paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
534752ec-66d5-4cd5-8e02-800b69fa6ceb | speaker-conditioned-target-speaker-extraction | 2104.04234 | null | https://arxiv.org/abs/2104.04234v1 | https://arxiv.org/pdf/2104.04234v1.pdf | Speaker-conditioned Target Speaker Extraction based on Customized LSTM Cells | Speaker-conditioned target speaker extraction systems rely on auxiliary information about the target speaker to extract the target speaker signal from a mixture of multiple speakers. Typically, a deep neural network is applied to isolate the relevant target speaker characteristics. In this paper, we focus on a single-c... | ['Simon Doclo', 'Christian Rollwage', 'Marvin Tammen', 'Ragini Sinha'] | 2021-04-09 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 4.68668461e-01 2.42768511e-01 6.50520623e-02 -3.96646947e-01
-9.66738641e-01 -3.30616057e-01 4.43359107e-01 1.01251602e-02
-5.09626210e-01 2.16046557e-01 1.22176655e-01 -2.06432700e-01
4.40808803e-01 -2.26553977e-01 -4.20179307e-01 -8.62397671e-01
2.06150729e-02 4.18058246e-01 -1.31583363e-02 -5.43785989... | [14.574952125549316, 6.074701309204102] |
26f42721-12bb-42d5-a316-59c524860009 | learning-to-represent-bilingual-dictionaries | 1808.03726 | null | https://arxiv.org/abs/1808.03726v3 | https://arxiv.org/pdf/1808.03726v3.pdf | Learning to Represent Bilingual Dictionaries | Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages. However, many applications, such as cross-lingual semantic search and question answering, can be largely benefited from the cross-lingual correspondence between sentences and lexicons. To bridge ... | ['Carlo Zaniolo', 'Kai-Wei Chang', 'Haochen Chen', 'Muhao Chen', 'Steven Skiena', 'Yingtao Tian'] | 2018-08-10 | learning-to-represent-bilingual-dictionaries-1 | https://aclanthology.org/K19-1015 | https://aclanthology.org/K19-1015.pdf | conll-2019-11 | ['reverse-dictionary'] | ['natural-language-processing'] | [-2.07378760e-01 -4.02569890e-01 -7.79440999e-01 -3.21653694e-01
-8.33082676e-01 -5.31284928e-01 5.52629173e-01 3.28791559e-01
-7.29253948e-01 3.70570272e-01 7.19018877e-01 -4.77641165e-01
4.67406660e-02 -7.86518753e-01 -4.61728930e-01 -1.88539937e-01
4.68759209e-01 4.80379730e-01 -1.51608974e-01 -6.63741350... | [11.104130744934082, 9.88132381439209] |
dfa9cff5-a7f0-48cd-b66d-30a23e86c98d | compnet-complementary-segmentation-network | 1804.00521 | null | http://arxiv.org/abs/1804.00521v2 | http://arxiv.org/pdf/1804.00521v2.pdf | CompNet: Complementary Segmentation Network for Brain MRI Extraction | Brain extraction is a fundamental step for most brain imaging studies. In
this paper, we investigate the problem of skull stripping and propose
complementary segmentation networks (CompNets) to accurately extract the brain
from T1-weighted MRI scans, for both normal and pathological brain images. The
proposed networks ... | ['Yi Hong', 'Raunak Dey'] | 2018-03-27 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 5.24802327e-01 4.90378141e-01 1.00864738e-01 -3.88615638e-01
-4.73942697e-01 -3.20403904e-01 4.10265386e-01 -1.58753961e-01
-7.89136827e-01 5.92328668e-01 -1.21558875e-01 5.24474308e-03
-3.67173314e-01 -4.65127438e-01 -6.99086070e-01 -7.27152288e-01
-3.45461071e-01 5.38163722e-01 6.30231261e-01 -1.51096405... | [14.307065963745117, -2.268117666244507] |
4cc51c08-4a4a-4c73-ab98-4923334af23d | deep-learning-for-distant-speech-recognition | 1712.06086 | null | http://arxiv.org/abs/1712.06086v1 | http://arxiv.org/pdf/1712.06086v1.pdf | Deep Learning for Distant Speech Recognition | Deep learning is an emerging technology that is considered one of the most
promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech
represents a crucial leap towards intelligent machines. Despite the great
efforts of the past dec... | ['Mirco Ravanelli'] | 2017-12-17 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.43640107e-02 -3.30841541e-02 5.71598887e-01 -2.46794373e-01
-6.03767872e-01 -3.05236697e-01 6.08952343e-01 -9.16928276e-02
-3.20404947e-01 4.30399328e-01 5.52710593e-01 -5.54953218e-01
-3.27165753e-01 -4.92321104e-01 -3.86063755e-01 -8.20038021e-01
1.59741770e-02 1.91589408e-02 -4.85519543e-02 -5.45123637... | [14.939098358154297, 5.908902168273926] |
91cb59ff-a535-4880-bd7d-7cbf013002fc | hybridized-feature-extraction-and-acoustic | 1506.02170 | null | http://arxiv.org/abs/1506.02170v1 | http://arxiv.org/pdf/1506.02170v1.pdf | Hybridized Feature Extraction and Acoustic Modelling Approach for Dysarthric Speech Recognition | Dysarthria is malfunctioning of motor speech caused by faintness in the human
nervous system. It is characterized by the slurred speech along with physical
impairment which restricts their communication and creates the lack of
confidence and affects the lifestyle. This paper attempt to increase the
efficiency of Automa... | ['Megha Rughani', 'D. Shivakrishna'] | 2015-06-06 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.86537202e-02 6.58494309e-02 5.45960426e-01 -2.72102594e-01
-4.95163538e-02 -3.73321444e-01 3.33658695e-01 -4.67087209e-01
-4.90052611e-01 7.42478430e-01 6.86395228e-01 -1.52730748e-01
-4.64314550e-01 -3.15796494e-01 -1.04575031e-01 -5.67178965e-01
2.95601636e-01 3.53771120e-01 1.84267219e-02 -7.76011765... | [14.524164199829102, 6.055976867675781] |
0c2f7852-95f2-4fd4-9c8f-30e44d801298 | deep-sketch-hashing-fast-free-hand-sketch | 1703.05605 | null | http://arxiv.org/abs/1703.05605v1 | http://arxiv.org/pdf/1703.05605v1.pdf | Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval | Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. Howev... | ['Yuming Shen', 'Fumin Shen', 'Xianglong Liu', 'Li Liu', 'Ling Shao'] | 2017-03-16 | deep-sketch-hashing-fast-free-hand-sketch-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Liu_Deep_Sketch_Hashing_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_Deep_Sketch_Hashing_CVPR_2017_paper.pdf | cvpr-2017-7 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [-2.36149877e-01 -7.27570653e-01 -2.62993217e-01 -3.26638609e-01
-1.07477570e+00 -5.37719548e-01 7.33420014e-01 -1.47929609e-01
-1.86659470e-01 7.69600496e-02 1.58698380e-01 1.53704494e-01
-1.46759704e-01 -9.18299079e-01 -4.33048069e-01 -5.45944393e-01
8.28089416e-02 5.14135838e-01 1.61481321e-01 -4.44015056... | [11.650494575500488, 0.6727721691131592] |
5365cbcf-0abb-4442-95c6-4e51c2eab629 | path-planning-for-autonomous-driving-the | 2303.09824 | null | https://arxiv.org/abs/2303.09824v4 | https://arxiv.org/pdf/2303.09824v4.pdf | Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives | Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being es... | ['Zhe XuanYuan', 'Fenghua Zhu', 'Dongsheng Yang', 'Lingxi Li', 'Yunfeng Ai', 'Long Chen', 'Xuemin Hu', 'Bai Li', 'Yuchen Li', 'Peng Deng', 'Siyu Teng'] | 2023-03-17 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.35503516e-01 2.70442814e-01 -6.89864993e-01 -3.67090166e-01
-3.75417769e-01 -6.81273222e-01 7.09875464e-01 -2.93042868e-01
-3.50780755e-01 5.43839276e-01 -1.27044573e-01 -8.63477647e-01
-1.30152479e-01 -4.66855675e-01 -4.52266604e-01 -3.19434702e-01
-1.69818506e-01 4.40221041e-01 6.46875143e-01 -4.13762033... | [5.6682538986206055, 1.0245548486709595] |
0ae21bea-7f86-487f-9034-db955ccd4b7b | centermask-real-time-anchor-free-instance-1 | 1911.06667 | null | https://arxiv.org/abs/1911.06667v6 | https://arxiv.org/pdf/1911.06667v6.pdf | CenterMask : Real-Time Anchor-Free Instance Segmentation | We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation m... | ['Jongyoul Park', 'Youngwan Lee'] | 2019-11-15 | centermask-real-time-anchor-free-instance | null | null | arxiv-2019-11 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 2.00158656e-01 5.33075273e-01 -1.91326201e-01 -2.28361219e-01
-7.48545885e-01 -4.19143438e-01 2.30904743e-01 -6.79322898e-01
-4.59217042e-01 6.77734256e-01 -1.50978208e-01 -2.53855616e-01
2.03067377e-01 -4.66741234e-01 -1.00478435e+00 -6.64570987e-01
2.40391225e-01 2.32184172e-01 7.28496015e-01 5.48484102... | [9.561720848083496, 0.08555518835783005] |
81153999-5123-4087-a2d1-0496d36658af | learn-to-predict-sets-using-feed-forward | 2001.11845 | null | https://arxiv.org/abs/2001.11845v2 | https://arxiv.org/pdf/2001.11845v2.pdf | Learn to Predict Sets Using Feed-Forward Neural Networks | This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tagging and object detection, have outputs that are naturally expressed a... | ['Laura Leal-Taixé', 'Farbod T. Motlagh', 'Roman Kaskman', 'Tianyu Zhu', 'Hamid Rezatofighi', 'Anton Milan', 'Qinfeng Shi', 'Daniel Cremers', 'Ian Reid'] | 2020-01-30 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.30754173e-01 -1.86854541e-01 2.29342207e-01 -5.43673754e-01
-3.70800406e-01 -9.33316290e-01 1.02718258e+00 2.42399171e-01
-5.82439423e-01 7.01891899e-01 -3.59224319e-01 -2.20522150e-01
-4.31812674e-01 -9.56813395e-01 -1.27533925e+00 -6.81358576e-01
-1.21351615e-01 1.01273656e+00 -3.62995337e-03 -1.22752197... | [9.408799171447754, 2.606431245803833] |
e15b42b8-1a9d-4165-a5db-bd7df807ae47 | disentangling-online-chats-with-dag | 2106.09024 | null | https://arxiv.org/abs/2106.09024v1 | https://arxiv.org/pdf/2106.09024v1.pdf | Disentangling Online Chats with DAG-Structured LSTMs | Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses a challenge for any task aiming to understand the content of the chat ... | ['Mohit Bansal', 'Ozan İrsoy', 'Marco Farina', 'Lisa Bauer', 'Duccio Pappadopulo'] | 2021-06-16 | null | https://aclanthology.org/2021.starsem-1.14 | https://aclanthology.org/2021.starsem-1.14.pdf | joint-conference-on-lexical-and-computational-1 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 3.05405110e-01 2.05886871e-01 -2.43180141e-01 -5.37039340e-01
-8.55843723e-01 -8.24859977e-01 1.00817358e+00 5.79830945e-01
-5.20558953e-01 8.00098181e-01 1.02512932e+00 -5.73688149e-01
-1.48838490e-01 -4.68497604e-01 -3.92605454e-01 -3.90476614e-01
-3.00506592e-01 8.82964075e-01 8.16410854e-02 -4.16955262... | [12.642892837524414, 7.830268383026123] |
c0d308d8-8216-4783-98c9-d76b6b185490 | robust-multi-agent-reinforcement-learning | null | null | https://openreview.net/forum?id=JvPsKam58LX | https://openreview.net/pdf?id=JvPsKam58LX | Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium | In this paper we deal with robust cooperative multi-agent reinforcement learning (CMARL). While CMARL has many potential applications, only a trained policy that is robust enough can be confidently deployed in real world. Existing works on robust MARL mainly apply vanilla adversarial training in centralized training an... | ['Zhanxing Zhu', 'Jun Wang', 'Yaodong Yang', 'Wulong Liu', 'Jianye Hao', 'Dong Li', 'Kun Shao', 'Yizheng Hu'] | 2021-01-01 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.94072759e-01 1.69109538e-01 -2.80303806e-01 4.85209338e-02
-1.07416093e+00 -8.57154369e-01 6.28662109e-01 -1.07621647e-01
-5.89670181e-01 1.23907781e+00 -6.46036565e-02 -3.70846570e-01
-2.44304925e-01 -7.15497792e-01 -9.01114941e-01 -1.16923988e+00
-5.91617405e-01 4.75050777e-01 -2.49956944e-03 -5.78727067... | [3.79510235786438, 2.272136688232422] |
0aca1209-5954-4f14-8954-cd7ae66ac915 | mfsnet-a-multi-focus-segmentation-network-for | 2203.14341 | null | https://arxiv.org/abs/2203.14341v2 | https://arxiv.org/pdf/2203.14341v2.pdf | MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation | Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer globally, and its early diagnosis is pivotal for the complete elimination of malignant t... | ['Ram Sarkar', 'Rohit Kundu', 'Hritam Basak'] | 2022-03-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.11111879e-01 2.10778892e-01 -1.64699346e-01 -6.83178008e-02
-5.70048034e-01 -1.09769203e-01 1.97691798e-01 -3.42825204e-02
-5.64407408e-01 4.94718581e-01 -8.72713923e-02 -1.00089900e-01
1.90476805e-01 -8.65388572e-01 -3.31751138e-01 -9.98826385e-01
3.67516935e-01 1.00827562e-02 2.76773304e-01 -2.58138217... | [15.57667350769043, -2.8937673568725586] |
dbf51341-2050-4892-848a-389d143aae87 | o-medal-online-active-deep-learning-for | 1908.10508 | null | https://arxiv.org/abs/1908.10508v2 | https://arxiv.org/pdf/1908.10508v2.pdf | O-MedAL: Online Active Deep Learning for Medical Image Analysis | Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Our novel sampling method queries the unlabeled examples that maxi... | ['Aurélio Campilho', 'Pei Zhang', 'Pedro Costa', 'Asim Smailagic', 'Alex Gaudio', 'Adrian Galdran', 'Susu Xu', 'Mostafa Mirshekari', 'Kartik Khandelwal', 'Hae Young Noh', 'Jonathon Fagert', 'Devesh Walawalkar'] | 2019-08-28 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 4.03511822e-01 9.89660382e-01 -8.38967085e-01 -7.37552702e-01
-1.19035244e+00 -3.51436734e-01 2.70512253e-01 5.89680433e-01
-9.85659242e-01 8.84336531e-01 8.02580789e-02 -2.80339606e-02
1.95775740e-02 -8.18508148e-01 -6.09666526e-01 -7.59599268e-01
-2.27037057e-01 7.06787109e-01 8.06428120e-02 3.45441550... | [14.910356521606445, -2.350311040878296] |
ef4a7d70-ad83-47e0-8bff-fbedcb82574c | fastgeodis-fast-generalised-geodesic-distance | 2208.00001 | null | https://arxiv.org/abs/2208.00001v2 | https://arxiv.org/pdf/2208.00001v2.pdf | FastGeodis: Fast Generalised Geodesic Distance Transform | The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware. In particular, it implements the paralellisable raster scan method from Criminisi et al. (2009), where elements in a row (... | ['Tom Vercauteren', 'Reuben Dorent', 'Muhammad Asad'] | 2022-07-26 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [-3.24324220e-01 -4.10553455e-01 4.29434448e-01 2.66514393e-03
-7.52751172e-01 -8.90980124e-01 8.34665596e-01 1.42005220e-01
-5.06163001e-01 3.71980906e-01 6.42182082e-02 -7.59223819e-01
-3.60026024e-02 -1.27629960e+00 -2.73097456e-01 -7.06079602e-01
-4.09161836e-01 7.78885007e-01 5.68664968e-01 -1.53978541... | [8.027230262756348, -2.8714067935943604] |
0849408a-70ac-47e4-8017-116618c8c5ff | zero-shot-video-editing-using-off-the-shelf | 2303.17599 | null | https://arxiv.org/abs/2303.17599v2 | https://arxiv.org/pdf/2303.17599v2.pdf | Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models | Large-scale text-to-image diffusion models achieve unprecedented success in image generation and editing. However, how to extend such success to video editing is unclear. Recent initial attempts at video editing require significant text-to-video data and computation resources for training, which is often not accessible... | ['Chunhua Shen', 'Xinlong Wang', 'Yue Cao', 'Hao Chen', 'Zide Liu', 'Kangyang Xie', 'Wen Wang'] | 2023-03-30 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 1.64081976e-01 -2.32423484e-01 -1.28560662e-01 -2.38100767e-01
-5.50018549e-01 -4.43926036e-01 6.62693620e-01 -3.73743325e-01
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8.46463963e-02 1.26163587e-01 3.17794412e-01 -1.12763517... | [10.910076141357422, -0.637012779712677] |
152f4c93-d60d-4085-9176-f11d91f74cf7 | improving-candidate-retrieval-with-entity | null | null | https://openreview.net/forum?id=jFOEfjXapqP | https://openreview.net/pdf?id=jFOEfjXapqP | Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking | There is little work on entity linking (EL) over Wikidata, even though it is the most extensive crowdsourced knowledge base. The scale of Wikidata can open up many new real-world applications, but its massive number of entities also makes EL challenging. To effectively narrow down the search space, we propose a novel c... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['pgtask'] | ['natural-language-processing'] | [-3.02661210e-01 1.25487983e-01 -6.99964345e-01 8.45799893e-02
-1.13271785e+00 -7.85437584e-01 6.89665556e-01 5.73625267e-01
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-6.50359541e-02 -9.75204110e-01 -7.17368424e-01 -1.68601856e-01
1.78535879e-01 1.04930055e+00 8.55828702e-01 -3.93781334... | [9.453444480895996, 8.855195045471191] |
be04968e-85ab-497c-bd42-ea5e1d024a28 | fine-grained-urban-flow-inference-with | null | null | https://ieeexplore.ieee.org/abstract/document/9723595 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9723595 | Fine-grained Urban Flow Inference with Incomplete Data | Fine-grained urban flow inference, which aims to infer the fine-grained urban flows of a city given the coarse-grained urban flow observations, is critically important to various smart city related applications such as urban planning and public safety. Previous works assume that the urban flow monitoring sensors are ev... | ['Jiyue Li; Senzhang Wang; Jiaqiang Zhang; Hao Miao; Junbo Zhang; Philip Yu'] | 2022-03-01 | null | null | null | ieee-transactions-on-knowledge-and-data-7 | ['fine-grained-urban-flow-inference'] | ['miscellaneous'] | [-1.19293004e-01 -2.98118979e-01 -3.53040457e-01 -4.35061872e-01
-6.82820022e-01 -2.04085678e-01 8.02219808e-01 -2.08976656e-01
-1.44735441e-01 9.15168524e-01 6.06812954e-01 -4.61657554e-01
-2.72184640e-01 -1.48608613e+00 -4.64130878e-01 -7.11149931e-01
-5.33559732e-02 5.63514650e-01 3.76165181e-01 -7.84400403... | [6.407787322998047, 2.0724289417266846] |
4df098ec-916c-44ad-ac96-ed97ba95bbc0 | modelling-temporal-information-using-discrete | 1603.06568 | null | http://arxiv.org/abs/1603.06568v2 | http://arxiv.org/pdf/1603.06568v2.pdf | Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos | With the widespread of user-generated Internet videos, emotion recognition in
those videos attracts increasing research efforts. However, most existing works
are based on framelevel visual features and/or audio features, which might fail
to model the temporal information, e.g. characteristics accumulated along time.
In... | ['Haimin Zhang', 'Min Xu'] | 2016-03-20 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [ 2.30746254e-01 -4.37658697e-01 -1.27691805e-01 -4.31896299e-01
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1.04160458e-01 -5.16516685e-01 -5.22647798e-01 -6.96247756e-01
-5.12070119e-01 -6.95386648e-01 -9.83549803e-02 7.22013786... | [13.285778999328613, 4.959600448608398] |
83f16f45-eb86-48c6-ad9b-c204072e54b8 | fully-bayesian-vib-deepssm | 2305.05797 | null | https://arxiv.org/abs/2305.05797v1 | https://arxiv.org/pdf/2305.05797v1.pdf | Fully Bayesian VIB-DeepSSM | Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variation... | ['Shireen Elhabian', 'Jadie Adams'] | 2023-05-09 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-7.51357228e-02 7.68805146e-01 -1.36838615e-01 -6.83909953e-01
-1.60182130e+00 -4.70214218e-01 2.74236768e-01 4.18716483e-02
-1.42700300e-01 8.42229664e-01 4.08986092e-01 -4.70067352e-01
-6.75841749e-01 -3.46533418e-01 -8.84077847e-01 -7.42510915e-01
-5.53944632e-02 9.96867180e-01 1.30513683e-01 4.10106242... | [14.000142097473145, -2.0068750381469727] |
4f162e89-7167-4062-bd8e-9d1f2090efd0 | multi-spectral-visual-odometry-without | 1908.08814 | null | https://arxiv.org/abs/1908.08814v1 | https://arxiv.org/pdf/1908.08814v1.pdf | Multi-Spectral Visual Odometry without Explicit Stereo Matching | Multi-spectral sensors consisting of a standard (visible-light) camera and a long-wave infrared camera can simultaneously provide both visible and thermal images. Since thermal images are independent from environmental illumination, they can help to overcome certain limitations of standard cameras under complicated ill... | ['Weichen Dai', 'Naira Hovakimyan', 'Yu Zhang', 'Ping Li', 'Donglei Sun'] | 2019-08-23 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 4.56183076e-01 -5.44335246e-01 1.39866129e-01 -2.74088860e-01
-4.37614202e-01 -4.99805212e-01 4.45171475e-01 -3.40326160e-01
-4.06351507e-01 6.22267663e-01 -2.65503049e-01 1.23357974e-01
-3.49174179e-02 -8.10540378e-01 -4.22361672e-01 -8.88461649e-01
8.92178774e-01 4.41694677e-01 4.24307019e-01 -2.14064777... | [9.13768196105957, -2.583188772201538] |
841c85d7-4bea-40f1-876c-252d4d3f4045 | trilateral-attention-network-for-real-time | 2106.09201 | null | https://arxiv.org/abs/2106.09201v1 | https://arxiv.org/pdf/2106.09201v1.pdf | Trilateral Attention Network for Real-time Medical Image Segmentation | Accurate segmentation of medical images into anatomically meaningful regions is critical for the extraction of quantitative indices or biomarkers. The common pipeline for segmentation comprises regions of interest detection stage and segmentation stage, which are independent of each other and typically performed using ... | ['Sameer Antani', 'Vandana Sachdev', 'Ghada Zamzmi'] | 2021-06-17 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.25194708e-01 3.56968567e-02 -1.15463436e-01 -4.30098385e-01
-6.97184741e-01 -5.86492181e-01 3.01407933e-01 4.90950674e-01
-6.66035891e-01 3.65672350e-01 3.11928801e-02 -2.85264194e-01
-1.34601767e-04 -5.95266283e-01 -4.30121005e-01 -7.02470303e-01
-2.76449233e-01 3.08963537e-01 5.57735920e-01 2.09762380... | [14.53760814666748, -2.50547456741333] |
424e3685-8346-4c0f-aafe-6cb54066112d | query-efficient-imitation-learning-for-end-to | 1605.06450 | null | http://arxiv.org/abs/1605.06450v1 | http://arxiv.org/pdf/1605.06450v1.pdf | Query-Efficient Imitation Learning for End-to-End Autonomous Driving | One way to approach end-to-end autonomous driving is to learn a policy
function that maps from a sensory input, such as an image frame from a
front-facing camera, to a driving action, by imitating an expert driver, or a
reference policy. This can be done by supervised learning, where a policy
function is tuned to minim... | ['Kyunghyun Cho', 'Jiakai Zhang'] | 2016-05-20 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 1.13408379e-01 3.53708535e-01 -5.18272184e-02 -4.13245231e-01
-8.04757297e-01 -7.38847971e-01 7.50906944e-01 -3.42681669e-02
-7.95953333e-01 8.69908869e-01 -2.27478012e-01 -4.31177378e-01
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1.15967236e-01 6.11635983e-01 5.84394991e-01 -3.76490802... | [4.776156425476074, 1.358668565750122] |
c5e0ce16-31cb-440e-a2f3-3e8e31a660bb | lpformer-lidar-pose-estimation-transformer | 2306.12525 | null | https://arxiv.org/abs/2306.12525v1 | https://arxiv.org/pdf/2306.12525v1.pdf | LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network | In this technical report, we present the 1st place solution for the 2023 Waymo Open Dataset Pose Estimation challenge. Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods have commonly relied on 2D image features and 2D sequential annotations for 3D human pose estimation. In co... | ['Hassan Foroosh', 'Zixiang Zhou', 'Weijia Chen', 'Yufei Xie', 'Dongqiangzi Ye'] | 2023-06-21 | null | null | null | null | ['pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.54088134e-01 7.60567710e-02 -2.74578005e-01 -2.07482561e-01
-8.66072714e-01 -2.92491078e-01 3.44244242e-01 -8.29544589e-02
-8.04954827e-01 6.40335083e-01 2.01494709e-01 2.24400371e-01
1.46439001e-02 -5.41986525e-01 -6.60150111e-01 -5.84012680e-02
-1.14504866e-01 9.14047420e-01 6.86986804e-01 -3.36195320... | [7.0066142082214355, -0.9120559096336365] |
2b481c15-5e1a-46fe-a00e-2c752f3cf460 | understanding-aesthetics-with-language-a | 2206.08614 | null | https://arxiv.org/abs/2206.08614v3 | https://arxiv.org/pdf/2206.08614v3.pdf | Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment | Computational inference of aesthetics is an ill-defined task due to its subjective nature. Many datasets have been proposed to tackle the problem by providing pairs of images and aesthetic scores based on human ratings. However, humans are better at expressing their opinion, taste, and emotions by means of language rat... | ['Clara Fernandez-Labrador', 'Luigi Celona', 'Daniel Vera Nieto'] | 2022-06-17 | null | null | null | null | ['aesthetic-image-captioning', 'aesthetics-quality-assessment'] | ['computer-vision', 'computer-vision'] | [-6.05550073e-02 1.61459804e-01 7.88604245e-02 -5.53785622e-01
-6.26600564e-01 -7.41067231e-01 5.36879361e-01 3.28959495e-01
-4.22021657e-01 2.65683860e-01 5.68914652e-01 4.63870801e-02
8.13457463e-03 -5.97684920e-01 -4.40024078e-01 -4.38523680e-01
4.68577176e-01 1.84126988e-01 -1.28127590e-01 -2.68912971... | [11.537833213806152, -0.9783839583396912] |
eb179d24-c691-4e61-b725-f9296121e9d7 | a-multi-objective-memetic-algorithm-for-auto | 2208.06984 | null | https://arxiv.org/abs/2208.06984v1 | https://arxiv.org/pdf/2208.06984v1.pdf | A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design | The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, with the rapid development of defense technologies, it also tends to be more diff... | ['Xiaoqian Chen', 'Tingsong Jiang', 'Wen Yao', 'Jialiang Sun'] | 2022-08-15 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-7.52428398e-02 -4.49188620e-01 2.54157931e-01 -1.72632188e-01
-3.00305873e-01 -8.39213073e-01 4.71822470e-01 -2.45970905e-01
-4.74900335e-01 6.10095084e-01 -2.21250970e-02 -3.24844241e-01
-3.51897091e-01 -9.90270317e-01 -5.35509586e-01 -1.07672203e+00
9.98581424e-02 2.46509537e-01 1.33285318e-02 -5.15154719... | [5.5426764488220215, 7.928229331970215] |
08211bf1-a56e-4856-85f1-10f26368e828 | bic-twitter-bot-detection-with-text-graph | 2208.08320 | null | https://arxiv.org/abs/2208.08320v2 | https://arxiv.org/pdf/2208.08320v2.pdf | BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency | Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify bots based on texts and networks on social media. Existing methods only leverage texts or networks alone, and while few works explored the sh... | ['Minnan Luo', 'Qinghua Zheng', 'Jundong Li', 'Zilong Chen', 'Shangbin Feng', 'Wenqian Zhang', 'Herun Wan', 'Zhenyu Lei'] | 2022-08-17 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.22387239e-02 -1.17153354e-01 -5.21521807e-01 1.41888589e-01
1.50768295e-01 -6.92663133e-01 1.03796363e+00 1.15042426e-01
-3.63837838e-01 1.54968128e-01 9.32384506e-02 -3.79818827e-01
2.90628761e-01 -8.18605304e-01 -1.34250119e-01 -1.70140550e-01
-5.03456108e-02 4.51417416e-01 5.70665300e-01 -4.18894082... | [8.09231948852539, 10.125280380249023] |
87728bc1-107e-4f89-925a-16708a64fb2e | method-for-the-generation-of-depth-images-for | 2006.16500 | null | https://arxiv.org/abs/2006.16500v1 | https://arxiv.org/pdf/2006.16500v1.pdf | Method for the generation of depth images for view-based shape retrieval of 3D CAD model from partial point cloud | A laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry information but also semantic information. However, recognition process is often ... | ['Hyungki Kim', 'Duhwan Mun', 'Moohyun Cha'] | 2020-06-30 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [ 7.42617473e-02 -8.79645407e-01 2.77332425e-01 -5.26275873e-01
-6.39594555e-01 -4.95644838e-01 4.06660765e-01 -6.59897551e-02
-1.82498336e-01 7.60533214e-02 -4.47917074e-01 2.64809672e-02
-3.12613159e-01 -1.30363560e+00 -5.77588141e-01 -6.46043777e-01
4.26164031e-01 8.84524345e-01 1.97630852e-01 -5.16662486... | [8.205423355102539, -3.216228723526001] |
e68ea6e7-af4c-49ff-ac1d-0af1d2324abd | rnn-based-counterfactual-time-series | 1712.03553 | null | https://arxiv.org/abs/1712.03553v7 | https://arxiv.org/pdf/1712.03553v7.pdf | RNN-based counterfactual prediction, with an application to homestead policy and public schooling | This paper proposes a method for estimating the effect of a policy intervention on an outcome over time. We train recurrent neural networks (RNNs) on the history of control unit outcomes to learn a useful representation for predicting future outcomes. The learned representation of control units is then applied to the t... | ['Shuxi Zeng', 'Jason Poulos'] | 2017-12-10 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.82074320e-02 3.36098969e-01 -1.07623541e+00 -5.27073264e-01
-6.98800445e-01 -1.72611743e-01 6.90609992e-01 9.83183160e-02
-2.91137457e-01 1.16994536e+00 1.24820697e+00 -8.64642143e-01
-1.96955487e-01 -9.85139489e-01 -9.31491673e-01 -6.32694542e-01
-3.69522303e-01 8.96137301e-03 -6.53400183e-01 2.82314986... | [7.995932102203369, 5.413512706756592] |
8d54ba75-7330-453d-acc6-119f2f8acdf2 | segsort-segmentation-by-discriminative | 1910.06962 | null | https://arxiv.org/abs/1910.06962v2 | https://arxiv.org/pdf/1910.06962v2.pdf | SegSort: Segmentation by Discriminative Sorting of Segments | Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels, but by decomposing it into perceptual groups and structures that are the basic building blocks of recognition. This motivates us to propos... | ['Liang-Chieh Chen', 'Tien-Ju Yang', 'Maxwell D. Collins', 'Jyh-Jing Hwang', 'Xiao Zhang', 'Stella X. Yu', 'Jianbo Shi'] | 2019-10-15 | segsort-segmentation-by-discriminative-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Hwang_SegSort_Segmentation_by_Discriminative_Sorting_of_Segments_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Hwang_SegSort_Segmentation_by_Discriminative_Sorting_of_Segments_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 4.25564617e-01 3.72255713e-01 -4.82272148e-01 -8.60981345e-01
-8.91614437e-01 -5.60689747e-01 4.64620441e-01 3.76933783e-01
-5.25481343e-01 2.71090746e-01 -7.13614672e-02 -3.92833501e-02
-7.76236951e-02 -7.65457690e-01 -7.52236545e-01 -6.55011714e-01
1.36626169e-01 5.99171162e-01 4.92901117e-01 2.58354217... | [9.568883895874023, 0.584109902381897] |
31103821-96e2-4380-b549-7fa7aeebfaa1 | glassloc-plenoptic-grasp-pose-detection-in | 1909.04269 | null | https://arxiv.org/abs/1909.04269v2 | https://arxiv.org/pdf/1909.04269v2.pdf | GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter | Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster ... | ['Odest Chadwicke Jenkins', 'Zheming Zhou', 'Haonan Chang', 'Tianyang Pan', 'Shiyu Wu'] | 2019-09-10 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 8.61398503e-02 -1.47930279e-01 4.09346491e-01 -1.46445092e-02
-2.59998441e-01 -1.08050716e+00 -1.59890458e-01 1.24930128e-01
-3.04334611e-02 3.92335981e-01 -3.14511210e-02 5.88859330e-05
-3.24435830e-01 -3.44282985e-01 -8.41647744e-01 -6.41135752e-01
-4.09803540e-01 8.35800529e-01 3.39701563e-01 -8.94441307... | [5.914651870727539, -1.0141469240188599] |
fdba5474-db43-444f-9c5e-8f6db19adde6 | featurebooster-boosting-feature-descriptors | 2211.15069 | null | https://arxiv.org/abs/2211.15069v3 | https://arxiv.org/pdf/2211.15069v3.pdf | FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network | We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The boosted ... | ['Danping Zou', 'Wenxian Yu', 'Wei Xi', 'Yu Hu', 'Zeyu Liu', 'Xinjiang Wang'] | 2022-11-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [-5.28296344e-02 -7.93044090e-01 -4.17706102e-01 -4.28334266e-01
-8.98240983e-01 -5.37080407e-01 6.26143396e-01 1.05837815e-01
-6.09804511e-01 1.98812023e-01 -1.43185526e-01 -1.50255591e-01
1.43722035e-02 -7.39247978e-01 -8.08074236e-01 -5.89282513e-01
-1.13597274e-01 8.91738459e-02 5.95162213e-01 -3.19027841... | [8.088260650634766, -1.8643444776535034] |
e3a77e1b-ff69-4739-baf4-a39289a73a24 | mla-bin-model-level-attention-and-batch | 2306.17008 | null | https://arxiv.org/abs/2306.17008v1 | https://arxiv.org/pdf/2306.17008v1.pdf | MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation | The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the perfor... | ['Weihua Zhou', 'Ni Yao', 'Jiaofen Nan', 'Yanting Li', 'Chuang Han', 'Yanhui Tian', 'Fubao Zhu'] | 2023-06-29 | null | null | null | null | ['medical-image-segmentation', 'domain-generalization'] | ['medical', 'methodology'] | [ 2.46659189e-01 9.64422598e-02 -3.30955476e-01 -6.57660306e-01
-7.03634799e-01 -3.09615433e-01 2.07589000e-01 3.02741248e-02
-4.23684359e-01 5.40849626e-01 1.13203451e-01 -9.89197120e-02
4.86973897e-02 -8.01112711e-01 -4.91266400e-01 -9.45877433e-01
2.48898298e-01 3.93142194e-01 1.21351421e-01 -3.71890212... | [14.536471366882324, -1.926310420036316] |
dd105136-ebdf-498b-81b1-0fa953468e21 | 3d-object-detection-and-instance-segmentation | null | null | https://www.mdpi.com/1424-8220/21/4/1213/htm | https://www.mdpi.com/1424-8220/21/4/1213/pdf | 3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images | Instance segmentation and object detection are significant problems in the fields of computer vision and robotics. We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images. A 3D convolutional-based system, named Frustu... | ['Ioannis Stamos', 'Xiaoke Shen 1'] | 2021-02-09 | null | null | null | sensors-2021-2 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.71505466e-01 8.17045644e-02 1.44179434e-01 -1.29673123e-01
-3.88117880e-01 -7.89997876e-01 3.57270330e-01 3.94888967e-01
-5.14343083e-01 -1.47821933e-01 -7.66055107e-01 -3.52614522e-01
3.94618303e-01 -1.19922495e+00 -8.09168279e-01 -2.50158399e-01
1.15729354e-01 7.15944171e-01 1.07146049e+00 -2.03040615... | [7.78834867477417, -2.6844089031219482] |
1ad2fd3b-7056-4877-bd0e-f5aa4216db9c | end-to-end-spoken-language-understanding-with | 2210.16554 | null | https://arxiv.org/abs/2210.16554v2 | https://arxiv.org/pdf/2210.16554v2.pdf | End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator | End-to-end spoken language understanding (SLU) suffers from the long-tail word problem. This paper exploits contextual biasing, a technique to improve the speech recognition of rare words, in end-to-end SLU systems. Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model comp... | ['Philip C. Woodland', 'Chao Zhang', 'Guangzhi Sun'] | 2022-10-29 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.71425983e-01 3.53884816e-01 -5.44515014e-01 -6.72922254e-01
-1.31822085e+00 -2.95691550e-01 3.06385756e-01 -5.58978058e-02
-6.55163229e-01 8.30284238e-01 6.33930981e-01 -7.03161120e-01
5.09937823e-01 -5.27588129e-01 -6.12065196e-01 -4.47679937e-01
9.13779214e-02 6.99696243e-01 4.61664677e-01 -3.91014963... | [14.043935775756836, 7.009949207305908] |
4de60527-0dab-449b-a271-74f59411f4cf | linear-dynamics-clustering-without | 1908.01039 | null | https://arxiv.org/abs/1908.01039v3 | https://arxiv.org/pdf/1908.01039v3.pdf | Linear Dynamics: Clustering without identification | Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical system is a venerable task, permitting provably efficient solutions only in special cases. This work shows that the eigenspectrum of unknown linear dynamics can be identified without... | ['Chloe Ching-Yun Hsu', 'Moritz Hardt', 'Michaela Hardt'] | 2019-08-02 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 5.27931228e-02 -3.49809974e-01 1.08598508e-01 1.00099698e-01
-4.83424157e-01 -1.06687903e+00 1.41811416e-01 -3.20891589e-01
1.54363617e-01 5.47158420e-01 -1.60434440e-01 -4.52339828e-01
-7.26925254e-01 5.20311408e-02 -2.13833407e-01 -9.60363448e-01
-7.68211186e-01 7.41946280e-01 -1.47020295e-01 -6.82685897... | [6.667965412139893, 3.5382533073425293] |
cf9d95a7-1b13-491e-8dd6-38a3b951807e | a-graph-neural-network-approach-for-temporal | 2306.13452 | null | https://arxiv.org/abs/2306.13452v1 | https://arxiv.org/pdf/2306.13452v1.pdf | A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence | We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph neural network that processes an augmented graph to estimate the correspondence. We have designe... | ['Shanmuganathan Raman', 'Prajwal Singh', 'Abhinav Narayan Harish', 'Aalok Gangopadhyay'] | 2023-06-23 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 1.41351044e-01 2.60862380e-01 1.12012178e-01 -2.60587752e-01
-3.65384281e-01 -2.31893182e-01 3.02884579e-01 -1.10775813e-01
-1.25282258e-01 5.97005367e-01 -3.95486653e-02 2.75213458e-02
1.19646460e-01 -1.15938365e+00 -1.10355413e+00 -1.47174180e-01
-3.40117663e-01 6.35411799e-01 2.91897893e-01 -1.74089879... | [7.356029987335205, -1.1137886047363281] |
cb481abf-125a-47d0-a03f-808abfcabc18 | cross-modality-data-augmentation-for-end-to | 2305.11096 | null | https://arxiv.org/abs/2305.11096v2 | https://arxiv.org/pdf/2305.11096v2.pdf | Cross-modality Data Augmentation for End-to-End Sign Language Translation | End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts directly without intermediate representations. It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data. To tackle these challenges, we propose a n... | ['Hui Xiong', 'Zhaopeng Tu', 'Xing Wang', 'Wenxiang Jiao', 'Jinhui Ye'] | 2023-05-18 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 4.12752420e-01 -1.52602971e-01 -2.09726751e-01 -4.55783844e-01
-1.05694699e+00 -4.84896213e-01 9.66119528e-01 -1.01354265e+00
-4.47009057e-01 4.57029969e-01 1.02793312e+00 -6.43663928e-02
3.37169558e-01 -2.15073392e-01 -7.18315601e-01 -8.15239429e-01
5.08362174e-01 3.38064581e-01 -2.20745966e-01 -1.80179358... | [9.214095115661621, -6.529625415802002] |
8d448d7d-7541-4b28-b3dd-151690179557 | deep-adversarial-decomposition-a-unified | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.pdf | Deep Adversarial Decomposition: A Unified Framework for Separating Superimposed Images | Separating individual image layers from a single mixed image has long been an important but challenging task. We propose a unified framework named "deep adversarial decomposition" for single superimposed image separation. Our method deals with both linear and non-linear mixtures under an adversarial training paradigm. ... | [' Jieping Ye', ' Zhenwei Shi', ' Tianyang Shi', ' Sen Lei', 'Zhengxia Zou'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['shadow-removal', 'reflection-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.42538726e-01 2.44643524e-01 -3.24503332e-02 -2.01526657e-01
-9.92724359e-01 -5.59778154e-01 5.39422870e-01 -6.34416997e-01
-3.32557827e-01 6.15513682e-01 -2.27407128e-01 -2.99165785e-01
2.76409209e-01 -2.32201129e-01 -1.10412836e+00 -1.26978993e+00
4.17227060e-01 1.70943197e-02 1.51192531e-01 -6.36477172... | [10.905517578125, -1.6385884284973145] |
1d8c4f2b-e215-4fc7-b7e6-b7ce2663ff0d | app-anytime-progressive-pruning | 2204.01640 | null | https://arxiv.org/abs/2204.01640v2 | https://arxiv.org/pdf/2204.01640v2.pdf | APP: Anytime Progressive Pruning | With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings. Although many methods have been investigated for optimal learning settings in scenarios where the data stream is continuous over time, sparse networks training in such set... | ['Irina Rish', 'Zhangyang Wang', 'Tianlong Chen', 'Bharat Runwal', 'Diganta Misra'] | 2022-04-04 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.68883100e-01 5.33205308e-02 -1.10317566e-01 -3.96144122e-01
-7.36242354e-01 -8.99814740e-02 3.66138667e-01 1.84379339e-01
-7.45500088e-01 6.42057776e-01 -3.18994522e-01 -3.74473065e-01
-3.72924507e-01 -7.78324723e-01 -1.11321545e+00 -7.69384444e-01
-3.06883931e-01 1.74317360e-01 4.97194141e-01 -2.05162778... | [8.858587265014648, 3.1997668743133545] |
07f91a4c-34d3-4066-bb3f-c4c47c49edbc | efficient-deep-learning-based-estimation-of | 2204.08989 | null | https://arxiv.org/abs/2204.08989v2 | https://arxiv.org/pdf/2204.08989v2.pdf | Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones | Nowadays, due to the widespread use of smartphones in everyday life and the improvement of computational capabilities of these devices, many complex tasks can now be deployed on them. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain types of diseases, the de... | ['Aboozar Ghaffari', 'Mahdi Farvardin', 'Taha Samavati'] | 2022-04-13 | null | null | null | null | ['spo2-estimation', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 5.60712032e-02 -7.62921646e-02 -1.20602995e-01 -5.88475585e-01
-4.17668253e-01 2.21393988e-01 -6.28664494e-02 -5.64304180e-02
-6.85159743e-01 6.76760137e-01 -1.56200469e-01 -4.30319220e-01
2.29214817e-01 -6.52190030e-01 -1.97405055e-01 -6.05070472e-01
-1.13209464e-01 -1.46239445e-01 8.85572359e-02 2.10762799... | [13.979601860046387, 3.099536180496216] |
da1eaa41-702b-431c-ac61-37ed17331689 | unsupervised-segmentation-of-fire-and-smoke | 1909.12937 | null | https://arxiv.org/abs/1909.12937v1 | https://arxiv.org/pdf/1909.12937v1.pdf | Unsupervised Segmentation of Fire and Smoke from Infra-Red Videos | This paper proposes a vision-based fire and smoke segmentation system which use spatial, temporal and motion information to extract the desired regions from the video frames. The fusion of information is done using multiple features such as optical flow, divergence and intensity values. These features extracted from th... | ['Manel Martínez-Ramón', 'Meenu Ajith'] | 2019-09-18 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 5.27463675e-01 -6.79131091e-01 -5.71585000e-02 -3.24840993e-01
-2.85099059e-01 -6.49044633e-01 7.07359672e-01 2.56117433e-01
-7.18097031e-01 6.54345930e-01 -5.57573549e-02 -2.07611352e-01
-3.44798654e-01 -1.16648388e+00 -1.32698998e-01 -1.05361974e+00
1.04820468e-01 3.48186493e-01 9.22831833e-01 2.92341143... | [9.163064956665039, -1.1978963613510132] |
8999bbcd-065f-4a40-b618-c738d49d6430 | tldr-at-semeval-2022-task-1-using | null | null | https://aclanthology.org/2022.semeval-1.6 | https://aclanthology.org/2022.semeval-1.6.pdf | TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations | We propose a pair of deep learning models, which employ unsupervised pretraining, attention mechanisms and contrastive learning for representation learning from dictionary definitions, and definition modeling from such representations. Our systems, the Transformers for Learning Dictionaries and Representations (TLDR), ... | ['Harsha Vardhan Vemulapati', 'Aditya Srivastava'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 1.81797639e-01 1.07685171e-01 -5.70058346e-01 -2.72989690e-01
-2.93175131e-01 -5.35629451e-01 1.04553449e+00 4.22407776e-01
-1.01777565e+00 3.76291901e-01 5.58458507e-01 -8.02361071e-01
4.05049697e-02 -6.55439615e-01 -3.03935647e-01 -1.24944426e-01
1.69339076e-01 9.66015041e-01 -4.74353105e-01 -5.59852660... | [10.641083717346191, 8.942037582397461] |
2c3a34d7-856c-4ede-913a-a596d2ef919d | an-analysis-of-vaccine-related-sentiments | 2306.13797 | null | https://arxiv.org/abs/2306.13797v1 | https://arxiv.org/pdf/2306.13797v1.pdf | An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines | Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic had fear and uncertainty about vaccines which has been well expressed on social media platforms such as Twitter. We analyse Twitter sentiments from the beginning of the C... | ['Cathy Yu', 'Janhavi Lande', 'Jayesh Sonawane', 'Rohitash Chandra'] | 2023-06-23 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.11979258e-02 2.50441551e-01 -4.12412919e-02 -1.51341811e-01
2.43044794e-02 -8.33446503e-01 1.11470878e+00 1.22319651e+00
-5.72164595e-01 3.35798889e-01 9.46018696e-01 -5.53878129e-01
1.45123526e-01 -8.77965152e-01 -7.43407786e-01 -6.51956856e-01
-3.61452818e-01 4.57530111e-01 -2.81220317e-01 -1.02506828... | [8.473944664001465, 9.768073081970215] |
ebcf2cfc-f2b8-4f4a-a839-00a3e8e4bb0f | towards-inter-character-relationship-driven | 2211.00676 | null | https://arxiv.org/abs/2211.00676v1 | https://arxiv.org/pdf/2211.00676v1.pdf | Towards Inter-character Relationship-driven Story Generation | In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story conti... | ['Snigdha Chaturvedi', 'Faeze Brahman', 'Anvesh Rao Vijjini'] | 2022-11-01 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.14421290e-01 6.89055562e-01 -3.79107088e-01 -7.79251397e-01
-5.06127656e-01 -4.58875626e-01 8.42977941e-01 -9.95951239e-03
4.77544755e-01 1.26741672e+00 9.44133461e-01 1.45493090e-01
-1.83990985e-01 -1.23642194e+00 -5.72081983e-01 -1.46401763e-01
1.23009846e-01 7.32170224e-01 -1.27805367e-01 -5.30711532... | [11.720860481262207, 8.854997634887695] |
94efbf6e-f295-4145-896e-a1dbfeb0bceb | moving-object-detection-for-event-based | 2109.01879 | null | https://arxiv.org/abs/2109.01879v4 | https://arxiv.org/pdf/2109.01879v4.pdf | Moving Object Detection for Event-based Vision using k-means Clustering | Moving object detection is important in computer vision. Event-based cameras are bio-inspired cameras that work by mimicking the working of the human eye. These cameras have multiple advantages over conventional frame-based cameras, like reduced latency, HDR, reduced motion blur during high motion, low power consumptio... | ['Mayukhmali Das', 'Anindya Mondal'] | 2021-09-04 | null | null | null | null | ['moving-object-detection', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 1.79076836e-01 -8.15814197e-01 7.65003711e-02 -2.47281138e-03
2.50626784e-02 -4.69393492e-01 3.05659205e-01 1.33939907e-01
-7.19620764e-01 6.66853130e-01 -1.64080307e-01 1.22723363e-01
-9.46748406e-02 -7.05272675e-01 -3.11294734e-01 -9.53428507e-01
2.96576440e-01 -4.40061063e-01 1.12146986e+00 3.72641504... | [8.624959945678711, -1.2285829782485962] |
d358c6c7-c0e0-424c-8282-1c3619435340 | rethinking-bayesian-deep-learning-methods-for-1 | 2206.09293 | null | https://arxiv.org/abs/2206.09293v1 | https://arxiv.org/pdf/2206.09293v1.pdf | Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation | Recently, several Bayesian deep learning methods have been proposed for semi-supervised medical image segmentation. Although they have achieved promising results on medical benchmarks, some problems are still existing. Firstly, their overall architectures belong to the discriminative models, and hence, in the early sta... | ['Thomas Lukasiewicz', 'JianFeng Wang'] | 2022-06-18 | rethinking-bayesian-deep-learning-methods-for | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-medical-image-segmentation', 'volumetric-medical-image-segmentation'] | ['computer-vision', 'medical'] | [-1.81640923e-01 2.20298842e-01 -3.65570545e-01 -7.21986592e-01
-6.32201672e-01 1.09731086e-01 3.14208299e-01 -1.13457158e-01
-2.66343355e-01 5.50743759e-01 4.87188138e-02 -1.97355747e-01
-2.92177171e-01 -8.61889124e-01 -4.97876555e-01 -1.11230230e+00
3.66133958e-01 7.08397150e-01 3.45263600e-01 2.30858386... | [14.56066608428955, -2.0379793643951416] |
fae095d2-51f8-4401-83de-7c8647a928e7 | learning-barycentric-representations-of-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Learning_Barycentric_Representations_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Xie_Learning_Barycentric_Representations_CVPR_2017_paper.pdf | Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval | Retrieving 3D shapes with sketches is a challenging problem since 2D sketches and 3D shapes are from two heterogeneous domains, which results in large discrepancy between them. In this paper, we propose to learn barycenters of 2D projections of 3D shapes for sketch-based 3D shape retrieval. Specifically, we first use t... | ['Fan Zhu', 'Yi Fang', 'Guoxian Dai', 'Jin Xie'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.44035530e-01 -6.27366304e-01 -9.98952016e-02 -4.05887872e-01
-7.21413195e-01 -7.69674778e-01 7.02055275e-01 -1.00125760e-01
-1.36192173e-01 1.80087358e-01 2.74304539e-01 7.35690594e-02
-3.78374487e-01 -9.00690913e-01 -5.37899435e-01 -5.97765982e-01
1.02597304e-01 7.63139486e-01 -2.00922284e-02 1.36751026... | [8.159345626831055, -3.865206241607666] |
5c6d89ca-7575-4f4b-a2aa-ec49e7dd6321 | evaluating-robustness-of-support-vector | 2306.02639 | null | https://arxiv.org/abs/2306.02639v1 | https://arxiv.org/pdf/2306.02639v1.pdf | Evaluating robustness of support vector machines with the Lagrangian dual approach | Adversarial examples bring a considerable security threat to support vector machines (SVMs), especially those used in safety-critical applications. Thus, robustness verification is an essential issue for SVMs, which can provide provable robustness against various kinds of adversary attacks. The evaluation results obtai... | ['Pan Qin', 'Hong Gu', 'YuTing Liu'] | 2023-06-05 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [-1.22749057e-04 -7.26316646e-02 -2.55738586e-01 -2.81098157e-01
-4.67039585e-01 -9.82355118e-01 4.94649857e-01 2.30730027e-02
-2.60013998e-01 8.59128952e-01 -5.10252237e-01 -6.96957171e-01
-2.70914793e-01 -9.48795438e-01 -9.21162069e-01 -9.76889670e-01
2.11228859e-02 -1.49570808e-01 5.67614853e-01 -4.91943359... | [5.677518367767334, 7.744203090667725] |
df4db926-5212-4f77-98de-8b979ac2e4e7 | discreetly-exploiting-inter-session | 2304.08894 | null | https://arxiv.org/abs/2304.08894v1 | https://arxiv.org/pdf/2304.08894v1.pdf | Discreetly Exploiting Inter-session Information for Session-based Recommendation | Limited intra-session information is the performance bottleneck of the early GNN based SBR models. Therefore, some GNN based SBR models have evolved to introduce additional inter-session information to facilitate the next-item prediction. However, we found that the introduction of inter-session information may bring in... | ['Haotong Wang', 'Gang Wu', 'Zihan Wang'] | 2023-04-18 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-2.52986699e-01 -4.80174601e-01 -6.03482068e-01 -3.41958761e-01
-2.15458479e-02 -2.46380955e-01 1.40254095e-01 2.12863367e-03
-3.93358856e-01 7.20121503e-01 9.40805487e-03 -5.74796319e-01
-5.67474127e-01 -6.02237999e-01 -4.14665490e-01 -4.62246060e-01
-1.64175272e-01 1.23739280e-01 7.17307389e-01 -6.64337754... | [10.119119644165039, 5.567831039428711] |
2a1afbf2-f189-4574-a2d8-a75e827a5465 | single-image-reflection-separation-with | 1806.05376 | null | http://arxiv.org/abs/1806.05376v1 | http://arxiv.org/pdf/1806.05376v1.pdf | Single Image Reflection Separation with Perceptual Losses | We present an approach to separating reflection from a single image. The
approach uses a fully convolutional network trained end-to-end with losses that
exploit low-level and high-level image information. Our loss function includes
two perceptual losses: a feature loss from a visual perception network, and an
adversari... | ['Ren Ng', 'Xuaner Zhang', 'Qifeng Chen'] | 2018-06-14 | single-image-reflection-separation-with-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Single_Image_Reflection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single_Image_Reflection_CVPR_2018_paper.pdf | cvpr-2018-6 | ['reflection-removal'] | ['computer-vision'] | [ 9.21955645e-01 -6.78139105e-02 2.79848158e-01 -4.39075828e-01
-1.04034328e+00 -2.16548532e-01 2.42378026e-01 -2.70622849e-01
-4.92453247e-01 4.14044440e-01 1.38750210e-01 -2.43022963e-01
2.70637721e-01 -7.60379732e-01 -9.53870654e-01 -6.19798362e-01
-3.15246373e-01 -8.58178914e-01 2.25025833e-01 -2.42321178... | [11.118754386901855, -2.116132974624634] |
85e48158-79a2-48ee-9681-b61ce2b2a3ab | computational-modelling-and-data-driven | 2107.05707 | null | https://arxiv.org/abs/2107.05707v2 | https://arxiv.org/pdf/2107.05707v2.pdf | Computational modelling and data-driven homogenisation of knitted membranes | Knitting is an effective technique for producing complex three-dimensional surfaces owing to the inherent flexibility of interlooped yarns and recent advances in manufacturing providing better control of local stitch patterns. Fully yarn-level modelling of large-scale knitted membranes is not feasible. Therefore, we us... | ['Fehmi Cirak', 'Xiao Xiao', 'Sumudu Herath'] | 2021-07-12 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.46706939e-01 1.16808504e-01 4.92421836e-01 2.98991382e-01
-4.45134938e-01 -4.35293943e-01 5.06050587e-01 1.91300541e-01
-8.88987482e-02 7.18060315e-01 -3.18874329e-01 1.20794006e-01
-2.81055748e-01 -7.72927761e-01 -9.25418019e-01 -1.16339684e+00
9.04444456e-02 8.88532221e-01 3.16847324e-01 1.51389008... | [6.346982479095459, 3.334249258041382] |
4532512b-58ad-4bcc-a51e-2269afbdfe07 | large-ai-models-in-health-informatics | 2303.11568 | null | https://arxiv.org/abs/2303.11568v1 | https://arxiv.org/pdf/2303.11568v1.pdf | Large AI Models in Health Informatics: Applications, Challenges, and the Future | Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which often reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A concrete example is the recent debut of ChatG... | ['Benny Lo', 'Dong Xu', 'Wu Yuan', 'Bo Xiao', 'Frank P. -W. Lo', 'Kyle Lam', 'Yinzhao Dong', 'Ruiyang Zhang', 'Peilun Shi', 'Jiachuan Peng', 'Jiankai Sun', 'Lin Li', 'Jianing Qiu'] | 2023-03-21 | null | null | null | null | ['drug-discovery', 'medical-diagnosis'] | ['medical', 'medical'] | [ 3.89475703e-01 4.05348748e-01 -2.04711735e-01 -2.77493279e-02
-5.38299739e-01 -2.49515384e-01 3.57313842e-01 3.40722293e-01
-3.39171797e-01 5.08989811e-01 3.52487445e-01 -3.36785793e-01
-4.48097348e-01 -6.61996961e-01 -6.77636087e-01 -6.47002935e-01
-3.30728561e-01 8.68990779e-01 -2.95778066e-01 -2.74105012... | [8.06909465789795, 6.793855667114258] |
48495de7-c413-44d2-b434-058f1aeb3c98 | vietnamese-transition-based-dependency | 1911.03726 | null | https://arxiv.org/abs/1911.03726v1 | https://arxiv.org/pdf/1911.03726v1.pdf | Vietnamese transition-based dependency parsing with supertag features | In recent years, dependency parsing is a fascinating research topic and has a lot of applications in natural language processing. In this paper, we present an effective approach to improve dependency parsing by utilizing supertag features. We performed experiments with the transition-based dependency parsing approach b... | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen'] | 2019-11-09 | null | null | null | null | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-4.37937707e-01 2.11208910e-01 -8.88573155e-02 -8.49559307e-01
-9.07893181e-01 -5.89610994e-01 3.23334247e-01 4.29373980e-01
-6.79441631e-01 1.04057181e+00 6.11875296e-01 -3.13707501e-01
3.91613126e-01 -8.42751205e-01 -1.24533325e-01 -5.41015804e-01
-2.98282027e-01 3.60313237e-01 6.10557795e-01 -6.72583759... | [10.331649780273438, 9.87930965423584] |
1922f8e8-9058-43df-b9a9-7e6259d600a8 | an-attention-based-deep-learning-approach-for | null | null | https://ieeexplore.ieee.org/document/9417097 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9417097 | An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG | Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on mul... | ['Cuntai Guan', 'XiaoLi Li', 'Chee-Keong Kwoh', 'Min Wu', 'Chengyu Liu', 'Zhenghua Chen', 'Emadeldeen Eldele'] | 2021-04-28 | null | null | null | null | ['sleep-stage-detection', 'sleep-quality-prediction', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical'] | [-2.51463830e-01 -3.15958440e-01 -1.86177325e-02 -6.20127678e-01
-4.71027583e-01 4.23849113e-02 4.06811744e-01 5.22366352e-03
-4.83875901e-01 6.15331113e-01 5.72151840e-01 -7.57807791e-02
-3.03468198e-01 -5.58493137e-01 -4.17768151e-01 -5.83934367e-01
-3.49126756e-01 -2.50054568e-01 4.69165258e-02 -1.98571607... | [13.497780799865723, 3.522317886352539] |
db5ac0fe-5521-4abb-8b56-280b8c8fab2b | pontogammarus-maeoticus-swarm-optimization-a | 1807.01844 | null | http://arxiv.org/abs/1807.01844v1 | http://arxiv.org/pdf/1807.01844v1.pdf | Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm | Nowadays, metaheuristic optimization algorithms are used to find the global
optima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization
(PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging
behavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny
creature found m... | ['Saeed Sharifian', 'Benyamin Ghojogh'] | 2018-07-05 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-8.54941383e-02 -4.50140297e-01 2.53056705e-01 1.60764605e-01
5.07134497e-01 -7.99773395e-01 1.12553701e-01 7.51496553e-02
-3.29033047e-01 1.15821242e+00 -2.10023373e-01 -6.16592914e-02
-3.07707906e-01 -1.05372143e+00 -3.53607684e-01 -1.41497779e+00
-1.77852094e-01 4.08051759e-01 -1.97237685e-01 -5.84748149... | [5.605627059936523, 3.4127299785614014] |
5870a6a4-61fb-4504-9b00-b89fa82deaf1 | event-based-video-reconstruction-using | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Weng_Event-Based_Video_Reconstruction_Using_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Weng_Event-Based_Video_Reconstruction_Using_Transformer_ICCV_2021_paper.pdf | Event-Based Video Reconstruction Using Transformer | Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency. Previous works have achieved impressive performances on event-based video reconstruction by introducing convolutional neural networks (CNNs). However, ... | ['Zhiwei Xiong', 'Yueyi Zhang', 'Wenming Weng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 3.85177322e-02 -7.11753666e-01 2.41080150e-02 -4.24593270e-01
-5.08590996e-01 -5.32833301e-02 5.99824905e-01 8.18075910e-02
-4.88846451e-01 5.27378261e-01 3.10590595e-01 1.77769437e-01
4.51740585e-02 -1.17990053e+00 -1.00134158e+00 -4.17791724e-01
-5.15222736e-02 -2.66525090e-01 7.71147192e-01 -9.26711857... | [8.610967636108398, -1.126115322113037] |
5787030b-0c36-4091-b877-0f429f3629b6 | feature-engineering-methods-on-multivariate | 2303.16117 | null | https://arxiv.org/abs/2303.16117v2 | https://arxiv.org/pdf/2303.16117v2.pdf | Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions | This paper is a work in progress. We are looking for collaborators to provide us financial datasets in Equity/Futures market to conduct more bench-marking studies. The authors have papers employing similar methods applied on the Numerai dataset, which is freely available but obfuscated. We apply different feature engin... | ['Mauricio Barahona', 'Thomas Wong'] | 2023-03-26 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-1.87144533e-01 -2.61910260e-01 -1.00488529e-01 -3.67130697e-01
-5.48413873e-01 -1.01787031e+00 8.84721935e-01 -3.88874441e-01
-2.02594250e-01 5.48673451e-01 1.93990648e-01 -7.86254883e-01
-2.13640973e-01 -1.10858989e+00 -1.49197042e-01 -1.77765101e-01
-6.92477167e-01 8.23996775e-03 1.42641068e-01 -4.92212325... | [4.533558368682861, 4.183145523071289] |
b5aebe1d-7b38-499f-ab4e-ddaaf8ec5fcc | de-abuse-tamilnlp-acl-2022-transliteration-as | null | null | https://aclanthology.org/2022.dravidianlangtech-1.5 | https://aclanthology.org/2022.dravidianlangtech-1.5.pdf | DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil | With the rise of social media and internet, thereis a necessity to provide an inclusive space andprevent the abusive topics against any gender,race or community. This paper describes thesystem submitted to the ACL-2022 shared taskon fine-grained abuse detection in Tamil. In ourapproach we transliterated code-mixed data... | ['Bharathi Raja Chakravarthi', 'Adeep Hande', 'Sean Benhur', 'Vasanth Palanikumar'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['transliteration', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-4.07255113e-01 3.37799862e-02 -5.63497841e-01 -3.69696796e-01
-8.70309293e-01 -5.50695539e-01 8.60013008e-01 2.35072181e-01
-7.91209280e-01 1.09865999e+00 1.86045155e-01 -2.80037522e-01
1.00262776e-01 -2.37681463e-01 -4.18541908e-01 -1.44669786e-01
2.67058481e-02 2.05171123e-01 3.55754107e-01 -2.18073010... | [8.72608757019043, 10.524541854858398] |
61ffe980-b124-4647-92a3-63919df563a9 | uncertainty-aware-system-identification-with | 2202.05844 | null | https://arxiv.org/abs/2202.05844v1 | https://arxiv.org/pdf/2202.05844v1.pdf | Uncertainty Aware System Identification with Universal Policies | Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real transfer is estimating the real-world environmental parameters to ground the simulated environment to. Although existing methods such as Domai... | ['Svetha Venkatesh', 'Santu Rana', 'Thommen George Karimpanal', 'Buddhika Laknath Semage'] | 2022-02-11 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.73594439e-01 -6.48093922e-03 -3.42102721e-02 -7.13068619e-02
-1.11503255e+00 -8.28018963e-01 1.10807025e+00 1.33759037e-01
-7.84421027e-01 1.16165495e+00 2.24101350e-01 -4.98406053e-01
-5.03075182e-01 -5.42821169e-01 -1.05180252e+00 -7.83208489e-01
3.29313278e-02 1.01262033e+00 4.12111968e-01 -1.59935638... | [4.3637871742248535, 1.9746309518814087] |
d37bca4f-4a8f-4e0b-b741-d65bf37ad17c | mmd-mix-value-function-factorisation-with | 2106.11652 | null | https://arxiv.org/abs/2106.11652v1 | https://arxiv.org/pdf/2106.11652v1.pdf | MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning | In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations. To solve such problems, many multi-agent reinforcement learning methods based on Centralized Training with Decentralized Execution have been proposed. One representative class of work is value d... | ['Guoliang Fan', 'Yunpeng Bai', 'Dapeng Li', 'Zhiwei Xu'] | 2021-06-22 | null | null | null | null | ['distributional-reinforcement-learning', 'smac-1', 'smac'] | ['methodology', 'playing-games', 'playing-games'] | [-6.08802915e-01 -5.84759451e-02 -4.21298772e-01 -1.12874798e-01
-8.46516848e-01 -4.59260166e-01 4.68675822e-01 -2.17300896e-02
-6.04677498e-01 1.41840446e+00 2.75829494e-01 6.83428273e-02
-3.61935049e-01 -9.90032077e-01 -3.90170604e-01 -1.24699080e+00
-9.08460021e-02 1.04552913e+00 -2.76111364e-01 -3.56023937... | [3.656989812850952, 2.1105685234069824] |
10bf9080-a8e5-4697-b22b-da276f17a912 | semantic-segmentation-of-surgical | 2303.10972 | null | https://arxiv.org/abs/2303.10972v1 | https://arxiv.org/pdf/2303.10972v1.pdf | Semantic segmentation of surgical hyperspectral images under geometric domain shifts | Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, however, although common in real-world open surgeries due to variations in surgical procedures or situs occlusions, remain a topic largely unad... | ['Lena Maier-Hein', 'Felix Nickel', 'Beat Peter Müller-Stich', 'Berkin Özdemir', 'Alessandro Motta', 'Alexander Studier-Fischer', 'Silvia Seidlitz', 'Jan Sellner'] | 2023-03-20 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.38701737e-01 6.78430140e-01 -2.01221872e-02 -1.94321856e-01
-7.16340303e-01 -8.43871891e-01 2.67549247e-01 4.97477621e-01
-5.95652938e-01 4.31736588e-01 1.31692782e-01 -5.49390674e-01
-2.19657749e-01 -5.42702854e-01 -7.14823127e-01 -8.82060170e-01
-1.24097213e-01 3.78886044e-01 2.10640579e-01 -3.96937996... | [14.326231956481934, -2.8145692348480225] |
c0b43e6e-e75f-4082-84b7-c6d00f77a140 | implementation-of-hand-detection-based | 1312.7560 | null | http://arxiv.org/abs/1312.7560v1 | http://arxiv.org/pdf/1312.7560v1.pdf | Implementation of Hand Detection based Techniques for Human Computer Interaction | The computer industry is developing at a fast pace. With this development
almost all of the fields under computers have advanced in the past couple of
decades. But the same technology is being used for human computer interaction
that was used in 1970s. Even today the same type of keyboard and mouse is used
for interact... | ['Vipul Honrao', 'Amiraj Dhawan'] | 2013-12-29 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.69841960e-01 -1.41878352e-01 -2.39962518e-01 1.41064664e-02
2.90042698e-01 -8.02819788e-01 4.95749861e-01 -3.22232372e-03
-6.64199233e-01 4.67548698e-01 -2.66728848e-01 -1.02955401e+00
2.22485706e-01 -8.14091742e-01 3.80499773e-02 -2.89819837e-01
3.16041112e-01 3.68846059e-01 7.44355619e-01 -4.92839187... | [6.507802486419678, -0.24195492267608643] |
895ca35e-7f9d-4ead-b302-2a4209a44100 | graftnet-towards-domain-generalized-stereo | 2204.00179 | null | https://arxiv.org/abs/2204.00179v1 | https://arxiv.org/pdf/2204.00179v1.pdf | GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature | Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shif... | ['Guodong Qi', 'Huimin Yu', 'Biyang Liu'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 1.64007828e-01 -2.95296788e-01 -1.87248170e-01 -6.51179790e-01
-5.17452121e-01 -2.95231968e-01 5.85109234e-01 -3.45678985e-01
-3.07643622e-01 4.87738758e-01 1.80893704e-01 -4.40197415e-04
-9.47673097e-02 -9.37733948e-01 -7.77264118e-01 -5.53629160e-01
2.37638429e-01 3.10907304e-01 2.33476728e-01 -4.30798948... | [8.748401641845703, -2.2233383655548096] |
c720fb54-1aeb-417b-a707-662eb84b2e1d | dyngraph2vec-capturing-network-dynamics-using | 1809.02657 | null | https://arxiv.org/abs/1809.02657v2 | https://arxiv.org/pdf/1809.02657v2.pdf | dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning | Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the propert... | ['Arquimedes Canedo', 'Palash Goyal', 'Sujit Rokka Chhetri'] | 2018-09-07 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-3.08344692e-01 1.34926990e-01 -4.06259418e-01 5.27227670e-02
2.18125537e-01 -6.92725956e-01 7.98998356e-01 1.21226646e-01
2.24473670e-01 4.53050196e-01 4.96416360e-01 -3.34254086e-01
-3.29261214e-01 -1.16359127e+00 -8.07252288e-01 -4.44682777e-01
-6.86500728e-01 7.06638813e-01 5.20072579e-01 -5.48162043... | [7.142436981201172, 6.105900764465332] |
0bdecddf-6ec4-4bf6-8994-2c2bfe1b9d8a | quality-of-syntactic-implication-of-rl-based | 1912.05493 | null | https://arxiv.org/abs/1912.05493v1 | https://arxiv.org/pdf/1912.05493v1.pdf | Quality of syntactic implication of RL-based sentence summarization | Work on summarization has explored both reinforcement learning (RL) optimization using ROUGE as a reward and syntax-aware models, such as models those input is enriched with part-of-speech (POS)-tags and dependency information. However, it is not clear what is the respective impact of these approaches beyond the standa... | ['Hoa T. Le', 'Claire Gardent', 'Christophe Cerisara'] | 2019-12-11 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.07700481e-02 2.67468601e-01 -3.49209607e-01 -2.17788666e-01
-1.15151858e+00 -7.40128040e-01 5.47441900e-01 7.50369966e-01
-7.71015644e-01 1.15455139e+00 1.10031664e+00 -2.75496066e-01
-1.63481474e-01 -5.05478203e-01 -5.94427526e-01 -4.31129307e-01
-1.49900585e-01 5.01512229e-01 1.03553973e-01 -5.47632694... | [12.415821075439453, 9.457637786865234] |
42b8fbba-677d-45fc-94ee-58ca08d63ea5 | seeded-ising-model-and-statistical-natures-of | 1802.02223 | null | https://arxiv.org/abs/1802.02223v1 | https://arxiv.org/pdf/1802.02223v1.pdf | Seeded Ising Model and Statistical Natures of Human Iris Templates | We propose a variant of Ising model, called the Seeded Ising Model, to model probabilistic nature of human iris templates. This model is an Ising model in which the values at certain lattice points are held fixed throughout Ising model evolution. Using this we show how to reconstruct the full iris template from partial... | ['Nam-Sook Wee', 'Song-Hwa Kwon', 'Sung Jin Lee', 'Hyeong In Choi'] | 2018-01-03 | null | null | null | null | ['2048'] | ['playing-games'] | [ 5.88782489e-01 5.83260834e-01 -1.46861479e-01 8.26964453e-02
-1.28179759e-01 -3.69219750e-01 5.85120499e-01 -4.39313203e-02
-3.48493725e-01 8.69350910e-01 1.05325490e-01 -5.61116710e-02
-5.16140401e-01 -6.48374319e-01 -6.31385028e-01 -1.11140454e+00
-1.23399504e-01 7.63382018e-01 1.36194125e-01 -1.21998854... | [5.658677577972412, 4.843847274780273] |
99886770-4ad7-4792-a02d-681d9d62a722 | a-new-approach-to-learning-in-dynamic | 1812.09027 | null | http://arxiv.org/abs/1812.09027v2 | http://arxiv.org/pdf/1812.09027v2.pdf | A new approach to learning in Dynamic Bayesian Networks (DBNs) | In this paper, we revisit the parameter learning problem, namely the
estimation of model parameters for Dynamic Bayesian Networks (DBNs). DBNs are
directed graphical models of stochastic processes that encompasses and
generalize Hidden Markov models (HMMs) and Linear Dynamical Systems (LDSs).
Whenever we apply these mo... | ['J. Atif', 'R. Laraki', 'E. Benhamou'] | 2018-12-21 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-1.38685822e-01 -3.80716519e-03 1.07238114e-01 2.82581355e-02
-1.60969645e-01 -6.50299847e-01 9.36617672e-01 -6.50473163e-02
-3.73469025e-01 7.85325408e-01 -1.78465739e-01 -7.72802413e-01
-6.60220623e-01 -7.24209964e-01 -3.80176306e-01 -9.55745757e-01
-2.61008441e-01 8.02549124e-01 5.79326928e-01 -1.94010586... | [6.037949085235596, 3.9877376556396484] |
3cef7cec-5255-4b9f-8961-98d42ea8711c | fast-and-flexible-human-program-induction-in | 2103.05823 | null | https://arxiv.org/abs/2103.05823v1 | https://arxiv.org/pdf/2103.05823v1.pdf | Fast and flexible: Human program induction in abstract reasoning tasks | The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collected from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). Although this subset of tasks contains considerabl... | ['Todd M. Gureckis', 'Brenden M. Lake', 'Wai Keen Vong', 'Aysja Johnson'] | 2021-03-10 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 1.76063925e-01 3.52015316e-01 5.79355955e-02 -4.67035472e-01
-3.12903732e-01 -7.99456775e-01 7.68336594e-01 5.50778925e-01
-3.10516953e-01 5.60594380e-01 3.73750776e-01 -2.96697140e-01
-1.21825658e-01 -5.97092390e-01 -7.10625470e-01 2.63005495e-02
-1.63092315e-01 5.87711930e-01 1.32925600e-01 4.50907275... | [8.34127426147461, 7.547027111053467] |
5ccbaf59-dea4-4f04-b999-75f36edcfcde | combining-global-and-local-merges-in-logic | 2305.16926 | null | https://arxiv.org/abs/2305.16926v2 | https://arxiv.org/pdf/2305.16926v2.pdf | Combining Global and Local Merges in Logic-based Entity Resolution | In the recently proposed Lace framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: all occurrences of those entity references (possibly across multiple database tup... | ['Yazmín Ibáñez-García', 'Víctor Gutiérrez-Basulto', 'Gianluca Cima', 'Meghyn Bienvenu'] | 2023-05-26 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-5.12233153e-02 3.68518203e-01 -2.45087013e-01 -2.73233712e-01
-3.37097317e-01 -7.68899202e-01 8.08607459e-01 1.02872527e+00
-4.73386019e-01 1.18973184e+00 6.58687055e-02 -2.67644614e-01
-5.02356350e-01 -1.17769790e+00 -4.18235809e-01 -3.16607118e-01
-2.77373284e-01 6.00176156e-01 5.70324898e-01 4.46592905... | [9.053485870361328, 7.699804782867432] |
211adcf2-9b1c-4da6-841a-fcb482d73050 | road-segmentation-for-remote-sensing-images | 2008.04021 | null | https://arxiv.org/abs/2008.04021v1 | https://arxiv.org/pdf/2008.04021v1.pdf | Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks | Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or... | ['Ruili Wang', 'Huiyu Zhou', 'Pourya Shamsolmoali', 'Jie Yang', 'Masoumeh Zareapoor'] | 2020-08-10 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 3.34792554e-01 1.40752243e-02 1.34871051e-01 -2.99127966e-01
-8.93055439e-01 -5.04088163e-01 5.73627710e-01 -3.89089227e-01
-3.48499447e-01 8.67624879e-01 -2.86551416e-01 -1.87249824e-01
-1.46283768e-02 -1.30083501e+00 -9.63286459e-01 -8.09726954e-01
1.61507592e-01 2.84341693e-01 4.36137050e-01 -2.08004624... | [9.844132423400879, 0.7459174990653992] |
b06c7317-119c-4f2d-a060-a305203b0b2b | enhancing-low-density-eeg-based-brain | 2212.03329 | null | https://arxiv.org/abs/2212.03329v1 | https://arxiv.org/pdf/2212.03329v1.pdf | Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation | Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience ... | ['Chun-Shu Wei', 'Sung-Yu Chen', 'Xin-Yao Huang'] | 2022-12-06 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.24354342e-01 -3.29045981e-01 2.70832628e-01 -1.70357481e-01
-7.03087747e-01 -6.48385584e-02 2.44371980e-01 -3.47805351e-01
-4.28535283e-01 1.19341266e+00 1.22158088e-01 -1.35361493e-01
-5.38169861e-01 -4.80899870e-01 -9.16038573e-01 -8.77382398e-01
-7.57082552e-02 1.58941746e-01 1.93926468e-01 1.42259672... | [13.098864555358887, 3.407172679901123] |
de8416cd-6e0c-49c9-a079-9fac77879799 | robust-face-recognition-using-local | 1212.02415 | null | http://arxiv.org/abs/1212.2415v1 | http://arxiv.org/pdf/1212.2415v1.pdf | Robust Face Recognition using Local Illumination Normalization and Discriminant Feature Point Selection | Face recognition systems must be robust to the variation of various factors
such as facial expression, illumination, head pose and aging. Especially, the
robustness against illumination variation is one of the most important problems
to be solved for the practical use of face recognition systems. Gabor wavelet
is widel... | ['Jongchol Jo', 'Cholhun Kim', 'Song Han', 'Jinsong Kim', 'Sunam Han'] | 2012-12-11 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.49327928e-01 -8.38377178e-01 -5.42934798e-02 -3.34198713e-01
2.74444193e-01 -1.24966919e-01 2.31937006e-01 -3.64374548e-01
-5.14489055e-01 6.38391733e-01 -2.45620087e-02 9.50900316e-02
-1.48308056e-03 -6.59142554e-01 -5.13180234e-02 -9.69163954e-01
7.79047608e-02 -3.54182541e-01 2.00614333e-01 -1.33760050... | [13.225645065307617, 0.7381916046142578] |
3902937f-0b95-47ce-9ac8-be7c706ef6bd | egocentric-object-manipulation-graphs | 2006.03201 | null | https://arxiv.org/abs/2006.03201v1 | https://arxiv.org/pdf/2006.03201v1.pdf | Egocentric Object Manipulation Graphs | We introduce Egocentric Object Manipulation Graphs (Ego-OMG) - a novel representation for activity modeling and anticipation of near future actions integrating three components: 1) semantic temporal structure of activities, 2) short-term dynamics, and 3) representations for appearance. Semantic temporal structure is mo... | ['Michael Maynord', 'Eadom Dessalene', 'Cornelia Fermuller', 'Chinmaya Devaraj', 'Yiannis Aloimonos'] | 2020-06-05 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 1.23795748e-01 1.06611900e-01 -1.93845183e-01 -2.98868641e-02
-1.43782392e-01 -7.86240160e-01 9.64382708e-01 -6.60454035e-02
-1.27839282e-01 1.69037059e-01 8.96744013e-01 1.62100539e-01
-3.64871144e-01 -4.75935161e-01 -6.21890366e-01 -2.51742572e-01
-6.20770216e-01 4.47055399e-01 2.80547112e-01 -2.66756743... | [8.155069351196289, 0.5351234674453735] |
478339e8-2402-4efe-b9ac-d4a95277684e | siod-single-instance-annotated-per-category | 2203.15353 | null | https://arxiv.org/abs/2203.15353v2 | https://arxiv.org/pdf/2203.15353v2.pdf | SIOD: Single Instance Annotated Per Category Per Image for Object Detection | Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instance-level annotation, while semi-supervised object detection (SSOD) remains challenging led by the inter-image discrepancy between labeled an... | ['Wei-Shi Zheng', 'Fan Tang', 'Ke Yan', 'Xingjia Pan', 'Hanjun Li'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.56611782e-01 2.85199493e-01 -3.68126333e-01 -4.75991309e-01
-1.05312967e+00 -3.33579183e-01 5.69110334e-01 -4.21764283e-03
-4.99938339e-01 6.48535550e-01 -3.00710678e-01 -4.19741645e-02
8.08703899e-02 -3.17667305e-01 -6.99486136e-01 -8.68292630e-01
3.68328214e-01 3.48397613e-01 5.10234058e-01 4.36895788... | [9.20046329498291, 1.278100609779358] |
d660ee0a-46a6-4ff7-a8cb-1dd53546ae01 | zero-shot-information-extraction-via-chatting | 2302.10205 | null | https://arxiv.org/abs/2302.10205v1 | https://arxiv.org/pdf/2302.10205v1.pdf | Zero-Shot Information Extraction via Chatting with ChatGPT | Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile, zero-shot IE reduces the time and effort that data labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, ChatGPT) show... | ['Wenjuan Han', 'Yong Jiang', 'Meishan Zhang', 'Yufeng Chen', 'Jinan Xu', 'Pengjun Xie', 'Shen Huang', 'Xin Zhang', 'Xiaobin Wang', 'Ning Cheng', 'Xingyu Cui', 'Xiang Wei'] | 2023-02-20 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.66216437e-02 5.56135654e-01 -4.87916619e-01 -3.28761458e-01
-1.37497008e+00 -4.57598567e-01 7.30787337e-01 1.81058452e-01
-4.87566561e-01 5.51970720e-01 4.69489485e-01 -5.18806219e-01
1.19039349e-01 -6.95429623e-01 -6.37834609e-01 -8.62846226e-02
2.36924931e-01 6.82595372e-01 3.34050208e-01 -3.37416649... | [9.789605140686035, 9.022651672363281] |
b9af430f-f042-42d2-88ab-206e30b4f99a | spatio-temporal-deep-learning-assisted | 2306.01570 | null | https://arxiv.org/abs/2306.01570v1 | https://arxiv.org/pdf/2306.01570v1.pdf | Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment | Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The constraints and data associated with SCUC are both geographically and temporally cor... | ['Xingpeng Li', 'Arun Venkatesh Ramesh'] | 2023-06-02 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-2.48291820e-01 -6.46582007e-01 -1.49291039e-01 1.45756260e-01
-3.74179274e-01 -6.68539286e-01 2.56542444e-01 4.90228534e-01
2.50749946e-01 1.15957940e+00 -4.87119615e-01 -6.67238057e-01
-8.49907398e-01 -8.83537829e-01 -1.37096066e-02 -8.77314031e-01
-1.10283124e+00 4.05265749e-01 -2.06991911e-01 -4.53571618... | [5.890623092651367, 2.5749335289001465] |
543b1746-e822-4cb7-9f13-5ff8307e0918 | reconstructing-video-from-interferometric | 1711.01357 | null | http://arxiv.org/abs/1711.01357v2 | http://arxiv.org/pdf/1711.01357v2.pdf | Reconstructing Video from Interferometric Measurements of Time-Varying Sources | Very long baseline interferometry (VLBI) makes it possible to recover images
of astronomical sources with extremely high angular resolution. Most recently,
the Event Horizon Telescope (EHT) has extended VLBI to short millimeter
wavelengths with a goal of achieving angular resolution sufficient for imaging
the event hor... | ['Adrian V. Dalca', 'Katherine L. Bouman', 'Sheperd S. Doeleman', 'Freek Roelofs', 'Andrew A. Chael', 'William T. Freeman', 'Michael D. Johnson'] | 2017-11-03 | null | null | null | null | ['image-imputation', 'radio-interferometry'] | ['computer-vision', 'miscellaneous'] | [ 1.07831605e-01 -1.15173250e-01 2.26412833e-01 2.28003666e-01
-2.68149972e-01 -5.53544343e-01 9.67978358e-01 -7.12008536e-01
-5.14505446e-01 5.96274316e-01 -3.54063839e-01 -3.66358280e-01
-1.65592879e-01 -6.37069523e-01 -6.64759040e-01 -1.09037924e+00
-2.96476156e-01 8.84513974e-01 4.56457555e-01 1.36570767... | [11.074676513671875, -2.5338053703308105] |
a25cbf79-5973-4a85-9c2b-25d0c5b58145 | automatic-extraction-of-nested-entities-in | null | null | https://dl.acm.org/doi/10.1145/3498324 | https://www.researchgate.net/publication/359803027_Automatic_Extraction_of_Nested_Entities_in_Clinical_Referrals_in_Spanish | Automatic Extraction of Nested Entities in Clinical Referrals in Spanish | Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, si... | ['Fabián Villena', 'Matías Rojas', 'Jocelyn Dunstan', 'Felipe Bravo-Marquez', 'Pablo Báez'] | 2022-04-07 | null | null | null | acm-transactions-on-computing-for-healthcare-1 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.76951349e-01 8.43816936e-01 -3.21094096e-01 -1.87251806e-01
-1.21507251e+00 -3.01700026e-01 2.80321360e-01 1.19864631e+00
-1.04654038e+00 7.32329190e-01 1.02081728e+00 -2.91011930e-01
-1.69945538e-01 -6.09708726e-01 -4.20040898e-02 -7.48822868e-01
-1.87110513e-01 9.86037016e-01 -1.68108165e-01 1.34835020... | [8.428577423095703, 8.708972930908203] |
f57b0fab-0a95-47ba-aeea-8deab42cec64 | az-whiteness-test-a-test-for-uncorrelated | 2204.11135 | null | https://arxiv.org/abs/2204.11135v1 | https://arxiv.org/pdf/2204.11135v1.pdf | AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs | We present the first whiteness test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph. The statistical test aims at finding serial dependencies among close-in-time observations, as well as spatial dependencies among neighboring observations given the underlying... | ['Cesare Alippi', 'Daniele Zambon'] | 2022-04-23 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 2.34689221e-01 1.01312116e-01 1.52108550e-01 1.05978668e-01
3.74854892e-01 -5.83484650e-01 6.75917566e-01 4.81967986e-01
1.10795870e-01 6.78140700e-01 -4.14902717e-02 -7.23895550e-01
-7.20158756e-01 -1.04359543e+00 -5.83521128e-01 -8.70277405e-01
-1.12977946e+00 3.17861617e-01 4.79701936e-01 -2.37680748... | [7.2009782791137695, 4.284841060638428] |
eaea8f85-4060-4641-9914-7ede885ab209 | probabilistic-uncertainty-aware-risk-spot | 2303.07181 | null | https://arxiv.org/abs/2303.07181v1 | https://arxiv.org/pdf/2303.07181v1.pdf | Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving | Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence... | ['Julian Eggert', 'Malte Probst', 'Tim Puphal'] | 2023-03-13 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.84311560e-02 4.97640893e-02 3.40904295e-02 -3.71987164e-01
-4.87115413e-01 -3.36016029e-01 9.53980982e-01 6.59634233e-01
-4.68128175e-01 7.55128384e-01 -2.79615670e-02 -1.06831753e+00
-7.16880858e-01 -9.93467748e-01 -4.81883287e-01 -5.93699574e-01
-4.68948662e-01 2.98984617e-01 7.42295027e-01 -4.11775202... | [5.674635410308838, 1.321842908859253] |
401d60e5-bde6-4c36-adcd-4c18e1adb852 | quadratic-programming-for-continuous-control | 2211.16720 | null | https://arxiv.org/abs/2211.16720v1 | https://arxiv.org/pdf/2211.16720v1.pdf | Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty | This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actu... | ['Zhong-Ping Jiang', 'Magnus Egerstedt', 'Tengfei Liu', 'Si Wu'] | 2022-11-30 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.44847830e-02 4.44190502e-01 -4.02384907e-01 5.68293929e-01
-4.24925029e-01 -6.84417844e-01 2.18483627e-01 3.25796545e-01
-2.21457481e-01 1.16472185e+00 -5.44739842e-01 -4.84720431e-02
-6.01084411e-01 -4.03663278e-01 -7.27247596e-01 -1.13610828e+00
-3.84047866e-01 2.54667215e-02 -5.22506386e-02 -5.48389733... | [5.189990997314453, 2.3567776679992676] |
ad923d04-15f6-44ac-ba8a-7102834dea04 | fast-event-based-optical-flow-estimation-by | 2212.12218 | null | https://arxiv.org/abs/2212.12218v1 | https://arxiv.org/pdf/2212.12218v1.pdf | Fast Event-based Optical Flow Estimation by Triplet Matching | Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not bee... | ['Guillermo Gallego', 'Yoshimitsu Aoki', 'Shintaro Shiba'] | 2022-12-23 | null | null | null | null | ['event-based-optical-flow', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 2.59329051e-01 -7.06342518e-01 -1.86997846e-01 -2.94204712e-01
-1.39492929e-01 -4.63129163e-01 4.13691163e-01 -4.65953611e-02
-6.94052994e-01 9.31349874e-01 7.14208782e-02 -4.22823504e-02
1.44203842e-01 -5.88101208e-01 -3.71623635e-01 -4.11800057e-01
-3.89175117e-01 -9.28745717e-02 8.10130656e-01 3.58002961... | [8.638633728027344, -1.260237693786621] |
eca1c269-0de8-49e6-99f0-eefc1f2559c5 | domain-randomization-enhanced-depth | 2208.03792 | null | https://arxiv.org/abs/2208.03792v2 | https://arxiv.org/pdf/2208.03792v2.pdf | Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects | Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks. To mitigate this problem, we propose a powerful RGBD fusion network, SwinDRNet, for depth restoration. We further propose Domain... | ['He Wang', 'Ping Tan', 'Ziyuan Liu', 'Hao Dong', 'Tianhao Wu', 'Qiwei Li', 'Jiyao Zhang', 'Qiyu Dai'] | 2022-08-07 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 3.80468100e-01 -1.11786142e-01 6.62754893e-01 -4.43557560e-01
-9.28762317e-01 -6.82010055e-01 5.03803074e-01 -4.26235557e-01
-9.95297506e-02 6.07350171e-01 2.98507452e-01 -7.88374022e-02
4.54721935e-02 -9.42078471e-01 -7.96444237e-01 -8.54578495e-01
2.40178749e-01 5.37038505e-01 4.50469971e-01 -2.67224580... | [8.722893714904785, -2.522937059402466] |
0283193e-62f7-4034-9c91-5d411811240b | unsupervised-domain-adaptation-for-question | null | null | https://aclanthology.org/2022.findings-naacl.183 | https://aclanthology.org/2022.findings-naacl.183.pdf | Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training | Question generation (QG) approaches based on large neural models require (i) large-scale and (ii) high-quality training data. These two requirements pose difficulties for specific application domains where training data is expensive and difficult to obtain. The trained QG models’ effectiveness can degrade significantly... | ['Claudia Hauff', 'Peide Zhu'] | null | null | null | null | findings-naacl-2022-7 | ['question-generation'] | ['natural-language-processing'] | [ 4.59079117e-01 3.38947177e-01 -2.86448836e-01 -5.43039382e-01
-1.18783891e+00 -5.95830202e-01 6.72563195e-01 6.00188896e-02
-4.72983003e-01 1.19394577e+00 3.72846514e-01 -1.80585742e-01
-1.06376097e-01 -8.65104258e-01 -5.46953142e-01 -2.08168864e-01
4.64610308e-01 1.11369669e+00 3.30796003e-01 -5.83644688... | [11.308891296386719, 8.276237487792969] |
46e7bb69-3255-4998-99cf-00221e320cd5 | deep-learning-for-segmentation-based-hepatic | 2210.15149 | null | https://arxiv.org/abs/2210.15149v3 | https://arxiv.org/pdf/2210.15149v3.pdf | Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study | Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver atten... | ['Xiangchun Liu', 'Yiyi Hui', 'Joshua Lin', 'Zezhong Ye', 'Huibin Nie', 'Ning Zhao', 'Feng Xia', 'Ziqiang Wang', 'Guixia Li', 'Zhongyi Zhang'] | 2022-10-27 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-5.03392637e-01 -2.09268227e-01 -2.66871065e-01 -1.18754141e-01
-7.18904197e-01 -6.92808330e-01 4.80907820e-02 4.82647330e-01
-1.64509580e-01 5.35629630e-01 5.46955109e-01 -7.08310664e-01
-1.19707435e-01 -7.80322134e-01 -2.56704122e-01 -6.96846247e-01
-5.39436102e-01 7.60791957e-01 5.98195232e-02 5.85287154... | [14.486478805541992, -2.6870498657226562] |
a81aff78-cf01-477d-b3d5-48f1a56b4a75 | new-insights-on-relieving-task-recency-bias | 2302.08243 | null | https://arxiv.org/abs/2302.08243v1 | https://arxiv.org/pdf/2302.08243v1.pdf | New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning | To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (CIL), where incoming samples from data stream can be used only once, is more challenging and can be encou... | ['Yanning Zhang', 'Shiyu Ji', 'Zhaoqiang Chen', 'Zhaojie Chen', 'Guoqiang Liang'] | 2023-02-16 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-7.11588860e-02 -9.57594439e-02 -5.58853030e-01 -4.13180292e-01
-2.52203375e-01 -1.64002091e-01 2.03682899e-01 1.89539745e-01
-4.65190291e-01 7.06841052e-01 3.44664305e-02 1.08493445e-02
-3.09341729e-01 -7.10087776e-01 -7.40857601e-01 -8.53520870e-01
9.40461159e-02 1.26410067e-01 4.61961567e-01 -1.54399157... | [9.761022567749023, 3.4322004318237305] |
996e8679-ba2f-46be-8c6c-d9056b823397 | emef-ensemble-multi-exposure-image-fusion | 2305.12734 | null | https://arxiv.org/abs/2305.12734v1 | https://arxiv.org/pdf/2305.12734v1.pdf | EMEF: Ensemble Multi-Exposure Image Fusion | Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don't utilize any synthesized gr... | ['Xuan Cheng', 'Ming Zeng', 'Yinglin Zheng', 'Haitao Cao', 'Chengyang Li', 'Renshuai Liu'] | 2023-05-22 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 1.75439671e-01 -4.50728178e-01 2.37247914e-01 -2.61471689e-01
-9.65385497e-01 -3.30172479e-01 3.12851936e-01 -4.09102172e-01
-3.88830811e-01 7.56455958e-01 1.18193023e-01 7.37306625e-02
-9.30539705e-03 -6.57454312e-01 -7.58268058e-01 -9.03828800e-01
6.87436283e-01 3.85971010e-01 1.59950316e-01 -3.72402340... | [10.722846984863281, -1.6263465881347656] |
d722d1a5-f97a-47b1-bc98-95c2efec41bc | spoken-language-change-detection-inspired-by | 2302.05265 | null | https://arxiv.org/abs/2302.05265v1 | https://arxiv.org/pdf/2302.05265v1.pdf | Spoken language change detection inspired by speaker change detection | Spoken language change detection (LCD) refers to identifying the language transitions in a code-switched utterance. Similarly, identifying the speaker transitions in a multispeaker utterance is known as speaker change detection (SCD). Since tasks-wise both are similar, the architecture/framework developed for the SCD t... | ['S. R. Mahadeva Prasanna', 'Jagabandhu Mishra'] | 2023-02-10 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.87765235e-01 9.31217894e-03 2.85659909e-01 -4.16262984e-01
-8.71980429e-01 -6.05540097e-01 7.77221203e-01 3.38262528e-01
-5.08436739e-01 3.21844459e-01 1.92701489e-01 -3.58426273e-01
3.20590377e-01 -1.83888689e-01 -3.30459654e-01 -5.52294850e-01
4.96730506e-02 1.14492513e-01 3.44121903e-01 -2.86677182... | [14.594295501708984, 6.372002601623535] |
740eb24a-5503-4ffd-a957-f6dfeb26ad3f | bayesian-models-of-functional-connectomics | 2301.06182 | null | https://arxiv.org/abs/2301.06182v1 | https://arxiv.org/pdf/2301.06182v1.pdf | Bayesian Models of Functional Connectomics and Behavior | The problem of jointly analysing functional connectomics and behavioral data is extremely challenging owing to the complex interactions between the two domains. In addition, clinical rs-fMRI studies often have to contend with limited samples, especially in the case of rare disorders. This data-starved regimen can sever... | ["Niharika Shimona D'Souza"] | 2023-01-15 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.71123010e-01 6.35565892e-02 -1.52631536e-01 -4.73196089e-01
-4.71321344e-01 -1.25403374e-01 2.72002339e-01 1.52665794e-01
-6.50014579e-01 8.13772440e-01 3.88577104e-01 -3.00595790e-01
-9.05487955e-01 -1.29102796e-01 -2.03124076e-01 -4.43633676e-01
-2.44719312e-01 7.83852816e-01 -3.60788584e-01 2.87464887... | [12.523724555969238, 3.370803117752075] |
5e3a842b-e5f0-4723-a185-58bb89d1d073 | propel-probabilistic-parametric-regression | 1807.10937 | null | https://arxiv.org/abs/1807.10937v2 | https://arxiv.org/pdf/1807.10937v2.pdf | PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks | In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic... | ['Greg Slabaugh', 'S M Masudur Rahman Al Arif', 'Rilwan Basaru', 'Muhammad Asad'] | 2018-07-28 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-1.44322768e-01 -2.73787789e-02 -8.01015943e-02 -6.96776986e-01
-9.81963634e-01 -1.74516495e-02 2.02333108e-01 -2.34014079e-01
-9.11050975e-01 9.79414940e-01 -2.27327362e-01 -2.18912251e-02
-1.67828441e-01 -4.22622770e-01 -8.76717031e-01 -8.12312245e-01
1.56059340e-01 4.05104339e-01 7.60101527e-02 2.55425602... | [8.651015281677246, 2.060908555984497] |
17406282-a490-42d5-8b6f-bbeb2fd14895 | anomaly-detection-by-recombining-gated | 2008.13763 | null | https://arxiv.org/abs/2008.13763v5 | https://arxiv.org/pdf/2008.13763v5.pdf | Anomaly Detection by Recombining Gated Unsupervised Experts | Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel ... | ['K. Böttinger', 'J. -P. Schulze', 'P. Sperl'] | 2020-08-31 | null | null | null | null | ['semi-supervised-anomaly-detection'] | ['computer-vision'] | [-3.33064012e-02 3.13885897e-01 1.91683263e-01 -6.68129861e-01
-1.68283239e-01 -5.31066000e-01 4.33956832e-01 2.13626251e-01
-3.57141763e-01 3.41118306e-01 -1.93800911e-01 -2.94453502e-01
-1.97007626e-01 -6.80444062e-01 -4.43423152e-01 -7.19451785e-01
5.56136630e-02 6.14794612e-01 5.10197401e-01 -1.88718468... | [7.603000164031982, 2.457871913909912] |
9d99a8ab-6287-4ddc-850d-b8144ce40029 | investigating-neural-architectures-for-short | null | null | https://aclanthology.org/W17-5017 | https://aclanthology.org/W17-5017.pdf | Investigating neural architectures for short answer scoring | Neural approaches to automated essay scoring have recently shown state-of-the-art performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on... | ['Chong MIn Lee', 'Brian Riordan', 'Andrea Horbach', 'Torsten Zesch', 'Aoife Cahill'] | 2017-09-01 | null | null | null | ws-2017-9 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-8.32214430e-02 4.10510935e-02 -3.95423800e-01 -5.62460959e-01
-1.04336965e+00 -6.69255495e-01 5.93380153e-01 3.59433651e-01
-6.04564428e-01 7.26447940e-01 7.83610106e-01 -2.28088960e-01
-2.71171153e-01 -7.94344962e-01 -3.25805992e-02 -3.30767892e-02
6.86500728e-01 5.70379257e-01 -9.92394164e-02 -4.55332249... | [11.300464630126953, 9.341715812683105] |
25ccd9d9-8adb-4c3a-8286-c7442bddd58b | spirit-diffusion-spirit-driven-score-based | 2212.11274 | null | https://arxiv.org/abs/2212.11274v1 | https://arxiv.org/pdf/2212.11274v1.pdf | SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging | Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterati... | ['Yanjie Zhu', 'Dong Liang', 'Hairong Zheng', 'Sen Jia', 'Jing Cheng', 'Zhuo-Xu Cui', 'Chentao Cao'] | 2022-12-14 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [-1.38524342e-02 -1.68824211e-01 -4.83751185e-02 -4.52967346e-01
-5.40762961e-01 -2.70927161e-01 3.66889775e-01 -5.41712165e-01
-2.92242974e-01 5.53719163e-01 5.99601150e-01 -2.44719684e-01
-4.43160206e-01 -2.24818334e-01 -1.23103067e-01 -8.34690452e-01
-3.53300780e-01 2.95336813e-01 3.55207294e-01 9.39017013... | [13.547285079956055, -2.384296417236328] |
8bb64d56-3a94-481a-aac8-479cd4a67266 | speed-up-object-detection-on-gigapixel-level | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.pdf | Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement | With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most existing works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch i... | ['Weiyao Lin', 'Aixin Zhang', 'John See', 'Wenjie Yang', 'Huabin Liu', 'Jiahao Fan'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['real-time-object-detection'] | ['computer-vision'] | [ 2.78157115e-01 -2.43151531e-01 -1.15701549e-01 1.41317174e-01
-6.75175071e-01 -1.24275915e-01 2.06369802e-01 2.13355333e-01
-1.43264383e-01 3.39451402e-01 -3.88184726e-01 1.70236919e-02
1.03190467e-02 -1.52525568e+00 -9.32172596e-01 -7.61614382e-01
-9.27437246e-02 1.99886039e-01 1.13785887e+00 1.96947232... | [8.885217666625977, -0.4921005070209503] |
f586b995-5fb8-481c-bf95-792c2f928ee7 | turn-segmentation-into-utterances-for-arabic | 1505.03081 | null | http://arxiv.org/abs/1505.03081v1 | http://arxiv.org/pdf/1505.03081v1.pdf | Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages | Text segmentation task is an essential processing task for many of Natural
Language Processing (NLP) such as text summarization, text translation,
dialogue language understanding, among others. Turns segmentation considered
the key player in dialogue understanding task for building automatic
Human-Computer systems. In ... | ['AbdelRahim A. Elmadany', 'Sherif M. Abdou', 'Mervat Gheith'] | 2015-05-12 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 3.91748786e-01 8.59453499e-01 1.98101312e-01 -5.30403554e-01
-1.07046556e+00 -9.17511344e-01 9.55430150e-01 4.05958533e-01
-2.22012743e-01 1.13770449e+00 7.03358531e-01 -5.17250896e-01
4.43036526e-01 -5.98383367e-01 -5.93089834e-02 -3.00165862e-01
3.34981382e-01 1.24786162e+00 1.26001880e-01 -7.32622743... | [12.649815559387207, 7.914575576782227] |
52910d8b-94fe-458b-8425-306fd95ab760 | towards-democratizing-joint-embedding-self | 2303.01986 | null | https://arxiv.org/abs/2303.01986v1 | https://arxiv.org/pdf/2303.01986v1.pdf | Towards Democratizing Joint-Embedding Self-Supervised Learning | Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid developments in recent years, due to its promise to effectively leverage large unlabeled data. The development of JE-SSL methods was driven primarily by the search for ever increasing downstream classification accuracies, using huge computational resource... | ['Pascal Vincent', 'Randall Balestriero', 'Florian Bordes'] | 2023-03-03 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.22591445e-01 3.69293571e-01 -1.66745484e-01 -4.30431187e-01
-9.07444656e-01 -4.77627844e-01 6.96827352e-01 2.68789887e-01
-4.74562675e-01 7.36689210e-01 3.04212928e-01 -5.13509989e-01
-8.50427002e-02 -5.71150124e-01 -5.62176824e-01 -7.61112154e-01
-1.59790486e-01 2.81224728e-01 5.97639233e-02 -4.02339756... | [9.286964416503906, 3.240518569946289] |
7640ecc6-0420-45f8-a79b-f2b0b309f7fe | multimodal-meta-learning-for-time-series | 2108.02842 | null | https://arxiv.org/abs/2108.02842v2 | https://arxiv.org/pdf/2108.02842v2.pdf | Multimodal Meta-Learning for Time Series Regression | Recent work has shown the efficiency of deep learning models such as Fully Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with Time Series Regression (TSR) problems. These models sometimes need a lot of data to be able to generalize, yet the time series are sometimes not long enough to be able ... | ['Lars Schmidt-Thieme', 'Kiran Madhusudhanan', 'Felix Heinrich', 'Sebastian Pineda Arango'] | 2021-08-05 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.05596155e-01 -4.07993913e-01 -2.25930497e-01 -5.83434820e-01
-8.68234873e-01 -3.30686510e-01 5.58979809e-01 1.16066430e-02
-4.63171989e-01 7.06007242e-01 2.20459908e-01 -4.27065194e-01
-3.14073414e-01 -7.57827640e-01 -9.58892226e-01 -4.85261649e-01
-2.89926916e-01 -2.00062487e-02 -3.66514355e-01 -4.83351767... | [7.042303085327148, 3.0009677410125732] |
b4d11af8-13b4-4f14-820e-99797086a3a8 | advanced-baseline-for-3d-human-pose | 2212.11344 | null | https://arxiv.org/abs/2212.11344v1 | https://arxiv.org/pdf/2212.11344v1.pdf | Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach | Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human... | ['Jungang Luo', 'Zichen Gui'] | 2022-12-21 | null | null | null | null | ['3d-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.58228844e-01 -1.04263499e-01 -9.46940780e-02 -3.18110794e-01
-4.56051737e-01 -1.17099494e-01 4.78319496e-01 -2.42521212e-01
-8.20602238e-01 4.97042030e-01 2.30412647e-01 7.72843435e-02
2.66254604e-01 -3.04139137e-01 -2.72771478e-01 -4.43216890e-01
-1.10951319e-01 6.93459392e-01 5.05257726e-01 -4.21361238... | [7.045414447784424, -0.8144016861915588] |
d555a808-c670-43c9-8d9a-bac712e857d0 | benchenas-a-benchmarking-platform-for-1 | null | null | https://ieeexplore.ieee.org/document/9697075 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9697075 | BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search | Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS methods, evolutionary computation-based NAS (ENAS) methods have recently gained much attention. Unfortun... | ['Xiangning Xie; Yuqiao Liu; Yanan Sun; Gary G. Yen; Bing Xue; Mengjie Zhang'] | 2022-12-01 | null | null | null | ieee-transactions-on-evolutionary-computation-3 | ['architecture-search'] | ['methodology'] | [-6.09715343e-01 -8.90652537e-01 2.88319230e-01 -2.09906891e-01
-1.66406557e-02 -3.73398870e-01 1.30099356e-01 -2.58635402e-01
-4.99645859e-01 5.95594347e-01 -3.30741316e-01 -3.67467940e-01
-9.60241184e-02 -8.74981046e-01 -6.46506965e-01 -8.79992604e-01
8.30129907e-02 1.34841233e-01 3.71407509e-01 -3.36160541... | [7.84597110748291, 3.3514459133148193] |
efd42929-5229-4dae-880f-362305049d47 | traffic-sign-detection-and-recognition-using | 2212.08387 | null | https://arxiv.org/abs/2212.08387v1 | https://arxiv.org/pdf/2212.08387v1.pdf | Traffic sign detection and recognition using event camera image reconstruction | This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neural network to reconstruct events into greyscale frames. Two YOLOv4 network models were trained, one based on greyscale images and the other ... | ['Tomasz Kryjak', 'Kamil Jeziorek'] | 2022-12-16 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.11116582e-01 -3.36496532e-01 2.67652273e-01 -2.59966254e-01
-5.48554547e-02 9.19620022e-02 8.00229013e-01 -5.09889185e-01
-7.99930334e-01 7.43219435e-01 -2.71968961e-01 -4.58511919e-01
1.81976601e-01 -1.07511306e+00 -3.92338097e-01 -8.09531629e-01
2.07662389e-01 -2.06959248e-01 6.39733851e-01 -1.80378780... | [8.416977882385254, -0.9635469913482666] |
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