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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7a883491-24fa-40ec-858d-737cd18e3f75 | a-review-of-3d-human-pose-estimation | 2010.06449 | null | https://arxiv.org/abs/2010.06449v3 | https://arxiv.org/pdf/2010.06449v3.pdf | A review of 3D human pose estimation algorithms for markerless motion capture | Human pose estimation is a very active research field, stimulated by its important applications in robotics, entertainment or health and sports sciences, among others. Advances in convolutional networks triggered noticeable improvements in 2D pose estimation, leading modern 3D markerless motion capture techniques to an... | ['Philippe Montesinos', 'Pierre Slangen', 'Denis Mottet', 'Yann Desmarais'] | 2020-10-13 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [-1.04670912e-01 3.74897160e-02 -5.47296405e-01 6.45923242e-03
-2.78916448e-01 -2.64432937e-01 4.82008070e-01 1.96621772e-02
-7.24727631e-01 7.82030404e-01 3.39371413e-01 1.67714238e-01
-1.59610584e-01 -3.53601456e-01 -4.31852221e-01 -2.83811659e-01
-3.65020752e-01 4.47083026e-01 3.04839432e-01 -1.67753294... | [7.006270885467529, -0.686739444732666] |
96e0e583-63df-4881-baba-1d3b369eb6f9 | leveraging-textures-in-zero-shot | 2203.11449 | null | https://arxiv.org/abs/2203.11449v2 | https://arxiv.org/pdf/2203.11449v2.pdf | How well does CLIP understand texture? | We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on various texture and material classification datasets; (2) represent compositional properties of texture such as red dots or yellow stripes on the... | ['Subhransu Maji', 'Chenyun Wu'] | 2022-03-22 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 2.22381040e-01 -1.44942343e-01 -2.20649764e-01 -4.50465053e-01
-2.43041456e-01 -7.88775206e-01 8.57522845e-01 -5.35692237e-02
2.06555769e-01 5.14820158e-01 3.98706079e-01 1.52839169e-01
2.68826094e-02 -9.19797182e-01 -6.27596319e-01 -8.23139787e-01
-2.52981991e-01 5.29836357e-01 1.97370619e-01 -2.25649163... | [10.288725852966309, -0.13394474983215332] |
ac4f20b6-0443-44fc-8a74-96aa383314ec | multi-resolution-factor-graph-based-stereo | 2202.01309 | null | https://arxiv.org/abs/2202.01309v1 | https://arxiv.org/pdf/2202.01309v1.pdf | Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm | A dense depth-map of a scene at an arbitrary view orientation can be estimated from dense view correspondences among multiple lower-dimensional views of the scene. These low-dimensional view correspondences are dependent on the geometrical relationship among the views and the scene. Determining dense view correspondenc... | ['Madhusudhanan Balasubramanian', 'Hanieh Shabanian'] | 2022-02-02 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 2.60775954e-01 -3.68064076e-01 2.65436292e-01 -3.95217031e-01
-4.82487589e-01 -5.74621618e-01 4.65733796e-01 1.18053705e-01
-2.44909421e-01 5.10037899e-01 3.37499201e-01 2.56813914e-01
-2.50452787e-01 -9.28035498e-01 -5.04286826e-01 -5.34070551e-01
4.14016664e-01 4.19877082e-01 7.67215431e-01 -1.38088912... | [9.247384071350098, -2.525109052658081] |
aa708e29-eb3d-4404-98f7-023492adfb58 | slgtformer-an-attention-based-approach-to | 2212.10746 | null | https://arxiv.org/abs/2212.10746v2 | https://arxiv.org/pdf/2212.10746v2.pdf | SLGTformer: An Attention-Based Approach to Sign Language Recognition | Sign language is the preferred method of communication of deaf or mute people, but similar to any language, it is difficult to learn and represents a significant barrier for those who are hard of hearing or unable to speak. A person's entire frontal appearance dictates and conveys specific meaning. However, this fronta... | ['Yu Xiang', 'Neil Song'] | 2022-12-21 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 5.36873937e-02 -1.72168329e-01 -1.68249905e-01 -2.61415064e-01
-9.02545333e-01 -4.14987326e-01 6.19279623e-01 -4.82993484e-01
-4.28347141e-01 1.84518218e-01 9.72477257e-01 -1.62976846e-01
-3.85378748e-01 -4.34589177e-01 -5.76525390e-01 -6.67487025e-01
-2.03907207e-01 3.65016639e-01 4.51520421e-02 -2.26994723... | [9.189470291137695, -6.486324787139893] |
d87df972-a513-42ba-8c3c-b7a411122a20 | probing-sentiment-oriented-pre-training | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Feng_Probing_Sentiment-Oriented_Pre-Training_Inspired_by_Human_Sentiment_Perception_Mechanism_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Feng_Probing_Sentiment-Oriented_Pre-Training_Inspired_by_Human_Sentiment_Perception_Mechanism_CVPR_2023_paper.pdf | Probing Sentiment-Oriented Pre-Training Inspired by Human Sentiment Perception Mechanism | Pre-training of deep convolutional neural networks (DCNNs) plays a crucial role in the field of visual sentiment analysis (VSA). Most proposed methods employ the off-the-shelf backbones pre-trained on large-scale object classification datasets (i.e., ImageNet). While it boosts performance for a big margin against i... | ['Jufeng Yang', 'Jiaxuan Liu', 'Tinglei Feng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['multi-label-learning'] | ['methodology'] | [ 4.81596649e-01 2.31169220e-02 -6.87893480e-02 -5.83314598e-01
-5.23123682e-01 -6.80110455e-01 7.19995916e-01 8.39070603e-02
-4.62346435e-01 3.26615065e-01 5.70961833e-02 -4.63469148e-01
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4.44083422e-01 7.84281865e-02 -7.75863230e-02 -3.51584196... | [10.327132225036621, 2.895373582839966] |
59e3e40e-17ae-42cc-9c97-6ec541979584 | text-style-transfer-back-translation | 2306.01318 | null | https://arxiv.org/abs/2306.01318v1 | https://arxiv.org/pdf/2306.01318v1.pdf | Text Style Transfer Back-Translation | Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the translation of inputs that share a similar style (to be more specific, translation-like inputs), since the source side of BT data is machine-transl... | ['Hao Yang', 'Zhengzhe Yu', 'Xiaoyu Chen', 'Jiaxin Guo', 'Minghan Wang', 'Zongyao Li', 'Hengchao Shang', 'Zhanglin Wu', 'Daimeng Wei'] | 2023-06-02 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 3.62726390e-01 -2.61630386e-01 -3.98986131e-01 -4.47756618e-01
-7.49736726e-01 -6.79517746e-01 7.29353130e-01 -3.83687347e-01
-3.44693244e-01 9.41320300e-01 2.46628568e-01 -6.09766424e-01
7.04578161e-01 -8.44252288e-01 -9.64502931e-01 -4.14889604e-01
8.42119992e-01 5.17310977e-01 1.21322996e-03 -8.41016769... | [11.647781372070312, 10.099991798400879] |
7e408641-b8a5-47fb-803f-70ca9d7c491d | class-similarity-weighted-knowledge | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Phan_Class_Similarity_Weighted_Knowledge_Distillation_for_Continual_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Phan_Class_Similarity_Weighted_Knowledge_Distillation_for_Continual_Semantic_Segmentation_CVPR_2022_paper.pdf | Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation | Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are ... | ['Abdesselam Bouzerdoum', 'Long Tran-Thanh', 'Son Lam Phung', 'The-Anh Ta', 'Minh Hieu Phan'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['continual-semantic-segmentation'] | ['computer-vision'] | [ 3.73659343e-01 1.20021179e-01 3.09114177e-02 -4.85521674e-01
-2.30591819e-01 -5.88572323e-01 4.59869206e-01 5.13484001e-01
-7.51881897e-01 9.74120378e-01 -2.06331462e-01 4.03092392e-02
7.39771947e-02 -7.84711182e-01 -9.51104045e-01 -7.19162405e-01
1.26608640e-01 6.39483869e-01 1.23456991e+00 2.79897987... | [9.424365043640137, 2.1000163555145264] |
8a119044-6f8f-42c4-8196-df9553213b2a | local-facial-makeup-transfer-via-disentangled | 2003.12065 | null | https://arxiv.org/abs/2003.12065v2 | https://arxiv.org/pdf/2003.12065v2.pdf | Local Facial Makeup Transfer via Disentangled Representation | Facial makeup transfer aims to render a non-makeup face image in an arbitrary given makeup one while preserving face identity. The most advanced method separates makeup style information from face images to realize makeup transfer. However, makeup style includes several semantic clear local styles which are still entan... | ['Zhaoyang Sun', 'Shengwu Xiong', 'Ryan Wen Liu', 'Wenxuan Liu', 'Feng Liu'] | 2020-03-27 | null | null | null | null | ['facial-makeup-transfer'] | ['computer-vision'] | [ 3.08778957e-02 2.24075355e-02 -6.41568229e-02 -5.02250075e-01
-2.67921507e-01 -8.95045519e-01 4.78657454e-01 -8.99641097e-01
2.25594372e-01 5.48559129e-01 3.11758846e-01 8.37121755e-02
2.76627362e-01 -1.06487679e+00 -7.84792006e-01 -8.42419744e-01
7.80172229e-01 7.29958490e-02 -2.71019518e-01 -4.44728881... | [12.71579360961914, -0.048966795206069946] |
298cd552-83d4-46fc-9afb-15903d329037 | one-peace-exploring-one-general | 2305.11172 | null | https://arxiv.org/abs/2305.11172v1 | https://arxiv.org/pdf/2305.11172v1.pdf | ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities | In this work, we explore a scalable way for building a general representation model toward unlimited modalities. We release ONE-PEACE, a highly extensible model with 4B parameters that can seamlessly align and integrate representations across vision, audio, and language modalities. The architecture of ONE-PEACE compris... | ['Chang Zhou', 'Xinggang Wang', 'Jingren Zhou', 'Xiaohuan Zhou', 'Shuai Bai', 'Junyang Lin', 'Shijie Wang', 'Peng Wang'] | 2023-05-18 | null | null | null | null | ['audio-classification', 'self-supervised-image-classification', 'visual-grounding', 'action-classification'] | ['audio', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.07399622e-01 -2.18913227e-01 7.40691945e-02 -3.32172096e-01
-1.27977467e+00 -8.55396926e-01 5.56145310e-01 -1.02193199e-01
-4.70849067e-01 1.10400781e-01 2.84583151e-01 -1.23086229e-01
5.15938876e-03 -5.20148754e-01 -7.96263278e-01 -4.00424361e-01
2.91271836e-01 3.32394928e-01 6.34319335e-02 -3.59237283... | [10.8817720413208, 1.5410795211791992] |
7f2739a8-a3ea-4932-a9d1-7059eeb3e5bd | perspective-purposeful-failure-in-artificial | 2102.12076 | null | https://arxiv.org/abs/2102.12076v1 | https://arxiv.org/pdf/2102.12076v1.pdf | Perspective: Purposeful Failure in Artificial Life and Artificial Intelligence | Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intel... | ['Lana Sinapayen'] | 2021-02-24 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-2.96200975e-03 1.02770358e-01 2.53906816e-01 2.72683471e-01
7.53309488e-01 -5.93007505e-01 9.14555788e-01 -1.93886891e-01
-3.91856521e-01 9.66323733e-01 -1.93281636e-01 -5.84316194e-01
-3.61845642e-01 -8.60612631e-01 -3.12577963e-01 -8.08659315e-01
-1.23189658e-01 3.00672889e-01 -7.86778629e-02 -8.12260807... | [5.5686516761779785, 4.150617599487305] |
67ea0134-54ce-4305-8d86-decd4902c10a | speaking-multiple-languages-affects-the-moral | 2211.07733 | null | https://arxiv.org/abs/2211.07733v2 | https://arxiv.org/pdf/2211.07733v2.pdf | Speaking Multiple Languages Affects the Moral Bias of Language Models | Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their performance is often much better on English than many other languages. We ex... | ['Kristian Kersting', 'Alexander Fraser', 'Constantin A. Rothkopf', 'Jindřich Libovický', 'Patrick Schramowski', 'Björn Deiseroth', 'Katharina Hämmerl'] | 2022-11-14 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.93473035e-01 2.53928751e-01 -2.33737603e-01 -3.76771778e-01
-5.62148035e-01 -7.29461551e-01 7.84781635e-01 3.02678794e-01
-1.01160657e+00 1.09874547e+00 5.63849747e-01 -4.52652931e-01
1.12455651e-01 -5.43135107e-01 -5.78056633e-01 -5.72659910e-01
2.60232598e-01 6.57619715e-01 -1.57704636e-01 -5.79405665... | [9.851529121398926, 10.22383975982666] |
107f64a6-0b73-4366-b53f-3cc3932413ec | a-convolutional-neural-network-approach-to | null | null | https://doi.org/10.1016/j.bspc.2019.101597 | https://www.sciencedirect.com/science/article/pii/S1746809419301776/pdfft?md5=ca17956e278efdd4a39ec925adfa2b16&pid=1-s2.0-S1746809419301776-main.pdf | A convolutional neural network approach to detect congestive heart failure | Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning,... | ['Mihaela Porumb', 'Leandro Pecchia', 'Sebastiano Massaro', 'Ernesto Iadanza'] | 2019-09-03 | null | null | null | biomedical-signal-processing-and-control | ['heart-rate-variability', 'congestive-heart-failure-detection', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'medical', 'methodology'] | [ 4.77910995e-01 -2.48371288e-01 -5.02898805e-02 -2.63349444e-01
-7.20307052e-01 -2.81199664e-01 -2.06154227e-01 7.40297258e-01
-4.45534796e-01 7.37124443e-01 4.73448783e-02 -5.17539740e-01
-2.11478129e-01 -5.44273436e-01 1.06722154e-01 -5.05044878e-01
-6.84476852e-01 4.44016367e-01 -7.24205613e-01 1.69337496... | [14.319178581237793, 3.279939889907837] |
b6768ff0-2eed-466d-a8a7-14ef380e703f | an-iterative-bp-cnn-architecture-for-channel | 1707.05697 | null | http://arxiv.org/abs/1707.05697v1 | http://arxiv.org/pdf/1707.05697v1.pdf | An Iterative BP-CNN Architecture for Channel Decoding | Inspired by recent advances in deep learning, we propose a novel iterative
BP-CNN architecture for channel decoding under correlated noise. This
architecture concatenates a trained convolutional neural network (CNN) with a
standard belief-propagation (BP) decoder. The standard BP decoder is used to
estimate the coded b... | ['Cong Shen', 'Feng Wu', 'Fei Liang'] | 2017-07-18 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 2.29168355e-01 -2.87626475e-01 1.77031755e-02 -1.03635557e-01
-6.06247842e-01 -9.12299380e-03 1.45590588e-01 1.45172656e-01
-5.80562294e-01 6.74627125e-01 -8.78776163e-02 -6.40555143e-01
2.07103223e-01 -6.89251423e-01 -9.77104485e-01 -1.02128446e+00
-7.03300759e-02 -1.66031197e-01 2.30389148e-01 9.16335508... | [6.4093499183654785, 1.488447904586792] |
85753ce3-55bc-489a-be5f-843d5b0cc95b | semi-centralised-multi-agent-reinforcement | 2209.01054 | null | https://arxiv.org/abs/2209.01054v2 | https://arxiv.org/pdf/2209.01054v2.pdf | Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction | Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms. Despite its popularity, it suffers from a critical drawback due to its reliance on learning from a single sample of the joint-action at a given state. As agents explor... | ['David Mguni', 'Jun Wang', 'Kun Shao', 'Matthew Taylor', 'Jianhong Wang', 'Zipeng Dai', 'Tianpei Yang', 'Juliusz Ziomek', 'Taher Jafferjee'] | 2022-09-02 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-4.61684614e-01 -3.49919647e-02 -4.70126271e-01 1.94782674e-01
-9.81564522e-01 -5.47585189e-01 7.74039447e-01 2.31885314e-01
-9.20668960e-01 1.25496352e+00 -1.47131845e-01 -4.74745542e-01
-1.28342137e-01 -5.75402379e-01 -7.88509548e-01 -8.47564697e-01
-5.40534198e-01 8.10386598e-01 2.57494152e-01 -2.09496394... | [3.9349992275238037, 2.1275386810302734] |
e9ac8f29-6c44-45f0-9884-dd7a1908bb3d | vpair-aerial-visual-place-recognition-and | 2205.11567 | null | https://arxiv.org/abs/2205.11567v1 | https://arxiv.org/pdf/2205.11567v1.pdf | VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments | Visual Place Recognition and Visual Localization are essential components in navigation and mapping for autonomous vehicles especially in GNSS-denied navigation scenarios. Recent work has focused on ground or close to ground applications such as self-driving cars or indoor-scenarios and low-altitude drone flights. Howe... | ['Daniel Cremers', 'Fahmi Rouatbi', 'Michael Schleiss'] | 2022-05-23 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.43411338e-01 -2.33239040e-01 3.20391804e-02 -6.42786682e-01
-9.71229225e-02 -9.99566615e-01 5.92574239e-01 -3.13351840e-01
-6.04780853e-01 7.62037158e-01 -4.68732238e-01 -4.66321826e-01
-4.37392034e-02 -1.06670702e+00 -7.02115297e-01 -4.45906669e-01
-4.15844202e-01 4.97704148e-01 3.40781391e-01 -8.28975439... | [7.266829967498779, -1.9391705989837646] |
5292bc9b-657b-4a96-a813-76f4dd6cbd65 | mts2graph-interpretable-multivariate-time | 2306.03834 | null | https://arxiv.org/abs/2306.03834v1 | https://arxiv.org/pdf/2306.03834v1.pdf | MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs | Conventional time series classification approaches based on bags of patterns or shapelets face significant challenges in dealing with a vast amount of feature candidates from high-dimensional multivariate data. In contrast, deep neural networks can learn low-dimensional features efficiently, and in particular, Convolut... | ['Zahra Ahmadi', 'Abdul Hakmeh', 'Raneen Younis'] | 2023-06-06 | null | null | null | null | ['graph-embedding', 'time-series-classification'] | ['graphs', 'time-series'] | [ 8.80263820e-02 -1.93273678e-01 -6.75691897e-03 -2.88261592e-01
-5.01955897e-02 -4.84823644e-01 5.72244167e-01 6.29364908e-01
-1.57520190e-01 4.49774086e-01 -2.05060933e-03 -2.15242058e-01
-8.71860862e-01 -7.57270157e-01 -4.37706590e-01 -7.40011215e-01
-8.15036952e-01 1.86107576e-01 -8.42550844e-02 -2.89563268... | [7.15551233291626, 2.982861280441284] |
4ce8fd74-44ba-401b-b9d1-ec62028c7e35 | rethinking-few-shot-class-incremental | 2207.09963 | null | https://arxiv.org/abs/2207.09963v1 | https://arxiv.org/pdf/2207.09963v1.pdf | Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry | Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously. The current protocol of FSCIL is built by mimicking the general class-incremental learning setting, while it is not totally appropri... | ['Li Liu', 'Wei Peng', 'Zitong Yu', 'Yawen Cui'] | 2022-07-20 | null | null | null | null | ['few-shot-class-incremental-learning', 'open-set-learning'] | ['methodology', 'miscellaneous'] | [ 2.15174884e-01 3.27362508e-01 -1.77187428e-01 -3.36692721e-01
-3.62854362e-01 -4.16464508e-01 4.72022742e-01 4.73676294e-01
-5.42052746e-01 7.79299259e-01 -2.65892237e-01 -7.02904090e-02
-5.53708673e-01 -1.00017798e+00 -6.48098528e-01 -9.56728816e-01
-3.93045172e-02 4.90581065e-01 5.55454373e-01 1.13451632... | [9.82714557647705, 3.324594497680664] |
c8b9603c-6030-40fd-b47d-10a9ee4e3e3a | 3d-semantic-segmentation-of-modular-furniture | null | null | https://ieeexplore.ieee.org/document/7926598 | http://web-info8.informatik.rwth-aachen.de/media/papers/egpaper_final.pdf | 3D Semantic Segmentation of Modular Furniture using rjMCMC | In this paper we propose a novel approach to identify and label the structural elements of furniture e.g. wardrobes, cabinets etc. Given a furniture item, the subdivision into its structural components like doors, drawers and shelves is difficult as the number of components and their spatial arrangements varies severel... | ['Bastian (*equal contribution)', 'Markus; Leibe', 'Manu; Mathias', 'Ishrat; Tom*', 'Badami*'] | 2017-05-15 | null | null | null | wacv-2017-2017-5 | ['furniture-segmentation'] | ['computer-vision'] | [ 1.98048726e-01 -6.56801835e-02 -1.03685874e-02 -1.15450267e-02
-2.89803833e-01 -1.01462615e+00 5.83023489e-01 2.96840191e-01
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-1.35319382e-01 -8.29128087e-01 -6.51166081e-01 -7.39216149e-01
3.72234499e-03 9.22127426e-01 3.70542794e-01 5.92901446... | [8.105355262756348, -2.499748468399048] |
301d8185-01bf-4aa4-a1af-0f8a0926aaa5 | generative-meta-learning-for-zero-shot | 2305.01920 | null | https://arxiv.org/abs/2305.01920v1 | https://arxiv.org/pdf/2305.01920v1.pdf | Generative Meta-Learning for Zero-Shot Relation Triplet Extraction | The zero-shot relation triplet extraction (ZeroRTE) task aims to extract relation triplets from a piece of text with unseen relation types. The seminal work adopts the pre-trained generative model to generate synthetic samples for new relations. However, current generative models lack the optimization process of model ... | ['Tieyun Qian', 'Wanli Li'] | 2023-05-03 | null | null | null | null | ['general-knowledge', 'zero-shot-relation-triplet-extraction'] | ['miscellaneous', 'natural-language-processing'] | [ 2.70449281e-01 6.86207056e-01 -4.03965533e-01 -4.78910416e-01
-9.64234114e-01 -1.28821954e-01 9.98365223e-01 -3.06883872e-01
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2.70252913e-01 9.37416673e-01 -2.58479547e-02 -4.96980250... | [9.476325035095215, 8.504120826721191] |
8fdedd28-18a1-4bbb-b970-c2387ba71434 | semantic-based-neural-network-repair | 2306.07995 | null | https://arxiv.org/abs/2306.07995v1 | https://arxiv.org/pdf/2306.07995v1.pdf | Semantic-Based Neural Network Repair | Recently, neural networks have spread into numerous fields including many safety-critical systems. Neural networks are built (and trained) by programming in frameworks such as TensorFlow and PyTorch. Developers apply a rich set of pre-defined layers to manually program neural networks or to automatically generate them ... | ['Jun Sun', 'Richard Schumi'] | 2023-06-12 | null | null | null | null | ['automl'] | ['methodology'] | [ 2.80604154e-01 3.15378934e-01 2.02073336e-01 -2.42541224e-01
-1.40746564e-01 -7.12283373e-01 6.16409965e-02 4.43540663e-02
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6.65627494e-02 -1.09983563e+00 -1.35692763e+00 -1.42595395e-01
-1.36385471e-01 1.50332853e-01 5.70940435e-01 -1.72543123... | [7.488786220550537, 7.674733638763428] |
a44e4f60-574b-4f65-b750-ff47039915c4 | vipr-visual-odometry-aided-pose-regression | 1912.08263 | null | https://arxiv.org/abs/1912.08263v3 | https://arxiv.org/pdf/1912.08263v3.pdf | ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization | Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a... | ['Christoffer Löffler', 'Felix Ott', 'Christopher Mutschler', 'Tobias Feigl'] | 2019-12-17 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-3.97202045e-01 -1.65977776e-01 -3.56061459e-01 -3.03919375e-01
-1.81071028e-01 -5.10105133e-01 6.55669332e-01 -2.01798484e-01
-5.52072763e-01 7.31321454e-01 3.21334630e-01 -6.68828860e-02
3.74648571e-02 -7.86838591e-01 -9.12711799e-01 -2.34999686e-01
-2.44341746e-01 6.66032195e-01 3.89905810e-01 -7.76560009... | [8.038272857666016, -2.1512744426727295] |
a4a4e986-2b52-4bb1-acce-2b99b11b6b0c | lite-light-field-transparency-estimation-for | 1910.00721 | null | https://arxiv.org/abs/1910.00721v4 | https://arxiv.org/pdf/1910.00721v4.pdf | LIT: Light-field Inference of Transparency for Refractive Object Localization | Translucency is prevalent in everyday scenes. As such, perception of transparent objects is essential for robots to perform manipulation. Compared with texture-rich or texture-less Lambertian objects, transparency induces significant uncertainty on object appearances. Ambiguity can be due to changes in lighting, viewpo... | ['Zheming Zhou', 'Odest Chadwicke Jenkins', 'Xiaotong Chen'] | 2019-10-02 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 4.50816154e-01 -5.03819957e-02 4.25613731e-01 -6.22836649e-01
-4.41100031e-01 -6.27292454e-01 3.78986388e-01 -4.96296912e-01
-2.09953710e-01 3.60202968e-01 -2.50017382e-02 2.89409190e-01
9.02316496e-02 -5.66887736e-01 -1.12513375e+00 -5.25722086e-01
3.24851900e-01 7.65284479e-01 2.42178306e-01 1.06829971... | [7.066502571105957, -2.108020782470703] |
a1445c46-e678-4231-894d-066030b2e1b2 | batchformer-learning-to-explore-sample | 2203.01522 | null | https://arxiv.org/abs/2203.01522v2 | https://arxiv.org/pdf/2203.01522v2.pdf | BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning | Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned issues, a variety of methods have been devised to explore the sample relationships in... | ['DaCheng Tao', 'Baosheng Yu', 'Zhi Hou'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hou_BatchFormer_Learning_To_Explore_Sample_Relationships_for_Robust_Representation_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hou_BatchFormer_Learning_To_Explore_Sample_Relationships_for_Robust_Representation_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 9.78057384e-02 -1.06713280e-01 -2.93351531e-01 -6.70619309e-01
-4.03368324e-01 -3.05672854e-01 3.88981283e-01 -2.14065779e-02
-2.90818006e-01 7.59740710e-01 9.15322006e-02 -2.15762436e-01
-3.34325999e-01 -7.79190183e-01 -6.03613675e-01 -9.14834440e-01
2.65465707e-01 3.71541172e-01 5.73657639e-02 -1.32450387... | [9.581974983215332, 3.523075819015503] |
2c903133-e990-4363-95b8-c1c1f648aca0 | distributional-lesk-effective-knowledge-based | null | null | https://aclanthology.org/W17-6931 | https://aclanthology.org/W17-6931.pdf | Distributional Lesk: Effective Knowledge-Based Word Sense Disambiguation | null | ['Gertjan van Noord', 'Dieke Oele'] | 2017-01-01 | null | null | null | ws-2017-1 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.394874095916748, 3.791229486465454] |
4cca679d-acdb-4cc1-80c4-1a4ef93c298a | deep-multi-frame-filtering-for-hearing-aids | 2305.08225 | null | https://arxiv.org/abs/2305.08225v1 | https://arxiv.org/pdf/2305.08225v1.pdf | Deep Multi-Frame Filtering for Hearing Aids | Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep filtering (DF) recently demonstrated its capabilities for low-latency scenarios like hearing aids with its complex multi-frame (MF) filter. Alternatively, the complex filte... | ['Andreas Maier', 'Alberto N. Escalante-B.', 'Tobias Rosenkranz', 'Hendrik Schröter'] | 2023-05-14 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.49533176e-01 -2.92991042e-01 3.33499283e-01 -1.19100578e-01
-1.10266674e+00 -3.93292010e-01 5.84938109e-01 -1.09505549e-01
-6.79405808e-01 6.03520274e-01 8.20759714e-01 -4.91156518e-01
-2.64000505e-01 -2.35684097e-01 -4.48404610e-01 -6.57255888e-01
-1.56308904e-01 -3.52253437e-01 3.45601857e-01 -8.40056017... | [15.125001907348633, 5.8861083984375] |
2e4ebafc-e30d-4377-9d44-9e3d9522fd0e | dense-network-expansion-for-class-incremental | 2303.12696 | null | https://arxiv.org/abs/2303.12696v1 | https://arxiv.org/pdf/2303.12696v1.pdf | Dense Network Expansion for Class Incremental Learning | The problem of class incremental learning (CIL) is considered. State-of-the-art approaches use a dynamic architecture based on network expansion (NE), in which a task expert is added per task. While effective from a computational standpoint, these methods lead to models that grow quickly with the number of tasks. A new... | ['Nuno Vasconcelos', 'Dashan Gao', 'Jiancheng Lyu', 'Yunsheng Li', 'Zhiyuan Hu'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Dense_Network_Expansion_for_Class_Incremental_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Dense_Network_Expansion_for_Class_Incremental_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['class-incremental-learning'] | ['computer-vision'] | [ 1.42816737e-01 2.57662028e-01 1.12546295e-01 -1.15621567e-01
-1.86978236e-01 -3.25407177e-01 6.23313904e-01 1.86533213e-01
-7.19898164e-01 6.86487079e-01 -1.47711754e-01 4.18632962e-02
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1.52616724e-01 4.64790553e-01 8.68417084e-01 -8.83095562... | [9.629202842712402, 3.375495195388794] |
5ba7da10-2c8e-4759-929f-a060667c8d89 | few-shot-inductive-learning-on-temporal | 2211.08169 | null | https://arxiv.org/abs/2211.08169v1 | https://arxiv.org/pdf/2211.08169v1.pdf | Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information | Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Simi... | ['Volker Tresp', 'Zhen Han', 'Yunpu Ma', 'Bailan He', 'Jingpei Wu', 'Zifeng Ding'] | 2022-11-15 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-3.91574621e-01 6.87368512e-01 -6.55207634e-01 -1.02409758e-01
-1.75272897e-01 -1.21030383e-01 3.61796945e-01 4.49198633e-01
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-4.70337451e-01 -1.48838627e+00 -7.69125283e-01 -1.04832046e-01
-6.72302902e-01 6.64553165e-01 6.44981146e-01 -2.78420627... | [8.688430786132812, 7.9607768058776855] |
578e44dd-2f68-4d0e-bb3c-0abe369798de | an-inter-and-intra-band-loss-for | 2008.05133 | null | https://arxiv.org/abs/2008.05133v1 | https://arxiv.org/pdf/2008.05133v1.pdf | An Inter- and Intra-Band Loss for Pansharpening Convolutional Neural Networks | Pansharpening aims to fuse panchromatic and multispectral images from the satellite to generate images with both high spatial and spectral resolution. With the successful applications of deep learning in the computer vision field, a lot of scholars have proposed many convolutional neural networks (CNNs) to solve the pa... | ['Bo Huang', 'Jiajun Cai'] | 2020-08-12 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 4.66021478e-01 -5.78590989e-01 -3.82733867e-02 -3.93330276e-01
-5.45731246e-01 -3.67918313e-01 3.05242062e-01 -3.98934603e-01
-4.88670081e-01 6.45548820e-01 3.19869444e-02 -1.53900370e-01
-4.23689157e-01 -1.24474955e+00 -6.49916947e-01 -9.45755661e-01
4.37225252e-01 -4.80273187e-01 1.17143579e-01 -6.05956852... | [10.210060119628906, -1.8878494501113892] |
f2914c63-b27d-4517-86c6-ade653710804 | histogram-equalization-of-the-image | 2108.12818 | null | https://arxiv.org/abs/2108.12818v1 | https://arxiv.org/pdf/2108.12818v1.pdf | Histogram Equalization Of The Image | The relevance and impact of probability distributions on image processing are the subject of this study.It may be characterized as a probability distribution function of brightness for a certain area, which might be a whole picture. To generate a histogram, the probability density function of the brightness is frequent... | ['Ibraheem Shayea', 'W. T Al-Shaibani', 'Melih Gokdemir', 'Irem Doken'] | 2021-08-29 | null | null | null | null | ['local-color-enhancement'] | ['computer-vision'] | [ 3.89788479e-01 -2.54305780e-01 -4.32082228e-02 -4.90396500e-01
-1.64652035e-01 -5.41287720e-01 4.50180441e-01 1.71588078e-01
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9.45159346e-02 -1.14494598e+00 -4.38045979e-01 -1.21308327e+00
1.26498282e-01 -9.48368087e-02 5.45856714e-01 1.67880997... | [10.862356185913086, -2.4072394371032715] |
ad575bb3-785b-48d9-a9db-5d38ac165a08 | latent-tree-learning-with-ordered-neurons | 2010.04926 | null | https://arxiv.org/abs/2010.04926v1 | https://arxiv.org/pdf/2010.04926v1.pdf | Latent Tree Learning with Ordered Neurons: What Parses Does It Produce? | Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures. One such model is ON-LSTM (Shen et al., 2019), which is trained on language modelling and has near-state-of-the-art performance on unsupervised parsing. In order to better understand the performanc... | ['Yian Zhang'] | 2020-10-10 | null | https://aclanthology.org/2020.blackboxnlp-1.11 | https://aclanthology.org/2020.blackboxnlp-1.11.pdf | emnlp-blackboxnlp-2020-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.54891038e-01 7.63989508e-01 -1.27088591e-01 -5.14539778e-01
-1.06078041e+00 -8.13133180e-01 5.28941631e-01 2.99375236e-01
-3.03661913e-01 5.63567698e-01 6.54476583e-01 -8.85612011e-01
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-2.87404060e-02 5.69322288e-01 1.61500499e-01 4.43066191... | [10.420512199401855, 9.518903732299805] |
63b0195c-f121-45e5-9b43-3fdc486fc617 | a-harmonic-based-fault-detection-algorithm | 2303.15957 | null | https://arxiv.org/abs/2303.15957v1 | https://arxiv.org/pdf/2303.15957v1.pdf | A Harmonic-based Fault detection algorithm for Microgrids | The trend toward Microgrids (MGs) is significantly increasing by employing Distributed Generators (DGs) which leads to new challenges, especially in the fault detection. This paper proposes an algorithm based on the Total Harmonic Distortion (THD) of the grid voltages to detect the events of faults in MGs. The algorith... | ['Josep. M. Guerrero', 'Jorge. El mariachet', 'Jose Matas', 'Wael Al Hanaineh'] | 2023-03-28 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-5.18901646e-01 -5.46459675e-01 5.22909403e-01 2.24887103e-01
-2.17145517e-01 -9.68309343e-01 5.65854013e-01 3.07551384e-01
5.97223878e-01 1.03171825e+00 -1.24833375e-01 -2.32362691e-02
-3.06391358e-01 -7.26748765e-01 1.36981621e-01 -1.13371551e+00
-6.07205451e-01 -1.35969277e-02 1.01584621e-01 -1.21849582... | [5.917745113372803, 2.5414061546325684] |
58033dc7-caa2-415e-8024-97cd7653b6e8 | fairness-and-diversity-in-recommender-systems | 2307.04644 | null | https://arxiv.org/abs/2307.04644v1 | https://arxiv.org/pdf/2307.04644v1.pdf | Fairness and Diversity in Recommender Systems: A Survey | Recommender systems are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to increasing attention to fairness-aware and diversity-aware recommender... | ['Tyler Derr', 'Charu Aggarwal', 'Xueqi Cheng', 'Yunchao Liu', 'Yu Wang', 'Yuying Zhao'] | 2023-07-10 | null | null | null | null | ['fairness', 'recommendation-systems', 'fairness'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [-3.92285854e-01 -1.37771308e-01 -7.30422676e-01 -6.27729952e-01
-1.14392184e-01 -6.04385257e-01 2.48959467e-01 2.52576381e-01
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-4.18430179e-01 -6.42129421e-01 1.52241513e-01 -2.71988750e-01
1.12129360e-01 -1.26879085e-02 -2.98733175e-01 -6.27625227... | [9.646844863891602, 5.660115718841553] |
10148710-4e1b-42d3-92ba-ab8a83fc5655 | enabling-surrogate-assisted-evolutionary | 2301.13374 | null | https://arxiv.org/abs/2301.13374v1 | https://arxiv.org/pdf/2301.13374v1.pdf | Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding | Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population us... | ['Ke Tang', 'Peng Yang', 'Guiying Li', 'Jinyuan Zhang', 'Xiaxi Li', 'Lan Tang'] | 2023-01-31 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-1.65348649e-01 -2.33904541e-01 1.40166461e-01 2.16366202e-02
-1.50839940e-01 -3.53879273e-01 4.39293534e-01 -6.77983239e-02
-1.11621559e+00 1.07062125e+00 -4.16443110e-01 -4.31338817e-01
-3.25931728e-01 -9.03822780e-01 -6.61183357e-01 -9.98748779e-01
1.82082672e-02 3.73932451e-01 7.04081804e-02 -5.11389017... | [4.107669353485107, 2.1440775394439697] |
ba46d17a-1408-40b3-8f12-528dd8f0fd0c | re-id-driven-localization-refinement-for | 1909.08580 | null | https://arxiv.org/abs/1909.08580v1 | https://arxiv.org/pdf/1909.08580v1.pdf | Re-ID Driven Localization Refinement for Person Search | Person search aims at localizing and identifying a query person from a gallery of uncropped scene images. Different from person re-identification (re-ID), its performance also depends on the localization accuracy of a pedestrian detector. The state-of-the-art methods train the detector individually, and the detected bo... | ['Jiacheng Ye', 'Xin Tan', 'Nong Sang', 'Chuchu Han', 'Changxin Gao', 'Yunshan Zhong', 'Chi Zhang'] | 2019-09-18 | re-id-driven-localization-refinement-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Han_Re-ID_Driven_Localization_Refinement_for_Person_Search_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Han_Re-ID_Driven_Localization_Refinement_for_Person_Search_ICCV_2019_paper.pdf | iccv-2019-10 | ['person-search'] | ['computer-vision'] | [-5.03742039e-01 -2.69377649e-01 1.30376682e-01 -3.43485206e-01
-8.73600006e-01 -4.69884932e-01 5.35313308e-01 -4.45062108e-02
-9.85886633e-01 4.74702060e-01 3.34138870e-01 4.00797129e-01
3.49285841e-01 -5.87038755e-01 -3.88608307e-01 -6.67131424e-01
2.98509926e-01 7.24576354e-01 5.21114469e-01 1.34668782... | [14.801182746887207, 0.7926192283630371] |
524e24ed-350b-423a-be34-3ae13d32b1f2 | fully-and-weakly-supervised-referring | 2212.10278 | null | https://arxiv.org/abs/2212.10278v1 | https://arxiv.org/pdf/2212.10278v1.pdf | Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning | Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the target according to the given language expression, has drawn increasing attention. Existing methods jointly consider the localization and segmentation steps, which rely on the fused visual and linguistic features for both steps. We... | ['Yao Zhao', 'Eng Gee Lim', 'Jimin Xiao', 'MingJie Sun', 'Hui Li'] | 2022-12-17 | null | null | null | null | ['referring-expression', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.56511927e-01 1.54874608e-01 -4.09255922e-01 -2.81323045e-01
-9.74484622e-01 -9.38895404e-01 4.53541577e-01 -1.01666292e-02
-6.21050537e-01 4.50097442e-01 -1.55082881e-01 -7.45537803e-02
4.87573445e-01 -4.46244776e-01 -8.35862994e-01 -7.08205581e-01
4.90294784e-01 3.22354257e-01 6.07681453e-01 9.37680602... | [9.961738586425781, 0.9575672745704651] |
4b67ff2d-5a0e-419e-bd50-008416259c17 | ganalyzer-analysis-and-manipulation-of-gans | 2302.00908 | null | https://arxiv.org/abs/2302.00908v1 | https://arxiv.org/pdf/2302.00908v1.pdf | GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis | Generative Adversarial Networks (GANs) are capable of synthesizing high-quality facial images. Despite their success, GANs do not provide any information about the relationship between the input vectors and the generated images. Currently, facial GANs are trained on imbalanced datasets, which generate less diverse imag... | ['Timothy Sweeny', 'Sarah Ariel Lamer', 'Mohammad H. Mahoor', 'Ali Pourramezan Fard'] | 2023-02-02 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 2.61977106e-01 5.01702785e-01 -4.32970859e-02 -6.15176857e-01
-4.16298360e-01 -5.54108620e-01 7.15929270e-01 -7.62781978e-01
1.69028059e-01 9.13712382e-01 2.00292289e-01 2.93802619e-01
4.24632430e-01 -1.04168546e+00 -6.08380020e-01 -1.13214707e+00
3.41265827e-01 5.31376421e-01 -8.40543687e-01 -2.25052908... | [12.761621475219727, 0.2664283514022827] |
9d690440-c733-4510-ba23-209787c87828 | road-images-augmentation-with-synthetic | 2101.04927 | null | https://arxiv.org/abs/2101.04927v1 | https://arxiv.org/pdf/2101.04927v1.pdf | Road images augmentation with synthetic traffic signs using neural networks | Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic t... | ['Vlad Shakhuro', 'Boris Faizov', 'Anton Konushin'] | 2021-01-13 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 4.97880578e-01 -1.63444862e-01 4.45330739e-02 -2.48203918e-01
-5.42200625e-01 -4.09223169e-01 8.83529007e-01 -1.18019724e+00
-1.35547638e-01 8.77930403e-01 -1.11147039e-01 -1.22703075e-01
3.06268066e-01 -6.99726939e-01 -1.06504595e+00 -8.28841150e-01
5.18900454e-01 5.62744141e-01 4.32605535e-01 -2.65204489... | [8.064159393310547, -0.8436663746833801] |
d16b3732-5415-43a2-9bf0-43ace7f6a04b | inter-and-intra-patient-ecg-heartbeat | 1812.07421 | null | http://arxiv.org/abs/1812.07421v1 | http://arxiv.org/pdf/1812.07421v1.pdf | Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach | Electrocardiogram (ECG) signal is a common and powerful tool to study heart
function and diagnose several abnormal arrhythmia. While there have been
remarkable improvements in cardiac arrhythmia classification methods, they
still cannot offer an acceptable performance in detecting different heart
conditions, especially... | ['Fatemeh Afghah', 'Sajad Mousavi'] | 2018-12-09 | inter-and-intra-patient-ecg-heartbeat-1 | null | null | arxiv181207421-2018-12 | ['arrhythmia-detection', 'heartbeat-classification'] | ['medical', 'medical'] | [ 5.50471097e-02 -3.54786545e-01 -9.58676189e-02 -2.85883963e-01
-5.89801431e-01 -5.00125706e-01 -4.99438606e-02 3.47477525e-01
-3.58384490e-01 8.07282805e-01 -4.30529416e-01 -3.85887891e-01
-3.26425612e-01 -5.32219470e-01 -4.01362814e-02 -6.28573060e-01
-3.58108014e-01 5.19051373e-01 -1.61443666e-01 2.30148777... | [14.323874473571777, 3.2819316387176514] |
60d0904c-23b2-405d-814c-abc723c602bf | just-go-with-the-flow-self-supervised-scene | 1912.00497 | null | https://arxiv.org/abs/1912.00497v2 | https://arxiv.org/pdf/1912.00497v2.pdf | Just Go with the Flow: Self-Supervised Scene Flow Estimation | When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular interest in the field of autonomous driving, in which many cars, people, bicycles, and other objects need to be accurately tracked. Current ... | ['David Held', 'Himangi Mittal', 'Brian Okorn'] | 2019-12-01 | just-go-with-the-flow-self-supervised-scene-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_Just_Go_With_the_Flow_Self-Supervised_Scene_Flow_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mittal_Just_Go_With_the_Flow_Self-Supervised_Scene_Flow_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-flow-estimation'] | ['computer-vision'] | [-4.16784100e-02 4.02692147e-02 -3.93478096e-01 -5.25845110e-01
-3.95724207e-01 -5.16618907e-01 8.94224703e-01 -9.12665576e-02
-7.07131386e-01 6.79502070e-01 -6.93416670e-02 -2.33762950e-01
7.13025033e-02 -7.18069851e-01 -7.95372725e-01 -3.48047167e-01
-2.61765927e-01 7.49849796e-01 9.55323100e-01 -3.34082574... | [8.501389503479004, -1.87962007522583] |
b00d16e9-181c-4e93-8df2-197a9e2da4ad | enhancing-egocentric-3d-pose-estimation-with | 2201.02017 | null | https://arxiv.org/abs/2201.02017v3 | https://arxiv.org/pdf/2201.02017v3.pdf | Enhancing Egocentric 3D Pose Estimation with Third Person Views | In this paper, we propose a novel approach to enhance the 3D body pose estimation of a person computed from videos captured from a single wearable camera. The key idea is to leverage high-level features linking first- and third-views in a joint embedding space. To learn such embedding space we introduce First2Third-Pos... | ['Francesc Moreno-Noguer', 'Albert Pumarola', 'Enric Corona', 'Mariella Dimiccoli', 'Ameya Dhamanaskar'] | 2022-01-06 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 9.21441540e-02 -7.77827874e-02 -1.61514342e-01 -4.28509891e-01
-7.16626763e-01 -5.97962379e-01 5.21213233e-01 -3.43635261e-01
-3.95115376e-01 3.42378438e-01 7.59342790e-01 7.71804810e-01
5.88689893e-02 -2.65939295e-01 -7.08133519e-01 -4.30735171e-01
-3.43798697e-01 3.32442492e-01 -1.69334024e-01 1.07755633... | [7.069746494293213, -0.79920893907547] |
7a497959-5831-4d02-b079-bf2b23cca4b6 | robust-representation-learning-with-reliable | 2305.16335 | null | https://arxiv.org/abs/2305.16335v1 | https://arxiv.org/pdf/2305.16335v1.pdf | Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering | Short text clustering is challenging since it takes imbalanced and noisy data as inputs. Existing approaches cannot solve this problem well, since (1) they are prone to obtain degenerate solutions especially on heavy imbalanced datasets, and (2) they are vulnerable to noises. To tackle the above issues, we propose a Ro... | ['Xinting Liao', 'Chaochao Chen', 'Weiming Liu', 'Mengling Hu', 'Xiaolin Zheng'] | 2023-05-23 | null | null | null | null | ['pseudo-label', 'text-clustering', 'short-text-clustering'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 2.01141126e-02 -3.05706203e-01 -1.82733849e-01 -5.63811481e-01
-1.24509943e+00 -3.92429829e-01 2.59879440e-01 3.80509973e-01
-2.14029863e-01 3.89187723e-01 3.90209436e-01 -3.11420858e-02
-7.21825585e-02 -5.70370257e-01 -4.62104529e-01 -8.45995545e-01
2.99891800e-01 6.04140460e-01 9.48727801e-02 -5.74940853... | [9.399024963378906, 3.9870641231536865] |
f0f53bfc-a52d-42db-8775-24ebd246e538 | portrait-a-hybrid-approach-to-create | 2305.11536 | null | https://arxiv.org/abs/2305.11536v1 | https://arxiv.org/pdf/2305.11536v1.pdf | PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT | Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of information posted significantly varies across disasters depending on several factors like the location, type, severity, etc. Verification of... | ['Sourav Kumar Dandapat', 'Roshni Chakraborty', 'Piyush Kumar Garg'] | 2023-05-19 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.63459603e-02 2.82484561e-01 -3.96717116e-02 -1.48301795e-01
-1.25076902e+00 -8.20853949e-01 7.30103135e-01 1.10485315e+00
-2.76088864e-01 9.96246636e-01 1.06052160e+00 7.14303702e-02
5.06664962e-02 -9.26210403e-01 -2.42597654e-01 -4.64957356e-01
-1.22344017e-01 5.57820141e-01 -1.17781051e-01 -4.87848639... | [12.505966186523438, 9.4557466506958] |
730a25df-2d78-4a7f-adfc-7a7876b7964b | efficient-few-shot-learning-for-pixel-precise | 2210.15570 | null | https://arxiv.org/abs/2210.15570v1 | https://arxiv.org/pdf/2210.15570v1.pdf | Efficient few-shot learning for pixel-precise handwritten document layout analysis | Layout analysis is a task of uttermost importance in ancient handwritten document analysis and represents a fundamental step toward the simplification of subsequent tasks such as optical character recognition and automatic transcription. However, many of the approaches adopted to solve this problem rely on a fully supe... | ['Claudio Piciarelli', 'Emanuela Colombi', 'Gian Luca Foresti', 'Matteo Paier', 'Silvia Zottin', 'Axel De Nardin'] | 2022-10-27 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 4.55946505e-01 -5.06339610e-01 -5.78604117e-02 -2.11361453e-01
-8.24661314e-01 -5.61655641e-01 7.25902498e-01 2.97253877e-01
-6.90925896e-01 7.80975103e-01 -3.67063247e-02 -2.91460752e-01
-2.10287198e-02 -6.35024428e-01 -4.43847030e-01 -8.55912685e-01
4.66543615e-01 7.64793634e-01 5.52427232e-01 -4.83918339... | [11.794281959533691, 2.5499942302703857] |
6a11d9a8-4c32-4c69-88e2-93796c2e3789 | perceptual-grouping-in-vision-language-models | 2210.09996 | null | https://arxiv.org/abs/2210.09996v2 | https://arxiv.org/pdf/2210.09996v2.pdf | Perceptual Grouping in Contrastive Vision-Language Models | Recent advances in zero-shot image recognition suggest that vision-language models learn generic visual representations with a high degree of semantic information that may be arbitrarily probed with natural language phrases. Understanding an image, however, is not just about understanding what content resides within an... | ['Jonathon Shlens', 'Alexander Toshev', 'Yinfei Yang', 'Sachin Ravi', 'Brandon McKinzie', 'Kanchana Ranasinghe'] | 2022-10-18 | null | null | null | null | ['unsupervised-semantic-segmentation-with', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.94093716e-01 -3.04709598e-02 -2.70529062e-01 -5.10456085e-01
-6.85436487e-01 -5.38079321e-01 1.01002717e+00 2.90686309e-01
-3.34691972e-01 1.54046059e-01 2.90727466e-01 -6.97453544e-02
-3.27658802e-01 -7.86239147e-01 -1.08546817e+00 -6.14980817e-01
1.47104012e-02 4.49884146e-01 3.32485557e-01 -3.75696011... | [9.926176071166992, 1.9866281747817993] |
9fb33e9a-1897-4e76-a68a-e6a97dfe0049 | efficient-subtyping-of-ovarian-cancer | 2302.08867 | null | https://arxiv.org/abs/2302.08867v2 | https://arxiv.org/pdf/2302.08867v2.pdf | Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning | Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process. We propose Discriminative Region Active Sampling for Multiple Instance Learning (DRAS-MIL), a computationally efficient slide classification met... | ['Nishant Ravikumar', 'Nicolas M. Orsi', 'Geoff Hall', 'Kieran Zucker', 'Katie Allen', 'Jack Breen'] | 2023-02-17 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 4.94929165e-01 4.10413802e-01 -4.47730899e-01 -7.99497738e-02
-1.56494713e+00 -3.44313860e-01 1.75498221e-02 6.82898164e-01
-7.55023062e-01 5.66854417e-01 1.18645422e-01 -6.98984444e-01
-6.98320642e-02 -6.08085275e-01 -2.68161893e-01 -1.19333804e+00
-7.13694692e-02 7.62409687e-01 6.09717518e-02 2.83823937... | [15.076101303100586, -3.033250570297241] |
17d69e41-8df4-4d53-a36d-3d34e26e8107 | changesim-towards-end-to-end-online-scene | 2103.05368 | null | https://arxiv.org/abs/2103.05368v2 | https://arxiv.org/pdf/2103.05368v2.pdf | ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments | We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in indust... | ['Jong-Hwan Kim', 'Ue-Hwan Kim', 'Sun-Kyung Lee', 'Sahng-Min Yoo', 'Jae-Hyuk Jang', 'Jin-Man Park'] | 2021-03-09 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 4.70325977e-01 -5.09210289e-01 4.18176740e-01 -5.08058488e-01
-7.12035239e-01 -8.63798320e-01 4.75212306e-01 8.68946239e-02
-3.32859546e-01 4.99887347e-01 -1.93919405e-01 1.06679834e-01
1.42048791e-01 -6.94175124e-01 -1.04109573e+00 -8.52359772e-01
-1.87353902e-02 5.08205652e-01 4.99003321e-01 -1.31345123... | [8.642467498779297, -2.201843023300171] |
36fada08-d33d-4d05-b364-f6aed0832acc | evaluation-of-the-spatio-temporal-features | 1904.01748 | null | http://arxiv.org/abs/1904.01748v1 | http://arxiv.org/pdf/1904.01748v1.pdf | Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System | Owing to the development and advancement of artificial intelligence, numerous
works were established in the human facial expression recognition system.
Meanwhile, the detection and classification of micro-expressions are attracting
attentions from various research communities in the recent few years. In this
paper, we ... | ['Kun-Hong Liu', 'Ran-Ke Lyu', 'Han-Zhe Zhang', 'Hao-Xuan Xua', 'Shu-Meng Lic', 'Sze-Teng Liong', 'Y. S. Gan', 'Danna Zheng'] | 2019-04-03 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 2.51685202e-01 -2.28801608e-01 -1.86570063e-02 -4.98483211e-01
-1.43920541e-01 1.18395535e-03 5.39986968e-01 -5.06728113e-01
-4.38279003e-01 7.32903719e-01 -2.17137709e-02 4.35031831e-01
2.76671767e-01 -6.79521441e-01 -1.04938708e-01 -9.08540428e-01
2.24389195e-01 -3.74258816e-01 -3.47584516e-01 -2.76193976... | [13.56795597076416, 1.7120131254196167] |
a86a8dde-e704-452f-a861-b0c1d2ec6da5 | uncertainty-aware-multi-view-co-training-for | 2006.16806 | null | https://arxiv.org/abs/2006.16806v1 | https://arxiv.org/pdf/2006.16806v1.pdf | Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation | Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely expensive in the field of medical image analysis. Unlabeled data, on the other hand, is much easier to acquire. Semi-supervised learning and u... | ['Zhuotun Zhu', 'Fengze Liu', 'Dong Yang', 'Zhiding Yu', 'Lequan Yu', 'Jinzheng Cai', 'Holger Roth', 'Daguang Xu', 'Alan Yuille', 'Yingda Xia'] | 2020-06-28 | null | null | null | null | ['semi-supervised-medical-image-segmentation', 'volumetric-medical-image-segmentation', 'pancreas-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 7.00005144e-02 2.78807610e-01 -4.12267536e-01 -6.43140018e-01
-1.03722525e+00 -6.91426873e-01 1.04888581e-01 1.36855900e-01
-4.34453577e-01 6.83751166e-01 1.59880482e-02 -1.91889271e-01
8.49881470e-02 -7.08910048e-01 -8.03978264e-01 -8.21588218e-01
1.91828117e-01 1.02839625e+00 1.87178373e-01 2.98275471... | [14.602252960205078, -2.104022979736328] |
5c16b06f-f59b-4fdb-a03a-63d7526a639a | revisiting-contrastive-methods-for | 2106.05967 | null | https://arxiv.org/abs/2106.05967v3 | https://arxiv.org/pdf/2106.05967v3.pdf | Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations | Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. However, current methods are still primarily applied to curated datasets like ImageNet. In this paper, we first study how biases in the dataset affect existing methods. Our resul... | ['Luc van Gool', 'Stamatios Georgoulis', 'Simon Vandenhende', 'Wouter Van Gansbeke'] | 2021-06-10 | null | http://proceedings.neurips.cc/paper/2021/hash/8757150decbd89b0f5442ca3db4d0e0e-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8757150decbd89b0f5442ca3db4d0e0e-Paper.pdf | neurips-2021-12 | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.11964214e-01 -5.86125962e-02 -6.45825624e-01 -4.32465881e-01
-7.41534173e-01 -7.69657731e-01 6.17716849e-01 -5.75364977e-02
-5.66828907e-01 4.77107942e-01 1.83227196e-01 -2.18683451e-01
1.45065328e-02 -5.98572910e-01 -9.80274737e-01 -5.02091706e-01
-8.18197150e-03 2.28346005e-01 5.73913395e-01 -2.92014509... | [9.648971557617188, 1.5565307140350342] |
d3a42b10-84e0-45b8-bf68-2681a2297f75 | interaction-modeling-with-multiplex-attention | 2208.10660 | null | https://arxiv.org/abs/2208.10660v2 | https://arxiv.org/pdf/2208.10660v2.pdf | Interaction Modeling with Multiplex Attention | Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present ... | ['Nick Haber', 'Jiajun Wu', 'Mykel Kochenderfer', 'Jiachen Li', 'Ruohan Zhang', 'Isaac Kauvar', 'Fan-Yun Sun'] | 2022-08-23 | null | null | null | null | ['trajectory-forecasting', 'social-navigation'] | ['computer-vision', 'robots'] | [-3.14936340e-01 2.67758928e-02 -2.05961630e-01 -1.11443043e-01
-1.64660528e-01 -4.05561507e-01 1.25811481e+00 1.42207012e-01
-1.69742420e-01 7.38004804e-01 4.47499752e-01 -3.26627791e-01
-5.06530881e-01 -9.15171087e-01 -8.58524561e-01 -3.75606000e-01
-8.21049869e-01 1.25323844e+00 2.57945865e-01 -6.92297101... | [5.819774627685547, 0.8505988717079163] |
9141c5a4-1f16-4454-952b-62847e0bcede | arabisc-context-sensitive-neural-spelling | null | null | https://aclanthology.org/2020.nlptea-1.2 | https://aclanthology.org/2020.nlptea-1.2.pdf | Arabisc: Context-Sensitive Neural Spelling Checker | Traditional statistical approaches to spelling correction usually consist of two consecutive processes — error detection and correction — and they are generally computationally intensive. Current state-of-the-art neural spelling correction models usually attempt to correct spelling errors directly over an entire senten... | ['Andy Way', 'Rejwanul Haque', 'Yasmin Moslem'] | 2020-12-01 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 4.97360021e-01 -1.83985934e-01 3.32568549e-02 -1.06988572e-01
-5.62937498e-01 -3.59948158e-01 5.74897945e-01 7.53240526e-01
-8.75286460e-01 7.62407184e-01 1.64697453e-01 -6.71228468e-01
4.47999090e-02 -7.65824080e-01 -7.32389688e-01 -4.50675577e-01
2.58836001e-01 1.61477536e-01 1.53404489e-01 -4.25289571... | [10.950892448425293, 10.736434936523438] |
7413abbe-3753-47b3-ad1c-79ca6fd5fd4c | cilex-an-investigation-of-context-information | null | null | https://aclanthology.org/2022.coling-1.362 | https://aclanthology.org/2022.coling-1.362.pdf | CILex: An Investigation of Context Information for Lexical Substitution Methods | Lexical substitution, which aims to generate substitutes for a target word given a context, is an important natural language processing task useful in many applications. Due to the paucity of annotated data, existing methods for lexical substitution tend to rely on manually curated lexical resources and contextual word... | ['Hanna Suominen', 'Artem Lenskiy', 'Elena Daskalaki', 'Sandaru Seneviratne'] | null | null | null | null | coling-2022-10 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 2.19028875e-01 -3.34013142e-02 -2.04864830e-01 -1.95246562e-01
-4.92526025e-01 -2.93335348e-01 6.46084189e-01 6.06265247e-01
-8.48164439e-01 7.18432248e-01 7.27273941e-01 -2.61868060e-01
1.87187240e-01 -7.26498485e-01 -4.41047579e-01 -4.09207493e-01
5.91383755e-01 1.71429351e-01 2.81100720e-01 -6.69540584... | [10.69241714477539, 9.201778411865234] |
0d420517-4213-4ed6-bdd7-dd188e7351ce | learning-geometry-aware-representations-by | 2304.08204 | null | https://arxiv.org/abs/2304.08204v1 | https://arxiv.org/pdf/2304.08204v1.pdf | Learning Geometry-aware Representations by Sketching | Understanding geometric concepts, such as distance and shape, is essential for understanding the real world and also for many vision tasks. To incorporate such information into a visual representation of a scene, we propose learning to represent the scene by sketching, inspired by human behavior. Our method, coined Lea... | ['Byoung-Tak Zhang', 'Kibeom Kim', 'Won-Seok Choi', 'Hyunsung Go', 'Inwoo Hwang', 'Hyundo Lee'] | 2023-04-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lee_Learning_Geometry-Aware_Representations_by_Sketching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lee_Learning_Geometry-Aware_Representations_by_Sketching_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.67633528e-01 1.03503941e-02 -1.50953576e-01 -7.05646276e-01
-3.28222960e-01 -1.04842389e+00 8.82693410e-01 -3.40541676e-02
-1.56492919e-01 2.99537033e-01 -2.12354716e-02 -9.22702923e-02
-7.81766772e-02 -8.89782250e-01 -1.07171965e+00 -4.04849738e-01
3.41821522e-01 6.14743173e-01 1.59273878e-01 -1.03777079... | [11.699152946472168, 0.3149799406528473] |
05800d6c-547d-4b34-a563-99d3cd586e1a | pointinst3d-segmenting-3d-instances-by-points | 2204.11402 | null | https://arxiv.org/abs/2204.11402v2 | https://arxiv.org/pdf/2204.11402v2.pdf | PointInst3D: Segmenting 3D Instances by Points | The current state-of-the-art methods in 3D instance segmentation typically involve a clustering step, despite the tendency towards heuristics, greedy algorithms, and a lack of robustness to the changes in data statistics. In contrast, we propose a fully-convolutional 3D point cloud instance segmentation method that wor... | ['Chunhua Shen', 'Wei Yin', 'Anton Van Den Hengel', 'Tong He'] | 2022-04-25 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 8.35490152e-02 -5.57386465e-02 -2.30923042e-01 -4.50874448e-01
-8.32664192e-01 -5.44546783e-01 7.25461125e-01 3.17044526e-01
-5.24500489e-01 3.71852130e-01 -4.44689691e-01 -2.38501668e-01
-2.11495548e-01 -7.74132907e-01 -7.44949281e-01 -5.57496548e-01
1.84153467e-01 1.11453652e+00 8.55701029e-01 1.29007280... | [8.043991088867188, -3.0657503604888916] |
e26bde70-dd00-4fa3-9cdd-03dffe5972dc | attentive-and-contrastive-learning-for-joint-1 | 2110.06853 | null | https://arxiv.org/abs/2110.06853v1 | https://arxiv.org/pdf/2110.06853v1.pdf | Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation | Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task that often relies on the so-called scene rigidity assumption. When observing a dynamic environment, this assumption is violated which leads to an ambiguity between the ego-motion of the camer... | ['In So Kweon', 'Fei Pan', 'Francois Rameau', 'Seokju Lee'] | 2021-10-13 | attentive-and-contrastive-learning-for-joint | http://openaccess.thecvf.com//content/ICCV2021/html/Lee_Attentive_and_Contrastive_Learning_for_Joint_Depth_and_Motion_Field_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lee_Attentive_and_Contrastive_Learning_for_Joint_Depth_and_Motion_Field_ICCV_2021_paper.pdf | iccv-2021-1 | ['motion-segmentation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-5.09482436e-02 -2.27339670e-01 -2.31484875e-01 -1.02122240e-01
-1.40300155e-01 -6.95208192e-01 6.50405586e-01 -7.57947624e-01
-4.03743297e-01 3.11251789e-01 1.10782102e-01 1.36036262e-01
2.95586079e-01 -3.21415871e-01 -8.51840019e-01 -7.35977948e-01
5.23415506e-01 5.79003394e-01 5.16770780e-01 3.00344050... | [8.51708984375, -2.040933609008789] |
a8dddf1d-0159-4a0f-8bdf-02e21eddf9ef | lqvsumm-a-corpus-of-linguistic-quality | null | null | https://aclanthology.org/L14-1467 | https://aclanthology.org/L14-1467.pdf | LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization | We present LQVSumm, a corpus of about 2000 automatically created extractive multi-document summaries from the TAC 2011 shared task on Guided Summarization, which we annotated with several types of linguistic quality violations. Examples for such violations include pronouns that lack antecedents or ungrammatical clauses... | ['Annemarie Friedrich', 'Marina Valeeva', 'Alexis Palmer'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['sentence-compression'] | ['natural-language-processing'] | [ 3.18958193e-01 7.10745692e-01 -1.27660885e-01 -5.64509273e-01
-1.66286051e+00 -1.00835991e+00 7.89575875e-01 9.95189428e-01
-3.86864066e-01 9.40887809e-01 1.22872913e+00 -1.41421920e-02
-3.54883224e-01 -4.69450593e-01 -4.70687121e-01 6.65381402e-02
3.72243166e-01 6.41306043e-01 1.08056180e-01 -2.41991028... | [12.133143424987793, 9.405505180358887] |
78974d55-3b90-43e9-af35-5b896392a39f | g-tuna-a-corpus-of-referring-expressions-in | null | null | https://aclanthology.org/W17-3522 | https://aclanthology.org/W17-3522.pdf | G-TUNA: a corpus of referring expressions in German, including duration information | Corpora of referring expressions elicited from human participants in a controlled environment are an important resource for research on automatic referring expression generation. We here present G-TUNA, a new corpus of referring expressions for German. Using the furniture stimuli set developed for the TUNA and D-TUNA c... | ['Jorrig Vogels', 'David Howcroft', 'Vera Demberg'] | 2017-09-01 | null | null | null | ws-2017-9 | ['referring-expression-generation'] | ['computer-vision'] | [-5.36350161e-02 6.67776391e-02 -3.02995052e-02 -6.04472697e-01
-9.72652495e-01 -6.63069606e-01 5.63904047e-01 1.85318198e-02
-4.30018783e-01 7.04904974e-01 5.02935886e-01 -3.02331746e-01
-2.42107511e-01 -4.07909483e-01 -1.95816662e-02 -3.50821614e-01
2.36197829e-01 2.83083797e-01 -1.49039268e-01 -7.16500401... | [10.390190124511719, 9.032584190368652] |
ca30f4e4-8506-402f-88fd-74a16dc37a56 | practical-algorithms-for-orientations-of | 2302.14386 | null | https://arxiv.org/abs/2302.14386v1 | https://arxiv.org/pdf/2302.14386v1.pdf | Practical Algorithms for Orientations of Partially Directed Graphical Models | In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data. In such cases, the learned causal model is commonly represented as a partially directed acyclic graph (PDAG), which contains both directed and undirected edges indic... | ['Maciej Liśkiewicz', 'Marcel Wienöbst', 'Malte Luttermann'] | 2023-02-28 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.29529852e-01 4.59758788e-01 -5.18423200e-01 -2.55023360e-01
-3.08803052e-01 -7.48253465e-01 6.13153815e-01 2.90206432e-01
8.17574039e-02 1.12700069e+00 1.30344808e-01 -7.53418326e-01
-7.94721127e-01 -8.16778421e-01 -8.62149119e-01 -6.83770120e-01
-7.13477194e-01 5.38593471e-01 1.28107473e-01 4.37685341... | [7.716587066650391, 5.2976155281066895] |
416885cb-9bad-429f-8a25-4b7792620919 | tm2d-bimodality-driven-3d-dance-generation | 2304.02419 | null | https://arxiv.org/abs/2304.02419v1 | https://arxiv.org/pdf/2304.02419v1.pdf | TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration | We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using a single modality such as music, our goal is to produce richer dance movements guided by the instructive information provided by the text. H... | ['Xinchao Wang', 'Zihang Jiang', 'Xinxin Zuo', 'Chuan Guo', 'Heng Chang', 'Dongze Lian', 'Kehong Gong'] | 2023-04-05 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 4.98884395e-02 -3.32909912e-01 -1.94787443e-01 5.90633117e-02
-1.01373148e+00 -8.36170673e-01 7.76682973e-01 -6.26601398e-01
-1.22865602e-01 3.93375844e-01 7.43541598e-01 6.75803749e-03
2.18593538e-01 -7.57729828e-01 -6.64481997e-01 -6.66329861e-01
4.30510491e-01 2.52083391e-01 -1.68283097e-02 -2.36413211... | [5.799209117889404, -0.17836861312389374] |
1522420b-7596-4514-a6e4-4381f8ce9210 | a-simple-lstm-model-for-transition-based | 1708.08959 | null | http://arxiv.org/abs/1708.08959v2 | http://arxiv.org/pdf/1708.08959v2.pdf | A Simple LSTM model for Transition-based Dependency Parsing | We present a simple LSTM-based transition-based dependency parser. Our model
is composed of a single LSTM hidden layer replacing the hidden layer in the
usual feed-forward network architecture. We also propose a new initialization
method that uses the pre-trained weights from a feed-forward neural network to
initialize... | ['Mohab El-karef', 'Bernd Bohnet'] | 2017-08-29 | null | null | null | null | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-1.34340763e-01 7.75465667e-01 -8.94108117e-02 -8.25835824e-01
-8.21640968e-01 -4.02518749e-01 -8.10903590e-03 -8.96872729e-02
-7.41486132e-01 7.49958038e-01 2.43412405e-01 -8.87600005e-01
6.77435875e-01 -7.95480013e-01 -8.20298076e-01 -4.94562358e-01
-1.07787542e-01 4.68145519e-01 4.10143197e-01 -2.34131277... | [10.265020370483398, 9.748156547546387] |
1fc86719-478c-4e57-8d87-5d6f96b5d8f3 | hgnet-learning-hierarchical-geometry-from | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yao_HGNet_Learning_Hierarchical_Geometry_From_Points_Edges_and_Surfaces_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yao_HGNet_Learning_Hierarchical_Geometry_From_Points_Edges_and_Surfaces_CVPR_2023_paper.pdf | HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces | Parsing an unstructured point set into constituent local geometry structures (e.g., edges or surfaces) would be helpful for understanding and representing point clouds. This motivates us to design a deep architecture to model the hierarchical geometry from points, edges, surfaces (triangles), to super-surfaces (adj... | ['Tao Mei', 'Yingwei Pan', 'Yehao Li', 'Ting Yao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-object-classification'] | ['computer-vision'] | [-7.64154410e-03 3.28988254e-01 8.44437033e-02 -6.08350277e-01
-7.85806954e-01 -5.64629495e-01 4.13993895e-01 4.22281981e-01
4.09972608e-01 -5.40540405e-02 -3.73009562e-01 -2.65018523e-01
-2.04109214e-02 -1.38187695e+00 -1.12484586e+00 -4.92533565e-01
-2.60658920e-01 4.80024606e-01 5.16964078e-01 -1.56890899... | [7.99152946472168, -3.539184808731079] |
88d483dd-b484-4ebc-ae9a-af3c0658975f | goca-guided-online-cluster-assignment-for | 2207.10158 | null | https://arxiv.org/abs/2207.10158v1 | https://arxiv.org/pdf/2207.10158v1.pdf | GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning | Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised representation learning methods typically cluster samples based on visually dominant features. While this works well for image-based self-supervision, it often fails for videos, which require understanding motion rather than ... | ['Chen Wang', 'Federico Tombari', 'Joshua L. Moore', 'Alireza Zareian', 'Huseyin Coskun'] | 2022-07-20 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [-1.52009046e-02 -3.05717587e-01 -3.97419602e-01 -3.36038619e-01
-5.55783629e-01 -4.87726718e-01 4.60217834e-01 1.34981573e-01
-3.48077565e-01 3.77786160e-01 3.36981177e-01 1.22432016e-01
3.33957411e-02 -6.09834075e-01 -7.72251129e-01 -9.67598021e-01
2.64424354e-01 1.40156731e-01 3.41803312e-01 1.78621307... | [8.771631240844727, 0.7204731702804565] |
6ae14a6a-17af-49ab-94fe-dd2af16b56ea | measuring-gender-bias-in-word-embeddings | null | null | https://aclanthology.org/W19-3804 | https://aclanthology.org/W19-3804.pdf | Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories | Prior work has shown that word embeddings capture human stereotypes, including gender bias. However, there is a lack of studies testing the presence of specific gender bias categories in word embeddings across diverse domains. This paper aims to fill this gap by applying the WEAT bias detection method to four sets of w... | ['Alfredo Maldonado', 'Kaytlin Chaloner'] | 2019-08-01 | null | null | null | ws-2019-8 | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [-3.67086202e-01 1.78219810e-01 -7.77508557e-01 -6.36760414e-01
1.52066723e-02 -7.45578408e-01 9.13485885e-01 8.30805838e-01
-9.94687557e-01 6.34218752e-01 7.58313477e-01 -4.72367853e-01
-1.38134763e-01 -9.73160267e-01 -2.42637217e-01 -3.49663556e-01
-2.73062736e-02 6.55454397e-01 6.73443079e-02 -4.95598048... | [9.365493774414062, 10.202165603637695] |
f267b2f9-4088-48c9-a5cc-f378f587c037 | casenet-deep-category-aware-semantic-edge | 1705.09759 | null | http://arxiv.org/abs/1705.09759v1 | http://arxiv.org/pdf/1705.09759v1.pdf | CASENet: Deep Category-Aware Semantic Edge Detection | Boundary and edge cues are highly beneficial in improving a wide variety of
vision tasks such as semantic segmentation, object recognition, stereo, and
object proposal generation. Recently, the problem of edge detection has been
revisited and significant progress has been made with deep learning. While
classical edge d... | ['Ming-Yu Liu', 'Zhiding Yu', 'Srikumar Ramalingam', 'Chen Feng'] | 2017-05-27 | casenet-deep-category-aware-semantic-edge-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Yu_CASENet_Deep_Category-Aware_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_CASENet_Deep_Category-Aware_CVPR_2017_paper.pdf | cvpr-2017-7 | ['object-proposal-generation'] | ['computer-vision'] | [ 3.01660210e-01 7.71335931e-03 5.22171780e-02 -6.20887578e-01
-4.60897774e-01 -3.30480427e-01 4.70839620e-01 2.66768664e-01
-7.46554792e-01 4.54926819e-01 -9.14966390e-02 -1.74758181e-01
2.37239882e-01 -8.92269909e-01 -7.33509839e-01 -5.07022560e-01
-6.90179924e-03 2.87812173e-01 6.69101894e-01 5.51000684... | [9.555041313171387, 0.4696494936943054] |
dfe08db9-d95e-45db-b278-4ad1f55f0be4 | attention-lstm-for-multivariate-traffic-state | 2301.02731 | null | https://arxiv.org/abs/2301.02731v1 | https://arxiv.org/pdf/2301.02731v1.pdf | Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads | Accurate traffic volume and speed prediction have a wide range of applications in transportation. It can result in useful and timely information for both travellers and transportation decision-makers. In this study, an Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic vo... | ['Seyedehsan Seyedabrishami', 'Amir Hossein Karbasi', 'Bilal Farooq', 'Elahe Sherafat'] | 2023-01-06 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-1.90976530e-01 -3.96788329e-01 -4.22010601e-01 -2.99980104e-01
-4.40654069e-01 -6.69712499e-02 5.27797639e-01 -1.48679558e-02
-5.69008112e-01 9.93659556e-01 5.50677255e-02 -8.26331019e-01
-5.92356265e-01 -1.10764170e+00 -4.16113853e-01 -7.20320046e-01
-3.28715563e-01 1.49844512e-01 2.68288046e-01 -3.80528450... | [6.305049419403076, 1.9650936126708984] |
6c539706-83ca-4ba1-9519-be2fd29fc6a9 | maximum-mean-discrepancy-kernels-for | 2301.09624 | null | https://arxiv.org/abs/2301.09624v1 | https://arxiv.org/pdf/2301.09624v1.pdf | Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images | How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a nove... | ['Fayyaz ul Amir Afsar Minhas', 'Muhammad Dawood', 'Piotr Keller'] | 2023-01-23 | null | null | null | null | ['whole-slide-images', 'survival-analysis'] | ['computer-vision', 'miscellaneous'] | [ 4.58733797e-01 -7.81746674e-03 -1.94279365e-02 -3.72100741e-01
-1.17782140e+00 -6.05620623e-01 2.97733307e-01 8.27956855e-01
-5.37252486e-01 4.58616585e-01 -1.49749801e-01 -3.54748696e-01
-6.43864274e-01 -6.98134661e-01 -3.73263717e-01 -1.30649900e+00
-3.38899314e-01 5.36400020e-01 4.85839754e-01 1.80503622... | [15.002342224121094, -2.912241220474243] |
b25e4ec4-aa81-4c0c-b8c8-12008911f0da | icfvr-2017-3rd-international-competition-on | 1801.01262 | null | http://arxiv.org/abs/1801.01262v1 | http://arxiv.org/pdf/1801.01262v1.pdf | ICFVR 2017: 3rd International Competition on Finger Vein Recognition | In recent years, finger vein recognition has become an important sub-field in
biometrics and been applied to real-world applications. The development of
finger vein recognition algorithms heavily depends on large-scale real-world
data sets. In order to motivate research on finger vein recognition, we
released the large... | ['Yingjie Chen', 'Wei Xu', 'Nasir Uddin Ahmed', 'Md. Shakil Ahmed', 'Liao Ni', 'Yilun Jin', 'Jingxuan Wen', 'Houjun Huang', 'Yi Zhang', 'Wenxin Li', 'Haifeng Zhang'] | 2018-01-04 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 2.70708978e-01 -3.48059952e-01 -1.92210823e-01 -4.09094155e-01
-1.80589780e-01 -8.80062819e-01 4.27184403e-01 -4.39456284e-01
-5.78793883e-01 6.24481261e-01 2.15750694e-01 3.01759224e-02
2.55390018e-01 -8.85244429e-01 1.84540913e-01 -3.40323657e-01
7.09409192e-02 2.50526756e-01 3.01313311e-01 1.07443318... | [13.030921936035156, 1.019033670425415] |
8d82fe59-3c86-4a4d-b21c-7d9b162b6839 | faceqan-face-image-quality-assessment-through | 2212.02127 | null | https://arxiv.org/abs/2212.02127v1 | https://arxiv.org/pdf/2212.02127v1.pdf | FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration | Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the ... | ['Vitomir Štruc', 'Peter Peer', 'Žiga Babnik'] | 2022-12-05 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.30964160e-01 -3.97875965e-01 1.70658574e-01 -5.95727980e-01
-9.39940274e-01 -3.62422168e-01 5.47485650e-01 -5.94678819e-01
-9.75849666e-03 5.96374869e-01 -1.78851530e-01 -8.02205503e-02
-2.55660236e-01 -6.11186922e-01 -7.21978605e-01 -6.81092203e-01
-1.38272241e-01 1.25203058e-01 -4.23066586e-01 -3.00522417... | [13.06754207611084, 0.6579708456993103] |
32bf9aff-b813-4290-b430-704488430398 | sasmu-boost-the-performance-of-generalized | 2306.01449 | null | https://arxiv.org/abs/2306.01449v1 | https://arxiv.org/pdf/2306.01449v1.pdf | SASMU: boost the performance of generalized recognition model using synthetic face dataset | Nowadays, deploying a robust face recognition product becomes easy with the development of face recognition techniques for decades. Not only profile image verification but also the state-of-the-art method can handle the in-the-wild image almost perfectly. However, the concern of privacy issues raise rapidly since mains... | ['Chinson Yeh', 'Haoyuan He', 'Yong-Sheng Chen', 'Pei-Chun Chang', 'Chia-Chun Chung'] | 2023-06-02 | null | null | null | null | ['robust-face-recognition', 'face-recognition', 'domain-generalization'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.31598133e-01 -1.94676429e-01 -1.63278908e-01 -7.07133293e-01
-6.35993898e-01 -4.29057956e-01 5.35409868e-01 -6.49595797e-01
-1.24845974e-01 7.95540810e-01 -2.39791870e-01 -2.39445150e-01
-1.61175460e-01 -5.97364545e-01 -5.26292086e-01 -8.00027490e-01
1.53308451e-01 5.04722260e-02 -2.20229417e-01 -2.56683290... | [13.065349578857422, 0.8576679825782776] |
532e5bbc-3165-4646-8119-b82e8facdcb3 | on-efficient-real-time-semantic-segmentation | 2206.08605 | null | https://arxiv.org/abs/2206.08605v2 | https://arxiv.org/pdf/2206.08605v2.pdf | On Efficient Real-Time Semantic Segmentation: A Survey | Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding and object detection. However, many of the top performing semantic segmentation models are extremely complex and cumbersome... | ['Muhammad Shafique', 'Christopher J. Holder'] | 2022-06-17 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 3.68538290e-01 -7.77154565e-02 -3.11342269e-01 -4.63950962e-01
-2.98039138e-01 -6.50256276e-01 5.94753802e-01 5.33995070e-02
-6.03389680e-01 1.72161892e-01 -8.49580228e-01 -6.50237918e-01
1.88355863e-01 -9.64702427e-01 -7.64689445e-01 -5.16099155e-01
5.01322076e-02 7.84147263e-01 9.50072944e-01 -6.11593835... | [8.314438819885254, -1.2517366409301758] |
82b20b94-29ad-47ab-815a-ca4b7186369f | deep-attention-q-network-for-personalized | 2307.01519 | null | https://arxiv.org/abs/2307.01519v1 | https://arxiv.org/pdf/2307.01519v1.pdf | Deep Attention Q-Network for Personalized Treatment Recommendation | Tailoring treatment for individual patients is crucial yet challenging in order to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations; however, they rely solely on current patient observations (vital signs, demographics) as the patient's... | ['Shihao Yang', 'Nicoleta Serban', 'Junghwan Lee', 'Simin Ma'] | 2023-07-04 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-4.26429249e-02 -9.57410634e-02 -5.45804560e-01 -3.06785643e-01
-5.46814561e-01 -1.79795459e-01 -1.58200800e-01 4.87355560e-01
-3.70450884e-01 1.06923282e+00 5.62546194e-01 -5.99658787e-01
-3.95090580e-01 -5.94797015e-01 -3.88916731e-01 -6.01989806e-01
-4.63214628e-02 7.89434195e-01 -3.81459266e-01 -1.66654214... | [4.011046409606934, 2.722944736480713] |
9898e350-7e5b-411e-bd6d-fd990508c80b | millimeter-wave-communications-with-an | 2002.10572 | null | https://arxiv.org/abs/2002.10572v3 | https://arxiv.org/pdf/2002.10572v3.pdf | Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning | In this paper, a novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station, which is assisted by a reconfigurable intelligent reflector (IR). In particular, a channel estimation approach is developed to measure the channel state information (CSI) in real-time. First... | ['Qianqian Zhang', 'Walid Saad', 'Mehdi Bennis'] | 2020-02-24 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.60585016e-02 3.10855746e-01 -1.61507502e-02 1.02019534e-01
-8.99387360e-01 -3.03452015e-01 -2.85244972e-01 -1.08352974e-01
-1.58666998e-01 1.04765463e+00 -4.92169484e-02 -4.72388923e-01
-4.24737543e-01 -1.11626017e+00 -6.64536476e-01 -1.22750533e+00
-3.23475629e-01 -9.38571990e-02 -4.95515257e-01 -2.77472943... | [6.074460506439209, 1.4476983547210693] |
5fc159d7-0bb3-4580-8f52-b9edf019a320 | lip-to-speech-synthesis-for-arbitrary | 2209.00642 | null | https://arxiv.org/abs/2209.00642v1 | https://arxiv.org/pdf/2209.00642v1.pdf | Lip-to-Speech Synthesis for Arbitrary Speakers in the Wild | In this work, we address the problem of generating speech from silent lip videos for any speaker in the wild. In stark contrast to previous works, our method (i) is not restricted to a fixed number of speakers, (ii) does not explicitly impose constraints on the domain or the vocabulary and (iii) deals with videos that ... | ['C. V. Jawahar', 'Vinay P Namboodiri', 'Rudrabha Mukhopadhyay', 'K R Prajwal', 'Sindhu B Hegde'] | 2022-09-01 | null | null | null | null | ['lip-to-speech-synthesis'] | ['computer-vision'] | [ 2.16212064e-01 9.14875790e-02 -1.32432655e-01 -3.59565914e-01
-1.15616035e+00 -7.73313284e-01 4.15399969e-01 -8.51671040e-01
-4.25700285e-02 6.15004182e-01 2.41700962e-01 -2.11491331e-01
4.29080784e-01 -1.10796079e-01 -7.61430740e-01 -6.94824815e-01
1.76223025e-01 1.56557336e-01 -2.91026812e-02 -3.22193727... | [13.304154396057129, -0.2822752296924591] |
a9e9c9ca-099d-4df6-8ae2-93c7ef45425a | a-generative-map-for-image-based-camera | 1902.11124 | null | http://arxiv.org/abs/1902.11124v4 | http://arxiv.org/pdf/1902.11124v4.pdf | A Generative Map for Image-based Camera Localization | In image-based camera localization systems, information about the environment
is usually stored in some representation, which can be referred to as a map.
Conventionally, most maps are built upon hand-crafted features. Recently,
neural networks have attracted attention as a data-driven map representation,
and have show... | ['Stefan Matthes', 'Mingpan Guo', 'Jiaojiao Ye', 'Hao Shen'] | 2019-02-18 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 1.12508230e-01 5.32325543e-03 -1.31992817e-01 -6.34494901e-01
-6.32442355e-01 -6.38984919e-01 7.15841293e-01 8.34970027e-02
-5.38841605e-01 7.44870961e-01 -3.00968718e-02 -1.97519585e-02
2.39436086e-02 -9.51913774e-01 -1.30063796e+00 -6.48737192e-01
2.70864338e-01 7.36549020e-01 3.32269818e-01 -1.20948002... | [7.5865302085876465, -2.0597851276397705] |
8c5452a0-d94e-4367-a4bd-5a33db01f525 | scaling-through-abstractions-high-performance | 2004.10519 | null | https://arxiv.org/abs/2004.10519v1 | https://arxiv.org/pdf/2004.10519v1.pdf | Scaling through abstractions -- high-performance vectorial wave simulations for seismic inversion with Devito | [Devito] is an open-source Python project based on domain-specific language and compiler technology. Driven by the requirements of rapid HPC applications development in exploration seismology, the language and compiler have evolved significantly since inception. Sophisticated boundary conditions, tensor contractions, s... | ['Rhodri Nelson', 'Philipp Witte', 'Felix J. Herrmann', 'Mathias Louboutin', 'Jan Thorbecke', 'Gerard Gorman', 'Fabio Luporini', 'George Bisbas'] | 2020-04-22 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [-1.10219263e-01 -3.89604092e-01 8.45727623e-01 2.39082752e-03
-4.20599073e-01 -2.92899340e-01 5.03069222e-01 -2.34683380e-01
-3.88252586e-01 6.94138944e-01 2.38883927e-01 -8.12560380e-01
-2.53871053e-01 -7.86550701e-01 -1.23110816e-01 -1.02017808e+00
-1.10490310e+00 5.08059561e-01 4.14873034e-01 -4.29820716... | [6.521066188812256, 3.148137331008911] |
e0161ddf-5a71-4ce8-9bdc-215d9e2e0e4d | universal-adversarial-perturbation-for-text | 1910.04618 | null | https://arxiv.org/abs/1910.04618v1 | https://arxiv.org/pdf/1910.04618v1.pdf | Universal Adversarial Perturbation for Text Classification | Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability. Unlike images on which a single fixed-size adversarial perturbation can be found, text is of ... | ['Hang Gao', 'Tim Oates'] | 2019-10-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 6.02153301e-01 2.27531835e-01 2.24989668e-01 -2.59274542e-01
-4.80877429e-01 -1.05764341e+00 8.14672410e-01 3.32133770e-02
-3.60964984e-01 6.29203618e-01 6.85878769e-02 -3.96611542e-01
4.88987356e-01 -1.10481071e+00 -1.36639571e+00 -9.25158799e-01
5.91947176e-02 4.89556849e-01 1.27403125e-01 -4.83963102... | [5.942464351654053, 8.073801040649414] |
f351b9ec-8328-4b6a-b2c5-33876ac8f0f9 | face-alignment-in-full-pose-range-a-3d-total | 1804.01005 | null | http://arxiv.org/abs/1804.01005v1 | http://arxiv.org/pdf/1804.01005v1.pdf | Face Alignment in Full Pose Range: A 3D Total Solution | Face alignment, which fits a face model to an image and extracts the semantic
meanings of facial pixels, has been an important topic in the computer vision
community. However, most algorithms are designed for faces in small to medium
poses (yaw angle is smaller than 45 degrees), which lack the ability to align
faces in... | ['Stan Z. Li', 'Zhen Lei', 'Xiaoming Liu', 'Xiangyu Zhu'] | 2018-04-02 | null | null | null | null | ['depth-image-estimation'] | ['computer-vision'] | [-6.63327724e-02 8.64549354e-02 -8.95669162e-02 -6.75162911e-01
-3.73179197e-01 -3.25663865e-01 4.61028188e-01 -7.92791843e-01
-5.70288338e-02 2.82253265e-01 2.13370323e-02 1.83458790e-01
2.10951954e-01 -5.64290702e-01 -6.95724487e-01 -5.43697536e-01
2.46943519e-01 5.01994133e-01 -2.33123794e-01 -9.72817838... | [13.312356948852539, 0.30389782786369324] |
5a56913f-dbc8-4808-93c0-dff3ebc3cd66 | low-resource-style-transfer-via-domain | null | null | https://openreview.net/forum?id=p_-ZgMkRD3 | https://openreview.net/pdf?id=p_-ZgMkRD3 | Low Resource Style Transfer via Domain Adaptive Meta Learning | Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of nonparallel data to guide transferring different text styles. (ii) colossal performance degradation when fine-tuning t... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 6.25923812e-01 -3.05659384e-01 5.10449968e-02 -5.22678673e-01
-8.80191147e-01 -7.98893332e-01 8.52776945e-01 -2.70278424e-01
-5.05247116e-01 8.79003942e-01 2.85546869e-01 -1.02664337e-01
3.99891496e-01 -7.42673934e-01 -9.20394659e-01 -5.12441814e-01
5.12667060e-01 9.88619745e-01 6.24207675e-01 -7.63304412... | [11.70520305633545, 9.577610969543457] |
7eb8ccc8-c157-4b59-83b4-07de2b73a776 | incremental-self-supervised-learning-based-on | 2303.17354 | null | https://arxiv.org/abs/2303.17354v4 | https://arxiv.org/pdf/2303.17354v4.pdf | ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization | In the realm of machine learning, the study of anomaly detection and localization within image data has gained substantial traction, particularly for practical applications such as industrial defect detection. While the majority of existing methods predominantly use Convolutional Neural Networks (CNN) as their primary ... | ['Li Zhu', 'Fei Guo', 'Wenping Jin'] | 2023-03-30 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 7.64698029e-01 6.45001084e-02 1.27947003e-01 -2.61456609e-01
-5.78602433e-01 -8.54725540e-02 3.99367332e-01 4.01670933e-01
-2.93407857e-01 2.53344029e-01 -2.58967578e-01 -4.10956174e-01
3.30070078e-01 -8.70338678e-01 -6.84646547e-01 -8.65209103e-01
1.24039754e-01 1.03403546e-01 3.75014573e-01 -8.01019650... | [7.629889965057373, 2.0210838317871094] |
4bd1378e-1b98-4f3f-a909-c383fbfea358 | prompt-learning-for-fine-grained-entity-1 | null | null | https://openreview.net/forum?id=7EemgCzGXAN | https://openreview.net/pdf?id=7EemgCzGXAN | Prompt-Learning for Fine-Grained Entity Typing | As an effective approach to tune pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, such... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['entity-typing'] | ['natural-language-processing'] | [-1.43832508e-02 -1.10037643e-02 -7.54108071e-01 -5.86717248e-01
-1.05898631e+00 -7.12856710e-01 6.60091698e-01 4.12991524e-01
-7.56714702e-01 7.81509876e-01 4.13259596e-01 -3.26805264e-01
1.47802368e-01 -7.03408957e-01 -7.39285469e-01 -3.73679936e-01
3.27925831e-01 4.73662317e-01 2.68139690e-01 -2.61199087... | [10.63659381866455, 8.204693794250488] |
26ae115c-1966-4b6a-90aa-b847e2ed01c5 | an-adaptive-threshold-for-the-canny-edge | 2209.08699 | null | https://arxiv.org/abs/2209.08699v1 | https://arxiv.org/pdf/2209.08699v1.pdf | An Adaptive Threshold for the Canny Edge Detection with Actor-Critic Algorithm | Visual surveillance aims to perform robust foreground object detection regardless of the time and place. Object detection shows good results using only spatial information, but foreground object detection in visual surveillance requires proper temporal and spatial information processing. In deep learning-based foregrou... | ['Jong-Eun Ha', 'Keong-Hun Choi'] | 2022-09-19 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 5.23564875e-01 -6.22916043e-01 2.71596223e-01 -2.94861495e-01
1.12259440e-01 -2.05559403e-01 6.41396344e-01 -2.86506683e-01
-8.20917964e-01 6.30356610e-01 -3.75315577e-01 -4.98838007e-01
1.83077961e-01 -8.98625135e-01 -7.26776302e-01 -1.30767453e+00
-7.30585381e-02 -2.56756358e-02 1.26410985e+00 1.14034861... | [8.811417579650879, -0.7954463362693787] |
c5989a2f-a3ee-4f3a-9102-27a6bacadca5 | evolving-tsukamoto-neuro-fuzzy-model-for | 2305.10421 | null | https://arxiv.org/abs/2305.10421v1 | https://arxiv.org/pdf/2305.10421v1.pdf | Evolving Tsukamoto Neuro Fuzzy Model for Multiclass Covid 19 Classification with Chest X Ray Images | Du e to rapid population growth and the need to use artificial intelligence to make quick decisions, developing a machine learning-based disease detection model and abnormality identification system has greatly improved the level of medical diagnosis Since COVID-19 has become one of the most severe diseases in the worl... | ['Maysam Orouskhani', 'Farzan Vahedifard', 'Hossein Abbasi', 'Negar Firoozeh', 'Sevda Molani', 'Marziyeh Rezaei'] | 2023-05-17 | null | null | null | null | ['medical-diagnosis', 'specificity'] | ['medical', 'natural-language-processing'] | [-4.17883834e-03 -5.95175087e-01 3.59842256e-02 8.98124948e-02
1.02743484e-01 -6.63043037e-02 -1.07218616e-01 1.46206588e-01
-5.31634152e-01 7.29068577e-01 -4.46544111e-01 -7.20520839e-02
-5.28838038e-01 -7.04433441e-01 1.39619291e-01 -8.85220647e-01
1.87055707e-01 8.45661223e-01 2.06956267e-01 -3.72682279... | [15.580198287963867, -1.6967624425888062] |
5dde2ef6-2cc5-4f98-ba2c-494afd0317fd | msr-gcn-multi-scale-residual-graph | 2108.07152 | null | https://arxiv.org/abs/2108.07152v2 | https://arxiv.org/pdf/2108.07152v2.pdf | MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction | Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursi... | ['Guiqing Li', 'Qing Zhang', 'Chengjiang Long', 'Yongwei Nie', 'Lingwei Dang'] | 2021-08-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dang_MSR-GCN_Multi-Scale_Residual_Graph_Convolution_Networks_for_Human_Motion_Prediction_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dang_MSR-GCN_Multi-Scale_Residual_Graph_Convolution_Networks_for_Human_Motion_Prediction_ICCV_2021_paper.pdf | iccv-2021-1 | ['human-pose-forecasting'] | ['computer-vision'] | [-3.08788307e-02 -2.12838605e-01 -8.35596099e-02 -2.01948628e-01
-4.02446330e-01 7.25667924e-02 2.77733624e-01 -3.43158036e-01
-3.09704840e-01 6.13670945e-01 4.49038327e-01 4.34379667e-01
2.10483521e-02 -5.94279945e-01 -6.95143580e-01 -6.06128871e-01
-5.93555085e-02 3.00439596e-01 4.43081766e-01 -3.10025513... | [7.29472017288208, -0.38634833693504333] |
083f69cf-1664-4c2d-a46a-c55e91654ba4 | scenehgn-hierarchical-graph-networks-for-3d | 2302.10237 | null | https://arxiv.org/abs/2302.10237v1 | https://arxiv.org/pdf/2302.10237v1.pdf | SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry | 3D indoor scenes are widely used in computer graphics, with applications ranging from interior design to gaming to virtual and augmented reality. They also contain rich information, including room layout, as well as furniture type, geometry, and placement. High-quality 3D indoor scenes are highly demanded while it requ... | ['Jie Yang', 'Leonidas J. Guibas', 'Yu-Kun Lai', 'Kaichun Mo', 'Jia-Mu Sun', 'Lin Gao'] | 2023-02-16 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 3.10209841e-01 1.65667951e-01 3.96024525e-01 -2.85804600e-01
-3.09264034e-01 -6.49602711e-01 4.00967270e-01 2.68817216e-01
4.18535143e-01 5.62579036e-01 3.32862526e-01 -4.19111818e-01
-9.11608711e-02 -1.35080945e+00 -8.01042557e-01 -4.45317686e-01
-4.52528447e-02 4.56198990e-01 2.05729097e-01 -3.88254851... | [9.147400856018066, -3.0214970111846924] |
77591232-80b7-455e-9d07-7e70d66d4a45 | weakly-supervised-temporal-action | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Weakly_Supervised_Temporal_Action_Localization_Through_Contrast_Based_Evaluation_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Weakly_Supervised_Temporal_Action_Localization_Through_Contrast_Based_Evaluation_Networks_ICCV_2019_paper.pdf | Weakly Supervised Temporal Action Localization Through Contrast Based Evaluation Networks | Weakly-supervised temporal action localization (WS-TAL) is a promising but challenging task with only video-level action categorical labels available during training. Without requiring temporal action boundary annotations in training data, WS-TAL could possibly exploit automatically retrieved video tags as video-level ... | [' Gang Hua', ' Nanning Zheng', ' Zhenxing Niu', ' Zhanning Gao', ' Qilin Zhang', ' Le Wang', 'Ziyi Liu'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.35426176e-01 -2.44500395e-02 -8.22199166e-01 -3.20265621e-01
-8.33663523e-01 -3.63987029e-01 6.34480000e-01 -1.46422118e-01
-7.17685521e-01 6.38768971e-01 4.63253140e-01 1.16863959e-01
3.98751907e-02 -2.62743860e-01 -7.20022619e-01 -6.52413905e-01
-3.49380463e-01 -5.65672293e-02 8.31103444e-01 1.33355543... | [8.501032829284668, 0.6233439445495605] |
53f9fce3-0fd7-4ce6-936c-17ead02b97b7 | unsupervised-text-embedding-space-generation | 2306.17181 | null | https://arxiv.org/abs/2306.17181v2 | https://arxiv.org/pdf/2306.17181v2.pdf | Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis | Generative Adversarial Networks (GAN) is a model for data synthesis, which creates plausible data through the competition of generator and discriminator. Although GAN application to image synthesis is extensively studied, it has inherent limitations to natural language generation. Because natural language is composed o... | ['Tae-Bin Ha', 'Jun-Min Lee'] | 2023-06-19 | null | null | null | null | ['image-generation', 'memorization', 'text-generation'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 6.65262043e-01 5.02486646e-01 7.68773854e-02 -1.06075957e-01
-4.97992843e-01 -4.21403915e-01 1.02928245e+00 -4.68534827e-01
-1.50175303e-01 9.83710170e-01 4.23862338e-01 -1.69388652e-01
6.87228858e-01 -1.32731724e+00 -9.65537906e-01 -7.70547211e-01
6.59290016e-01 3.31403583e-01 -2.45343104e-01 -3.49348158... | [11.895118713378906, 9.354439735412598] |
1bf8d3e2-728e-440e-a012-ad3b949e0287 | figure-descriptive-text-extraction-using | 2208.06040 | null | https://arxiv.org/abs/2208.06040v1 | https://arxiv.org/pdf/2208.06040v1.pdf | Figure Descriptive Text Extraction using Ontological Representation | Experimental research publications provide figure form resources including graphs, charts, and any type of images to effectively support and convey methods and results. To describe figures, authors add captions, which are often incomplete, and more descriptions reside in body text. This work presents a method to extrac... | ['Line Pouchard', 'Julia Rayz', 'Gilchan Park'] | 2022-08-11 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 4.32422347e-02 4.00955319e-01 -1.40588343e-01 -3.21205348e-01
-5.08335173e-01 -7.28497565e-01 6.60171151e-01 9.08909917e-01
4.99822944e-02 8.55452418e-01 5.23843944e-01 -5.69166005e-01
5.97871898e-04 -1.01897168e+00 -6.12853944e-01 7.95482695e-02
4.27886061e-02 4.31106575e-02 1.27043933e-01 -1.57476082... | [11.339344024658203, 2.2110841274261475] |
151ef7a9-22de-40a4-87da-23dd9556bdac | efficient-and-safe-exploration-in | 1904.01068 | null | http://arxiv.org/abs/1904.01068v1 | http://arxiv.org/pdf/1904.01068v1.pdf | Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models | We propose a safe exploration algorithm for deterministic Markov Decision
Processes with unknown transition models. Our algorithm guarantees safety by
leveraging Lipschitz-continuity to ensure that no unsafe states are visited
during exploration. Unlike many other existing techniques, the provided safety
guarantee is d... | ['Erdem Biyik', 'Shahrouz Ryan Alimo', 'Jonathan Margoliash', 'Dorsa Sadigh'] | 2019-04-01 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.54047042e-01 4.80886906e-01 -5.17944634e-01 -2.62706578e-01
-9.60315585e-01 -8.03944170e-01 5.04726470e-01 2.08096072e-01
-6.27858281e-01 1.01028490e+00 1.92321092e-01 -1.01243675e+00
-1.27263278e-01 -8.14463735e-01 -8.02313149e-01 -5.37835181e-01
-8.73765171e-01 2.22062126e-01 5.07079244e-01 -9.52708349... | [4.5413408279418945, 2.1394903659820557] |
c3d2b3cf-eaea-43e1-a139-4e5d982c40f1 | recovering-the-unbiased-scene-graphs-from-the | 2107.02112 | null | https://arxiv.org/abs/2107.02112v1 | https://arxiv.org/pdf/2107.02112v1.pdf | Recovering the Unbiased Scene Graphs from the Biased Ones | Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects. Recently, more efforts have been paid to the long tail problem in SGG; however, the imbalance in the fraction of missing labels of different classes, or report... | ['Jiashi Feng', 'Roger Zimmermann', 'Changhu Wang', 'Hanshu Yan', 'Henghui Ding', 'Meng-Jiun Chiou'] | 2021-07-05 | null | null | null | null | ['visual-relationship-detection', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 3.89464110e-01 1.85212702e-01 -5.51035404e-01 -3.18536103e-01
-9.00010645e-01 -5.64010739e-01 4.55820471e-01 -1.96115933e-02
-2.61301585e-02 9.52428997e-01 2.52356887e-01 -1.42062873e-01
6.58445507e-02 -5.30618906e-01 -9.63747323e-01 -9.11646307e-01
2.32157260e-01 5.04726291e-01 9.07445922e-02 3.40866804... | [10.139814376831055, 1.9870936870574951] |
0bd224c8-f1c4-44a4-b041-32915a0f4faf | ris-aided-joint-localization-and-1 | 2204.13484 | null | https://arxiv.org/abs/2204.13484v1 | https://arxiv.org/pdf/2204.13484v1.pdf | RIS-aided Joint Localization and Synchronization with a Single-Antenna Receiver: Beamforming Design and Low-Complexity Estimation | Reconfigurable intelligent surfaces (RISs) have attracted enormous interest thanks to their ability to overcome line-of-sight blockages in mmWave systems, enabling in turn accurate localization with minimal infrastructure. Less investigated are however the benefits of exploiting RIS with suitably designed beamforming s... | ['Gonzalo Seco-Granados', 'Henk Wymeersch', 'Angelo Coluccia', 'Musa Furkan Keskin', 'Alessio Fascista'] | 2022-04-28 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.61886707e-01 2.94610769e-01 3.88709933e-01 -1.94644053e-02
-8.46953869e-01 -4.86578822e-01 3.00952852e-01 1.27579004e-01
-2.72938877e-01 6.68574035e-01 -1.82783958e-02 -2.72980362e-01
-8.10188949e-01 -6.40793025e-01 -5.35303831e-01 -1.39311254e+00
-2.22050563e-01 1.28256202e-01 -1.64006442e-01 -1.18761934... | [6.286422252655029, 1.3048655986785889] |
07b63485-f8e2-447e-912a-5ffb48345a23 | cross-domain-deep-feature-combination-for | 1811.10199 | null | http://arxiv.org/abs/1811.10199v1 | http://arxiv.org/pdf/1811.10199v1.pdf | Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data | In recent decade, many state-of-the-art algorithms on image classification as
well as audio classification have achieved noticeable successes with the
development of deep convolutional neural network (CNN). However, most of the
works only exploit single type of training data. In this paper, we present a
study on classi... | ['Takuya Akashi', 'Chao Zhang', 'Bold Naranchimeg'] | 2018-11-26 | null | null | null | null | ['bird-species-classification-with-audio-visual'] | ['audio'] | [ 1.34304062e-01 -5.76767147e-01 -4.05484699e-02 -2.50329792e-01
-5.01399577e-01 -5.68101108e-01 7.14294732e-01 2.70829409e-01
-7.88183093e-01 4.42899883e-01 1.41774878e-01 9.32416543e-02
-8.64898786e-02 -7.46279418e-01 -6.14636958e-01 -5.71177721e-01
-2.58892715e-01 -1.15129113e-01 2.02217132e-01 -3.56151640... | [15.15077018737793, 5.092846393585205] |
74ddce97-d48c-419b-bf2c-9440c98a4c0c | disconnected-emerging-knowledge-graph | 2209.01397 | null | https://arxiv.org/abs/2209.01397v1 | https://arxiv.org/pdf/2209.01397v1.pdf | Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction | Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on pred... | ['Lei Zhao', 'Wei Chen', 'Pengpeng Zhao', 'Hongzhi Yin', 'Weiqing Wang', 'Yufeng Zhang'] | 2022-09-03 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-2.18185648e-01 7.02683985e-01 -5.81107020e-01 5.94909079e-02
7.84042701e-02 -3.24132472e-01 4.40015405e-01 4.97609168e-01
4.50215518e-01 9.29304481e-01 -6.28756657e-02 -2.57122189e-01
-6.56258345e-01 -1.47918952e+00 -7.59937942e-01 -4.02853042e-01
-7.26997495e-01 4.85857576e-01 8.53755355e-01 -4.18491423... | [8.664121627807617, 7.917446613311768] |
a7e2361c-b40b-4018-be31-e18518e8d47d | in-vitro-micropropagation-and-apocarotenoid | 2208.13292 | null | https://arxiv.org/abs/2208.13292v1 | https://arxiv.org/pdf/2208.13292v1.pdf | In vitro micropropagation and apocarotenoid gene expression in saffron | Saffron (Crocus sativus L.) is a triploid, sterile, monocot plant belonging to the family Iridaceae, sub-family Crocoideae. C.sativus only blooms once a year and should be collected within a very short duration, the stigmas of Saffron flowers are harvested manually and subjected to desiccation then have been used as a ... | ['Mandana Mirbakhsh'] | 2022-08-28 | null | null | null | null | ['culture'] | ['speech'] | [ 2.00970009e-01 -1.05847999e-01 -3.41758043e-01 1.84235454e-01
2.96085507e-01 -1.24056840e+00 3.86187822e-01 5.36917210e-01
1.61584422e-01 8.24837983e-01 2.14404374e-01 -4.48627949e-01
-3.59004512e-02 -8.24473679e-01 6.12568706e-02 -1.10095918e+00
-3.34318668e-01 1.01940237e-01 1.58642437e-02 -3.14337909... | [4.662353038787842, 5.089835166931152] |
4ce58e23-992c-4a23-851f-45e105db5353 | multi-modal-attention-network-for-stock | 2112.13593 | null | https://arxiv.org/abs/2112.13593v5 | https://arxiv.org/pdf/2112.13593v5.pdf | Multi-modal Attention Network for Stock Movements Prediction | Stock prices move as piece-wise trending fluctuation rather than a purely random walk. Traditionally, the prediction of future stock movements is based on the historical trading record. Nowadays, with the development of social media, many active participants in the market choose to publicize their strategies, which pro... | ['Shi Gu', 'Shwai He'] | 2021-12-27 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.65802002e-01 -2.97324508e-01 -5.77790499e-01 -2.91548878e-01
-5.87260783e-01 -7.40529001e-01 7.28919327e-01 1.07994080e-01
-4.78946030e-01 7.65541911e-01 7.51869023e-01 -7.89166912e-02
7.24038631e-02 -1.18921471e+00 -6.01990521e-01 -3.46339375e-01
-1.10901065e-01 2.60516167e-01 4.72830445e-01 -6.64780259... | [4.383845806121826, 4.278177261352539] |
83b23b7d-6fca-4537-a6c9-ab862867a60e | using-data-augmentations-and-vtln-to-reduce | 2307.02009 | null | https://arxiv.org/abs/2307.02009v1 | https://arxiv.org/pdf/2307.02009v1.pdf | Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems | Speech technology has improved greatly for norm speakers, i.e., adult native speakers of a language without speech impediments or strong accents. However, non-norm or diverse speaker groups show a distinct performance gap with norm speakers, which we refer to as bias. In this work, we aim to reduce bias against differe... | ['Odette Scharenborg', 'Tanvina Patel'] | 2023-07-05 | null | null | null | null | ['anatomy', 'speech-recognition'] | ['miscellaneous', 'speech'] | [ 2.03707933e-01 4.43711251e-01 1.95985273e-01 -2.72925586e-01
-9.09917355e-01 -6.35028839e-01 3.37818056e-01 -5.57553880e-02
-6.52965903e-01 3.47160071e-01 6.39701605e-01 -5.11283636e-01
1.41236708e-01 -1.75662488e-01 -5.18862128e-01 -5.68836331e-01
2.25532129e-01 9.04922709e-02 -6.84613362e-02 -3.33744824... | [14.459238052368164, 6.4495320320129395] |
47c5c8d5-5334-4414-80ed-54e527017657 | a-25d-cascaded-convolutional-neural-network | 1806.01018 | null | http://arxiv.org/abs/1806.01018v2 | http://arxiv.org/pdf/1806.01018v2.pdf | A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images | In recent years, intravital skin imaging has been increasingly used in
mammalian skin research to investigate cell behaviors. A fundamental step of
the investigation is mitotic cell (cell division) detection. Because of the
complex backgrounds (normal cells), the majority of the existing methods cause
several false pos... | ['Yen-Wei Chen', 'Satoko Takemoto', 'Xian-Hau Han', 'Titinunt Kitrungrotsakul', 'Yutaro Iwamoto', 'Tomomi Nemoto', 'Hideo Yokota', 'Xiong Wei', 'Sari Ipponjima'] | 2018-06-04 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.08497046e-01 -5.09336948e-01 2.35704258e-02 2.33848870e-01
-3.58678550e-01 -3.74001294e-01 4.31119740e-01 2.19865859e-01
-7.85873652e-01 8.76062989e-01 -5.93497038e-01 -1.00366235e-01
4.91574705e-01 -9.04174626e-01 -4.40158308e-01 -1.10709774e+00
8.59748647e-02 1.70776710e-01 9.93365467e-01 1.25397697... | [14.724593162536621, -3.2014238834381104] |
812afb71-82ae-4d91-b8f3-d4aec7579b18 | two-headed-eye-segmentation-approach-for | 2209.15471 | null | https://arxiv.org/abs/2209.15471v1 | https://arxiv.org/pdf/2209.15471v1.pdf | Two-headed eye-segmentation approach for biometric identification | Iris-based identification systems are among the most popular approaches for person identification. Such systems require good-quality segmentation modules that ideally identify the regions for different eye components. This paper introduces the new two-headed architecture, where the eye components and eyelashes are segm... | ['Christian Brendel', 'Tobias Zillig', 'Tanguy Jeanneau', 'Maciej Zieba', 'Wiktor Lazarski'] | 2022-09-30 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.79193512e-01 1.17035776e-01 -1.26715779e-01 -5.37447333e-01
-3.97210121e-01 -4.69462484e-01 4.28784519e-01 -1.94937170e-01
-5.68709433e-01 6.16011977e-01 -1.99526995e-01 -1.94181383e-01
-2.36791492e-01 -3.29025030e-01 -4.87293482e-01 -7.18602657e-01
1.89302176e-01 3.57243985e-01 -2.58095503e-01 1.43263534... | [3.7445249557495117, -3.6309854984283447] |
f69acabb-d075-4d5d-a13a-a8a082d4ce01 | low-light-image-and-video-enhancement-via | 2203.04889 | null | https://arxiv.org/abs/2203.04889v1 | https://arxiv.org/pdf/2203.04889v1.pdf | Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity | Image acquisition in low-light conditions suffers from poor quality and significant degradation in visual aesthetics. This affects the visual perception of the acquired image and the performance of various computer vision and image processing algorithms applied after acquisition. Especially for videos, the additional t... | ['Matthias Trapp', 'Jürgen Döllner', 'Sebastian Pasewaldt', 'Amir Semmo', 'Max Reimann', 'Sumit Shekhar'] | 2022-03-09 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 5.53378105e-01 -6.11454487e-01 3.48464221e-01 -2.02399239e-01
-2.31877670e-01 -5.83302438e-01 5.15432537e-01 1.32298559e-01
-7.02430010e-01 5.94575107e-01 -2.27664575e-01 -4.56341803e-02
1.43061783e-02 -7.07684815e-01 -5.51962495e-01 -1.08843315e+00
1.10751070e-01 -5.31877637e-01 3.48613828e-01 -2.45619193... | [10.715812683105469, -2.4876012802124023] |
a1376457-1b22-48f2-89e5-cacc88f02ebd | learning-classifier-synthesis-for-generalized | 1906.02944 | null | https://arxiv.org/abs/1906.02944v5 | https://arxiv.org/pdf/1906.02944v5.pdf | Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning | Object recognition in the real-world requires handling long-tailed or even open-ended data. An ideal visual system needs to recognize the populated head visual concepts reliably and meanwhile efficiently learn about emerging new tail categories with a few training instances. Class-balanced many-shot learning and few-sh... | ['De-Chuan Zhan', 'Han-Jia Ye', 'Hexiang Hu'] | 2019-06-07 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 1.55854762e-01 1.37256682e-02 -4.15048331e-01 -5.36948383e-01
-8.40058208e-01 -3.66524935e-01 8.27348113e-01 -1.21931449e-01
-3.68163973e-01 5.66405833e-01 3.83687764e-02 1.59706220e-01
1.22871466e-01 -7.07948983e-01 -8.43317866e-01 -7.21592486e-01
1.26336487e-02 6.30322993e-01 6.32842481e-01 -2.73569196... | [9.998780250549316, 2.684119939804077] |
af84a4d3-6fde-4a5f-8949-f3a9143451ba | a-resource-light-method-for-cross-lingual | 1801.06436 | null | http://arxiv.org/abs/1801.06436v1 | http://arxiv.org/pdf/1801.06436v1.pdf | A Resource-Light Method for Cross-Lingual Semantic Textual Similarity | Recognizing semantically similar sentences or paragraphs across languages is
beneficial for many tasks, ranging from cross-lingual information retrieval and
plagiarism detection to machine translation. Recently proposed methods for
predicting cross-lingual semantic similarity of short texts, however, make use
of tools ... | ['Marc Franco-Salvador', 'Goran Glavaš', 'Simone Paolo Ponzetto', 'Paolo Rosso'] | 2018-01-19 | null | null | null | null | ['cross-lingual-information-retrieval', 'cross-lingual-semantic-textual-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.56970495e-02 -3.63479197e-01 -3.64303589e-01 -2.10396618e-01
-9.82257783e-01 -9.05969679e-01 9.69682097e-01 6.40444338e-01
-8.26457262e-01 4.86834288e-01 3.37334812e-01 -5.30738294e-01
4.75536101e-02 -6.95629954e-01 -4.75024194e-01 -4.06314790e-01
4.24501717e-01 4.97835606e-01 6.77787438e-02 -4.28801268... | [11.017614364624023, 9.902726173400879] |
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