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dc79a49e-5a3e-4fcc-ad29-9775fd5f9f69 | bts-a-bi-lingual-benchmark-for-text | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_BTS_A_Bi-Lingual_Benchmark_for_Text_Segmentation_in_the_Wild_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_BTS_A_Bi-Lingual_Benchmark_for_Text_Segmentation_in_the_Wild_CVPR_2022_paper.pdf | BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild | As a prerequisite of many text-related tasks such as text erasing and text style transfer, text segmentation arouses more and more attention recently. Current researches mainly focus on only English characters and digits, while few work studies Chinese characters due to the lack of public large-scale and high-quali... | ['XiaoHu Qie', 'Ying Shan', 'Honglun Zhang', 'jianqi ma', 'Zhongang Qi', 'Xixi Xu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.96768242e-01 -5.94303787e-01 -7.24635422e-02 -3.30067635e-01
-4.31227475e-01 -4.18866038e-01 5.04414022e-01 -2.61307657e-01
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7.86105216e-01 4.45979387e-01 6.63511455e-01 -5.63260838... | [12.021485328674316, 2.2409257888793945] |
e80802ab-2059-41af-88ae-e7b8a6f5855a | connecting-the-dots-multivariate-time-series | 2005.11650 | null | https://arxiv.org/abs/2005.11650v1 | https://arxiv.org/pdf/2005.11650v1.pdf | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks | Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that ... | ['Chengqi Zhang', 'Zonghan Wu', 'Xiaojun Chang', 'Jing Jiang', 'Shirui Pan', 'Guodong Long'] | 2020-05-24 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [-1.80095702e-01 -2.57929653e-01 -2.91790336e-01 -3.66524041e-01
1.75861552e-01 -3.81132096e-01 5.70194662e-01 3.25420231e-01
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-3.80110055e-01 -1.02624953e+00 -5.52233934e-01 -5.25032222e-01
-8.36434424e-01 3.26399803e-01 2.71544039e-01 -3.53575230... | [6.7893476486206055, 2.7674167156219482] |
31a39b10-6f1d-4205-af5b-e04cb34ea353 | sleep-staging-based-on-serialized-dual | 2107.08442 | null | https://arxiv.org/abs/2107.08442v2 | https://arxiv.org/pdf/2107.08442v2.pdf | Sleep Staging Based on Multi Scale Dual Attention Network | Sleep staging plays an important role on the diagnosis of sleep disorders. In general, experts classify sleep stages manually based on polysomnography (PSG), which is quite time-consuming. Meanwhile, the acquisition process of multiple signals is much complex, which can affect the subject's sleep. Therefore, the use of... | ['Wanquan Liu', 'Pingshu Zhang', 'Xiaodong Yuan', 'Zhimin Hu', 'Qi Zhang', 'Chonggang Lu', 'Huafeng Wang'] | 2021-07-18 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-2.32832894e-01 -4.53492522e-01 2.18976274e-01 -3.36122543e-01
-1.80055514e-01 -1.12453863e-01 -1.49420857e-01 -1.19960211e-01
-6.14900410e-01 7.97363579e-01 -3.32240053e-02 -4.71196473e-02
-1.99354202e-01 -3.21135163e-01 1.13112792e-01 -7.67695606e-01
-5.57733513e-02 -2.31789686e-02 2.62520373e-01 -1.43226564... | [13.479604721069336, 3.4920172691345215] |
146d0f84-07b6-4158-9a80-be31577b8b90 | hsurf-net-normal-estimation-for-3d-point | 2210.07158 | null | https://arxiv.org/abs/2210.07158v1 | https://arxiv.org/pdf/2210.07158v1.pdf | HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces | We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on learning point weights to fit neighborhoods into a geometric surface approximated by a polynomial function with a predefined order, based on w... | ['Zhizhong Han', 'Yi Fang', 'Cheng Wang', 'Jin-San Cheng', 'Yu-Shen Liu', 'Qing Li'] | 2022-10-13 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [ 1.28974721e-01 -1.97303697e-01 8.68720636e-02 -6.08420014e-01
-7.30438292e-01 -7.98276588e-02 2.76833922e-01 -3.81970517e-02
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9.79079306e-02 7.07707047e-01 4.26325440e-01 2.44859196... | [8.218914985656738, -3.5113980770111084] |
16ad2cb8-6f76-4805-a736-d9596d934895 | deep-learning-techniques-for-blind-image | 2306.09426 | null | https://arxiv.org/abs/2306.09426v1 | https://arxiv.org/pdf/2306.09426v1.pdf | Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation | Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL) techniques, they do not usually design evaluations with high scaling factors, capping it at 2x or 4x. Moreover, the datasets are generally benchmarks which do not truly encompass ... | ['Valdivino Alexandre de Santiago Júnior'] | 2023-06-15 | null | null | null | null | ['image-super-resolution', 'image-quality-assessment', 'super-resolution', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.26193950e-01 -4.01935935e-01 -5.65402433e-02 -4.47025523e-02
-1.00496686e+00 -4.47706819e-01 6.45538390e-01 -5.62771618e-01
-2.04001278e-01 7.65374064e-01 5.22205412e-01 -1.88245669e-01
-3.94401252e-01 -5.22035897e-01 -4.92489636e-01 -7.99414098e-01
-8.32855999e-02 -2.77063716e-02 6.64496645e-02 -6.06084049... | [11.192216873168945, -1.9543488025665283] |
7e76e706-91a6-4511-aa8e-e10ae8e19a06 | wsnet-learning-compact-and-efficient-networks | null | null | https://openreview.net/forum?id=H1I3M7Z0b | https://openreview.net/pdf?id=H1I3M7Z0b | WSNet: Learning Compact and Efficient Networks with Weight Sampling | We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via \emph{ad hoc} processing such as model pruning or filter factorization. Alternatively, WSNet... | ['Yingzhen Yang', 'Xiaojie Jin', 'Jianchao Yang', 'Ning Xu', 'Jiashi Feng', 'Shuicheng Yan'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['paper-generation'] | ['natural-language-processing'] | [ 4.49484348e-01 5.99876642e-02 -1.28044754e-01 -5.74474216e-01
-5.78800678e-01 -1.78221554e-01 1.02855735e-01 3.01930495e-02
-1.03003836e+00 5.30643165e-01 -1.22508802e-01 -1.75996497e-01
-4.39088315e-01 -6.39692664e-01 -1.03513515e+00 -6.25531077e-01
-2.23331824e-01 3.45369160e-01 -5.59424385e-02 1.24446772... | [8.599371910095215, 3.2152457237243652] |
0b740e6f-710d-4726-ae8f-90c53352d8a0 | a-hybrid-deep-learning-model-for-arabic-text | 2009.01987 | null | https://arxiv.org/abs/2009.01987v1 | https://arxiv.org/pdf/2009.01987v1.pdf | A Hybrid Deep Learning Model for Arabic Text Recognition | Arabic text recognition is a challenging task because of the cursive nature of Arabic writing system, its joint writing scheme, the large number of ligatures and many other challenges. Deep Learning DL models achieved significant progress in numerous domains including computer vision and sequence modelling. This paper ... | ['Bassam Hammo', 'Mohammad Fasha', 'Jabir Widian', 'Nadim Obeid'] | 2020-09-04 | null | null | null | null | ['irregular-text-recognition'] | ['computer-vision'] | [ 6.01388663e-02 -5.70205629e-01 4.54363614e-01 -2.88201869e-01
-2.43029803e-01 -1.14499557e+00 8.15747917e-01 -3.01032513e-01
-3.60597998e-01 6.26318276e-01 -7.70428553e-02 -2.01129630e-01
2.62483299e-01 -6.33593559e-01 -3.19956869e-01 -7.25705206e-01
1.96653441e-01 9.14278805e-01 2.35749885e-01 -5.12383461... | [11.852977752685547, 2.5889556407928467] |
e04e4120-af24-4dbc-8d50-418db24d3019 | ev-mgrflownet-motion-guided-recurrent-network | 2305.07853 | null | https://arxiv.org/abs/2305.07853v1 | https://arxiv.org/pdf/2305.07853v1.pdf | EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss | Event cameras offer promising properties, such as high temporal resolution and high dynamic range. These benefits have been utilized into many machine vision tasks, especially optical flow estimation. Currently, most existing event-based works use deep learning to estimate optical flow. However, their networks have not... | ['Zheng Fang', 'Chenming Hu', 'Delei Kong', 'Kuanxu Hou', 'XinJie Huang', 'Hao Zhuang'] | 2023-05-13 | null | null | null | null | ['event-based-optical-flow', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [-6.54754639e-02 -3.76565754e-01 -3.23513746e-01 -1.95788950e-01
-2.09191963e-01 -9.60293859e-02 4.90595430e-01 -3.67533505e-01
-4.37377572e-01 6.97574139e-01 5.44241965e-01 -6.36205077e-02
9.26957279e-02 -6.61856830e-01 -6.88921511e-01 -5.02124846e-01
1.15819194e-03 -2.97897398e-01 6.13728940e-01 1.42424792... | [8.883075714111328, -1.5517407655715942] |
ebf60239-c6b0-43fa-8cd2-34a59062add7 | improving-event-temporal-relation | null | null | https://aclanthology.org/2022.ccl-1.76 | https://aclanthology.org/2022.ccl-1.76.pdf | Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning | “Event Temporal Relation Classification (ETRC) is crucial to natural language understanding. In recent years, the mainstream ETRC methods may not take advantage of lots of semantic information contained in golden temporal relation labels, which is lost by the discrete one-hot labels. To alleviate the loss of semantic i... | ['Li Lishuang', 'Sun Tiesen'] | null | null | null | null | ccl-2022-10 | ['temporal-relation-classification', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.07506621e-01 1.64422631e-01 -4.10570055e-01 -6.17008030e-01
-9.30474102e-01 -1.35038584e-01 6.73213482e-01 -3.90274711e-02
-6.92955434e-01 6.50241017e-01 4.24284667e-01 -5.60800396e-02
-1.63706720e-01 -6.69712782e-01 -6.78758323e-01 -5.66364110e-01
1.36649702e-03 6.69739604e-01 2.10671529e-01 -2.49869168... | [9.556543350219727, 3.2574658393859863] |
cb544604-0a15-4f46-b1cd-5a6d2ad3b21c | an-efficient-deep-learning-approach-using | 2109.02629 | null | https://arxiv.org/abs/2109.02629v1 | https://arxiv.org/pdf/2109.02629v1.pdf | An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving | Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). In autonomous driving, the appearance of incomplete point clouds losing geometric and semantic information is inevitable owing to limitations of occlusion, sensor resolution, and viewing angle when the Light Detec... | ['Francesco Piccialli', 'Gang Mei', 'Jingzhi Tu'] | 2021-09-01 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-5.49162775e-02 -1.15937874e-01 2.50839442e-01 -1.93597317e-01
-3.67770493e-01 -3.32707852e-01 4.04632717e-01 -2.87823230e-01
-9.02285054e-02 6.62287772e-01 -6.06290102e-01 -5.48161685e-01
-7.27043301e-02 -1.11813664e+00 -1.09963000e+00 -8.00776124e-01
2.93440670e-01 6.00606024e-01 3.62106949e-01 -2.02284366... | [8.15957260131836, -2.763535976409912] |
eb30ea09-28f4-41b4-bb9d-ab0e19327cbc | shuffle-qudio-accelerate-distributed-vqe-with | 2209.12454 | null | https://arxiv.org/abs/2209.12454v1 | https://arxiv.org/pdf/2209.12454v1.pdf | Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction | The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems outperforming classical approaches. To gain such computational advantages on large-scale problems, a feasible solution is the QUantum DIstributed Optimization (QUDIO... | ['DaCheng Tao', 'Yuxuan Du', 'Yang Qian'] | 2022-09-26 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 3.09441648e-02 -2.62542427e-01 1.56132206e-01 4.82410081e-02
-1.00621235e+00 -5.74216127e-01 8.46004263e-02 3.08562636e-01
-6.58138335e-01 9.16778445e-01 -4.72516268e-01 -6.24950171e-01
-2.57010698e-01 -1.23211253e+00 -8.14737022e-01 -1.24132502e+00
-1.29971221e-01 3.83459836e-01 6.44011945e-02 -4.64652568... | [5.583877086639404, 4.932258605957031] |
468f3dcf-f1b3-4c18-8baa-9787a5d942b2 | bringing-blurry-alive-at-high-frame-rate-with | 1903.06531 | null | https://arxiv.org/abs/1903.06531v3 | https://arxiv.org/pdf/1903.06531v3.pdf | High Frame Rate Video Reconstruction based on an Event Camera | Event-based cameras measure intensity changes (called `events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the `active pixel sensor' (APS), the `Dynamic and Active-pixel Vision Sensor' (DAVIS) allows the simultaneous output of intensity frames and events. However, the ou... | ['Miaomiao Liu', 'Xin Yu', 'Cedric Scheerlinck', 'Richard Hartley', 'Liyuan Pan', 'Yuchao Dai'] | 2019-03-12 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 4.81470168e-01 -5.34311295e-01 3.50874275e-01 -2.18623459e-01
-6.51402295e-01 -2.90283233e-01 2.87458062e-01 -2.68692583e-01
-5.73679030e-01 7.17443049e-01 1.86050721e-02 3.53428930e-01
-1.34424776e-01 -5.22884190e-01 -9.25694168e-01 -9.64528799e-01
9.41172838e-02 -2.40174368e-01 4.97196794e-01 3.40754777... | [10.939847946166992, -2.1599936485290527] |
8d5d6dba-2f25-447d-bcf4-2e5c99b56863 | proximity-forest-2-0-a-new-effective-and | 2304.05800 | null | https://arxiv.org/abs/2304.05800v2 | https://arxiv.org/pdf/2304.05800v2.pdf | Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series | Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed, includi... | ['Geoffrey I. Webb', 'Mahsa Salehi', 'Chang Wei Tan', 'Matthieu Herrmann'] | 2023-04-12 | null | null | null | null | ['time-series-classification', 'dynamic-time-warping'] | ['time-series', 'time-series'] | [-3.93172838e-02 -6.59301579e-01 -1.62001491e-01 -1.33914977e-01
-4.82788891e-01 -6.07846439e-01 7.74341226e-01 4.35882360e-01
-3.56292337e-01 4.90688622e-01 3.74075510e-02 -3.74519110e-01
-7.88577497e-01 -7.63536572e-01 -6.43542707e-02 -6.39834881e-01
-8.64068329e-01 4.07551616e-01 7.16670752e-01 -6.19656324... | [7.251235008239746, 3.2935171127319336] |
d4e8751e-64a0-426d-b4a3-49893fc34006 | how-to-design-a-three-stage-architecture-for | 2106.03932 | null | https://arxiv.org/abs/2106.03932v2 | https://arxiv.org/pdf/2106.03932v2.pdf | How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild | Successful active speaker detection requires a three-stage pipeline: (i) audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation modeling between a reference speaker and the background speakers within each frame, and (iii) temporal modeling for the reference speaker. Each stage of this pipeline ... | ['Gerhard Rigoll', 'Maja Taseska', 'Okan Köpüklü'] | 2021-06-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kopuklu_How_To_Design_a_Three-Stage_Architecture_for_Audio-Visual_Active_Speaker_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kopuklu_How_To_Design_a_Three-Stage_Architecture_for_Audio-Visual_Active_Speaker_ICCV_2021_paper.pdf | iccv-2021-1 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 2.34691337e-01 1.55983418e-01 -2.94423811e-02 -4.16693598e-01
-1.48829985e+00 -3.95214736e-01 7.04227567e-01 6.66781068e-02
-2.69040406e-01 5.34263346e-03 3.88418436e-01 -4.34897952e-02
2.76420176e-01 -9.71511900e-02 -5.08373141e-01 -7.16561019e-01
-3.58366221e-01 2.57495791e-01 6.33521318e-01 1.50957301... | [14.421168327331543, 5.19271993637085] |
6ab562d2-42c0-46e9-bacd-a74efbcbd0a0 | template-based-approach-to-zero-shot-intent | 2206.10914 | null | https://arxiv.org/abs/2206.10914v1 | https://arxiv.org/pdf/2206.10914v1.pdf | Template-based Approach to Zero-shot Intent Recognition | The recent advances in transfer learning techniques and pre-training of large contextualized encoders foster innovation in real-life applications, including dialog assistants. Practical needs of intent recognition require effective data usage and the ability to constantly update supported intents, adopting new ones, an... | ['Irina Piontkovskaya', 'Andrey Bout', 'Valentin Malykh', 'Ekaterina Artemova', 'Pavel Burnyshev', 'Dmitry Lamanov'] | 2022-06-22 | null | null | null | null | ['intent-recognition', 'sentence-pair-modeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.02055532e-01 3.49523902e-01 -8.37332234e-02 -8.06045175e-01
-6.51447296e-01 -3.51081401e-01 7.80031919e-01 2.76793063e-01
-8.74972939e-01 8.06666017e-01 4.22046751e-01 -4.03154731e-01
2.32302189e-01 -6.12359762e-01 -2.84173012e-01 -2.43345633e-01
9.81645510e-02 7.30742574e-01 1.11329854e-01 -6.71959519... | [12.34579849243164, 7.620105266571045] |
3d6f6ab7-173e-43b1-868b-0e993f309f44 | a-survey-on-geocoding-algorithms-and-datasets | null | null | https://openreview.net/forum?id=-koTfmSDsM | https://openreview.net/pdf?id=-koTfmSDsM | A Survey on Geocoding: Algorithms and Datasets for Toponym Resolution | Geocoding, the task of converting unstructured text to structured spatial data, has recently seen progress thanks to a variety of new datasets, evaluation metrics, and machine-learning algorithms. We provide a survey to review, organize and analyze recent work on geocoding (also known as toponym resolution) where the t... | ['Anonymous'] | 2021-07-17 | null | https://openreview.net/forum?id=p_ibGwUc8_C | https://openreview.net/pdf?id=p_ibGwUc8_C | acl-arr-may-2021-5 | ['toponym-resolution'] | ['natural-language-processing'] | [ 1.29932240e-01 3.39262813e-01 -4.34410632e-01 -6.15407407e-01
-1.00856400e+00 -5.82313716e-01 1.09913945e+00 5.51434934e-01
-4.15300906e-01 9.25732970e-01 1.13690960e+00 -2.95151830e-01
-4.11842585e-01 -1.30344605e+00 -1.67339876e-01 -2.27339014e-01
-4.28641319e-01 4.32125390e-01 1.44040033e-01 -1.74429923... | [9.362208366394043, 9.147689819335938] |
12f45325-b9af-4b2c-86ad-6f61877e4c32 | working-hard-to-know-your-neighbors-margins | 1705.10872 | null | http://arxiv.org/abs/1705.10872v4 | http://arxiv.org/pdf/1705.10872v4.pdf | Working hard to know your neighbor's margins: Local descriptor learning loss | We introduce a novel loss for learning local feature descriptors which is
inspired by the Lowe's matching criterion for SIFT. We show that the proposed
loss that maximizes the distance between the closest positive and closest
negative patch in the batch is better than complex regularization methods; it
works well for b... | ['Jiri Matas', 'Anastasiya Mishchuk', 'Dmytro Mishkin', 'Filip Radenovic'] | 2017-05-30 | working-hard-to-know-your-neighbors-margins-1 | http://papers.nips.cc/paper/7068-working-hard-to-know-your-neighbors-margins-local-descriptor-learning-loss | http://papers.nips.cc/paper/7068-working-hard-to-know-your-neighbors-margins-local-descriptor-learning-loss.pdf | neurips-2017-12 | ['patch-matching'] | ['computer-vision'] | [-2.00225145e-01 -4.39168304e-01 -5.10008335e-01 -6.16742551e-01
-9.59136009e-01 -3.62677932e-01 5.54079413e-01 3.00609350e-01
-7.91307926e-01 1.69591695e-01 1.47307232e-01 1.84269510e-02
-3.22992086e-01 -7.46073544e-01 -9.15415227e-01 -5.90645611e-01
-2.69279867e-01 5.79157583e-02 3.97894561e-01 -1.14214949... | [8.26047134399414, -1.898010015487671] |
c6a18bbe-b035-4aae-95c0-167b82753afa | attention-based-context-aware-reasoning-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Cooray_Attention-Based_Context_Aware_Reasoning_for_Situation_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cooray_Attention-Based_Context_Aware_Reasoning_for_Situation_Recognition_CVPR_2020_paper.pdf | Attention-Based Context Aware Reasoning for Situation Recognition | Situation Recognition (SR) is a fine-grained action recognition task where the model is expected to not only predict the salient action of the image, but also predict values of all associated semantic roles of the action. Predicting semantic roles is very challenging: a vast variety of possibilities can be the match fo... | [' Wei Lu', ' Ngai-Man Cheung', 'Thilini Cooray'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['grounded-situation-recognition', 'situation-recognition', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.90903735e-01 9.86075178e-02 -3.23686719e-01 -4.94335145e-01
-7.07820892e-01 -5.03949285e-01 7.87730575e-01 7.04311728e-02
-1.24548256e-01 4.23182368e-01 6.26030803e-01 -3.16222459e-01
-6.50292039e-02 -6.45192146e-01 -6.69462204e-01 -2.36769497e-01
3.88158560e-01 2.90766656e-01 7.87637115e-01 -3.89594376... | [10.32751178741455, 1.2205402851104736] |
11335599-abe4-41b0-a0cf-629c015b0e8e | topologically-aware-deformation-fields-for | 2205.06267 | null | https://arxiv.org/abs/2205.06267v2 | https://arxiv.org/pdf/2205.06267v2.pdf | Topologically-Aware Deformation Fields for Single-View 3D Reconstruction | We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned im... | ['Deepak Pathak', 'Shivam Duggal'] | 2022-05-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Duggal_Topologically-Aware_Deformation_Fields_for_Single-View_3D_Reconstruction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Duggal_Topologically-Aware_Deformation_Fields_for_Single-View_3D_Reconstruction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-9.89300907e-02 -1.30275026e-01 6.30066022e-02 -6.13207996e-01
-7.86987841e-01 -9.74924207e-01 6.98025525e-01 -2.07687721e-01
-4.59483825e-02 8.25379938e-02 -3.69094796e-02 -6.21055141e-02
-5.14084613e-03 -8.13595414e-01 -1.23078096e+00 -3.95737052e-01
7.98677951e-02 1.19948506e+00 2.67015040e-01 4.70887944... | [8.349109649658203, -3.2173285484313965] |
3499325d-35d0-4f6e-964d-c17ac89c3305 | learning-first-order-rules-with | 2204.13570 | null | https://arxiv.org/abs/2204.13570v1 | https://arxiv.org/pdf/2204.13570v1.pdf | Learning First-Order Rules with Differentiable Logic Program Semantics | Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a novel differentiable inductive logic programming (ILP) model, called differentiable first-order rule learner (DFOL), which finds the correct LPs... | ['Hanpin Wang', 'Yongzhi Cao', 'Katsumi Inoue', 'Kun Gao'] | 2022-04-28 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 1.43322244e-01 7.26059377e-01 -5.73011398e-01 -8.11784625e-01
-3.91653478e-01 -6.50581241e-01 5.32739401e-01 -6.00514002e-02
5.32832593e-02 7.52159774e-01 2.52968848e-01 -6.63606286e-01
-5.84877014e-01 -1.00113750e+00 -1.51173723e+00 -3.01725060e-01
-4.46886152e-01 9.74882722e-01 -1.34760216e-01 -3.71805996... | [9.15035343170166, 7.556769371032715] |
014f797d-91e1-4d25-bc5d-47aad3aea580 | infogan-interpretable-representation-learning | 1606.03657 | null | http://arxiv.org/abs/1606.03657v1 | http://arxiv.org/pdf/1606.03657v1.pdf | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets | This paper describes InfoGAN, an information-theoretic extension to the
Generative Adversarial Network that is able to learn disentangled
representations in a completely unsupervised manner. InfoGAN is a generative
adversarial network that also maximizes the mutual information between a small
subset of the latent varia... | ['Pieter Abbeel', 'John Schulman', 'Ilya Sutskever', 'Yan Duan', 'Rein Houthooft', 'Xi Chen'] | 2016-06-12 | infogan-interpretable-representation-learning-1 | http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets | http://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf | neurips-2016-12 | ['unsupervised-image-classification', 'unsupervised-mnist'] | ['computer-vision', 'methodology'] | [ 2.79914290e-01 5.98382354e-01 -7.54866526e-02 -5.38090527e-01
-2.75171220e-01 -9.12224829e-01 8.73509169e-01 -8.25081050e-01
2.02252910e-01 5.84924698e-01 3.68813634e-01 -2.40036231e-02
-7.90698677e-02 -7.62595117e-01 -7.95743525e-01 -9.03179884e-01
6.11868873e-03 8.11433613e-01 -7.92503417e-01 -1.00523531... | [11.633016586303711, -0.03266732394695282] |
87539c1e-3006-4083-9be0-74ea63e75104 | voicefixer-a-unified-framework-for-high | 2204.05841 | null | https://arxiv.org/abs/2204.05841v2 | https://arxiv.org/pdf/2204.05841v2.pdf | VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration | Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on a single type of distortion, such as speech denoising or dereverberation. However, speech signals can be degraded by several different distortions simultaneously in the real world. It is thus important to extend speech restor... | ['Yuxuan Wang', 'Chuanzeng Huang', 'DeLiang Wang', 'Yan Zhao', 'Qiao Tian', 'Qiuqiang Kong', 'Xubo Liu', 'Haohe Liu'] | 2022-04-12 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 9.93374363e-02 -1.75351128e-01 4.10922080e-01 -1.49679676e-01
-9.40207958e-01 -4.22633320e-01 2.80096382e-01 -2.78937608e-01
1.08932316e-01 5.88035464e-01 8.26208830e-01 -5.82140684e-01
2.55905867e-01 -4.00092363e-01 -6.01231515e-01 -6.41019344e-01
1.30514219e-01 -3.95035774e-01 -1.74907614e-02 -4.35133636... | [14.935456275939941, 6.042664527893066] |
8d4ce18e-01e7-45ea-9b4f-91d6b0f81292 | what-does-a-car-ssette-tape-tell | 1905.13448 | null | https://arxiv.org/abs/1905.13448v2 | https://arxiv.org/pdf/1905.13448v2.pdf | Audio Caption in a Car Setting with a Sentence-Level Loss | Captioning has attracted much attention in image and video understanding while a small amount of work examines audio captioning. This paper contributes a Mandarin-annotated dataset for audio captioning within a car scene. A sentence-level loss is proposed to be used in tandem with a GRU encoder-decoder model to generat... | ['Mengyue Wu', 'Xuenan Xu', 'Kai Yu', 'Heinrich Dinkel'] | 2019-05-31 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 7.91555882e-01 6.27083898e-01 -5.65944724e-02 -4.81099308e-01
-1.53454876e+00 -3.27116907e-01 7.99807489e-01 1.20765734e-02
-2.46550635e-01 9.28053558e-01 7.04259872e-01 8.29980597e-02
4.61593539e-01 -3.05248231e-01 -1.02804470e+00 -3.06309789e-01
9.52585191e-02 4.21850383e-01 8.75906944e-02 -1.06972933... | [15.255708694458008, 4.8449015617370605] |
ff77a3fd-046b-4c46-b0c7-b2452aaf1e17 | contrastive-feature-learning-for-fault | 2208.13288 | null | https://arxiv.org/abs/2208.13288v1 | https://arxiv.org/pdf/2208.13288v1.pdf | Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications | A railway is a complex system comprising multiple infrastructure and rolling stock assets. To operate the system safely, reliably, and efficiently, the condition many components needs to be monitored. To automate this process, data-driven fault detection and diagnostics models can be employed. In practice, however, the... | ['Olga Fink', 'Stefan Koller', 'Wilfried Bürzle', 'Lucian-Stefan Ancu', 'Kajan Ratnasabapathy', 'Gabriel Michau', 'Katharina Rombach'] | 2022-08-28 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 4.28833216e-01 -1.50446996e-01 2.94241875e-01 -3.56603742e-01
-4.78483945e-01 1.04540564e-01 3.07729691e-01 2.37204120e-01
-1.51318416e-01 5.47506034e-01 -3.52013886e-01 3.32157835e-02
-6.99045062e-01 -9.41095233e-01 -6.75939143e-01 -1.02332127e+00
-1.82646945e-01 4.76661146e-01 4.47986573e-01 -5.50680935... | [6.797791004180908, 2.371485471725464] |
ad931477-ae6b-43af-9e1c-bb0964eb5b43 | quota-the-quantile-option-architecture-for | 1811.02073 | null | http://arxiv.org/abs/1811.02073v2 | http://arxiv.org/pdf/1811.02073v2.pdf | QUOTA: The Quantile Option Architecture for Reinforcement Learning | In this paper, we propose the Quantile Option Architecture (QUOTA) for
exploration based on recent advances in distributional reinforcement learning
(RL). In QUOTA, decision making is based on quantiles of a value distribution,
not only the mean. QUOTA provides a new dimension for exploration via making
use of both opt... | ['Linglong Kong', 'Shangtong Zhang', 'Borislav Mavrin', 'Hengshuai Yao', 'Bo Liu'] | 2018-11-05 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.05582178e-01 1.91725776e-01 -6.25658274e-01 -2.32222110e-01
-5.25670588e-01 -4.19267297e-01 3.98672938e-01 8.77812505e-02
-6.24429762e-01 1.30467713e+00 2.57899940e-01 -4.53196853e-01
-3.71681243e-01 -1.03858221e+00 -5.46075284e-01 -5.75323224e-01
-4.63599533e-01 4.13380235e-01 -1.18879460e-01 -6.28800035... | [4.157121658325195, 2.4862983226776123] |
e6da164b-b5ce-4b93-98dc-678aeef4db4a | adversarial-unsupervised-domain-adaptation | 2101.00701 | null | https://arxiv.org/abs/2101.00701v1 | https://arxiv.org/pdf/2101.00701v1.pdf | Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation | This paper addresses the problem of domain adaptation for the task of music source separation. Using datasets from two different domains, we compare the performance of a deep learning-based harmonic-percussive source separation model under different training scenarios, including supervised joint training using data fro... | ['Patrik Ohlsson', 'Sven Ahlbäck', 'Simon Dixon', 'Emmanouil Benetos', 'Carlos Lordelo'] | 2021-01-03 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 4.55566227e-01 -4.22967486e-02 -8.05456191e-03 -9.76665616e-02
-1.46727693e+00 -9.86641169e-01 4.09660131e-01 -2.82423615e-01
-2.89093316e-01 8.25584710e-01 3.54259640e-01 1.61187395e-01
-4.74944651e-01 -2.36867771e-01 -5.67672014e-01 -9.25626516e-01
-1.71053000e-02 7.57498622e-01 1.73002258e-01 -2.74562359... | [15.549175262451172, 5.474595069885254] |
982bd7b1-3ccd-4ba0-a170-6d9d367b65d0 | consistency-and-accuracy-of-celeba-attribute | 2210.07356 | null | https://arxiv.org/abs/2210.07356v2 | https://arxiv.org/pdf/2210.07356v2.pdf | Consistency and Accuracy of CelebA Attribute Values | We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentiall... | ['Kevin W. Bowyer', 'Michael C. King', 'Terrance Boult', 'Manuel Günther', 'Grace Bezold', 'Haiyu Wu'] | 2022-10-13 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [-1.73258021e-01 5.20124793e-01 -1.84183165e-01 -8.05955827e-01
-5.79265654e-01 -5.23842037e-01 3.39214176e-01 2.65186816e-01
-2.41899252e-01 6.06520832e-01 -6.20888658e-02 -6.20192029e-02
-4.45409082e-02 -4.54447538e-01 -4.69954252e-01 -5.38216293e-01
-1.04221746e-01 6.81448936e-01 4.62268181e-02 6.77615218... | [13.048161506652832, 1.193516492843628] |
4ddb04f2-77f5-4fe3-b182-25b8fade67a6 | df-vo-what-should-be-learnt-for-visual | 2103.00933 | null | https://arxiv.org/abs/2103.00933v1 | https://arxiv.org/pdf/2103.00933v1.pdf | DF-VO: What Should Be Learnt for Visual Odometry? | Multi-view geometry-based methods dominate the last few decades in monocular Visual Odometry for their superior performance, while they have been vulnerable to dynamic and low-texture scenes. More importantly, monocular methods suffer from scale-drift issue, i.e., errors accumulate over time. Recent studies show that d... | ['Ian Reid', 'Ravi Garg', 'Jia-Wang Bian', 'Chamara Saroj Weerasekera', 'Huangying Zhan'] | 2021-03-01 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-3.18717062e-01 -4.25721884e-01 -2.09229261e-01 -3.26161444e-01
-5.87122381e-01 -6.70224428e-01 4.57918763e-01 -7.32867837e-01
-1.83270127e-01 5.48353016e-01 1.79785043e-01 3.48362140e-02
1.82844162e-01 -6.13238931e-01 -1.02108979e+00 -6.37131691e-01
2.87654698e-01 4.28208500e-01 1.07589580e-01 -1.22643605... | [8.228723526000977, -2.21358323097229] |
b85c9661-c7cc-4ec2-b27f-d674d38a737d | improving-automatic-quotation-attribution-in | 2307.03734 | null | https://arxiv.org/abs/2307.03734v1 | https://arxiv.org/pdf/2307.03734v1.pdf | Improving Automatic Quotation Attribution in Literary Novels | Current models for quotation attribution in literary novels assume varying levels of available information in their training and test data, which poses a challenge for in-the-wild inference. Here, we approach quotation attribution as a set of four interconnected sub-tasks: character identification, coreference resoluti... | ['Adam Hammond', 'Graeme Hirst', 'Frank Rudzicz', 'Krishnapriya Vishnubhotla'] | 2023-07-07 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 2.78307438e-01 4.40857649e-01 -3.22877914e-01 -3.58556360e-01
-1.22507918e+00 -9.12524819e-01 1.18413794e+00 2.50766426e-01
-4.99095380e-01 7.62138724e-01 8.65504205e-01 -5.38538024e-03
2.71345377e-01 -7.74749964e-02 -4.13989216e-01 -1.09465398e-01
6.03853047e-01 1.15566576e+00 1.15136802e-01 -6.00814819... | [11.092257499694824, 8.924667358398438] |
1ca659b6-d37a-4a5c-b9c5-b02a43213370 | a-large-scale-study-of-a-sleep-tracking-and | 2211.02592 | null | https://arxiv.org/abs/2211.02592v1 | https://arxiv.org/pdf/2211.02592v1.pdf | A Large-Scale Study of a Sleep Tracking and Improving Device with Closed-loop and Personalized Real-time Acoustic Stimulation | Various intervention therapies ranging from pharmaceutical to hi-tech tailored solutions have been available to treat difficulty in falling asleep commonly caused by insomnia in modern life. However, current techniques largely remain ill-suited, ineffective, and unreliable due to their lack of precise real-time sleep t... | ['Tam Vu', 'Sangtae Ha', 'Sy Duong-Quy', 'Hoang Huu Nguyen', 'Linh Nguyen', 'Nhat Pham', 'Hoang Truong', 'Nam Bui', 'Ban Xuan Dong', 'Galen Pogoncheff', 'Anh Nguyen'] | 2022-11-04 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-1.71088874e-01 -1.58867374e-01 1.74933583e-01 -3.16971719e-01
-8.08154702e-01 -3.56017947e-01 -4.67115134e-01 -6.72637671e-02
-5.98470688e-01 8.30585539e-01 4.88463759e-01 -2.11606935e-01
-1.04250811e-01 -2.29864806e-01 -1.62839025e-01 -5.15137076e-01
-1.33231580e-01 2.96381593e-01 1.14627808e-01 -1.04923718... | [13.541888236999512, 3.3997299671173096] |
7c71d14c-dd4c-458d-bafb-42eb4df6a7e8 | deepscaffold-a-comprehensive-tool-for | 1908.07209 | null | https://arxiv.org/abs/1908.07209v4 | https://arxiv.org/pdf/1908.07209v4.pdf | DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning | The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molec... | ['Zhenming Liu', 'Jianxing Hu', 'Yibo Li', 'Jielong Zhou', 'Yanxing Wang', 'Liangren Zhang'] | 2019-08-20 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.15899694e-01 4.29747067e-02 -6.49303913e-01 -1.22427158e-01
-3.21204633e-01 -7.91643977e-01 2.87351936e-01 3.94130200e-01
1.14078417e-01 1.50714314e+00 4.82266657e-02 -5.37017226e-01
-5.30921035e-02 -9.53214824e-01 -6.70716286e-01 -7.93852568e-01
-1.40020192e-01 3.87313128e-01 2.71395177e-01 -1.58494830... | [4.965819358825684, 5.726573467254639] |
df014c51-149c-40e8-84c4-9463c4a5b19a | reading-car-license-plates-using-deep | 1601.05610 | null | http://arxiv.org/abs/1601.05610v1 | http://arxiv.org/pdf/1601.05610v1.pdf | Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs | In this work, we tackle the problem of car license plate detection and
recognition in natural scene images. Inspired by the success of deep neural
networks (DNNs) in various vision applications, here we leverage DNNs to learn
high-level features in a cascade framework, which lead to improved performance
on both detecti... | ['Chunhua Shen', 'Hui Li'] | 2016-01-21 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [ 4.62866366e-01 -3.36387873e-01 2.58287955e-02 -2.06001267e-01
-8.14600766e-01 -5.68643630e-01 5.60534179e-01 -3.58671844e-01
-7.04888880e-01 3.77555102e-01 -3.51536065e-01 -1.79988816e-01
4.56398517e-01 -8.77549529e-01 -8.30238819e-01 -8.66165161e-01
5.40887296e-01 2.40144819e-01 6.22249246e-01 -4.60307114... | [9.844403266906738, -4.933106899261475] |
4fe69ba8-8d72-4d2a-8af8-379b7b56bc43 | a-robust-and-lightweight-deep-attention | 2206.00455 | null | https://arxiv.org/abs/2206.00455v1 | https://arxiv.org/pdf/2206.00455v1.pdf | A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations | Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainstream strategy for predicting genetic alterations using WSIs although bag-based models along with self-attention mechanism-based algorithms h... | ['Xu Steven Xu', 'Hong Zhang', 'Miaomiao Yang', 'Xingyu Li', 'Bangwei Guo'] | 2022-05-31 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 2.88073957e-01 2.47462526e-01 -4.35628086e-01 -6.59068227e-02
-1.31872571e+00 -1.44613340e-01 2.88668871e-01 6.17001235e-01
-1.40755326e-01 8.08410883e-01 8.89414027e-02 -5.13057053e-01
-3.00515056e-01 -6.25265598e-01 -7.99581349e-01 -1.21991360e+00
-1.44213885e-01 3.89808863e-01 1.46434858e-01 -1.46128535... | [15.207609176635742, -2.8497698307037354] |
c534b163-ab07-4af7-89e2-47c202f614d7 | how-to-train-bert-with-an-academic-budget | 2104.07705 | null | https://arxiv.org/abs/2104.07705v2 | https://arxiv.org/pdf/2104.07705v2.pdf | How to Train BERT with an Academic Budget | While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget? We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep l... | ['Omer Levy', 'Moshe Berchansky', 'Peter Izsak'] | 2021-04-15 | null | https://aclanthology.org/2021.emnlp-main.831 | https://aclanthology.org/2021.emnlp-main.831.pdf | emnlp-2021-11 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-3.22150409e-01 3.10676903e-01 -1.89702734e-01 -6.69296682e-01
-1.28348780e+00 -7.38245666e-01 2.89374858e-01 -1.05233416e-01
-6.06463730e-01 6.61517620e-01 6.14991300e-02 -7.93038368e-01
4.04918164e-01 -5.38401663e-01 -9.29484546e-01 -1.46229282e-01
-9.05882716e-02 9.63480532e-01 1.14491813e-01 -2.78816968... | [10.583257675170898, 8.187274932861328] |
04f9421f-3651-4e5e-905b-8026de0c0c5d | neural-machine-translation-with-contrastive | 2212.03140 | null | https://arxiv.org/abs/2212.03140v1 | https://arxiv.org/pdf/2212.03140v1.pdf | Neural Machine Translation with Contrastive Translation Memories | Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories~(TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holi... | ['Rui Yan', 'Dongyan Zhao', 'Lemao Liu', 'Shen Gao', 'Xin Cheng'] | 2022-12-06 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.71705943e-01 -1.76321015e-01 -2.68464327e-01 -2.35795796e-01
-1.31328452e+00 -1.76838428e-01 8.58671367e-01 5.49222566e-02
-3.65333974e-01 7.90791154e-01 4.67696816e-01 -3.24685961e-01
1.72840655e-01 -5.81049442e-01 -1.04492962e+00 -7.93706894e-01
3.54124039e-01 7.65291512e-01 -5.25072403e-02 -4.96191531... | [11.702323913574219, 10.017871856689453] |
c7547934-dcf6-4081-9d8b-788db5a59650 | pay-more-attention-to-history-a-context | 2112.08735 | null | https://arxiv.org/abs/2112.08735v2 | https://arxiv.org/pdf/2112.08735v2.pdf | Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL | Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL (Structured Query Language) representations. One of the most intractable problems of conversational text-to-SQL is modelling the semantics of multi-turn queries and gathering the proper information required fo... | ['Yan Zhang', 'Wei Wu', 'Sirui Wang', 'Yutian Li', 'Hanchu Zhang', 'Yuntao Li'] | 2021-12-16 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.83046162e-01 6.41342878e-01 -1.39976948e-01 -9.77741480e-01
-1.31784046e+00 -7.37334311e-01 7.22249687e-01 2.04486266e-01
-5.05654365e-02 5.31696379e-01 6.44166350e-01 -5.66638947e-01
2.68269330e-01 -1.15460002e+00 -7.38549888e-01 2.20444147e-02
3.15996230e-01 1.06810236e+00 4.85945404e-01 -6.37361944... | [10.036736488342285, 7.886224269866943] |
9c8a5085-040d-4c4f-b031-c625e8c85595 | mobiprox-supporting-dynamic-approximate | 2303.11291 | null | https://arxiv.org/abs/2303.11291v1 | https://arxiv.org/pdf/2303.11291v1.pdf | Mobiprox: Supporting Dynamic Approximate Computing on Mobiles | Runtime-tunable context-dependent network compression would make mobile deep learning adaptable to often varying resource availability, input "difficulty", or user needs. The existing compression techniques significantly reduce the memory, processing, and energy tax of deep learning, yet, the resulting models tend to b... | ['Veljko Pejović', 'Saša Misailović', 'Yifan Zhao', 'Hashim Sharif', 'Octavian Machidon', 'Matevž Fabjančič'] | 2023-03-16 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.42476459e-02 -2.19167352e-01 -6.54191077e-01 -4.52814460e-01
-5.54419339e-01 -4.14669275e-01 7.41246194e-02 4.95933287e-04
-6.79598093e-01 2.62349010e-01 -1.67014420e-01 -5.84844351e-01
-3.24469149e-01 -7.09643185e-01 -8.05755258e-01 -5.90331197e-01
1.16182953e-01 3.17099482e-01 1.87830091e-01 1.17390245... | [8.587942123413086, 3.0493111610412598] |
2ebfb4cf-04fc-4eda-b2e4-bd1f601ff07b | inception-residual-block-based-neural-network | 1810.13169 | null | http://arxiv.org/abs/1810.13169v2 | http://arxiv.org/pdf/1810.13169v2.pdf | Inception-Residual Block based Neural Network for Thermal Image Denoising | Thermal cameras show noisy images due to their limited thermal resolution,
especially for the scenes of a low temperature difference. In order to deal
with a noise problem, this paper proposes a novel neural network architecture
with repeatable denoising inception-residual blocks(DnIRB) for noise learning.
Each DnIRB h... | ['Jin-Young Kim', 'Seungyou Na', 'Nazeer Shahid', 'Huy Toan Nguyen', 'Seongmin Hwang', 'Doseong Sin', 'Gwanghyun Yu'] | 2018-10-31 | null | null | null | null | ['thermal-image-denoising'] | ['computer-vision'] | [ 3.22451204e-01 -6.39370203e-01 5.92063904e-01 -3.56302381e-01
-3.55099946e-01 2.03409512e-03 2.39323199e-01 -6.02848530e-01
-7.34822571e-01 5.91914117e-01 5.68199120e-02 1.68696959e-02
6.03432907e-03 -4.89746958e-01 -4.81445372e-01 -1.20025432e+00
2.43632779e-01 -6.64817214e-01 3.74364972e-01 -1.68767467... | [11.390233039855957, -2.346128225326538] |
5756ab42-c735-4661-8719-b081feea9465 | metaadvdet-towards-robust-detection-of | 1908.02199 | null | https://arxiv.org/abs/1908.02199v1 | https://arxiv.org/pdf/1908.02199v1.pdf | MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks | Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving c... | ['Hailin Shi', 'Chenxu Zhao', 'Li Chen', 'Junhai Yong', 'Dan Zeng', 'Chen Ma'] | 2019-08-06 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 2.18730286e-01 -3.78474444e-01 2.31160671e-01 -9.86473337e-02
-4.87290353e-01 -6.80920660e-01 7.26217687e-01 -2.96807736e-01
-5.78663707e-01 5.14035940e-01 -2.79981256e-01 -7.03029633e-02
3.62626463e-01 -1.02497804e+00 -7.88258195e-01 -8.08780670e-01
-9.63323638e-02 2.37308607e-01 8.08719516e-01 -4.93413299... | [5.544356346130371, 7.92865514755249] |
a2a7e16c-4136-4719-a7c0-bd77fec7007a | bias-reduced-hindsight-experience-replay-with | 1905.05498 | null | https://arxiv.org/abs/1905.05498v5 | https://arxiv.org/pdf/1905.05498v5.pdf | Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization | Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm for sparse reward functions. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode. Virtual goals are randomly selected, irrespective of which are most instructive for the... | ['Armin Biess', 'Binyamin Manela'] | 2019-05-14 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.26435772e-01 2.15881422e-01 -1.20093450e-01 -2.26299211e-01
-7.17285573e-01 -5.18779337e-01 4.21562761e-01 5.05571961e-02
-6.64714336e-01 1.45014167e+00 3.40573788e-01 -1.72393993e-02
-3.89711350e-01 -7.03668833e-01 -6.05020285e-01 -8.64695549e-01
-4.05081987e-01 5.30614555e-01 -1.44874565e-02 -5.15772760... | [4.110002517700195, 1.6647841930389404] |
9e4cc64e-f227-46eb-b399-0cebfef21cf0 | knowledge-graph-contrastive-learning-for | 2205.00976 | null | https://arxiv.org/abs/2205.00976v2 | https://arxiv.org/pdf/2205.00976v2.pdf | Knowledge Graph Contrastive Learning for Recommendation | Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of such methods relies on the high quality knowledge graphs,... | ['Chenliang Li', 'Lianghao Xia', 'Chao Huang', 'Yuhao Yang'] | 2022-05-02 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-2.68741012e-01 2.86244471e-02 -7.67362773e-01 -3.08298409e-01
-2.16405228e-01 -3.53308260e-01 1.12127893e-01 1.32239312e-01
7.64468759e-02 7.10696816e-01 7.19865024e-01 2.01274958e-02
-6.37723148e-01 -1.14016497e+00 -7.47246504e-01 -5.29871881e-01
-2.25521848e-02 2.32586756e-01 -1.18769728e-01 -5.49775362... | [10.218192100524902, 5.603754997253418] |
6df2aacf-826c-438a-b7b7-2f1bda33b48a | sparse-spn-depth-completion-from-sparse | 2212.00987 | null | https://arxiv.org/abs/2212.00987v1 | https://arxiv.org/pdf/2212.00987v1.pdf | Sparse SPN: Depth Completion from Sparse Keypoints | Our long term goal is to use image-based depth completion to quickly create 3D models from sparse point clouds, e.g. from SfM or SLAM. Much progress has been made in depth completion. However, most current works assume well distributed samples of known depth, e.g. Lidar or random uniform sampling, and perform poorly on... | ['Derek Hoiem', 'Jae Yong Lee', 'Yuqun Wu'] | 2022-12-02 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 1.21776342e-01 8.94109756e-02 -1.67888597e-01 -3.29196334e-01
-9.02543604e-01 -4.76096481e-01 5.17004550e-01 -2.31440607e-02
-2.74305373e-01 8.77142310e-01 6.62710965e-02 4.24290039e-02
3.18553805e-01 -8.56963098e-01 -9.21280324e-01 -2.35125035e-01
-7.11667985e-02 1.07728028e+00 4.63063687e-01 1.95482969... | [8.459550857543945, -2.9710702896118164] |
15c75391-d312-4540-bc2d-95ef74c6ae4d | macro-action-based-multi-agent-robot-deep | 2209.10003 | null | https://arxiv.org/abs/2209.10003v2 | https://arxiv.org/pdf/2209.10003v2.pdf | Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability | The state-of-the-art multi-agent reinforcement learning (MARL) methods have provided promising solutions to a variety of complex problems. Yet, these methods all assume that agents perform synchronized primitive-action executions so that they are not genuinely scalable to long-horizon real-world multi-agent/robot tasks... | ['Yuchen Xiao'] | 2022-09-20 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-1.44193128e-01 2.08579436e-01 -2.85569042e-01 -1.01866849e-01
-9.24570799e-01 -5.10306776e-01 9.91999984e-01 3.75731021e-01
-8.23688745e-01 1.37410176e+00 -5.09826355e-02 -2.42071733e-01
-4.26941186e-01 -4.69993353e-01 -5.62432587e-01 -1.03140318e+00
-8.32846045e-01 1.23752522e+00 2.88921446e-01 -2.58889794... | [3.8937315940856934, 2.0011274814605713] |
11ccdc41-701e-40ed-a721-9c9aa1f38495 | robust-policy-optimization-in-deep | 2212.07536 | null | https://arxiv.org/abs/2212.07536v1 | https://arxiv.org/pdf/2212.07536v1.pdf | Robust Policy Optimization in Deep Reinforcement Learning | The policy gradient method enjoys the simplicity of the objective where the agent optimizes the cumulative reward directly. Moreover, in the continuous action domain, parameterized distribution of action distribution allows easy control of exploration, resulting from the variance of the representing distribution. Entro... | ['Yexiang Xue', 'Md Masudur Rahman'] | 2022-12-14 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.28120616e-01 1.67907491e-01 -7.55758822e-01 1.39941216e-01
-4.95295674e-01 -4.46900308e-01 5.67105114e-01 6.09868877e-02
-8.01422656e-01 1.27441096e+00 3.91222596e-01 -2.71181554e-01
-3.29461366e-01 -5.48791885e-01 -6.96089327e-01 -9.66450155e-01
-1.39556780e-01 3.15716594e-01 -1.09266847e-01 -3.06713521... | [4.122490882873535, 2.1387715339660645] |
56fcfdd5-124f-4e19-98b2-e8d21463b7a6 | botartist-twitter-bot-detection-machine | 2306.00037 | null | https://arxiv.org/abs/2306.00037v2 | https://arxiv.org/pdf/2306.00037v2.pdf | BotArtist: Twitter bot detection Machine Learning model based on Twitter suspension | Twitter as one of the most popular social networks, offers a means for communication and online discourse, which unfortunately has been the target of bots and fake accounts, leading to the manipulation and spreading of false information. Towards this end, we gather a challenging, multilingual dataset of social discours... | ['Alexander Shevtsov', 'Sotiris Ioannidis', 'Polyvios Pratikakis', 'Ioannis Lamprou', 'Despoina Antonakaki'] | 2023-05-31 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-3.37936252e-01 3.87119442e-01 -5.03798604e-01 2.37991795e-01
-6.11069322e-01 -8.96616697e-01 1.43915641e+00 5.71214318e-01
-6.30521357e-01 8.94037485e-01 2.95001030e-01 -3.21381062e-01
4.49031651e-01 -1.00780010e+00 -3.95675153e-01 -3.95905465e-01
1.03434324e-01 7.50060499e-01 3.76231283e-01 -5.41759908... | [8.150965690612793, 10.198779106140137] |
33428b91-13fc-40a5-a25a-33c37764d5e9 | ganmex-one-vs-one-attributions-using-gan | 2011.06015 | null | https://arxiv.org/abs/2011.06015v4 | https://arxiv.org/pdf/2011.06015v4.pdf | GANMEX: One-vs-One Attributions Guided by GAN-based Counterfactual Explanation Baselines | Attribution methods have been shown as promising approaches for identifying key features that led to learned model predictions. While most existing attribution methods rely on a baseline input for performing feature perturbations, limited research has been conducted to address the baseline selection issues. Poor choice... | ['Zohar Karnin', 'Pin-Ju Tien', 'Sheng-Min Shih'] | 2020-11-11 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.36931121e-01 5.10441184e-01 -3.62451643e-01 -5.05625248e-01
-3.46306354e-01 -6.07277632e-01 9.45202589e-01 -1.20947950e-01
-1.29512133e-04 9.38339531e-01 2.28573248e-01 -1.97372243e-01
-2.21650764e-01 -7.50631392e-01 -9.80858445e-01 -6.83649004e-01
1.42426282e-01 4.13748056e-01 2.45310664e-02 -2.50182778... | [8.803476333618164, 5.533175468444824] |
7b1437f5-a2db-4864-b245-9e8620df3574 | a-rich-morphological-tagger-for-english | null | null | https://aclanthology.org/E17-2018 | https://aclanthology.org/E17-2018.pdf | A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax | A traditional claim in linguistics is that all human languages are equally expressive{---}able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morph... | ['John Sylak-Glassman', 'Rebecca Knowles', 'Matt Post', 'Ryan Cotterell', 'Christo Kirov'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.41029991e-02 1.06130660e-01 -3.02267194e-01 -7.01746523e-01
-6.02657497e-01 -1.01535428e+00 6.20438159e-01 5.48608363e-01
-6.65002406e-01 6.31157756e-01 6.38352096e-01 -8.19998860e-01
-1.86317608e-01 -7.89307177e-01 -2.75923940e-03 -2.99871832e-01
3.49739432e-01 3.92237872e-01 1.52381614e-01 -7.28031933... | [10.428389549255371, 9.90538501739502] |
65572ec8-bbac-439c-a9b6-ac8fc1e1e8fd | hierarchical-feature-alignment-network-for | 2207.08485 | null | https://arxiv.org/abs/2207.08485v2 | https://arxiv.org/pdf/2207.08485v2.pdf | Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation | Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS). Most of the previous methods directly extract and fuse the motion and appearance features for segmenting target objects in the UVOS setting. However, optical flow is intrinsically an instantaneous velocity ... | ['Jinhui Tang', 'Zhenmin Tang', 'Fumin Shen', 'Guo-Sen Xie', 'Yazhou Yao', 'Gensheng Pei'] | 2022-07-18 | null | null | null | null | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.83190063e-01 -4.07122850e-01 -4.11093533e-01 -3.80724341e-01
-3.22080344e-01 -3.83997232e-01 4.10527349e-01 -2.18671620e-01
-3.77651185e-01 3.31204683e-01 -7.49773160e-03 1.81161121e-01
-1.84444606e-01 -5.07135391e-01 -5.52212894e-01 -8.71118128e-01
1.49032876e-01 -1.88008267e-02 4.27641571e-01 5.79962432... | [9.236352920532227, -0.22447825968265533] |
5c72773f-ccf3-4f49-aec0-7e3b6956efa4 | acrnet-attention-cube-regression-network-for | 2210.05130 | null | https://arxiv.org/abs/2210.05130v1 | https://arxiv.org/pdf/2210.05130v1.pdf | ACRNet: Attention Cube Regression Network for Multi-view Real-time 3D Human Pose Estimation in Telemedicine | Human pose estimation (HPE) for 3D skeleton reconstruction in telemedicine has long received attention. Although the development of deep learning has made HPE methods in telemedicine simpler and easier to use, addressing low accuracy and high latency remains a big challenge. In this paper, we propose a novel multi-view... | ['Sunil K. Agrawal', 'Xupeng Ai', 'Chenfei Zhu', 'Boce Hu'] | 2022-10-11 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-2.62475789e-01 1.18200794e-01 -1.24848597e-01 -1.04643553e-01
-8.28444839e-01 -2.12599356e-02 -1.85840860e-01 -4.02618170e-01
-3.63858223e-01 5.43368638e-01 5.07912934e-01 6.80633113e-02
-2.36548826e-01 -4.41062182e-01 -8.66926193e-01 -6.67825580e-01
-9.19808671e-02 7.18946874e-01 5.99972419e-02 -2.64411539... | [7.1663055419921875, -0.552754819393158] |
29de8135-4dd5-46c5-8792-b627be193060 | direct-and-indirect-transactions-and | 1911.11569 | null | https://arxiv.org/abs/1911.11569v5 | https://arxiv.org/pdf/1911.11569v5.pdf | Direct and indirect transactions and requirements | The indirect transactions between sectors of an economic system has been a long-standing open problem. There have been numerous attempts to conceptually define and mathematically formulate this notion in various other scientific fields in literature as well. The existing direct and indirect effects formulations, howeve... | ['Huseyin Coskun', 'Husna Betul Coskun'] | 2019-11-23 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.27638653e-01 -3.39887813e-02 -3.41094434e-01 2.02889666e-01
9.80530307e-02 -8.70912492e-01 1.08056986e+00 -5.83297536e-02
-6.31921813e-02 1.04160190e+00 1.16778053e-01 -4.09979522e-01
-1.12971103e+00 -7.91532040e-01 -2.59703070e-01 -9.42945242e-01
-3.85371596e-01 2.10922867e-01 -2.58920759e-01 -7.54563391... | [5.627202033996582, 3.501335382461548] |
9a542ab4-d507-4239-8fa8-12f3b6a87128 | simple-vs-oversampling-based-classification | null | null | https://aclanthology.org/2020.wanlp-1.24 | https://aclanthology.org/2020.wanlp-1.24.pdf | Simple vs Oversampling-based Classification Methods for Fine Grained Arabic Dialect Identification in Twitter | In this paper, we present a description of our experiments on country-level Arabic dialect identification. A comparison study between a set of classifiers has been carried out. The best results were achieved using the Linear Support Vector Classification (LSVC) model by applying a Random Over Sampling (ROS) process yie... | ['Mourad Abbas', 'Mohamed Lichouri'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-2.42805764e-01 -1.16542399e-01 7.00810701e-02 -6.03083491e-01
-7.98000276e-01 -8.17672372e-01 1.12863505e+00 4.33553278e-01
-5.79162121e-01 7.02660263e-01 -1.77103847e-01 -2.81561583e-01
4.44810465e-02 -5.72995484e-01 -3.80344950e-02 -7.67919302e-01
-1.45443991e-01 6.75016761e-01 3.54811698e-01 -8.44612777... | [10.20732307434082, 10.703264236450195] |
f8db18ea-7320-4a05-bc54-11d426e5d51e | end-to-end-one-shot-human-parsing | 2105.01241 | null | https://arxiv.org/abs/2105.01241v2 | https://arxiv.org/pdf/2105.01241v2.pdf | End-to-end One-shot Human Parsing | Previous human parsing models are limited to parsing humans into pre-defined classes, which is inflexible for practical fashion applications that often have new fashion item classes. In this paper, we define a novel one-shot human parsing (OSHP) task that requires parsing humans into an open set of classes defined by a... | ['Bohan Zhuang', 'DaCheng Tao', 'Jianfei Cai', 'Jing Zhang', 'Haoyu He'] | 2021-05-04 | null | null | null | null | ['one-shot-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 4.06582467e-02 2.38724694e-01 -3.38898540e-01 -7.32542753e-01
-8.54207039e-01 -5.32652140e-01 1.54937521e-01 3.12391692e-03
-5.68780243e-01 3.66066426e-01 2.76234001e-03 1.08465672e-01
3.94759886e-02 -1.01016939e+00 -6.60917580e-01 -6.07898772e-01
1.56111851e-01 5.26649117e-01 5.72291613e-01 -5.33760823... | [8.96111011505127, 0.384874165058136] |
64d17180-51a7-4e89-b4f6-6e8be0e11876 | deepa2-a-modular-framework-for-deep-argument | 2110.01509 | null | https://arxiv.org/abs/2110.01509v3 | https://arxiv.org/pdf/2110.01509v3.pdf | DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models | In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst -- a T5 model (Raffel et al. 2020) set up and trained within DeepA2 -- reconstructs argumentative texts, which advance an infor... | ['Kyle Richardson', 'Gregor Betz'] | 2021-10-04 | null | https://aclanthology.org/2022.starsem-1.2 | https://aclanthology.org/2022.starsem-1.2.pdf | sem-naacl-2022-7 | ['logical-reasoning-reading-comprehension'] | ['natural-language-processing'] | [-1.22332731e-02 1.15612543e+00 -2.95653850e-01 -1.92639083e-01
-9.38217938e-01 -1.09232461e+00 1.13293517e+00 5.32701075e-01
-2.50059575e-01 9.23354626e-01 8.77621889e-01 -1.24176955e+00
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2.26226345e-01 9.56807554e-01 -1.43985495e-01 -3.19994330... | [9.66491985321045, 9.567083358764648] |
8fba1199-5ff2-415e-a372-9c05e3d85859 | a-3d-object-detection-and-pose-estimation | 1703.03940 | null | http://arxiv.org/abs/1703.03940v1 | http://arxiv.org/pdf/1703.03940v1.pdf | A 3D Object Detection and Pose Estimation Pipeline Using RGB-D Images | 3D object detection and pose estimation has been studied extensively in
recent decades for its potential applications in robotics. However, there still
remains challenges when we aim at detecting multiple objects while retaining
low false positive rate in cluttered environments. This paper proposes a robust
3D object d... | ['Juan Rojas', 'Yisheng Guan', 'Ruotao He'] | 2017-03-11 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [ 2.98804045e-01 -2.86509007e-01 1.60823017e-01 -2.14777276e-01
-7.48526156e-01 -6.39306724e-01 4.35810655e-01 4.21500504e-01
-6.30059361e-01 7.87115693e-02 -6.34946823e-01 1.17775761e-01
-1.61135122e-01 -5.74647963e-01 -6.03208125e-01 -5.67025185e-01
-2.10686773e-01 1.11084485e+00 1.00034690e+00 7.98255354... | [7.360309600830078, -2.3249313831329346] |
0491f76e-6864-4d27-9f3a-8ca710da005b | end-to-end-trainable-trident-person-search | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Han_End-to-End_Trainable_Trident_Person_Search_Network_Using_Adaptive_Gradient_Propagation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Han_End-to-End_Trainable_Trident_Person_Search_Network_Using_Adaptive_Gradient_Propagation_ICCV_2021_paper.pdf | End-to-End Trainable Trident Person Search Network Using Adaptive Gradient Propagation | Person search suffers from the conflicting objectives of commonness and uniqueness between the person detection and re-identification tasks that make the end-to-end training of person search networks difficult. In this paper, we propose a trident network for person search that performs detection, re-identification,... | ['Jae-Young Sim', 'Kuhyeun Ko', 'Byeong-Ju Han'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['person-search'] | ['computer-vision'] | [-2.35773072e-01 -4.78795171e-01 -3.86619046e-02 -5.25216162e-01
-2.88691670e-01 -3.47650319e-01 5.12176454e-01 -3.40831280e-01
-1.02837193e+00 4.96083826e-01 2.77384728e-01 2.63776898e-01
-2.73129046e-01 -4.49251354e-01 -2.99398929e-01 -4.20368612e-01
1.66760057e-01 7.46005774e-01 1.22685961e-01 4.99048978... | [14.825587272644043, 0.8201420903205872] |
ae71f43f-62a5-450f-9640-b91838e1a520 | learning-to-quantize-vulnerability-patterns | 2306.06109 | null | https://arxiv.org/abs/2306.06109v1 | https://arxiv.org/pdf/2306.06109v1.pdf | Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities | Deep learning (DL) models have become increasingly popular in identifying software vulnerabilities. Prior studies found that vulnerabilities across different vulnerable programs may exhibit similar vulnerable scopes, implicitly forming discernible vulnerability patterns that can be learned by DL models through supervis... | ['Dinh Phung', 'Chakkrit Tantithamthavorn', 'Van Nguyen', 'Trung Le', 'Michael Fu'] | 2023-05-26 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-9.12757665e-02 -2.45212689e-01 -5.18756270e-01 -2.99471349e-01
-1.00164175e+00 -1.01346540e+00 8.09256956e-02 6.60258651e-01
2.09817767e-01 1.57338515e-01 2.00419962e-01 -8.81557882e-01
1.71064198e-01 -9.76451933e-01 -7.17063785e-01 -2.21174657e-01
-4.84151095e-01 -4.97522593e-01 3.32520545e-01 -8.17573443... | [7.0875701904296875, 7.773055553436279] |
dc4d4d19-4014-41f3-ae39-d24a5f3ff6b6 | neural-fine-gray-monotonic-neural-networks | 2305.06703 | null | https://arxiv.org/abs/2305.06703v1 | https://arxiv.org/pdf/2305.06703v1.pdf | Neural Fine-Gray: Monotonic neural networks for competing risks | Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest. Despite competitive performances in tackling this problem, machine learning methods often ignore other competing risks that preclude the event of inter... | ['Jessica Barrett', 'Brian Tom', 'Chang Ho Yoon', 'Vincent Jeanselme'] | 2023-05-11 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.34005266e-01 1.62708610e-01 -6.34672165e-01 -5.61451972e-01
-9.31119204e-01 -1.51898533e-01 3.92446250e-01 4.95584637e-01
-7.42442131e-01 1.16812396e+00 3.85880649e-01 -8.48790705e-01
-5.54857314e-01 -5.74084222e-01 -1.95264176e-01 -6.92249954e-01
-4.11043644e-01 5.72671056e-01 -3.87117088e-01 2.63941735... | [7.847569942474365, 5.478878021240234] |
09c67701-d7f9-4ef7-991e-bfab7fcb7871 | multi-task-learning-of-cascaded-cnn-for | 1805.01290 | null | http://arxiv.org/abs/1805.01290v1 | http://arxiv.org/pdf/1805.01290v1.pdf | Multi-task Learning of Cascaded CNN for Facial Attribute Classification | Recently, facial attribute classification (FAC) has attracted significant
attention in the computer vision community. Great progress has been made along
with the availability of challenging FAC datasets. However, conventional FAC
methods usually firstly pre-process the input images (i.e., perform face
detection and ali... | ['Ni Zhuang', 'Yan Yan', 'Si Chen', 'Hanzi Wang'] | 2018-05-03 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 0.12227622 -0.29689044 0.03770065 -0.7446982 -0.5281681 0.0579512
0.44165736 -0.00875005 -0.5008215 0.3745443 -0.11991737 0.26102033
-0.19133332 -0.7211579 -0.49484843 -0.8260844 0.22132598 0.5054006
-0.20091513 -0.02609576 0.01418687 0.6085771 -1.6325521 -0.04100299
0.9518074 1.5754902 0.05... | [13.511107444763184, 0.7706761360168457] |
75128921-1348-41cd-98a0-57f73a246f2e | improving-temporal-relation-extraction-with | null | null | https://aclanthology.org/W16-2914 | https://aclanthology.org/W16-2914.pdf | Improving Temporal Relation Extraction with Training Instance Augmentation | null | ['Guergana Savova', 'Dmitriy Dligach', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2016-08-01 | null | null | null | ws-2016-8 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.282594203948975, 3.763812780380249] |
8eac8bee-cf36-4312-8f70-28a0953e730d | u3e-unsupervised-and-erasure-based-evidence | 2210.02621 | null | https://arxiv.org/abs/2210.02621v3 | https://arxiv.org/pdf/2210.02621v3.pdf | U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension | More tasks in Machine Reading Comprehension(MRC) require, in addition to answer prediction, the extraction of evidence sentences that support the answer. However, the annotation of supporting evidence sentences is usually time-consuming and labor-intensive. In this paper, to address this issue and considering that most... | ['Chenghao Wu', 'Shumin Shi', 'Suzhe He'] | 2022-10-06 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 3.57491404e-01 8.60124305e-02 -1.97300360e-01 -3.40825021e-01
-5.17354012e-01 -3.11870098e-01 2.67097741e-01 9.32117760e-01
-5.98757207e-01 9.67967987e-01 5.82867503e-01 -3.91748220e-01
-1.88878790e-01 -8.23856473e-01 -7.27582872e-01 -6.64017498e-02
3.91578913e-01 8.77812877e-02 5.85070491e-01 7.58395791... | [11.412104606628418, 8.293479919433594] |
6c06af3c-2376-49d0-883c-4c82d0d71070 | learning-joint-embedding-with-multimodal-cues | null | null | https://dl.acm.org/citation.cfm?id=3206064 | http://www.cs.cmu.edu/~fmetze/interACT/Publications_files/publications/ICMR2018_Camera_Ready.pdf | Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval | Constructing a joint representation invariant across different modalities (e.g., video, language) is of significant importance in many multimedia applications. While there are a number of recent successes in developing effective image-text retrieval methods by learning
joint representations, the video-text retrieval t... | ['Amit K. Roy-Chowdhury', 'Niluthpol Chowdhury Mithun', 'Juncheng Li', 'Florian Metze'] | 2018-06-11 | null | null | null | icmr-2018-6 | ['video-text-retrieval'] | ['computer-vision'] | [ 2.62889504e-01 -8.51824522e-01 -3.33457470e-01 -2.62994409e-01
-1.61254692e+00 -4.51462656e-01 7.73973644e-01 5.07508256e-02
-4.44857985e-01 3.71541440e-01 3.34098607e-01 2.20015511e-01
-3.55467290e-01 -2.09007949e-01 -4.71048892e-01 -7.47351527e-01
2.39327222e-01 2.74688974e-02 1.27679929e-01 1.12496883... | [10.442986488342285, 0.9765657186508179] |
8a6da194-1831-4a7d-94e9-21403e963035 | your-local-gan-designing-two-dimensional | 1911.12287 | null | https://arxiv.org/abs/1911.12287v2 | https://arxiv.org/pdf/1911.12287v2.pdf | Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models | We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality. We show that by just replacing the dense attention layer of SAGAN with our construction, we obtain very significant FID, Inception score and pure visual improvements. FID score is improved from $18.65$ to $15.94$ on Im... | ['Han Zhang', 'Augustus Odena', 'Giannis Daras', 'Alexandros G. Dimakis'] | 2019-11-27 | your-local-gan-designing-two-dimensional-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Daras_Your_Local_GAN_Designing_Two_Dimensional_Local_Attention_Mechanisms_for_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Daras_Your_Local_GAN_Designing_Two_Dimensional_Local_Attention_Mechanisms_for_CVPR_2020_paper.pdf | cvpr-2020-6 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.52348485e-02 4.10792470e-01 3.11512291e-01 -5.96717708e-02
-5.74096322e-01 -6.72802389e-01 6.21010005e-01 -3.42340916e-01
-2.46603608e-01 6.67820036e-01 2.65873224e-01 -1.74618527e-01
-1.00986809e-01 -9.73161340e-01 -1.15146637e+00 -6.49862111e-01
-2.29256898e-01 1.13172047e-01 2.00760499e-01 -3.95158887... | [11.454115867614746, -0.5203142762184143] |
d5f3d06f-00e3-4e86-a2e8-d64e46f88b5e | clanet-a-comprehensive-framework-for-cross | 2306.16538 | null | https://arxiv.org/abs/2306.16538v1 | https://arxiv.org/pdf/2306.16538v1.pdf | CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images | Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, batch effects, a significant issue st... | ['Huiyu Zhou', 'Yinhai Wang', 'Jonathan Orme', 'Kerry Hallbrook', 'Navin Rathna Kumar', 'Adam Corrigan', 'Lei Tong'] | 2023-06-28 | null | null | null | null | ['self-supervised-learning', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [-5.16761746e-03 -8.61294448e-01 -1.04343750e-01 3.39348055e-02
-1.06007552e+00 -5.58612585e-01 4.90429908e-01 8.04659545e-01
-5.24913311e-01 9.42086816e-01 -1.30281478e-01 -4.13282253e-02
-1.82966769e-01 -6.43852174e-01 -4.90668118e-01 -1.36856616e+00
3.11243329e-02 4.95081455e-01 -1.55503109e-01 -9.93449688... | [15.009220123291016, -3.0943586826324463] |
1a5e8215-c6e6-415b-93f3-83b45ee42dc0 | image-completion-via-inference-in-deep | 2102.12037 | null | https://arxiv.org/abs/2102.12037v3 | https://arxiv.org/pdf/2102.12037v3.pdf | Conditional Image Generation by Conditioning Variational Auto-Encoders | We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture and training objective capable of leveraging a foundation model in the form of a pretrained unconditional VAE. To train the conditional VAE, we only need to train an artifact to pe... | ['Frank Wood', 'Saeid Naderiparizi', 'William Harvey'] | 2021-02-24 | conditional-image-generation-by-conditioning | https://openreview.net/forum?id=7MV6uLzOChW | https://openreview.net/pdf?id=7MV6uLzOChW | iclr-2022-4 | ['conditional-image-generation'] | ['computer-vision'] | [ 5.33073664e-01 5.15589058e-01 1.60650790e-01 -4.08184022e-01
-1.11728144e+00 -1.61977783e-01 7.63576150e-01 -3.37799519e-01
-3.47165346e-01 8.96524191e-01 1.08870648e-01 -3.26210499e-01
1.45073012e-01 -9.11441028e-01 -1.33720434e+00 -6.91095293e-01
2.57562757e-01 6.38420165e-01 -1.17559522e-01 1.56366929... | [11.544386863708496, -0.32839682698249817] |
3e80003e-0d73-4b36-a589-7781cf0d1eab | drone-as-a-service-composition-under | 2103.06513 | null | https://arxiv.org/abs/2103.06513v1 | https://arxiv.org/pdf/2103.06513v1.pdf | Drone-as-a-Service Composition Under Uncertainty | We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of thr... | ['Athman Bouguettaya', 'Azadeh Ghari Neiat', 'Du Yong Kim', 'Flora D. Salim', 'Ali Hamdi'] | 2021-03-11 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-3.78063053e-01 -5.26588738e-01 -1.93726018e-01 -6.52519107e-01
-5.37720263e-01 -8.18229139e-01 7.46590018e-01 1.26135047e-03
-2.64243595e-02 6.89354241e-01 -1.08283207e-01 -6.30669743e-02
-1.04830849e+00 -1.20462930e+00 -5.03279924e-01 -7.81621516e-01
-5.46574950e-01 9.50126827e-01 4.59554166e-01 -6.41847789... | [5.526534557342529, 1.9600681066513062] |
d8a218a0-5147-41e1-9991-d501dbfababc | mask-guided-feature-extraction-and | 2109.07755 | null | https://arxiv.org/abs/2109.07755v1 | https://arxiv.org/pdf/2109.07755v1.pdf | Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization | While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have been understudied. FGVC aims at classifying objects from the same species (very similar categories), while the Ultra-FGVC targets at more ch... | ['Yongsheng Gao', 'Miaohua Zhang', 'Xiaohan Yu', 'Zicheng Pan'] | 2021-09-16 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.74196208e-01 -4.36179399e-01 -1.05389453e-01 -8.69005993e-02
-4.76641238e-01 -5.60574412e-01 6.69933259e-01 3.13528657e-01
-2.94957578e-01 5.97534299e-01 -7.90420324e-02 -2.64078397e-02
-1.73299655e-01 -7.23590195e-01 -3.47906172e-01 -8.20903480e-01
7.65384212e-02 7.69810285e-03 6.00438654e-01 7.05399215... | [9.633298873901367, 1.9892250299453735] |
745473d2-b541-49ba-ba9a-287a008ce0dc | customizing-an-affective-tutoring-system | 2111.14262 | null | https://arxiv.org/abs/2111.14262v1 | https://arxiv.org/pdf/2111.14262v1.pdf | Customizing an Affective Tutoring System Based on Facial Expression and Head Pose Estimation | In recent years, the main problem in e-learning has shifted from analyzing content to personalization of learning environment by Intelligence Tutoring Systems (ITSs). Therefore, by designing personalized teaching models, learners are able to have a successful and satisfying experience in achieving their learning goals.... | ['Ebrahim Mousavi', 'Gholam Ali Montazer', 'Mahdi Pourmirzaei'] | 2021-11-21 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.53166044e-01 4.95932877e-01 1.59714252e-01 -5.58241487e-01
-3.22826542e-02 -3.94410551e-01 1.29960239e-01 3.30702513e-01
-2.49688402e-01 4.96319503e-01 2.03140676e-02 -5.03771789e-02
-5.45472726e-02 -8.48678768e-01 -3.75160426e-01 -5.53696990e-01
4.07827049e-01 1.39297798e-01 2.91314691e-01 -3.45322728... | [13.436083793640137, 2.9597575664520264] |
76b479aa-e4b4-46b4-8777-4426868d2103 | make-a-story-visual-memory-conditioned | 2211.13319 | null | https://arxiv.org/abs/2211.13319v3 | https://arxiv.org/pdf/2211.13319v3.pdf | Make-A-Story: Visual Memory Conditioned Consistent Story Generation | There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for mor... | ['Leonid Sigal', 'Shweta Mahajan', 'Sergey Tulyakov', 'Jian Ren', 'Hsin-Ying Lee', 'Tanzila Rahman'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['story-visualization', 'story-generation'] | ['computer-vision', 'natural-language-processing'] | [ 5.98568246e-02 -2.81172931e-01 1.43575564e-01 -1.30553916e-01
-4.59963739e-01 -4.20669138e-01 1.13139272e+00 -1.21834010e-01
7.88340420e-02 7.65924990e-01 6.25741363e-01 2.73305357e-01
1.58594415e-01 -8.38751793e-01 -9.06901956e-01 -5.70370138e-01
3.64785284e-01 3.03629458e-01 6.51815414e-01 -3.26069057... | [11.143046379089355, 0.5451802611351013] |
45f772ea-b347-45e4-a62d-9f4ae146c268 | damage-accumulation-during-high-temperature | 2102.13575 | null | https://arxiv.org/abs/2102.13575v1 | https://arxiv.org/pdf/2102.13575v1.pdf | Damage accumulation during high temperature fatigue of Ti/SiC$_f$ metal matrix composites under different stress amplitudes | The damage mechanisms and load redistribution of high strength TC17 titanium alloy/unidirectional SiC fibre composite (fibre diameter = 100 $\mu$m) under high temperature (350 {\deg}C) fatigue cycling have been investigated in situ using synchrotron X-ray computed tomography (CT) and X-ray diffraction (XRD) for high cy... | ['Timothy L. Burnett', 'Xiaorong Zhou', 'Philip J. Withers', 'Yingwei Fan', 'Stefan Michalik', 'Katherine J. Dobson', 'Michael Atkinson', 'Yuan Chai', 'Samuel A. McDonald', 'Nan Li', 'Wenxia Zhao', 'Xu Xu', 'Ying Wang'] | 2021-02-26 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 3.60136926e-01 2.14816496e-01 2.21827462e-01 6.44607723e-01
-2.59967029e-01 -8.88951868e-02 2.46668369e-01 2.38617696e-02
-4.38083798e-01 4.29801792e-01 1.33148625e-01 3.77867632e-02
-4.05816674e-01 -7.21032560e-01 -4.17090476e-01 -1.12141740e+00
-1.79560795e-01 7.54267573e-01 8.42514336e-01 -4.58548695... | [6.14755916595459, 3.2689406871795654] |
72a9eedf-636e-4c17-9911-e31faa050320 | supervised-hyperalignment-for-multi-subject | 2001.02894 | null | https://arxiv.org/abs/2001.02894v1 | https://arxiv.org/pdf/2001.02894v1.pdf | Supervised Hyperalignment for multi-subject fMRI data alignment | Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) datasets. Most of the existing HA methods utilized unsupervised approaches, where they only maximized the correlation be... | ['Liangxiu Han', 'Alessandro Selvitella', 'Daoqiang Zhang', 'Muhammad Yousefnezhad'] | 2020-01-09 | null | null | null | null | ['multi-subject-fmri-data-alignment'] | ['medical'] | [ 4.69991356e-01 -3.05749923e-01 1.48701116e-01 -5.23359537e-01
-3.97950768e-01 -5.48150949e-02 3.16532254e-01 1.93178020e-02
-4.81741250e-01 6.54441476e-01 9.79419276e-02 2.88341165e-01
-7.83434868e-01 -4.29648280e-01 -3.13715905e-01 -1.10877264e+00
-4.73647565e-01 2.70722926e-01 2.25215912e-01 1.54489443... | [12.671313285827637, 3.3923285007476807] |
63c96483-ff80-475b-8032-f8d1d4a0d0d9 | ofa-2-a-multi-objective-perspective-for-the | 2303.13683 | null | https://arxiv.org/abs/2303.13683v1 | https://arxiv.org/pdf/2303.13683v1.pdf | OFA$^2$: A Multi-Objective Perspective for the Once-for-All Neural Architecture Search | Once-for-All (OFA) is a Neural Architecture Search (NAS) framework designed to address the problem of searching efficient architectures for devices with different resources constraints by decoupling the training and the searching stages. The computationally expensive process of training the OFA neural network is done o... | ['Fernando J. Von Zuben', 'Rafael C. Ito'] | 2023-03-23 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.43529847e-01 1.01894245e-01 -1.43638209e-01 -7.86133483e-02
-2.53660679e-01 -5.20142257e-01 -7.52666406e-03 2.70239003e-02
-4.67429459e-01 8.13283503e-01 -4.84481812e-01 -4.54223216e-01
-8.24046016e-01 -1.01958656e+00 -5.75872421e-01 -8.30142856e-01
-1.06491975e-01 8.56633902e-01 -8.29193965e-02 -1.87170714... | [8.157873153686523, 3.207883834838867] |
d655bdb2-0471-4ee1-910f-d1385b35e4de | wikichat-a-few-shot-llm-based-chatbot | 2305.14292 | null | https://arxiv.org/abs/2305.14292v1 | https://arxiv.org/pdf/2305.14292v1.pdf | WikiChat: A Few-Shot LLM-Based Chatbot Grounded with Wikipedia | Despite recent advances in Large Language Models (LLMs), users still cannot trust the information provided in their responses. LLMs cannot speak accurately about events that occurred after their training, which are often topics of great interest to users, and, as we show in this paper, they are highly prone to hallucin... | ['Monica S. Lam', 'Heidi C. Zhang', 'Violet Z. Yao', 'Sina J. Semnani'] | 2023-05-23 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-4.15340275e-01 4.14294094e-01 -1.29496962e-01 -2.10158408e-01
-1.43852890e+00 -7.38912165e-01 1.04627359e+00 4.76726174e-01
-3.47977877e-01 8.99421811e-01 8.14964414e-01 -1.01585448e-01
-5.07497750e-02 -4.25739557e-01 -3.66948515e-01 -1.38256282e-01
1.47621185e-01 6.79416001e-01 3.06106269e-01 -5.71038485... | [12.293328285217285, 8.129841804504395] |
ea6c5496-0be8-470f-99f5-fd8d176a8393 | modal-regression-based-atomic-representation | 1711.05861 | null | http://arxiv.org/abs/1711.05861v1 | http://arxiv.org/pdf/1711.05861v1.pdf | Modal Regression based Atomic Representation for Robust Face Recognition | Representation based classification (RC) methods such as sparse RC (SRC) have
shown great potential in face recognition in recent years. Most previous RC
methods are based on the conventional regression models, such as lasso
regression, ridge regression or group lasso regression. These regression models
essentially imp... | ['Luoqing Li', 'Hong Chen', 'Yulong Wang', 'Yuan Yan Tang'] | 2017-11-05 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.44369477e-01 -4.50932980e-01 -2.29712531e-01 -5.24267435e-01
-7.70437181e-01 5.43611385e-02 3.71694475e-01 -6.59778297e-01
-5.16190492e-02 6.41736269e-01 6.98885471e-02 -4.34013084e-02
-4.43118513e-01 -5.57083726e-01 -5.08078098e-01 -1.02971494e+00
3.67637068e-01 1.55967742e-01 -6.28673732e-01 -2.34863475... | [12.545652389526367, 0.3993406295776367] |
82ba0357-ed4e-4592-9f78-80bd700470fe | adaptive-threshold-selective-self-attention | null | null | https://aclanthology.org/2022.coling-1.157 | https://aclanthology.org/2022.coling-1.157.pdf | Adaptive Threshold Selective Self-Attention for Chinese NER | Recently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context. However, the fully connected way of self-attention may scatter the attention distribution and allow some... | ['Yong Dou', 'Ziwen Zhang', 'Minghao Hu', 'Zhen Huang', 'Biao Hu'] | null | null | null | null | coling-2022-10 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 4.90954984e-03 -1.84513494e-01 3.21201459e-02 -3.04816425e-01
-7.84913301e-01 -3.91577035e-01 1.12807579e-01 2.71068662e-01
-8.85972679e-01 6.15946949e-01 5.32599151e-01 -2.65125901e-01
2.52583236e-01 -7.20866263e-01 -3.65308523e-01 -7.95152783e-01
2.67754167e-01 2.96171010e-01 5.77142119e-01 -2.06189141... | [9.791481018066406, 9.824719429016113] |
a9906084-a50e-472b-9bed-5de8e3d2d7fd | multi-person-absolute-3d-human-pose | 2004.03989 | null | https://arxiv.org/abs/2004.03989v1 | https://arxiv.org/pdf/2004.03989v1.pdf | Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision | In 3D human pose estimation one of the biggest problems is the lack of large, diverse datasets. This is especially true for multi-person 3D pose estimation, where, to our knowledge, there are only machine generated annotations available for training. To mitigate this issue, we introduce a network that can be trained wi... | ['Andras Lorincz', 'Marton Veges'] | 2020-04-08 | null | null | null | null | ['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative'] | ['computer-vision', 'computer-vision'] | [-2.15673238e-01 6.55541196e-03 -7.06946552e-02 -2.75538713e-01
-7.41311729e-01 -5.41989684e-01 2.94222534e-01 -1.18644647e-01
-7.23109245e-01 8.38027239e-01 4.60225910e-01 4.08580303e-01
3.01765859e-01 -3.92495424e-01 -7.08461821e-01 -2.15660706e-01
5.98821454e-02 8.13256800e-01 3.64442140e-01 -2.41554305... | [7.00563383102417, -0.9182350039482117] |
a1370600-71d0-4e5d-b13e-091332a99d89 | spatio-temporal-deformable-convolution-for | null | null | https://www.aiide.org/ojs/index.php/AAAI/article/view/6697 | https://www.aiide.org/ojs/index.php/AAAI/article/download/6697/6551 | Spatio-temporal deformable convolution for compressed video quality enhancement | Recent years have witnessed remarkable success of deep learning methods in quality enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compressio... | ['Jianing Deng'] | 2020-04-03 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 2.85139054e-01 -7.55433977e-01 5.64143285e-02 -3.48871112e-01
-6.32151246e-01 -3.02076280e-01 2.23664671e-01 -9.59217623e-02
-4.77755010e-01 6.39634967e-01 3.06417972e-01 3.46431062e-02
-1.55282095e-01 -6.99442565e-01 -5.47823012e-01 -7.43253410e-01
1.74923055e-02 -5.39770603e-01 3.01521659e-01 3.18741538... | [11.124772071838379, -1.771977424621582] |
8b443e8a-b1e4-4db8-b9ed-b7402ae9ff8a | nnrepair-constraint-based-repair-of-neural | 2103.12535 | null | https://arxiv.org/abs/2103.12535v2 | https://arxiv.org/pdf/2103.12535v2.pdf | NNrepair: Constraint-based Repair of Neural Network Classifiers | We present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the last layer. NNrepair first uses fault localization to find potentially faulty network parameters (such as the weights) and then performs repai... | ['Corina Pasareanu', 'Yannic Noller', 'Youcheng Sun', 'Divya Gopinath', 'Muhammad Usman'] | 2021-03-23 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 6.40173778e-02 4.80373204e-01 -2.14434434e-02 -2.89644539e-01
-6.35456979e-01 -1.15235579e+00 1.10820509e-01 1.18034072e-01
-2.99258471e-01 7.73447692e-01 -2.59660482e-01 -8.01358283e-01
-1.79701731e-01 -6.68045163e-01 -1.31091428e+00 -6.23596847e-01
-1.45244211e-01 6.32819161e-02 4.22518790e-01 7.11937100... | [5.975515365600586, 7.706060886383057] |
57767812-63bb-417b-a117-fc21de173e6a | long-tail-prediction-uncertainty-aware | 2207.00788 | null | https://arxiv.org/abs/2207.00788v2 | https://arxiv.org/pdf/2207.00788v2.pdf | Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles | A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles. Recently, deep learning technology has been widely adopted to design prediction models due to their impressive performance. However, such models may fail in the "long-tail" driving cases where ... | ['Kun Jiang', 'Yunkang Xu', 'Diange Yang', 'Xiaoyu Liu', 'Nanshan Deng', 'Zhong Cao', 'Weitao Zhou'] | 2022-07-02 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-3.43636036e-01 2.26798967e-01 -2.71685570e-01 -4.30758864e-01
-4.54347372e-01 -2.63647661e-02 5.28694868e-01 -2.04069912e-01
-2.08865508e-01 7.95310616e-01 1.54543787e-01 -6.47510946e-01
-2.28096500e-01 -1.05774164e+00 -7.71144271e-01 -7.22782254e-01
-7.27878660e-02 2.07908005e-01 3.05227876e-01 -2.65140831... | [5.692999362945557, 1.2393370866775513] |
0c97e716-6013-4d00-9bb9-6af9edab095a | multiverse-transformer-1st-place-solution-for | 2306.11868 | null | https://arxiv.org/abs/2306.11868v1 | https://arxiv.org/pdf/2306.11868v1.pdf | Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023 | This technical report presents our 1st place solution for the Waymo Open Sim Agents Challenge (WOSAC) 2023. Our proposed MultiVerse Transformer for Agent simulation (MVTA) effectively leverages transformer-based motion prediction approaches, and is tailored for closed-loop simulation of agents. In order to produce simu... | ['Fan Yi', 'Tiebiao Zhao', 'Yu Wang'] | 2023-06-20 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-5.44724166e-01 -1.36030361e-01 -8.05373862e-02 3.30370575e-01
-6.31352484e-01 -6.22511148e-01 1.07393777e+00 -3.10625136e-02
-4.01964873e-01 9.60642755e-01 1.99843988e-01 -6.06179655e-01
-6.66856617e-02 -7.08305478e-01 -6.95042074e-01 -3.75858694e-01
-7.50494123e-01 7.26382494e-01 4.66176748e-01 -6.66130126... | [5.20245885848999, 1.1606420278549194] |
4f4f1cd0-763a-43a9-8b78-8b9520c930c3 | postech-etris-submission-to-the-wmt2020-ape | null | null | https://aclanthology.org/2020.wmt-1.82 | https://aclanthology.org/2020.wmt-1.82.pdf | POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model | This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked lang... | ['Jong-Hyeok Lee', 'Young-Kil Kim', 'Baikjin Jung', 'Jaehun Shin', 'WonKee Lee', 'Jihyung Lee'] | null | null | null | null | wmt-emnlp-2020-11 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [-1.57109816e-02 7.25951940e-02 -1.86678469e-01 -4.49104816e-01
-1.52277744e+00 -4.64788198e-01 9.08987284e-01 -2.11695731e-01
-7.13661849e-01 9.73881185e-01 4.03641909e-01 -5.96277475e-01
5.70464730e-01 -3.16287577e-01 -1.04595804e+00 -4.30937409e-02
3.07722718e-01 7.66933203e-01 -2.66074896e-01 -3.66189718... | [11.659098625183105, 10.291336059570312] |
f8b56f49-ad31-4eef-88e3-32c7306cbca0 | learning-robust-representations-for-continual | 2210.04497 | null | https://arxiv.org/abs/2210.04497v1 | https://arxiv.org/pdf/2210.04497v1.pdf | Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation | Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations seriously degrades when the model learns new relations. Most previous work attributes catastrophic forg... | ['Zhifang Sui', 'Sujian Li', 'Yunbo Cao', 'Binghuai Lin', 'Tianyu Liu', 'YiFan Song', 'Peiyi Wang'] | 2022-10-10 | null | null | null | null | ['continual-relation-extraction'] | ['natural-language-processing'] | [ 1.69938803e-01 6.16034329e-01 -4.31227297e-01 -2.93717593e-01
-3.17639083e-01 -3.44761074e-01 5.60806572e-01 3.89018238e-01
-6.47800341e-02 1.08727372e+00 -4.87049818e-02 -2.61379391e-01
-3.61872092e-02 -1.13277900e+00 -9.68092620e-01 -3.96374673e-01
-2.71863997e-01 5.52968442e-01 4.05113399e-01 -5.65134108... | [9.19062614440918, 8.515437126159668] |
3750afb3-45b4-430a-97fd-5c2bdf4854ff | learn-and-review-enhancing-continual-named | null | null | https://aclanthology.org/2022.findings-acl.179 | https://aclanthology.org/2022.findings-acl.179.pdf | Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples | Traditional methods for named entity recognition (NER) classify mentions into a fixed set of pre-defined entity types. However, in many real-world scenarios, new entity types are incrementally involved. To investigate this problem, continual learning is introduced for NER. However, the existing method depends on the re... | ['Dai Dai', 'Sujian Li', 'Wenhao Wu', 'Yong Zhu', 'Yajuan Lyu', 'Quan Wang', 'Yu Xia'] | null | null | null | null | findings-acl-2022-5 | ['continual-named-entity-recognition'] | ['natural-language-processing'] | [-1.29998565e-01 3.04005388e-02 4.00452651e-02 -5.92113674e-01
-6.13658845e-01 -6.98376715e-01 3.86714816e-01 3.17661256e-01
-9.67409968e-01 9.48351264e-01 5.91030531e-02 -2.17371255e-01
1.06986508e-01 -7.88074970e-01 -5.52463472e-01 -5.04032254e-01
4.73024786e-01 4.37526703e-01 3.97104472e-01 -1.31629810... | [9.659504890441895, 9.494075775146484] |
8e6f96ef-8028-4895-976d-ee176cae3630 | copy-and-paste-gan-face-hallucination-from | 2002.10650 | null | https://arxiv.org/abs/2002.10650v3 | https://arxiv.org/pdf/2002.10650v3.pdf | Copy and Paste GAN: Face Hallucination from Shaded Thumbnails | Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper pro... | ['Yang Zhang', 'Yawei Luo', 'Changhui Hu', 'Xin Yu', 'Xiaobo Lu', 'Ivor Tsang'] | 2020-02-25 | copy-and-paste-gan-face-hallucination-from-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Copy_and_Paste_GAN_Face_Hallucination_From_Shaded_Thumbnails_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Copy_and_Paste_GAN_Face_Hallucination_From_Shaded_Thumbnails_CVPR_2020_paper.pdf | cvpr-2020-6 | ['face-hallucination'] | ['computer-vision'] | [ 4.96681750e-01 1.77398011e-01 1.87450260e-01 -3.34929347e-01
-7.39008904e-01 -4.71318245e-01 4.94612217e-01 -1.11994636e+00
5.09903617e-02 7.16933310e-01 3.13890606e-01 2.18821943e-01
4.42663193e-01 -8.85148346e-01 -8.48676443e-01 -8.12412322e-01
5.98881006e-01 -2.81489819e-01 -4.74814951e-01 -2.45720148... | [12.806071281433105, -0.06740497797727585] |
445b43f4-d6de-409a-abfd-13bf981ba1e0 | simipu-simple-2d-image-and-3d-point-cloud | 2112.04680 | null | https://arxiv.org/abs/2112.04680v2 | https://arxiv.org/pdf/2112.04680v2.pdf | SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations | Pre-training has become a standard paradigm in many computer vision tasks. However, most of the methods are generally designed on the RGB image domain. Due to the discrepancy between the two-dimensional image plane and the three-dimensional space, such pre-trained models fail to perceive spatial information and serve a... | ['Hang Zhao', 'Bolei Zhou', 'Junjun Jiang', 'Xianming Liu', 'Qinhong Jiang', 'Liangji Fang', 'Ang Li', 'Zehui Chen', 'Zhenyu Li'] | 2021-12-09 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 9.15577561e-02 -2.43490368e-01 -1.56350911e-01 -5.00332475e-01
-6.27094388e-01 -3.74368429e-01 5.96406639e-01 -8.52482840e-02
-4.15464550e-01 2.14027703e-01 -1.85649633e-01 -1.99528024e-01
-2.21659243e-01 -9.30439234e-01 -1.01784348e+00 -6.32369757e-01
1.80093527e-01 3.47377926e-01 1.49134278e-01 -2.05662563... | [8.352745056152344, -3.093148946762085] |
98e12f51-77bd-43ff-a90e-22f1450e1168 | icdar-2023-competition-on-structured-text | 2306.03287 | null | https://arxiv.org/abs/2306.03287v1 | https://arxiv.org/pdf/2306.03287v1.pdf | ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images | Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate the... | ['Xiang Bai', 'Jingdong Wang', 'Xing Sun', 'Dimosthenis Karatzas', 'Min Zhang', 'Shuicheng Yan', 'Jiebo Luo', 'Cheng-Lin Liu', 'Errui Ding', 'Wanxiang Che', 'Yuliang Liu', 'Yuechen Yu', 'Kun Yao', 'Pengyuan Lyu', 'Xiaoguang Hu', 'Shikun Feng', 'Yuning Du', 'Mengjun Cheng', 'Jianfeng Kuang', 'Mingrui Chen', 'MingYu Liu'... | 2023-06-05 | null | null | null | null | ['document-ai', 'entity-linking'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.12492499e-02 1.40839547e-01 -2.46734381e-01 -9.69019085e-02
-8.93935204e-01 -7.93373048e-01 8.69827569e-01 2.87474573e-01
-5.40632606e-01 5.48767626e-01 3.44299704e-01 -3.05530354e-02
-1.37796223e-01 -4.24203873e-01 -3.72830003e-01 -2.27752432e-01
-7.30070993e-02 6.30547643e-01 2.06551984e-01 4.00031991... | [11.767138481140137, 2.5922393798828125] |
f3b6d615-08c4-44be-b841-80055c6b1b1c | mutual-exclusivity-training-and-primitive | 2211.15578 | null | https://arxiv.org/abs/2211.15578v1 | https://arxiv.org/pdf/2211.15578v1.pdf | Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality | Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. In this work, we analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias (i.e., a source sequence already mapped to a target sequence is less likely ... | ['Mohit Bansal', 'Xiang Zhou', 'Yichen Jiang'] | 2022-11-28 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 9.51616466e-01 2.73682982e-01 -9.08089429e-02 -4.18641597e-01
-6.88934445e-01 -1.03765023e+00 5.07799387e-01 1.20804369e-01
-4.90594059e-01 1.09778380e+00 2.87977308e-01 -7.84739316e-01
2.34366104e-01 -7.17050314e-01 -1.25559330e+00 -7.77351081e-01
-7.57193342e-02 3.24243993e-01 2.83206373e-01 -3.25703561... | [11.259979248046875, 9.15325927734375] |
c442f94c-4810-4127-82c3-ca37edfca59b | reinforcement-learning-based-speech | 1811.04224 | null | http://arxiv.org/abs/1811.04224v1 | http://arxiv.org/pdf/1811.04224v1.pdf | Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition | Conventional deep neural network (DNN)-based speech enhancement (SE)
approaches aim to minimize the mean square error (MSE) between enhanced speech
and clean reference. The MSE-optimized model may not directly improve the
performance of an automatic speech recognition (ASR) system. If the target is
to minimize the reco... | [] | 2018-11-10 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.40483484e-01 -2.27507710e-01 4.32827324e-01 -3.92335832e-01
-8.13045263e-01 8.81553665e-02 1.64310157e-01 -3.52206379e-01
-5.84309042e-01 5.55660963e-01 3.17795038e-01 -4.27358687e-01
-7.08048344e-02 -4.43910152e-01 -3.78733873e-01 -9.38853800e-01
1.73848346e-01 -5.49800396e-01 -2.18400404e-01 -4.58573848... | [14.793742179870605, 6.017111301422119] |
4aff43b8-99ca-4855-9b21-d5e4f692e2a5 | homotopic-policy-mirror-descent-policy | 2201.09457 | null | https://arxiv.org/abs/2201.09457v9 | https://arxiv.org/pdf/2201.09457v9.pdf | Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity | We propose a new policy gradient method, named homotopic policy mirror descent (HPMD), for solving discounted, infinite horizon MDPs with finite state and action spaces. HPMD performs a mirror descent type policy update with an additional diminishing regularization term, and possesses several computational properties t... | ['Guanghui Lan', 'Tuo Zhao', 'Yan Li'] | 2022-01-24 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-7.87532330e-02 2.68947124e-01 -4.03344989e-01 1.81039214e-01
-6.65385842e-01 -8.04044962e-01 4.87761050e-01 2.66438395e-01
-8.36775541e-01 1.16726482e+00 8.64264742e-02 -7.38306999e-01
-5.82184613e-01 -5.41128159e-01 -8.68511140e-01 -9.62290168e-01
-4.94758576e-01 3.25888604e-01 1.16674960e-01 -2.95055062... | [4.2863874435424805, 2.6770644187927246] |
ea263c20-6fcc-4e80-b0a9-5c0967b995f9 | large-scale-nonlinear-granger-causality-for | null | null | https://www.nature.com/articles/s41598-021-87316-6 | https://www.nature.com/articles/s41598-021-87316-6.pdf | Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data | A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observations remains an important, y... | ['M. Ali Vosoughi & Anas Abidin', 'Adora M. DSouza', 'Axel Wismüller'] | 2021-04-09 | null | null | null | null | ['learning-network-representations'] | ['methodology'] | [ 3.21995109e-01 -3.52716029e-01 7.83603787e-02 -1.21759318e-01
-1.32690877e-01 -6.98333979e-01 1.09523070e+00 1.72291785e-01
-8.53007566e-03 1.08457541e+00 3.54123473e-01 -6.21244192e-01
-8.92182708e-01 -4.50964332e-01 -5.25417924e-01 -7.94450402e-01
-1.31142044e+00 4.04833078e-01 -5.41402251e-02 -7.42746592... | [7.7075581550598145, 5.18927526473999] |
4a296b33-e0f6-4b6d-8332-6eed86cc4705 | handwritten-arabic-numeral-recognition-using | 1702.04663 | null | http://arxiv.org/abs/1702.04663v1 | http://arxiv.org/pdf/1702.04663v1.pdf | Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks | Handwritten character recognition is an active area of research with
applications in numerous fields. Past and recent works in this field have
concentrated on various languages. Arabic is one language where the scope of
research is still widespread, with it being one of the most popular languages
in the world and being... | ['Akm Ashiquzzaman', 'Abdul Kawsar Tushar'] | 2017-02-15 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-2.14698344e-01 -5.41008115e-01 1.48642913e-01 -3.27675074e-01
-2.99848020e-01 -3.29173267e-01 2.58389294e-01 1.46081567e-01
-5.86325884e-01 8.88324976e-01 -1.61998987e-01 -4.71652031e-01
-3.33310962e-01 -9.19138134e-01 -5.84581912e-01 -6.95730925e-01
-1.72933079e-02 2.20110491e-01 2.61140943e-01 -4.11454588... | [11.868346214294434, 2.7103567123413086] |
690bf77a-5eb5-4575-88f3-aa76c86829d0 | exact-recovery-of-community-detection-in | 2209.14859 | null | https://arxiv.org/abs/2209.14859v1 | https://arxiv.org/pdf/2209.14859v1.pdf | Exact Recovery of Community Detection in dependent Gaussian Mixture Models | We study the community detection problem on a Gaussian mixture model, in which (1) vertices are divided into $k\geq 2$ distinct communities that are not necessarily equally-sized; (2) the Gaussian perturbations for different entries in the observation matrix are not necessarily independent or identically distributed. W... | ['Sichen Yang', 'Zhongyang Li'] | 2022-09-23 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.64320413e-02 1.22254796e-01 1.37085348e-01 1.46083653e-01
-3.64626169e-01 -6.68516457e-01 6.99878782e-02 9.82172266e-02
-1.61910027e-01 5.17769158e-01 -4.60049897e-01 -3.66095871e-01
-2.54812628e-01 -8.72421563e-01 -6.51487947e-01 -1.03307450e+00
-5.29027939e-01 8.12577009e-01 1.38371676e-01 2.05626979... | [6.946370601654053, 5.1103434562683105] |
a82e25b8-b9c3-4716-80a3-b1cd66d7d665 | pointattn-you-only-need-attention-for-point | 2203.08485 | null | https://arxiv.org/abs/2203.08485v1 | https://arxiv.org/pdf/2203.08485v1.pdf | PointAttN: You Only Need Attention for Point Cloud Completion | Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of deep neural networks, researches on point cloud completion have made great progress in recent years. However, the explicit local region par... | ['Chunhua Shen', 'Qingshan Liu', 'Junxia Li', 'Dongyan Guo', 'Ying Cui', 'Jun Wang'] | 2022-03-16 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-3.94941598e-01 -1.94217995e-01 1.27328187e-01 -4.86487180e-01
-6.72297955e-01 -2.89851278e-01 4.55515176e-01 -8.67980048e-02
4.71824035e-02 1.59544155e-01 1.70002177e-01 -2.60509700e-01
-1.92127481e-01 -8.81930947e-01 -1.25951135e+00 -5.06635129e-01
7.96601847e-02 5.38859904e-01 5.27347326e-02 -2.43714318... | [8.17312240600586, -3.5738234519958496] |
0c4728c8-f353-44c5-8bde-c495434d8e3b | 3d-ldm-neural-implicit-3d-shape-generation | 2212.00842 | null | https://arxiv.org/abs/2212.00842v2 | https://arxiv.org/pdf/2212.00842v2.pdf | 3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models | Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion mode... | ['Paul Guerrero', 'Linqi Zhou', 'Alberto Tono', 'Andrew Rodriguez', 'Mariem Khlifi', 'Gimin Nam'] | 2022-12-01 | null | null | null | null | ['3d-shape-generation', 'image-to-3d', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.32856828e-01 5.64956784e-01 1.31250873e-01 2.04818752e-02
-8.32498193e-01 -7.19916165e-01 8.70844603e-01 -3.40378165e-01
3.33898783e-01 6.10684156e-01 4.49707150e-01 -1.59101263e-01
3.27061981e-01 -1.20464182e+00 -9.67369735e-01 -6.02944136e-01
2.29918018e-01 5.40901124e-01 -1.79417834e-01 -8.49466547... | [9.082757949829102, -3.541922092437744] |
f0618763-6bae-4ddc-aba8-a72fcfa5233a | moving-average-options-machine-learning-and | 2108.11141 | null | https://arxiv.org/abs/2108.11141v1 | https://arxiv.org/pdf/2108.11141v1.pdf | Moving average options: Machine Learning and Gauss-Hermite quadrature for a double non-Markovian problem | Evaluating moving average options is a tough computational challenge for the energy and commodity market as the payoff of the option depends on the prices of a certain underlying observed on a moving window so, when a long window is considered, the pricing problem becomes high dimensional. We present an efficient metho... | ['Antonino Zanette', 'Andrea Molent', 'Ludovic Goudenège'] | 2021-08-25 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-5.38974464e-01 -2.91698277e-01 1.47823423e-01 3.12717676e-01
-5.05569875e-01 -4.61056888e-01 5.37327230e-01 1.18077442e-01
-5.15352190e-01 9.19393182e-01 -4.11909759e-01 -4.01466668e-01
-2.71662533e-01 -1.20287621e+00 -5.44430137e-01 -1.00593913e+00
-2.43258119e-01 6.31905079e-01 -2.61509046e-02 -7.06119835... | [5.700814723968506, 3.854480266571045] |
67aaf371-45b6-4c00-a844-b89e11d8ea74 | predator-registration-of-3d-point-clouds-with | 2011.13005 | null | https://arxiv.org/abs/2011.13005v3 | https://arxiv.org/pdf/2011.13005v3.pdf | PREDATOR: Registration of 3D Point Clouds with Low Overlap | We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region. Different from previous work, our model is specifically designed to handle (also) point-cloud pairs with low overlap. Its key novelty is an overlap-attention block for early information exchange between the l... | ['Konrad Schindler', 'Andreas Wieser', 'Mikhail Usvyatsov', 'Zan Gojcic', 'Shengyu Huang'] | 2020-11-25 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Huang_Predator_Registration_of_3D_Point_Clouds_With_Low_Overlap_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Huang_Predator_Registration_of_3D_Point_Clouds_With_Low_Overlap_CVPR_2021_paper.pdf | cvpr-2021-1 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.20838694e-01 2.61661917e-01 -2.12460399e-01 -4.82231565e-02
-1.22858799e+00 -5.04445672e-01 7.34636605e-01 5.51220357e-01
-2.43289843e-01 5.37356995e-02 1.82223722e-01 1.74110606e-01
-3.17478888e-02 -7.06054091e-01 -1.12996030e+00 -5.60390532e-01
-4.01831001e-01 8.80137801e-01 4.83718127e-01 -1.82315916... | [7.677492141723633, -3.0915026664733887] |
8ad0dc59-3626-4eb0-abcf-041990fa2de8 | dmrf-unet-a-two-stage-deep-learning-scheme | 2205.07567 | null | https://arxiv.org/abs/2205.07567v1 | https://arxiv.org/pdf/2205.07567v1.pdf | DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions | Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to ... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Hai-Han Sun', 'Yee Hui Lee', 'Qiqi Dai'] | 2022-05-16 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.21266776e-01 -1.08259737e-01 8.75116765e-01 -4.90803510e-01
-6.85425997e-01 2.19891995e-01 -1.26676053e-01 -2.42482185e-01
-1.39486343e-01 8.19379389e-01 -2.65488611e-03 -3.21499079e-01
-5.36575198e-01 -1.40327299e+00 -8.09290290e-01 -1.22072875e+00
-4.16162640e-01 5.22039533e-01 6.04341738e-03 -4.20620650... | [6.894832134246826, 1.5023306608200073] |
f9722646-9130-4b72-937d-23586fef0b20 | deepsinger-singing-voice-synthesis-with-data | 2007.04590 | null | https://arxiv.org/abs/2007.04590v2 | https://arxiv.org/pdf/2007.04590v2.pdf | DeepSinger: Singing Voice Synthesis with Data Mined From the Web | In this paper, we develop DeepSinger, a multi-lingual multi-singer singing voice synthesis (SVS) system, which is built from scratch using singing training data mined from music websites. The pipeline of DeepSinger consists of several steps, including data crawling, singing and accompaniment separation, lyrics-to-singi... | ['Tie-Yan Liu', 'Yi Ren', 'Zhou Zhao', 'Xu Tan', 'Tao Qin', 'Jian Luan'] | 2020-07-09 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-0.04668039 -0.39242712 0.0787253 -0.10447875 -1.5172086 -0.99946386
0.08901045 -0.77775896 0.07694104 0.26530343 0.5786697 -0.17955017
0.06036597 -0.2675305 -0.49941227 -0.51033044 0.33105296 0.465656
-0.15624598 -0.307801 -0.2009144 0.13566008 -1.6965176 0.3426024
0.56762284 0.70885205 0.384... | [15.504436492919922, 6.106106758117676] |
a6be4b14-7145-458f-b864-a0ebcd8c0297 | diffusion-and-volume-maximization-based | 2203.09992 | null | https://arxiv.org/abs/2203.09992v3 | https://arxiv.org/pdf/2203.09992v3.pdf | Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images | Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off... | ['James M. Murphy', 'David A. Coomes', 'Aland H. Y. Chan', 'Robert J. Plemmons', 'Kangning Cui', 'Sam L. Polk'] | 2022-03-18 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 7.72829056e-01 -2.72026956e-01 -1.53168216e-01 -5.04131280e-02
-4.15613919e-01 -5.93473434e-01 4.04409260e-01 7.59915635e-02
-8.44068080e-02 6.51479840e-01 -8.06303024e-02 -1.93784252e-01
-5.18197417e-01 -1.10808992e+00 -8.00179094e-02 -1.09135699e+00
-3.09938401e-01 6.15484834e-01 -6.51359111e-02 2.31363606... | [9.891876220703125, -1.868166446685791] |
82cdba52-746c-4201-9316-acd87054e8c0 | interactive-open-ended-learning-for-3d-object | 1912.09539 | null | https://arxiv.org/abs/1912.09539v1 | https://arxiv.org/pdf/1912.09539v1.pdf | Interactive Open-Ended Learning for 3D Object Recognition | The thesis contributes in several important ways to the research area of 3D object category learning and recognition. To cope with the mentioned limitations, we look at human cognition, in particular at the fact that human beings learn to recognize object categories ceaselessly over time. This ability to refine knowled... | ['S. Hamidreza Kasaei'] | 2019-12-19 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 9.81029272e-02 1.24050051e-01 2.30609089e-01 -4.83839720e-01
4.26591814e-01 -7.10662603e-01 7.14691818e-01 3.60978603e-01
-4.89974350e-01 5.31160057e-01 -4.70597416e-01 6.92429021e-03
-3.71933550e-01 -7.41714895e-01 -5.27815044e-01 -4.92228478e-01
-4.08854574e-01 6.67315245e-01 4.75284576e-01 -2.54023790... | [7.605556011199951, -1.1871367692947388] |
7de52007-fcf6-44fb-98d4-26a18cb0efce | uncertainty-aware-gait-recognition-via | 2211.08007 | null | https://arxiv.org/abs/2211.08007v1 | https://arxiv.org/pdf/2211.08007v1.pdf | Uncertainty-aware Gait Recognition via Learning from Dirichlet Distribution-based Evidence | Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery. However, existing methods often neglect that the gallery may not contain identities corresponding to the probes, leading to recognition errors rather than raising an a... | ['Xin Yu', 'Robby T. Tan', 'Lincheng Li', 'Chen Liu', 'Beibei Lin'] | 2022-11-15 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-3.61466147e-02 -2.41084367e-01 -3.57265592e-01 -2.02874362e-01
-1.31262827e+00 -4.44859982e-01 3.67851436e-01 -1.20009094e-01
-2.35199910e-02 7.70481050e-01 3.17938216e-02 3.02051485e-01
-1.74935181e-02 -9.59785104e-01 -7.88170159e-01 -7.85314023e-01
-7.61433765e-02 8.34677756e-01 2.43815869e-01 3.30723584... | [13.989349365234375, 1.2563611268997192] |
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