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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3c2c973e-69f7-4296-9ae5-9e8ae2ad141c | novel-algorithm-to-generate-shortest-edit | null | null | https://raw.githubusercontent.com/ppml38/shortest_edit_script/main/paper/shortest_edit_script_algorithm.pdf | https://raw.githubusercontent.com/ppml38/shortest_edit_script/main/paper/shortest_edit_script_algorithm.pdf | Novel algorithm to generate shortest edit script using Levenshtein distance algorithm | String similarity, longest common subsequence and shortest edit scripts are the triplets of problem that related to each other. There are different algorithms exist to generate edit script by solving longest common subsequence problem. This paper proposes an algorithm that uses string similarity problem to generate sho... | ['P. Prakash Maria Liju'] | 2022-08-16 | null | null | null | github-2022-8 | ['edit-script-generation', 'file-difference'] | ['computer-code', 'computer-code'] | [ 5.60174346e-01 -3.23576391e-01 2.72349328e-01 -6.87414348e-01
2.18801796e-01 -1.10573804e+00 2.24916711e-01 4.69271123e-01
-4.00046259e-01 6.71965718e-01 3.43566060e-01 -3.44025850e-01
-3.47524256e-01 -1.03787029e+00 -4.05055761e-01 -4.35037538e-02
1.33881986e-01 1.74652100e-01 6.47111475e-01 -6.20885849... | [4.989056587219238, 5.224582672119141] |
4c5ed98e-283c-4e72-b2a8-705724881ab7 | graphflow-exploiting-conversation-flow-with | 1908.00059 | null | https://arxiv.org/abs/1908.00059v2 | https://arxiv.org/pdf/1908.00059v2.pdf | GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension | Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or... | ['Lingfei Wu', 'Yu Chen', 'Mohammed J. Zaki'] | 2019-07-31 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.36651316e-01 2.17717260e-01 -6.99649528e-02 -4.55703050e-01
-3.53532851e-01 -6.65849805e-01 6.64370000e-01 5.87515175e-01
-1.60772517e-01 5.82752883e-01 8.47952306e-01 -7.96598494e-01
-1.50264502e-01 -8.96477401e-01 -1.78962857e-01 -1.36070520e-01
7.99120441e-02 6.49815679e-01 4.51819509e-01 -7.45021522... | [12.156363487243652, 7.919756889343262] |
cf802f9a-9d2c-4a5a-97a6-a167de3fe0f3 | coot-cooperative-hierarchical-transformer-for | 2011.00597 | null | https://arxiv.org/abs/2011.00597v1 | https://arxiv.org/pdf/2011.00597v1.pdf | COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning | Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In this paper, we propose a Cooperative hierarchical Transformer (COOT) to leverage this hierarchy information and model the interactions between... | ['Thomas Brox', 'Hamed Pirsiavash', 'Mohammadreza Zolfaghari', 'Simon Ging'] | 2020-11-01 | null | http://proceedings.neurips.cc/paper/2020/hash/ff0abbcc0227c9124a804b084d161a2d-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/ff0abbcc0227c9124a804b084d161a2d-Paper.pdf | neurips-2020-12 | ['video-text-retrieval'] | ['computer-vision'] | [ 2.44156737e-02 -3.62423718e-01 -3.43184739e-01 -3.74444962e-01
-8.98996115e-01 -6.25747263e-01 9.06510592e-01 4.14169461e-01
-2.48158917e-01 4.20430303e-01 6.97922349e-01 2.50812888e-01
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-8.35518390e-02 1.02363206e-01 7.45043814e-01 1.52216658... | [9.943297386169434, 0.5529834032058716] |
184955c7-baba-4408-a0ab-ff9bc94e777f | tuning-models-of-code-with-compiler-generated | 2305.18341 | null | https://arxiv.org/abs/2305.18341v1 | https://arxiv.org/pdf/2305.18341v1.pdf | Tuning Models of Code with Compiler-Generated Reinforcement Learning Feedback | Large Language Models (LLMs) pre-trained on code have recently emerged as the dominant approach to program synthesis. However, the code that these models produce can violate basic language-level invariants, leading to lower performance in downstream tasks. We address this issue through an approach, called RLCF, that fu... | ['Chris Jermaine', 'Thomas Reps', 'Swarat Chaudhuri', 'Chima Adiole', 'Abhinav Jain'] | 2023-05-25 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-8.52428749e-02 3.74348611e-01 -5.54360092e-01 -4.35710281e-01
-1.35490620e+00 -7.73357213e-01 5.36755145e-01 3.95777762e-01
-7.27090612e-02 2.74451077e-01 2.71542102e-01 -1.03718841e+00
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-9.41253155e-02 2.38916621e-01 2.80295968e-01 -2.01059997... | [7.865427494049072, 7.696400165557861] |
adb45170-e369-4615-954b-63c5d93cad98 | an-improved-air-light-estimation-scheme-for | null | null | https://ieeexplore.ieee.org/document/9201388 | https://ieeexplore.ieee.org/document/9201388 | An Improved Air-Light Estimation Scheme for Single Haze Images Using Color Constancy Prior | Hazy environment attenuates the scene radiance and
causes difficulty in distinguishing the color and texture of the scene.
A crucial step in dehazing is the recovery of the global air-light
vector. Traditional methods usually interpret the RGB value of
the brightest region in haze images as the air-light. In this l... | ['B.K. Panigrahi', 'Tapan Kumar Gandhi', 'Sidharth Gautam'] | 2020-09-21 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.06640947e-01 -7.09319949e-01 5.42312086e-01 -1.58126250e-01
-8.53767768e-02 -2.56439596e-01 3.69302034e-01 -4.50561255e-01
-6.21300936e-02 7.23984480e-01 -6.12248182e-02 -1.38995886e-01
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4.30420756e-01 -3.68395329e-01 3.20621312e-01 -5.01883984... | [10.833403587341309, -3.1369264125823975] |
d7ac21ab-f68f-4b8b-9a16-256bd3e133b6 | self-training-for-class-incremental-semantic | 2012.03362 | null | https://arxiv.org/abs/2012.03362v3 | https://arxiv.org/pdf/2012.03362v3.pdf | Self-Training for Class-Incremental Semantic Segmentation | In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that le... | ['Joost Van de Weijer', 'Xialei Liu', 'Lu Yu'] | 2020-12-06 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 5.42454779e-01 4.12482649e-01 -2.89791107e-01 -5.23129463e-01
-8.30335677e-01 -6.05376184e-01 3.83114994e-01 5.04680425e-02
-7.21205771e-01 1.11563122e+00 -1.35416379e-02 -1.46939635e-01
2.53369063e-01 -3.65811080e-01 -9.52496290e-01 -7.12971747e-01
1.94057554e-01 5.99841893e-01 4.99417245e-01 1.22994147... | [9.450080871582031, 2.2746169567108154] |
d8f9918a-efbc-4317-b307-774f1dee114e | sgbanet-semantic-gan-and-balanced-attention | 2207.10256 | null | https://arxiv.org/abs/2207.10256v1 | https://arxiv.org/pdf/2207.10256v1.pdf | SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition | Scene text recognition is a challenging task due to the complex backgrounds and diverse variations of text instances. In this paper, we propose a novel Semantic GAN and Balanced Attention Network (SGBANet) to recognize the texts in scene images. The proposed method first generates the simple semantic feature using Sema... | ['Yue Lu', 'Umapada Pal', 'Jiajia Wu', 'Bing Yin', 'Palaiahnakote Shivakumara', 'Shujing Lyu', 'Dajian Zhong'] | 2022-07-21 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.08381689e-01 -2.89397240e-01 7.52999410e-02 -4.31949615e-01
-5.67662179e-01 -4.05039281e-01 7.56510735e-01 -5.87645292e-01
-5.56072965e-02 3.39745611e-01 2.34626889e-01 1.19312651e-01
2.63323277e-01 -7.51813352e-01 -6.72660112e-01 -1.06402278e+00
1.10942459e+00 6.41315162e-01 -1.32885734e-02 -4.57089320... | [11.391117095947266, -0.038199156522750854] |
f45b0f73-36cf-47d5-a2f4-cb7c296661c9 | dudonet-encoding-mask-projection-to-reduce-ct | 2001.00340 | null | https://arxiv.org/abs/2001.00340v3 | https://arxiv.org/pdf/2001.00340v3.pdf | Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography | Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. However, they are inherently local in the sinogram domain. Thus, one possible approach to MAR is to exploit the latter characteristic by learning to reduce... | ['Jing-Jing Lu', 'S. Kevin Zhou', 'Wei-An Lin', 'Yuanyuan Lyu', 'Haofu Liao'] | 2020-01-02 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.04159808e-01 1.99001372e-01 2.73595512e-01 -1.06066957e-01
-7.28507757e-01 -1.46164328e-01 6.56606853e-02 -1.27121478e-01
-2.67282158e-01 8.94115090e-01 2.73990154e-01 3.18283811e-02
-4.02283520e-01 -6.00242376e-01 -6.66444600e-01 -1.02032113e+00
-6.33987263e-02 2.76833326e-01 4.52370137e-01 6.92768991... | [13.492403984069824, -2.552842378616333] |
e6705f8c-3b12-4d5e-b874-cba1006afdc6 | symphony-generation-with-permutation | 2205.05448 | null | https://arxiv.org/abs/2205.05448v2 | https://arxiv.org/pdf/2205.05448v2.pdf | Symphony Generation with Permutation Invariant Language Model | In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation. We propose a novel Multi-track Multi-instrument Repeatable (MMR) representation for symphonic music and model the music sequence using a Transformer-based auto-regressive language model wi... | ['Maosong Sun', 'Feng Yu', 'Xiaobing Li', 'Xinran Zhang', 'Zehua Cheng', 'Yuanliang Dong', 'Jiafeng Liu'] | 2022-05-10 | null | null | null | null | ['audio-generation', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [ 3.20558816e-01 -1.40000850e-01 -2.19189599e-02 9.76803973e-02
-1.12861550e+00 -8.54556739e-01 4.52558279e-01 -6.56523824e-01
1.14817962e-01 6.76120400e-01 5.80476582e-01 -3.60702090e-02
-3.29674602e-01 -6.90575123e-01 -7.99419999e-01 -5.25884926e-01
2.30562672e-01 5.08260667e-01 -3.81506115e-01 -5.45439363... | [16.02560043334961, 5.516234874725342] |
511f2100-ea71-498a-82ba-66ad14759771 | robust-lane-detection-through-self-pre | 2305.17271 | null | https://arxiv.org/abs/2305.17271v1 | https://arxiv.org/pdf/2305.17271v1.pdf | Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss | Lane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelati... | ['Yongqi Dong', 'Ruohan Li'] | 2023-05-26 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-1.6787034e-02 -1.1635632e-01 -2.8684003e-02 -6.2667370e-01
-4.1807675e-01 -8.3366588e-02 3.1105918e-01 -3.1461698e-01
-7.3719049e-01 6.2558568e-01 -4.5137629e-01 -4.0997961e-01
1.8443343e-01 -7.4600816e-01 -6.6496223e-01 -9.5112967e-01
1.0791019e-01 -1.8367307e-02 7.5675595e-01 -6.9419682e-02
3.1843510e-01... | [8.069791793823242, -1.292013168334961] |
c31930bb-0b45-4d88-aae8-14d8e40c2967 | spatial-temporal-graph-learning-with | 2306.10683 | null | https://arxiv.org/abs/2306.10683v1 | https://arxiv.org/pdf/2306.10683v1.pdf | Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation | Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction. However, most existing models are vulnerable to the quality of the generated region... | ['Ruihua Han', 'SiuMing Yiu', 'Zheng Wang', 'Lianghao Xia', 'Chao Huang', 'Qianru Zhang'] | 2023-06-19 | null | null | null | null | ['contrastive-learning', 'graph-learning', 'contrastive-learning'] | ['computer-vision', 'graphs', 'methodology'] | [ 1.75618440e-01 1.13950767e-01 -6.03628933e-01 -4.39449400e-01
-8.83279920e-01 -4.62067664e-01 5.66890657e-01 3.55434448e-01
1.00349016e-01 6.09772861e-01 5.09625912e-01 -4.29798216e-01
-1.73930377e-01 -1.07402480e+00 -7.90687978e-01 -4.57210094e-01
-2.61132061e-01 2.43035018e-01 2.53269315e-01 -3.72875780... | [6.531975746154785, 2.1012609004974365] |
5d2aa744-a108-4fc1-acc5-75ed8554e190 | knowing-the-distance-understanding-the-gap | 2303.15219 | null | https://arxiv.org/abs/2303.15219v1 | https://arxiv.org/pdf/2303.15219v1.pdf | Knowing the Distance: Understanding the Gap Between Synthetic and Real Data For Face Parsing | The use of synthetic data for training computer vision algorithms has become increasingly popular due to its cost-effectiveness, scalability, and ability to provide accurate multi-modality labels. Although recent studies have demonstrated impressive results when training networks solely on synthetic data, there remains... | ['Orly Zvitia', 'Moran Rubin', 'Max Kogan', 'Vladimir Loginov', 'Alexey Gruzdev', 'Assaf Lehr', 'Eli Friedman'] | 2023-03-27 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 4.08585399e-01 1.48307696e-01 -1.11418463e-01 -3.64586502e-01
-8.71433258e-01 -6.67194664e-01 7.67021060e-01 3.86916250e-02
-5.53224564e-01 5.32830000e-01 8.51857886e-02 -2.30158582e-01
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6.15830123e-01 4.53146428e-01 -2.48326715e-02 3.44508812... | [11.22565746307373, 1.3554472923278809] |
26fa6a0c-1fa9-44f8-a29d-356f530fed92 | aspect-category-opinion-sentiment-extraction | null | null | https://ieeexplore.ieee.org/document/10013820 | https://ieeexplore.ieee.org/document/10013820 | Aspect-Category-Opinion-Sentiment Extraction Using Generative Transformer Model | Sentiment analysis is one of Natural Language Processing's applications that aims to process and extract sentiment information quickly and effectively. To expand upon the previous triplet extraction, that being aspect-opinion-sentiment triplets, Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction was created.... | ['Ngoc Hong Tran', 'Quang Vinh Dinh', 'Cao Duy Hoang'] | 2023-01-18 | null | null | null | rifv-2023-1 | ['aspect-based-sentiment-analysis', 'aspect-category-opinion-sentiment-quadruple'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.70540616e-01 2.43019268e-01 1.09145187e-01 -8.08203518e-01
-7.24730909e-01 -8.02599728e-01 6.84137166e-01 5.92174053e-01
-2.62637109e-01 5.04382014e-01 3.24452907e-01 -5.03024280e-01
5.67948222e-02 -7.26372898e-01 1.30587325e-01 -5.07311106e-01
2.85184175e-01 4.59979057e-01 1.26242921e-01 -8.83464515... | [11.296218872070312, 6.78853702545166] |
ea513108-d2b1-4344-bcb6-5dd62980604e | gesture-recognition-with-mmwave-wi-fi-access | 2306.17062 | null | https://arxiv.org/abs/2306.17062v1 | https://arxiv.org/pdf/2306.17062v1.pdf | Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned | In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave Wi-Fi signals so that they can ... | ['Jeroen Famaey', 'Rafael Berkvens', 'Nabeel Nisar Bhat'] | 2023-06-29 | null | null | null | null | ['pose-estimation', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.65179837e-01 -4.84012067e-02 -3.31635058e-01 -3.99521202e-01
-9.82640684e-01 -2.18005627e-01 2.54325867e-01 -6.02870941e-01
-5.36317468e-01 6.83508277e-01 4.08321798e-01 -3.81851047e-01
-2.60297954e-01 -8.14592123e-01 -3.93234253e-01 -1.16874588e+00
-3.51517439e-01 -1.16368815e-01 -4.66161408e-02 9.26074758... | [6.600583553314209, 0.7499961256980896] |
94beb042-42b9-4c5a-ab6a-c86baed96797 | learning-multimodal-data-augmentation-in | 2212.14453 | null | https://arxiv.org/abs/2212.14453v2 | https://arxiv.org/pdf/2212.14453v2.pdf | Learning Multimodal Data Augmentation in Feature Space | The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modal... | ['Andrew Gordon Wilson', 'Anshumali Shrivastava', 'Mu Li', 'Aston Zhang', 'Xingjian Shi', 'Zhiqiang Tang', 'Zichang Liu'] | 2022-12-29 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 5.96423149e-01 -6.42833710e-02 -1.14174776e-01 -3.67605239e-01
-8.81931007e-01 -8.19943011e-01 8.64926994e-01 2.23951697e-01
-4.62819546e-01 6.88982427e-01 3.32965463e-01 -2.90866733e-01
2.21629322e-01 -4.60838109e-01 -8.45289707e-01 -4.80902135e-01
2.85533875e-01 4.43898499e-01 -3.01338285e-01 -2.68879294... | [10.821537971496582, 1.5795531272888184] |
48dadc59-dc1c-4244-bd6f-aa8600a89c77 | instant-multi-view-head-capture-through-1 | 2306.07437 | null | https://arxiv.org/abs/2306.07437v1 | https://arxiv.org/pdf/2306.07437v1.pdf | Instant Multi-View Head Capture through Learnable Registration | Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow, and commonly address the problem in two separate steps; multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3D Meshes from Performanc... | ['Michael J. Black', 'Tianye Li', 'Timo Bolkart'] | 2023-06-12 | instant-multi-view-head-capture-through | http://openaccess.thecvf.com//content/CVPR2023/html/Bolkart_Instant_Multi-View_Head_Capture_Through_Learnable_Registration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bolkart_Instant_Multi-View_Head_Capture_Through_Learnable_Registration_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration', '3d-face-reconstruction', 'semantic-correspondence', 'multi-view-3d-shape-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-6.17207550e-02 2.38632441e-01 1.21513925e-01 -6.80025399e-01
-1.11626935e+00 -4.57505703e-01 4.79187518e-01 -9.60130915e-02
-2.16829434e-01 3.51392806e-01 3.62571031e-01 3.65483582e-01
3.23109031e-01 -7.16795683e-01 -1.03013337e+00 -5.08255184e-01
3.43614548e-01 1.09458768e+00 2.01501429e-01 1.51222438... | [13.302314758300781, 0.031331371515989304] |
4720ab46-82db-4f67-9039-d0a8a95ef94c | accurate-gigapixel-crowd-counting-by | 2305.09271 | null | https://arxiv.org/abs/2305.09271v1 | https://arxiv.org/pdf/2305.09271v1.pdf | Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement | The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting. Such resolutions are far beyond the memory and computation limits of current GPUs, and available deep neural network architectures and training procedures are not designed for such massive inputs. Although several method... | ['Alexandros Iosifidis', 'Qi Zhang', 'Arian Bakhtiarnia'] | 2023-05-16 | null | null | null | null | ['crowd-counting'] | ['computer-vision'] | [-3.18366468e-01 -2.80682862e-01 2.65930057e-01 -3.81540880e-02
-2.18674779e-01 -1.03978351e-01 5.93994617e-01 1.76557243e-01
-9.46361065e-01 1.08761251e+00 2.43151635e-01 -3.74826714e-02
2.92363256e-01 -1.15639997e+00 -3.68354410e-01 -4.88151819e-01
2.66657081e-02 7.79555261e-01 6.19877875e-01 -7.98043087... | [8.40416431427002, -0.33159422874450684] |
97e533da-766c-4151-bd63-dab4d1ebc473 | backdoor-attack-is-a-devil-in-federated-gan | 2207.00762 | null | https://arxiv.org/abs/2207.00762v2 | https://arxiv.org/pdf/2207.00762v2.pdf | Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis | Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research. Training generative adversarial neural networks (GAN) usually requires large amounts of training data. Federated learning (FL) provides a way of training a central model using ... | ['Xiaoxiao Li', 'Ruinan Jin'] | 2022-07-02 | null | null | null | null | ['data-poisoning', 'medical-image-generation'] | ['adversarial', 'medical'] | [ 3.01577508e-01 4.80870157e-01 1.04781927e-03 7.06071854e-02
-9.77501214e-01 -1.03843272e+00 5.54418862e-01 -2.20804617e-01
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4.65012826e-02 3.64333272e-01 -2.81658798e-01 -9.88027379... | [5.982293128967285, 7.07939338684082] |
c43db37a-3d09-4e32-bfa2-bd2c26a715aa | mher-model-based-hindsight-experience-replay | 2107.00306 | null | https://arxiv.org/abs/2107.00306v2 | https://arxiv.org/pdf/2107.00306v2.pdf | MHER: Model-based Hindsight Experience Replay | Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to alleviate issues raised from sparse rewards. However, these methods are still limited in efficiency and cannot make full use of experiences.... | ['Xiu Li', 'Feng Luo', 'Yali Du', 'Lei Han', 'Meng Fang', 'Rui Yang'] | 2021-07-01 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.43389761e-01 1.95303649e-01 -2.27962688e-01 -8.91612843e-02
-9.19316590e-01 -3.32152873e-01 3.64477307e-01 4.50976397e-04
-6.80959582e-01 1.24378455e+00 4.14785296e-01 2.60950495e-02
-4.04215813e-01 -6.13704979e-01 -8.55612040e-01 -7.80286789e-01
-3.50813389e-01 3.29154909e-01 -2.04197004e-01 -4.62330401... | [4.097320079803467, 1.704741358757019] |
e5dc285f-f063-435f-afa7-bc8667bd4544 | disentangle-align-and-fuse-for-multimodal-and | 1911.04417 | null | https://arxiv.org/abs/1911.04417v5 | https://arxiv.org/pdf/1911.04417v5.pdf | Disentangle, align and fuse for multimodal and semi-supervised image segmentation | Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common information shared between modalities (an organ's anatomy) is beneficial for m... | ['Rohan Dharmakumar', 'Scott Semple', 'Chengjia Wang', 'Agisilaos Chartsias', 'Sotirios A. Tsaftaris', 'Giorgos Papanastasiou', 'David E. Newby'] | 2019-11-11 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 6.78028107e-01 -8.00820962e-02 -3.29855055e-01 -5.27073920e-01
-1.01442397e+00 -8.61349761e-01 3.42879385e-01 2.56903321e-01
-4.64654893e-01 4.54839051e-01 2.59468406e-01 -1.96219802e-01
-1.95003480e-01 -2.79073268e-01 -4.46583569e-01 -9.73409772e-01
-2.73892283e-01 2.87356198e-01 1.33587539e-01 2.16617092... | [13.999917030334473, -2.352886438369751] |
9aa2e826-fde2-48cf-81c6-5b5e886f16df | won-t-get-fooled-again-answering-questions | 2307.02394 | null | https://arxiv.org/abs/2307.02394v1 | https://arxiv.org/pdf/2307.02394v1.pdf | Won't Get Fooled Again: Answering Questions with False Premises | Pre-trained language models (PLMs) have shown unprecedented potential in various fields, especially as the backbones for question-answering (QA) systems. However, they tend to be easily deceived by tricky questions such as "How many eyes does the sun have?". Such frailties of PLMs often allude to the lack of knowledge ... | ['Maosong Sun', 'Zhiyuan Liu', 'Xingyi Cheng', 'Huadong Wang', 'Yifan Luo', 'Shengding Hu'] | 2023-07-05 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [ 8.09981376e-02 7.25806594e-01 1.25948250e-01 -4.90011483e-01
-9.30626571e-01 -1.03516722e+00 5.71414232e-01 -6.88241348e-02
-1.32671371e-01 8.89255047e-01 -1.07035577e-01 -9.31708395e-01
-8.93637538e-03 -9.40689504e-01 -8.41644943e-01 -3.04177761e-01
4.63530540e-01 5.25139213e-01 4.15484875e-01 -7.99535990... | [10.991774559020996, 7.9343485832214355] |
904fda51-a8a8-4d3a-86c1-50f02012f8d8 | reader-guided-passage-reranking-for-open | 2101.00294 | null | https://arxiv.org/abs/2101.00294v3 | https://arxiv.org/pdf/2101.00294v3.pdf | Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering | Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer. In this paper, we propose a simple and effective passage reranking method, named Reader-guIDEd Reranker (... | ['Weizhu Chen', 'Jiawei Han', 'Jianfeng Gao', 'Yelong Shen', 'Xiaodong Liu', 'Pengcheng He', 'Yuning Mao'] | 2021-01-01 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 8.06396753e-02 8.59768223e-03 -5.84629104e-02 -2.21806262e-02
-1.82988548e+00 -8.97390246e-01 7.26969004e-01 4.91261452e-01
-9.24320161e-01 8.88741136e-01 6.50829077e-01 -3.68689150e-01
-3.58502954e-01 -7.69426227e-01 -7.96770334e-01 -8.32454041e-02
3.24006349e-01 1.18837047e+00 8.65428090e-01 -8.96836162... | [11.448826789855957, 7.760507583618164] |
408f46b9-1e58-4d23-829c-8060f96c1ea3 | pre-trained-contextual-embedding-of-source-1 | 2001.00059 | null | https://arxiv.org/abs/2001.00059v3 | https://arxiv.org/pdf/2001.00059v3.pdf | Learning and Evaluating Contextual Embedding of Source Code | Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A significant advancement in natural-language understanding has come with the development of pre-trained contextual embeddings, such as BERT, which ca... | ['Aditya Kanade', 'Petros Maniatis', 'Kensen Shi', 'Gogul Balakrishnan'] | 2019-12-21 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf | icml-2020-1 | ['contextual-embedding-for-source-code', 'program-repair', 'variable-misuse', 'exception-type', 'swapped-operands', 'function-docstring-mismatch', 'wrong-binary-operator', 'program-repair'] | ['computer-code', 'computer-code', 'computer-code', 'computer-code', 'computer-code', 'computer-code', 'computer-code', 'reasoning'] | [ 1.09479934e-01 4.53702062e-02 -3.93339515e-01 -3.51715982e-01
-1.10107112e+00 -6.13729239e-01 4.23197687e-01 3.58751714e-01
-3.00429940e-01 2.67843217e-01 6.42394900e-01 -8.41663957e-01
1.08380541e-01 -5.76300979e-01 -8.09584856e-01 -1.33678228e-01
-1.76816970e-01 1.29825482e-02 2.32713446e-01 -3.29709679... | [7.612033843994141, 7.865365982055664] |
f4955f9b-410c-4507-9571-d6fe1d0c3355 | towards-scene-understanding-for-autonomous | null | null | https://openaccess.thecvf.com/content/ACCV2022W/MLCSA/html/Steininger_Towards_Scene_Understanding_for_Autonomous_Operations_on_Airport_Aprons_ACCVW_2022_paper.html | https://openaccess.thecvf.com/content/ACCV2022W/MLCSA/papers/Steininger_Towards_Scene_Understanding_for_Autonomous_Operations_on_Airport_Aprons_ACCVW_2022_paper.pdf | Towards Scene Understanding for Autonomous Operations on Airport Aprons | Enhancing logistics vehicles on airport aprons with assistant and autonomous capabilities offers the potential to significantly increase
safety and efficiency of operations. However, this research area is still underrepresented compared to other automotive domains, especially regarding available image data, which is e... | ['Oliver Zendel', 'Julia Simon', 'Verena Widhalm', 'Wolfgang Pointner', 'Andreas Kriegler', 'Daniel Steininger'] | 2022-12-04 | null | null | null | asian-conference-on-computer-vision-accv | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.93923950e-01 -2.79631585e-01 4.05414179e-02 -5.06528437e-01
-2.30379969e-01 -7.98102319e-01 7.90232539e-01 4.15422350e-01
-6.38330162e-01 5.91890097e-01 -4.34677839e-01 -2.75886744e-01
-4.25838441e-01 -9.07984138e-01 -6.30340457e-01 -8.00704241e-01
-3.69677752e-01 6.29739523e-01 3.68504286e-01 -7.34990656... | [8.029773712158203, -1.1346460580825806] |
c9b33952-8f69-40f9-8067-62139b3132aa | protecting-the-protected-group-circumventing | 1905.10546 | null | https://arxiv.org/abs/1905.10546v3 | https://arxiv.org/pdf/1905.10546v3.pdf | Protecting the Protected Group: Circumventing Harmful Fairness | Machine Learning (ML) algorithms shape our lives. Banks use them to determine if we are good borrowers; IT companies delegate them recruitment decisions; police apply ML for crime-prediction, and judges base their verdicts on ML. However, real-world examples show that such automated decisions tend to discriminate again... | ['Moshe Tennenholtz', 'Omer Ben-Porat', 'Fedor Sandomirskiy'] | 2019-05-25 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 6.48750663e-02 4.01233047e-01 -8.49051416e-01 -6.42235100e-01
-5.08183658e-01 -5.27536690e-01 4.23395097e-01 3.93762767e-01
-8.47679019e-01 1.06324661e+00 2.43541971e-01 -7.67722547e-01
-2.78776646e-01 -1.06230474e+00 -2.45977696e-02 -7.68731654e-01
3.62863123e-01 5.78544736e-01 -5.67996562e-01 -5.54464422... | [8.83655834197998, 5.3765740394592285] |
48763f05-6eb8-4a06-8d95-d7d4b9c9c54a | 4d-or-semantic-scene-graphs-for-or-domain | 2203.11937 | null | https://arxiv.org/abs/2203.11937v1 | https://arxiv.org/pdf/2203.11937v1.pdf | 4D-OR: Semantic Scene Graphs for OR Domain Modeling | Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are capable of understanding all the links and interactions in such a demanding environment. This paper aims to bring the community one step clo... | ['Nassir Navab', 'Federico Tombari', 'Tobias Czempiel', 'Ulrich Eck', 'Evin Pınar Örnek', 'Ege Özsoy'] | 2022-03-22 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.73967499e-01 7.39713192e-01 1.98699087e-01 -2.47254103e-01
-3.68355811e-01 -4.18349147e-01 3.32848281e-01 5.30743361e-01
-8.97638276e-02 2.50353098e-01 5.52652657e-01 -3.41375887e-01
-6.21845067e-01 -4.15248990e-01 -7.03151762e-01 -3.69965971e-01
-1.95112735e-01 6.35048449e-01 3.29558887e-02 -1.10768348... | [14.012417793273926, -3.4129183292388916] |
23a433ca-d1f0-455d-940b-a09cb1a76302 | wavefront-sensor-for-millimeter-submillimeter | 2102.09286 | null | https://arxiv.org/abs/2102.09286v1 | https://arxiv.org/pdf/2102.09286v1.pdf | Wavefront sensor for millimeter/submillimeter-wave adaptive optics based on aperture-plane interferometry | We present a concept of a millimeter wavefront sensor that allows real-time sensing of the surface of a ground-based millimeter/submillimeter telescope. It is becoming important for ground-based millimeter/submillimeter astronomy to make telescopes larger with keeping their surface accurate. To establish `millimetric a... | ['Kotaro Kohno', 'Toshikazu Onishi', 'Tai Oshima', 'Tatsuya Takekoshi', 'Mikio Kurita', 'Tomoko Nakamura', 'Sachiko Okumura', 'Keiichi Matsuda', 'Satoya Nakano', 'Masato Hagimoto', 'Yohei Togami', 'Nario Kuno', 'Noriyuki Kawaguchi', 'Tetsuhiro Minamidani', 'Ikumi Hashimoto', 'Hideo Ogawa', 'Nozomi Okada', 'Akio Taniguc... | 2021-02-18 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 2.52314895e-01 3.77258182e-01 8.61860812e-01 -3.38338315e-01
-3.22024465e-01 -6.50954604e-01 2.07962424e-01 -8.23058486e-01
-2.03217834e-01 5.15189648e-01 8.17748010e-02 -4.56381410e-01
-1.69481218e-01 -8.11298788e-01 -2.88919568e-01 -5.10367513e-01
-2.24264674e-02 9.48973715e-01 5.09418368e-01 -2.50460595... | [9.757906913757324, -2.716773271560669] |
2f087010-6210-412d-a170-10589dbcfc00 | constructing-dreams-using-generative-ai | 2305.12013 | null | https://arxiv.org/abs/2305.12013v1 | https://arxiv.org/pdf/2305.12013v1.pdf | Constructing Dreams using Generative AI | Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand ho... | ['Cynthia Breazeal', 'Prerna Ravi', 'Randi Williams', 'Daniella DiPaola', 'Safinah Ali'] | 2023-05-19 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.82125652e-01 1.02772117e+00 1.00187138e-01 7.89339095e-02
-1.08033791e-01 -7.88442552e-01 8.15974295e-01 -1.62377745e-01
1.24546610e-01 6.52016103e-01 6.37195468e-01 -3.09861720e-01
1.20831430e-01 -8.34722281e-01 -8.35268021e-01 -3.77567202e-01
4.96723801e-01 3.19035977e-01 -1.52756274e-02 -4.40778434... | [9.43839168548584, 6.417750358581543] |
a762f08e-0ecc-4277-804e-d021c5919a5d | 190600901 | 1906.00901 | null | https://arxiv.org/abs/1906.00901v2 | https://arxiv.org/pdf/1906.00901v2.pdf | The iMet Collection 2019 Challenge Dataset | Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropo... | ['Christine Kaeser-Chen', 'Serge Belongie', 'Jennie Choi', 'Chenyang Zhang', 'Maria Kessler', 'Grace Vesom'] | 2019-06-03 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 3.44539911e-01 -4.66102362e-01 -2.25270927e-01 -4.29501981e-01
-5.16743720e-01 -7.73316801e-01 9.03264701e-01 -2.20818013e-01
-3.28278929e-01 4.04973090e-01 3.09889466e-01 3.71417552e-01
-3.83871794e-01 -7.00680673e-01 -6.30436063e-01 -3.01899463e-01
4.55105603e-01 7.68632472e-01 2.66631860e-02 1.16382740... | [11.327999114990234, 0.5163640379905701] |
2df11ddf-751d-4d36-b79a-75838360a670 | a-convolutional-spiking-network-for-gesture | 2304.11106 | null | https://arxiv.org/abs/2304.11106v2 | https://arxiv.org/pdf/2304.11106v2.pdf | A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces | Brain-computer interfaces are being explored for a wide variety of therapeutic applications. Typically, this involves measuring and analyzing continuous-time electrical brain activity via techniques such as electrocorticogram (ECoG) or electroencephalography (EEG) to drive external devices. However, due to the inherent... | ['Bipin Rajendran', 'Yiming Ai'] | 2023-04-21 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 8.33808005e-01 -5.38630784e-01 3.20178539e-01 -2.09096119e-01
-3.56526762e-01 -4.80457842e-01 3.94572288e-01 -1.62771180e-01
-6.61115289e-01 9.74653602e-01 -3.38129997e-01 -1.92890540e-01
-4.66990471e-01 -4.43921447e-01 -4.63054001e-01 -9.39824820e-01
-1.53822735e-01 8.96419808e-02 8.31291527e-02 2.12044343... | [12.954687118530273, 3.3770713806152344] |
9b884d6e-ce84-4c08-b6b2-e4d75020bae9 | faceforensics-learning-to-detect-manipulated | 1901.08971 | null | https://arxiv.org/abs/1901.08971v3 | https://arxiv.org/pdf/1901.08971v3.pdf | FaceForensics++: Learning to Detect Manipulated Facial Images | The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper... | ['Matthias Nießner', 'Andreas Rössler', 'Luisa Verdoliva', 'Justus Thies', 'Davide Cozzolino', 'Christian Riess'] | 2019-01-25 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 4.97994542e-01 1.09381117e-01 3.12372539e-02 -1.84890971e-01
-6.52963221e-01 -5.95656097e-01 8.84941518e-01 -8.93930718e-02
-2.57129222e-01 6.11027181e-01 2.80239820e-01 -4.31596972e-02
6.49781749e-02 -6.80638969e-01 -8.13929319e-01 -5.51002204e-01
-3.30658406e-02 1.03531107e-01 4.29479312e-03 -4.38728750... | [12.570416450500488, 1.0974845886230469] |
12b16584-eaf9-49c3-ab2b-f453b8c76b5a | joint-detection-and-identification-feature | 1604.01850 | null | http://arxiv.org/abs/1604.01850v3 | http://arxiv.org/pdf/1604.01850v3.pdf | Joint Detection and Identification Feature Learning for Person Search | Existing person re-identification benchmarks and methods mainly focus on
matching cropped pedestrian images between queries and candidates. However, it
is different from real-world scenarios where the annotations of pedestrian
bounding boxes are unavailable and the target person needs to be searched from
a gallery of w... | ['Shuang Li', 'Tong Xiao', 'Liang Lin', 'Bochao Wang', 'Xiaogang Wang'] | 2016-04-07 | joint-detection-and-identification-feature-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Xiao_Joint_Detection_and_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Xiao_Joint_Detection_and_CVPR_2017_paper.pdf | cvpr-2017-7 | ['person-search'] | ['computer-vision'] | [-1.52777255e-01 -6.04512393e-01 -1.16653815e-02 -6.21274173e-01
-7.91136205e-01 -5.52387714e-01 5.09504139e-01 -6.75743958e-03
-1.04187620e+00 7.49112129e-01 1.30963847e-01 7.85171911e-02
3.41665357e-01 -7.66062558e-01 -8.68316650e-01 -4.20591265e-01
1.90824583e-01 5.98043799e-01 3.68172258e-01 2.13552058... | [14.80420970916748, 0.8566433787345886] |
9c2ffedf-ec54-4d98-b116-af8d8c677475 | sea-a-spatially-explicit-architecture-for | 2304.12532 | null | https://arxiv.org/abs/2304.12532v1 | https://arxiv.org/pdf/2304.12532v1.pdf | SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning | Spatial information is essential in various fields. How to explicitly model according to the spatial location of agents is also very important for the multi-agent problem, especially when the number of agents is changing and the scale is enormous. Inspired by the point cloud task in computer vision, we propose a spatia... | ['Guoliang Fan', 'Bin Zhang', 'Zhiwei Xu', 'Dapeng Li'] | 2023-04-25 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-6.30218506e-01 -3.35124969e-01 -1.53966904e-01 9.02307779e-03
-2.61981100e-01 -5.37903011e-01 9.74760056e-01 2.08048269e-01
-7.72187412e-01 9.64088619e-01 1.04857564e-01 6.43123221e-03
-3.31979752e-01 -1.12271154e+00 -6.80871308e-01 -8.61055672e-01
-3.28020394e-01 7.74487853e-01 8.39716434e-01 -4.67191130... | [3.797667980194092, 1.9734996557235718] |
00b64a4a-08cb-4e24-90cb-8d9fa8fbc5ac | locking-on-leveraging-dynamic-vehicle-imposed | 2306.17529 | null | https://arxiv.org/abs/2306.17529v1 | https://arxiv.org/pdf/2306.17529v1.pdf | Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization | Most 6-DoF localization and SLAM systems use static landmarks but ignore dynamic objects because they cannot be usefully incorporated into a typical pipeline. Where dynamic objects have been incorporated, typical approaches have attempted relatively sophisticated identification and localization of these objects, limiti... | ['Michael Milford', 'Ankit Vora', 'Shubham Shrivastava', 'Punarjay Chakravarty', 'Sourav Garg', 'Stephen Hausler'] | 2023-06-30 | null | null | null | null | ['visual-localization', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [-1.57661363e-01 -2.04040617e-01 -3.07710245e-02 -5.59518516e-01
-7.15382040e-01 -1.06814837e+00 7.62531579e-01 2.45591253e-02
-9.14843976e-01 4.08871859e-01 -3.37125421e-01 -2.08435327e-01
4.25570086e-02 -4.85669196e-01 -8.67517769e-01 -3.65029812e-01
-1.39771134e-01 9.28543925e-01 8.50274980e-01 -1.92106262... | [7.373980522155762, -2.1359565258026123] |
ad34b65a-da3c-420f-bfee-ce6422e6ae57 | removing-supervision-in-semantic-segmentation | 2303.17410 | null | https://arxiv.org/abs/2303.17410v1 | https://arxiv.org/pdf/2303.17410v1.pdf | Removing supervision in semantic segmentation with local-global matching and area balancing | Removing supervision in semantic segmentation is still tricky. Current approaches can deal with common categorical patterns yet resort to multi-stage architectures. We design a novel end-to-end model leveraging local-global patch matching to predict categories, good localization, area and shape of objects for semantic ... | ['Fiora Pirri', 'Nico Samà', 'Simone Rossetti'] | 2023-03-30 | null | null | null | null | ['unsupervised-semantic-segmentation', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 2.40964651e-01 6.47662699e-01 -3.26398790e-01 -5.22198081e-01
-1.16292930e+00 -8.77851546e-01 3.46891820e-01 1.69001520e-01
-3.51790845e-01 2.45344296e-01 -3.71943384e-01 3.75703461e-02
1.85445771e-01 -6.76207483e-01 -1.02222061e+00 -4.32474405e-01
1.92315113e-02 9.19712722e-01 5.94511926e-01 3.65971588... | [9.582404136657715, 0.6460556387901306] |
96dced80-cb8c-443f-8c4f-3568737da69e | henet-forcing-a-network-to-think-more-for | 2110.10872 | null | https://arxiv.org/abs/2110.10872v1 | https://arxiv.org/pdf/2110.10872v1.pdf | HENet: Forcing a Network to Think More for Font Recognition | Although lots of progress were made in Text Recognition/OCR in recent years, the task of font recognition is remaining challenging. The main challenge lies in the subtle difference between these similar fonts, which is hard to distinguish. This paper proposes a novel font recognizer with a pluggable module solving the ... | ['Youdong Ding', 'Shugong Xu', 'Shiyi Mu', 'Jingchao Chen'] | 2021-10-21 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 1.41456157e-01 -5.29887140e-01 1.08451039e-01 -5.87678671e-01
-3.14862669e-01 -8.78464937e-01 2.73266733e-01 -2.55207777e-01
-9.43250060e-02 4.82469052e-01 -1.38158724e-01 -6.52705073e-01
1.31376535e-01 -4.73130375e-01 -4.73004699e-01 -8.20966244e-01
3.80557925e-01 1.28069609e-01 2.81400979e-01 -2.70612150... | [11.955574035644531, 2.094174385070801] |
1f461b8a-a1bb-4e2e-b664-fd92f71ce438 | alignscore-evaluating-factual-consistency | 2305.16739 | null | https://arxiv.org/abs/2305.16739v1 | https://arxiv.org/pdf/2305.16739v1.pdf | AlignScore: Evaluating Factual Consistency with a Unified Alignment Function | Many text generation applications require the generated text to be factually consistent with input information. Automatic evaluation of factual consistency is challenging. Previous work has developed various metrics that often depend on specific functions, such as natural language inference (NLI) or question answering ... | ['Zhiting Hu', 'Ruichen Li', 'Yichi Yang', 'Yuheng Zha'] | 2023-05-26 | null | null | null | null | ['fact-verification', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.25457990e-01 2.93790221e-01 8.18019733e-02 -3.97043049e-01
-1.39927316e+00 -7.19808578e-01 1.07784283e+00 5.47759771e-01
2.43247226e-02 1.15178740e+00 7.16776669e-01 -1.55813619e-01
-2.74255633e-01 -5.01538396e-01 -6.89952970e-01 -1.72225460e-01
4.37064946e-01 6.25592649e-01 2.06553601e-02 -5.16598582... | [12.00667953491211, 9.211189270019531] |
0757e6df-fe0e-48ab-8b87-5591dc7b7f4e | curriculum-learning-meets-weakly-supervised | 2212.07619 | null | https://arxiv.org/abs/2212.07619v1 | https://arxiv.org/pdf/2212.07619v1.pdf | Curriculum Learning Meets Weakly Supervised Modality Correlation Learning | In the field of multimodal sentiment analysis (MSA), a few studies have leveraged the inherent modality correlation information stored in samples for self-supervised learning. However, they feed the training pairs in a random order without consideration of difficulty. Without human annotation, the generated training pa... | ['Haifeng Hu', 'Ya Sun', 'Sijie Mai'] | 2022-12-15 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 2.31901497e-01 1.62305385e-01 -4.39097375e-01 -5.85732400e-01
-1.07671392e+00 -5.44319510e-01 4.00791734e-01 3.97776991e-01
-4.80629265e-01 4.67412651e-01 1.50445938e-01 -7.38349631e-02
2.02139709e-02 -6.95664644e-01 -5.45912087e-01 -9.81822968e-01
1.75091058e-01 4.45442379e-01 5.43064885e-02 -2.01029971... | [12.961502075195312, 4.985927104949951] |
bd07bdea-f050-4b5c-a561-5d35ffaf621e | dense-procedure-captioning-in-narrated | null | null | https://aclanthology.org/P19-1641 | https://aclanthology.org/P19-1641.pdf | Dense Procedure Captioning in Narrated Instructional Videos | Understanding narrated instructional videos is important for both research and real-world web applications. Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos which are a sequence of step-wise clips with description. Previous works on video dense ca... | ['Zhendong Niu', 'Botian Shi', 'Yaobo Liang', 'Nan Duan', 'Ming Zhou', 'Peng Chen', 'Lei Ji'] | 2019-07-01 | null | null | null | acl-2019-7 | ['dense-captioning'] | ['computer-vision'] | [ 8.80032182e-01 2.88761854e-01 -4.11720902e-01 -3.69886100e-01
-1.42401493e+00 -9.20534313e-01 5.33719063e-01 -5.85329197e-02
-5.52201904e-02 7.82042205e-01 1.03883779e+00 -4.97713983e-02
3.15154821e-01 -3.40156078e-01 -1.29027915e+00 -3.61197412e-01
8.57748464e-02 5.92374206e-02 -1.06775336e-01 1.25976190... | [10.362436294555664, 0.7279420495033264] |
07c23fc1-6bb2-4e69-b792-9470f8c075f9 | robust-statistics-and-no-reference-image | 1902.03842 | null | http://arxiv.org/abs/1902.03842v1 | http://arxiv.org/pdf/1902.03842v1.pdf | Robust statistics and no-reference image quality assessment in Curvelet domain | This paper uses robust statistics and curvelet transform to learn a
general-purpose no-reference (NR) image quality assessment (IQA) model. The new
approach, here called M1, competes with the Curvelet Quality Assessment
proposed in 2014 (Curvelet2014). The central idea is to use descriptors based
on robust statistics t... | ['Ramon Giostri Campos', 'Evandro Ottoni Teatini Salles'] | 2019-02-11 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [-8.94180462e-02 -4.02832121e-01 2.04297706e-01 -1.11676492e-01
-1.06065476e+00 -5.25913894e-01 6.46954834e-01 2.98785210e-01
-4.82892543e-01 5.84845185e-01 3.08594882e-01 1.51654318e-01
-4.98518020e-01 -4.40203846e-01 -3.53407890e-01 -7.42779374e-01
-3.54738444e-01 -1.43562227e-01 3.03677917e-01 -2.15315953... | [11.777389526367188, -1.931288480758667] |
c58da3d9-dffc-4a05-a827-f8e3291a03ca | a-robust-attentional-framework-for-license | 2006.03919 | null | https://arxiv.org/abs/2006.03919v2 | https://arxiv.org/pdf/2006.03919v2.pdf | A Robust Attentional Framework for License Plate Recognition in the Wild | Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, eg, shooting in frontal and horizontal view-angles and under good lighting conditions. However, th... | ['Peng Wang', 'Yanning Zhang', 'Chunhua Shen', 'Linjiang Zhang', 'Hui Li', 'Zhen Li'] | 2020-06-06 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 1.66725311e-02 -8.83032620e-01 4.14172746e-02 -2.63546765e-01
-7.75959611e-01 -9.64582682e-01 4.96443123e-01 -1.02343726e+00
-2.02919886e-01 5.28278828e-01 -2.25374043e-01 -1.59934461e-01
4.31984395e-01 -7.76919186e-01 -7.00526297e-01 -9.41316068e-01
7.18558133e-01 1.86906710e-01 4.40814704e-01 -3.23145688... | [9.85318374633789, -4.921056747436523] |
5fb4edad-6eec-4c4e-b5b8-090e08ec4da6 | implicit-behavioral-cloning | 2109.00137 | null | https://arxiv.org/abs/2109.00137v1 | https://arxiv.org/pdf/2109.00137v1.pdf | Implicit Behavioral Cloning | We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments dis... | ['Jonathan Tompson', 'Igor Mordatch', 'Johnny Lee', 'Adrian Wong', 'Laura Downs', 'Ayzaan Wahid', 'Oscar Ramirez', 'Andy Zeng', 'Corey Lynch', 'Pete Florence'] | 2021-09-01 | null | null | null | null | ['d4rl'] | ['robots'] | [ 3.05261780e-02 3.75114113e-01 -5.80855668e-01 2.88384198e-03
-5.58777928e-01 -5.65241933e-01 8.09310436e-01 -1.89377889e-01
-7.78862715e-01 1.21525300e+00 -1.49198964e-01 -3.27934384e-01
-4.71966743e-01 -2.49442514e-02 -1.14637566e+00 -1.05720246e+00
-5.50456941e-01 9.15739775e-01 2.75476128e-02 -3.23189706... | [4.3894243240356445, 1.03813898563385] |
081be8ef-5c1d-4d65-babf-fac5432383c1 | deep-brain-state-classification-of-meg-data | 2007.00897 | null | https://arxiv.org/abs/2007.00897v2 | https://arxiv.org/pdf/2007.00897v2.pdf | Deep brain state classification of MEG data | Neuroimaging techniques have shown to be useful when studying the brain's activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), in combination with various deep artificial neural network models to perform brain decoding. More specifically, here we investigate to wh... | ['Jesus Garcia Fernandez', 'Siamak Mehrkanoon', 'Ismail Alaoui Abdellaoui', 'Caner Sahinli'] | 2020-07-02 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 5.36005311e-02 -9.79653969e-02 5.65736890e-01 -6.97340667e-01
-3.52682732e-02 -1.31959781e-01 7.07629383e-01 1.26780391e-01
-5.03766894e-01 7.76678622e-01 4.78659004e-01 1.17747031e-01
-4.57957029e-01 -5.77830255e-01 -5.48899651e-01 -7.24043846e-01
-2.24874243e-01 2.34316304e-01 1.22148305e-01 -1.53955922... | [12.68569278717041, 3.3846426010131836] |
a49f2c10-d4d2-49d9-bf91-a1f7ecee14cd | dynamic-observation-policies-in-observation | 2307.02620 | null | https://arxiv.org/abs/2307.02620v1 | https://arxiv.org/pdf/2307.02620v1.pdf | Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning | Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step... | ['Isaac Tamblyn', 'Mark Crowley', 'Colin Bellinger'] | 2023-07-05 | null | null | null | null | ['reinforcement-learning-1', 'text-generation', 'openai-gym'] | ['methodology', 'natural-language-processing', 'playing-games'] | [ 1.11229308e-01 3.86895180e-01 3.13063823e-02 1.60112038e-01
-1.60444185e-01 -5.87527454e-01 6.69804335e-01 3.41835976e-01
-1.00866461e+00 1.32385635e+00 -3.65324587e-01 -1.59560725e-01
-4.34236884e-01 -8.91283095e-01 -8.05310130e-01 -9.71973896e-01
-7.94470087e-02 7.42556632e-01 1.20170325e-01 -3.07494819... | [4.381244659423828, 1.9527369737625122] |
45f2037c-9f80-4f32-815a-9e5da75c3d73 | groupformer-group-activity-recognition-with | 2108.12630 | null | https://arxiv.org/abs/2108.12630v1 | https://arxiv.org/pdf/2108.12630v1.pdf | GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer | Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group representations. However, previous methods either model spatial and temporal information separately, or directly aggregate individual featur... | ['Shuai Yi', 'Jun Hou', 'Shinan Liu', 'Kunlin Yang', 'Lingbo Liu', 'Qianggang Cao', 'Shuaicheng Li'] | 2021-08-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Li_GroupFormer_Group_Activity_Recognition_With_Clustered_Spatial-Temporal_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Li_GroupFormer_Group_Activity_Recognition_With_Clustered_Spatial-Temporal_Transformer_ICCV_2021_paper.pdf | iccv-2021-1 | ['group-activity-recognition'] | ['computer-vision'] | [ 0.04636412 -0.30907422 -0.4435891 -0.53892165 -0.5072464 -0.2787737
0.58423996 0.16616358 -0.26108158 0.4272255 0.56206346 0.18082123
-0.4800772 -0.7686857 -0.5796931 -0.7321687 -0.1316728 0.07193415
0.21695668 0.11148048 0.31111914 0.04087991 -1.692633 0.4168192
1.3448132 1.0981296 0.33... | [8.206324577331543, 0.7047435641288757] |
1f3bc620-31c9-4dbc-a1d3-0caf1452e0cf | pay-better-attention-to-attention-head | 2106.10840 | null | https://arxiv.org/abs/2106.10840v1 | https://arxiv.org/pdf/2106.10840v1.pdf | Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling | Multi-head attention has each of the attention heads collect salient information from different parts of an input sequence, making it a powerful mechanism for sequence modeling. Multilingual and multi-domain learning are common scenarios for sequence modeling, where the key challenge is to maximize positive transfer an... | ['Xian Li', 'Juan Pino', 'Yun Tang', 'Hongyu Gong'] | 2021-06-21 | null | http://proceedings.neurips.cc/paper/2021/hash/15c00b5250ddedaabc203b67f8b034fd-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/15c00b5250ddedaabc203b67f8b034fd-Paper.pdf | neurips-2021-12 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 6.42451197e-02 -7.76285455e-02 -3.96438628e-01 -4.33934957e-01
-1.36313188e+00 -8.87277663e-01 3.22252214e-01 -3.12899262e-01
-6.94650888e-01 9.32066083e-01 3.54918957e-01 -5.29472709e-01
5.06843150e-01 -2.05560938e-01 -8.88280690e-01 -3.80314797e-01
1.82716161e-01 6.90605581e-01 -9.65301469e-02 -6.34808302... | [11.68820858001709, 10.087145805358887] |
308ef62c-32de-4a1a-8324-49be489c4b7d | socially-compliant-navigation-dataset-scand-a | 2203.15041 | null | https://arxiv.org/abs/2203.15041v2 | https://arxiv.org/pdf/2203.15041v2.pdf | Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation | Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans. With the emergence of autonomously navigating mobile robots in human populated environments (e.g., domestic service robots in homes and re... | ['Peter Stone', 'Joydeep Biswas', 'Justin Hart', 'Alexander Toshev', 'Soeren Pirk', 'Garrett Warnell', 'Xuesu Xiao', 'Anirudh Nair', 'Haresh Karnan'] | 2022-03-28 | null | null | null | null | ['social-navigation'] | ['robots'] | [-3.10206175e-01 2.16285333e-01 2.42876694e-01 -4.29935366e-01
-2.60754347e-01 -4.65766281e-01 5.47800720e-01 -3.07249099e-01
-1.03286910e+00 9.93669212e-01 -7.57581294e-02 -2.42546886e-01
-2.94027537e-01 -4.41094786e-01 -7.33040869e-01 -4.07631636e-01
-5.38054943e-01 9.10270572e-01 2.78126806e-01 -8.02221000... | [4.8320207595825195, 0.96552973985672] |
b1a8abcd-2326-44df-8c23-7efb21600477 | constrained-crystals-deep-convolutional | null | null | https://www.nature.com/articles/s41524-021-00526-4 | https://www.nature.com/articles/s41524-021-00526-4.pdf | Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures | Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep ... | ['Zhang H.', 'Gutfleisch O.', 'Shen C.', 'Samathrakis I.', 'Zhang Y.', 'Opahle I.', 'Fortunato N.M.', 'Long T.'] | 2021-05-10 | null | null | null | npj-computational-materials-2021-5 | ['formation-energy'] | ['miscellaneous'] | [ 1.97368294e-01 4.11029868e-02 -1.21203333e-01 -1.56963989e-01
-6.54649317e-01 -1.81976557e-01 4.89296287e-01 -5.23610003e-02
-1.28926724e-01 1.04457974e+00 2.99331844e-02 -6.73719868e-02
-4.17136401e-01 -1.00710118e+00 -6.68866098e-01 -1.54961610e+00
-1.76383201e-02 9.45424020e-01 1.13289140e-01 -3.06487143... | [5.211695671081543, 5.301068305969238] |
94fb02d3-a928-4805-bcb5-c098f4c44677 | mac-mining-activity-concepts-for-language | 1811.08925 | null | http://arxiv.org/abs/1811.08925v1 | http://arxiv.org/pdf/1811.08925v1.pdf | MAC: Mining Activity Concepts for Language-based Temporal Localization | We address the problem of language-based temporal localization in untrimmed
videos. Compared to temporal localization with fixed categories, this problem
is more challenging as the language-based queries not only have no pre-defined
activity list but also may contain complex descriptions. Previous methods
address the p... | ['JIyang Gao', 'Ram Nevatia', 'Runzhou Ge', 'Kan Chen'] | 2018-11-21 | null | null | null | null | ['language-based-temporal-localization'] | ['computer-vision'] | [-1.57451674e-01 -5.97525001e-01 -7.19507217e-01 -3.27486128e-01
-1.05491543e+00 -7.02224910e-01 6.92136526e-01 -2.89377067e-02
-6.63677275e-01 5.36519766e-01 5.91067255e-01 3.36111784e-01
-3.83689627e-02 -2.31309637e-01 -7.08174407e-01 -5.44852376e-01
-6.07754469e-01 -1.58760086e-01 6.14880025e-01 1.55429915... | [9.550318717956543, 0.7174724340438843] |
e0b06a18-8ec3-447c-973b-1d8438b88449 | dialogueein-emotion-interaction-network-for | null | null | https://aclanthology.org/2022.coling-1.57 | https://aclanthology.org/2022.coling-1.57.pdf | DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis | Emotion Recognition in Conversation (ERC) has attracted increasing attention in the affective computing research field. Previous works have mainly focused on modeling the semantic interactions in the dialogue and implicitly inferring the evolution of the speakers’ emotional states. Few works have considered the emotion... | ['Qin Jin', 'Ruichen Li', 'Jingwen Hu', 'Jinming Zhao', 'Yuchen Liu'] | null | null | null | null | coling-2022-10 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-7.17393637e-01 1.26894221e-01 1.92780375e-01 -7.62336254e-01
9.36518684e-02 -2.87812501e-01 6.16307497e-01 6.64280429e-02
-2.31879920e-01 5.01690507e-01 5.48267841e-01 4.17388648e-01
3.34235251e-01 -3.89043689e-01 2.40916327e-01 -4.94763166e-01
-8.86812210e-02 2.81056345e-01 -2.70141333e-01 -6.26853108... | [13.024286270141602, 6.0434746742248535] |
af3e4ea6-1372-40e9-ac87-70ae743f3074 | unsupervised-meta-learning-via-latent-space | null | null | https://openreview.net/forum?id=-pLftu7EpXz | https://openreview.net/pdf?id=-pLftu7EpXz | Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling | Meta-learning aims to learn a model from a stream of tasks such that the model is able to generalize across tasks and rapidly adapt to new tasks. We propose to learn an energy-based model (EBM) in the latent space of a top-down generative
model such that the EBM in the low dimensional latent space is able to be learne... | ['Ying Nian Wu', 'Bo Pang', 'Deqian Kong'] | 2021-09-30 | null | null | null | 5th-workshop-on-meta-learning-at-neurips-2021 | ['unsupervised-few-shot-image-classification'] | ['computer-vision'] | [ 1.54577553e-01 -7.41625205e-02 -3.43661249e-01 -5.45076072e-01
-8.62036288e-01 -2.17233792e-01 8.51597846e-01 5.70722669e-02
-3.88319671e-01 4.87967789e-01 1.53474689e-01 4.23260003e-01
-9.78554264e-02 -6.77857578e-01 -8.22328269e-01 -7.71121323e-01
7.62913236e-03 8.33538353e-01 8.18733275e-02 1.17807947... | [9.863201141357422, 3.018612861633301] |
d5fbadb1-a56d-4411-92d0-a9b4e175158c | differentially-private-topological-data | 2305.03609 | null | https://arxiv.org/abs/2305.03609v1 | https://arxiv.org/pdf/2305.03609v1.pdf | Differentially Private Topological Data Analysis | This paper is the first to attempt differentially private (DP) topological data analysis (TDA), producing near-optimal private persistence diagrams. We analyze the sensitivity of persistence diagrams in terms of the bottleneck distance, and we show that the commonly used \v{C}ech complex has sensitivity that does not d... | ['Jordan Awan', 'Jinwon Sohn', 'Sehwan Kim', 'Taegyu Kang'] | 2023-05-05 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 1.32379681e-01 2.77658731e-01 -6.67936280e-02 5.49463220e-02
-6.37942851e-01 -9.50573564e-01 2.18652084e-01 3.02712440e-01
-6.10037565e-01 8.22543800e-01 -1.90751538e-01 -5.41302204e-01
-4.80852872e-01 -1.01532710e+00 -1.04238355e+00 -9.68410850e-01
-7.22339749e-01 6.62738681e-02 3.86002153e-01 -1.28261939... | [6.08095645904541, 6.639958381652832] |
13ed9fd7-b43e-4b0b-b349-aa8081289b56 | instance-aware-domain-generalization-for-face | 2304.05640 | null | https://arxiv.org/abs/2304.05640v1 | https://arxiv.org/pdf/2304.05640v1.pdf | Instance-Aware Domain Generalization for Face Anti-Spoofing | Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-... | ['Lizhuang Ma', 'Shouhong Ding', 'Ran Yi', 'Xuequan Lu', 'Taiping Yao', 'Ke-Yue Zhang', 'Qianyu Zhou'] | 2023-04-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Instance-Aware_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Instance-Aware_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-anti-spoofing'] | ['computer-vision'] | [ 3.49755585e-01 -3.98465067e-01 -2.71970004e-01 -6.44100964e-01
-4.23476905e-01 -8.05727124e-01 5.86190701e-01 -2.06667051e-01
-8.62627402e-02 6.50669634e-01 1.63234055e-01 2.87236534e-02
-1.67483002e-01 -8.29730451e-01 -3.67501050e-01 -9.97763634e-01
2.29335502e-01 1.46351889e-01 1.82362229e-01 -4.12990332... | [13.220630645751953, 1.1286412477493286] |
f4a24006-a24d-4bdf-af62-9124e7f4b4e0 | a-deeper-look-at-3d-shape-classifiers | 1809.02560 | null | http://arxiv.org/abs/1809.02560v2 | http://arxiv.org/pdf/1809.02560v2.pdf | A Deeper Look at 3D Shape Classifiers | We investigate the role of representations and architectures for classifying
3D shapes in terms of their computational efficiency, generalization, and
robustness to adversarial transformations. By varying the number of training
examples and employing cross-modal transfer learning we study the role of
initialization of ... | ['Jong-Chyi Su', 'Subhransu Maji', 'Rui Wang', 'Matheus Gadelha'] | 2018-09-07 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 4.11028750e-02 2.45820805e-01 2.71419078e-01 -3.65756810e-01
-8.90558660e-01 -1.15640485e+00 9.25338507e-01 5.01391701e-02
-1.22298360e-01 1.71941817e-01 -1.37327656e-01 -3.66010875e-01
1.95942849e-01 -9.43737566e-01 -1.17984927e+00 -4.82020080e-01
-1.31759062e-01 6.31270409e-01 -2.31598578e-02 -3.99971813... | [8.26539421081543, -3.9105582237243652] |
95852a07-a03c-4de5-b6b8-ba4d09703acd | arhnet-adaptive-region-harmonization-for | 2307.01220 | null | https://arxiv.org/abs/2307.01220v1 | https://arxiv.org/pdf/2307.01220v1.pdf | ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance | Accurately segmenting brain lesions in MRI scans is critical for providing patients with prognoses and neurological monitoring. However, the performance of CNN-based segmentation methods is constrained by the limited training set size. Advanced data augmentation is an effective strategy to improve the model's robustnes... | ['Rachel Sparks', 'Sebastien Ourselin', 'Alejandro Granados', 'Xi Ouyang', 'Yang Liu', 'Jiayu Huo'] | 2023-07-02 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 9.57158580e-02 1.36552960e-01 -1.66554555e-01 -3.63177657e-01
-6.83843851e-01 -1.91154331e-01 3.57292205e-01 -5.22383228e-02
-5.69648564e-01 6.31098330e-01 4.74047847e-02 -1.11559242e-01
3.94502312e-01 -5.15123188e-01 -5.17407119e-01 -8.29963386e-01
1.88622311e-01 2.05586806e-01 7.76123881e-01 -1.44182995... | [14.489340782165527, -2.237053871154785] |
0b48d66d-b80c-4f22-8388-c301aba6efed | improving-continuous-sign-language-1 | 2212.13023 | null | https://arxiv.org/abs/2212.13023v1 | https://arxiv.org/pdf/2212.13023v1.pdf | Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal | Most deep-learning-based continuous sign language recognition (CSLR) models share a similar backbone consisting of a visual module, a sequential module, and an alignment module. However, due to limited training samples, a connectionist temporal classification loss may not train such CSLR backbones sufficiently. In this... | ['Brian Mak', 'Ronglai Zuo'] | 2022-12-26 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 0.05365623 -0.27874994 -0.17789061 -0.5487478 -0.55654395 -0.34369957
0.6444366 -0.7131609 -0.55293155 0.35602242 0.4424501 -0.05175062
0.08628836 -0.24588093 -0.62766206 -0.97137797 0.15623601 -0.18218398
0.1722108 -0.24315293 0.06179138 0.44269308 -1.2754958 0.33945006
1.0031712 0.98715776 0.... | [9.21527099609375, -6.512218475341797] |
a0f9dce1-9bdc-425a-b8bf-b7d7ec3e7122 | on-distillation-of-guided-diffusion-models | 2210.03142 | null | https://arxiv.org/abs/2210.03142v3 | https://arxiv.org/pdf/2210.03142v3.pdf | On Distillation of Guided Diffusion Models | Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALLE-2, Stable Diffusion and Imagen. However, a downside of classifier-free guided diffusion models is that they are ... | ['Stefano Ermon', 'Robin Rombach', 'Tim Salimans', 'Jonathan Ho', 'Diederik P. Kingma', 'Ruiqi Gao', 'Chenlin Meng'] | 2022-10-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-guided-image-editing'] | ['computer-vision'] | [ 3.71033996e-01 -1.81789312e-03 1.82707727e-01 -1.94952279e-01
-1.07260215e+00 -3.73764098e-01 8.74702573e-01 -1.96122065e-01
-5.66745460e-01 6.57431960e-01 1.38384908e-01 -1.64037332e-01
5.69121167e-02 -9.49403048e-01 -6.85781717e-01 -7.39072263e-01
8.05950686e-02 3.92748505e-01 4.22693402e-01 -8.88591781... | [11.273727416992188, -0.4743518829345703] |
b79a5c60-2d06-4026-89be-71ecb57923b0 | pids-joint-point-interaction-dimension-search | 2211.15759 | null | https://arxiv.org/abs/2211.15759v2 | https://arxiv.org/pdf/2211.15759v2.pdf | PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud | The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This over... | ['Yiran Chen', 'Hai Li', 'Feng Yan', 'Mingyuan Ma', 'Tunhou Zhang'] | 2022-11-28 | null | null | null | null | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [-2.09849700e-02 7.87808597e-02 -4.41621929e-01 -3.05653363e-01
-6.70913935e-01 -5.62470376e-01 5.01179039e-01 2.57626865e-02
-1.05255850e-01 -4.98957783e-02 -6.41425699e-02 -4.64491874e-01
-4.47676599e-01 -7.54799902e-01 -8.20296347e-01 -4.22157794e-01
-6.98205158e-02 1.12650132e+00 7.79512703e-01 -4.72567379... | [7.964248180389404, -3.3733766078948975] |
e8a6149a-f192-46b7-ade1-a064fea64449 | anticipative-feature-fusion-transformer-for | 2210.12649 | null | https://arxiv.org/abs/2210.12649v1 | https://arxiv.org/pdf/2210.12649v1.pdf | Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation | Although human action anticipation is a task which is inherently multi-modal, state-of-the-art methods on well known action anticipation datasets leverage this data by applying ensemble methods and averaging scores of unimodal anticipation networks. In this work we introduce transformer based modality fusion techniques... | ['Jürgen Beyerer', 'Rainer Stiefelhagen', 'Michael Voit', 'David Schneider', 'Zeyun Zhong'] | 2022-10-23 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.72897142e-01 -6.62235618e-02 -4.28924449e-02 -2.44607046e-01
-1.19924915e+00 -2.73437202e-01 8.05620372e-01 6.44111708e-02
-3.85550052e-01 5.59000552e-01 9.77184355e-01 4.57309753e-01
-4.05416191e-01 -3.04062814e-01 -3.73988837e-01 -5.64437807e-01
-4.16766316e-01 2.71532923e-01 1.42282531e-01 -5.46885490... | [8.226682662963867, 0.5863378047943115] |
7d5939b4-3909-4e9e-9486-11b520726768 | wavepf-a-novel-fusion-approach-based-on | 2305.17376 | null | https://arxiv.org/abs/2305.17376v2 | https://arxiv.org/pdf/2305.17376v2.pdf | DePF: A Novel Fusion Approach based on Decomposition Pooling for Infrared and Visible Images | Infrared and visible image fusion aims to generate synthetic images simultaneously containing salient features and rich texture details, which can be used to boost downstream tasks. However, existing fusion methods are suffering from the issues of texture loss and edge information deficiency, which result in suboptimal... | ['Xiaoning Song', 'Zhongwei Shen', 'Chunyang Cheng', 'Yongbiao Xiao', 'Hui Li'] | 2023-05-27 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 4.04487818e-01 -3.11862826e-01 1.07093930e-01 -2.86665171e-01
-7.88951218e-01 5.92279807e-02 4.54833746e-01 2.74040792e-02
-1.99643865e-01 7.26817846e-01 5.10816813e-01 2.13595510e-01
-3.89885940e-02 -8.83952796e-01 -6.11757457e-01 -1.18362784e+00
4.84974474e-01 -7.45676875e-01 1.52046725e-01 -4.12555635... | [10.556697845458984, -1.8569436073303223] |
8a6b1c24-fa7a-4d5b-81de-8e7a33a1a646 | synchronized-audio-visual-frames-with | 2112.14088 | null | https://arxiv.org/abs/2112.14088v1 | https://arxiv.org/pdf/2112.14088v1.pdf | Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation | Video-to-Text (VTT) is the task of automatically generating descriptions for short audio-visual video clips, which can support visually impaired people to understand scenes of a YouTube video for instance. Transformer architectures have shown great performance in both machine translation and image captioning, lacking a... | ['Rainer Lienhart', 'Moritz Einfalt', 'Philipp Harzig'] | 2021-12-28 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.37825048e-01 -1.24039799e-01 -1.57414138e-01 -3.39692205e-01
-1.12582815e+00 -4.65382427e-01 6.91174388e-01 -2.75924951e-01
-3.38442624e-01 7.21343815e-01 4.94426221e-01 -1.38279557e-01
2.35588104e-01 -4.16031718e-01 -8.94653201e-01 -3.19398135e-01
3.32642184e-03 3.18964571e-01 2.64199048e-01 -3.12816978... | [10.6807222366333, 0.8798953890800476] |
fde0e387-5f3c-4aea-9250-82e4f8e3db2d | image-generation-from-freehand-scene-sketches | 2003.02683 | null | https://arxiv.org/abs/2003.02683v5 | https://arxiv.org/pdf/2003.02683v5.pdf | SketchyCOCO: Image Generation from Freehand Scene Sketches | We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports hi... | ['Li-Min Wang', 'Jianzhuang Liu', 'Changqing Zou', 'Chengying Gao', 'Qi Xu', 'Qi Liu'] | 2020-03-05 | sketchycoco-image-generation-from-freehand | http://openaccess.thecvf.com/content_CVPR_2020/html/Gao_SketchyCOCO_Image_Generation_From_Freehand_Scene_Sketches_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Gao_SketchyCOCO_Image_Generation_From_Freehand_Scene_Sketches_CVPR_2020_paper.pdf | cvpr-2020-6 | ['sketch-to-image-translation'] | ['computer-vision'] | [ 5.35340369e-01 3.53970557e-01 9.07159001e-02 6.91906661e-02
-8.92556906e-01 -8.57423544e-01 1.15261388e+00 -8.39497924e-01
1.81197554e-01 7.59228051e-01 1.46017242e-02 -1.97176725e-01
3.13464373e-01 -1.13047540e+00 -8.54603350e-01 -4.70267266e-01
1.00852162e-01 2.96870440e-01 -1.85424656e-01 -3.34264785... | [11.650811195373535, -0.43018606305122375] |
59d005a9-d5cc-455d-bdfa-1974b92c3d17 | sparse-group-learning-with-lipschitz-loss | 1910.08880 | null | https://arxiv.org/abs/1910.08880v7 | https://arxiv.org/pdf/1910.08880v7.pdf | Improved error rates for sparse (group) learning with Lipschitz loss functions | We study a family of sparse estimators defined as minimizers of some empirical Lipschitz loss function -- which include the hinge loss, the logistic loss and the quantile regression loss -- with a convex, sparse or group-sparse regularization. In particular, we consider the L1 norm on the coefficients, its sorted Slope... | ['Antoine Dedieu'] | 2019-10-20 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.71116471e-01 3.44078302e-01 -3.86937171e-01 -3.49249870e-01
-1.44110107e+00 -2.53134340e-01 -3.87906611e-01 1.41461298e-01
-4.67279166e-01 1.00664890e+00 -8.32345635e-02 -2.55950391e-01
-4.97816443e-01 -5.99640429e-01 -1.10827231e+00 -9.88776624e-01
-8.21249962e-01 1.54403061e-01 -2.88263649e-01 -5.60050905... | [6.7392897605896, 4.554749965667725] |
2e37513c-aaf0-40fd-908a-ed2b1fc81f6d | mot20-a-benchmark-for-multi-object-tracking | 2003.09003 | null | https://arxiv.org/abs/2003.09003v1 | https://arxiv.org/pdf/2003.09003v1.pdf | MOT20: A benchmark for multi object tracking in crowded scenes | Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTCh... | ['Laura Leal-Taixé', 'Stefan Roth', 'Anton Milan', 'Hamid Rezatofighi', 'Patrick Dendorfer', 'Javen Shi', 'Konrad Schindler', 'Daniel Cremers', 'Ian Reid'] | 2020-03-19 | null | null | null | null | ['multiple-people-tracking', 'multiple-object-tracking-with-transformer'] | ['computer-vision', 'computer-vision'] | [-2.17684925e-01 -5.09739339e-01 3.32468003e-02 -1.85556579e-02
-5.43287098e-01 -4.67548013e-01 7.49770641e-01 1.59709468e-01
-7.82714188e-01 9.26237166e-01 -5.79741858e-02 2.03658432e-01
1.29837334e-01 -1.90765977e-01 -6.90368593e-01 -5.63666224e-01
-1.50523067e-01 8.00246477e-01 9.47660446e-01 -1.62781421... | [6.373203277587891, -1.9927502870559692] |
59594ede-f831-438a-bec2-411847357983 | unleashing-the-power-of-neural-discourse | 2011.03203 | null | https://arxiv.org/abs/2011.03203v1 | https://arxiv.org/pdf/2011.03203v1.pdf | Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining | RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-o... | ['Giuseppe Carenini', 'Patrick Huber', 'Grigorii Guz'] | 2020-11-06 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.70251393e-01 9.33490753e-01 -5.64687550e-01 -3.77474666e-01
-1.31047618e+00 -6.94771349e-01 9.34022725e-01 5.80530584e-01
-3.69750977e-01 1.11468446e+00 1.14840555e+00 -8.44943464e-01
3.45095575e-01 -6.52730823e-01 -5.04965067e-01 -3.41403484e-01
-2.40411267e-01 5.64307332e-01 5.04150331e-01 -6.28919899... | [10.810503959655762, 9.457355499267578] |
7f49623d-58ea-4ba4-b951-a87ecc0f217e | aim-2020-challenge-on-rendering-realistic | 2011.04988 | null | https://arxiv.org/abs/2011.04988v1 | https://arxiv.org/pdf/2011.04988v1.pdf | AIM 2020 Challenge on Rendering Realistic Bokeh | This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh data... | ['Jay Zou', 'Hulk Wong', 'Max Zheng', 'Tengyao Wang', 'Xueqin Chen', 'Ge Wu', 'Praseeda S', 'Sanjana A R', 'Minnu A L', 'Saagara M B', 'A. N. Rajagopalan', 'Maitreya Suin', 'Praveen Kandula', 'Kuldeep Purohit', 'Nisarg A. Shah', 'Sourya Dipta Das', 'Saikat Dutta', 'Melvin Kuriakose', 'Hrishikesh P S', 'Jiji C V', 'Dens... | 2020-11-10 | null | null | null | null | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 1.75210699e-01 -2.82696158e-01 6.89999938e-01 -4.47614700e-01
-1.04176342e+00 -3.45937908e-01 4.30707008e-01 -3.46161723e-01
-5.30175209e-01 3.87798429e-01 1.10376358e-01 -8.36971849e-02
-4.48011570e-02 -3.26213270e-01 -8.48594725e-01 -6.12793863e-01
-2.22067133e-01 1.42276272e-01 3.47471893e-01 -1.41796902... | [10.537376403808594, -2.3173508644104004] |
347d841b-c583-488e-954d-82ec6ea3cebd | colored-transparent-object-matting-from-a | 1910.02222 | null | https://arxiv.org/abs/1910.02222v1 | https://arxiv.org/pdf/1910.02222v1.pdf | Colored Transparent Object Matting from a Single Image Using Deep Learning | This paper proposes a deep learning based method for colored transparent object matting from a single image. Existing approaches for transparent object matting often require multiple images and long processing times, which greatly hinder their applications on real-world transparent objects. The recently proposed TOM-Ne... | ['Kwan-Yee Kenneth Wong', 'Jamal Ahmed Rahim'] | 2019-10-05 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 2.59270877e-01 -1.84882641e-01 5.37440360e-01 -3.12251896e-01
-3.60594422e-01 -3.83714706e-01 4.29770127e-02 -8.62211347e-01
-1.49005294e-01 5.88432074e-01 -3.53539228e-01 -2.47136563e-01
5.65665662e-01 -7.57050693e-01 -1.01720583e+00 -7.94100165e-01
3.48804623e-01 3.49738359e-01 4.21089768e-01 3.41681838... | [10.514371871948242, -1.038562536239624] |
28b6a075-21bb-4c6e-92e8-f6f57c9f4d7b | dibimt-a-novel-benchmark-for-measuring-word | null | null | https://aclanthology.org/2022.acl-long.298 | https://aclanthology.org/2022.acl-long.298.pdf | DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation | Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. Over the last few decades, multiple efforts have been undertaken to investigate incorrect translations caused by the polysemous nature of words. Within this body of research, some studies have posited that models pick up semanti... | ['Roberto Navigli', 'Francesco Saina', 'Federico Martelli', 'Niccolò Campolungo'] | null | null | null | null | acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.63104460e-01 -1.01742469e-01 -3.80031675e-01 -4.11098212e-01
-8.63245964e-01 -8.75649869e-01 9.95756924e-01 1.81151867e-01
-5.63350260e-01 1.20037973e+00 3.26721191e-01 -6.74401700e-01
3.43525499e-01 -4.20704603e-01 -5.79164743e-01 -1.99778900e-01
6.23935997e-01 1.01716971e+00 -1.06435210e-01 -7.93478191... | [11.423468589782715, 10.308006286621094] |
83d2ffb6-d519-45c7-92cf-337fbc9cf599 | evaluation-of-deep-neural-networks-for | null | null | https://www.sciencedirect.com/science/article/abs/pii/S092523121830924X | https://www.sciencedirect.com/science/article/abs/pii/S092523121830924X | Evaluation of deep neural networks for traffic sign detection systems | Traffic sign detection systems constitute a key component in trending real-world applications, such as autonomous driving, and driver safety and assistance. This paper analyses the state-of-the-art of several object-detection systems (Faster R-CNN, R-FCN, SSD, and YOLO V2) combined with various feature extractors (Resn... | ['Luis M. Soria-Morillo', 'Juan Antonio Álvarez-García', 'Álvaro Arcos-García'] | 2018-11-17 | null | null | null | neurocomputing-2018-11 | ['traffic-sign-detection'] | ['computer-vision'] | [-1.28640398e-01 -4.73534971e-01 -3.13238651e-01 -8.14067200e-02
-3.12313020e-01 -1.85805321e-01 6.28782153e-01 -4.96074617e-01
-8.85465205e-01 2.03260601e-01 -3.17632049e-01 -7.40619481e-01
-1.87887073e-01 -5.33374369e-01 -4.18570310e-01 -5.89508712e-01
8.73804837e-02 2.12849155e-01 9.94750619e-01 -3.95842165... | [7.9945902824401855, -0.8087912201881409] |
02017271-cba1-4529-92a2-348ea052a69a | ngep-a-graph-based-event-planning-framework | 2210.10602 | null | https://arxiv.org/abs/2210.10602v1 | https://arxiv.org/pdf/2210.10602v1.pdf | NGEP: A Graph-based Event Planning Framework for Story Generation | To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to gua... | ['Frank Guerin', 'Chenghua Lin', 'Tyler Loakman', 'Zhihao Zhang', 'Chen Tang'] | 2022-10-19 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.10356462e-01 7.85665929e-01 -8.45564082e-02 -1.79225609e-01
-8.32708597e-01 -5.15506208e-01 1.29601312e+00 1.64591536e-01
2.05405876e-01 8.90633345e-01 1.23395705e+00 -1.09778620e-01
5.43458536e-02 -1.18908346e+00 -6.90167904e-01 1.22516416e-01
-6.55447543e-02 6.86674893e-01 8.11722651e-02 -3.43071252... | [11.660073280334473, 8.892730712890625] |
ed85c86f-284a-4e0a-9cdd-d74f7ac5d97f | discrete-cosine-transform-network-for-guided | 2104.06977 | null | https://arxiv.org/abs/2104.06977v3 | https://arxiv.org/pdf/2104.06977v3.pdf | Discrete Cosine Transform Network for Guided Depth Map Super-Resolution | Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected with suboptimal conditions with the help of HR RGB images of the same scene. To solve the challenges in interpreting the working mechanism, ex... | ['Hanspeter Pfister', 'Zudi Lin', 'Shuang Xu', 'Jiangshe Zhang', 'Zixiang Zhao'] | 2021-04-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Discrete_Cosine_Transform_Network_for_Guided_Depth_Map_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Discrete_Cosine_Transform_Network_for_Guided_Depth_Map_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['depth-map-super-resolution'] | ['computer-vision'] | [ 4.41161633e-01 6.37172014e-02 -8.79785568e-02 -3.12408954e-01
-1.30611205e+00 -1.22115612e-01 2.99126953e-01 -5.52854180e-01
-3.93629849e-01 6.24483168e-01 4.67664123e-01 1.33155182e-01
-7.31521696e-02 -9.95696127e-01 -4.64149982e-01 -8.97814333e-01
2.79654294e-01 -2.59709507e-01 3.89933735e-01 -2.93541521... | [9.786229133605957, -2.4010820388793945] |
16337c6d-a4ac-4ad1-b5c3-77b5b4090878 | color-inference-from-semantic-labeling-for | 1911.13114 | null | https://arxiv.org/abs/1911.13114v2 | https://arxiv.org/pdf/1911.13114v2.pdf | Color inference from semantic labeling for person search in videos | We propose an explainable model to generate semantic color labels for person search. In this context, persons are described from their semantic parts, such as hat, shirt, etc. Person search consists in looking for people based on these descriptions. In this work, we aim to improve the accuracy of color labels for peopl... | ['Guillaume-Alexandre Bilodeau', 'Harshad Mahadik', 'Jules Simon', 'David Steele'] | 2019-11-29 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.63285416e-02 -1.93681106e-01 4.09077927e-02 -6.78391397e-01
-3.81245404e-01 -8.34416509e-01 6.84113145e-01 8.53134543e-02
-4.61689770e-01 5.68560481e-01 -1.11027136e-01 9.27594230e-02
-4.58181463e-02 -8.04776609e-01 -5.41674256e-01 -2.84965605e-01
4.99399275e-01 1.08153665e+00 2.30594471e-01 6.21816963... | [9.059786796569824, 0.13283585011959076] |
9b059526-46aa-4535-bc53-3d2bf13127bc | learnable-graph-matching-incorporating-graph | 2103.16178 | null | https://arxiv.org/abs/2103.16178v1 | https://arxiv.org/pdf/2103.16178v1.pdf | Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking | Data association across frames is at the core of Multiple Object Tracking (MOT) task. This problem is usually solved by a traditional graph-based optimization or directly learned via deep learning. Despite their popularity, we find some points worth studying in current paradigm: 1) Existing methods mostly ignore the co... | ['Zhaoxiang Zhang', 'Naiyan Wang', 'Zehao Huang', 'JiaWei He'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['online-multi-object-tracking', 'graph-partitioning'] | ['computer-vision', 'graphs'] | [-2.83076048e-01 -1.59772560e-01 -3.62760007e-01 -2.83671767e-01
-5.07658362e-01 -3.61923784e-01 1.62476137e-01 -6.23373874e-02
-2.31781155e-01 5.33691347e-01 -1.87223747e-01 -1.33256689e-01
-1.46780610e-01 -6.04688883e-01 -1.02978146e+00 -7.14738369e-01
2.67782602e-02 4.09983963e-01 3.80149543e-01 6.60748333... | [6.3659443855285645, -2.08918833732605] |
2308c416-c26a-4aa6-99b0-9d06b0af0fc0 | egcn-an-ensemble-based-learning-framework-for | null | null | https://www.ijcai.org/proceedings/2022/511 | https://www.ijcai.org/proceedings/2022/0511.pdf | EGCN: An Ensemble-based Learning Framework for Exploring Effective Skeleton-based Rehabilitation Exercise Assessment | Recently, some skeleton-based physical therapy systems have been attempted to automatically evaluate the correctness or quality of an exercise performed by rehabilitation subjects. However, in terms of algorithms and evaluation criteria, the task remains not fully explored regarding making full use of different skeleto... | ['Keith C.C. Chan', 'Gong Chen', 'Xiang Zhang', 'Yan Liu', 'Bruce X.B. Yu'] | 2022-07-01 | null | null | null | ijcai-2022-7 | ['action-assessment'] | ['computer-vision'] | [ 3.39961052e-01 -6.57908246e-02 -4.91352260e-01 -1.83355600e-01
-6.18546188e-01 1.15785547e-01 2.01950192e-01 -1.30228117e-01
-3.52052003e-01 8.30767214e-01 6.59649134e-01 -1.58064976e-01
-4.22126502e-01 -8.37986588e-01 -2.22229823e-01 -3.57958317e-01
-2.47538164e-01 2.03562498e-01 1.94284022e-01 -1.89452752... | [7.320581912994385, 0.22588685154914856] |
9d390157-fbb3-4fa0-814d-b7c04ed4f848 | iteratively-selecting-an-easy-reference-frame | 2112.12402 | null | https://arxiv.org/abs/2112.12402v1 | https://arxiv.org/pdf/2112.12402v1.pdf | Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier | Unsupervised video object segmentation (UVOS) is a per-pixel binary labeling problem which aims at separating the foreground object from the background in the video without using the ground truth (GT) mask of the foreground object. Most of the previous UVOS models use the first frame or the entire video as a reference ... | ['Euntai Kim', 'Hongje Seong', 'Youngjo Lee'] | 2021-12-23 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 4.91417915e-01 -1.40300974e-01 -4.58082378e-01 -1.88790664e-01
-4.04900938e-01 -3.33257645e-01 3.83811504e-01 -1.29553899e-01
-5.82351506e-01 5.36248863e-01 -1.79304019e-01 -3.39351356e-01
1.51354179e-01 -8.23145688e-01 -6.61305726e-01 -9.16924417e-01
4.54315603e-01 2.75275141e-01 1.14940608e+00 1.23487450... | [9.144041061401367, -0.30029332637786865] |
7cfc273c-71cb-49bb-9cf8-3e8ecb12ecc1 | can-chatgpt-pass-an-introductory-level | 2305.02230 | null | https://arxiv.org/abs/2305.02230v2 | https://arxiv.org/pdf/2305.02230v2.pdf | Can ChatGPT Pass An Introductory Level Functional Language Programming Course? | The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code togethe... | ['Yihan Zhang', 'Xujie Si', 'Brigitte Pientka', 'Chuqin Geng'] | 2023-04-29 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [-2.85636038e-02 1.05568446e-01 -2.31762081e-01 -1.30489171e-01
-1.03326368e+00 -9.90213096e-01 3.37672353e-01 8.69718313e-01
-2.02059731e-01 4.26190078e-01 3.66054364e-02 -9.18013275e-01
5.60508706e-02 -6.52643681e-01 -7.23659158e-01 -1.21166319e-01
1.15164585e-01 -3.54273915e-02 2.45657027e-01 -3.64483684... | [9.750856399536133, 7.311288833618164] |
3747ee65-2299-43dc-a494-770162704002 | efficient-video-semantic-segmentation-with | 1912.11844 | null | https://arxiv.org/abs/1912.11844v1 | https://arxiv.org/pdf/1912.11844v1.pdf | Efficient Video Semantic Segmentation with Labels Propagation and Refinement | This paper tackles the problem of real-time semantic segmentation of high definition videos using a hybrid GPU / CPU approach. We propose an Efficient Video Segmentation(EVS) pipeline that combines: (i) On the CPU, a very fast optical flow method, that is used to exploit the temporal aspect of the video and propagate s... | ['Luc van Gool', 'Radu Timofte', 'Matthieu Paul', 'Christoph Mayer'] | 2019-12-26 | null | null | null | null | ['2048'] | ['playing-games'] | [ 3.87954652e-01 1.05169132e-01 8.89495090e-02 -2.05405563e-01
-6.08065665e-01 -3.22060794e-01 3.32954347e-01 6.93494007e-02
-7.53708124e-01 4.61093575e-01 -1.85901999e-01 -2.67636538e-01
5.30265749e-01 -8.99774015e-01 -6.30970836e-01 -4.90924031e-01
4.16629612e-02 6.10460162e-01 1.12123656e+00 1.23228682... | [9.186528205871582, -0.17200618982315063] |
ad9a0e05-023a-4762-bb5f-130d5609c54c | transferable-graph-backdoor-attack | 2207.00425 | null | https://arxiv.org/abs/2207.00425v3 | https://arxiv.org/pdf/2207.00425v3.pdf | Transferable Graph Backdoor Attack | Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node features for better graph representation learning. Despite the success of GNNs, and similar to other types of deep neural networks, GNNs are foun... | ['Salil S. Kanhere', 'Damith C. Ranasinghe', 'Seyit Camtepe', 'Tamas Abraham', 'Olivier De Vel', 'Paul Montague', 'Bao Gia Doan', 'Shuiqiao Yang'] | 2022-06-21 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 4.52548563e-01 4.00553912e-01 -1.47836670e-01 1.90697595e-01
-3.33217591e-01 -1.17776418e+00 5.44266701e-01 1.89195022e-01
1.12152502e-01 5.50941885e-01 -5.18299490e-02 -6.28865004e-01
2.50322185e-02 -1.37176788e+00 -1.25230896e+00 -4.83368784e-01
-6.38696015e-01 1.17023043e-01 2.89444715e-01 -6.27695858... | [6.107460975646973, 7.3392181396484375] |
1a4cbd10-b166-4502-b17f-d3659f195842 | deepore-a-deep-learning-workflow-for-rapid | 2005.03759 | null | https://arxiv.org/abs/2005.03759v2 | https://arxiv.org/pdf/2005.03759v2.pdf | DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials | DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images. By combining naturally occurring porous textures we generated 17700 semi-real 3-D micro-structures of porous geo-materials with size of 256^3 voxels and 30 physical prop... | ['Traiwit Chung', 'Ying Da Wang', 'Reza Shams', 'Arash Rabbani', 'Masoud Babaei'] | 2020-05-03 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-4.08500545e-02 2.40526304e-01 8.18049312e-01 1.77069604e-02
-5.10069370e-01 1.74369410e-01 5.52367687e-01 4.75579947e-01
-8.42993140e-01 1.19404185e+00 -3.96288261e-02 -2.67383456e-01
-4.23281074e-01 -1.46260381e+00 -1.10701466e+00 -1.11103594e+00
-6.17574692e-01 1.07779670e+00 4.57865566e-01 -4.70595770... | [6.412940979003906, 3.3438875675201416] |
d200b7a9-b718-4541-921c-8911fa895a08 | unsupervised-lifelong-person-re | 2203.06468 | null | https://arxiv.org/abs/2203.06468v1 | https://arxiv.org/pdf/2203.06468v1.pdf | Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal | Existing unsupervised person re-identification (ReID) methods focus on adapting a model trained on a source domain to a fixed target domain. However, an adapted ReID model usually only works well on a certain target domain, but can hardly memorize the source domain knowledge and generalize to upcoming unseen data. In t... | ['Francois Bremond', 'Benoit Lagadec', 'Hao Chen'] | 2022-03-12 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.23213823e-02 -3.46877426e-01 2.95563471e-02 -6.80509806e-01
-4.04512048e-01 -6.21919990e-01 6.96862280e-01 1.28571481e-01
-9.28608239e-01 9.01259124e-01 2.60307997e-01 5.02767026e-01
5.70347868e-02 -6.52373314e-01 -6.52070105e-01 -5.13938546e-01
1.85690522e-01 9.52025414e-01 2.99748063e-01 -7.80375898... | [14.741759300231934, 1.123348593711853] |
6307a7f3-28b2-48bf-a5be-445e99e6c2ff | segmenting-moving-objects-via-an-object | 2207.02206 | null | https://arxiv.org/abs/2207.02206v2 | https://arxiv.org/pdf/2207.02206v2.pdf | Segmenting Moving Objects via an Object-Centric Layered Representation | The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video. We make four contributions: First, we introduce an object-centric segmentation model with a depth-ordered layer representation. This is implemented using a variant of the transformer architecture that ... | ['Andrew Zisserman', 'Weidi Xie', 'Junyu Xie'] | 2022-07-05 | null | null | null | null | ['unsupervised-object-segmentation', 'motion-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.57367045e-01 5.38871512e-02 -1.13975003e-01 -7.70508870e-02
-7.51419425e-01 -8.10701847e-01 6.28082514e-01 -2.89215714e-01
-3.32848996e-01 5.68140507e-01 -1.56103969e-01 -1.42491415e-01
6.95963800e-02 -4.20961618e-01 -1.14963806e+00 -5.00169754e-01
-1.92656577e-01 7.79642522e-01 1.05450726e+00 -2.06895601... | [9.129650115966797, -0.09626523405313492] |
efee579b-8e03-433d-b161-318e54858c65 | selective-query-processing-a-risk-sensitive | 2305.18311 | null | https://arxiv.org/abs/2305.18311v1 | https://arxiv.org/pdf/2305.18311v1.pdf | Selective Query Processing: a Risk-Sensitive Selection of System Configurations | In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the system configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selec... | ['Md Zia Ullah', 'Josiane Mothe'] | 2023-05-17 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 3.58738266e-02 -4.18288767e-01 -4.77610797e-01 -2.28992134e-01
-1.03993952e+00 -8.23738873e-01 5.51512659e-01 5.69516480e-01
-8.25099528e-01 5.47119141e-01 2.27547064e-02 -4.08589810e-01
-6.83165252e-01 -9.13357258e-01 -9.78012756e-02 -5.20505250e-01
1.00856721e-02 9.90464509e-01 7.01851487e-01 -3.74448657... | [11.491726875305176, 7.53472900390625] |
2c9af49b-d266-429e-ad3e-c68d29597d28 | using-program-induction-to-interpret | 1708.00376 | null | http://arxiv.org/abs/1708.00376v1 | http://arxiv.org/pdf/1708.00376v1.pdf | Using Program Induction to Interpret Transition System Dynamics | Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which ... | ['Svetlin Penkov', 'Subramanian Ramamoorthy'] | 2017-07-26 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.11644071e-01 4.57976818e-01 -2.80211926e-01 -5.75557768e-01
-5.58420680e-02 -3.69864367e-02 8.58326852e-01 4.39337134e-01
1.13747269e-01 6.48021579e-01 -9.55212042e-02 -9.95655715e-01
-4.63061601e-01 -8.90379190e-01 -1.13028550e+00 -3.04234326e-01
-7.04367220e-01 5.81336319e-01 4.17730547e-02 -1.59635425... | [8.423381805419922, 7.255739212036133] |
a8cf2194-e51e-4604-b251-af95de1344e3 | sharcs-shared-concept-space-for-explainable | 2307.00316 | null | https://arxiv.org/abs/2307.00316v1 | https://arxiv.org/pdf/2307.00316v1.pdf | SHARCS: Shared Concept Space for Explainable Multimodal Learning | Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given modelling task. While various deep learning approaches have successfully addressed these challenges, their reasoning process is often opaque; l... | ['Nikola Simidjievski', 'Pietro Liò', 'Lucie Charlotte Magister', 'Pietro Barbiero', 'Gabriele Dominici'] | 2023-07-01 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 3.00722033e-01 3.86158377e-01 -3.64308834e-01 -5.15597820e-01
-1.16200852e+00 -6.55503929e-01 7.25277841e-01 2.60344237e-01
1.12156853e-01 6.10630572e-01 4.16475415e-01 -3.40095162e-01
-4.31556165e-01 -3.98127884e-01 -6.94257081e-01 -4.86055702e-01
1.61696076e-01 7.81555176e-01 -3.99811655e-01 -1.59245238... | [10.760170936584473, 1.7241144180297852] |
72b36da5-1067-4f1b-9360-dbfaee02dc5b | new-wrapper-method-based-on-normalized-mutual | 2210.14346 | null | https://arxiv.org/abs/2210.14346v1 | https://arxiv.org/pdf/2210.14346v1.pdf | New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images | Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In this context, we propose a new wrapper method based on normalized mutual informa... | ['Ahmed Hammouch', 'Elkebir Sarhrouni', 'Asma Elmaizi', 'Hasna Nhaila'] | 2022-10-25 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 7.45979249e-01 -6.85033321e-01 1.43901378e-01 -4.30858761e-01
-2.42794231e-01 -6.13691211e-01 3.51313591e-01 1.21478178e-01
-2.33634815e-01 9.27936792e-01 -1.82858825e-01 -3.75821106e-02
-9.40687001e-01 -9.15614784e-01 5.27060926e-02 -1.01621735e+00
-1.32365003e-01 2.73780767e-02 -1.46171838e-01 -7.41895661... | [9.781513214111328, -1.8349343538284302] |
a5685750-f508-4a02-b77c-4e17ef03d65c | world-models | 1803.10122 | null | http://arxiv.org/abs/1803.10122v4 | http://arxiv.org/pdf/1803.10122v4.pdf | World Models | We explore building generative neural network models of popular reinforcement
learning environments. Our world model can be trained quickly in an
unsupervised manner to learn a compressed spatial and temporal representation
of the environment. By using features extracted from the world model as inputs
to an agent, we c... | ['Jürgen Schmidhuber', 'David Ha'] | 2018-03-27 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-4.92166787e-01 5.21217167e-01 -1.03107490e-01 -1.60971448e-01
-1.89546987e-01 -5.76264560e-01 7.53364205e-01 -4.64960039e-01
-4.92984325e-01 1.01547730e+00 4.77397978e-01 2.45520566e-03
9.63309631e-02 -1.18631315e+00 -1.00011599e+00 -7.37528741e-01
-2.70662874e-01 8.19398701e-01 -1.64199173e-01 -3.63306612... | [4.21512508392334, 1.4773885011672974] |
6e901e39-465d-4b5c-9ad2-ddf5b889e6f9 | a-discrete-cvae-for-response-generation-on-1 | 1911.09845 | null | https://arxiv.org/abs/1911.09845v1 | https://arxiv.org/pdf/1911.09845v1.pdf | A Discrete CVAE for Response Generation on Short-Text Conversation | Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled la-tent vari... | ['Xiaojiang Liu', 'Jun Gao', 'Shuming Shi', 'Junhui Li', 'Guodong Zhou', 'Wei Bi'] | 2019-11-22 | a-discrete-cvae-for-response-generation-on | https://aclanthology.org/D19-1198 | https://aclanthology.org/D19-1198.pdf | ijcnlp-2019-11 | ['short-text-conversation'] | ['natural-language-processing'] | [ 2.57138256e-02 2.69727081e-01 -9.23721120e-02 -6.97117686e-01
-1.11858642e+00 -3.53089809e-01 6.70021653e-01 -5.33264279e-01
-2.72395853e-02 1.12038779e+00 7.66063511e-01 1.34962142e-01
2.19102800e-01 -8.12704384e-01 -3.81912917e-01 -7.44416654e-01
6.09328806e-01 8.43228936e-01 -3.39977205e-01 -2.88257420... | [12.559650421142578, 8.376569747924805] |
7c880f49-1505-45c9-a5cb-3b113cd49609 | physics-informed-neural-networks-for-pathloss | 2211.12986 | null | https://arxiv.org/abs/2211.12986v1 | https://arxiv.org/pdf/2211.12986v1.pdf | Physics-informed neural networks for pathloss prediction | This paper introduces a physics-informed machine learning approach for pathloss prediction. This is achieved by including in the training phase simultaneously (i) physical dependencies between spatial loss field and (ii) measured pathloss values in the field. It is shown that the solution to a proposed learning problem... | ['Nicola Michailow', 'Alberto Martinez Alba', 'Steffen Limmer'] | 2022-11-23 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 7.11874589e-02 1.43739164e-01 -5.19440055e-01 -5.87104380e-01
-6.72093987e-01 -7.66485408e-02 1.79800212e-01 5.30359924e-01
-3.20768595e-01 1.06965733e+00 -4.43414599e-01 -9.03840423e-01
-8.93159509e-01 -9.02682245e-01 -6.28150344e-01 -8.27522993e-01
-6.73533022e-01 3.90203744e-01 5.40392816e-01 -1.29554227... | [6.199511528015137, 1.4216413497924805] |
73c62e71-86d3-4e05-b142-2c92d34654bf | t3l-translate-and-test-transfer-learning-for | 2306.04996 | null | https://arxiv.org/abs/2306.04996v1 | https://arxiv.org/pdf/2306.04996v1.pdf | T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification | Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language mo... | ['Massimo Piccardi', 'Gholamreza Haffari', 'Inigo Jauregi Unanue'] | 2023-06-08 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 2.10787822e-02 -2.34467447e-01 -3.99275303e-01 -6.05627060e-01
-1.23142123e+00 -6.76135957e-01 9.61907506e-01 1.47089317e-01
-8.40750277e-01 8.32164466e-01 -7.08427578e-02 -6.46784306e-01
5.83713531e-01 -4.86407369e-01 -8.05424750e-01 -4.52997059e-01
5.20934403e-01 9.11050856e-01 -8.88282806e-02 -2.58921921... | [11.126908302307129, 9.967049598693848] |
d5c9a864-e4f3-4d7b-9e72-0113f940fb2f | inpaintfusion-incremental-rgb-d-inpainting | null | null | https://mugichoko445.github.io/InpaintFusion/ | https://arbook.icg.tugraz.at/schmalstieg/Schmalstieg_380.pdf | InpaintFusion: Incremental RGB-D Inpainting for 3D Scenes | State-of-the-art methods for diminished reality propagate pixel information from a keyframe to subsequent frames for real-time inpainting. However, these approaches produce artifacts, if the scene geometry is not sufficiently planar. In this paper, we present InpaintFusion, a new real-time method that extends inpaintin... | ['Denis Kalkofen', 'Dieter Schmalstieg', 'Hideo Saito', 'Wolfgang Broll', 'Okan Erat', 'Shohei Mori'] | 2020-10-01 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 5.63789427e-01 5.26006892e-02 5.91956794e-01 -3.16886127e-01
-7.93613255e-01 -7.00662017e-01 3.97476614e-01 -1.01445988e-01
-3.42667341e-01 7.88563669e-01 3.14449682e-03 1.82014331e-01
2.50536621e-01 -9.50637221e-01 -8.05146873e-01 -5.61060667e-01
3.91843766e-01 4.86702800e-01 5.37729800e-01 -6.57080859... | [9.40311336517334, -2.99514102935791] |
ddacbbae-4e1c-4c7f-a71a-e05e8105bf3a | thermal-object-detection-using-domain | 2006.00821 | null | https://arxiv.org/abs/2006.00821v2 | https://arxiv.org/pdf/2006.00821v2.pdf | Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving | Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving. The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals. A thermal camera captures an image using the... | ['Witold Pedrycz', 'Muhammd Aasim Rafique', 'Ahmad Muqeem Sheri', 'Shoaib Azam', 'Moongu Jeon', 'Farzeen Munir'] | 2020-06-01 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 8.63799036e-01 -3.37801784e-01 1.64589629e-01 -4.97099519e-01
-4.73163724e-01 -8.52666199e-01 6.12334073e-01 -8.55162740e-01
-5.03444135e-01 6.78322911e-01 -4.12126392e-01 -1.26088083e-01
2.18358368e-01 -6.69155538e-01 -6.25750303e-01 -1.16445553e+00
5.28500736e-01 -9.36189666e-02 1.10304527e-01 -4.57001716... | [9.215988159179688, -1.8126875162124634] |
053ba867-10db-4bf3-88dd-a0d5120b0591 | fast-effective-and-self-supervised | 2104.08027 | null | https://arxiv.org/abs/2104.08027v2 | https://arxiv.org/pdf/2104.08027v2.pdf | Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders | Pretrained Masked Language Models (MLMs) have revolutionised NLP in recent years. However, previous work has indicated that off-the-shelf MLMs are not effective as universal lexical or sentence encoders without further task-specific fine-tuning on NLI, sentence similarity, or paraphrasing tasks using annotated task dat... | ['Nigel Collier', 'Anna Korhonen', 'Ivan Vulić', 'Fangyu Liu'] | 2021-04-16 | null | https://aclanthology.org/2021.emnlp-main.109 | https://aclanthology.org/2021.emnlp-main.109.pdf | emnlp-2021-11 | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [ 6.28793836e-01 2.15842545e-01 -1.57123998e-01 -6.80140138e-01
-1.02626920e+00 -7.02947795e-01 7.18595028e-01 4.53857213e-01
-7.10927606e-01 8.21283460e-01 4.14630532e-01 -2.97799140e-01
3.38676780e-01 -6.45791650e-01 -8.48696411e-01 -2.03199938e-01
2.43863776e-01 5.00119686e-01 2.83929348e-01 -8.14660668... | [10.90769100189209, 8.744810104370117] |
fadeb983-d1ad-4e93-b479-ba79944e72f0 | end-to-end-dense-video-captioning-as-sequence-1 | 2204.08121 | null | https://arxiv.org/abs/2204.08121v2 | https://arxiv.org/pdf/2204.08121v2.pdf | End-to-end Dense Video Captioning as Sequence Generation | Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage generative process, which first proposes a segment for each event, then renders a caption for each identified segment. Recent advances in lar... | ['Ashish V. Thapliyal', 'Radu Soricut', 'William Yang Wang', 'Bo Pang', 'Wanrong Zhu'] | 2022-04-18 | null | https://aclanthology.org/2022.coling-1.498 | https://aclanthology.org/2022.coling-1.498.pdf | coling-2022-10 | ['dense-video-captioning'] | ['computer-vision'] | [ 5.32797217e-01 2.45082274e-01 -1.49616420e-01 -3.80518049e-01
-1.11576939e+00 -4.38973784e-01 8.98427010e-01 -2.58730769e-01
-1.00091904e-01 1.00218987e+00 7.43432820e-01 3.97122698e-03
7.63702869e-01 -3.22875232e-01 -1.02111292e+00 -4.79151785e-01
1.08206244e-02 9.12554502e-01 6.81997091e-02 -1.12045528... | [10.477128028869629, 0.7270507216453552] |
0ac58112-988f-4063-ae97-3afc4bd2a68a | optimizing-neural-network-hyperparameters | 1609.08703 | null | http://arxiv.org/abs/1609.08703v1 | http://arxiv.org/pdf/1609.08703v1.pdf | Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification | Systems based on artificial neural networks (ANNs) have achieved
state-of-the-art results in many natural language processing tasks. Although
ANNs do not require manually engineered features, ANNs have many
hyperparameters to be optimized. The choice of hyperparameters significantly
impacts models' performances. Howeve... | ['Franck Dernoncourt', 'Ji Young Lee'] | 2016-09-27 | null | null | null | null | ['dialog-act-classification'] | ['natural-language-processing'] | [-4.77033854e-02 1.17078505e-01 3.55744734e-03 -6.68128908e-01
-6.58707619e-01 -4.32829022e-01 8.00940752e-01 4.36421782e-01
-8.25913131e-01 6.93623126e-01 -1.02795556e-01 -3.29002798e-01
-1.95531994e-01 -7.58664072e-01 -1.61986351e-01 -7.54123449e-01
2.84657598e-01 1.12223327e+00 3.10635805e-01 -1.41326517... | [6.882308006286621, 3.9900219440460205] |
666435cc-06ea-4231-9992-2dac6be7686e | minimally-supervised-structure-rich-text | 2102.11479 | null | https://arxiv.org/abs/2102.11479v1 | https://arxiv.org/pdf/2102.11479v1.pdf | Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks | Text categorization is an essential task in Web content analysis. Considering the ever-evolving Web data and new emerging categories, instead of the laborious supervised setting, in this paper, we focus on the minimally-supervised setting that aims to categorize documents effectively, with a couple of seed documents an... | ['Jiawei Han', 'Jingbo Shang', 'Luna Xin Dong', 'Chenwei Zhang', 'Xinyang Zhang'] | 2021-02-23 | null | null | null | null | ['product-categorization', 'text-categorization'] | ['miscellaneous', 'natural-language-processing'] | [ 4.06647205e-01 2.74462581e-01 -6.29690528e-01 -6.51075244e-01
-7.58308470e-01 -8.83211076e-01 7.21839666e-01 5.13925433e-01
-3.42328399e-01 1.34414360e-01 2.83035189e-01 -4.04047430e-01
-1.08122632e-01 -8.30449402e-01 -5.36709309e-01 -4.85348046e-01
1.31437242e-01 8.30877602e-01 -1.50580853e-01 3.39410864... | [10.360474586486816, 6.674609184265137] |
31e02a7b-05c3-45bc-b66b-7ea80d3d1b19 | learning-illumination-from-diverse-portraits | 2008.02396 | null | https://arxiv.org/abs/2008.02396v1 | https://arxiv.org/pdf/2008.02396v1.pdf | Learning Illumination from Diverse Portraits | We present a learning-based technique for estimating high dynamic range (HDR), omnidirectional illumination from a single low dynamic range (LDR) portrait image captured under arbitrary indoor or outdoor lighting conditions. We train our model using portrait photos paired with their ground truth environmental illuminat... | ['Christoph Rhemann', 'Rohit Pandey', 'Wan-Chun Ma', 'Sean Fanello', 'Paul Debevec', 'Jay Busch', 'Jason Dourgarian', 'Chloe LeGendre'] | 2020-08-05 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 7.25343645e-01 -3.14988554e-01 3.75635058e-01 -4.30379629e-01
-1.03049052e+00 -7.58083940e-01 3.92449975e-01 -9.87088621e-01
2.69908849e-02 7.97961414e-01 1.51996225e-01 5.37766367e-02
4.94298756e-01 -6.31501675e-01 -1.05795979e+00 -7.37526417e-01
2.16719642e-01 -4.41893972e-02 -5.52290559e-01 -1.06499039... | [9.896072387695312, -2.914862871170044] |
fb60acec-9fa5-45fe-8697-de98fc73b657 | novel-classification-of-ischemic-heart | 2011.09801 | null | https://arxiv.org/abs/2011.09801v1 | https://arxiv.org/pdf/2011.09801v1.pdf | Novel Classification of Ischemic Heart Disease Using Artificial Neural Network | Ischemic heart disease (IHD), particularly in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. Machine learning techniques applied to parameters extracted form heart rate variability (HRV) signal seem to be a v... | ['Agostino Accardo', 'Gianfranco Sinagra', 'Luca Restivo', 'Marco Merlo', 'Giulia Silveri'] | 2020-11-19 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 8.29122365e-02 -1.05138846e-01 -1.22047924e-01 -2.36344814e-01
1.68222919e-01 -1.93188876e-01 1.03548467e-01 3.72429013e-01
-4.08022374e-01 1.08294415e+00 -4.02909145e-02 -4.03558046e-01
-3.69927734e-01 -7.34312415e-01 3.49584609e-01 -6.66802764e-01
-4.70404804e-01 6.51576400e-01 -3.52761209e-01 -2.75578424... | [14.063065528869629, 3.1151304244995117] |
439274d6-e486-418d-a5c9-72fd1944d642 | hamiltonian-prior-to-disentangle-content-and | 2112.01641 | null | https://arxiv.org/abs/2112.01641v4 | https://arxiv.org/pdf/2112.01641v4.pdf | Hamiltonian latent operators for content and motion disentanglement in image sequences | We introduce \textit{HALO} -- a deep generative model utilising HAmiltonian Latent Operators to reliably disentangle content and motion information in image sequences. The \textit{content} represents summary statistics of a sequence, and \textit{motion} is a dynamic process that determines how information is expressed ... | ['Amos Storkey', 'Asif Khan'] | 2021-12-02 | null | null | null | null | ['motion-disentanglement'] | ['computer-vision'] | [ 3.26402277e-01 -6.23442791e-02 -1.57266304e-01 2.20014721e-01
-4.72520292e-02 -1.05641389e+00 1.18962383e+00 -3.25138897e-01
-2.24568456e-01 7.27561593e-01 3.23949158e-01 -5.01976945e-02
-2.24724472e-01 -6.71907008e-01 -4.22738463e-01 -1.38981891e+00
-3.06997865e-01 2.55424529e-01 -5.54543920e-02 -1.63919613... | [10.881400108337402, -0.7239387631416321] |
8151ff1c-ca75-4ccf-98e0-18f24793a9b7 | calibrate-the-inter-observer-segmentation | 2208.03016 | null | https://arxiv.org/abs/2208.03016v1 | https://arxiv.org/pdf/2208.03016v1.pdf | Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle | On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite g... | ['Yanwu Xu', 'Huiying Liu', 'Weihua Yang', 'Mingkui Tan', 'Lixin Duan', 'Hoayi Xiong', 'Huihui Fang', 'Junde Wu'] | 2022-08-05 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 3.91122431e-01 5.64252257e-01 -5.72334170e-01 -6.32722616e-01
-1.32672656e+00 -3.92103076e-01 1.42468914e-01 6.71771914e-02
-3.36294919e-01 7.15437353e-01 -1.19126610e-01 -3.51942420e-01
-2.65190005e-01 -5.32087743e-01 -4.45649743e-01 -1.14030933e+00
6.98092163e-01 8.57442737e-01 2.84256544e-02 4.51258540... | [14.616496086120605, -2.086751699447632] |
895c7470-dd9a-43dd-a410-99f2faaf6d8d | polycentric-clustering-and-structural | 2210.07463 | null | https://arxiv.org/abs/2210.07463v1 | https://arxiv.org/pdf/2210.07463v1.pdf | Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation | Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. Most existing methods assign pseudo-labels to the target data by generating feature prototypes. However, due to the discrepancy in the data d... | ['Huiyu Zhou', 'Ningzhong Liu', 'Han Sun', 'Xinyu Guan'] | 2022-10-14 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 1.51873529e-01 -3.31248343e-01 -3.31797898e-01 -4.33738589e-01
-5.72370291e-01 -4.56144363e-01 4.20936882e-01 -7.79574141e-02
-1.87830970e-01 7.14207053e-01 8.06479007e-02 2.94647753e-01
-6.67944700e-02 -6.48367643e-01 -5.60993254e-01 -1.14705622e+00
6.45008266e-01 5.43613255e-01 2.11217105e-01 -6.16977271... | [10.348424911499023, 3.082895278930664] |
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