and semantic resources can improve the performance of natural language processing (NLP) tasks. The existing methods mostly perform simple integration through concatenating these resources successively, seldom con... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['term-extraction', 'aspect-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-3.85663748e-01 -1.17378412e-02 -4.89485204e-01 -2.88663685e-01
-2.91063935e-01 -5.56647182e-01 5.24642646e-01 2.40176871e-01
-5.69434702e-01 6.60057485e-01 5.54269671e-01 -5.44352710e-01
-1.94128275e-01 -1.16951787e+00 -3.47239941e-01 -7.67858922e-02
1.27718955e-01 4.12501007e-01 6.48627162e-01 -5.11363387... | [9.928942680358887, 8.898032188415527] |
5225bb0a-f4d9-41b8-844e-bcb48e104db8 | msw-transformer-multi-scale-shifted-windows | 2306.12098 | null | https://arxiv.org/abs/2306.12098v1 | https://arxiv.org/pdf/2306.12098v1.pdf | MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification | Automatic classification of electrocardiogram (ECG) signals plays a crucial role in the early prevention and diagnosis of cardiovascular diseases. While ECG signals can be used for the diagnosis of various diseases, their pathological characteristics exhibit minimal variations, posing a challenge to automatic classific... | ['Jingfeng Guo', 'Lei Xie', 'Shuxin Zhuang', 'Zhemin Zhuang', 'Renjie Cheng'] | 2023-06-21 | null | null | null | null | ['ecg-classification', 'classification-1'] | ['medical', 'methodology'] | [ 1.90557390e-01 -4.76916879e-01 -7.31764734e-02 -4.04782534e-01
-7.78302252e-01 -2.73814499e-01 -8.61987993e-02 2.59238541e-01
-2.88712502e-01 6.29939616e-01 -4.24789004e-02 -3.00175339e-01
-4.50384468e-01 -6.09818399e-01 -1.17086865e-01 -8.26926231e-01
-3.71310860e-01 2.20862195e-01 1.53822392e-01 -1.66078046... | [14.270788192749023, 3.232184648513794] |
a13efb84-7e48-4f02-99aa-91f0320c0f1a | damo-nlp-at-semeval-2022-task-11-a-knowledge | 2203.00545 | null | https://arxiv.org/abs/2203.00545v3 | https://arxiv.org/pdf/2203.00545v3.pdf | DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition | The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team DAMO-NLP proposes a knowledge-based system, where... | ['Yong Jiang', 'Wei Lu', 'Kewei Tu', 'Yueting Zhuang', 'Weiming Lu', 'Fei Huang', 'Pengjun Xie', 'Xiaobin Wang', 'Tao Wang', 'Jiong Cai', 'Yongliang Shen', 'Xinyu Wang'] | 2022-03-01 | null | https://aclanthology.org/2022.semeval-1.200 | https://aclanthology.org/2022.semeval-1.200.pdf | semeval-naacl-2022-7 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-2.05329508e-01 -6.64310828e-02 -1.93348899e-01 -4.91031796e-01
-1.32798600e+00 -1.15953434e+00 4.30354536e-01 3.97356987e-01
-9.98697221e-01 1.05991232e+00 6.51167452e-01 -2.25707605e-01
7.32004121e-02 -7.59456396e-01 -5.77105105e-01 7.32270703e-02
1.09343484e-01 5.81013083e-01 2.63090640e-01 -5.98599792... | [9.776711463928223, 9.531672477722168] |
468f9ea5-8d21-4760-90bc-525487c5ba03 | vietnamese-word-segmentation-with-svm | 2006.07804 | null | https://arxiv.org/abs/2006.07804v1 | https://arxiv.org/pdf/2006.07804v1.pdf | Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix Capture | In this paper, we approach Vietnamese word segmentation as a binary classification by using the Support Vector Machine classifier. We inherit features from prior works such as n-gram of syllables, n-gram of syllable types, and checking conjunction of adjacent syllables in the dictionary. We propose two novel ways to fe... | ['Ngan Luu-Thuy Nguyen', 'Duc-Vu Nguyen', 'Kiet Van Nguyen', 'Dang Van Thin'] | 2020-06-14 | null | null | null | null | ['vietnamese-word-segmentation', 'vietnamese-datasets'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.79231137e-01 -2.82382786e-01 -3.76992136e-01 -3.44414651e-01
-3.22631329e-01 -8.87601674e-01 4.26145881e-01 3.54572952e-01
-8.79694283e-01 8.23404908e-01 -2.25262165e-01 -6.88724875e-01
1.91211030e-01 -9.15620267e-01 -3.10787857e-01 -6.89537764e-01
1.26736239e-01 5.39474905e-01 5.71201801e-01 -4.71721828... | [10.263628005981445, 10.143024444580078] |
ae72ffd5-987a-460c-a2cf-cc33d9861474 | boundary-aware-information-maximization-for | 2202.02371 | null | https://arxiv.org/abs/2202.02371v2 | https://arxiv.org/pdf/2202.02371v2.pdf | Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation | Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns a discriminative representation by constructing positive and negative pairs. However, it is not trivial to build reasonable pairs for a segm... | ['Christian Desrosiers', 'Marco Pedersoli', 'Ping Wang', 'Jizong Peng'] | 2022-02-04 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 5.96357644e-01 2.24717379e-01 -5.19708455e-01 -8.25006187e-01
-1.10465086e+00 -3.53170365e-01 4.36352581e-01 2.06415787e-01
-7.15935588e-01 7.24482000e-01 1.29278572e-02 -2.45470092e-01
2.11503245e-02 -6.53956532e-01 -5.09798527e-01 -8.28067482e-01
1.80563822e-01 4.85692441e-01 4.17278111e-01 -4.75702621... | [14.741292953491211, -2.0901145935058594] |
4b3a4f9d-7c07-4567-9042-bc845f9a10d9 | benchmarking-scalable-methods-for-streaming | null | null | https://aclanthology.org/2021.acl-long.364 | https://aclanthology.org/2021.acl-long.364.pdf | Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference | Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time. T... | ['Dan Bikel', 'Sameer Singh', 'Andrew McCallum', 'Robert L Logan IV'] | 2021-08-01 | null | null | null | acl-2021-5 | ['entity-disambiguation'] | ['natural-language-processing'] | [-0.14950727 0.10599585 -0.26451612 -0.37802586 -0.95764047 -0.99253255
0.8093878 0.86304474 -0.82890904 0.88727623 0.57304615 -0.16141509
-0.21146601 -0.8097355 -0.752261 -0.17745863 -0.4778144 0.78119683
0.54673487 -0.14367571 0.06183771 0.45603278 -1.4442861 0.01690047
0.85429186 0.47433433 -0.... | [9.372690200805664, 8.934479713439941] |
804cba3a-7572-4845-b8fd-44fbc4d33ab0 | contrasting-centralized-and-decentralized | 2102.04402 | null | https://arxiv.org/abs/2102.04402v2 | https://arxiv.org/pdf/2102.04402v2.pdf | Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning | Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community. In particular, actor-critic methods with a centralized critic and decentralized actors... | ['Christopher Amato', 'Brett Daley', 'Yuchen Xiao', 'Xueguang Lyu'] | 2021-02-08 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-5.69430470e-01 1.55278683e-01 -2.88656890e-01 -1.74356729e-01
-4.95149434e-01 -8.04304421e-01 6.24168634e-01 2.46417031e-01
-5.66752851e-01 7.63610661e-01 3.50753158e-01 -5.48948050e-01
-1.69294283e-01 -3.44714254e-01 -3.33824426e-01 -7.91917622e-01
-1.07632354e-01 6.31043375e-01 1.21938311e-01 -3.88591588... | [3.825136423110962, 2.0878751277923584] |
f26051ea-0f02-4850-a66b-1b6be67fae9c | csecu-dsg-at-semeval-2021-task-5-leveraging | null | null | https://aclanthology.org/2021.semeval-1.135 | https://aclanthology.org/2021.semeval-1.135.pdf | CSECU-DSG at SemEval-2021 Task 5: Leveraging Ensemble of Sequence Tagging Models for Toxic Spans Detection | The upsurge of prolific blogging and microblogging platforms enabled the abusers to spread negativity and threats greater than ever. Detecting the toxic portions substantially aids to moderate or exclude the abusive parts for maintaining sound online platforms. This paper describes our participation in the SemEval 2021... | ['Abu Nowshed Chy', 'Radiathun Tasnia', 'Fareen Tasneem', 'Jannatun Naim', 'Tashin Hossain'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-2.42217839e-01 -1.95084795e-01 -2.36406058e-01 -2.25278050e-01
-8.84686410e-01 -9.64884937e-01 4.21202451e-01 8.75584856e-02
-6.34824753e-01 8.78000975e-01 5.29686272e-01 -3.92615020e-01
4.28911656e-01 -4.44769502e-01 -5.68221249e-02 -2.93275297e-01
-2.44600579e-01 1.49509162e-01 -1.12897471e-01 -3.89445096... | [8.903630256652832, 10.617754936218262] |
ae631cc2-2dc3-429a-9bc8-a4117c14e144 | encoder-decoder-architecture-for-3d-seismic | 2207.14789 | null | https://arxiv.org/abs/2207.14789v1 | https://arxiv.org/pdf/2207.14789v1.pdf | Encoder-Decoder Architecture for 3D Seismic Inversion | Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For exampl... | ['Mauricio Araya-Polo', 'Yen Sun', 'Amir Adler', 'Maayan Gelboim'] | 2022-07-29 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 9.01046544e-02 6.73482791e-02 9.66243982e-01 -2.20678434e-01
-1.25253701e+00 -3.33669364e-01 5.28430194e-02 1.54413432e-01
-4.77374375e-01 3.00761044e-01 3.25577885e-01 -4.42457020e-01
-3.60891461e-01 -1.10346770e+00 -8.91701996e-01 -5.00673592e-01
-8.00952196e-01 5.29352903e-01 2.56010324e-01 -3.88978750... | [6.87230920791626, 2.506937265396118] |
427e2620-1503-46d3-aee5-8d18180fc1d1 | a-mountain-shaped-single-stage-network-for | 2305.05146 | null | https://arxiv.org/abs/2305.05146v1 | https://arxiv.org/pdf/2305.05146v1.pdf | A Mountain-Shaped Single-Stage Network for Accurate Image Restoration | Image restoration is the task of aiming to obtain a high-quality image from a corrupt input image, such as deblurring and deraining. In image restoration, it is typically necessary to maintain a complex balance between spatial details and contextual information. Although a multi-stage network can optimally balance thes... | ['Depeng Dang', 'Jingfan Yang', 'Ning Wang', 'Ying Zhang', 'Jing Yang', 'Hu Gao'] | 2023-05-09 | null | null | null | null | ['deblurring', 'single-image-deraining'] | ['computer-vision', 'computer-vision'] | [ 4.65018779e-01 -1.17313847e-01 2.63329782e-02 -9.72320959e-02
-5.77239513e-01 -1.15234070e-01 3.12685698e-01 -2.81090975e-01
-3.48111898e-01 5.87628663e-01 3.77343029e-01 -1.92621037e-01
1.33557156e-01 -7.39841104e-01 -8.49688649e-01 -8.97490919e-01
5.42775333e-01 -6.49976194e-01 4.24389809e-01 -2.33212799... | [11.204832077026367, -2.1805310249328613] |
1bce8d43-206e-4a14-bca1-506c20ba96ee | predicting-sector-index-movement-with | null | null | https://aclanthology.org/Y15-1065 | https://aclanthology.org/Y15-1065.pdf | Predicting Sector Index Movement with Microblogging Public Mood Time Series on Social Issues | null | ['Kotaro Sakamoto', 'Jinlong Guo', 'Hideyuki Shibuki', 'Tatsunori Mori', 'Yujie Lu'] | 2015-10-01 | predicting-sector-index-movement-with-1 | https://aclanthology.org/Y15-1065 | https://aclanthology.org/Y15-1065.pdf | paclic-2015-10 | ['stock-prediction'] | ['time-series'] | [-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... | [-7.406898021697998, 3.7285566329956055] |
f1c763b0-3f69-437d-9af3-1693aca198b7 | strong-generalization-and-efficiency-in | 2007.03629 | null | https://arxiv.org/abs/2007.03629v2 | https://arxiv.org/pdf/2007.03629v2.pdf | Strong Generalization and Efficiency in Neural Programs | We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction. By carefully designing the input / output interfaces of the neural model and through imitation, we are able to learn models that produce correct results for arbitrary input sizes, achieving stron... | ['Oriol Vinyals', 'Yujia Li', 'Felix Gimeno', 'Pushmeet Kohli'] | 2020-07-07 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.64125437e-01 1.39853433e-01 -4.77389246e-01 -1.36737481e-01
-5.74463904e-01 -9.63375747e-01 1.63566917e-01 5.02580345e-01
-8.04878771e-01 5.48001528e-01 -2.33840346e-01 -7.62987852e-01
-2.67874181e-01 -1.19113040e+00 -1.66143334e+00 -4.31468844e-01
-6.95861518e-01 7.14669526e-01 2.38611296e-01 -5.37032448... | [8.663511276245117, 7.28926944732666] |
f6b0520e-b415-4e1a-b05e-13ecaffd03fc | multi-target-tracking-and-occlusion-handling | 1511.01726 | null | http://arxiv.org/abs/1511.01726v1 | http://arxiv.org/pdf/1511.01726v1.pdf | Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model | This paper considers the problem of multiple human target tracking in a
sequence of video data. A solution is proposed which is able to deal with the
challenges of a varying number of targets, interactions and when every target
gives rise to multiple measurements. The developed novel algorithm comprises
variational Bay... | ['Ata-ur-Rehman', 'Syed Mohsen Naqvi', 'Lyudmila Mihaylova', 'Jonathon Chambers'] | 2015-11-05 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.84579432e-01 -4.72746640e-01 2.44159356e-01 -9.31788683e-02
-6.68551147e-01 -5.57932913e-01 1.08290505e+00 4.02506560e-01
-6.63010299e-01 8.55910122e-01 -1.23072363e-01 1.49828315e-01
-4.74639297e-01 -3.48639667e-01 -4.69526619e-01 -7.25020587e-01
-2.40690455e-01 1.04308164e+00 9.96906996e-01 4.92293090... | [6.586842060089111, -1.989241361618042] |
e4c5f796-af85-4e6d-a617-27eaf3796fa5 | 3d-speaker-a-large-scale-multi-device-multi | 2306.15354 | null | https://arxiv.org/abs/2306.15354v2 | https://arxiv.org/pdf/2306.15354v2.pdf | 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement | Disentangling uncorrelated information in speech utterances is a crucial research topic within speech community. Different speech-related tasks focus on extracting distinct speech representations while minimizing the affects of other uncorrelated information. We present a large-scale speech corpus to facilitate the res... | ['Qian Chen', 'Hui Wang', 'Yafeng Chen', 'Luyao Cheng', 'Siqi Zheng'] | 2023-06-27 | null | null | null | null | ['self-supervised-learning', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 3.72360423e-02 2.85324991e-01 -4.87924308e-01 -1.99033692e-01
-1.07159305e+00 -9.12162364e-01 9.27337229e-01 -3.98176610e-01
1.69134721e-01 3.01793545e-01 9.27212119e-01 -3.70566905e-01
-1.55867174e-01 -1.63786635e-01 -1.90039456e-01 -1.00022995e+00
-2.00166851e-01 7.02986121e-01 -3.42321664e-01 -3.22093725... | [14.809454917907715, 6.474897861480713] |
fc68eaac-6a1c-4cdd-a07d-fcfe3139fadb | spectral-spatial-classification-of-2 | null | null | https://doi.org/10.3390/rs9010067 | https://www.mdpi.com/2072-4292/9/1/67/pdf?version=1484303115 | Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network | Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the s... | ['Qiang Shen', 'Haokui Zhang', 'Ying Li'] | 2017-01-13 | null | null | null | remote-sensing-2017-1 | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.96882659e-01 -6.41199291e-01 2.66254216e-01 -4.06105459e-01
-6.56709433e-01 -2.97462493e-01 3.42738926e-01 -8.99140313e-02
-2.33469442e-01 5.53110540e-01 7.35518634e-02 -2.96041697e-01
-4.27822709e-01 -1.24689531e+00 -5.78968227e-01 -1.16560745e+00
-2.39487574e-01 -2.03867823e-01 6.12917319e-02 -1.95273772... | [9.91744327545166, -1.6334120035171509] |
9f587561-575d-4cd2-935b-919bcddbd5f5 | freeseg-unified-universal-and-open-vocabulary | 2303.17225 | null | https://arxiv.org/abs/2303.17225v1 | https://arxiv.org/pdf/2303.17225v1.pdf | FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation | Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios. However, existing methods devote to designing specialized architectures or parameters for specific segme... | ['Xingang Wang', 'Xin Pan', 'Shilei Wen', 'Rui Wang', 'Yitong Wang', 'Xuefeng Xiao', 'Ren Yuxi', 'Ming Li', 'Pengxiang Yan', 'Jie Wu', 'Jie Qin'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_FreeSeg_Unified_Universal_and_Open-Vocabulary_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_FreeSeg_Unified_Universal_and_Open-Vocabulary_Image_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.21595430e-01 -7.31254220e-02 -4.72407162e-01 -5.73715270e-01
-9.14022505e-01 -6.80967450e-01 3.95732075e-01 -1.77321345e-01
-5.71813107e-01 3.59026462e-01 -2.55453825e-01 -1.77222624e-01
-8.50761496e-03 -6.22911751e-01 -4.10634220e-01 -8.32495213e-01
2.45146185e-01 7.47780919e-01 6.06467068e-01 -1.62248164... | [9.648784637451172, 0.6401199102401733] |
d5d10fda-4565-4f5a-8fc6-0c5e42b36cd8 | quantum-gaussian-process-regression-for | 2304.12923 | null | https://arxiv.org/abs/2304.12923v1 | https://arxiv.org/pdf/2304.12923v1.pdf | Quantum Gaussian Process Regression for Bayesian Optimization | Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits. By employing a hardware-efficient feature map and careful regularization of the Gram matrix, we demonstrate that the... | ['Marco Roth', 'Frederic Rapp'] | 2023-04-25 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 3.01319718e-01 1.05737001e-01 1.63464829e-01 -1.52039781e-01
-1.16307724e+00 -4.62251216e-01 7.75035083e-01 9.02249664e-02
-6.57906294e-01 6.64650202e-01 -1.69781446e-01 -2.96227753e-01
-1.64464220e-01 -9.31379378e-01 -7.07317173e-01 -1.24363017e+00
3.22619021e-01 8.29142153e-01 2.47255355e-01 -7.55252466... | [5.637090682983398, 4.872980117797852] |
c5e3ef3c-7f1a-4d9c-9044-c8bfa1188eff | improving-human-image-synthesis-with-residual | 2205.12022 | null | https://arxiv.org/abs/2205.12022v2 | https://arxiv.org/pdf/2205.12022v2.pdf | Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance | With the rapid development of the Metaverse, virtual humans have emerged, and human image synthesis and editing techniques, such as pose transfer, have recently become popular. Most of the existing techniques rely on GANs, which can generate good human images even with large variants and occlusions. But from our best k... | ['Jing Xiao', 'Jianzong Wang', 'Shijing Si', 'Jianhan Wu'] | 2022-05-24 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.34675571e-01 -3.63794640e-02 1.27876818e-01 4.92029414e-02
-4.67931390e-01 -1.70035020e-01 4.56327856e-01 -7.34089613e-01
-1.71981975e-02 7.95885801e-01 1.42460540e-01 7.77504593e-02
4.41003829e-01 -8.46633255e-01 -5.46596944e-01 -8.48350644e-01
4.10612881e-01 1.59899354e-01 4.81513649e-01 -3.72236699... | [11.418187141418457, -0.8984583616256714] |
b8a5dc4c-59da-4d9d-940c-0e63aef50f49 | one-shot-imitation-learning-via-interaction | 2306.12392 | null | https://arxiv.org/abs/2306.12392v1 | https://arxiv.org/pdf/2306.12392v1.pdf | One-shot Imitation Learning via Interaction Warping | Imitation learning of robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration. We infer the 3D mesh of each object in the environment using shape warping, a technique for alig... | ['Robert Platt', 'Lawson L. S. Wong', 'Jan-Willem van de Meent', 'Thomas Kipf', 'Robin Walters', 'Elise van der Pol', 'Abhinav Kumar', 'Kishore Reddy Pagidi', 'Skye Thompson', 'Ondrej Biza'] | 2023-06-21 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-7.88646340e-02 1.75269201e-01 -9.04614776e-02 -1.05807118e-01
-4.41134870e-01 -7.89201736e-01 6.15649998e-01 -2.64300793e-01
-3.46945792e-01 7.62466550e-01 -2.15887874e-01 1.28703654e-01
-9.34650376e-02 -2.87576765e-01 -1.34603906e+00 -4.74604875e-01
-2.89318234e-01 1.10277128e+00 4.53722566e-01 -2.18993038... | [4.733774662017822, 0.5542905926704407] |
28c53dc0-e4fa-4002-b6c8-ab1d8745be1d | negvsr-augmenting-negatives-for-generalized | 2305.14669 | null | https://arxiv.org/abs/2305.14669v1 | https://arxiv.org/pdf/2305.14669v1.pdf | NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution | The capability of video super-resolution (VSR) to synthesize high-resolution (HR) video from ideal datasets has been demonstrated in many works. However, applying the VSR model to real-world video with unknown and complex degradation remains a challenging task. First, existing degradation metrics in most VSR methods ar... | ['Yukai Shi', 'Yuming Fan', 'Zhijing Yang', 'Xiaoyu Xian', 'Meilin Wang', 'Yexing Song'] | 2023-05-24 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.75568390e-01 -6.53615773e-01 9.00224224e-02 -1.38555542e-02
-8.87908161e-01 -2.04766750e-01 2.67164409e-01 -7.76952326e-01
-5.97647764e-02 9.05489981e-01 2.05862030e-01 1.40527681e-01
-8.86346176e-02 -5.37272155e-01 -5.59041560e-01 -7.75835812e-01
2.70843655e-01 -3.27453524e-01 3.65976721e-01 -5.67797065... | [11.092811584472656, -2.1140694618225098] |
23c10ff2-3b60-4850-bf23-807ae409d194 | encoder-decoder-based-unified-semantic-role | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/17514 | https://ojs.aaai.org/index.php/AAAI/article/view/17514/17321 | Encoder-decoder based unified semantic role labeling with label-aware syntax | Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests. Recent works show that leveraging the syntax knowledge significantly enhances the SRL performances. In this paper, we investigate a novel unified S... | ['Donghong Ji', 'Bobo Li', 'Fei Li', 'Hao Fei'] | 2021-05-18 | null | null | null | conference-2021-5 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.32987237e-01 4.23021138e-01 -4.34616029e-01 -6.25387788e-01
-8.91842365e-01 -8.88940752e-01 2.22388953e-01 1.90269500e-01
-3.44969511e-01 4.17393297e-01 6.64701164e-01 -5.42720854e-01
5.94695397e-02 -9.49905097e-01 -7.37275064e-01 -3.32926214e-01
2.44766235e-01 4.13434684e-01 6.39035404e-01 -4.07073110... | [10.328024864196777, 9.25981330871582] |
75d03e69-39ba-46fe-aa60-ab6d8d3ed0e0 | ditch-the-gold-standard-re-evaluating-1 | 2112.08812 | null | https://arxiv.org/abs/2112.08812v2 | https://arxiv.org/pdf/2112.08812v2.pdf | Ditch the Gold Standard: Re-evaluating Conversational Question Answering | Conversational question answering aims to provide natural-language answers to users in information-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can re... | ['Danqi Chen', 'Manan Goenka', 'Tianyu Gao', 'Huihan Li'] | 2021-12-16 | null | https://aclanthology.org/2022.acl-long.555 | https://aclanthology.org/2022.acl-long.555.pdf | acl-2022-5 | ['question-rewriting'] | ['natural-language-processing'] | [ 6.63927123e-02 7.07322061e-01 2.21577913e-01 -6.49043858e-01
-1.32370949e+00 -8.49042594e-01 1.05268836e+00 1.08885564e-01
-3.17177385e-01 7.93737769e-01 7.55695164e-01 -8.08772683e-01
1.17238350e-01 -5.83749592e-01 -3.95264365e-02 1.89277772e-02
3.39189559e-01 1.28251648e+00 4.71912205e-01 -9.92888510... | [12.062211990356445, 7.982449531555176] |
788f5bdb-f1ad-4ff3-bcaa-0d0e615fe0be | meta-sage-scale-meta-learning-scheduled | 2306.02688 | null | https://arxiv.org/abs/2306.02688v2 | https://arxiv.org/pdf/2306.02688v2.pdf | Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization | This paper proposes Meta-SAGE, a novel approach for improving the scalability of deep reinforcement learning models for combinatorial optimization (CO) tasks. Our method adapts pre-trained models to larger-scale problems in test time by suggesting two components: a scale meta-learner (SML) and scheduled adaptation with... | ['Jinkyoo Park', 'Hyeonah Kim', 'Minsu Kim', 'Jiwoo Son'] | 2023-06-05 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [-1.83214828e-01 -1.11355104e-01 -5.67774892e-01 -2.74465173e-01
-1.01445377e+00 -4.54849064e-01 3.25021118e-01 2.66580462e-01
-7.17099845e-01 8.52175415e-01 2.71527261e-01 -2.06588030e-01
-3.11777204e-01 -7.23099589e-01 -7.91653097e-01 -6.06192887e-01
-3.68940145e-01 7.90580571e-01 1.22833006e-01 -2.11762920... | [4.135000705718994, 1.954849362373352] |
b10d2d35-dfbc-4867-b206-c983fb20b34a | no-reference-image-quality-assessment-in-the | null | null | https://ieeexplore.ieee.org/document/6272356 | https://live.ece.utexas.edu/publications/2012/TIP%20BRISQUE.pdf | No-Reference Image Quality Assessment in the Spatial Domain | We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blo... | ['and Alan Conrad Bovik', 'Anush Krishna Moorthy', 'Anish Mittal'] | 2012-08-17 | null | null | null | ieee-transacations-on-image-processing-2012-8 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.48975956e-01 -7.69561827e-01 3.06315720e-01 -1.51993468e-01
-1.28634751e+00 -7.32045949e-01 6.60411239e-01 -1.48646096e-02
-3.60761017e-01 4.57722366e-01 4.77713376e-01 -3.04566503e-01
-4.65048194e-01 -5.14854252e-01 -4.37554389e-01 -9.81823862e-01
-3.67582738e-02 -4.71675694e-01 9.25337300e-02 -2.63934672... | [11.719024658203125, -1.981359839439392] |
45b9e042-8465-425d-b342-250a407e2b79 | optimized-preprocessing-and-tiny-ml-for | 2303.11371 | null | https://arxiv.org/abs/2303.11371v1 | https://arxiv.org/pdf/2303.11371v1.pdf | Optimized preprocessing and Tiny ML for Attention State Classification | In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task and compared it to other state... | ['Van-Tam Nguyen', 'Pavlo Mozharovskyi', 'Enzo Tartaglione', 'Rémi Nahon', 'Yinghao Wang'] | 2023-03-20 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 3.60761732e-01 -3.76345813e-01 -1.40829101e-01 -5.57143748e-01
-4.28108215e-01 -4.12249118e-02 4.62467492e-01 3.60991567e-01
-6.69275403e-01 1.09869325e+00 -2.04658896e-01 -1.28211394e-01
-3.53266269e-01 -4.87913162e-01 -7.26470053e-02 -4.76655930e-01
-4.08509165e-01 2.68087804e-01 9.40529406e-02 -6.76113516... | [13.24582290649414, 3.384467601776123] |
ebe5a5e2-6aa8-4a50-996a-ac3f13c78acf | docextractor-an-off-the-shelf-historical | 2012.08191 | null | https://arxiv.org/abs/2012.08191v1 | https://arxiv.org/pdf/2012.08191v1.pdf | docExtractor: An off-the-shelf historical document element extraction | We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation. We demonstrate it provides high-quality performances as an off-the-shelf system across a wide variety of datasets and leads to results on p... | ['Mathieu Aubry', 'Tom Monnier'] | 2020-12-15 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 2.39839509e-01 1.15841195e-01 1.66511923e-01 -6.73377514e-02
-1.06949282e+00 -1.06762075e+00 1.01058376e+00 2.31786489e-01
-4.04218763e-01 5.06239533e-01 5.71786538e-02 -4.90958244e-01
-8.45864043e-02 -6.43425703e-01 -1.02978694e+00 -1.68227807e-01
-2.29188800e-03 6.06295228e-01 4.64678168e-01 -3.03364068... | [11.696634292602539, 2.649200439453125] |
3ffb02d0-4f1b-4793-aad6-5ef16c8b9f00 | particlesfm-exploiting-dense-point | 2207.09137 | null | https://arxiv.org/abs/2207.09137v1 | https://arxiv.org/pdf/2207.09137v1.pdf | ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild | Estimating the pose of a moving camera from monocular video is a challenging problem, especially due to the presence of moving objects in dynamic environments, where the performance of existing camera pose estimation methods are susceptible to pixels that are not geometrically consistent. To tackle this challenge, we p... | ['Yong-Jin Liu', 'Wenping Wang', 'Hengkai Guo', 'Shaohui Liu', 'Wang Zhao'] | 2022-07-19 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-2.59185523e-01 -5.25896549e-01 -1.73058156e-02 -1.79906607e-01
-7.24879980e-01 -8.13989520e-01 4.01422650e-01 -3.90911758e-01
-5.77057421e-01 4.35833037e-01 1.95570782e-01 1.36020109e-01
1.57908916e-01 -4.08973783e-01 -1.10543847e+00 -6.22835815e-01
-1.21205606e-01 5.67389548e-01 4.56239820e-01 6.85482472... | [8.380518913269043, -1.9897937774658203] |
879145be-9a88-456c-a50f-653bc14e41c1 | a-novel-augmented-reality-ultrasound | 2205.04350 | null | https://arxiv.org/abs/2205.04350v1 | https://arxiv.org/pdf/2205.04350v1.pdf | A Novel Augmented Reality Ultrasound Framework Using an RGB-D Camera and a 3D-printed Marker | Purpose. Ability to locate and track ultrasound images in the 3D operating space is of great benefit for multiple clinical applications. This is often accomplished by tracking the probe using a precise but expensive optical or electromagnetic tracking system. Our goal is to develop a simple and low cost augmented reali... | ['Laurent Launay', 'Albert Murienne', 'Pierre Le Gargasson', 'Guillaume Pasquier', 'Boris Labbé', 'Gaétan Lelu', 'Yitian Zhou'] | 2022-05-09 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 1.38930529e-02 1.39072835e-01 4.52360958e-01 -1.31486692e-02
-8.99444103e-01 -7.89123237e-01 -1.25444874e-01 -7.85209760e-02
-4.44752961e-01 1.26583263e-01 -1.10994913e-01 -7.01253355e-01
-3.36363055e-02 -3.24193954e-01 -6.42813861e-01 -5.10837197e-01
-5.01291990e-01 3.42904776e-01 3.81856143e-01 -6.82728877... | [13.783023834228516, -2.9872264862060547] |
86d5ee29-264b-4a6c-b903-402f3dc715df | data-driven-and-automatic-surface-texture | 2110.10005 | null | https://arxiv.org/abs/2110.10005v1 | https://arxiv.org/pdf/2110.10005v1.pdf | Data-driven and Automatic Surface Texture Analysis Using Persistent Homology | Surface roughness plays an important role in analyzing engineering surfaces. It quantifies the surface topography and can be used to determine whether the resulting surface finish is acceptable or not. Nevertheless, while several existing tools and standards are available for computing surface roughness, these methods ... | ['Firas A. Khasawneh', 'Melih C. Yesilli'] | 2021-10-19 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 6.63558900e-01 -2.20451862e-01 5.41598916e-01 -7.42050409e-02
-6.90144420e-01 -4.99848574e-01 4.84494954e-01 6.78725898e-01
-9.79095548e-02 4.10715878e-01 -4.35839087e-01 -3.25240701e-01
-4.28517133e-01 -1.40263391e+00 -4.50783879e-01 -6.74903810e-01
-7.14310333e-02 4.74024504e-01 7.08946407e-01 -5.20610392... | [8.607358932495117, -2.528127431869507] |
3c1b992a-956e-46c7-b2b6-8d07f0952cbc | learning-activation-functions-for-sparse | 2305.10964 | null | https://arxiv.org/abs/2305.10964v2 | https://arxiv.org/pdf/2305.10964v2.pdf | Learning Activation Functions for Sparse Neural Networks | Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigat... | ['Marius Lindauer', 'Mehdi Asadi', 'Aditya Mohan', 'Mohammad Loni'] | 2023-05-18 | null | null | null | null | ['automl', 'hyperparameter-optimization'] | ['methodology', 'methodology'] | [-8.62194151e-02 1.16720088e-01 -2.05230683e-01 -3.57919753e-01
-3.50985110e-01 -3.58281255e-01 2.77852297e-01 -1.96961373e-01
-5.81615210e-01 9.73687053e-01 -1.87879950e-02 -3.85753542e-01
-2.02644497e-01 -8.39144647e-01 -8.15963447e-01 -7.59211540e-01
-7.30158836e-02 4.03710812e-01 2.24411532e-01 -2.35523954... | [8.599345207214355, 3.1686863899230957] |
7010d59c-930c-47aa-bf3e-233d5cc3b1ff | jointly-learning-topic-specific-word-and | null | null | https://openreview.net/forum?id=Vx8l4vwv94 | https://openreview.net/pdf?id=Vx8l4vwv94 | JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING | Document embedding generally ignores underlying topics, which fails to capture polysemous terms that can mislead to improper thematic representation. Moreover, embedding a new document during the test process needs a complex and expensive inference method. Some models first learn word embeddings and later learn underly... | ['Zuping Zhang', 'Farid Uddin'] | 2021-09-29 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-1.10053793e-01 -9.25195962e-02 -4.59848285e-01 -4.13321495e-01
-4.35109198e-01 -4.33903426e-01 8.85077238e-01 5.40829480e-01
-3.16071659e-01 3.81381214e-01 6.26057684e-01 -9.56260711e-02
-3.99453491e-01 -8.50163639e-01 -2.08565474e-01 -8.40809286e-01
1.28144369e-01 5.04569054e-01 -3.93538699e-02 1.00717060... | [10.442865371704102, 7.107545375823975] |
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