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369ca808-3fea-44c0-9f7e-a4c2f47e596f | the-cone-of-silence-speech-separation-by | 2010.06007 | null | https://arxiv.org/abs/2010.06007v1 | https://arxiv.org/pdf/2010.06007v1.pdf | The Cone of Silence: Speech Separation by Localization | Given a multi-microphone recording of an unknown number of speakers talking concurrently, we simultaneously localize the sources and separate the individual speakers. At the core of our method is a deep network, in the waveform domain, which isolates sources within an angular region $\theta \pm w/2$, given an angle of ... | ['Ira Kemelmacher-Shlizerman', 'Steve Seitz', 'Vivek Jayaram', 'Teerapat Jenrungrot'] | 2020-10-12 | null | http://proceedings.neurips.cc/paper/2020/hash/f056bfa71038e04a2400266027c169f9-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/f056bfa71038e04a2400266027c169f9-Paper.pdf | neurips-2020-12 | ['audio-source-separation'] | ['audio'] | [ 8.69290158e-02 -2.27824867e-01 2.57487297e-01 5.19568361e-02
-1.55137861e+00 -8.19460392e-01 -7.34260157e-02 3.80657874e-02
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-8.52227151e-01 3.16329867e-01 2.91881830e-01 1.84964374... | [15.147488594055176, 5.65294885635376] |
e217ad1d-0a96-4751-a462-e32f93e4b62f | one-step-distributional-reinforcement | 2304.14421 | null | https://arxiv.org/abs/2304.14421v1 | https://arxiv.org/pdf/2304.14421v1.pdf | One-Step Distributional Reinforcement Learning | Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return. In the distributional RL (DistrRL) paradigm, the agent goes beyond the limit of the expected value, to capture the underlying probability distribution of the return across all time steps. ... | ['Eric Moulines', 'Kirill Fedyanin', 'Yasser Abdelaziz Dahou Djilali', 'REDA ALAMI', 'Mastane Achab'] | 2023-04-27 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.66162068e-01 8.96814242e-02 -2.38230914e-01 1.69265315e-01
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-1.90407574e-01 4.44205105e-01 9.14487150e-03 -2.33910978... | [4.166033744812012, 2.4865119457244873] |
7273bda7-1a81-40e7-abd7-446dda1a1933 | the-acl-ocl-corpus-advancing-open-science-in | 2305.14996 | null | https://arxiv.org/abs/2305.14996v1 | https://arxiv.org/pdf/2305.14996v1.pdf | The ACL OCL Corpus: advancing Open science in Computational Linguistics | We present a scholarly corpus from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain, named as ACL OCL. Compared with previous ARC and AAN versions, ACL OCL includes structured full-texts with logical sections, references to figures, and links to a large knowledge resource (se... | ['Min-Yen Kan', 'Niranjana Unnithan', 'Benjamin Aw', 'Yanxia Qin', 'Shaurya Rohatgi'] | 2023-05-24 | null | null | null | null | ['chunking'] | ['natural-language-processing'] | [-5.36316395e-01 6.89313710e-01 -6.08252287e-01 -4.51534316e-02
-1.11575389e+00 -7.87194073e-01 6.64675295e-01 5.46793163e-01
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1.56338230e-01 6.84342027e-01 -9.79550853e-02 -7.68032670... | [9.779560089111328, 8.540742874145508] |
c6e2c46c-f3f2-4903-adcd-7284a43d8254 | quad-networks-unsupervised-learning-to-rank | 1611.07571 | null | http://arxiv.org/abs/1611.07571v2 | http://arxiv.org/pdf/1611.07571v2.pdf | Quad-networks: unsupervised learning to rank for interest point detection | Several machine learning tasks require to represent the data using only a
sparse set of interest points. An ideal detector is able to find the
corresponding interest points even if the data undergo a transformation typical
for a given domain. Since the task is of high practical interest in computer
vision, many hand-cr... | ['Marc Pollefeys', 'Torsten Sattler', 'Lubor Ladicky', 'Akihito Seki', 'Nikolay Savinov'] | 2016-11-22 | quad-networks-unsupervised-learning-to-rank-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Savinov_Quad-Networks_Unsupervised_Learning_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Savinov_Quad-Networks_Unsupervised_Learning_CVPR_2017_paper.pdf | cvpr-2017-7 | ['interest-point-detection'] | ['computer-vision'] | [ 3.31813842e-01 1.23066135e-01 -8.68841708e-02 -4.04279530e-01
-1.25511980e+00 -5.47040641e-01 7.68435657e-01 3.88451874e-01
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9.39594209e-03 7.36963809e-01 5.91820598e-01 -5.04268780... | [8.065537452697754, -1.941015601158142] |
18a664c5-c5f4-4850-986d-f3c1305c74b9 | neuralfield-ldm-scene-generation-with | 2304.09787 | null | https://arxiv.org/abs/2304.09787v1 | https://arxiv.org/pdf/2304.09787v1.pdf | NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models | Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing complex 3D environments. We leverage Latent Diffusion Models that have been suc... | ['Sanja Fidler', 'Antonio Torralba', 'Robin Rombach', 'Daiqing Li', 'Katja Schwarz', 'Karsten Kreis', 'Kangxue Yin', 'Bradley Brown', 'Seung Wook Kim'] | 2023-04-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_NeuralField-LDM_Scene_Generation_With_Hierarchical_Latent_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_NeuralField-LDM_Scene_Generation_With_Hierarchical_Latent_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-generation'] | ['computer-vision'] | [ 4.94124353e-01 3.21436018e-01 4.57369417e-01 -3.50529015e-01
-4.93407100e-01 -4.81123418e-01 1.05820227e+00 -1.89265117e-01
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4.57089534e-03 8.71263325e-01 5.79336397e-02 -1.58816308... | [9.153534889221191, -3.157683849334717] |
d46c71a7-3c2d-45ed-a6d7-c7d3b415d059 | focus-manipulation-detection-via-photometric | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Focus_Manipulation_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Focus_Manipulation_Detection_CVPR_2018_paper.pdf | Focus Manipulation Detection via Photometric Histogram Analysis | With the rise of misinformation spread via social media channels, enabled by the increasing automation and realism of image manipulation tools, image forensics is an increasingly relevant problem. Classic image forensic methods leverage low-level cues such as metadata, sensor noise fingerprints, and others that are ea... | ['Can Chen', 'Scott McCloskey', 'Jingyi Yu'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['image-forensics'] | ['computer-vision'] | [ 5.91689229e-01 -2.13115156e-01 4.07932162e-01 3.57692018e-02
-7.68645823e-01 -8.92162800e-01 6.36252701e-01 3.91314505e-03
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-2.17847452e-02 -4.13000643e-01 -8.52047265e-01 -5.19423306e-01
-3.70226800e-02 -7.70992190e-02 2.50074923e-01 -8.24793875... | [12.40837287902832, 1.0267765522003174] |
47fdb896-9ee5-4f17-82a7-9e081f59f14c | towards-robust-and-transferable-iiot-sensor | 2110.03440 | null | https://arxiv.org/abs/2110.03440v1 | https://arxiv.org/pdf/2110.03440v1.pdf | Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence | The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms on industrial assets enables great opportunities for anomaly classification in industrial plants. The performance of such a classification model depends highly on the available training data. Models perform well when the training data comes fr... | ['Daniel Schall', 'Stefan von Dosky', 'Herbert Grieb', 'Thomas Bierweiler', 'Jana Kemnitz'] | 2021-10-07 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 9.64244008e-02 -3.46880406e-01 2.77565897e-01 -1.51031524e-01
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-1.26427501e-01 6.01786852e-01 1.73411682e-01 -1.85269609... | [6.8937788009643555, 2.3538951873779297] |
51b9905f-e6aa-4783-b6c8-6ad27bbdab88 | a-vision-transformer-approach-for-efficient | 2305.02074 | null | https://arxiv.org/abs/2305.02074v2 | https://arxiv.org/pdf/2305.02074v2.pdf | A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution | In this paper, we develop a novel super-resolution algorithm for near-field synthetic-aperture radar (SAR) under irregular scanning geometries. As fifth-generation (5G) millimeter-wave (mmWave) devices are becoming increasingly affordable and available, high-resolution SAR imaging is feasible for end-user applications ... | ['Murat Torlak', 'Geetika Vedula', 'Yusef Alimam', 'Josiah Smith'] | 2023-05-03 | null | null | null | null | ['image-super-resolution', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 9.19214249e-01 -2.48204827e-01 1.85391322e-01 -3.70264977e-01
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0218097b-43c5-411f-8459-08771c93e73c | development-of-a-fire-detection-system-on | 2212.03709 | null | https://arxiv.org/abs/2212.03709v1 | https://arxiv.org/pdf/2212.03709v1.pdf | Development Of A Fire Detection System On Satellite Images | This paper discusses the development of a convolutional architecture of a deep neural network for the recognition of wildfires on satellite images. Based on the results of image classification, a fuzzy cognitive map of the analysis of the macroeconomic situation was built. The paper also considers the prospect of using... | ['Alexey Averkin', 'Sergey Yarushev'] | 2022-12-07 | null | null | null | null | ['fire-detection'] | ['time-series'] | [-2.55648464e-01 -2.81053066e-01 6.34740472e-01 -3.50616753e-01
2.40881130e-01 -3.60727280e-01 6.49503887e-01 1.54106155e-01
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-3.98598760e-01 2.09901974e-01 -2.27824256e-01 -9.73328769... | [9.558145523071289, -1.4554810523986816] |
784dd9cb-0c7e-4cbf-b826-a635f60e83b0 | sketch-based-video-object-localization | 2304.00450 | null | https://arxiv.org/abs/2304.00450v1 | https://arxiv.org/pdf/2304.00450v1.pdf | Sketch-based Video Object Localization | We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch. We first outline the challenges in the SVOL task and build the Sketch-Video Attention Network (SVANet) with the following design principles: (i) to consider tempo... | ['Changick Kim', 'Sumin Lee', 'Minji Son', 'Jinyoung Park', 'So-Yeong Jeon', 'Sangmin Woo'] | 2023-04-02 | null | null | null | null | ['object-localization'] | ['computer-vision'] | [-8.46643746e-02 -5.62951386e-01 -3.65645677e-01 -3.45396325e-02
-6.76341712e-01 -6.76269412e-01 6.35247111e-01 -2.93618411e-01
-2.98486650e-01 2.38995224e-01 1.82287946e-01 -2.01750305e-02
-7.56143034e-02 -4.87043709e-01 -8.56362820e-01 -2.83768624e-01
-3.96067142e-01 2.94754475e-01 2.67838746e-01 1.64642021... | [9.74278450012207, 0.7647556662559509] |
51598989-89d8-40fb-85db-aecb6cd4ec05 | not-only-generative-art-stable-diffusion-for | 2304.10278 | null | https://arxiv.org/abs/2304.10278v1 | https://arxiv.org/pdf/2304.10278v1.pdf | Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis | The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of art, and style to the way it is expressed. This duality poses an important challenge for computer vision. The visual appearance of objects a... | ['Noa Garcia', 'Yuta Nakashima', 'Yankun Wu'] | 2023-04-20 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 1.52261004e-01 -1.70134619e-01 3.48799825e-02 -2.80544162e-01
1.28246203e-01 -1.00147617e+00 1.12653530e+00 4.47634608e-02
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4.42230225e-01 6.73420846e-01 3.03495415e-02 -4.32886273... | [11.369782447814941, 0.13686326146125793] |
5e79a28b-253c-401a-8417-10a83d83754d | face-phylogeny-tree-using-basis-functions | 2002.09068 | null | https://arxiv.org/abs/2002.09068v2 | https://arxiv.org/pdf/2002.09068v2.pdf | Face Phylogeny Tree Using Basis Functions | Photometric transformations, such as brightness and contrast adjustment, can be applied to a face image repeatedly creating a set of near-duplicate images. Identifying the original image from a set of such near-duplicates and deducing the relationship between them are important in the context of digital image forensics... | ['Sudipta Banerjee', 'Arun Ross'] | 2020-02-21 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 4.43114549e-01 -1.28441155e-01 4.01151717e-01 -4.24099475e-01
-3.16125870e-01 -7.52266824e-01 6.93085670e-01 -1.92190737e-01
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2.18620244e-02 3.42829973e-01 -5.79694547e-02 -6.24475963... | [12.7443265914917, 0.7849080562591553] |
5dbdffc5-a55f-4a86-8f5d-b98c3527d194 | medical-diffusion-denoising-diffusion | 2211.03364 | null | https://arxiv.org/abs/2211.03364v7 | https://arxiv.org/pdf/2211.03364v7.pdf | Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation | Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimen... | ['Daniel Truhn', 'Jakob Nikolas Kather', 'Sven Nebelung', 'Christiane Kuhl', 'Johannes Stegmaier', 'Sebastian Foersch', 'Bettina Baessler', 'Sandy Engelhardt', 'Philipp Schad', 'Maximilian Schulze-Hagen', 'Christoph Haarburger', 'Tianyu Han', 'Soroosh Tayebi Arasteh', 'Gustav Mueller-Franzes', 'Firas Khader'] | 2022-11-07 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 3.64544898e-01 6.72613263e-01 3.62686776e-02 -5.12523413e-01
-1.01959574e+00 -5.89170694e-01 5.77276170e-01 2.42029831e-01
-6.22660339e-01 8.54012311e-01 3.66459996e-01 -4.56400961e-01
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-1.46946888e-02 7.83060908e-01 4.00927365e-02 2.95064181... | [14.291831970214844, -1.9556490182876587] |
71c09a32-091f-4884-b98d-ac81bbd00d94 | sess-saliency-enhancing-with-scaling-and | 2207.01769 | null | https://arxiv.org/abs/2207.01769v1 | https://arxiv.org/pdf/2207.01769v1.pdf | SESS: Saliency Enhancing with Scaling and Sliding | High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techniques have been developed to generate better saliency using neural networks. However, they are often limited to specific saliency visualisati... | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Osman Tursun'] | 2022-07-05 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 4.81802434e-01 1.38816284e-02 -1.75517216e-01 -3.67571563e-01
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9.62471366e-02 -1.47746220e-01 1.21499336e+00 -3.51610720... | [9.807555198669434, -0.35700753331184387] |
07b3bd00-cc1d-4386-98ac-a9e93fe9ee88 | an-alternative-to-wsss-an-empirical-study-of | 2305.01586 | null | https://arxiv.org/abs/2305.01586v2 | https://arxiv.org/pdf/2305.01586v2.pdf | An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems | The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the application of SAM in Weakly-Supervised Semantic Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generation pipeline given o... | ['Nick Barnes', 'Yiran Zhong', 'Yanhao Zhang', 'Zheyuan Liu', 'Weixuan Sun'] | 2023-05-02 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 5.42477369e-01 2.25776464e-01 -3.47896725e-01 -7.49768853e-01
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1.31752044e-01 5.75138748e-01 8.45214009e-01 1.08423904... | [9.543098449707031, 0.5813727378845215] |
c075f15b-3cbf-4b1c-9837-e8643e31ffd5 | a-comprehensive-analysis-of-acknowledgement | 2210.09716 | null | https://arxiv.org/abs/2210.09716v1 | https://arxiv.org/pdf/2210.09716v1.pdf | A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains | Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher's collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The... | ['Philipp Mayr', 'Nina Smirnova'] | 2022-10-18 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-4.53914642e-01 1.81319043e-01 -3.12603205e-01 2.42107660e-01
-3.00651163e-01 -8.13616753e-01 8.25018585e-01 5.43024242e-01
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3.63978446e-01 2.22173318e-01 -3.58449831e-03 5.33217564... | [9.590310096740723, 8.296892166137695] |
009a51d0-b4eb-48e2-8ae1-d8f7b95794ea | towards-optimizing-reiters-hs-tree-for | 1907.12130 | null | https://arxiv.org/abs/1907.12130v1 | https://arxiv.org/pdf/1907.12130v1.pdf | Towards Optimizing Reiter's HS-Tree for Sequential Diagnosis | Reiter's HS-Tree is one of the most popular diagnostic search algorithms due to its desirable properties and general applicability. In sequential diagnosis, where the addressed diagnosis problem is subject to successive change through the acquisition of additional knowledge about the diagnosed system, HS-Tree is used i... | ['Patrick Rodler'] | 2019-07-28 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 3.73173058e-01 5.18621802e-01 -3.00843388e-01 -2.11791620e-01
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-2.42888164e-02 1.03856552e+00 8.79867315e-01 1.20607583... | [5.419331073760986, 2.7204294204711914] |
c21da666-90b7-4a3b-8fd7-404a258aee07 | instructedit-improving-automatic-masks-for | 2305.18047 | null | https://arxiv.org/abs/2305.18047v1 | https://arxiv.org/pdf/2305.18047v1.pdf | InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions | Recent works have explored text-guided image editing using diffusion models and generated edited images based on text prompts. However, the models struggle to accurately locate the regions to be edited and faithfully perform precise edits. In this work, we propose a framework termed InstructEdit that can do fine-graine... | ['Peter Wonka', 'Michael Birsak', 'Biao Zhang', 'Qian Wang'] | 2023-05-29 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 4.36279476e-01 4.07366678e-02 2.55568653e-01 -4.17801708e-01
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5.65976679e-01 5.69163859e-01 7.56958187e-01 -4.18834761... | [11.265953063964844, -0.52339106798172] |
f2d03543-9b5f-4b44-ad0d-cd91efcc13d3 | one-shot-recognition-of-any-material-anywhere | 2212.00648 | null | https://arxiv.org/abs/2212.00648v4 | https://arxiv.org/pdf/2212.00648v4.pdf | One-shot recognition of any material anywhere using contrastive learning with physics-based rendering | Visual recognition of materials and their states is essential for understanding most aspects of the world, from determining whether food is cooked, metal is rusted, or a chemical reaction has occurred. However, current image recognition methods are limited to specific classes and properties and can't handle the vast nu... | ['Alan Aspuru-Guzik', 'Han Hao', 'Jolina Li', 'Sagi Eppel', 'Manuel S. Drehwald'] | 2022-12-01 | null | null | null | null | ['material-classification', 'material-recognition', 'one-shot-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.05733311e-01 -6.02465332e-01 -2.80489862e-01 -1.76313639e-01
-1.74448162e-01 -7.95434833e-01 7.99818695e-01 3.16215456e-01
7.45519772e-02 5.70838079e-02 -1.72587052e-01 9.66841653e-02
1.42411903e-01 -8.94009888e-01 -9.46026921e-01 -9.43313718e-01
-2.54094470e-02 6.17475569e-01 3.30970913e-01 -4.47331995... | [10.176905632019043, -0.08873289823532104] |
f4b98134-212b-4d0e-8c35-4e9a7d8f101f | back-to-the-feature-learning-robust-camera | 2103.09213 | null | https://arxiv.org/abs/2103.09213v2 | https://arxiv.org/pdf/2103.09213v2.pdf | Back to the Feature: Learning Robust Camera Localization from Pixels to Pose | Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go... | ['Torsten Sattler', 'Fredrik Kahl', 'Lars Hammarstrand', 'Vincent Lepetit', 'Marc Pollefeys', 'Viktor Larsson', 'Carl Toft', 'Hugo Germain', 'Måns Larsson', 'Ajaykumar Unagar', 'Paul-Edouard Sarlin'] | 2021-03-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sarlin_Back_to_the_Feature_Learning_Robust_Camera_Localization_From_Pixels_CVPR_2021_paper.pdf | cvpr-2021-1 | ['camera-localization'] | ['computer-vision'] | [-1.59455732e-01 2.56780796e-02 -1.24706984e-01 -5.22245526e-01
-1.13274455e+00 -8.42344999e-01 5.60641587e-01 -1.94453999e-01
-3.32537025e-01 1.43234387e-01 9.32134241e-02 1.18929952e-01
-1.51898891e-01 -4.43258464e-01 -1.14653659e+00 -4.06695753e-01
2.26347715e-01 1.00556755e+00 6.56645745e-02 1.12577572... | [7.765594005584717, -2.484361410140991] |
6e33fe4f-fb8c-4876-a4d8-3df6c271cb4c | audiocaps-generating-captions-for-audios-in | null | null | https://aclanthology.org/N19-1011 | https://aclanthology.org/N19-1011.pdf | AudioCaps: Generating Captions for Audios in The Wild | We explore the problem of Audio Captioning: generating natural language description for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Ou... | ['Chris Dongjoo Kim', 'Byeongchang Kim', 'Hyunmin Lee', 'Gunhee Kim'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['audio-captioning'] | ['audio'] | [ 5.62855601e-01 3.53828043e-01 1.07454151e-01 -4.40483272e-01
-1.91426408e+00 -7.81369686e-01 3.57445538e-01 -1.83195651e-01
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-2.01902002e-01 5.07033169e-01 1.84147462e-01 -3.56530339... | [15.294219017028809, 4.93602180480957] |
76e49071-3c13-47f6-9f67-6a4ac8eeb550 | synthesizing-annotated-image-and-video-data | 2209.14448 | null | https://arxiv.org/abs/2209.14448v1 | https://arxiv.org/pdf/2209.14448v1.pdf | Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition | An insufficient number of training samples is a common problem in neural network applications. While data augmentation methods require at least a minimum number of samples, we propose a novel, rendering-based pipeline for synthesizing annotated data sets. Our method does not modify existing samples but synthesizes enti... | ['André Kaup', 'Christian Riess', 'Jürgen Seiler', 'Denise Moussa', 'Anatol Maier', 'Maximilane Gruber', 'Andreas Spruck'] | 2022-09-28 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 8.59464049e-01 1.24628522e-01 3.31858754e-01 -3.74788254e-01
-8.13625634e-01 -5.62545717e-01 6.65193081e-01 5.04771285e-02
-7.86232173e-01 7.74440348e-01 -3.76443356e-01 -7.35784993e-02
4.18308884e-01 -7.92039812e-01 -8.75691414e-01 -6.21550083e-01
4.90571469e-01 5.11872113e-01 3.01517189e-01 6.96239769... | [9.949886322021484, -4.577886581420898] |
7c407fe7-cad2-43b9-93fe-5cc11413d099 | sketch-guided-text-to-image-diffusion-models | 2211.13752 | null | https://arxiv.org/abs/2211.13752v1 | https://arxiv.org/pdf/2211.13752v1.pdf | Sketch-Guided Text-to-Image Diffusion Models | Text-to-Image models have introduced a remarkable leap in the evolution of machine learning, demonstrating high-quality synthesis of images from a given text-prompt. However, these powerful pretrained models still lack control handles that can guide spatial properties of the synthesized images. In this work, we introdu... | ['Daniel Cohen-Or', 'Kfir Aberman', 'Andrey Voynov'] | 2022-11-24 | null | null | null | null | ['sketch-to-image-translation'] | ['computer-vision'] | [ 6.25664949e-01 1.94437444e-01 -1.42340243e-01 -3.41165870e-01
-8.92442703e-01 -6.11263990e-01 1.05093050e+00 -5.62190354e-01
-4.67122048e-02 6.14273906e-01 9.87733081e-02 -9.03809816e-02
4.35544029e-02 -9.94203389e-01 -1.10253513e+00 -9.15061295e-01
4.30027694e-01 5.74374080e-01 1.78518385e-01 -3.93781215... | [11.51987361907959, -0.5101370811462402] |
aadecf70-6a71-45ea-b0e9-cdd5ab157847 | 3d-colored-shape-reconstruction-from-a-single | 2302.05573 | null | https://arxiv.org/abs/2302.05573v1 | https://arxiv.org/pdf/2302.05573v1.pdf | 3D Colored Shape Reconstruction from a Single RGB Image through Diffusion | We propose a novel 3d colored shape reconstruction method from a single RGB image through diffusion model. Diffusion models have shown great development potentials for high-quality 3D shape generation. However, most existing work based on diffusion models only focus on geometric shape generation, they cannot either acc... | ['Bin Liu', 'Fengwei Chen', 'Xiaolin Wei', 'Bo Li'] | 2023-02-11 | null | null | null | null | ['3d-shape-generation', '3d-shape-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.18747979e-01 -2.05607489e-01 4.69618469e-01 2.49039866e-02
-3.81838471e-01 -3.65688443e-01 4.30196911e-01 -4.34861004e-01
-1.32590503e-01 1.57283917e-01 -2.24100873e-01 -3.58700871e-01
6.92290217e-02 -1.31900811e+00 -4.90875393e-01 -9.31950331e-01
5.30605853e-01 5.22963464e-01 4.35029000e-01 -1.22572429... | [8.7689847946167, -3.3939883708953857] |
ec2d31b2-3317-4ff1-8350-fed701549108 | aggressionnet-generalised-multi-modal-deep | 2001.05493 | null | https://arxiv.org/abs/2001.05493v2 | https://arxiv.org/pdf/2001.05493v2.pdf | A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts | Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. Majority of such conversations contains code-mixed languages[28]. Additionally, the way used to expr... | ['Niraj Kumar', 'Anant Khandelwal'] | 2020-01-15 | null | null | null | null | ['style-generalization', 'aggression-identification'] | ['computer-vision', 'natural-language-processing'] | [-8.36431444e-01 -1.26346350e-01 9.58473384e-02 -2.17763454e-01
-3.45813572e-01 -3.25525999e-01 4.28255588e-01 2.35503271e-01
-5.76174140e-01 7.35116720e-01 3.46617699e-01 -2.52818942e-01
6.58068880e-02 -7.18126059e-01 -3.21851403e-01 -3.13628018e-01
-1.76170953e-02 3.71500291e-02 -1.12844974e-01 -5.53969860... | [8.957769393920898, 10.588452339172363] |
a14e8623-7480-40a4-89c8-6cc792d4c91c | maximising-weather-forecasting-accuracy | 2301.12471 | null | https://arxiv.org/abs/2301.12471v1 | https://arxiv.org/pdf/2301.12471v1.pdf | Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs | Weather forecasting is an essential task to tackle global climate change. Weather forecasting requires the analysis of multivariate data generated by heterogeneous meteorological sensors. These sensors comprise of ground-based sensors, radiosonde, and sensors mounted on satellites, etc., To analyze the data generated b... | ['Shreelakshmi C R', 'Surya Durbha', 'Gaganpreet Singh'] | 2023-01-29 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.32368773e-01 -2.30951354e-01 9.09993127e-02 -4.93238986e-01
5.39685845e-01 -2.72346228e-01 6.42884910e-01 3.63599151e-01
2.16782675e-03 9.79774356e-01 1.23366013e-01 -7.65067279e-01
-1.05016112e-01 -1.60243773e+00 -2.00804010e-01 -6.81363225e-01
-6.76657200e-01 2.22338811e-01 3.54700714e-01 -9.18062627... | [6.630339622497559, 2.7950057983398438] |
951ae201-a010-4b67-baaf-251275d084e9 | flexible-and-explainable-solutions-for-multi | 2109.08299 | null | https://arxiv.org/abs/2109.08299v1 | https://arxiv.org/pdf/2109.08299v1.pdf | Flexible and Explainable Solutions for Multi-Agent Path Finding Problems | The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. The real-world applicati... | ['Aysu Bogatarkan'] | 2021-09-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.98813155e-01 3.72743428e-01 -9.45651159e-02 -1.64282426e-01
-3.61367092e-02 -1.05096614e+00 1.71648413e-01 5.92065513e-01
-1.29049987e-01 1.03105438e+00 -5.38988352e-01 -3.43471169e-01
-9.68551993e-01 -9.88093495e-01 -6.00731015e-01 -5.20447671e-01
-6.35203123e-01 1.04925466e+00 3.37057650e-01 -6.18917286... | [4.952594757080078, 1.7424193620681763] |
cb336239-d5d6-4506-a5a9-c59a3874e625 | distributed-heuristic-multi-agent-path | 2106.11365 | null | https://arxiv.org/abs/2106.11365v1 | https://arxiv.org/pdf/2106.11365v1.pdf | Distributed Heuristic Multi-Agent Path Finding with Communication | Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn cooperation to handle congeste... | ['Hang Ma', 'Yudong Luo', 'Ziyuan Ma'] | 2021-06-21 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.07331234e-01 4.86440569e-01 -2.94064283e-01 6.63102269e-02
-6.35238469e-01 -4.67206031e-01 5.44947922e-01 1.23090170e-01
-8.88369203e-01 1.28341627e+00 3.26034799e-02 -5.38431883e-01
-5.37725270e-01 -9.28613305e-01 -7.69021153e-01 -8.80826354e-01
-7.59181738e-01 9.60843682e-01 4.31475699e-01 -7.20663071... | [3.8705053329467773, 1.9294856786727905] |
9cdcdb29-d64c-4a6e-a046-7ed186f322fc | resource-allocation-algorithm-for-mec-based | null | null | https://ieeexplore.ieee.org/document/9679368 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9679368 | Resource allocation algorithm for MEC based on Deep Reinforcement Learning | In recent years, driven by the commercialization of the 6th Generation Communication Technology (6G), an increasing number of 6G devices connected to mobile networks produces computation-intensive tasks such as ultra-high-resolution video streaming, inter-active visual reality (VR) gaming, augmented reality (AR). Howev... | ['Shougang Du', 'Ying Chen', 'Xin Chen', 'Yijie Wang'] | 2022-01-20 | null | null | null | ieee-2022-1 | ['edge-computing'] | ['time-series'] | [-2.03156948e-01 -1.36808038e-01 -3.21518958e-01 4.49535072e-01
1.20606206e-01 -3.71111393e-01 -1.35739639e-01 -7.10379958e-01
-8.19945410e-02 7.64496505e-01 8.61444175e-02 -4.42502350e-01
-3.08044463e-01 -9.04885113e-01 -1.15838714e-01 -7.73118258e-01
-2.74621248e-01 2.30820373e-01 3.61410499e-01 6.58394992... | [5.969771385192871, 1.5838299989700317] |
a66b443e-d61d-4cd3-94d3-738cefb78363 | dressing-in-order-recurrent-person-image | 2104.07021 | null | https://arxiv.org/abs/2104.07021v3 | https://arxiv.org/pdf/2104.07021v3.pdf | Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing | We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different order... | ['Svetlana Lazebnik', 'Daniel McKee', 'Aiyu Cui'] | 2021-04-14 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cui_Dressing_in_Order_Recurrent_Person_Image_Generation_for_Pose_Transfer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cui_Dressing_in_Order_Recurrent_Person_Image_Generation_for_Pose_Transfer_ICCV_2021_paper.pdf | iccv-2021-1 | ['pose-transfer'] | ['computer-vision'] | [ 4.59862798e-01 2.24249810e-01 4.18716908e-01 -1.11717835e-01
-3.38938594e-01 -6.60294473e-01 3.80818963e-01 -4.00786281e-01
2.80872405e-01 7.16666818e-01 6.15551889e-01 3.55691254e-01
1.97994515e-01 -9.35134470e-01 -1.06017196e+00 -2.81890273e-01
2.27103621e-01 6.12476289e-01 -1.69291452e-01 -6.15182877... | [11.90518856048584, -0.8050904273986816] |
fad30418-068e-4301-ab8e-81918d895f59 | generalized-object-search | 2301.10121 | null | https://arxiv.org/abs/2301.10121v2 | https://arxiv.org/pdf/2301.10121v2.pdf | Generalized Object Search | Future collaborative robots must be capable of finding objects. As such a fundamental skill, we expect object search to eventually become an off-the-shelf capability for any robot, similar to e.g., object detection, SLAM, and motion planning. However, existing approaches either make unrealistic compromises (e.g., reduc... | ['Kaiyu Zheng'] | 2023-01-24 | null | null | null | null | ['motion-planning'] | ['robots'] | [-3.66459221e-01 1.30018353e-01 -1.25969276e-01 -1.30721509e-01
-4.49158847e-01 -8.28375578e-01 4.59021568e-01 1.25923917e-01
-5.30988514e-01 6.85895860e-01 -5.80751240e-01 -4.32397753e-01
-6.41961932e-01 -3.71352375e-01 -5.12576163e-01 -4.24959958e-01
-4.82991248e-01 1.23457909e+00 3.68796706e-01 -9.23218355... | [4.88897180557251, 1.0883888006210327] |
eb4deca5-6d6c-4d66-b33d-e54040ebde97 | diffusion-probabilistic-models-for-3d-point | 2103.01458 | null | https://arxiv.org/abs/2103.01458v2 | https://arxiv.org/pdf/2103.01458v2.pdf | Diffusion Probabilistic Models for 3D Point Cloud Generation | We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation. Inspired by the diffusion process in non-equilibrium thermodynamics, we view points in point clouds as particles in a thermodynamic system ... | ['Wei Hu', 'Shitong Luo'] | 2021-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Luo_Diffusion_Probabilistic_Models_for_3D_Point_Cloud_Generation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Luo_Diffusion_Probabilistic_Models_for_3D_Point_Cloud_Generation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-generation'] | ['computer-vision'] | [ 2.23497540e-01 1.56976119e-01 1.33604005e-01 -1.45423964e-01
-8.33719909e-01 -6.03974044e-01 8.62493396e-01 -1.70542032e-01
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2.89400697e-01 -1.02386200e+00 -1.02135301e+00 -9.48270917e-01
4.10882324e-01 9.51072097e-01 -2.18414158e-01 2.74476886... | [8.876977920532227, -3.650486707687378] |
5861ceaa-09a2-4f03-a610-79cf2e5b9e27 | is-this-a-joke-detecting-humor-in-spanish | 1703.09527 | null | http://arxiv.org/abs/1703.09527v1 | http://arxiv.org/pdf/1703.09527v1.pdf | Is This a Joke? Detecting Humor in Spanish Tweets | While humor has been historically studied from a psychological, cognitive and
linguistic standpoint, its study from a computational perspective is an area
yet to be explored in Computational Linguistics. There exist some previous
works, but a characterization of humor that allows its automatic recognition
and generatio... | ['Guillermo Moncecchi', 'Matías Cubero', 'Diego Garat', 'Santiago Castro'] | 2017-03-28 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-4.00558263e-01 3.14090878e-01 -2.48718604e-01 -2.95467824e-01
-1.26470178e-01 -4.62361246e-01 8.13863337e-01 5.03245950e-01
-2.72476196e-01 7.18742728e-01 5.41940928e-01 -2.00631365e-01
4.77603853e-01 -7.10034847e-01 2.65593603e-02 -4.13595706e-01
3.14708084e-01 6.12804830e-01 1.72605842e-01 -5.28946936... | [8.908088684082031, 10.973648071289062] |
d38d835e-0eec-40ec-bb1e-c3a1245e9d84 | machine-learning-for-music-genre-multifaceted | 1911.12618 | null | https://arxiv.org/abs/1911.12618v1 | https://arxiv.org/pdf/1911.12618v1.pdf | Machine learning for music genre: multifaceted review and experimentation with audioset | Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there ... | ['Jaime Ramírez', 'M. Julia Flores'] | 2019-11-28 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.51276785e-01 -3.70973676e-01 -3.93900692e-01 1.84018742e-02
-7.02588558e-01 -8.26626956e-01 4.60444957e-01 1.37229979e-01
-2.55858749e-01 5.04571199e-01 3.02302867e-01 3.31173182e-01
-6.56880617e-01 -3.13846081e-01 5.74886799e-04 -7.39286840e-01
-1.85628757e-01 6.31098628e-01 -1.33440405e-01 -4.57283169... | [15.934735298156738, 5.187588214874268] |
5a34e213-c921-4c39-b57d-a7b6a69818ce | investigating-annotator-bias-in-abusive | null | null | https://aclanthology.org/2021.ranlp-main.170 | https://aclanthology.org/2021.ranlp-main.170.pdf | Investigating Annotator Bias in Abusive Language Datasets | Nowadays, social media platforms use classification models to cope with hate speech and abusive language. The problem of these models is their vulnerability to bias. A prevalent form of bias in hate speech and abusive language datasets is annotator bias caused by the annotator’s subjective perception and the complexity... | ['Georg Groh', 'Gerhard Hagerer', 'Christian Widmer', 'Maximilian Wich'] | null | null | https://aclanthology.org/2021.ranlp-1.170 | https://aclanthology.org/2021.ranlp-1.170.pdf | ranlp-2021-9 | ['abusive-language'] | ['natural-language-processing'] | [-3.20413053e-01 1.31469414e-01 -4.41576988e-01 -4.15561110e-01
-2.11274400e-02 -1.01708305e+00 5.43963432e-01 2.48397127e-01
-4.86100733e-01 5.82231462e-01 5.58089495e-01 -3.75583358e-02
3.98578942e-01 -2.76648045e-01 1.43801168e-01 -3.87715816e-01
5.04741430e-01 1.41217604e-01 4.86090779e-02 -4.84812915... | [8.693800926208496, 10.533914566040039] |
7202338e-59a0-4425-ba0f-68ca34dd97ae | memseg-a-semi-supervised-method-for-image | 2205.00908 | null | https://arxiv.org/abs/2205.00908v1 | https://arxiv.org/pdf/2205.00908v1.pdf | MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities | Under the semi-supervised framework, we propose an end-to-end memory-based segmentation network (MemSeg) to detect surface defects on industrial products. Considering the small intra-class variance of products in the same production line, from the perspective of differences and commonalities, MemSeg introduces artifici... | ['Hui Feng', 'Jing Liu', 'Peng Wu', 'Minghui Yang'] | 2022-05-02 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.08849633e-01 2.81077594e-01 -2.69359887e-01 -4.56368059e-01
-5.90303421e-01 -1.17994696e-01 -1.32105961e-01 1.04323275e-01
4.76858206e-02 2.39172697e-01 -7.63802409e-01 -1.94742709e-01
-2.82773703e-01 -9.71651673e-01 -8.43151927e-01 -8.44206631e-01
1.36436656e-01 5.40065110e-01 2.96293586e-01 2.95310020... | [7.50601053237915, 1.968276023864746] |
ee56a6c7-b98a-479b-ae33-c0d5ae46ac80 | a-white-box-analysis-of-colbert | 2012.09650 | null | https://arxiv.org/abs/2012.09650v1 | https://arxiv.org/pdf/2012.09650v1.pdf | A White Box Analysis of ColBERT | Transformer-based models are nowadays state-of-the-art in ad-hoc Information Retrieval, but their behavior is far from being understood. Recent work has claimed that BERT does not satisfy the classical IR axioms. However, we propose to dissect the matching process of ColBERT, through the analysis of term importance and... | ['Stéphane Clinchant', 'Benjamin Piwowarski', 'Thibault Formal'] | 2020-12-17 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.08325869e-01 1.01346135e-01 -5.25595188e-01 -1.19710386e-01
-6.64490640e-01 -7.08279550e-01 1.14538682e+00 7.43551135e-01
-2.99740255e-01 4.73925978e-01 1.78276330e-01 -3.34118932e-01
-9.92664516e-01 -8.03995430e-01 -3.94847631e-01 -4.10944909e-01
-1.42600104e-01 1.15507138e+00 5.40406704e-01 -8.74905825... | [9.493657112121582, 8.060552597045898] |
ce94971d-aba6-49bb-a62b-a1711743c328 | meta-particle-flow-for-sequential-bayesian | 1902.00640 | null | https://arxiv.org/abs/1902.00640v3 | https://arxiv.org/pdf/1902.00640v3.pdf | Particle Flow Bayes' Rule | We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. Its neural parameterization can be trained in a meta-learning framework, allowing this operator to re... | ['Xinshi Chen', 'Hanjun Dai', 'Le Song'] | 2019-02-02 | null | null | null | null | ['sequential-bayesian-inference'] | ['time-series'] | [ 1.42400771e-01 1.89456239e-01 1.12437466e-02 -2.36657634e-01
-2.46757284e-01 -2.57536769e-01 1.06343901e+00 -4.21183184e-02
-5.11537790e-01 7.91582048e-01 2.58573025e-01 -1.86273545e-01
-3.44922513e-01 -1.10301185e+00 -9.46337044e-01 -8.33670199e-01
-4.27648038e-01 8.37940991e-01 6.33929372e-01 2.11051524... | [6.853137969970703, 3.8492517471313477] |
1d2e4b27-116a-4533-a318-147945e16a86 | milestones-in-autonomous-driving-and-1 | 2305.11239 | null | https://arxiv.org/abs/2305.11239v2 | https://arxiv.org/pdf/2305.11239v2.pdf | Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors | Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directi... | ['Fei-Yue Wang', 'Nanning Zheng', 'Dongpu Cao', 'Jinjun Wang', 'Chen Lv', 'Siyu Teng', 'Zhongxu Hu', 'Li Li', 'Daxin Tian', 'Yang Xing', 'Chao Huang', 'Yuchen Li', 'Long Chen'] | 2023-05-12 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-1.36031479e-01 1.48390949e-01 -4.27447021e-01 -4.58510071e-01
4.44919690e-02 -6.01309538e-01 5.48129559e-01 -2.50864983e-01
-8.37965533e-02 4.57296222e-01 -1.34367660e-01 -6.89311266e-01
-1.76955126e-02 -7.52418339e-01 -5.66066086e-01 -4.31169093e-01
6.91103563e-02 -1.20611511e-01 4.87874120e-01 -5.95275879... | [5.65463399887085, 1.0613548755645752] |
f7553ad1-be5a-4d97-a7ec-6df6494c96eb | leaving-the-nest-going-beyond-local-loss | 2305.16830 | null | https://arxiv.org/abs/2305.16830v1 | https://arxiv.org/pdf/2305.16830v1.pdf | Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize | Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, "How can the structure of a decision-making task be used to tailor ML models for that specific task?" To this end, recent work has proposed learning task-specific loss ... | ['Milind Tambe', 'Bryan Wilder', 'Andrew Perrault', 'Sanket Shah'] | 2023-05-26 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 9.43286940e-02 8.18080232e-02 -5.64399958e-01 -7.90037751e-01
-1.24788940e+00 -5.02170205e-01 4.73468035e-01 2.78422624e-01
-5.02452850e-01 9.32373405e-01 -4.55481075e-02 -6.01311326e-01
-1.61477193e-01 -4.16195214e-01 -7.37666786e-01 -5.59190691e-01
1.82964310e-01 5.87379932e-01 1.01842202e-01 1.92892224... | [8.505668640136719, 4.130135536193848] |
ef478c13-6c0a-4526-9686-57f785879ecc | dafa-diversity-aware-feature-aggregation-for | null | null | https://doi.org/10.1109/ACCESS.2022.3203399 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9874741 | DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection | We present a framework for attention-based video object detection using a simple yet effective external memory management algorithm. An attention mechanism has been adopted in video object detection task to enrich the features of key frames using adjacent frames. Although several recent studies utilized frame-level fir... | ['Ki-Seok Chung', 'Si-Dong Roh'] | 2022-09-01 | null | null | null | ieee-access-2022-9 | ['video-object-detection'] | ['computer-vision'] | [-1.51346266e-01 -5.39100945e-01 -2.52363712e-01 -3.92090306e-02
-5.23810446e-01 -8.12012330e-03 2.05409840e-01 2.03803435e-01
-8.34360063e-01 6.25492275e-01 -4.64734286e-02 7.74770975e-02
-4.19733375e-02 -8.40575576e-01 -5.16183913e-01 -5.64077795e-01
-2.45097175e-01 4.23313119e-02 9.95033801e-01 1.26473397... | [8.99653148651123, -0.09715411067008972] |
6423d5e2-ba40-4fa8-84fe-4888627e19ce | deep-extreme-multi-label-learning | 1704.03718 | null | http://arxiv.org/abs/1704.03718v4 | http://arxiv.org/pdf/1704.03718v4.pdf | Deep Extreme Multi-label Learning | Extreme multi-label learning (XML) or classification has been a practical and
important problem since the boom of big data. The main challenge lies in the
exponential label space which involves $2^L$ possible label sets especially
when the label dimension $L$ is huge, e.g., in millions for Wikipedia labels.
This paper ... | ['Wenjie Zhang', 'Hongyuan Zha', 'Xiangfeng Wang', 'Junchi Yan'] | 2017-04-12 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.80953350e-02 2.05211863e-01 -2.52218902e-01 -4.80224758e-01
-8.34874809e-01 -4.45028573e-01 1.38335899e-01 4.89160776e-01
-4.23709244e-01 4.03076023e-01 1.03160292e-01 -2.05177426e-01
-3.16375464e-01 -8.23104084e-01 -3.31130087e-01 -8.93968701e-01
2.10534539e-02 5.82769692e-01 -1.99128792e-01 -7.82167241... | [9.600759506225586, 4.303903102874756] |
439dd7df-d3ee-40ca-bf53-6ff8d35ddd4d | unsupervised-dialogue-topic-segmentation-with | 2305.02747 | null | https://arxiv.org/abs/2305.02747v1 | https://arxiv.org/pdf/2305.02747v1.pdf | Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation | Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to assess topic similarity for unsupervised dialogue segmentation. However, the topic similarity cannot be fully identified via semantic simil... | ['Yongbin Li', 'Fei Huang', 'Min Yang', 'Yuchuan Wu', 'Ting-En Lin', 'Rui Wang', 'Haoyu Gao'] | 2023-05-04 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.34265719e-02 5.73149204e-01 -2.23734900e-01 -9.08139944e-01
-1.00125587e+00 -6.96978271e-01 7.53069878e-01 2.47112021e-01
-7.27349147e-02 7.33560324e-01 6.62799954e-01 -2.09807694e-01
3.75310153e-01 -4.91021901e-01 -1.04289964e-01 -4.02703047e-01
4.55676019e-01 8.79873157e-01 4.85862285e-01 -5.22045076... | [12.607833862304688, 8.061488151550293] |
b6810260-9886-4f94-94d8-3ec6e499e680 | detection-and-classification-of-brain-tumors | 2208.13264 | null | https://arxiv.org/abs/2208.13264v1 | https://arxiv.org/pdf/2208.13264v1.pdf | Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks | Abnormal development of tissues in the body as a result of swelling and morbid enlargement is known as a tumor. They are mainly classified as Benign and Malignant. Tumour in the brain is fatal as it may be cancerous, so it can feed on healthy cells nearby and keep increasing in size. This may affect the soft tissues, n... | ['Harsh Kirty', 'Ranit Sen', 'Gopinath Balaji'] | 2022-08-28 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-5.78489490e-02 2.87505776e-01 3.31899583e-01 -4.17887084e-02
3.61197352e-01 2.16387300e-04 5.19088566e-01 1.80951595e-01
-6.31542146e-01 8.42015147e-01 1.15431167e-01 -4.47615325e-01
2.25274250e-01 -8.58076453e-01 -4.32913929e-01 -8.74445140e-01
-3.07197511e-01 3.30573797e-01 5.45686603e-01 -2.37930968... | [14.827262878417969, -2.593012571334839] |
65576461-dad5-41e9-83f9-c8844c5dcb23 | multi-scale-geometry-aware-transformer-for-3d | 2304.05694 | null | https://arxiv.org/abs/2304.05694v1 | https://arxiv.org/pdf/2304.05694v1.pdf | Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification | Self-attention modules have demonstrated remarkable capabilities in capturing long-range relationships and improving the performance of point cloud tasks. However, point cloud objects are typically characterized by complex, disordered, and non-Euclidean spatial structures with multiple scales, and their behavior is oft... | ['Xuan Tang', 'Arafat Al-Jawari', 'Jian Yang', 'Zhengyu Li', 'Shing-Ho Jonathan Lin', 'Muyu Wang', 'Xian Wei'] | 2023-04-12 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-4.84560788e-01 -3.43767732e-01 2.51981080e-01 -2.81542033e-01
-7.37200260e-01 -3.69626731e-01 3.89927477e-01 3.36812995e-02
1.00602381e-01 6.24002144e-02 -1.11234911e-01 -4.84044757e-03
-2.98541337e-01 -1.05166507e+00 -1.05278146e+00 -7.10903466e-01
-1.41795576e-01 6.11388087e-01 4.00180459e-01 -4.21264857... | [7.935511589050293, -3.472709894180298] |
0e6cb3ba-bfe6-4fad-96da-54aa41d2a70d | low-resource-spoken-language-identification | 2106.00052 | null | https://arxiv.org/abs/2106.00052v1 | https://arxiv.org/pdf/2106.00052v1.pdf | Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions | This memo describes NTR/TSU winning submission for Low Resource ASR challenge at Dialog2021 conference, language identification track. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. Traditionally, the ASR task requires large volumes of lab... | ['Nikolay Mikhaylovskiy', 'Roman Bedyakin'] | 2021-05-31 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-3.91552180e-01 -2.55744923e-02 -1.06698208e-01 -5.12605608e-01
-8.29195261e-01 -6.98773623e-01 8.65211785e-01 -1.14582507e-02
-9.08145905e-01 7.26960123e-01 5.35087526e-01 -5.62378883e-01
3.08269352e-01 5.44795096e-02 -2.70573169e-01 -3.77836436e-01
1.88530758e-01 8.42566192e-01 -2.91396290e-01 -4.75297332... | [14.161019325256348, 6.677275657653809] |
f41f584d-0b30-4b66-b97c-b1b86119ea7a | roweisposes-including-eigenposes-supervised | 2006.15736 | null | https://arxiv.org/abs/2006.15736v1 | https://arxiv.org/pdf/2006.15736v1.pdf | Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition | Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition. ... | ['Benyamin Ghojogh', 'Fakhri Karray', 'Mark Crowley'] | 2020-06-28 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 9.61446390e-02 -1.54192105e-01 -2.78938621e-01 -2.58014709e-01
-2.56262273e-01 -6.87603205e-02 4.84612733e-01 -1.16864741e+00
-2.11155429e-01 3.69672060e-01 6.99052989e-01 8.60775039e-02
-3.49752486e-01 -2.29248002e-01 -1.75511256e-01 -9.28067207e-01
-8.22952092e-02 3.17610681e-01 -2.36991718e-01 -2.31600851... | [7.847659111022949, 0.3877544105052948] |
1db3c544-d4bc-4890-861f-0036fe77577b | an-improved-relevance-feedback-in-cbir | 2006.11821 | null | https://arxiv.org/abs/2006.11821v2 | https://arxiv.org/pdf/2006.11821v2.pdf | An Improved Relevance Feedback in CBIR | Relevance Feedback in Content-Based Image Retrieval is a method where the feedback of the performance is being used to improve itself. Prior works use feature re-weighting and classification techniques as the Relevance Feedback methods. This paper shows a novel addition to the prior methods to further improve the retri... | ['Subhadip Maji', 'Smarajit Bose'] | 2020-06-21 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.28162819e-01 -5.14990866e-01 -4.58023280e-01 -2.48612761e-01
-9.02975202e-01 -1.44003928e-01 6.06008470e-01 2.90179610e-01
-4.11633462e-01 6.18753612e-01 3.32018018e-01 4.14251648e-02
-4.24869359e-01 -6.22992635e-01 -4.07975093e-02 -5.13698339e-01
-9.79197025e-03 4.44235690e-02 5.47968984e-01 -6.61432624... | [10.792852401733398, 0.1187131404876709] |
23c941b4-9741-4c36-81f5-5a9c53da5761 | time-varying-transition-matrices-with-multi | 2306.11772 | null | https://arxiv.org/abs/2306.11772v1 | https://arxiv.org/pdf/2306.11772v1.pdf | Time-Varying Transition Matrices with Multi-task Gaussian Processes | In this paper, we present a kernel-based, multi-task Gaussian Process (GP) model for approximating the underlying function of an individual's mobility state using a time-inhomogeneous Markov Process with two states: moves and pauses. Our approach accounts for the correlations between the transition probabilities by cre... | ['Ekin Ugurel'] | 2023-06-20 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 4.10970449e-02 -1.09362036e-01 -1.61072463e-01 -2.36232653e-01
-3.82200718e-01 -3.62827271e-01 9.15415108e-01 -8.38317052e-02
-4.86401558e-01 8.91404092e-01 1.28170028e-01 -3.19265753e-01
-2.78511703e-01 -5.53215504e-01 -5.32376230e-01 -9.27957296e-01
-4.70331073e-01 8.60063672e-01 2.45732799e-01 2.53512055... | [6.9413604736328125, 3.9000885486602783] |
ed1466f5-34db-4edf-92f3-a9b38c9be34e | one-model-for-the-learning-of-language | 1711.06301 | null | http://arxiv.org/abs/1711.06301v2 | http://arxiv.org/pdf/1711.06301v2.pdf | One Model for the Learning of Language | A major target of linguistics and cognitive science has been to understand
what class of learning systems can acquire the key structures of natural
language. Until recently, the computational requirements of language have been
used to argue that learning is impossible without a highly constrained
hypothesis space. Here... | ['Yuan Yang'] | 2017-11-16 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.45046139e-01 4.48102802e-01 1.09371059e-02 -4.17777449e-01
-5.26496291e-01 -6.33807898e-01 7.79203355e-01 2.97637910e-01
-5.29020190e-01 7.58775234e-01 -4.16400731e-01 -1.16932678e+00
-2.91164249e-01 -1.14939213e+00 -8.37412894e-01 -5.22279561e-01
-6.70826793e-01 4.49544042e-01 4.38866347e-01 -5.42509794... | [8.884347915649414, 7.164343357086182] |
251ce38f-4680-4f02-8794-5e37aa229331 | low-variance-gradient-estimation-in-unrolled | 2304.11153 | null | https://arxiv.org/abs/2304.11153v1 | https://arxiv.org/pdf/2304.11153v1.pdf | Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single | We propose an evolution strategies-based algorithm for estimating gradients in unrolled computation graphs, called ES-Single. Similarly to the recently-proposed Persistent Evolution Strategies (PES), ES-Single is unbiased, and overcomes chaos arising from recursive function applications by smoothing the meta-loss lands... | ['Kevin Swersky', 'Zico Kolter', 'Paul Vicol'] | 2023-04-21 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 4.67783697e-02 2.70427018e-02 6.46856949e-02 3.61165434e-01
-6.43955290e-01 -4.57280546e-01 2.76120424e-01 2.18608305e-01
-5.25887609e-01 9.15657699e-01 -6.33693933e-02 -2.88071111e-02
-1.87512845e-01 -6.22523904e-01 -8.14956784e-01 -1.03953373e+00
-1.44773066e-01 4.54443932e-01 2.86300421e-01 -2.70022005... | [7.202095985412598, 3.8753445148468018] |
aa53d0b4-eb68-4238-a2a3-4b9f92c8eae0 | a-unified-transformer-framework-for-group | 2203.04708 | null | https://arxiv.org/abs/2203.04708v2 | https://arxiv.org/pdf/2203.04708v2.pdf | A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection | Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researches focus on co-segmentation (CoS), co-saliency detection (CoSD) and video salient object detection (VSOD) to discover the co-occurrent objects. However, p... | ['Qingyao Wu', 'Guosheng Lin', 'Ruizhou Sun', 'Jingliang Deng', 'Yukun Su'] | 2022-03-09 | null | null | null | null | ['co-saliency-detection', 'video-salient-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.18013838e-01 -1.93486050e-01 -2.08227813e-01 -2.41867140e-01
-2.92916268e-01 -1.51854038e-01 4.00434285e-01 2.34145224e-02
-6.08895779e-01 4.73768860e-01 -3.46513242e-02 1.23243898e-01
-4.13842909e-02 -5.53915620e-01 -8.99558783e-01 -4.87512946e-01
6.17386661e-02 -8.73287469e-02 1.09168470e+00 -2.15733066... | [9.610576629638672, -0.2885572016239166] |
e04afb92-52ad-4cf1-b8c5-71ca13ea4fa2 | scientific-language-models-for-biomedical | 2106.09700 | null | https://arxiv.org/abs/2106.09700v2 | https://arxiv.org/pdf/2106.09700v2.pdf | Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study | Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applications, such as drug design and repurposing. Recent work has shown that general-domain language models (LMs) can serve as "soft" KGs, and that t... | ['Tom Hope', 'Hannaneh Hajishirzi', 'Noah A. Smith', 'Iz Beltagy', 'David Wadden', 'Rahul Nadkarni'] | 2021-06-17 | null | https://openreview.net/forum?id=4Exq_UvWKY8 | https://openreview.net/pdf?id=4Exq_UvWKY8 | akbc-2021-10 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-3.13303508e-02 7.64530361e-01 -8.99813414e-01 -2.19919384e-01
-7.00906336e-01 -5.71869671e-01 3.74150306e-01 6.97477162e-01
-1.48695797e-01 9.54353988e-01 6.98721588e-01 -5.54529488e-01
-4.37186867e-01 -8.76676679e-01 -1.05966389e+00 -3.19199711e-01
-4.56041038e-01 5.94294786e-01 1.70016084e-02 6.25130236... | [8.552440643310547, 7.943466663360596] |
4b9c7bf6-7c31-455e-b783-2086729d4636 | retinal-image-segmentation-with-small | 2303.05110 | null | https://arxiv.org/abs/2303.05110v1 | https://arxiv.org/pdf/2303.05110v1.pdf | Retinal Image Segmentation with Small Datasets | Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal m... | ['Yongmin Li', 'Zidong Wang', 'Alina Miron', 'Nchongmaje Ndipenoch'] | 2023-03-09 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-1.26124904e-01 -8.60683993e-02 1.81658253e-01 -2.97996789e-01
-3.50527138e-01 -1.98273763e-01 3.32260281e-02 -2.66148031e-01
-5.43936133e-01 8.91631842e-01 1.07346572e-01 -4.37687337e-01
-9.07042176e-02 -3.50257933e-01 -1.98431775e-01 -6.17625654e-01
-1.59389645e-01 3.24916482e-01 4.50033933e-01 4.17613834... | [15.814611434936523, -3.9891960620880127] |
38554476-3106-4022-b3cb-d8f076004539 | maximum-state-entropy-exploration-using | 2306.14808 | null | https://arxiv.org/abs/2306.14808v1 | https://arxiv.org/pdf/2306.14808v1.pdf | Maximum State Entropy Exploration using Predecessor and Successor Representations | Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misplaced items. These exploration skills necessarily track where they have been so that they can plan for finding items with relative efficiency. Contemporary exploration algorithms o... | ['Glen Berseth', 'Irina Rish', 'Lucas Lehnert', 'Arnav Kumar Jain'] | 2023-06-26 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [-3.72279063e-02 8.23953468e-03 -6.27032161e-01 -3.50189716e-01
-2.74959177e-01 -4.67912108e-01 2.50207216e-01 4.92373556e-01
-7.94285595e-01 9.13370252e-01 4.46062610e-02 -4.94675636e-01
-5.02703607e-01 -1.07228732e+00 -7.92259276e-01 -5.69642603e-01
-1.01266706e+00 4.94540393e-01 -1.45081192e-01 -1.04441062... | [3.966669797897339, 1.6859065294265747] |
bc959165-164f-45da-85e9-b56d70d2751f | signed-link-representation-in-continuous-time | 2207.03408 | null | https://arxiv.org/abs/2207.03408v3 | https://arxiv.org/pdf/2207.03408v3.pdf | Representation Learning in Continuous-Time Dynamic Signed Networks | Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of th... | ['Kartik Sharma', 'Srijan Kumar', 'Yeon Chang Lee', 'Anand Kumar M', 'Mohit Raghavendra'] | 2022-07-07 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [-8.13672766e-02 2.61936992e-01 -6.28848732e-01 -3.56346250e-01
7.89563835e-01 -6.29461467e-01 9.06728506e-01 2.78322488e-01
3.97853591e-02 7.46847510e-01 2.43971631e-01 -3.23202193e-01
-6.83981776e-01 -1.17601228e+00 -7.45028019e-01 -4.95161384e-01
-9.60572898e-01 7.31383324e-01 4.78700697e-01 -7.31149971... | [7.183151721954346, 6.170722484588623] |
ddc22dfd-99c0-4823-95c8-7db385ccf27f | visual-analysis-of-discrimination-in-machine | 2007.15182 | null | https://arxiv.org/abs/2007.15182v2 | https://arxiv.org/pdf/2007.15182v2.pdf | Visual Analysis of Discrimination in Machine Learning | The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learnin... | ['Zhutian Chen', 'Shixia Liu', 'Qianwen Wang', 'Zhenhua Xu', 'Yong Wang', 'Huamin Qu'] | 2020-07-30 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 1.25483215e-01 5.06234281e-02 -4.99119371e-01 -5.67691803e-01
-7.14249304e-03 -7.39905894e-01 4.44777995e-01 9.28533256e-01
-2.30068251e-01 6.96384847e-01 3.57554674e-01 -1.20802522e+00
-6.35986507e-01 -6.27163947e-01 2.29616642e-01 -2.32210115e-01
-2.11562783e-01 2.88389862e-01 -1.93528712e-01 4.28581201... | [8.807538986206055, 5.47583532333374] |
15384655-6bfa-48bf-a84b-a0cedacf561a | learning-to-speak-fluently-in-a-foreign | 1907.04448 | null | https://arxiv.org/abs/1907.04448v2 | https://arxiv.org/pdf/1907.04448v2.pdf | Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning | We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages. Moreover, the model is able to transfer voices across languages, e.g. synthesize fluent Spanish speech using an English speaker's voice, without training on a... | ['Bhuvana Ramabhadran', 'RJ Skerry-Ryan', 'Ye Jia', 'Ron J. Weiss', 'Andrew Rosenberg', 'Yonghui Wu', 'Zhifeng Chen', 'Yu Zhang', 'Heiga Zen'] | 2019-07-09 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 8.62506330e-02 3.47354650e-01 -7.02318177e-03 -1.93099484e-01
-1.02474046e+00 -1.00802469e+00 6.60617948e-01 -5.69840431e-01
-2.25875333e-01 6.42428219e-01 6.29859984e-01 -7.67367601e-01
5.74082494e-01 -4.34482217e-01 -7.80033886e-01 -4.61549550e-01
2.27190226e-01 5.24085104e-01 -1.50732681e-01 -3.54177803... | [14.729727745056152, 6.8204216957092285] |
5e78ba77-3193-433d-9688-a3573e1164f5 | hybrid-approach-to-identify-druglikeness | 2208.06362 | null | https://arxiv.org/abs/2208.06362v3 | https://arxiv.org/pdf/2208.06362v3.pdf | Hybrid Approach to Identify Druglikeness Leading Compounds against COVID-19 3CL Protease | SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for t... | ['Abdul Majid', 'Imra Aqeel'] | 2022-08-03 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.37480184e-01 -4.78805840e-01 -3.05214792e-01 9.42913350e-03
-3.30330878e-01 -6.65234268e-01 2.69050092e-01 5.57626605e-01
-1.67977855e-01 1.54828310e+00 4.37801257e-02 -9.15644467e-01
-1.53339013e-01 -6.70828104e-01 -4.63685900e-01 -7.72465587e-01
-3.82412106e-01 3.59258503e-01 -9.87285003e-02 -3.18569034... | [4.672873497009277, 5.146955966949463] |
bf7ba012-88c3-457f-bd8f-d3772d9f06f3 | clustering-without-knowing-how-to-application | 2209.10267 | null | https://arxiv.org/abs/2209.10267v3 | https://arxiv.org/pdf/2209.10267v3.pdf | Clustering Without Knowing How To: Application and Evaluation | Crowdsourcing allows running simple human intelligence tasks on a large crowd of workers, enabling solving problems for which it is difficult to formulate an algorithm or train a machine learning model in reasonable time. One of such problems is data clustering by an under-specified criterion that is simple for humans,... | ['Dmitry Ustalov', 'Daniil Fedulov', 'Daniil Likhobaba'] | 2022-09-21 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-1.49426237e-01 8.80711600e-02 2.78297424e-01 -3.15723568e-01
-3.92501980e-01 -8.05724621e-01 3.74422282e-01 1.24906406e-01
-5.80770016e-01 6.42108560e-01 -1.19769603e-01 -1.39509022e-01
1.49196967e-01 -4.46841359e-01 -6.65228188e-01 -5.79974413e-01
3.62735987e-01 1.02523303e+00 5.99472463e-01 -5.88136435... | [9.707919120788574, 4.745421409606934] |
1d38958f-6ced-4596-9146-4383ec2dcb4e | reprogramming-audio-driven-talking-face | 2306.16003 | null | https://arxiv.org/abs/2306.16003v1 | https://arxiv.org/pdf/2306.16003v1.pdf | Reprogramming Audio-driven Talking Face Synthesis into Text-driven | In this paper, we propose a method to reprogram pre-trained audio-driven talking face synthesis models to be able to operate with text inputs. As the audio-driven talking face synthesis model takes speech audio as inputs, in order to generate a talking avatar with the desired speech content, speech recording needs to b... | ['Yong Man Ro', 'Se Jin Park', 'Minsu Kim', 'Jeongsoo Choi'] | 2023-06-28 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 6.02213860e-01 4.08804655e-01 1.02788381e-01 -5.19370139e-01
-7.02808142e-01 -3.48733664e-01 5.82979441e-01 -7.64460862e-01
1.98459104e-01 3.33207816e-01 4.23184723e-01 3.52215208e-02
2.50007212e-01 -7.39202857e-01 -9.24726605e-01 -8.32457125e-01
4.29652631e-01 2.11785927e-01 -3.50631416e-01 1.04261041... | [13.238214492797852, -0.3803410828113556] |
dab3b074-e71d-4a66-8c9b-179bfa37cd77 | video-restoration-with-a-deep-plug-and-play | 2209.02854 | null | https://arxiv.org/abs/2209.02854v2 | https://arxiv.org/pdf/2209.02854v2.pdf | Video Restoration with a Deep Plug-and-Play Prior | This paper presents a novel method for restoring digital videos via a Deep Plug-and-Play (PnP) approach. Under a Bayesian formalism, the method consists in using a deep convolutional denoising network in place of the proximal operator of the prior in an alternating optimization scheme. We distinguish ourselves from pri... | ['Andrés Almansa', 'Matias Tassano', 'Julie Delon', 'Antoine Monod'] | 2022-09-06 | null | null | null | null | ['video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 4.65544015e-01 3.51104699e-02 4.02702779e-01 -6.68771416e-02
-8.17989349e-01 -1.54233590e-01 7.52322316e-01 -4.11033243e-01
-4.79430616e-01 7.55598068e-01 3.71211976e-01 -1.27666533e-01
-1.35691732e-01 -6.09644294e-01 -1.03909922e+00 -1.16922617e+00
2.15656936e-01 3.42940688e-02 2.52772987e-01 -2.67931521... | [11.535454750061035, -2.335871934890747] |
6b179541-6900-44db-a346-c4f85aa0c0ba | an-eeg-channel-selection-framework-for-driver | 2304.14920 | null | https://arxiv.org/abs/2304.14920v1 | https://arxiv.org/pdf/2304.14920v1.pdf | An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance | Drowsy driving has a crucial influence on driving safety, creating an urgent demand for driver drowsiness detection. Electroencephalogram (EEG) signal can accurately reflect the mental fatigue state and thus has been widely studied in drowsiness monitoring. However, the raw EEG data is inherently noisy and redundant, w... | ['Yang Liu', 'Liming Zhai', 'Chenyu Liu', 'Jiaping Xiao', 'Ziyu Jia', 'Dan Lin', 'Xinliang Zhou'] | 2023-04-26 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.32090819e-01 -1.78818062e-01 1.07264556e-01 -5.48083782e-01
-5.65753996e-01 -2.70315826e-01 4.63536754e-02 -5.20522557e-02
-3.80105942e-01 6.21723652e-01 7.19528587e-04 -3.48965377e-01
-4.97734189e-01 -3.81331325e-01 -4.18146133e-01 -7.85376251e-01
3.78234506e-01 -1.39154896e-01 1.33173153e-01 -2.68139869... | [13.133111953735352, 3.2142913341522217] |
bab03106-3979-44c7-907f-5c36bfa8c1d0 | radar-robust-ai-text-detection-via | 2307.03838 | null | https://arxiv.org/abs/2307.03838v1 | https://arxiv.org/pdf/2307.03838v1.pdf | RADAR: Robust AI-Text Detection via Adversarial Learning | Recent advances in large language models (LLMs) and the intensifying popularity of ChatGPT-like applications have blurred the boundary of high-quality text generation between humans and machines. However, in addition to the anticipated revolutionary changes to our technology and society, the difficulty of distinguishin... | ['Tsung-Yi Ho', 'Pin-Yu Chen', 'Xiaomeng Hu'] | 2023-07-07 | null | null | null | null | ['fairness', 'fairness', 'text-generation'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 3.01824003e-01 1.02573685e-01 8.17786679e-02 8.01538378e-02
-7.54876256e-01 -7.53067315e-01 1.25619698e+00 -8.47916305e-02
-2.75535583e-01 6.53958380e-01 2.79050440e-01 -5.15962660e-01
5.72750211e-01 -7.53042161e-01 -6.97391987e-01 -1.51698798e-01
3.87668788e-01 7.16912925e-01 1.48609474e-01 -6.17105246... | [6.304945945739746, 8.260127067565918] |
4871415a-6443-465f-9cd2-e24bf78723bd | talk-to-edit-fine-grained-facial-editing-via | 2109.04425 | null | https://arxiv.org/abs/2109.04425v1 | https://arxiv.org/pdf/2109.04425v1.pdf | Talk-to-Edit: Fine-Grained Facial Editing via Dialog | Facial editing is an important task in vision and graphics with numerous applications. However, existing works are incapable to deliver a continuous and fine-grained editing mode (e.g., editing a slightly smiling face to a big laughing one) with natural interactions with users. In this work, we propose Talk-to-Edit, an... | ['Ziwei Liu', 'Chen Change Loy', 'Xingang Pan', 'Ziqi Huang', 'Yuming Jiang'] | 2021-09-09 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jiang_Talk-To-Edit_Fine-Grained_Facial_Editing_via_Dialog_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jiang_Talk-To-Edit_Fine-Grained_Facial_Editing_via_Dialog_ICCV_2021_paper.pdf | iccv-2021-1 | ['facial-editing'] | ['computer-vision'] | [ 1.48043454e-01 1.34618089e-01 1.20659016e-01 -7.60043085e-01
-2.92179227e-01 -7.68975735e-01 7.98037827e-01 -4.62657064e-01
3.97139974e-02 3.75682652e-01 4.45904434e-01 7.61506259e-02
2.01722965e-01 -5.22115290e-01 -4.99023020e-01 -3.69726628e-01
5.98856568e-01 3.31169009e-01 -2.19064727e-01 -3.43470722... | [12.647252082824707, -0.3278326094150543] |
6e37e18a-a8aa-4ab7-9680-756c9dfd5c07 | meta-prediction-model-for-distillation-aware | 2305.16948 | null | https://arxiv.org/abs/2305.16948v1 | https://arxiv.org/pdf/2305.16948v1.pdf | Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets | Distillation-aware Neural Architecture Search (DaNAS) aims to search for an optimal student architecture that obtains the best performance and/or efficiency when distilling the knowledge from a given teacher model. Previous DaNAS methods have mostly tackled the search for the neural architecture for fixed datasets and ... | ['Sung Ju Hwang', 'Minseon Kim', 'Sohyun An', 'Hayeon Lee'] | 2023-05-26 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-7.68491402e-02 1.99988182e-03 -5.25777452e-02 -3.90247077e-01
-9.38399971e-01 -5.46405315e-01 3.43214631e-01 -4.16473262e-02
-6.43942952e-01 5.45932829e-01 -3.81279737e-01 -3.41829211e-01
-3.16734612e-01 -6.35452628e-01 -9.32722211e-01 -7.96252728e-01
4.03417438e-01 1.01163411e+00 2.38580406e-01 -1.19611189... | [9.442245483398438, 3.312537908554077] |
a9965d2d-0cfa-4e7a-981e-adcfc9f88a3c | analyzing-the-robustness-of-unsupervised | 2110.03509 | null | https://arxiv.org/abs/2110.03509v5 | https://arxiv.org/pdf/2110.03509v5.pdf | Analyzing the Robustness of Unsupervised Speech Recognition | Unsupervised speech recognition (unsupervised ASR) aims to learn the ASR system with non-parallel speech and text corpus only. Wav2vec-U has shown promising results in unsupervised ASR by self-supervised speech representations coupled with Generative Adversarial Network (GAN) training, but the robustness of the unsuper... | ['Yu Tsao', 'Hung-Yi Lee', 'Da-Rong Liu', 'Chan-Jan Hsu', 'Guan-Ting Lin'] | 2021-10-07 | null | null | null | null | ['unsupervised-speech-recognition'] | ['speech'] | [ 4.70626682e-01 2.76798606e-01 1.71796039e-01 -3.95940602e-01
-9.15780246e-01 -5.38439870e-01 7.68967688e-01 -4.37950492e-01
-3.08684617e-01 5.15780687e-01 7.43404150e-01 -3.60513628e-01
4.21432883e-01 -4.80799556e-01 -5.36693871e-01 -8.25367332e-01
2.90046096e-01 4.85607505e-01 -2.04521388e-01 -4.21567947... | [14.635765075683594, 6.525681018829346] |
b08755ab-a03f-45fb-b14c-33e8804a4484 | sapphire-approaches-for-enhanced-concept-to | 2108.06643 | null | https://arxiv.org/abs/2108.06643v2 | https://arxiv.org/pdf/2108.06643v2.pdf | SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation | We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART an... | ['Varun Gangal', 'Eduard Hovy', 'Chaitanya Narisetty', 'Jessica Huynh', 'Steven Y. Feng'] | 2021-08-15 | null | https://aclanthology.org/2021.inlg-1.21 | https://aclanthology.org/2021.inlg-1.21.pdf | inlg-acl-2021-8 | ['concept-to-text-generation'] | ['natural-language-processing'] | [ 5.51090002e-01 7.67140985e-01 -1.50273129e-01 -2.32240826e-01
-9.70934033e-01 -7.20173597e-01 1.10097766e+00 -5.42509463e-03
-1.48007825e-01 1.14033675e+00 8.54830921e-01 -3.60910654e-01
-3.74789760e-02 -5.92340887e-01 -5.50493777e-01 -4.36910242e-02
5.68875015e-01 1.13802719e+00 -2.11063355e-01 -7.91517675... | [11.289871215820312, 8.754678726196289] |
b0ecd14a-de2d-466f-9e9c-ff3f38983e0d | multisubs-a-large-scale-multimodal-and | 2103.01910 | null | https://arxiv.org/abs/2103.01910v3 | https://arxiv.org/pdf/2103.01910v3.pdf | MultiSubs: A Large-scale Multimodal and Multilingual Dataset | This paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dataset consists of images selected to unambiguously illustrate concepts expressed in sentences from movie subtitles. The dataset is a valuable... | ['Lucia Specia', 'Chiraag Lala', 'Josiel Figueiredo', 'Pranava Madhyastha', 'Josiah Wang'] | 2021-03-02 | null | https://aclanthology.org/2022.lrec-1.730 | https://aclanthology.org/2022.lrec-1.730.pdf | lrec-2022-6 | ['multimodal-lexical-translation', 'multimodal-text-prediction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.93840712e-01 -4.17434685e-02 -1.76648259e-01 -4.22319680e-01
-8.02802980e-01 -7.66701698e-01 9.77303624e-01 3.99139941e-01
-6.95879519e-01 6.59710169e-01 4.61277068e-01 -4.33655024e-01
1.98838532e-01 -4.03716296e-01 -8.18690479e-01 -3.08880687e-01
2.76127040e-01 5.67372501e-01 2.62199581e-01 -4.90999609... | [11.198681831359863, 1.3080523014068604] |
3fb9eb4a-25fd-4157-8cac-68fd01208e9e | a-method-to-disambiguate-a-word-by-using | null | null | https://aclanthology.org/2021.icon-main.47 | https://aclanthology.org/2021.icon-main.47.pdf | A Method to Disambiguate a Word by Using Restricted Boltzmann Machine | Finding the correct sense of a word is of great importance in many textual data related applications such as information retrieval, text mining and natural language processing. We have proposed one novel Word Sense Disambiguation (WSD) method according to its context. Based on collocation extraction score, the proposed... | ['Bhogeswar Borah', 'Nazreena Rahman'] | null | null | null | null | icon-2021-12 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 3.10776144e-01 -2.45208800e-01 -1.45191342e-01 -1.12282075e-01
-7.38305211e-01 -5.88421702e-01 8.64205122e-01 8.39434266e-01
-1.17578876e+00 9.40204024e-01 6.07820451e-01 -1.76010013e-01
-2.53203422e-01 -6.79206908e-01 2.65105013e-02 -3.79491329e-01
2.89005727e-01 4.16252613e-01 4.65243816e-01 -6.85853601... | [10.159637451171875, 9.034948348999023] |
7c20e540-8c8f-4b34-a0f8-2e77368474e6 | deep-learning-for-joint-acoustic-echo-and | 2305.01637 | null | https://arxiv.org/abs/2305.01637v2 | https://arxiv.org/pdf/2305.01637v2.pdf | Deep Learning for Joint Acoustic Echo and Acoustic Howling Suppression in Hybrid Meetings | Hybrid meetings have become increasingly necessary during the post-COVID period and also brought new challenges for solving audio-related problems. In particular, the interplay between acoustic echo and acoustic howling in a hybrid meeting makes the joint suppression of them difficult. This paper proposes a deep learni... | ['Dong Yu', 'Meng Yu', 'Hao Zhang'] | 2023-05-02 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.36430818e-01 4.05319547e-03 4.96818393e-01 -1.41554708e-02
-1.21839607e+00 -2.06120118e-01 3.38077992e-01 -2.91476727e-01
-2.62524217e-01 4.45470363e-01 5.25827467e-01 5.98433688e-02
-2.99577981e-01 -4.49564196e-02 -4.05177742e-01 -9.81188774e-01
1.02955028e-02 -2.07396582e-01 -1.26485556e-01 -2.85539269... | [15.017820358276367, 5.886640548706055] |
96022027-4ba4-463a-9fcd-e7384665da09 | to-scene-a-large-scale-dataset-for | 2203.09440 | null | https://arxiv.org/abs/2203.09440v3 | https://arxiv.org/pdf/2203.09440v3.pdf | TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes | Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e.g., coffee tables, writing desks). It is indispensable to understanding tabletop scenes in 3D indoor scene parsing applications. Unfortunately, it is hard to meet this demand by directly deploying data-driven algorit... | ['Xiaoguang Han', 'Haolin Liu', 'Pei Chen', 'Mutian Xu'] | 2022-03-17 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 3.23622108e-01 -2.92748243e-01 -1.57018661e-01 -4.71202970e-01
-9.28109288e-01 -7.36569345e-01 4.82721142e-02 -2.02192530e-01
6.29698783e-02 1.10808305e-01 -2.55318373e-01 -1.74942210e-01
6.50567114e-02 -7.08703578e-01 -1.13784468e+00 -3.75474095e-01
2.78744221e-01 7.26643980e-01 6.49816394e-01 -3.47939059... | [7.076288223266602, -1.9184362888336182] |
7805e721-d649-4503-b124-48018f699634 | sesql-yet-another-large-scale-session-level | 2208.12711 | null | https://arxiv.org/abs/2208.12711v1 | https://arxiv.org/pdf/2208.12711v1.pdf | SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset | As the first session-level Chinese dataset, CHASE contains two separate parts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and 3,456 sessions translated from English SParC (CHASE-T). We find the two parts are highly discrepant and incompatible as training and evaluation data. In this work, we pres... | ['Min Zhang', 'Hua Wu', 'Xinyan Xiao', 'Fukang Yan', 'Chenhui Dou', 'Zeyang Liu', 'Zhenghua Li', 'Lijie Wang', 'Saihao Huang'] | 2022-08-26 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-2.04306558e-01 -3.13912891e-02 1.09609868e-02 -9.98230517e-01
-1.85472846e+00 -1.07185721e+00 -3.23799923e-02 3.78518760e-01
-6.35862529e-01 7.85621107e-01 3.73558521e-01 -6.62220418e-01
8.54663923e-02 -8.81458640e-01 -1.10465777e+00 1.63633779e-01
2.47945815e-01 7.94263184e-01 5.26931167e-01 -3.65295589... | [9.93319034576416, 7.880223751068115] |
3d45dec7-aa77-4da5-8568-695aa93446e0 | layout-aware-dreamer-for-embodied-referring | 2212.00171 | null | https://arxiv.org/abs/2212.00171v2 | https://arxiv.org/pdf/2212.00171v2.pdf | Layout-aware Dreamer for Embodied Referring Expression Grounding | In this work, we study the problem of Embodied Referring Expression Grounding, where an agent needs to navigate in a previously unseen environment and localize a remote object described by a concise high-level natural language instruction. When facing such a situation, a human tends to imagine what the destination may ... | ['Marie-Francine Moens', 'Tinne Tuytelaars', 'Zehao Wang', 'Mingxiao Li'] | 2022-11-30 | null | null | null | null | ['referring-expression', 'common-sense-reasoning'] | ['computer-vision', 'reasoning'] | [-6.92970008e-02 2.77782351e-01 3.86019021e-01 -4.77101207e-01
-7.47590303e-01 -5.97397566e-01 3.67658824e-01 4.38254535e-01
-5.19847393e-01 5.83574176e-01 2.94905156e-01 -3.53998989e-01
-9.17496085e-02 -9.75854218e-01 -1.12748659e+00 -8.19698870e-01
-7.80836493e-02 7.21746564e-01 -1.19284809e-01 -5.07203758... | [4.491841793060303, 0.5150647163391113] |
877b03bd-13c1-41fe-918d-0076f8a3998d | performance-comparison-of-large-language | 2307.02288 | null | https://arxiv.org/abs/2307.02288v2 | https://arxiv.org/pdf/2307.02288v2.pdf | Performance Comparison of Large Language Models on VNHSGE English Dataset: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard | This paper presents a performance comparison of three large language models (LLMs), namely OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard, on the VNHSGE English dataset. The results show that BingChat is better than ChatGPT and Bard. Therefore, BingChat and Bard can replace ChatGPT while ChatGPT is not yet offici... | ['Xuan-Quy Dao'] | 2023-07-05 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-1.1608789e+00 -1.9392239e-01 -4.4754732e-01 1.0566199e-01
-9.7775859e-01 -6.0358751e-01 3.9871374e-01 4.9235240e-01
-6.4108521e-01 8.8256723e-01 1.4305198e-01 -1.1938204e+00
-7.1559615e-02 -8.0176228e-01 -3.6174998e-01 -2.9618427e-01
5.0726789e-01 2.8320915e-01 3.3785290e-01 -7.0123452e-01
2.3807980e-01... | [10.96613597869873, 9.130141258239746] |
2b236dd1-9438-459e-ad6f-44eb855ae001 | the-way-to-my-heart-is-through-contrastive-1 | 2111.09748 | null | https://arxiv.org/abs/2111.09748v1 | https://arxiv.org/pdf/2111.09748v1.pdf | The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video | The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring. In this work we propose a new approach to remote photoplethysmography (rPPG) - the measurement of blood volume changes from observations of a person's face or skin. Similar to current state-... | ['Simon Stent', 'John Gideon'] | 2021-11-18 | the-way-to-my-heart-is-through-contrastive | http://openaccess.thecvf.com//content/ICCV2021/html/Gideon_The_Way_to_My_Heart_Is_Through_Contrastive_Learning_Remote_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Gideon_The_Way_to_My_Heart_Is_Through_Contrastive_Learning_Remote_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-variation'] | ['computer-vision'] | [ 4.68435138e-01 4.01272655e-01 3.89396623e-02 -5.88929176e-01
-7.29398906e-01 -1.19060606e-01 3.56573731e-01 -6.37504235e-02
-3.99257153e-01 7.27266371e-01 4.37006801e-01 2.18982950e-01
3.04280072e-01 -2.99086392e-01 -6.48886859e-01 -7.47717083e-01
-1.51364848e-01 -3.11316717e-02 1.45453870e-01 1.05797522... | [13.896225929260254, 2.7822697162628174] |
49a700f5-d2d4-4d71-9533-f77dd767a726 | restoration-of-the-jpeg-maximum-lossy | 2306.12757 | null | https://arxiv.org/abs/2306.12757v1 | https://arxiv.org/pdf/2306.12757v1.pdf | Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator | When a JPEG image is compressed using the loss compression method with a high compression rate, a blocking phenomenon can occur in the image, making it necessary to restore the image to its original quality. In particular, restoring compressed images that are unrecognizable presents an innovative challenge. Therefore, ... | ['Sungyoung Kim', 'Jongwook Si'] | 2023-06-22 | null | null | null | null | ['image-restoration', 'blocking'] | ['computer-vision', 'natural-language-processing'] | [ 5.72280705e-01 7.67771080e-02 1.82755709e-01 -1.00068688e-01
-4.18769479e-01 -1.51554972e-01 3.56505901e-01 -2.40607619e-01
-1.52385697e-01 7.42130876e-01 2.65321523e-01 -1.72426328e-02
2.07861468e-01 -1.27584577e+00 -9.41992164e-01 -7.65048265e-01
-2.46205673e-01 -2.22818732e-01 5.56400195e-02 -5.33884466... | [11.352043151855469, -1.6113883256912231] |
7e7071bd-3c05-406b-b9f4-46b940f706cb | rovist-learning-robust-metrics-for-visual-2 | null | null | https://aclanthology.org/2022.findings-naacl.206 | https://aclanthology.org/2022.findings-naacl.206.pdf | RoViST: Learning Robust Metrics for Visual Storytelling | Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have poor correl... | ['Josiah Poon', 'Caren Han', 'Eileen Wang'] | null | null | null | null | findings-naacl-2022-7 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.46267235e-01 3.65485936e-01 4.25431505e-02 -1.97880760e-01
-7.02314973e-01 -6.04516923e-01 1.39625001e+00 6.69809937e-01
-1.13458961e-01 8.22450459e-01 7.82551050e-01 -1.57841772e-01
-1.56685337e-01 -8.36102068e-01 -6.18433475e-01 -3.71446401e-01
2.09157273e-01 5.07796586e-01 4.80689555e-01 -2.87469000... | [11.75562572479248, 8.791535377502441] |
34cd6c43-657d-4c85-9fc7-2b209fdc7ab3 | discover-the-mysteries-of-the-maya-selected | 2208.03163 | null | https://arxiv.org/abs/2208.03163v2 | https://arxiv.org/pdf/2208.03163v2.pdf | Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021 | The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021). Remote sensing has greatly acce... | ['Žiga Kokalj', 'Ivica Dimitrovski', 'Ana Kostovska', 'Nikola Simidjievski', 'Dragi Kocev'] | 2022-08-05 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 1.46960586e-01 -7.16189817e-02 -6.25042170e-02 -2.22237706e-02
-9.33948815e-01 -4.01528299e-01 8.92530203e-01 1.72339112e-01
-5.96615434e-01 7.65449464e-01 -2.58339614e-01 -5.54006457e-01
-3.80658984e-01 -1.20486939e+00 -3.96790236e-01 -6.70177877e-01
-4.54257041e-01 7.95541644e-01 1.26987129e-01 -5.61566770... | [9.425747871398926, -1.4595485925674438] |
0f184ac8-4d15-4a4e-9195-9b223d6d6f23 | iterative-span-selection-self-emergence-of | null | null | https://aclanthology.org/2022.coling-1.478 | https://aclanthology.org/2022.coling-1.478.pdf | Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling | Semantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates. Semantic arguments and their predicates are related in various distinct manners, of which certain semantic arguments are a necessity while others serve as an auxiliary to their predicates. To consider such roles and ... | ['Satoshi Sekine', 'Kentaro Inui', 'Hiroki Ouchi', 'Shuhei Kurita'] | null | null | null | null | coling-2022-10 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 6.37223303e-01 5.93967438e-01 -7.62810290e-01 -6.25873685e-01
-6.42727733e-01 -1.07919061e+00 6.49341166e-01 4.45344567e-01
-3.92441988e-01 9.55671012e-01 6.31403625e-01 -3.90041322e-01
-5.39771795e-01 -6.39586151e-01 -7.34138429e-01 -4.03318554e-01
5.14686815e-02 1.05603790e+00 6.89010561e-01 -4.30355340... | [10.21533489227295, 9.275347709655762] |
5af8ab51-6b37-4db5-832b-b8434cf82c18 | read-look-or-listen-what-s-needed-for-solving | 2307.04532 | null | https://arxiv.org/abs/2307.04532v1 | https://arxiv.org/pdf/2307.04532v1.pdf | Read, Look or Listen? What's Needed for Solving a Multimodal Dataset | The prevalence of large-scale multimodal datasets presents unique challenges in assessing dataset quality. We propose a two-step method to analyze multimodal datasets, which leverages a small seed of human annotation to map each multimodal instance to the modalities required to process it. Our method sheds light on the... | ['Roy Schwartz', 'Yonatan Bitton', 'Netta Madvil'] | 2023-07-06 | null | null | null | null | ['video-question-answering', 'question-answering', 'speaker-identification'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 3.70960295e-01 2.07224451e-02 9.43848714e-02 -2.82096863e-01
-1.52962697e+00 -1.27248228e+00 7.64011025e-01 1.49301276e-01
-4.91431534e-01 3.77552629e-01 5.41715264e-01 -1.99555084e-01
-2.63742894e-01 -2.81370789e-01 -6.68745637e-01 -4.68817711e-01
3.24542850e-01 5.90330899e-01 2.34584972e-01 -3.13242525... | [10.694540977478027, 1.5864102840423584] |
c1570595-f9ea-45c6-bf5f-3fe57ad296c4 | semi-supervised-semantic-segmentation-methods | 2212.13486 | null | https://arxiv.org/abs/2212.13486v2 | https://arxiv.org/pdf/2212.13486v2.pdf | Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment | People with diabetes are more likely to develop diabetic retinopathy (DR) than healthy people. However, DR is the leading cause of blindness. At present, the diagnosis of diabetic retinopathy mainly relies on the experienced clinician to recognize the fine features in color fundus images. This is a time-consuming task.... | ['Zeyu Ding', 'Hizmawati Madzin', 'Zhuoyi Tan'] | 2022-12-27 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 1.23809300e-01 -6.28317799e-03 -7.11049065e-02 -6.43244147e-01
-6.47341669e-01 -3.72302234e-01 -7.68468678e-02 -1.91983998e-01
-4.90155965e-01 6.85170949e-01 2.12680191e-01 -5.10872483e-01
-2.16570616e-01 -7.46344984e-01 5.88143878e-02 -9.10395265e-01
2.39546224e-01 1.39954463e-01 2.75691390e-01 2.37354681... | [15.833372116088867, -3.997908115386963] |
ff243df3-2f66-4386-8cbe-8af3e8374379 | linear-stochastic-bandits-over-a-bit | 2203.01198 | null | https://arxiv.org/abs/2203.01198v1 | https://arxiv.org/pdf/2203.01198v1.pdf | Linear Stochastic Bandits over a Bit-Constrained Channel | One of the primary challenges in large-scale distributed learning stems from stringent communication constraints. While several recent works address this challenge for static optimization problems, sequential decision-making under uncertainty has remained much less explored in this regard. Motivated by this gap, we int... | ['George J. Pappas', 'Hamed Hassani', 'Aritra Mitra'] | 2022-03-02 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.21416527e-01 3.69153291e-01 -4.40541893e-01 -2.55466938e-01
-1.35700345e+00 -7.08227992e-01 -1.74534082e-01 3.88829172e-01
-4.59665626e-01 1.12440705e+00 -2.08915770e-02 -5.26041687e-01
-5.63357472e-01 -7.25575209e-01 -1.14452386e+00 -1.07509840e+00
-3.96052659e-01 5.14096022e-01 -4.37802672e-01 2.78656334... | [4.605367660522461, 3.3791158199310303] |
e984dfd6-d6c9-4d3e-9e87-e76a09edbb0b | meme-generating-rnn-model-explanations-via-1 | null | null | https://openreview.net/forum?id=0beaSUVK_n4 | https://openreview.net/pdf?id=0beaSUVK_n4 | MEME: Generating RNN Model Explanations via Model Extraction | Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering RNN-based approaches is improving their explainability and interpretability. In this work we present MEME: a model extraction approach capable of approximating RNNs with interpretable models repre... | ['Anonymous'] | 2020-10-15 | null | null | null | neurips-workshop-hamlets-2020-12 | ['occupation-prediction'] | ['natural-language-processing'] | [ 2.42962018e-01 1.01087260e+00 -3.44056077e-02 -7.20934451e-01
-2.17408583e-01 -2.91366894e-02 3.86349648e-01 8.09989274e-02
9.98057500e-02 9.10423696e-01 9.47937131e-01 -7.51994908e-01
-3.38167965e-01 -7.14746714e-01 -5.35219491e-01 1.18040890e-01
1.76957443e-01 9.64173794e-01 -9.19418275e-01 -1.41752958... | [8.334943771362305, 6.058230400085449] |
93d7f7b6-c300-463f-9d24-0c69b326435c | hintedbt-augmenting-back-translation-with | 2109.04443 | null | https://arxiv.org/abs/2109.04443v1 | https://arxiv.org/pdf/2109.04443v1.pdf | HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints | Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve effectiveness of the available BT data, we introduce HintedBT -- a family of techniques which provides hints (through tags) to the ... | ['Aravindan Raghuveer', 'Abhirut Gupta', 'Melvin Johnson', 'Sahana Ramnath'] | 2021-09-09 | null | https://aclanthology.org/2021.emnlp-main.129 | https://aclanthology.org/2021.emnlp-main.129.pdf | emnlp-2021-11 | ['transliteration'] | ['natural-language-processing'] | [ 2.92160422e-01 -7.16807917e-02 -5.02812564e-01 -5.40318668e-01
-1.34111381e+00 -9.20788109e-01 8.26679468e-01 -1.46212295e-01
-5.23643255e-01 9.02438819e-01 4.06373233e-01 -8.92266452e-01
5.21854222e-01 -3.24005544e-01 -1.07379901e+00 -4.04657632e-01
3.57004493e-01 8.21977854e-01 -3.33504081e-01 -5.34613431... | [11.598931312561035, 10.302660942077637] |
aaa94c78-40a1-46a2-878a-d5c9265c36f1 | threat-detection-in-self-driving-vehicles | 2209.02438 | null | https://arxiv.org/abs/2209.02438v1 | https://arxiv.org/pdf/2209.02438v1.pdf | Threat Detection In Self-Driving Vehicles Using Computer Vision | On-road obstacle detection is an important field of research that falls in the scope of intelligent transportation infrastructure systems. The use of vision-based approaches results in an accurate and cost-effective solution to such systems. In this research paper, we propose a threat detection mechanism for autonomous... | ['Poonam Saini', 'Taresh Sharma', 'Akshay Singh', 'Param Patil', 'Aaryan Jagetia', 'Umang Goenka'] | 2022-09-06 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-2.06286281e-01 -3.75809491e-01 -2.92777712e-03 -1.92047372e-01
-3.49678993e-01 -2.73939013e-01 3.83195758e-01 -1.44271255e-01
-6.58617198e-01 4.74214941e-01 -2.87248909e-01 -7.54529238e-01
-6.68215752e-02 -1.03593373e+00 -4.82880503e-01 -4.26844239e-01
4.15402025e-01 -1.14759997e-01 1.13346303e+00 -5.24753928... | [7.923628330230713, -1.125646948814392] |
ce634d01-515b-4100-8fe3-d43db7c2f909 | enforcing-almost-sure-reachability-in-pomdps | 2007.00085 | null | https://arxiv.org/abs/2007.00085v3 | https://arxiv.org/pdf/2007.00085v3.pdf | Enforcing Almost-Sure Reachability in POMDPs | Partially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information. We consider the EXPTIME-hard problem of synthesising policies that almost-surely reach some goal state without ever visiting a bad state. In particular, we are interested i... | ['Sanjit A. Seshia', 'Nils Jansen', 'Sebastian Junges'] | 2020-06-30 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.05088124e-01 6.16997004e-01 -1.18281305e-01 7.50186071e-02
-7.02727735e-01 -7.69687295e-01 6.31702363e-01 1.26142040e-01
-4.17751461e-01 1.15561330e+00 -1.64050702e-02 -8.79627228e-01
-4.76483434e-01 -9.51973021e-01 -4.68216360e-01 -9.18149352e-01
-2.83591568e-01 8.67806435e-01 5.13649464e-01 5.17479442... | [4.347409725189209, 2.1167449951171875] |
4e6206d9-6bb1-47c6-a21c-7d5a1fd30568 | generative-modeling-of-high-resolution-global | 2210.12504 | null | https://arxiv.org/abs/2210.12504v1 | https://arxiv.org/pdf/2210.12504v1.pdf | Generative Modeling of High-resolution Global Precipitation Forecasts | Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change. Making accurate high-resolution precipitation forecasts using traditional physical models remains a major challenge in operational weather forecasting as ... | ['Peter Harrington', 'Shashank Subramanian', 'James Duncan'] | 2022-10-22 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-4.06316638e-01 -2.22696483e-01 2.90379137e-01 -4.81155217e-01
-6.37239873e-01 -5.23190200e-01 7.44624794e-01 -3.43777649e-02
1.63072664e-02 1.24610877e+00 2.33714536e-01 -7.92955697e-01
2.57772833e-01 -1.29784596e+00 -5.41713953e-01 -9.29238796e-01
-4.20409709e-01 5.44933796e-01 -2.91248679e-01 -8.26504350... | [6.5774712562561035, 2.950648069381714] |
22c9096d-eccd-4a60-b8ef-8885396e3789 | learning-answer-generation-using-supervision | 2305.15344 | null | https://arxiv.org/abs/2305.15344v1 | https://arxiv.org/pdf/2305.15344v1.pdf | Learning Answer Generation using Supervision from Automatic Question Answering Evaluators | Recent studies show that sentence-level extractive QA, i.e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers using the top-k answer sentences ranked by AS2 models (a la retrieval-augmented generation style). In this paper, we propose a novel traini... | ['Alessandro Moschitti', 'Rik Koncel-Kedziorski', 'Siddhant Garg', 'Matteo Gabburo'] | 2023-05-24 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [ 4.12111670e-01 4.65129822e-01 5.00291169e-01 -3.65236938e-01
-1.70313466e+00 -8.03667128e-01 7.56876171e-01 1.24263786e-01
-2.60856867e-01 1.13565052e+00 3.07932436e-01 -4.98113722e-01
1.82477962e-02 -1.19783688e+00 -7.06780255e-01 -6.01200402e-01
5.14880121e-01 1.23761714e+00 3.12814415e-01 -6.59915388... | [11.47999095916748, 8.208985328674316] |
a67d7066-d1cd-4f9c-bc56-e9fdda5d6bc4 | cyberloc-towards-accurate-long-term-visual | 2301.02403 | null | https://arxiv.org/abs/2301.02403v1 | https://arxiv.org/pdf/2301.02403v1.pdf | CyberLoc: Towards Accurate Long-term Visual Localization | This technical report introduces CyberLoc, an image-based visual localization pipeline for robust and accurate long-term pose estimation under challenging conditions. The proposed method comprises four modules connected in a sequence. First, a mapping module is applied to build accurate 3D maps of the scene, one map fo... | ['Jiangwei Li', 'Wei Luo', 'Yuxiang Wen', 'Yangdong Liu', 'Miao Jia', 'Qichao Xu', 'Xiao Liang', 'Yukai Lin', 'Liu Liu'] | 2023-01-06 | null | null | null | null | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [-4.45755422e-01 -3.70771587e-01 -5.67083210e-02 -4.59389120e-01
-1.21992838e+00 -9.34531033e-01 5.49163222e-01 4.86913435e-02
-5.44627547e-01 1.91106483e-01 -2.74190336e-01 1.66485682e-02
-9.49907750e-02 -3.91816080e-01 -9.17444170e-01 -4.70320582e-01
-1.11300960e-01 7.98298836e-01 6.33724332e-01 -1.57362893... | [7.366966247558594, -2.1978719234466553] |
72d1a3a6-4bba-4226-bed7-a491cc9b98f2 | how-good-is-a-video-summary-a-new | 2101.10514 | null | https://arxiv.org/abs/2101.10514v1 | https://arxiv.org/pdf/2101.10514v1.pdf | How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization | Automatic video summarization is still an unsolved problem due to several challenges. The currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking video dataset called VISIOCITY (VIdeo SummarIzatiOn based on Continuity, Intent and Dive... | ['Ganesh Ramakrishnan', 'Rishabh Iyer', 'Anshul Tomar', 'Suraj Kothawade', 'Vishal Kaushal'] | 2021-01-26 | null | null | null | null | ['supervised-video-summarization'] | ['computer-vision'] | [ 2.97039598e-01 1.01983033e-01 -3.51958066e-01 -2.55824536e-01
-1.28355849e+00 -7.33843803e-01 6.95705831e-01 4.34171438e-01
-2.31441513e-01 9.27799702e-01 8.93336356e-01 2.23467171e-01
-6.00090511e-02 -3.85133326e-01 -7.41933703e-01 -6.40307903e-01
-8.31857771e-02 3.42183501e-01 2.95653164e-01 1.54618099... | [10.478824615478516, 0.47169992327690125] |
ac3e2ac7-9ae5-4303-846d-a53bfbe217c3 | creating-corpora-for-research-in-feedback | null | null | https://aclanthology.org/2020.lrec-1.42 | https://aclanthology.org/2020.lrec-1.42.pdf | Creating Corpora for Research in Feedback Comment Generation | In this paper, we report on datasets that we created for research in feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning. There has been almost no such corpus open to the public and accordingly there has been a very limited am... | ["Shin{'}ichiro Ishikawa", 'Ryo Nagata', 'Kentaro Inui'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['comment-generation'] | ['natural-language-processing'] | [ 3.33009750e-01 8.29967976e-01 -2.02539936e-01 -4.39462930e-01
-1.14899313e+00 -6.37495279e-01 7.43054688e-01 6.07944429e-01
-3.11864942e-01 1.26268601e+00 1.11787677e+00 -8.19975376e-01
5.28831959e-01 -1.74809694e-01 -3.46926451e-01 -1.77739888e-01
5.53872347e-01 2.56052136e-01 2.39457741e-01 -4.64495122... | [11.833080291748047, 9.043704986572266] |
75733332-38a7-4cf8-8a6f-c4eba0197099 | high-throughput-cotton-phenotyping-big-data | 2305.05423 | null | https://arxiv.org/abs/2305.05423v1 | https://arxiv.org/pdf/2305.05423v1.pdf | High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks | In this study, we propose a big data pipeline for cotton bloom detection using a Lambda architecture, which enables real-time and batch processing of data. Our proposed approach leverages Azure resources such as Data Factory, Event Grids, Rest APIs, and Databricks. This work is the first to develop and demonstrate the ... | ['Glen Rains', 'Javad Mohammadpour Velni', 'Alireza Ebrahimi', 'Amanda Issac'] | 2023-05-09 | null | null | null | null | ['plant-phenotyping', 'automl'] | ['computer-vision', 'methodology'] | [-3.49133343e-01 -3.94924939e-01 4.01858717e-01 -5.86880110e-02
-7.27978572e-02 -7.85804868e-01 2.40865201e-01 8.83895755e-01
4.05820049e-02 -1.59341414e-02 -6.31303668e-01 -4.31238502e-01
3.74015234e-02 -1.53533065e+00 -7.10076928e-01 -9.49711621e-01
-2.89555848e-01 3.20248425e-01 4.24823076e-01 -3.16276476... | [9.155543327331543, -1.5072975158691406] |
6468b941-bc90-4758-8c25-21e8bf201da2 | view-volume-network-for-semantic-scene | 1806.05361 | null | http://arxiv.org/abs/1806.05361v1 | http://arxiv.org/pdf/1806.05361v1.pdf | View-volume Network for Semantic Scene Completion from a Single Depth Image | We introduce a View-Volume convolutional neural network (VVNet) for inferring
the occupancy and semantic labels of a volumetric 3D scene from a single depth
image. The VVNet concatenates a 2D view CNN and a 3D volume CNN with a
differentiable projection layer. Given a single RGBD image, our method extracts
the detailed... | ['Yu-Xiao Guo', 'Xin Tong'] | 2018-06-14 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 1.07130013e-01 6.52741864e-02 8.28989875e-03 -6.86740994e-01
-5.07465482e-01 -4.65842366e-01 4.82825071e-01 -3.08011323e-01
-2.47996584e-01 2.31074452e-01 2.54091918e-01 -1.43557817e-01
2.86850750e-01 -1.39723647e+00 -9.43954885e-01 -4.03619111e-01
1.23087220e-01 5.28213680e-01 1.57329425e-01 3.04944426... | [8.468034744262695, -2.9555981159210205] |
fa140bb7-00ab-4a77-acd3-0eaad7c91678 | single-shot-neural-relighting-and-svbrdf | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3151_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640086.pdf | Single-Shot Neural Relighting and SVBRDF Estimation | We present a novel physically-motivated deep network for joint shape and material estimation, as well as relighting under novel illumination conditions, using a single image captured by a mobile phone camera. Our physically-based modeling leverages a deep cascaded architecture trained on a large-scale synthetic dataset... | ['Shen Sang', 'Manmohan Chandraker'] | null | null | null | null | eccv-2020-8 | ['svbrdf-estimation'] | ['computer-vision'] | [ 2.16151416e-01 1.00248996e-02 4.57882911e-01 -3.25455666e-01
-9.11747575e-01 -6.92198575e-01 8.25926125e-01 -3.73594046e-01
1.42373279e-01 6.84763432e-01 3.65820140e-01 -2.34319374e-01
1.43325299e-01 -9.54799592e-01 -1.18550313e+00 -2.50620842e-01
6.57553300e-02 5.80292583e-01 -1.82050452e-01 -1.38153479... | [9.299371719360352, -3.2532169818878174] |
08f962d5-5f6d-45cf-b5b8-7a6598f25ddd | re-iqa-unsupervised-learning-for-image | 2304.00451 | null | https://arxiv.org/abs/2304.00451v2 | https://arxiv.org/pdf/2304.00451v2.pdf | Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild | Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupe... | ['Alan C. Bovik', 'Sandeep Mishra', 'Avinab Saha'] | 2023-04-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Saha_Re-IQA_Unsupervised_Learning_for_Image_Quality_Assessment_in_the_Wild_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Saha_Re-IQA_Unsupervised_Learning_for_Image_Quality_Assessment_in_the_Wild_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-quality-assessment'] | ['computer-vision'] | [ 4.36892480e-01 -1.32873684e-01 1.30389914e-01 -4.57969397e-01
-1.13920939e+00 -5.59166670e-01 5.12331307e-01 2.06212327e-01
-3.70857775e-01 2.80179083e-01 3.35370719e-01 -8.64794552e-02
5.17569073e-02 -9.22779977e-01 -9.53394294e-01 -3.79075646e-01
-3.10565867e-02 -2.11417735e-01 3.79306786e-02 -9.06594992... | [11.797201156616211, -1.7734094858169556] |
226edcc0-abf0-4462-93a2-1008a52dc5c6 | an-unsupervised-attentive-adversarial | 2202.09635 | null | https://arxiv.org/abs/2202.09635v2 | https://arxiv.org/pdf/2202.09635v2.pdf | Deep Single Image Deraining using An Asymetric Cycle Generative and Adversarial Framework | In reality, rain and fog are often present at the same time, which can greatly reduce the clarity and quality of the scene image. However, most unsupervised single image deraining methods mainly focus on rain streak removal by disregarding the fog, which leads to low-quality deraining performance. In addition, the samp... | ['Zixiang Xiong', 'Tao Lu', 'Cheng Chen', 'Rui Jiang', 'Wei Liu'] | 2022-02-19 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 6.34058937e-02 -3.87180537e-01 6.06691897e-01 -2.94883847e-01
-3.87903154e-01 -4.63216752e-01 3.85332793e-01 -7.01099098e-01
-2.79130284e-02 7.93216228e-01 6.03279173e-02 2.07688436e-02
2.37013429e-01 -1.02505934e+00 -7.03243732e-01 -1.41199434e+00
2.44347498e-01 -1.38226807e-01 6.09050989e-02 -5.51118612... | [10.936129570007324, -3.1900570392608643] |
8e4de4d2-8934-4cd5-b503-90637b8a6b61 | beyond-single-receptive-field-a-receptive | 2207.10278 | null | https://arxiv.org/abs/2207.10278v1 | https://arxiv.org/pdf/2207.10278v1.pdf | Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification | The classification of airborne laser scanning (ALS) point clouds is a critical task of remote sensing and photogrammetry fields. Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain... | ['Martin Weinmann', 'Kun fu', 'Xiaonan Lu', 'Xian Sun', 'Wenhui Diao', 'Kaiqiang Chen', 'Yongqiang Mao'] | 2022-07-21 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 7.79714137e-02 -1.99893773e-01 2.80113101e-01 -5.90230882e-01
-6.21192813e-01 -4.44617301e-01 4.08043265e-01 -7.11804256e-02
-2.91401982e-01 4.16398168e-01 -4.11214769e-01 -2.88359404e-01
-2.67291278e-01 -1.23898077e+00 -9.48989153e-01 -7.73926258e-01
-1.88363865e-01 3.73097688e-01 1.70154467e-01 -3.70536715... | [7.996367931365967, -2.978407621383667] |
afb414d9-cf1c-4807-92cd-3148003b5327 | semantic-based-pre-training-for-dialogue | 2209.09146 | null | https://arxiv.org/abs/2209.09146v1 | https://arxiv.org/pdf/2209.09146v1.pdf | Semantic-based Pre-training for Dialogue Understanding | Pre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main semantic meaning of a dialogue context. We investigate Abstract Meaning Representation (AMR) as explicit semantic knowledge f... | ['Yue Zhang', 'Linfeng Song', 'Xuefeng Bai'] | 2022-09-19 | null | https://aclanthology.org/2022.coling-1.49 | https://aclanthology.org/2022.coling-1.49.pdf | coling-2022-10 | ['dialogue-understanding'] | ['natural-language-processing'] | [ 5.40734828e-01 1.05734634e+00 2.68934853e-02 -6.20938480e-01
-2.28138074e-01 -5.16521335e-01 1.07094276e+00 2.75003880e-01
-2.76997596e-01 7.39352763e-01 7.12072194e-01 -5.19283414e-01
2.01794356e-01 -9.59956229e-01 -2.45898604e-01 8.89538750e-02
2.75949568e-01 7.79277265e-01 2.50293881e-01 -9.96737659... | [12.42521858215332, 8.150345802307129] |
45e1307b-28aa-4440-a2d5-910c75ae488a | are-training-trajectories-of-deep-single | 2306.08744 | null | https://arxiv.org/abs/2306.08744v1 | https://arxiv.org/pdf/2306.08744v1.pdf | Are training trajectories of deep single-spike and deep ReLU network equivalent? | Communication by binary and sparse spikes is a key factor for the energy efficiency of biological brains. However, training deep spiking neural networks (SNNs) with backpropagation is harder than with artificial neural networks (ANNs), which is puzzling given that recent theoretical results provide exact mapping algori... | ['Wulfram Gerstner', 'Angeliki Pantazi', 'Giovanni Cherubini', 'Guillaume Bellec', 'Stanisław Woźniak', 'Ana Stanojevic'] | 2023-06-14 | null | null | null | null | ['quantization'] | ['methodology'] | [ 5.08750737e-01 9.03152395e-03 3.40263307e-01 -2.44182289e-01
1.99513555e-01 -4.50224996e-01 4.21572089e-01 -8.08460042e-02
-8.28790486e-01 1.04652333e+00 -4.31760281e-01 -2.47423723e-01
-3.50369602e-01 -8.01560998e-01 -1.08167756e+00 -1.14862800e+00
-1.19108364e-01 2.95526415e-01 3.00440997e-01 -2.68825293... | [8.164657592773438, 2.579181432723999] |
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