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84b433e4-1706-4f2d-97e4-a2aeeeaf9012 | diffucd-unsupervised-hyperspectral-image | 2305.1241 | null | https://arxiv.org/abs/2305.12410v1 | https://arxiv.org/pdf/2305.12410v1.pdf | DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model | Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing due to its ability to detect subtle changes on the earth's surface. Recently, diffusional denoising probabilistic models (DDPM) have demonstrated remarkable performance in the generative domain. Apart from their image... | ['Licheng Jiao', 'Huiyu Zhou', 'Guanchun Wang', 'Shunli Tian', 'Xiangrong Zhang'] | 2023-05-21 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.52568376e-01 -5.40290475e-01 2.48896793e-01 -2.70225942e-01
-8.39555025e-01 -6.62665784e-01 6.85998440e-01 8.22549313e-02
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1.11323759e-01 -1.25466853e-01 2.07363516e-01 -8.71135890... | [10.10805606842041, -1.9497205018997192] |
8eee917f-24ca-451e-bcf9-332242602097 | with-a-little-push-nli-models-can-robustly | 2305.16819 | null | https://arxiv.org/abs/2305.16819v1 | https://arxiv.org/pdf/2305.16819v1.pdf | With a Little Push, NLI Models can Robustly and Efficiently Predict Faithfulness | Conditional language models still generate unfaithful output that is not supported by their input. These unfaithful generations jeopardize trust in real-world applications such as summarization or human-machine interaction, motivating a need for automatic faithfulness metrics. To implement such metrics, NLI models seem... | ['Katja Markert', 'Anette Frank', 'Juri Opitz', 'Julius Steen'] | 2023-05-26 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.49966222e-01 6.71559632e-01 -2.22655714e-01 -4.83954608e-01
-9.10410881e-01 -5.81562340e-01 1.19425678e+00 2.77870297e-01
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3.89121175e-01 8.52417767e-01 1.93474237e-02 -3.94705236... | [12.214714050292969, 8.916519165039062] |
f0af3240-8e11-4dce-976e-e1c6162ad04c | labrad-or-lightweight-memory-scene-graphs-for | 2303.13293 | null | https://arxiv.org/abs/2303.13293v1 | https://arxiv.org/pdf/2303.13293v1.pdf | LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms | Modern surgeries are performed in complex and dynamic settings, including ever-changing interactions between medical staff, patients, and equipment. The holistic modeling of the operating room (OR) is, therefore, a challenging but essential task, with the potential to optimize the performance of surgical teams and aid ... | ['Nassir Navab', 'Chantal Pellegrini', 'Felix Holm', 'Tobias Czempiel', 'Ege Özsoy'] | 2023-03-23 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 1.15402237e-01 1.22730307e-01 -2.26769179e-01 -3.03208113e-01
-2.83448517e-01 -3.88056636e-01 4.51518416e-01 7.25955248e-01
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-1.80815876e-01 4.50027823e-01 2.92945921e-01 -1.45729095... | [14.064148902893066, -3.353748321533203] |
c1bbeb75-773a-4e31-aac7-2d7cd53bf63d | towards-controllable-and-personalized-review | 1910.03506 | null | https://arxiv.org/abs/1910.03506v2 | https://arxiv.org/pdf/1910.03506v2.pdf | Towards Controllable and Personalized Review Generation | In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic information. RevGAN utilizes the combination of three novel components, including self-attentive recursive autoencoders, conditional discriminat... | ['Alexander Tuzhilin', 'Pan Li'] | 2019-09-30 | towards-controllable-and-personalized-review-1 | https://aclanthology.org/D19-1319 | https://aclanthology.org/D19-1319.pdf | ijcnlp-2019-11 | ['review-generation'] | ['natural-language-processing'] | [-2.65586853e-01 5.46556890e-01 -3.44939642e-02 -5.62177181e-01
-6.15245998e-01 -5.79548776e-01 8.28770697e-01 -3.48734915e-01
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3.40484262e-01 5.76127827e-01 -3.72910678e-01 -8.14370871... | [11.99781322479248, 9.06580638885498] |
e8ac01fd-1171-4b24-b77c-da7c5424ba69 | hands-on-wireless-sensing-with-wi-fi-a | 2206.09532 | null | https://arxiv.org/abs/2206.09532v1 | https://arxiv.org/pdf/2206.09532v1.pdf | Hands-on Wireless Sensing with Wi-Fi: A Tutorial | With the rapid development of wireless communication technology, wireless access points (AP) and internet of things (IoT) devices have been widely deployed in our surroundings. Various types of wireless signals (e.g., Wi-Fi, LoRa, LTE) are filling out our living and working spaces. Previous researches reveal the fact t... | ['Guidong Zhang', 'Guoxuan Chi', 'Yi Zhang', 'Zheng Yang'] | 2022-06-20 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.52242756e-01 -3.13113570e-01 -2.52849132e-01 -1.33871600e-01
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-1.57708719e-01 -5.25130332e-01 2.36465782e-02 -1.01915374... | [6.569962501525879, 0.8358908295631409] |
f06f831a-b896-4dfc-9eb8-7b861a4eb9a1 | neural-code-summarization | 2103.01025 | null | https://arxiv.org/abs/2103.01025v2 | https://arxiv.org/pdf/2103.01025v2.pdf | Neural Code Summarization | Code summarization is the task of generating readable summaries that are semantically meaningful and can accurately describe the presumed task of a software. Program comprehension has become one of the most tedious tasks for knowledge transfer. As the codebase evolves over time, the description needs to be manually upd... | ['Piyush Shrivastava'] | 2021-02-26 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.96229029e-01 4.54035401e-01 -2.21827373e-01 -3.73723209e-01
-1.03597248e+00 -6.03372991e-01 3.18131834e-01 9.39436197e-01
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-9.36472267e-02 3.51088822e-01 3.29078346e-01 -3.19793016... | [7.623711109161377, 7.939359188079834] |
38a89164-2a1b-403b-846b-e8832175f275 | object-preserving-siamese-network-for-single | 2301.12057 | null | https://arxiv.org/abs/2301.12057v1 | https://arxiv.org/pdf/2301.12057v1.pdf | Object Preserving Siamese Network for Single Object Tracking on Point Clouds | Obviously, the object is the key factor of the 3D single object tracking (SOT) task. However, previous Siamese-based trackers overlook the negative effects brought by randomly dropped object points during backbone sampling, which hinder trackers to predict accurate bounding boxes (BBoxes). Exploring an approach that se... | ['Zhengwei Hu', 'Jingchao Peng', 'Zhongze Wang', 'Haitao Zhao', 'Kaijie Zhao'] | 2023-01-28 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.23770136e-01 -1.94923401e-01 -3.87681931e-01 5.70612736e-02
-5.43014705e-01 -6.06815577e-01 3.52178603e-01 -1.43332496e-01
-4.84884113e-01 5.80555320e-01 -1.51235744e-01 3.35988045e-01
-2.13973388e-01 -2.69428879e-01 -7.75616884e-01 -8.04655612e-01
-3.67809087e-01 4.91010576e-01 1.13783658e+00 1.14829473... | [6.498702049255371, -2.259793758392334] |
21accc8a-9abe-4bd0-b8c1-57fd5e33434d | pdq-tmk-pdqf-a-test-drive-of-facebooks | 1912.07745 | null | https://arxiv.org/abs/1912.07745v1 | https://arxiv.org/pdf/1912.07745v1.pdf | PDQ & TMK + PDQF -- A Test Drive of Facebook's Perceptual Hashing Algorithms | Efficient and reliable automated detection of modified image and multimedia files has long been a challenge for law enforcement, compounded by the harm caused by repeated exposure to psychologically harmful materials. In August 2019 Facebook open-sourced their PDQ and TMK + PDQF algorithms for image and video similarit... | ['Janis Dalins', 'Douglas Boudry', 'Campbell Wilson'] | 2019-12-16 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [ 4.88544703e-01 -2.74160177e-01 -2.29189172e-01 -1.79131135e-01
-7.64962316e-01 -7.79912472e-01 4.94392961e-01 5.67096949e-01
-5.01835346e-01 3.94763589e-01 2.04757527e-01 -1.81036845e-01
-4.96867687e-01 -4.41608936e-01 -1.39198110e-01 -2.40473315e-01
-6.27597347e-02 2.83272509e-02 1.80825651e-01 -3.92211042... | [12.49780559539795, 1.0658634901046753] |
080acfa4-e501-4b06-8dfd-68e894b982ae | ammunition-component-classification-using | 2208.12863 | null | https://arxiv.org/abs/2208.12863v1 | https://arxiv.org/pdf/2208.12863v1.pdf | Ammunition Component Classification Using Deep Learning | Ammunition scrap inspection is an essential step in the process of recycling ammunition metal scrap. Most ammunition is composed of a number of components, including case, primer, powder, and projectile. Ammo scrap containing energetics is considered to be potentially dangerous and should be separated before the recycl... | ['Hang Shi', 'Chengjun Liu', 'Hadi Ghahremannezhad'] | 2022-08-26 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 9.52920243e-02 -4.48497325e-01 3.82673085e-01 -5.17255217e-02
-4.12958443e-01 -5.89019001e-01 4.13814723e-01 1.52868524e-01
-1.34512037e-01 4.98550981e-01 -1.12899333e-01 -1.29288450e-01
-3.20884824e-01 -1.02667987e+00 -4.56040382e-01 -9.26113427e-01
7.88559094e-02 6.59488916e-01 -1.61449052e-02 -1.28796190... | [7.435753345489502, 1.7795143127441406] |
6729b9da-0ac1-4fc1-b771-c342eb820db9 | efficient-debiasing-with-contrastive-weight | 2210.05247 | null | https://arxiv.org/abs/2210.05247v3 | https://arxiv.org/pdf/2210.05247v3.pdf | Training Debiased Subnetworks with Contrastive Weight Pruning | Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: ``Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?" While empirical evid... | ['Jong Chul Ye', 'Sang Wan Lee', 'Sangmin Lee', 'Geon Yeong Park'] | 2022-10-11 | training-debiased-subnetworks-with | http://openaccess.thecvf.com//content/CVPR2023/html/Park_Training_Debiased_Subnetworks_With_Contrastive_Weight_Pruning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Park_Training_Debiased_Subnetworks_With_Contrastive_Weight_Pruning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['facial-attribute-classification'] | ['computer-vision'] | [ 7.65834928e-01 3.99847656e-01 -5.16867876e-01 -5.06329715e-01
-5.87924004e-01 -4.17372137e-01 2.23716334e-01 -2.56222934e-01
-3.23712766e-01 1.53021300e+00 1.99934721e-01 -3.71539056e-01
-6.09684646e-01 -5.82446873e-01 -9.65809643e-01 -9.86407280e-01
-1.68079343e-02 4.42149609e-01 3.73493284e-01 9.42012668... | [8.559683799743652, 4.114323616027832] |
c63bfa2a-6d6f-4d8b-b6fd-8ab226c34db6 | a-survey-on-deep-learning-of-small-sample-in | 1908.00473 | null | https://arxiv.org/abs/1908.00473v1 | https://arxiv.org/pdf/1908.00473v1.pdf | A Survey on Deep Learning of Small Sample in Biomedical Image Analysis | The success of deep learning has been witnessed as a promising technique for computer-aided biomedical image analysis, due to end-to-end learning framework and availability of large-scale labelled samples. However, in many cases of biomedical image analysis, deep learning techniques suffer from the small sample learnin... | ['Xiaoqiong Li', 'Yulin Deng', 'Xiaoying Tang', 'Pengyi Zhang', 'Yunxin Zhong'] | 2019-08-01 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 3.26786369e-01 6.03117466e-01 -4.04215485e-01 -3.69188160e-01
-7.96855688e-01 1.04306728e-01 1.99812636e-01 3.69319946e-01
-4.00281608e-01 8.33879530e-01 2.53824055e-01 -3.91363800e-01
-1.70824826e-01 -4.79251564e-01 -4.18922931e-01 -1.00645244e+00
-1.52804092e-01 5.44961154e-01 -7.33708441e-02 8.43869597... | [14.846482276916504, -2.2302772998809814] |
1b2f2f92-eedd-4863-9e9c-a8a2633d2a68 | nbd-gap-non-blind-image-deblurring-without | 2209.09498 | null | https://arxiv.org/abs/2209.09498v1 | https://arxiv.org/pdf/2209.09498v1.pdf | NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images | In recent years, deep neural network-based restoration methods have achieved state-of-the-art results in various image deblurring tasks. However, one major drawback of deep learning-based deblurring networks is that large amounts of blurry-clean image pairs are required for training to achieve good performance. Moreove... | ['Vishal M. Patel', 'Rajeev Yasarla', 'Nithin Gopalakrishnan Nair'] | 2022-09-20 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 9.03371200e-02 -7.39291191e-01 4.64376509e-01 -6.03034832e-02
-5.06009400e-01 -3.10306311e-01 4.89668697e-01 -6.89393222e-01
-2.74235517e-01 8.40744674e-01 4.74536240e-01 -1.28554642e-01
-6.43616393e-02 -3.42817187e-01 -6.55060649e-01 -1.00404692e+00
2.06135660e-01 -1.33886606e-01 -1.77867904e-01 -7.56447464... | [11.601874351501465, -2.6881301403045654] |
ffa9d278-68d5-4a54-a8c6-dd9008abc453 | lidar-flow-dense-scene-flow-estimation-from | 1910.14453 | null | https://arxiv.org/abs/1910.14453v2 | https://arxiv.org/pdf/1910.14453v2.pdf | LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images | We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of LiDAR to resolve the lack of information in some regions of stereo images due to textureless objects, shadows, ill-conditioned light environment ... | ['Oliver Wasenmüller', 'René Schuster', 'Ramy Battrawy', 'Didier Stricker', 'Qing Rao'] | 2019-10-31 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 3.62054527e-01 -5.79796314e-01 3.94608639e-02 -3.15994352e-01
-5.89494169e-01 -4.30889279e-01 3.85240763e-01 -1.75846238e-02
-3.85696381e-01 9.07437563e-01 -2.65286397e-02 -1.42894655e-01
-1.11322261e-01 -9.89544451e-01 -4.02616948e-01 -3.30855697e-01
2.81259626e-01 5.56025803e-01 9.28854465e-01 -1.85588319... | [8.626466751098633, -2.481154203414917] |
47b3232b-e81f-45f3-a869-936981c318c1 | collective-intelligence-for-object | 2211.15136 | null | https://arxiv.org/abs/2211.15136v3 | https://arxiv.org/pdf/2211.15136v3.pdf | Collective Intelligence for 2D Push Manipulations with Mobile Robots | While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative 2D push manipulations using mobile robots. Although conventional works demon... | ['Yujin Tang', 'Shixiang Shane Gu', 'Yutaka Matsuo', 'Hiroki Furuta', 'Jumpei Arima', 'Tatsuya Matsushima', 'So Kuroki'] | 2022-11-28 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-1.14889920e-01 3.05994749e-01 3.20362672e-02 4.36640196e-02
-1.52534798e-01 -6.63535058e-01 4.87939179e-01 -3.88315544e-02
-3.74355018e-01 7.68596828e-01 -8.63907801e-04 -1.36820376e-01
-4.50944960e-01 -5.22446871e-01 -9.30698216e-01 -5.76101780e-01
-3.35956275e-01 8.28697383e-01 3.20841998e-01 -7.48377144... | [4.474392414093018, 1.1183671951293945] |
e84c2c75-0ca6-48c7-a236-a7f144569a04 | transforming-graphs-for-enhanced-attribute | 2306.11307 | null | https://arxiv.org/abs/2306.11307v2 | https://arxiv.org/pdf/2306.11307v2.pdf | Transforming Graphs for Enhanced Attribute-Based Clustering: An Innovative Graph Transformer Method | Graph Representation Learning (GRL) is an influential methodology, enabling a more profound understanding of graph-structured data and aiding graph clustering, a critical task across various domains. The recent incursion of attention mechanisms, originally an artifact of Natural Language Processing (NLP), into the real... | ['Jiacheng Liu', 'Li Tao', 'Yinan Chen', 'Jiayun Wu', 'Shuo Han'] | 2023-06-20 | null | null | null | null | ['graph-clustering', 'graph-embedding', 'graph-attention', 'graph-learning', 'clustering', 'graph-representation-learning'] | ['graphs', 'graphs', 'graphs', 'graphs', 'methodology', 'methodology'] | [-2.13507041e-02 4.57809687e-01 -2.28757590e-01 -1.45410985e-01
-4.80464138e-02 -4.66966271e-01 6.99431002e-01 5.36499560e-01
-1.80908620e-01 9.58096460e-02 3.48220378e-01 -4.89713430e-01
-1.84988931e-01 -9.43646550e-01 -4.55987692e-01 -8.40878904e-01
-3.30300063e-01 3.72527808e-01 -1.48685679e-01 -6.41435161... | [7.069454669952393, 6.260171890258789] |
d2fbed87-ee64-4f6b-b059-0660e06c37cb | towards-an-embodied-semantic-fovea-semantic | 1807.10561 | null | http://arxiv.org/abs/1807.10561v1 | http://arxiv.org/pdf/1807.10561v1.pdf | Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos | Incorporating the physical environment is essential for a complete
understanding of human behavior in unconstrained every-day tasks. This is
especially important in ego-centric tasks where obtaining 3 dimensional
information is both limiting and challenging with the current 2D video analysis
methods proving insufficien... | ['A. Aldo Faisal', 'Pavel Orlov', 'Stefan Leutenegger', 'Noyan Songur', 'Mickey Li'] | 2018-07-27 | null | null | null | null | ['3d-scene-reconstruction', 'semantic-slam'] | ['computer-vision', 'computer-vision'] | [-1.84113551e-02 -1.51535317e-01 8.02311450e-02 -6.57790005e-01
-3.32548440e-01 -7.32326031e-01 1.97651237e-01 -2.13842452e-01
-4.25034076e-01 2.97944605e-01 1.31296158e-01 -2.69973092e-02
-7.85732344e-02 4.09674123e-02 -7.78700233e-01 -2.86848098e-01
1.37581781e-01 4.25838232e-01 2.78572172e-01 -1.87914401... | [14.105823516845703, 0.08291333168745041] |
054cf603-e8fb-431c-949f-1130e235d71a | sfi-swin-symmetric-face-inpainting-with-swin | 2301.0313 | null | https://arxiv.org/abs/2301.03130v1 | https://arxiv.org/pdf/2301.03130v1.pdf | SFI-Swin: Symmetric Face Inpainting with Swin Transformer by Distinctly Learning Face Components Distributions | Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models can fill out the missing parts of an image while considering the symmetry and homogeneity of the pictur... | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'MohammadHossein Givkashi', 'Mohammadreza Naderi'] | 2023-01-09 | null | null | null | null | ['face-image-quality', 'facial-inpainting', 'image-inpainting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.27988535e-02 3.97053838e-01 1.13685600e-01 -3.30505013e-01
-3.19785595e-01 -2.01272935e-01 2.20457613e-01 -2.50074297e-01
-3.21418494e-02 7.70031631e-01 3.05652648e-01 4.48462784e-01
-2.33414307e-01 -7.47761428e-01 -5.17654181e-01 -5.96917748e-01
4.34907883e-01 2.89265215e-01 -1.41354531e-01 -2.01592654... | [12.706623077392578, -0.0973181501030922] |
0fca832b-e6c5-405d-bec0-ec2f08075a36 | pointinverter-point-cloud-reconstruction-and | 2211.08702 | null | https://arxiv.org/abs/2211.08702v1 | https://arxiv.org/pdf/2211.08702v1.pdf | PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors | In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on SP-GAN, a state-of-the-art sphere-guided 3D point cloud generator. We derive an efficient way to encode an input 3D point cloud to the late... | ['Sai-Kit Yeung', 'Duc Thanh Nguyen', 'Binh-Son Hua', 'Jaeyeon Kim'] | 2022-11-16 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-1.37800887e-01 3.27798247e-01 1.51980713e-01 6.10621870e-02
-9.79246855e-01 -9.15906191e-01 8.52061152e-01 -5.98459899e-01
3.05396140e-01 4.66189325e-01 -1.41012251e-01 -3.87053937e-01
3.92738611e-01 -1.29718125e+00 -1.22727001e+00 -6.78511322e-01
3.83063376e-01 1.14753675e+00 -1.39681160e-01 -1.66139409... | [8.858694076538086, -3.663846492767334] |
a7116806-c141-4eb4-bb16-dabd6e0e6ee6 | program-analysis-of-probabilistic-programs | 2204.06868 | null | https://arxiv.org/abs/2204.06868v1 | https://arxiv.org/pdf/2204.06868v1.pdf | Program Analysis of Probabilistic Programs | Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No single inference algorithm can be used as a probabilistic programming back-end that is simultaneously re... | ['Maria I. Gorinova'] | 2022-04-14 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.87185854e-01 1.92864791e-01 -1.10203266e-01 -5.83306968e-01
-7.12864101e-01 -7.62509942e-01 6.19107723e-01 2.92030305e-01
-2.98354268e-01 6.24939561e-01 -1.37494221e-01 -9.93139505e-01
-4.09533173e-01 -1.07929003e+00 -2.85814613e-01 -8.01262140e-01
7.51940683e-02 8.16034257e-01 5.85996866e-01 1.19689219... | [8.494254112243652, 6.6041693687438965] |
dc633c7a-6c1b-4adc-8d1e-4ee87c79afd6 | direct-acoustics-to-word-models-for-english | 1703.07754 | null | http://arxiv.org/abs/1703.07754v1 | http://arxiv.org/pdf/1703.07754v1.pdf | Direct Acoustics-to-Word Models for English Conversational Speech Recognition | Recent work on end-to-end automatic speech recognition (ASR) has shown that
the connectionist temporal classification (CTC) loss can be used to convert
acoustics to phone or character sequences. Such systems are used with a
dictionary and separately-trained Language Model (LM) to produce word
sequences. However, they a... | ['George Saon', 'Bhuvana Ramabhadran', 'Kartik Audhkhasi', 'Michael Picheny', 'David Nahamoo'] | 2017-03-22 | null | null | null | null | ['english-conversational-speech-recognition'] | ['speech'] | [ 4.16230977e-01 -7.29848370e-02 7.26830885e-02 -2.78732359e-01
-1.59889853e+00 -6.87619567e-01 5.19624472e-01 -1.45913705e-01
-6.50325835e-01 5.89501083e-01 1.72719285e-01 -9.67636347e-01
6.28548861e-01 -1.79980218e-01 -6.18790090e-01 -7.06018269e-01
5.71275204e-02 4.78785872e-01 2.17198908e-01 -1.11575857... | [14.443574905395508, 6.734960556030273] |
0acb3126-4610-4e5b-9f9b-46e6fd583758 | puzzlenet-scene-text-detection-by-segment | 2002.11371 | null | https://arxiv.org/abs/2002.11371v1 | https://arxiv.org/pdf/2002.11371v1.pdf | PuzzleNet: Scene Text Detection by Segment Context Graph Learning | Recently, a series of decomposition-based scene text detection methods has achieved impressive progress by decomposing challenging text regions into pieces and linking them in a bottom-up manner. However, most of them merely focus on linking independent text pieces while the context information is underestimated. In th... | ['Yiqing Hu', 'Hao Liu', 'Deqiang Jiang', 'Bo Ren', 'Antai Guo'] | 2020-02-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.84108585e-01 -1.16722196e-01 1.03403352e-01 9.00952965e-02
-7.00911582e-01 -5.99489331e-01 5.33589482e-01 1.60323590e-01
2.49950334e-01 9.42048356e-02 1.60109967e-01 -1.63869247e-01
1.02139920e-01 -8.94715548e-01 -7.20156312e-01 -3.85259241e-01
2.29635671e-01 7.12143898e-01 7.46720612e-01 -3.61008018... | [12.07944393157959, 2.2892282009124756] |
0657e740-e1a7-434b-a5aa-ddc4f955e1ab | high-resolution-and-fast-face-completion-via | null | null | https://openreview.net/forum?id=Hkxx3o0qFX | https://openreview.net/pdf?id=Hkxx3o0qFX | High Resolution and Fast Face Completion via Progressively Attentive GANs | Face completion is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial netw... | ['Christopher G. Healey', 'Shaoliang Nie', 'Zeyuan Chen', 'Tianfu Wu'] | null | null | null | null | iclr-2019-5 | ['facial-inpainting'] | ['computer-vision'] | [ 3.31894666e-01 4.20149803e-01 4.27166820e-01 -4.60849166e-01
-8.32872629e-01 -2.56738633e-01 5.91004312e-01 -6.03945613e-01
-1.79959700e-01 7.98503995e-01 3.07176828e-01 2.98274159e-01
3.20475840e-04 -8.71831179e-01 -9.27894294e-01 -3.33505809e-01
-1.70847818e-01 6.39801085e-01 -2.71425724e-01 -2.37122133... | [12.637664794921875, -0.2049209177494049] |
e027c5f7-0a30-4fdd-b40c-acccb71188d7 | cross-domain-iterative-network-for | 2305.10326 | null | https://arxiv.org/abs/2305.10326v1 | https://arxiv.org/pdf/2305.10326v1.pdf | Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT | Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of ischemic heart diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-angle (LA) SPECT enables faster scanning and reduced hardware costs but results in lower reconstruction a... | ['Chi Liu', 'Albert J. Sinusas', 'Qiong Liu', 'Xueqi Guo', 'Huidong Xie', 'Bo Zhou', 'Xiongchao Chen'] | 2023-05-17 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 1.91677049e-01 -3.03230226e-01 -9.19456258e-02 -4.88952577e-01
-1.05574369e+00 -2.62233198e-01 -4.32832837e-02 -1.29074305e-01
-5.35415828e-01 7.27676988e-01 1.57303348e-01 -3.51277351e-01
-7.51203373e-02 -7.11839139e-01 -3.84735912e-01 -6.50477946e-01
1.59915596e-01 5.29316604e-01 3.99900794e-01 2.78336257... | [13.504866600036621, -2.524232864379883] |
66396cb8-3256-4570-8b4d-dd061cb6f4a5 | variable-length-hashing | 1603.05414 | null | http://arxiv.org/abs/1603.05414v1 | http://arxiv.org/pdf/1603.05414v1.pdf | Variable-Length Hashing | Hashing has emerged as a popular technique for large-scale similarity search.
Most learning-based hashing methods generate compact yet correlated hash codes.
However, this redundancy is storage-inefficient. Hence we propose a lossless
variable-length hashing (VLH) method that is both storage- and
search-efficient. Stor... | ['XiaoLi Li', 'Hong Wei Ng', 'Pierre Moulin', 'Honghai Yu'] | 2016-03-17 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.32472277e-01 -3.12438667e-01 -4.06454116e-01 -6.40936475e-03
-1.32741153e+00 -4.90932286e-01 3.91485900e-01 7.10171223e-01
-6.36484087e-01 7.21850336e-01 1.64123047e-02 -1.21629670e-01
-2.02718481e-01 -1.11853182e+00 -3.77876788e-01 -7.82937229e-01
-2.01963797e-01 2.26996675e-01 5.94234824e-01 -1.98962819... | [11.178378105163574, 1.133490800857544] |
0290c4d0-295f-4a72-b21c-ca6a4b942bb8 | developing-a-fidelity-evaluation-approach-for | 2106.08492 | null | https://arxiv.org/abs/2106.08492v1 | https://arxiv.org/pdf/2106.08492v1.pdf | Developing a Fidelity Evaluation Approach for Interpretable Machine Learning | Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency. Explainable AI (XAI) methods are used to improve the interpretability of these complex models, and in doing so impro... | ['Renuka Sindhgatta', 'Catarina Moreira', 'Chun Ouyang', 'Mythreyi Velmurugan'] | 2021-06-16 | null | null | null | null | ['explanation-fidelity-evaluation'] | ['methodology'] | [ 7.64078498e-02 5.62064648e-01 -3.39279324e-01 -5.18137932e-01
-4.32376742e-01 -7.30333686e-01 8.56371403e-01 2.88827032e-01
3.20984304e-01 5.73647201e-01 4.73854274e-01 -8.69663477e-01
-5.18273830e-01 -4.19115394e-01 -6.66644037e-01 1.45283702e-03
1.72949404e-01 6.36595726e-01 -4.12503123e-01 1.10906765... | [8.831499099731445, 5.9264726638793945] |
19d3f41d-a056-4746-a02f-76755af81d65 | an-image-source-method-framework-for | 1906.12227 | null | http://arxiv.org/abs/1906.12227v1 | http://arxiv.org/pdf/1906.12227v1.pdf | An Image Source Method Framework for Arbitrary Reflecting Boundaries | We propose a theoretical framework for the image source method that
generalizes to arbitrary reflecting boundaries, e.g. boundaries that are curved
or even with certain openings. Furthermore, it can seamlessly incorporate
boundary absorption, source directivity, and nonspecular reflections. This
framework is based on t... | [] | 2019-06-28 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 6.12997115e-01 1.61367342e-01 4.47734714e-01 -7.45599642e-02
-5.33585370e-01 -8.00046027e-01 7.06805587e-01 -1.43286616e-01
2.56967187e-01 4.75571126e-01 3.59119833e-01 -3.61912876e-01
-3.30829889e-01 -1.01501977e+00 -4.55362201e-01 -8.08229625e-01
-1.46567538e-01 -1.02833569e-01 2.11681426e-01 -3.94231290... | [15.143163681030273, 5.674461364746094] |
de361482-886b-4b3a-bfb5-30745c020217 | fusiondepth-complement-self-supervised | 2305.06036 | null | https://arxiv.org/abs/2305.06036v1 | https://arxiv.org/pdf/2305.06036v1.pdf | FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume | Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame depth estimation framework which monocular depth can be refined continuously by multi-frame sequent... | ['Yong liu', 'Ying Chen', 'Shang Xu', 'Jianlin Liu', 'Zhuofei Huang'] | 2023-05-10 | null | null | null | null | ['stereo-depth-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 7.17280898e-03 5.71994595e-02 -3.96242619e-01 -6.15056396e-01
-5.08755744e-01 -3.16684008e-01 5.53754508e-01 -5.98483741e-01
-5.09412706e-01 9.38470960e-01 1.59374624e-01 1.60056695e-01
2.20395043e-01 -7.75866032e-01 -7.45669603e-01 -6.17124259e-01
4.38558549e-01 3.88473958e-01 6.46075666e-01 3.60613644... | [8.792044639587402, -2.4210281372070312] |
e82a5951-8ab8-4801-818e-addb1081fe00 | buda-boundless-unsupervised-domain-adaptation | 2004.0113 | null | https://arxiv.org/abs/2004.01130v2 | https://arxiv.org/pdf/2004.01130v2.pdf | Handling new target classes in semantic segmentation with domain adaptation | In this work, we define and address a novel domain adaptation (DA) problem in semantic scene segmentation, where the target domain not only exhibits a data distribution shift w.r.t. the source domain, but also includes novel classes that do not exist in the latter. Different to "open-set" and "universal domain adaptati... | ['Patrick Pérez', 'Tuan-Hung Vu', 'Matthieu Cord', 'Maxime Bucher'] | 2020-04-02 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 3.55256349e-01 1.32087335e-01 -3.31432253e-01 -5.02361119e-01
-9.73232210e-01 -7.23860025e-01 5.59487462e-01 6.15983875e-03
-4.65457290e-01 8.55545402e-01 -1.15049861e-01 -8.15344080e-02
2.65893966e-01 -6.75578177e-01 -8.01439047e-01 -6.39995933e-01
3.84415388e-01 7.69771516e-01 7.20686495e-01 -1.95661396... | [9.816248893737793, 1.8266125917434692] |
ba651242-728b-40e8-9b07-260df449213f | pacs-a-dataset-for-physical-audiovisual | 2203.1113 | null | https://arxiv.org/abs/2203.11130v3 | https://arxiv.org/pdf/2203.11130v3.pdf | PACS: A Dataset for Physical Audiovisual CommonSense Reasoning | In order for AI to be safely deployed in real-world scenarios such as hospitals, schools, and the workplace, it must be able to robustly reason about the physical world. Fundamental to this reasoning is physical common sense: understanding the physical properties and affordances of available objects, how they can be ma... | ['Louis-Philippe Morency', 'Ruslan Salakhutdinov', 'Paul Pu Liang', 'Peter Wu', 'Samuel Yu'] | 2022-03-21 | null | null | null | null | ['physical-commonsense-reasoning'] | ['reasoning'] | [ 5.33304870e-01 1.76085904e-01 3.88054959e-02 -1.51674315e-01
-7.27938652e-01 -6.60300553e-01 6.19000614e-01 3.32205683e-01
-1.28149286e-01 5.72208941e-01 4.81750309e-01 -2.10426360e-01
-4.81121719e-01 -6.24235570e-01 -7.98945308e-01 -3.79224002e-01
1.41520485e-01 2.07238451e-01 3.52160692e-01 -4.60456878... | [10.47431755065918, 1.2280741930007935] |
7bede14f-f89f-416b-ab5e-d7a0979be0ef | enhancing-diversity-in-teacher-student | 2011.13776 | null | https://arxiv.org/abs/2011.13776v1 | https://arxiv.org/pdf/2011.13776v1.pdf | Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification | The objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without labor-intensive identity annotations. State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled images in the target domain and learn from these noisy pseudo labels. Recently introduced Mean Te... | ['Francois Bremond', 'Benoit Lagadec', 'Hao Chen'] | 2020-11-27 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.34969908e-01 5.16511537e-02 -1.46256328e-01 -7.03318119e-01
-3.47304195e-01 -5.08594215e-01 5.94725072e-01 -1.18770264e-01
-7.03544974e-01 7.76380002e-01 5.34656122e-02 3.70780438e-01
-4.57791612e-02 -5.00226855e-01 -4.27382916e-01 -9.15115118e-01
3.72865140e-01 6.53218567e-01 1.14804417e-01 2.27906108... | [14.80106258392334, 1.0843011140823364] |
d55522bb-6430-4e7d-a5c0-f8c8eed5aef0 | knowledge-distillation-for-end-to-endperson | 1909.01058 | null | https://arxiv.org/abs/1909.01058v2 | https://arxiv.org/pdf/1909.01058v2.pdf | Knowledge Distillation for End-to-End Person Search | We introduce knowledge distillation for end-to-end person search. End-to-End methods are the current state-of-the-art for person search that solve both detection and re-identification jointly. These approaches for joint optimization show their largest drop in performance due to a sub-optimal detector. We propose two di... | ['Fabio Galasso', 'Sikandar Amin', 'Bharti Munjal'] | 2019-09-03 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 7.35172778e-02 4.02756482e-02 -1.15501307e-01 -1.81827620e-01
-1.16273129e+00 -4.95501637e-01 4.67664242e-01 2.56122887e-01
-1.09267700e+00 4.56849664e-01 -7.00786635e-02 4.48478870e-02
-3.92809331e-01 -4.30677265e-01 -7.40239501e-01 -7.03551710e-01
3.27395290e-01 1.30055475e+00 5.07023633e-01 7.38563836... | [14.831236839294434, 0.8226412534713745] |
3cf81688-0415-4d4b-9b82-9b8b7bed54ea | a-new-split-for-evaluating-true-zero-shot | 2107.13029 | null | https://arxiv.org/abs/2107.13029v2 | https://arxiv.org/pdf/2107.13029v2.pdf | A New Split for Evaluating True Zero-Shot Action Recognition | Zero-shot action recognition is the task of classifying action categories that are not available in the training set. In this setting, the standard evaluation protocol is to use existing action recognition datasets(e.g. UCF101) and randomly split the classes into seen and unseen. However, most recent work builds on rep... | ['Marcus Rohrbach', 'Frank Keller', 'Kiyoon Kim', 'Laura Sevilla-Lara', 'Shreyank N Gowda'] | 2021-07-27 | null | null | null | null | ['zero-shot-action-recognition', 'few-shot-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.12120950e-01 2.01822415e-01 -3.48384589e-01 -2.49706745e-01
-9.45001721e-01 -2.97818989e-01 7.75494516e-01 -2.42687508e-01
-4.85675871e-01 8.33278775e-01 4.58052814e-01 8.99538621e-02
-7.84217268e-02 -7.10807681e-01 -5.92481494e-01 -8.90288651e-01
1.71411008e-01 4.50800031e-01 6.87986553e-01 -1.90770403... | [8.517664909362793, 0.9436069130897522] |
7db06643-a352-4bec-92ea-4fc8029da6d9 | from-noisy-prediction-to-true-label-noisy | 2205.0069 | null | https://arxiv.org/abs/2205.00690v3 | https://arxiv.org/pdf/2205.00690v3.pdf | From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model | Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused on modifying the classifier during the training procedure. It has two potential problems. First, these m... | ['Il-Chul Moon', 'Kyungwoo Song', 'Byeonghu Na', 'JoonHo Jang', 'Seungjae Shin', 'HeeSun Bae'] | 2022-05-02 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 5.98868787e-01 1.86941847e-01 -1.38819769e-01 -5.72753072e-01
-9.96123433e-01 -7.02608466e-01 4.82088089e-01 1.89667046e-02
-2.85676479e-01 8.58172953e-01 -2.36212283e-01 -2.57199645e-01
-2.42499672e-02 -7.28339672e-01 -8.46410990e-01 -9.15458441e-01
4.25100297e-01 3.46818417e-01 8.71635750e-02 1.48546547... | [9.407529830932617, 4.004250526428223] |
7733ff97-6220-4fe4-b73a-16cc1c0bec8e | codec-complex-document-and-entity-collection | 2205.04546 | null | https://arxiv.org/abs/2205.04546v2 | https://arxiv.org/pdf/2205.04546v2.pdf | CODEC: Complex Document and Entity Collection | CODEC is a document and entity ranking benchmark that focuses on complex research topics. We target essay-style information needs of social science researchers, i.e. "How has the UK's Open Banking Regulation benefited Challenger Banks?". CODEC includes 42 topics developed by researchers and a new focused web corpus wit... | ['Jeffrey Dalton', 'Sean MacAvaney', 'Sophie Fischer', 'Carlos Gemmell', 'Paul Owoicho', 'Iain Mackie'] | 2022-05-09 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-5.05058408e-01 1.21391609e-01 -6.78324044e-01 -1.59563705e-01
-1.34749520e+00 -9.03706968e-01 8.90373051e-01 7.93333113e-01
-8.94031584e-01 6.78277671e-01 8.90888929e-01 -4.53913063e-01
-4.55551773e-01 -6.47816777e-01 -3.78218085e-01 2.07162142e-01
1.69562493e-02 1.10505509e+00 2.24316314e-01 -7.46580720... | [11.271039009094238, 7.8105292320251465] |
cafde926-c2ec-4023-9191-48a935ba3e2f | self-supervised-representation-learning-from-4 | 2103.12676 | null | https://arxiv.org/abs/2103.12676v2 | https://arxiv.org/pdf/2103.12676v2.pdf | Self-supervised representation learning from 12-lead ECG data | Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity remains a central challenge in the field. Self-supervised learning represents a promising way to alleviate this issue. In this work, we put forw... | ['Nils Strodthoff', 'Temesgen Mehari'] | 2021-03-23 | null | null | null | null | ['ecg-classification', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 7.71794736e-01 1.93107337e-01 -1.26002848e-01 -5.46404719e-01
-1.11411798e+00 -5.32130718e-01 1.79765165e-01 6.06413782e-01
-4.60630655e-01 8.73233914e-01 1.23051912e-01 -2.37847522e-01
-5.10746300e-01 -4.35601890e-01 -4.56380635e-01 -7.25198090e-01
-4.34074730e-01 6.29396856e-01 -1.65414006e-01 -6.18073940... | [14.293757438659668, 3.3287816047668457] |
10d9c1b6-a5dc-4c8f-aa73-e30b1ee7983e | mitosis-detection-for-breast-cancer-pathology | 2109.01526 | null | https://arxiv.org/abs/2109.01526v3 | https://arxiv.org/pdf/2109.01526v3.pdf | Mitosis Detection for Breast Cancer Pathology Images Using UV-Net | The difficulty of detecting mitosis and its similarity to non-mitosis objects has remained a challenge in computational pathology. The lack of publicly available data has added more complexity. Deep learning algorithms have shown potentials in mitosis detection tasks. However, they face challenges when applied to patho... | ['April Khademi', 'Susan Done', 'Salar Razavi', 'Samir Mitha', 'Seyed H. Mirjahanmardi'] | 2021-09-02 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.67929250e-01 1.24187358e-01 -3.90259549e-02 -1.88913822e-01
-8.36036623e-01 -3.12497079e-01 5.69077313e-01 4.44533646e-01
-7.77807832e-01 1.07247221e+00 -5.35439923e-02 1.01777144e-01
1.89149171e-01 -6.47953093e-01 -2.56975502e-01 -1.39578080e+00
1.01292357e-01 3.92401755e-01 4.26273078e-01 8.73622298... | [15.076482772827148, -3.1472885608673096] |
d69c6239-565b-41b0-a341-05eff5a41385 | reformulating-ctr-prediction-learning | 2304.13643 | null | https://arxiv.org/abs/2304.13643v1 | https://arxiv.org/pdf/2304.13643v1.pdf | Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation | Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Ri... | ['Yongdong Zhang', 'Xiangnan He', 'Dingxian Wang', 'Wenjie Wang', 'Fuli Feng', 'Tianhao Shi', 'Yang Zhang'] | 2023-04-26 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-2.86133915e-01 -5.85250139e-01 -1.56148911e-01 -4.68968958e-01
-4.30043906e-01 -5.46647251e-01 4.46302682e-01 -2.37854123e-02
-2.46239334e-01 5.91063440e-01 3.20837796e-01 -4.05887038e-01
-6.12753093e-01 -7.58735478e-01 -6.30765140e-01 -7.34858334e-01
-3.31944585e-01 1.61283668e-02 -1.20071627e-01 -4.82808143... | [10.103321075439453, 5.448392391204834] |
e0ffa32f-2ecd-4cac-8d20-f545672e963c | high-speed-and-high-quality-text-to-lip | 2107.06831 | null | https://arxiv.org/abs/2107.06831v2 | https://arxiv.org/pdf/2107.06831v2.pdf | Parallel and High-Fidelity Text-to-Lip Generation | As a key component of talking face generation, lip movements generation determines the naturalness and coherence of the generated talking face video. Prior literature mainly focuses on speech-to-lip generation while there is a paucity in text-to-lip (T2L) generation. T2L is a challenging task and existing end-to-end wo... | ['Nicholas Yuan', 'Baoxing Huai', 'Wencan Huang', 'Zhou Zhao', 'Yi Ren', 'Zhiying Zhu', 'Jinglin Liu'] | 2021-07-14 | null | null | null | null | ['text-to-face-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.18016742e-01 3.85368466e-02 -2.84782082e-01 -2.73819000e-01
-8.95270944e-01 -2.09341228e-01 5.20921409e-01 -6.75545812e-01
2.05831751e-01 7.19889224e-01 6.66425467e-01 1.40065653e-02
3.70239735e-01 -3.90312523e-01 -6.44578457e-01 -9.16199625e-01
3.37722957e-01 -2.41466150e-01 -1.35461137e-01 1.57504976... | [13.277749061584473, -0.37426289916038513] |
d91afcf2-6de9-4d54-b067-1fe312706018 | exploring-non-verbal-predicates-in-semantic | 2307.0187 | null | https://arxiv.org/abs/2307.01870v1 | https://arxiv.org/pdf/2307.01870v1.pdf | Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities | Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs. Conversely, predicates can also be expressed using other parts of speech, e.g., nouns and adjectives. However, non-verbal predicates appear ... | ['Roberto Navigli', 'Simone Conia', 'Riccardo Orlando'] | 2023-07-04 | null | null | null | null | ['transfer-learning', 'semantic-role-labeling'] | ['miscellaneous', 'natural-language-processing'] | [ 3.02640289e-01 5.95702589e-01 -6.89590335e-01 -4.37793553e-01
-5.89133382e-01 -1.06957400e+00 9.18313444e-01 7.60001421e-01
-6.49750471e-01 1.24448466e+00 8.22510302e-01 -3.02655637e-01
-1.09435245e-01 -8.83726120e-01 -7.24259257e-01 -2.86408186e-01
1.20099805e-01 8.85918319e-01 6.01089895e-01 -9.41257417... | [10.30292797088623, 9.364359855651855] |
af9c7816-3a9a-4262-88c9-5b6706390221 | a-deep-learning-approach-to-language | null | null | https://aclanthology.org/W19-3630 | https://aclanthology.org/W19-3630.pdf | A Deep Learning Approach to Language-independent Gender Prediction on Twitter | This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users{'} tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, Fren... | ['REYHANEH HASHEMPOUR'] | 2019-08-01 | null | null | null | ws-2019-8 | ['gender-prediction'] | ['computer-vision'] | [-4.02512342e-01 -7.14087412e-02 -3.88096869e-01 -5.13178289e-01
-3.61639522e-02 -3.53140891e-01 9.50384319e-01 3.14593822e-01
-8.88124824e-01 8.46148789e-01 1.33045211e-01 -3.27069789e-01
1.54273480e-01 -8.01238477e-01 -3.66206735e-01 -4.64623928e-01
8.81975889e-02 6.36594236e-01 -1.80059355e-02 -3.70201528... | [9.47107219696045, 10.361076354980469] |
62e8f7fe-f749-4c08-b8f6-4f7ad688768a | a-generalised-directional-laplacian | 1708.04816 | null | http://arxiv.org/abs/1708.04816v1 | http://arxiv.org/pdf/1708.04816v1.pdf | A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation | Directional or Circular statistics are pertaining to the analysis and
interpretation of directions or rotations. In this work, a novel probability
distribution is proposed to model multidimensional sparse directional data. The
Generalised Directional Laplacian Distribution (DLD) is a hybrid between the
Laplacian distri... | ['Nikolaos Mitianoudis'] | 2017-08-16 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [-1.79526061e-01 -4.18061703e-01 1.04379875e-03 -1.19642556e-01
-9.65912461e-01 -6.98897839e-01 3.09830576e-01 -5.98105550e-01
5.40785715e-02 6.66953504e-01 5.18235028e-01 -1.80307075e-01
-6.44266427e-01 -4.88680303e-02 -9.19136554e-02 -1.13286853e+00
-1.86054394e-01 1.64141342e-01 -7.09332526e-02 5.17779827... | [15.23755931854248, 5.664351463317871] |
1e583fa8-70db-4a97-bdef-f7f52c80ae4b | underwater-single-channel-acoustic-signal | null | null | https://asa.scitation.org/doi/abs/10.1121/10.0009852 | https://asa.scitation.org/doi/10.1121/10.0009852 | Underwater single-channel acoustic signal multitarget recognition using convolutional neural networks | The radiated noise from ships is of great significance to target recognition, and several deep learning methods have been developed for the recognition of underwater acoustic signals. Previous studies have focused on single-target recognition, with relatively few reports on multitarget recognition. This paper proposes ... | ['Kejun Wang', 'Qinggang Sun'] | 2022-03-31 | null | null | null | the-journal-of-the-acoustical-society-of | ['audio-multiple-target-classification'] | ['audio'] | [ 5.09712875e-01 -4.97853667e-01 7.10426450e-01 -4.89527613e-01
-1.27568400e+00 -6.47009671e-01 2.53712118e-01 -6.01828285e-02
-7.74631977e-01 5.81027925e-01 1.38766006e-01 2.02287966e-03
-2.49504715e-01 -7.07072496e-01 -5.70568383e-01 -1.08989596e+00
-5.07853866e-01 -1.18528284e-01 1.00262374e-01 -3.40267420... | [8.538020133972168, -1.1572321653366089] |
2d2478a8-71d6-46cc-bd5a-08d642a40ae0 | building-powerful-and-equivariant-graph | 2006.15107 | null | https://arxiv.org/abs/2006.15107v3 | https://arxiv.org/pdf/2006.15107v3.pdf | Building powerful and equivariant graph neural networks with structural message-passing | Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning local structures in order to achieve good generalization. However, current message-passing architectures have a limited representation power a... | ['Andreas Loukas', 'Pascal Frossard', 'Clement Vignac'] | 2020-06-26 | null | http://proceedings.neurips.cc/paper/2020/hash/a32d7eeaae19821fd9ce317f3ce952a7-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a32d7eeaae19821fd9ce317f3ce952a7-Paper.pdf | neurips-2020-12 | ['graph-regression'] | ['graphs'] | [ 3.31617445e-01 7.16138929e-02 -3.80826384e-01 -5.63945293e-01
-3.27097118e-01 -7.01003194e-01 7.77106464e-01 8.28944027e-01
-2.51249135e-01 6.84748054e-01 1.29022971e-01 -4.29752469e-01
-4.29445416e-01 -1.05706322e+00 -1.08595300e+00 -8.29066813e-01
-5.65218687e-01 5.28138638e-01 3.10834765e-01 -5.11751711... | [6.742607116699219, 6.043454170227051] |
548cee94-8b2d-4970-b847-a61d24f27590 | syntax-controlled-knowledge-graph-to-text | 2207.00719 | null | https://arxiv.org/abs/2207.00719v1 | https://arxiv.org/pdf/2207.00719v1.pdf | Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency | The knowledge graph (KG) stores a large amount of structural knowledge, while it is not easy for direct human understanding. Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the... | ['Huijuan Xu', 'Fengyu Zhou', 'Chongfeng Fan', 'Jin Liu'] | 2022-07-02 | null | https://aclanthology.org/2022.findings-naacl.95 | https://aclanthology.org/2022.findings-naacl.95.pdf | findings-naacl-2022-7 | ['kg-to-text'] | ['natural-language-processing'] | [ 4.09248859e-01 7.53962398e-01 -1.67189062e-01 -4.91000384e-01
-8.40514183e-01 -3.93467933e-01 3.02767247e-01 1.43578678e-01
-1.68462902e-01 9.97783124e-01 4.77559239e-01 -1.68310314e-01
5.44255413e-02 -1.02457559e+00 -1.01569521e+00 -5.68790793e-01
4.49309528e-01 5.70959449e-01 7.43834376e-02 -2.45489299... | [11.796923637390137, 8.896486282348633] |
15a7bb33-8c88-4021-93f7-e99f38244c50 | firl-fast-imitation-and-policy-reuse-learning | 2203.00251 | null | https://arxiv.org/abs/2203.00251v2 | https://arxiv.org/pdf/2203.00251v2.pdf | A Versatile Agent for Fast Learning from Human Instructors | In recent years, a myriad of superlative works on intelligent robotics policies have been done, thanks to advances in machine learning. However, inefficiency and lack of transfer ability hindered algorithms from pragmatic applications, especially in human-robot collaboration, when few-shot fast learning and high flexib... | ['Chee-Meng Chew', 'Marcelo Ang', 'Jiayi Tan', 'Haofeng Liu', 'Zedong Zhang', 'YiWen Chen'] | 2022-03-01 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 4.18697484e-02 2.99360156e-01 -3.03171545e-01 -1.13758177e-01
-3.53934020e-01 -8.55731905e-01 7.43636250e-01 -1.27289698e-01
-7.02806592e-01 1.11722660e+00 -6.18869588e-02 -4.52684760e-01
-4.28016663e-01 -2.97869444e-01 -7.08440542e-01 -6.17293298e-01
-2.13570103e-01 6.86083794e-01 2.68404365e-01 -4.78042632... | [4.218785762786865, 1.3027145862579346] |
024d522a-922d-408f-925d-f53defd989a9 | continual-one-shot-learning-of-hidden-spike | 1708.09072 | null | http://arxiv.org/abs/1708.09072v1 | http://arxiv.org/pdf/1708.09072v1.pdf | Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions | This paper presents a constructive algorithm that achieves successful
one-shot learning of hidden spike-patterns in a competitive detection task. It
has previously been shown (Masquelier et al., 2008) that spike-timing-dependent
plasticity (STDP) and lateral inhibition can result in neurons competitively
tuned to repea... | ['Tien-Fu Lu', 'Steven Grainger', 'Toby Lightheart'] | 2017-08-30 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [ 5.08159757e-01 6.07861057e-02 6.21893346e-01 1.46698728e-02
9.72358808e-02 -3.58398259e-01 7.50833273e-01 -1.07299851e-03
-7.10245490e-01 1.08469176e+00 -5.87243319e-01 -2.34029204e-01
-3.80123295e-02 -8.21007967e-01 -9.51250553e-01 -1.18776882e+00
-3.56234998e-01 5.12594938e-01 7.15405941e-01 -3.05306256... | [8.070778846740723, 2.8253636360168457] |
8e4bdc6b-6902-4aee-bdf9-744e8555dd4a | knowledge-authoring-for-rules-and-actions | 2305.07763 | null | https://arxiv.org/abs/2305.07763v1 | https://arxiv.org/pdf/2305.07763v1.pdf | Knowledge Authoring for Rules and Actions | Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledg... | ['Michael Kifer', 'Paul Fodor', 'Yuheng Wang'] | 2023-05-12 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-3.01568836e-01 6.60110593e-01 -2.62334734e-01 -3.31589073e-01
-2.81508058e-01 -7.93936193e-01 8.01199734e-01 1.77489758e-01
-4.04907987e-02 9.31441247e-01 6.12055250e-02 -1.09610116e+00
-6.75704956e-01 -1.27217102e+00 -7.73693979e-01 2.80867457e-01
-2.60149734e-03 8.85793746e-01 5.29510498e-01 -7.24055648... | [9.098252296447754, 7.218609809875488] |
854e2ef9-ae9f-430d-a242-f4312cc2fe94 | designing-a-3d-aware-stylenerf-encoder-for | 2302.09467 | null | https://arxiv.org/abs/2302.09467v1 | https://arxiv.org/pdf/2302.09467v1.pdf | Designing a 3D-Aware StyleNeRF Encoder for Face Editing | GAN inversion has been exploited in many face manipulation tasks, but 2D GANs often fail to generate multi-view 3D consistent images. The encoders designed for 2D GANs are not able to provide sufficient 3D information for the inversion and editing. Therefore, 3D-aware GAN inversion is proposed to increase the 3D editin... | ['Jing Dong', 'Bo Peng', 'Wei Wang', 'Songlin Yang'] | 2023-02-19 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 1.38268456e-01 1.50741339e-01 -2.55652238e-02 -4.38854784e-01
-3.20053846e-01 -7.25335598e-01 5.83642483e-01 -1.06318724e+00
4.62386727e-01 6.48660362e-01 2.80248314e-01 8.02533999e-02
1.18043266e-01 -8.28380823e-01 -8.77364099e-01 -7.79272974e-01
5.78924537e-01 4.24571902e-01 -5.22316933e-01 -2.06774890... | [12.588520050048828, -0.4032139480113983] |
97c2c0f8-d3b9-40a3-b77d-dc56e84d0dda | combining-deep-learning-and-argumentative | null | null | https://aclanthology.org/J18-4011 | https://aclanthology.org/J18-4011.pdf | Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets | The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus expl... | ['Oana Cocarascu', 'Francesca Toni'] | 2018-12-01 | null | null | null | cl-2018-12 | ['deception-detection'] | ['miscellaneous'] | [ 6.16677739e-02 4.92671132e-01 -4.39505130e-01 -3.47914398e-01
-5.00335813e-01 -6.84924960e-01 1.17036009e+00 1.06357348e+00
-5.03416479e-01 7.47108996e-01 5.47406435e-01 -6.49547577e-01
1.09525174e-01 -9.49854255e-01 -4.80925262e-01 -5.88468552e-01
2.11333379e-01 3.74593675e-01 -1.14164490e-03 -5.35472333... | [8.528620719909668, 10.275663375854492] |
7d4ca4f6-c4bf-4fdb-9d75-d54b158a4132 | learning-deep-multiresolution-representations | 2102.08423 | null | https://arxiv.org/abs/2102.08423v1 | https://arxiv.org/pdf/2102.08423v1.pdf | Learning deep multiresolution representations for pansharpening | Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening. This paper proposes a pyramid based deep fusion framework that preserves spectral and spatial characteristics at different scales. The spectral information is preserved by passin... | ['Junaid Imtiaz', 'Muhammad Imran Qureshi', 'Syed Abdul Mannan Kirmani', 'Muhammad Mohsin Riaz', 'Syed Sohaib Ali', 'Hannan Adeel'] | 2021-02-16 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 2.53953904e-01 -7.13838220e-01 -1.15885846e-01 -1.59360662e-01
-6.94339931e-01 -8.19160700e-01 2.41963312e-01 -2.46779561e-01
-3.05910349e-01 6.16831779e-01 3.97583187e-01 1.25956059e-01
-3.87109071e-01 -1.29246736e+00 -5.91892064e-01 -9.77496564e-01
1.22608103e-01 -4.67402399e-01 2.66816705e-01 -5.53563654... | [10.152706146240234, -1.9261870384216309] |
545123bc-af56-4b9f-bf26-9dee4f369caf | unnoticeable-backdoor-attacks-on-graph-neural | 2303.01263 | null | https://arxiv.org/abs/2303.01263v1 | https://arxiv.org/pdf/2303.01263v1.pdf | Unnoticeable Backdoor Attacks on Graph Neural Networks | Graph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. Recent studies find that GNNs are vulnerable to adversarial attacks. However, effective backdoor attacks on graphs are still an open problem. In particular, backdoor attack poisons the gra... | ['Suhang Wang', 'Xiang Zhang', 'Minhua Lin', 'Enyan Dai'] | 2023-02-11 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 2.97927320e-01 2.57965386e-01 -4.50480282e-01 1.89997420e-01
-2.02242926e-01 -1.37612879e+00 5.78603625e-01 1.59217983e-01
-3.33822444e-02 6.77025378e-01 -3.87133390e-01 -5.88644862e-01
1.28537016e-02 -1.41508365e+00 -7.40959942e-01 -7.91587651e-01
-4.44333166e-01 2.66304404e-01 6.61241531e-01 -2.24835873... | [6.091660976409912, 7.340043067932129] |
5accf2d0-972b-4361-a605-f738d85c0aaa | re-evaluating-lidar-scene-flow-for-autonomous | 2304.0215 | null | https://arxiv.org/abs/2304.02150v1 | https://arxiv.org/pdf/2304.02150v1.pdf | Re-Evaluating LiDAR Scene Flow for Autonomous Driving | Current methods for self-supervised LiDAR scene flow estimation work poorly on real data. A variety of flaws in common evaluation protocols have caused leading approaches to focus on problems that do not exist in real data. We analyze a suite of recent works and find that despite their focus on deep learning, the main ... | ['Simon Lucey', 'Deva Ramanan', 'Nathaniel Chodosh'] | 2023-04-04 | null | null | null | null | ['motion-compensation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-6.50512725e-02 -3.82498831e-01 -3.84440541e-01 -5.20243466e-01
-5.88805676e-01 -6.65501058e-01 5.22194862e-01 -5.68627179e-01
-5.85930228e-01 7.87396669e-01 7.63716102e-02 -2.56872416e-01
-2.18214784e-02 -5.74596286e-01 -6.15977228e-01 -5.14583051e-01
7.46810064e-02 9.13615704e-01 6.75297081e-01 -3.21661204... | [8.513090133666992, -2.0340614318847656] |
4309305b-7ddd-46b6-94f8-f8b4b6f9da00 | context-aware-stand-alone-neural-spelling | 2011.06642 | null | https://arxiv.org/abs/2011.06642v1 | https://arxiv.org/pdf/2011.06642v1.pdf | Context-aware Stand-alone Neural Spelling Correction | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the stand-alone spelling correction problem, which only corrects... | ['Liang Huang', 'Hairong Liu', 'Xiangci Li'] | 2020-11-12 | null | https://aclanthology.org/2020.findings-emnlp.37 | https://aclanthology.org/2020.findings-emnlp.37.pdf | findings-of-the-association-for-computational | ['spelling-correction'] | ['natural-language-processing'] | [ 5.18963516e-01 -2.29301378e-01 1.84552968e-01 -3.59842747e-01
-9.52488840e-01 -8.32466602e-01 3.53844613e-01 8.46181393e-01
-8.03273082e-01 7.15907335e-01 2.38555580e-01 -3.03484648e-01
6.52282834e-01 -5.72683215e-01 -7.38547206e-01 -4.51819450e-01
5.40586948e-01 1.20001964e-01 2.55960226e-01 -1.40571922... | [10.973601341247559, 10.712369918823242] |
1ffc39df-3fa8-4f19-87e8-d538f2d9a39f | rates-of-convergence-of-spectral-methods-for | 1709.03183 | null | http://arxiv.org/abs/1709.03183v1 | http://arxiv.org/pdf/1709.03183v1.pdf | Rates of Convergence of Spectral Methods for Graphon Estimation | This paper studies the problem of estimating the grahpon model - the
underlying generating mechanism of a network. Graphon estimation arises in many
applications such as predicting missing links in networks and learning user
preferences in recommender systems. The graphon model deals with a random graph
of $n$ vertices... | ['Jiaming Xu'] | 2017-09-10 | rates-of-convergence-of-spectral-methods-for-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2335 | http://proceedings.mlr.press/v80/xu18a/xu18a.pdf | icml-2018-7 | ['graphon-estimation'] | ['graphs'] | [ 2.17787310e-01 5.40806174e-01 -1.01466380e-01 1.20795280e-01
-6.73404276e-01 -4.58704323e-01 -3.50548595e-01 2.15359464e-01
-2.56779939e-01 6.26643717e-01 -4.50992912e-01 -4.40071672e-01
-6.75912559e-01 -1.02754283e+00 -8.96429420e-01 -8.84118974e-01
-9.93187487e-01 3.58823597e-01 -4.78996150e-02 -1.29191294... | [6.700377941131592, 4.877086162567139] |
2ba5343c-2ec2-4d5b-ace5-02e0531a36a9 | based-benchmarking-analysis-and-structural | 2305.17477 | null | https://arxiv.org/abs/2305.17477v1 | https://arxiv.org/pdf/2305.17477v1.pdf | BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring | This paper discusses the challenges of evaluating deblurring-methods quality and proposes a reduced-reference metric based on machine learning. Traditional quality-assessment metrics such as PSNR and SSIM are common for this task, but not only do they correlate poorly with subjective assessments, they also require grou... | ['Dmitriy Vatolin', 'Mikhail Dremin', 'Egor Chistov', 'Nikita Alutis'] | 2023-05-27 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 1.35350049e-01 -8.19274127e-01 1.86359718e-01 -3.19913507e-01
-8.87579203e-01 -7.74231911e-01 4.45808232e-01 -2.43370771e-01
-3.90794933e-01 9.14668024e-01 6.25106633e-01 -1.73989236e-01
-3.32700387e-02 -1.38231680e-01 -3.24951619e-01 -6.36624098e-01
-1.07151449e-01 -4.91982132e-01 1.77363619e-01 1.54420093... | [11.614188194274902, -2.720612049102783] |
cf8dfd73-ed2b-4429-babf-dc8bcca10241 | training-of-a-skull-stripping-neural-network | 1810.10853 | null | http://arxiv.org/abs/1810.10853v1 | http://arxiv.org/pdf/1810.10853v1.pdf | Training of a Skull-Stripping Neural Network with efficient data augmentation | Skull-stripping methods aim to remove the non-brain tissue from acquisition
of brain scans in magnetic resonance (MR) imaging. Although several methods
sharing this common purpose have been presented in literature, they all suffer
from the great variability of the MR images. In this work we propose a novel
approach bas... | ['Gabriele Valvano', 'Dante Chiappino', 'Emiliano Ricciardi', 'Nicola Martini', 'Gianmarco Santini', 'Daniele Della Latta', 'Andrea Leo'] | 2018-10-25 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 3.34698856e-01 2.59330004e-01 3.74804497e-01 -6.08968019e-01
-5.83051801e-01 -2.71143485e-02 3.36020470e-01 7.27742091e-02
-9.62013304e-01 7.44014323e-01 -8.96346010e-03 -7.60062933e-02
-2.00035289e-01 -5.67225754e-01 -5.29381037e-01 -6.12432361e-01
-3.90099347e-01 4.05331552e-01 2.90761203e-01 2.90803872... | [14.184041023254395, -2.261899709701538] |
126f392b-9e1e-48d5-a9e1-a8d6b5f1d0ab | blind-image-deblurring-by-spectral-properties | 1209.2082 | null | http://arxiv.org/abs/1209.2082v3 | http://arxiv.org/pdf/1209.2082v3.pdf | Blind Image Deblurring by Spectral Properties of Convolution Operators | In this paper, we study the problem of recovering a sharp version of a given
blurry image when the blur kernel is unknown. Previous methods often introduce
an image-independent regularizer (such as Gaussian or sparse priors) on the
desired blur kernel. We shall show that the blurry image itself encodes rich
information... | ['Shiyu Chang', 'Yi Ma', 'Guangcan Liu'] | 2012-09-10 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.49140030e-01 -2.79028207e-01 1.76966101e-01 -1.49291977e-01
-2.53294080e-01 -6.58112168e-01 8.81185234e-02 -7.25776315e-01
-5.57311438e-02 8.27120304e-01 6.45906746e-01 -1.48893250e-02
-1.54589713e-01 -2.74272829e-01 -7.96532452e-01 -9.77725506e-01
-9.80248675e-02 -2.98700362e-01 -2.10720628e-01 -1.18682850... | [11.623647689819336, -2.7639243602752686] |
c68fa6fb-ef76-40e1-8c3e-96368d91704d | cypur-nn-crop-yield-prediction-using | 2011.13265 | null | https://arxiv.org/abs/2011.13265v1 | https://arxiv.org/pdf/2011.13265v1.pdf | CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks | Our recent study using historic data of paddy yield and associated conditions include humidity, luminescence, and temperature. By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. Simulations indicate that our model can predict paddy yield with hig... | ['A Balachandra', 'Thulasiram Gunta', 'Varun Yadav', 'Anirudh Hebbar', 'Sandesh Ramesh'] | 2020-11-26 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-2.50869036e-01 -3.12314898e-01 -1.57031238e-01 -1.30123794e-01
1.60674796e-01 -5.38514733e-01 5.83240688e-02 1.94443375e-01
2.15537623e-01 7.91311145e-01 -3.37914795e-01 -8.36848557e-01
1.19714625e-01 -1.73358643e+00 -3.28306645e-01 -7.34598279e-01
-4.18923140e-01 -7.85299316e-02 2.37967014e-01 -7.26562023... | [9.346327781677246, -1.6144925355911255] |
a2c58f8e-e93f-4c14-9c0a-18276408ff5b | revisiting-self-training-for-few-shot | 2110.01256 | null | https://arxiv.org/abs/2110.01256v1 | https://arxiv.org/pdf/2110.01256v1.pdf | Revisiting Self-Training for Few-Shot Learning of Language Model | As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learn... | ['Haizhou Li', 'Ran Cheng', 'Grandee Lee', 'Chen Zhang', 'Yan Zhang', 'Yiming Chen'] | 2021-10-04 | null | https://aclanthology.org/2021.emnlp-main.718 | https://aclanthology.org/2021.emnlp-main.718.pdf | emnlp-2021-11 | ['sentence-pair-classification', 'sentence-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.87696093e-01 1.49715513e-01 -6.23492718e-01 -5.75142086e-01
-1.26331615e+00 -3.99880797e-01 7.43693173e-01 1.97198004e-01
-6.54016733e-01 9.04478312e-01 4.10834223e-01 -8.00860524e-02
3.03684324e-01 -5.17242789e-01 -4.30768281e-01 -4.83378977e-01
4.06944573e-01 6.00711763e-01 3.04572821e-01 -4.16855901... | [10.884246826171875, 7.79044246673584] |
9058c272-dec3-4686-b331-02301f764671 | fault-prognosis-in-particle-accelerator-power | 2209.1557 | null | https://arxiv.org/abs/2209.15570v1 | https://arxiv.org/pdf/2209.15570v1.pdf | Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning | Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, ... | ['Sarah Cousineau', 'Pradeep Ramuhalli', 'Mark Wezensky', 'Chris Pappas', 'Majdi I. Radaideh'] | 2022-09-30 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.80547142e-01 -6.34775460e-02 3.58705640e-01 -1.70126170e-01
-3.06741714e-01 -8.94801617e-02 4.83255416e-01 1.14163600e-01
-2.21640453e-01 8.31579626e-01 -3.70628804e-01 -4.32254583e-01
-5.74724674e-01 -6.51174128e-01 -2.09431216e-01 -1.02483726e+00
-2.95572191e-01 8.65668535e-01 4.29460168e-01 -2.85532802... | [6.660383224487305, 2.4353444576263428] |
859ab39b-0878-43cf-aded-d803ecf64b1a | classifier-stacking-for-native-language | null | null | https://aclanthology.org/W17-5044 | https://aclanthology.org/W17-5044.pdf | Classifier Stacking for Native Language Identification | This paper reports our contribution (team WLZ) to the NLI Shared Task 2017 (essay track). We first extract lexical and syntactic features from the essays, perform feature weighting and selection, and train linear support vector machine (SVM) classifiers each on an individual feature type. The output of base classifiers... | ['Wen Li', 'Liang Zou'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 7.93702453e-02 8.86071026e-02 -8.03384900e-01 -5.65124452e-01
-9.36483979e-01 -6.93288624e-01 7.78433383e-01 3.97587150e-01
-7.70515561e-01 8.87357891e-01 4.26130027e-01 -5.04492104e-01
-1.73976030e-02 -5.76112628e-01 -4.49154764e-01 -2.20511302e-01
2.53801495e-01 4.93363798e-01 -2.89439917e-01 -3.29629853... | [10.502076148986816, 10.336982727050781] |
8f362d93-6218-4ade-924d-48816831df5a | mimic-extract-a-data-extraction-preprocessing | 1907.08322 | null | https://arxiv.org/abs/1907.08322v2 | https://arxiv.org/pdf/1907.08322v2.pdf | MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III | Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data... | ['Shirly Wang', 'Michael C. Hughes', 'Geeticka Chauhan', 'Tristan Naumann', 'Matthew B. A. McDermott', 'Marzyeh Ghassemi'] | 2019-07-19 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [ 0.29481182 -0.02438877 -0.09211102 -0.47305465 -1.3084937 -0.55477935
0.03642758 1.0110091 -0.35221294 0.4835894 0.59197766 -0.56166375
-0.347326 -0.4546411 -0.47688097 -0.47519153 -0.17897427 0.5632105
-0.26462656 0.19950482 -0.24068178 0.40327457 -1.0798764 0.7866175
0.577542 0.9124393 -0.16... | [7.904660701751709, 6.271363735198975] |
413fcfab-3516-4b0f-9e9d-3c9d4f08fd44 | abrupt-motion-tracking-via-nearest-neighbor | 1410.7484 | null | http://arxiv.org/abs/1410.7484v2 | http://arxiv.org/pdf/1410.7484v2.pdf | Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling | Stochastic sampling based trackers have shown good performance for abrupt
motion tracking so that they have gained popularity in recent years. However,
conventional methods tend to use a two-stage sampling paradigm, in which the
search space needs to be uniformly explored with an inefficient preliminary
sampling phase.... | ['Jian Zhang', 'Yao Lu', 'Tianfei Zhou', 'Huijun Di', 'Feng Lv', 'Qingjie Zhao'] | 2014-10-28 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 6.30691126e-02 -6.26061440e-01 -4.55685109e-01 -2.03008369e-01
-6.69765592e-01 -2.77579516e-01 5.55384517e-01 -1.02050871e-01
-3.58655840e-01 6.51353955e-01 1.12148002e-01 1.84465405e-02
-1.02413237e-01 -5.50026536e-01 -3.90068263e-01 -8.93090069e-01
1.11564010e-01 4.00657393e-02 8.12492669e-01 3.14122409... | [6.410825729370117, -2.0736398696899414] |
2a6706ea-76d0-4a00-a827-061d54ed9b01 | a-deep-learning-approach-for-diabetic | null | null | https://ieeexplore.ieee.org/abstract/document/9298201/ | https://ieeexplore.ieee.org/abstract/document/9298201/ | A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning | Diabetic Retinopathy is a primary complication of diabetes which more often than not, affects both eyes and anyone with type-1 or type-2 diabetes can develop it. A Diabetic patient should undergo eye tests periodically as the pace of development of this condition is slow. A Dataset of Fundus Photographs of retina is co... | ['Himangi Pande', 'Karan Javali', 'Shardul Pharande', 'Bhargav Patil', 'Satwik Ramchandre'] | 2021-01-01 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 9.41039845e-02 -4.17473242e-02 -2.97284015e-02 -4.90536302e-01
-2.51474082e-01 6.30452260e-02 1.18936360e-01 -1.23374403e-01
-4.35734302e-01 8.67602944e-01 5.17775938e-02 -4.04684544e-01
-6.75836951e-02 -7.80692816e-01 -2.17037946e-01 -8.06902468e-01
3.07801723e-01 5.78912832e-02 2.03811973e-02 1.26514360... | [15.84024429321289, -3.993350028991699] |
c06de952-30d6-49b7-8638-b0a897f5f331 | customer-profiling-segmentation-and-sales | 2302.01786 | null | https://arxiv.org/abs/2302.01786v1 | https://arxiv.org/pdf/2302.01786v1.pdf | Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing | In an increasingly customer-centric business environment, effective communication between marketing and senior management is crucial for success. With the rise of globalization and increased competition, utilizing new data mining techniques to identify potential customers is essential for direct marketing efforts. This... | ['Islam Taj-Eddin', 'Mohamed Hamada', 'Mahmoud SalahEldin Kasem'] | 2023-02-03 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.02039874e-01 -1.71431854e-01 -6.41027212e-01 -9.29756343e-01
-2.14441016e-01 -1.37214556e-01 1.17289415e-02 6.84886873e-01
-4.16645288e-01 5.03031909e-01 1.48075074e-01 -6.51986063e-01
-4.23741490e-01 -1.01384044e+00 6.80783167e-02 -3.81833971e-01
3.71309310e-01 9.29047465e-01 -1.60325453e-01 -3.33089828... | [9.25742244720459, 5.911329746246338] |
1e21ea6a-efa6-4b07-b985-5cf836c6cb8f | compressive-sensing-of-ecg-signals-using-plug | 2210.08204 | null | https://arxiv.org/abs/2210.08204v1 | https://arxiv.org/pdf/2210.08204v1.pdf | Compressive Sensing of ECG Signals using Plug-and-Play Regularization | Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original signal from such compressed measurements remains a challenging problem. Traditional recovery methods are based on solving a regularized minimi... | ['Kunal Narayan Chaudhury', 'Ruturaj Gavaskar', 'Unni VS'] | 2022-10-15 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.15288520e-01 -1.64467424e-01 1.21098138e-01 -1.01131037e-01
-9.05631721e-01 -1.24678724e-01 1.30857050e-01 -5.84664904e-02
-3.53450388e-01 7.89257765e-01 6.23732433e-02 -8.32916424e-02
-4.48214889e-01 -4.23504263e-01 -6.41353726e-01 -1.06149006e+00
-9.07095969e-02 1.36145297e-02 -2.26414323e-01 -9.65688974... | [11.79118824005127, -2.395260810852051] |
7312f22e-f114-4de9-a880-018c290d7062 | continuously-indexed-domain-adaptation | 2007.01807 | null | https://arxiv.org/abs/2007.01807v2 | https://arxiv.org/pdf/2007.01807v2.pdf | Continuously Indexed Domain Adaptation | Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously indexed domains. For example, in medical applications, one often needs to transfer disease analysis and prediction across patients of different... | ['Hao Wang', 'Dina Katabi', 'Hao He'] | 2020-07-03 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1417-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1417-Paper.pdf | icml-2020-1 | ['continuously-indexed-domain-adaptation'] | ['methodology'] | [ 6.76463425e-01 7.48980232e-03 -4.38361198e-01 -4.46925193e-01
-8.09865892e-01 -6.87347770e-01 3.89945060e-01 2.20011026e-01
-3.27262431e-01 1.21299338e+00 1.49736419e-01 -1.82799697e-01
-1.26878634e-01 -9.19099748e-01 -8.48575592e-01 -5.93791127e-01
-2.53831856e-02 8.89527082e-01 1.76017776e-01 -2.67085999... | [10.362872123718262, 3.1112613677978516] |
084c836e-b99d-4c2e-ada7-7615ad89cf22 | wantwords-an-open-source-online-reverse | null | null | https://aclanthology.org/2020.emnlp-demos.23 | https://aclanthology.org/2020.emnlp-demos.23.pdf | WantWords: An Open-source Online Reverse Dictionary System | A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions. Reverse dictionaries have great practical value such as solving the tip-of-the-tongue problem and helping new language learners. There have been some online reverse dictionary systems, but they supp... | ['Maosong Sun', 'Zhiyuan Liu', 'Yanhui Yang', 'Lei Zhang', 'Fanchao Qi'] | 2020-10-01 | null | null | null | emnlp-2020-11 | ['reverse-dictionary'] | ['natural-language-processing'] | [-6.37661517e-01 -5.54198086e-01 -7.08854735e-01 -2.43586734e-01
-6.03529096e-01 -1.02039170e+00 3.96048993e-01 1.84060752e-01
-7.89297938e-01 5.56464851e-01 4.28394675e-01 -7.87121654e-01
2.51888275e-01 -8.42896163e-01 -1.46134287e-01 -1.91019416e-01
7.46590137e-01 6.03294909e-01 2.46981978e-01 -1.06829381... | [11.00733470916748, 9.867774963378906] |
c582835d-5b45-4176-967c-110ad0464db8 | eventclip-adapting-clip-for-event-based | 2306.06354 | null | https://arxiv.org/abs/2306.06354v1 | https://arxiv.org/pdf/2306.06354v1.pdf | EventCLIP: Adapting CLIP for Event-based Object Recognition | Recent advances in 2D zero-shot and few-shot recognition often leverage large pre-trained vision-language models (VLMs) such as CLIP. Due to a shortage of suitable datasets, it is currently infeasible to train such models for event camera data. Thus, leveraging existing models across modalities is an important research... | ['Igor Gilitschenski', 'Xudong Liu', 'Ziyi Wu'] | 2023-06-10 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.47829205e-01 -5.02650499e-01 -2.62873739e-01 -5.81521034e-01
-8.99280667e-01 -2.46481657e-01 1.01305747e+00 1.21824756e-01
-4.53103274e-01 1.04243286e-01 3.81956071e-01 1.82572767e-01
2.34351069e-01 -5.65255523e-01 -8.88356149e-01 -3.68549585e-01
1.12839654e-01 1.20918527e-02 4.46701497e-01 8.99598897... | [8.898645401000977, 0.9232564568519592] |
3e5bc5a9-3fdf-43d2-ae1f-852afafa62ee | combo-pre-training-representations-of-binary | 2210.05102 | null | https://arxiv.org/abs/2210.05102v1 | https://arxiv.org/pdf/2210.05102v1.pdf | COMBO: Pre-Training Representations of Binary Code Using Contrastive Learning | Compiled software is delivered as executable binary code. Developers write source code to express the software semantics, but the compiler converts it to a binary format that the CPU can directly execute. Therefore, binary code analysis is critical to applications in reverse engineering and computer security tasks wher... | ['Yu Huang', 'Kevin Leach', 'Huajie Shao', 'Scott Thomas Andersen', 'Kevin Cao', 'Yueke Zhang', 'Chen Huang', 'Yifan Zhang'] | 2022-10-11 | null | null | null | null | ['vulnerability-detection', 'computer-security'] | ['miscellaneous', 'miscellaneous'] | [ 1.28900200e-01 -2.89736420e-01 -7.73708642e-01 -2.94974864e-01
-6.81776464e-01 -8.75926971e-01 3.15429121e-01 8.30502391e-01
7.12237433e-02 2.02838052e-02 1.59440920e-01 -1.15887189e+00
3.73042405e-01 -8.80803704e-01 -6.14226162e-01 -3.85369137e-02
-1.38909951e-01 -6.64610136e-03 1.26240123e-02 -2.98242658... | [7.235850811004639, 7.818892002105713] |
897c1fbe-cf38-4a28-9ca8-cb29527d2e74 | crack-segmentation-for-low-resolution-images | null | null | https://ieeexplore.ieee.org/abstract/document/9511400 | http://www.mva-org.jp/Proceedings/2021/papers/O1-1-2.pdf | Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution | This paper proposes a method for crack segmentation on low-resolution images. Detailed cracks on their high-resolution images are estimated by super resolution from the low-resolution images. Our proposed method optimizes super-resolution images for the crack segmentation. For this method, we propose the Boundary Combo... | ['Norimichi Ukita', 'Yuki Kondo'] | 2021-07-25 | null | null | null | international-conference-on-machine-vision | ['crack-segmentation'] | ['computer-vision'] | [ 2.58956850e-01 -3.35944779e-02 1.22088991e-01 -1.51428431e-01
-1.34495854e+00 2.09099591e-01 -1.61738023e-01 -4.68814403e-01
-2.40606233e-01 6.58902049e-01 2.20383555e-02 6.64116800e-01
3.13689381e-01 -9.98448431e-01 -2.87336946e-01 -8.07335258e-01
2.66472369e-01 3.58051300e-01 1.11894238e+00 -1.25059396... | [10.969758033752441, -2.2942800521850586] |
1bc7def2-948b-4fd3-b11d-fc8f0dccec1c | joint-matrix-decomposition-for-deep | 2107.04386 | null | https://arxiv.org/abs/2107.04386v3 | https://arxiv.org/pdf/2107.04386v3.pdf | Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression | Deep convolutional neural networks (CNNs) with a large number of parameters require intensive computational resources, and thus are hard to be deployed in resource-constrained platforms. Decomposition-based methods, therefore, have been utilized to compress CNNs in recent years. However, since the compression factor an... | ['Jiahao Zhou', 'Lei Huang', 'Weize Sun', 'Shaowu Chen'] | 2021-07-09 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 1.09795727e-01 -3.06204230e-01 1.44932221e-03 -3.81105840e-01
-1.01922676e-01 -1.44149363e-02 1.36122644e-01 -2.12610781e-01
-7.64039457e-01 4.12387758e-01 1.87404633e-01 -2.01240540e-01
-2.15015382e-01 -6.70281410e-01 -7.70914435e-01 -7.89504886e-01
3.55502963e-01 -2.25234568e-01 3.29743892e-01 -6.85833022... | [8.49351692199707, 3.086530923843384] |
06b01333-c3ce-45d4-bcca-bde6aca1346d | fast-road-segmentation-via-uncertainty-aware | 2203.04537 | null | https://arxiv.org/abs/2203.04537v1 | https://arxiv.org/pdf/2203.04537v1.pdf | Fast Road Segmentation via Uncertainty-aware Symmetric Network | The high performance of RGB-D based road segmentation methods contrasts with their rare application in commercial autonomous driving, which is owing to two reasons: 1) the prior methods cannot achieve high inference speed and high accuracy in both ways; 2) the different properties of RGB and depth data are not well-exp... | ['Anlong Ming', 'Wenteng Liang', 'Fei Sheng', 'Feng Xue', 'Yicong Chang'] | 2022-03-09 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.01354785e-01 5.55408821e-02 -1.67608544e-01 -4.75766331e-01
-6.86740518e-01 1.33454371e-02 4.54549640e-01 -5.03705628e-02
-4.66469377e-01 7.50065684e-01 -3.72418612e-01 -2.66958982e-01
-1.84048846e-01 -1.23245120e+00 -7.45403886e-01 -8.23263228e-01
3.53161484e-01 2.09415272e-01 7.69338906e-01 -1.74924508... | [8.409512519836426, -2.365255832672119] |
ba14d6d2-9c13-4f70-ad8d-e362835ccbdc | the-neural-data-router-adaptive-control-flow | 2110.07732 | null | https://arxiv.org/abs/2110.07732v4 | https://arxiv.org/pdf/2110.07732v4.pdf | The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization | Despite progress across a broad range of applications, Transformers have limited success in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail to find intuitive solutions that route relevant information to the right node/operation at the right time... | ['Jürgen Schmidhuber', 'Kazuki Irie', 'Róbert Csordás'] | 2021-10-14 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 1.21092230e-01 1.30823568e-01 -2.70631760e-01 -2.92800277e-01
-5.52996516e-01 -8.78254414e-01 1.91600531e-01 4.13189471e-01
-4.47486416e-02 8.06474030e-01 3.51650298e-01 -1.10427403e+00
-3.39383662e-01 -1.07454836e+00 -7.05423772e-01 -1.90468833e-01
-4.16012228e-01 6.22366548e-01 2.20108986e-01 -6.39992833... | [9.466938972473145, 7.117514610290527] |
92cf9d9b-ff3a-44e2-a659-1268aac90014 | discriminant-projection-representation-based | 1712.01643 | null | http://arxiv.org/abs/1712.01643v1 | http://arxiv.org/pdf/1712.01643v1.pdf | Discriminant Projection Representation-based Classification for Vision Recognition | Representation-based classification methods such as sparse
representation-based classification (SRC) and linear regression classification
(LRC) have attracted a lot of attentions. In order to obtain the better
representation, a novel method called projection representation-based
classification (PRC) is proposed for ima... | ['Qingxiang Feng', 'Yicong Zhou'] | 2017-11-19 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 3.42732817e-01 -3.66625458e-01 -1.80109799e-01 -3.59187722e-01
-7.43851304e-01 4.46077824e-01 3.52140814e-01 -3.46499950e-01
-1.72910109e-01 4.11177278e-01 4.97477278e-02 1.23013102e-01
-4.41620111e-01 -7.98237741e-01 -3.38709384e-01 -9.25458670e-01
1.39702857e-01 -1.28082493e-02 -6.60464540e-02 9.08326171... | [12.47513198852539, 0.4217372536659241] |
38faaf79-0268-4de2-a9a6-9e1b1bf05f61 | cooperative-decision-making-in-shared-spaces | 2306.14617 | null | https://arxiv.org/abs/2306.14617v1 | https://arxiv.org/pdf/2306.14617v1.pdf | Cooperative Decision-Making in Shared Spaces: Making Urban Traffic Safer through Human-Machine Cooperation | In this paper, a cooperative decision-making is presented, which is suitable for intention-aware automated vehicle functions. With an increasing number of highly automated and autonomous vehicles on public roads, trust is a very important issue regarding their acceptance in our society. The most challenging scenarios a... | ['Sören Hohmann', 'Dongxu Yang', 'Balint Varga'] | 2023-06-26 | null | null | null | null | ['autonomous-vehicles', 'decision-making'] | ['computer-vision', 'reasoning'] | [-1.79766223e-01 5.61758041e-01 -2.22284794e-01 -7.17433691e-01
1.43553400e-02 1.74268886e-01 9.37772155e-01 1.18313715e-01
-6.89176142e-01 1.10060394e+00 -4.02208090e-01 -5.39997339e-01
-1.59538835e-01 -8.62423599e-01 -4.14636850e-01 -5.51537514e-01
-1.26537710e-01 7.59557188e-01 8.39289248e-01 -6.77370369... | [5.6112260818481445, 1.315854549407959] |
1aa624a3-1e8b-4f04-be93-ba9c63e31ee6 | plug-and-play-vqa-zero-shot-vqa-by-conjoining | 2210.08773 | null | https://arxiv.org/abs/2210.08773v3 | https://arxiv.org/pdf/2210.08773v3.pdf | Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training | Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) ... | ['Steven C. H. Hoi', 'Silvio Savarese', 'Boyang Li', 'Junnan Li', 'Anthony Meng Huat Tiong'] | 2022-10-17 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [-2.41986457e-02 3.89793605e-01 -1.25877529e-01 -3.76703173e-01
-1.22629535e+00 -5.93855381e-01 7.13549495e-01 -3.37271780e-01
-3.89515817e-01 4.12098289e-01 3.19744498e-01 -5.55809021e-01
4.17133123e-01 -6.76162899e-01 -9.28829253e-01 -2.52239525e-01
6.94386125e-01 5.73536992e-01 5.03936768e-01 -4.13609326... | [10.885479927062988, 1.692568063735962] |
2a74472c-e802-4ee7-95ef-b3705bb98109 | efficient-column-generation-for-cell | 1709.07337 | null | http://arxiv.org/abs/1709.07337v1 | http://arxiv.org/pdf/1709.07337v1.pdf | Efficient Column Generation for Cell Detection and Segmentation | We study the problem of instance segmentation in biological images with
crowded and compact cells. We formulate this task as an integer program where
variables correspond to cells and constraints enforce that cells do not
overlap. To solve this integer program, we propose a column generation
formulation where the prici... | ['Julian Yarkony', 'Miguel A. Gonzalez-Ballester', 'Shaofei Wang', 'Chong Zhang'] | 2017-09-21 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 5.24150252e-01 4.65784848e-01 -1.07726216e-01 -2.44525701e-01
-9.55397427e-01 -7.99254060e-01 1.62691757e-01 4.50998724e-01
-7.28791296e-01 1.42906260e+00 -5.84894598e-01 -9.85452458e-02
-1.50970832e-01 -5.50235808e-01 -1.11449301e+00 -1.01355338e+00
-7.23784044e-02 1.25647116e+00 1.45536557e-01 3.47156912... | [14.492613792419434, -3.189276933670044] |
3a21c84b-5cc7-4d8d-a927-85278a8ec473 | towards-streaming-egocentric-action | 2110.05386 | null | https://arxiv.org/abs/2110.05386v2 | https://arxiv.org/pdf/2110.05386v2.pdf | Towards Streaming Egocentric Action Anticipation | Egocentric action anticipation is the task of predicting the future actions a camera wearer will likely perform based on past video observations. While in a real-world system it is fundamental to output such predictions before the action begins, past works have not generally paid attention to model runtime during evalu... | ['Giovanni Maria Farinella', 'Antonino Furnari'] | 2021-10-11 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.12250674e-01 3.17163378e-01 -1.99087888e-01 -4.09142643e-01
-1.78960606e-01 -8.61034095e-02 8.09196472e-01 4.15234864e-01
-9.06045616e-01 5.28001666e-01 5.26604116e-01 6.92077279e-02
-1.77819222e-01 -5.84068716e-01 -9.02612507e-01 -4.13682580e-01
-2.95555413e-01 4.52877969e-01 3.66173446e-01 -9.29237008... | [8.141510009765625, 0.40105077624320984] |
3b36938b-b060-4477-8ad2-c4ab8ff2bc4f | 3d-deeply-supervised-network-for-automatic | 1607.00582 | null | http://arxiv.org/abs/1607.00582v1 | http://arxiv.org/pdf/1607.00582v1.pdf | 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes | Automatic liver segmentation from CT volumes is a crucial prerequisite yet
challenging task for computer-aided hepatic disease diagnosis and treatment. In
this paper, we present a novel 3D deeply supervised network (3D DSN) to address
this challenging task. The proposed 3D DSN takes advantage of a fully
convolutional a... | ['Pheng-Ann Heng', 'Yueming Jin', 'Qi Dou', 'Lequan Yu', 'Hao Chen', 'Jing Qin'] | 2016-07-03 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-2.00704560e-01 8.73718485e-02 -2.93872714e-01 -5.97205579e-01
-1.06870663e+00 -1.76416874e-01 3.60265106e-01 1.93758056e-01
-4.89203960e-01 4.21172768e-01 1.94322467e-01 -4.83805448e-01
1.37972534e-01 -5.23672044e-01 -5.11605203e-01 -8.67834508e-01
-3.08796465e-01 6.18602753e-01 1.30215123e-01 2.79300392... | [14.510231018066406, -2.6239445209503174] |
759ac6f0-c53f-4eab-a5b1-601c99b77695 | cross-document-language-modeling | 2101.00406 | null | https://arxiv.org/abs/2101.00406v2 | https://arxiv.org/pdf/2101.00406v2.pdf | CDLM: Cross-Document Language Modeling | We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First, instead of considering documents in isolation, we pretrain over sets of multiple related documents, encouraging the model to learn cross-do... | ['Ido Dagan', 'Arie Cattan', 'Matthew E. Peters', 'Iz Beltagy', 'Arman Cohan', 'Avi Caciularu'] | 2021-01-02 | null | https://aclanthology.org/2021.findings-emnlp.225 | https://aclanthology.org/2021.findings-emnlp.225.pdf | findings-emnlp-2021-11 | ['cross-document-language-modeling'] | ['natural-language-processing'] | [-1.29737318e-01 6.60477951e-02 -3.78894120e-01 -5.84331155e-01
-1.32396889e+00 -6.63814783e-01 9.87320125e-01 1.89716741e-01
-4.20879364e-01 5.34296334e-01 4.42278892e-01 -4.49515283e-01
2.28520334e-01 -2.09472597e-01 -8.88215125e-01 -3.97567481e-01
-3.52830768e-01 6.22661412e-01 1.67350426e-01 -2.69771338... | [10.790103912353516, 8.753933906555176] |
f4f88971-b1c0-451e-8edc-6ea3b61f867e | intelligent-robotic-sonographer-mutual | 2307.03705 | null | https://arxiv.org/abs/2307.03705v1 | https://arxiv.org/pdf/2307.03705v1.pdf | Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations | Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to high inter-operator variability, resulting images highly depend on operators' experience. In this work, an intelligent robotic sonographer is pro... | ['and Nassir Navab', 'Michael Burke', 'Ying Hu', 'Mingchuan Zhou', 'Yuan Bi', 'Zhongliang Jiang'] | 2023-07-07 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 2.20549807e-01 2.17089698e-01 1.65862128e-01 -4.82411057e-01
-8.03784132e-01 -4.69512314e-01 -6.50101379e-02 1.09303690e-01
-5.67514658e-01 6.27808154e-01 -2.03371152e-01 1.21674500e-01
-5.89739561e-01 -2.76464880e-01 -6.53533161e-01 -9.62245524e-01
-3.30476612e-01 3.02045822e-01 -7.17175901e-02 1.80203617... | [14.423158645629883, -2.133993625640869] |
599aa5ed-9efb-4585-a7b2-c0dd1072f507 | general-multi-label-image-classification-with | 2011.14027 | null | https://arxiv.org/abs/2011.14027v1 | https://arxiv.org/pdf/2011.14027v1.pdf | General Multi-label Image Classification with Transformers | Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the comp... | ['Yanjun Qi', 'Vicente Ordonez', 'Tianlu Wang', 'Jack Lanchantin'] | 2020-11-27 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.61323249e-01 2.85332412e-01 -2.74152935e-01 -7.03628540e-01
-8.73960197e-01 -8.03617954e-01 8.03075969e-01 2.64195532e-01
-1.82513550e-01 5.30088961e-01 -2.00605273e-01 -2.52336651e-01
1.59225643e-01 -6.32127345e-01 -1.00058877e+00 -6.59139454e-01
1.31733015e-01 5.36962867e-01 1.71575770e-01 2.86115944... | [9.643339157104492, 3.996398687362671] |
b485ea6c-9224-46c6-8fb9-9004b5e70ff4 | examining-the-mapping-functions-of-denoising | 1904.06157 | null | https://arxiv.org/abs/1904.06157v2 | https://arxiv.org/pdf/1904.06157v2.pdf | Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation | The goal of this work is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks based on the denoising autoencoder (DAE) model that are conditioned on the mixture magnitude spectra. To approximate the mapping functions, w... | ['Gerald Schuller', 'Estefanía Cano', 'Konstantinos Drossos', 'Stylianos Ioannis Mimilakis'] | 2019-04-12 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.19456697e-01 2.83445138e-02 2.08874449e-01 -5.48605658e-02
-4.84208047e-01 -4.87028658e-01 2.64796913e-01 -4.68395859e-01
-2.28983954e-01 3.37994605e-01 4.26187068e-01 -1.85687672e-02
-4.57745641e-01 -5.67867458e-01 -7.91340828e-01 -7.66485155e-01
-1.46741122e-01 2.66972184e-02 -2.25737140e-01 -2.99164444... | [15.439133644104004, 5.8208794593811035] |
89e20914-78d1-4e08-a203-904912c67607 | on-building-machine-learning-pipelines-for | 2306.07118 | null | https://arxiv.org/abs/2306.07118v1 | https://arxiv.org/pdf/2306.07118v1.pdf | On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities | As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. For market holders and researchers, in particular, the large num... | ['Huang Shengqiang', 'Ronnie Salvador Giagone', 'Yang Zhou', 'Anandharaju Durai Raju', 'Ibrahim Abualhaol', 'Masoud Mehrabi Koushki'] | 2023-06-12 | null | null | null | null | ['dimensionality-reduction', 'android-malware-detection'] | ['methodology', 'miscellaneous'] | [ 2.17132911e-01 -2.47283638e-01 -8.54843616e-01 4.48225923e-02
-4.74555910e-01 -9.30673361e-01 6.17469192e-01 -1.49145350e-01
1.10290952e-01 4.71759260e-01 -3.69387902e-02 -9.60067868e-01
-8.68592337e-02 -3.42154384e-01 -3.70515883e-01 -3.23514104e-01
-8.85791052e-03 1.50657058e-01 8.72142091e-02 1.68970376... | [14.419124603271484, 9.678455352783203] |
887700cc-bf94-4110-956c-881bbcacf12a | fine-grained-ecg-classification-based-on-deep | 1901.06469 | null | https://arxiv.org/abs/1901.06469v3 | https://arxiv.org/pdf/1901.06469v3.pdf | Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification | Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world... | ['Xiaobin Xu', 'Jing Tian', 'Jing Zhang', 'Yuxiang Yang', 'Yang Cao'] | 2019-01-19 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.52025628e-01 -5.42067111e-01 1.46198466e-01 -6.49662137e-01
-8.61560702e-01 -4.97535050e-01 -1.37819290e-01 3.26300532e-01
-2.54577488e-01 7.12474942e-01 8.31906423e-02 -1.02025077e-01
-4.16848212e-01 -4.19490904e-01 -1.56492069e-02 -6.58617973e-01
-5.98557651e-01 6.34628683e-02 -1.13131572e-02 -8.72035790... | [14.2920560836792, 3.2956340312957764] |
8a4faaf7-3bd9-4605-a175-ab78c902c3c8 | arch-animation-ready-clothed-human | 2108.07845 | null | https://arxiv.org/abs/2108.07845v4 | https://arxiv.org/pdf/2108.07845v4.pdf | ARCH++: Animation-Ready Clothed Human Reconstruction Revisited | We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the visible regions from input views and the unseen regions. While prior work shows great promise of reconstructing animatable clothed humans wit... | ['Tony Tung', 'Stefano Soatto', 'Shunsuke Saito', 'Yuanlu Xu', 'Tong He'] | 2021-08-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/He_ARCH_Animation-Ready_Clothed_Human_Reconstruction_Revisited_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/He_ARCH_Animation-Ready_Clothed_Human_Reconstruction_Revisited_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-reconstruction-from-a-single-image'] | ['computer-vision'] | [ 1.42117947e-01 6.88088834e-02 4.59702492e-01 -1.60396099e-01
-4.04519618e-01 -5.57105541e-01 4.13675845e-01 -5.47369838e-01
6.57365024e-02 4.15727109e-01 3.71743858e-01 3.01165760e-01
3.42980742e-01 -8.04022849e-01 -1.10098457e+00 -3.62607867e-01
2.06313044e-01 7.60021687e-01 3.16145301e-01 -6.06832206... | [7.277676582336426, -1.2697633504867554] |
b7e47a4f-bcfe-4c7a-85e0-efdd8c10ac97 | non-rigid-point-cloud-registration-with | 2205.12796 | null | https://arxiv.org/abs/2205.12796v3 | https://arxiv.org/pdf/2205.12796v3.pdf | Non-rigid Point Cloud Registration with Neural Deformation Pyramid | Non-rigid point cloud registration is a key component in many computer vision and computer graphics applications. The high complexity of the unknown non-rigid motion make this task a challenging problem. In this paper, we break down this problem via hierarchical motion decomposition. Our method called Neural Deformatio... | ['Tatsuya Harada', 'Yang Li'] | 2022-05-25 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 1.29315212e-01 -1.93289116e-01 -1.81784853e-01 5.95785072e-03
-9.62002635e-01 -4.14284676e-01 4.91299868e-01 -3.51418614e-01
-3.18345368e-01 3.03246409e-01 2.19784915e-01 -3.26966569e-02
1.28459156e-01 -6.69924736e-01 -1.17942691e+00 -1.02005553e+00
-1.61297649e-01 8.82905483e-01 4.36007708e-01 -1.70392804... | [8.166386604309082, -2.4280550479888916] |
ec62b3ca-4261-4b70-889b-7c585421915d | interpretable-deep-learning-for-forecasting | 2302.05762 | null | https://arxiv.org/abs/2302.05762v1 | https://arxiv.org/pdf/2302.05762v1.pdf | Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape | As advertisers increasingly shift their budgets toward digital advertising, forecasting advertising costs is essential for making budget plans to optimize marketing campaign returns. In this paper, we perform a comprehensive study using a variety of time-series forecasting methods to predict daily average cost-per-clic... | ['Maximilian Kaiser', 'Qiwei Han', 'Fynn Oldenburg'] | 2023-02-11 | null | null | null | null | ['marketing', 'time-series-clustering'] | ['miscellaneous', 'time-series'] | [-8.88671279e-02 -2.50789315e-01 -9.85277057e-01 -1.04282236e+00
-7.24981427e-01 -4.73565638e-01 7.52401888e-01 2.61498958e-01
-2.46236876e-01 2.39154458e-01 6.04456127e-01 -8.28270495e-01
-4.47153568e-01 -6.42058849e-01 -5.21829963e-01 -1.98861491e-02
-4.87269104e-01 4.01100755e-01 -2.19790339e-01 -1.34123340... | [9.636994361877441, 5.623012065887451] |
38ea765d-d7fd-47e6-9c5a-cbf31e806b71 | multi-angle-point-cloud-vae-unsupervised | 1907.12704 | null | https://arxiv.org/abs/1907.12704v1 | https://arxiv.org/pdf/1907.12704v1.pdf | Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction | Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from self-reconstruction. However, these methods are still suffering from ineffective learning of local geometry, which significantly limits the ... | ['Matthias Zwicker', 'Yu-Shen Liu', 'Zhizhong Han', 'Xiyang Wang'] | 2019-07-30 | multi-angle-point-cloud-vae-unsupervised-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Han_Multi-Angle_Point_Cloud-VAE_Unsupervised_Feature_Learning_for_3D_Point_Clouds_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Han_Multi-Angle_Point_Cloud-VAE_Unsupervised_Feature_Learning_for_3D_Point_Clouds_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-point-cloud-linear-classification', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision'] | [-2.28890732e-01 -1.43499345e-01 -2.43756716e-04 -6.68664575e-01
-1.01291740e+00 -6.41493499e-01 4.99836832e-01 1.49446642e-02
-6.51797578e-02 2.54563808e-01 -1.19559921e-01 -3.18787992e-02
-8.93779099e-02 -1.00865889e+00 -1.14521575e+00 -7.23380327e-01
1.51552439e-01 8.54809821e-01 1.73176765e-01 -1.24214716... | [8.198442459106445, -3.4305944442749023] |
660bb7ca-d6ed-485d-9ae5-3d3509659f4d | how-does-beam-search-improve-span-level | 2212.10767 | null | https://arxiv.org/abs/2212.10767v1 | https://arxiv.org/pdf/2212.10767v1.pdf | How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling? | Text-to-text generation models have increasingly become the go-to solution for a wide variety of sequence labeling tasks (e.g., entity extraction and dialog slot filling). While most research has focused on the labeling accuracy, a key aspect -- of vital practical importance -- has slipped through the cracks: understan... | ['Karthik Raman', 'Iftekhar Naim', 'Kazuma Hashimoto'] | 2022-12-21 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 3.14928204e-01 5.94826937e-01 -4.69502330e-01 -6.50074184e-01
-1.43571138e+00 -7.85559237e-01 3.16498995e-01 2.21570238e-01
-2.16425329e-01 1.03923619e+00 3.50625455e-01 -7.58979321e-01
3.48653406e-01 -4.22101766e-01 -6.62240386e-01 -2.27835640e-01
4.43639308e-01 8.58451307e-01 -4.46882695e-02 -5.15953861... | [10.838382720947266, 8.777430534362793] |
2bac0234-6755-4d81-8b02-6741206abb3d | integration-of-automatic-sentence | null | null | https://aclanthology.org/2020.lt4hala-1.8 | https://aclanthology.org/2020.lt4hala-1.8.pdf | Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model | The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition. Tasks such as lexical analysis need to be based on sentence segmentation because of the reason that a plenty of ancient books are not punctuated. How... | ['Changwei Xu', 'Liming Xiao', 'Ning Cheng', 'Xingyue Hao', 'Sijia Ge', 'Minxuan Feng', 'Bin Li'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-1.50969341e-01 1.18229106e-01 1.32156178e-01 -4.88931447e-01
-5.80148995e-01 -4.83823150e-01 3.69674573e-03 3.59586328e-01
-1.03227043e+00 8.84847462e-01 4.81956750e-02 -5.10562181e-01
2.93752968e-01 -8.08134198e-01 -3.89744520e-01 -4.76463854e-01
2.65405774e-01 2.72364229e-01 4.77624774e-01 -1.58756375... | [10.115629196166992, 10.130635261535645] |
90e0eaab-bc71-4281-a1e8-72e7a0ffa4d3 | towards-globally-consistent-stochastic-human | 2305.12554 | null | https://arxiv.org/abs/2305.12554v1 | https://arxiv.org/pdf/2305.12554v1.pdf | Towards Globally Consistent Stochastic Human Motion Prediction via Motion Diffusion | Stochastic human motion prediction aims to predict multiple possible upcoming pose sequences based on past human motion trajectories. Prior works focused heavily on generating diverse motion samples, leading to inconsistent, abnormal predictions from the immediate past observations. To address this issue, in this work,... | ['Girish Chowdhary', 'Jiarui Sun'] | 2023-05-21 | null | null | null | null | ['motion-prediction', 'stochastic-human-motion-prediction', 'human-motion-prediction'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 1.40857711e-01 3.37904580e-02 -1.10456839e-01 1.21368589e-02
-3.84930342e-01 -1.68871835e-01 6.15136564e-01 -4.41403538e-01
-1.08889617e-01 6.67181551e-01 6.40990555e-01 5.92517555e-02
1.31700769e-01 -5.32696426e-01 -5.42423487e-01 -4.80821848e-01
-3.14779043e-01 3.63114536e-01 4.10879314e-01 -7.38647506... | [7.330674171447754, -0.14473344385623932] |
64067071-ac55-4a45-b559-6b81bcafa116 | hidanet-rgb-d-salient-object-detection-via | 2301.07405 | null | https://arxiv.org/abs/2301.07405v1 | https://arxiv.org/pdf/2301.07405v1.pdf | HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness | RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challengin... | ['Cédric Demonceaux', 'Chao Ma', 'Fabrice Meriaudeau', 'Guillaume Allibert', 'Zongwei Wu'] | 2023-01-18 | null | null | null | null | ['rgb-d-salient-object-detection', 'saliency-detection'] | ['computer-vision', 'computer-vision'] | [ 6.55519888e-02 -1.75600290e-01 -1.96386158e-01 -4.12043452e-01
-9.35290456e-01 -2.60211021e-01 4.48345274e-01 3.34213257e-01
-1.54769763e-01 3.78344893e-01 3.65005404e-01 1.86689958e-01
-8.21972862e-02 -6.30643010e-01 -7.65767992e-01 -7.19792247e-01
3.55590999e-01 -2.47267067e-01 8.31176460e-01 -2.60422230... | [9.714252471923828, -0.7000154852867126] |
811e3161-4f73-42fc-ac3f-3708308d128c | image-to-image-retrieval-by-learning | 2012.147 | null | https://arxiv.org/abs/2012.14700v1 | https://arxiv.org/pdf/2012.14700v1.pdf | Image-to-Image Retrieval by Learning Similarity between Scene Graphs | As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by ... | ['Eun-Sol Kim', 'Jonghun Park', 'Changjin Han', 'SeongEun Lee', 'Sungwook Jeon', 'Woo Young Kang', 'Sangwoong Yoon'] | 2020-12-29 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 5.40922523e-01 1.74057558e-01 -3.31434190e-01 -7.34902918e-01
-7.37581193e-01 -3.95623773e-01 8.22424054e-01 8.39576423e-01
-4.23544914e-01 1.88999679e-02 6.23090446e-01 1.16017476e-01
-1.82061821e-01 -7.79222190e-01 -7.79756486e-01 -1.01085111e-01
1.83820143e-01 3.50621015e-01 3.20740730e-01 -1.55390248... | [10.729267120361328, 1.4149819612503052] |
3012b3a0-078c-4c77-8eb9-fbd0f55f3b2e | single-image-deep-defocus-estimation-and-its | 2107.14443 | null | https://arxiv.org/abs/2107.14443v2 | https://arxiv.org/pdf/2107.14443v2.pdf | Single image deep defocus estimation and its applications | Depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the lev... | ['Guang Deng', 'Fernando J. Galetto'] | 2021-07-30 | null | null | null | null | ['defocus-estimation'] | ['computer-vision'] | [ 5.25922418e-01 -5.79129100e-01 3.73419166e-01 -4.82527673e-01
-1.18756637e-01 -2.14578092e-01 2.20928416e-01 -3.69100839e-01
-4.56283033e-01 7.97524631e-01 2.64625460e-01 -7.10928589e-02
-1.77192569e-01 -5.71958005e-01 -6.05687022e-01 -9.58850265e-01
1.88997507e-01 -1.06753252e-01 2.41412893e-01 2.06965670... | [11.392026901245117, -2.747540235519409] |
9771846e-f7a5-4e41-b446-d05ff2bf2c53 | a-research-platform-for-multi-robot-dialogue-1 | 1910.05624 | null | https://arxiv.org/abs/1910.05624v1 | https://arxiv.org/pdf/1910.05624v1.pdf | A Research Platform for Multi-Robot Dialogue with Humans | This paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which users can verbally issue search, navigate, and follow instructions to two robotic teammates: a simulated ground robot and an aerial robot. Th... | ['Jesse Bloecker', 'Stephanie M. Lukin', 'Matthew Marge', 'Clare Voss', 'Eric Holder', 'Stephen Nogar', 'Cory J. Hayes'] | 2019-10-12 | a-research-platform-for-multi-robot-dialogue | https://aclanthology.org/N19-4023 | https://aclanthology.org/N19-4023.pdf | naacl-2019-6 | ['dialogue-management'] | ['natural-language-processing'] | [-2.16442525e-01 6.03996158e-01 3.02349478e-01 -5.82757413e-01
-2.04703629e-01 -8.53193760e-01 7.88261890e-01 -3.31945205e-03
-4.71735865e-01 1.11945331e+00 -3.82300206e-02 -3.70914519e-01
-5.98411381e-01 -3.63755524e-01 1.72180101e-01 -2.25082323e-01
-1.34946495e-01 9.97379124e-01 3.78440320e-01 -1.07739627... | [4.441879749298096, 0.6466375589370728] |
55b7cf8e-b479-4b1e-9fdb-ad5bd47395cc | from-shallow-to-deep-compositional-reasoning | 2206.12533 | null | https://arxiv.org/abs/2206.12533v1 | https://arxiv.org/pdf/2206.12533v1.pdf | From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering | In order to achieve a general visual question answering (VQA) system, it is essential to learn to answer deeper questions that require compositional reasoning on the image and external knowledge. Meanwhile, the reasoning process should be explicit and explainable to understand the working mechanism of the model. It is ... | ['Zihao Zhu'] | 2022-06-25 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-4.09207791e-02 6.78397000e-01 4.68915440e-02 -4.42094922e-01
-8.06145668e-02 -5.95914125e-01 3.67371798e-01 1.34150982e-02
1.11659102e-01 2.70021498e-01 3.43678564e-01 -6.56087518e-01
1.04016095e-01 -1.05916572e+00 -9.10038233e-01 -1.31581664e-01
3.64896029e-01 4.49305862e-01 3.87193501e-01 -3.58678252... | [10.61657428741455, 1.8302829265594482] |
3b236bb4-7edf-4c48-ad00-872b08aef973 | td-gen-graph-generation-with-tree | 2106.10656 | null | https://arxiv.org/abs/2106.10656v2 | https://arxiv.org/pdf/2106.10656v2.pdf | TD-GEN: Graph Generation With Tree Decomposition | We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation. The framework includes a permutation invariant tree generation model which forms the backbone of graph generation. Tree nodes are supernodes, e... | ['Greg Mori', 'Amir H. Abdi', 'Hossein Hajimirsadeghi', 'Hamed Shirzad'] | 2021-06-20 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 6.65415943e-01 8.32939744e-01 -2.62371391e-01 1.55116301e-02
-3.41930479e-01 -8.28742862e-01 6.72065318e-01 2.64328748e-01
1.30197734e-01 8.11571777e-01 -1.18011422e-01 -4.70944613e-01
-3.62660468e-01 -1.37353206e+00 -4.52546537e-01 -6.19729161e-01
-5.78012884e-01 7.25443900e-01 3.75121891e-01 7.56101087... | [7.006231784820557, 5.401900768280029] |
1ca69239-9932-43e5-952e-5c219e305d32 | pstr-end-to-end-one-step-person-search-with | 2204.0334 | null | https://arxiv.org/abs/2204.03340v1 | https://arxiv.org/pdf/2204.03340v1.pdf | PSTR: End-to-End One-Step Person Search With Transformers | We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along with a discriminative r... | ['Fahad Shahbaz Khan', 'Mubarak Shah', 'Jin Xie', 'Hisham Cholakkal', 'Rao Muhammad Anwer', 'Yanwei Pang', 'Jiale Cao'] | 2022-04-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cao_PSTR_End-to-End_One-Step_Person_Search_With_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cao_PSTR_End-to-End_One-Step_Person_Search_With_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['person-search'] | ['computer-vision'] | [ 8.82904008e-02 -1.55703232e-01 -7.97902793e-02 -5.53283930e-01
-1.25719774e+00 -2.90195674e-01 6.71517134e-01 -1.52076229e-01
-6.64593101e-01 3.18111837e-01 4.46555346e-01 3.02748054e-01
7.24657401e-02 -4.31702495e-01 -6.19908631e-01 -2.88322508e-01
1.96502551e-01 9.17130888e-01 2.66322464e-01 -1.10558860... | [14.83632755279541, 0.818330705165863] |
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