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c992763e-d241-4467-b973-4c7ac96781b8 | on-target-representation-in-continuous-output | null | null | https://aclanthology.org/2022.repl4nlp-1.24 | https://aclanthology.org/2022.repl4nlp-1.24.pdf | On Target Representation in Continuous-output Neural Machine Translation | Continuous generative models proved their usefulness in high-dimensional data, such as image and audio generation. However, continuous models for text generation have received limited attention from the community. In this work, we study continuous text generation using Transformers for neural machine translation (NMT).... | ['Vlad Niculae', 'Evgeniia Tokarchuk'] | null | null | null | null | repl4nlp-acl-2022-5 | ['audio-generation'] | ['audio'] | [ 3.50298852e-01 3.56909335e-01 -5.69815636e-02 5.94669282e-02
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3.82970095e-01 8.10383737e-01 -2.95123488e-01 -5.74984848... | [11.845649719238281, 9.591038703918457] |
1d94d8d0-efe4-435d-9129-d3f6fab8aaff | no-reference-video-quality-assessment-based | null | null | https://www.mdpi.com/2079-9292/10/22/2768 | https://www.mdpi.com/2079-9292/10/22/2768 | No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features | No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts.... | ['Domonkos Varga'] | 2021-11-12 | null | null | null | electronics-2021-11 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.74682662e-01 -6.82485044e-01 -9.99869108e-02 -4.13885683e-01
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794d0b05-d2f0-47d3-a188-5da581b4bcc1 | towards-predicting-fine-finger-motions-from | 2202.05204 | null | https://arxiv.org/abs/2202.05204v2 | https://arxiv.org/pdf/2202.05204v2.pdf | Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation | A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks. Existing systems typically perform discrete gestures such as pointing or grasping, by employing electromyography (EMG) ... | ['Alex M. Bronstein', 'Alon Wolf', 'Oren Salzman', 'Dean Zadok'] | 2022-02-10 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 3.18274945e-01 1.66513160e-01 -3.37892532e-01 6.33191764e-02
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-2.45602816e-01 4.58324105e-01 2.03212604e-01 -3.13356549... | [6.794686317443848, 0.1579710990190506] |
c7a7c7eb-1cca-4f52-958a-02f5ec6ab14b | a-simple-baseline-for-direct-2d-multi-person | 2302.01110 | null | https://arxiv.org/abs/2302.01110v2 | https://arxiv.org/pdf/2302.01110v2.pdf | DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles | Existing head pose estimation (HPE) mainly focuses on single person with pre-detected frontal heads, which limits their applications in real complex scenarios with multi-persons. We argue that these single HPE methods are fragile and inefficient for Multi-Person Head Pose Estimation (MPHPE) since they rely on the separ... | ['Hongtao Lu', 'Fei Jiang', 'Huayi Zhou'] | 2023-02-02 | null | null | null | null | ['head-detection', 'head-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.02510405e-01 2.71653980e-01 2.30428815e-01 -5.64364135e-01
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-3.98487262e-02 6.05966389e-01 2.21383154e-01 -9.86781940... | [13.621349334716797, 0.3228411376476288] |
94888115-46ee-461b-a16f-ac17a5cf31aa | inverse-reinforcement-learning-from-diverse | 2207.14299 | null | https://arxiv.org/abs/2207.14299v2 | https://arxiv.org/pdf/2207.14299v2.pdf | Graph Inverse Reinforcement Learning from Diverse Videos | Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks. However, most prior works are still limited by training from a relatively restricted domain of videos. In this paper, we argue that the true potential ... | ['Xiaolong Wang', 'Rishabh Jangir', 'Nicklas Hansen', 'Jonathan Zamora', 'Sateesh Kumar'] | 2022-07-28 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 5.08421659e-02 2.29932368e-01 -3.25881064e-01 -1.64930686e-01
-6.31617606e-01 -5.81820667e-01 4.01023656e-01 -5.21583915e-01
-5.08793712e-01 8.13333690e-01 4.51125234e-01 1.23583145e-01
-3.17566127e-01 -2.91548278e-02 -1.21693468e+00 -4.84970212e-01
-6.35012031e-01 2.66929686e-01 2.25629285e-01 -4.13540244... | [4.575743198394775, 0.8225477933883667] |
63e4efe1-a8be-4c2e-9230-1a9b99e101f4 | xformal-a-benchmark-for-multilingual | 2104.04108 | null | https://arxiv.org/abs/2104.04108v1 | https://arxiv.org/pdf/2104.04108v1.pdf | XFORMAL: A Benchmark for Multilingual Formality Style Transfer | We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicat... | ['Joel Tetreault', 'Ke Zhang', 'Di Lu', 'Eleftheria Briakou'] | 2021-04-08 | null | null | null | null | ['formality-style-transfer'] | ['natural-language-processing'] | [-1.50335729e-01 5.19240424e-02 -2.11466849e-01 -5.13697326e-01
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2.97920287e-01 8.81958306e-01 -4.13628221e-02 -9.63507175... | [11.431241035461426, 10.004508972167969] |
dfcea4b9-16f2-4061-953d-65cf76c344e9 | document-level-relation-extraction-with-dual | null | null | https://aclanthology.org/2020.coling-main.143 | https://aclanthology.org/2020.coling-main.143.pdf | Document-level Relation Extraction with Dual-tier Heterogeneous Graph | Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result. In this paper, we propose a novel graph-based model with Dual-tier H... | ['Li Guo', 'Wang Yubin', 'Hengzhu Tang', 'Tingwen Liu', 'Xiaobo Shu', 'Bowen Yu', 'Zhenyu Zhang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 3.19984347e-01 4.08273160e-01 -3.03872108e-01 -8.02215487e-02
-7.22725809e-01 -2.94864625e-01 6.56949759e-01 8.51849318e-01
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-3.18341911e-01 -1.43774426e+00 -4.67449874e-01 -1.55628413e-01
-2.27940947e-01 4.18465137e-01 4.36007440e-01 -4.07230765... | [9.262062072753906, 8.564370155334473] |
b320f6e7-8e07-4f04-8cab-1041699e55fe | learning-word-representations-from-scarce-and | null | null | https://aclanthology.org/P15-1104 | https://aclanthology.org/P15-1104.pdf | Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces | null | ["M{\\'a}rio Silva", 'Ramon Astudillo', 'Wang Ling', 'Silvio Amir', 'Isabel Trancoso'] | 2015-07-01 | learning-word-representations-from-scarce-and-1 | https://aclanthology.org/P15-1104 | https://aclanthology.org/P15-1104.pdf | ijcnlp-2015-7 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.468672275543213, 3.5347490310668945] |
d007ad6e-d656-4b66-8849-701bd7a884e1 | cellular-network-speech-enhancement-removing | 2301.09027 | null | https://arxiv.org/abs/2301.09027v1 | https://arxiv.org/pdf/2301.09027v1.pdf | Cellular Network Speech Enhancement: Removing Background and Transmission Noise | The primary objective of speech enhancement is to reduce background noise while preserving the target's speech. A common dilemma occurs when a speaker is confined to a noisy environment and receives a call with high background and transmission noise. To address this problem, the Deep Noise Suppression (DNS) Challenge f... | ['Ojas Bhargave', 'Joseph Konan', 'Shikhar Agnihotri', 'Haohui Liu', 'Hamza Khalid', 'Amanda Shu'] | 2023-01-22 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 5.65041900e-02 -3.20795864e-01 3.25404443e-02 1.25020832e-01
-9.57132339e-01 -3.97033393e-01 5.48192747e-02 -3.32448781e-01
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-4.72398624e-02 -4.62786406e-01 -1.14476442e-01 -8.60138655e-01
6.60345554e-02 -1.79508060e-01 -4.30431627e-02 -4.83124673... | [14.947736740112305, 5.989822864532471] |
ebc73d29-9517-4ff3-9002-52c0e562a924 | type-enriched-hierarchical-contrastive | 2208.10081 | null | https://arxiv.org/abs/2208.10081v1 | https://arxiv.org/pdf/2208.10081v1.pdf | Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing | Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in text. Modern methods for FET mainly focus on learning what a certain type looks like. And few works directly model the type differences, that is, let models know the extent that one type is different from others. To allevi... | ['Yu Luo', 'Zhou Fang', 'Shuang Zeng', 'Ning Jing', 'Haijin Liang', 'Xinyu Zuo'] | 2022-08-22 | null | https://aclanthology.org/2022.coling-1.212 | https://aclanthology.org/2022.coling-1.212.pdf | coling-2022-10 | ['entity-typing'] | ['natural-language-processing'] | [-3.05706710e-01 1.65466130e-01 -4.64658111e-01 -5.71801126e-01
-2.99895614e-01 -8.28956544e-01 4.97954160e-01 4.88955706e-01
-5.04379034e-01 5.82913160e-01 3.44530106e-01 -3.38092685e-01
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3.34794849e-01 4.16903645e-01 5.55865288e-01 -2.73138791... | [9.648143768310547, 8.762304306030273] |
15349305-5b00-4c18-bf3e-9aba8d26cf8f | analysis-of-recent-trends-in-face-recognition | 2304.11725 | null | https://arxiv.org/abs/2304.11725v1 | https://arxiv.org/pdf/2304.11725v1.pdf | Analysis of Recent Trends in Face Recognition Systems | With the tremendous advancements in face recognition technology, face modality has been widely recognized as a significant biometric identifier in establishing a person's identity rather than any other biometric trait like fingerprints that require contact sensors. However, due to inter-class similarities and intra-cla... | ['Krishnendu K. S'] | 2023-04-23 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 4.97232258e-01 -2.33612686e-01 1.94634683e-02 -6.98443770e-01
-2.03959003e-01 -4.46848691e-01 6.82616293e-01 -3.10512543e-01
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-3.41957390e-01 -7.19819903e-01 -1.41915083e-01 -8.65938485e-01
1.94578573e-01 2.10394531e-01 -1.13850310e-01 2.12637782... | [13.258362770080566, 0.9296302199363708] |
212e464c-9f02-4463-a7d8-35b6232295c0 | assessing-mortality-prediction-through | 2207.10872 | null | https://arxiv.org/abs/2207.10872v1 | https://arxiv.org/pdf/2207.10872v1.pdf | Assessing mortality prediction through different representation models based on concepts extracted from clinical notes | Recent years have seen particular interest in using electronic medical records (EMRs) for secondary purposes to enhance the quality and safety of healthcare delivery. EMRs tend to contain large amounts of valuable clinical notes. Learning of embedding is a method for converting notes into a format that makes them compa... | ['Maryam Lotfi Shahreza', 'Nasser Ghadiri', 'Hoda Memarzadeh'] | 2022-07-22 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 8.19819281e-04 2.09183455e-01 -2.62543291e-01 -1.65145621e-01
-8.96469235e-01 -5.17652214e-01 3.57446730e-01 1.00607562e+00
-6.11840963e-01 5.05257487e-01 8.93961728e-01 -4.45771068e-01
-4.39014554e-01 -9.97002423e-01 -3.05604190e-01 -4.16255027e-01
-2.30361167e-02 5.61226070e-01 -2.69469708e-01 -2.32463792... | [8.012188911437988, 7.045865058898926] |
924156f6-34b0-4594-a632-e09763dcc1fc | multi-target-normal-behaviour-models-for-wind | 2012.03074 | null | https://arxiv.org/abs/2012.03074v3 | https://arxiv.org/pdf/2012.03074v3.pdf | Multi-target normal behaviour models for wind farm condition monitoring | The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to detect anomalous deviations from normal operation based on the turbi... | ['Angela Meyer'] | 2020-12-05 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [-1.30393326e-01 -5.65640628e-01 7.15623200e-02 -1.94475830e-01
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-5.07951140e-01 1.48272336e-01 1.97318017e-01 -2.35723719... | [6.477437973022461, 2.507061719894409] |
c3a00751-7dda-4137-9a69-9c2d3210a655 | studyformer-attention-based-and-dynamic-multi | 2302.11840 | null | https://arxiv.org/abs/2302.11840v1 | https://arxiv.org/pdf/2302.11840v1.pdf | StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images | Chest X-ray images are commonly used in medical diagnosis, and AI models have been developed to assist with the interpretation of these images. However, many of these models rely on information from a single view of the X-ray, while multiple views may be available. In this work, we propose a novel approach for combinin... | ['Andre Dourson', 'Diane Wilson', 'Michael Fitzke', 'Lucas Wannenmacher'] | 2023-02-23 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 3.08906794e-01 1.03477947e-02 -2.41199657e-01 -6.78214252e-01
-1.14205861e+00 -4.15873051e-01 6.32772923e-01 2.99249977e-01
-2.31537104e-01 1.07962973e-01 5.49079441e-02 -2.62495428e-01
-1.19639434e-01 -7.86710799e-01 -6.62429631e-01 -5.05045116e-01
4.30039287e-01 6.12127244e-01 1.78459167e-01 1.74897805... | [15.13623046875, -1.8959739208221436] |
45720039-8df8-46bd-b55d-aa2953a04d0b | semantic-segmentation-assisted-scene | 2109.11453 | null | https://arxiv.org/abs/2109.11453v1 | https://arxiv.org/pdf/2109.11453v1.pdf | Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds | Outdoor scene completion is a challenging issue in 3D scene understanding, which plays an important role in intelligent robotics and autonomous driving. Due to the sparsity of LiDAR acquisition, it is far more complex for 3D scene completion and semantic segmentation. Since semantic features can provide constraints and... | ['Hongbo Zhang', 'Feng Wen', 'Wanlong Li', 'Yong liu', 'Tianxin Huang', 'Xin Kong', 'Hao Zou', 'Xuemeng Yang'] | 2021-09-23 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 2.36176923e-01 2.11960170e-02 -9.75683853e-02 -7.40274906e-01
-2.92253435e-01 -3.54327053e-01 1.67135105e-01 -4.15808633e-02
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-4.94437618e-03 -9.30164278e-01 -7.66960919e-01 -5.44811964e-01
1.81986988e-01 4.33608145e-01 5.52720070e-01 -6.54715253... | [8.361595153808594, -2.881075143814087] |
582af57f-5869-40bf-9f81-848e34b6fc0c | infrared-and-visible-image-fusion-based-on | 2201.10739 | null | https://arxiv.org/abs/2201.10739v1 | https://arxiv.org/pdf/2201.10739v1.pdf | Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model | The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states). Such scheme is prone to produce an inaccurate statistical model for the high frequency subband and reduces the quality of fusion result. In this paper, a fine-grained multi-state contextu... | ['Xiao-Jun Wu', 'Zhancheng Zhang', 'Anqi Wang', 'Yuting Jiang', 'Xiaoqing Luo'] | 2022-01-26 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.79128185e-01 -6.21636033e-01 -2.52018571e-01 -1.77911773e-01
-7.51828849e-01 4.06732820e-02 3.86656612e-01 3.56599428e-02
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-1.84070915e-01 -8.51046741e-01 1.44300178e-01 -1.47153497e+00
1.66518971e-01 -5.39120257e-01 4.17552680e-01 3.57525982... | [10.544173240661621, -1.9588836431503296] |
9b965b48-9156-4915-9382-da6b90ff834d | toward-grammatical-error-detection-from | 1906.01154 | null | https://arxiv.org/abs/1906.01154v6 | https://arxiv.org/pdf/1906.01154v6.pdf | Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition | We propose a new, more actionable view of neural network interpretability and data analysis by leveraging the remarkable matching effectiveness of representations derived from deep networks, guided by an approach for class-conditional feature detection. The decomposition of the filter-ngram interactions of a convolutio... | ['Allen Schmaltz'] | 2019-06-04 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 8.20429146e-01 6.74046993e-01 -4.97756451e-01 -8.57897460e-01
-9.54109430e-01 -7.63391078e-01 5.53365469e-01 4.05027419e-01
-4.74189550e-01 7.96886861e-01 2.10346460e-01 -3.66005629e-01
-1.50631340e-02 -8.09676170e-01 -1.02633965e+00 -6.90297842e-01
-2.81445161e-02 7.69737601e-01 -4.63862270e-02 7.76965022... | [9.751568794250488, 7.05945348739624] |
965ce0bf-c3ed-4dce-a5b9-ce64ebf305e1 | lade-the-first-comprehensive-last-mile | 2306.10675 | null | https://arxiv.org/abs/2306.10675v1 | https://arxiv.org/pdf/2306.10675v1.pdf | LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry | Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile delivery dataset exists to support research in this field. In this paper, we introd... | ['Huaiyu Wan', 'Youfang Lin', 'Roger Zimmermann', 'Liuqing Yang', 'Yuxuan Liang', 'Junhong Lou', 'Jianbin Zhen', 'Ergang Shan', 'Yutong Xia', 'Xiaowei Mao', 'Haoyuan Hu', 'Haomin Wen', 'Lixia Wu'] | 2023-06-19 | null | null | null | null | ['management'] | ['miscellaneous'] | [-5.95183253e-01 -9.62657213e-01 -3.45522761e-01 -5.68323374e-01
-7.11693943e-01 -8.30178261e-01 4.55886573e-01 3.94238949e-01
-2.40530819e-02 5.70369184e-01 2.48224914e-01 -3.84391963e-01
-6.74797595e-01 -8.79499078e-01 -6.99332476e-01 -7.63068795e-01
-6.96142733e-01 8.14981341e-01 -2.64753938e-01 -2.86301553... | [7.087385654449463, 2.8577821254730225] |
0dd0af01-6f78-410f-a6ce-779c495b458a | learning-to-steer-by-mimicking-features-from | 1811.02759 | null | http://arxiv.org/abs/1811.02759v1 | http://arxiv.org/pdf/1811.02759v1.pdf | Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks | The training of many existing end-to-end steering angle prediction models
heavily relies on steering angles as the supervisory signal. Without learning
from much richer contexts, these methods are susceptible to the presence of
sharp road curves, challenging traffic conditions, strong shadows, and severe
lighting chang... | ['Chen Change Loy', 'Yuenan Hou', 'Zheng Ma', 'Chunxiao Liu'] | 2018-11-07 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 2.84884572e-02 1.31855039e-02 -3.32960814e-01 -5.92061162e-01
-6.01103008e-01 -5.48388720e-01 4.75419223e-01 -4.21527356e-01
-4.37092841e-01 9.03564334e-01 1.90313965e-01 -4.59338129e-01
2.16812432e-01 -7.08983004e-01 -9.91012335e-01 -8.18867505e-01
-7.62540624e-02 3.01953018e-01 3.94928724e-01 -3.89310718... | [8.198147773742676, -1.4124958515167236] |
a420d34d-94e7-423e-ac10-e17b6a82a532 | deep-single-image-portrait-relighting | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Deep_Single-Image_Portrait_Relighting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Deep_Single-Image_Portrait_Relighting_ICCV_2019_paper.pdf | Deep Single-Image Portrait Relighting | Conventional physically-based methods for relighting portrait images need to solve an inverse rendering problem, estimating face geometry, reflectance and lighting. However, the inaccurate estimation of face components can cause strong artifacts in relighting, leading to unsatisfactory results. In this work, we apply a... | [' David W. Jacobs', ' Kalyan Sunkavalli', ' Sunil Hadap', 'Hao Zhou'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['single-image-portrait-relighting'] | ['computer-code'] | [ 3.67748857e-01 -1.93308443e-01 3.75968516e-01 -3.57684851e-01
-7.96107292e-01 -4.00306940e-01 3.67405444e-01 -6.76118195e-01
1.99270591e-01 8.91694784e-01 -2.00839937e-02 -5.51322103e-02
2.64939815e-01 -1.19482911e+00 -9.35534298e-01 -7.60539353e-01
4.63665068e-01 9.92803425e-02 -3.72726470e-01 -3.12542528... | [12.396029472351074, -0.41851750016212463] |
346aa442-f0ba-4c82-8db7-cb494c9a548d | sinet-a-scale-insensitive-convolutional | 1804.00433 | null | http://arxiv.org/abs/1804.00433v2 | http://arxiv.org/pdf/1804.00433v2.pdf | SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection | Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic ... | ['Pheng-Ann Heng', 'Hao Chen', 'Yongjie Xiao', 'Xuemiao Xu', 'Xiaowei Hu', 'Shengfeng He', 'Jing Qin'] | 2018-04-02 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [ 8.73000994e-02 -4.89508420e-01 -6.18616678e-02 -3.40013415e-01
-4.80335742e-01 -3.99817616e-01 2.83357233e-01 -2.55595863e-01
-6.23311162e-01 1.79167151e-01 -3.22489738e-01 -2.84239441e-01
1.42659202e-01 -8.49179804e-01 -9.27057743e-01 -6.53545976e-01
-3.10809821e-01 -1.34255692e-01 1.28043485e+00 -5.01396000... | [8.610787391662598, -0.5739319324493408] |
8cd6feed-068e-474d-88be-ab6b3a2063f6 | playing-chess-with-limited-look-ahead | 2007.02130 | null | https://arxiv.org/abs/2007.02130v1 | https://arxiv.org/pdf/2007.02130v1.pdf | Playing Chess with Limited Look Ahead | We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of this research lies with creating a chess engine that is highly capable, but restricted in its look ah... | ['Arman Maesumi'] | 2020-07-04 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-3.25701326e-01 -1.35272413e-01 -1.11793287e-01 -2.53683180e-01
-4.58339214e-01 -9.55665290e-01 5.54570556e-01 7.25219101e-02
-9.84803617e-01 5.85494399e-01 -8.14033449e-02 -6.71281934e-01
-5.67015767e-01 -1.08640289e+00 -7.56664693e-01 -4.05141592e-01
-2.04493567e-01 6.54333770e-01 8.22343290e-01 -1.08211851... | [3.4365477561950684, 1.4183486700057983] |
ab4d713a-044d-40d0-a702-2adf8a4c5332 | a-large-scale-homography-benchmark-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Barath_A_Large-Scale_Homography_Benchmark_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Barath_A_Large-Scale_Homography_Benchmark_CVPR_2023_paper.pdf | A Large-Scale Homography Benchmark | We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estim... | ['Jiri Matas', 'Wolfgang Förstner', 'Michal Polic', 'Dmytro Mishkin', 'Daniel Barath'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['homography-estimation'] | ['computer-vision'] | [-5.83946854e-02 -2.20290676e-01 -1.01390423e-03 -2.28711188e-01
-9.87651765e-01 -6.89185023e-01 7.95503736e-01 -3.00038844e-01
-6.42896444e-03 1.00184500e-01 3.04451257e-01 3.01395804e-01
-1.97227105e-01 -7.13463306e-01 -1.31976473e+00 -3.68193954e-01
-2.01308072e-01 9.70372021e-01 4.07988012e-01 -1.22501001... | [8.103046417236328, -2.3380258083343506] |
b9419abc-8d2d-41a9-bd62-117b75d17d27 | syntactically-guided-generative-embeddings-1 | 2101.11530 | null | https://arxiv.org/abs/2101.11530v2 | https://arxiv.org/pdf/2101.11530v2.pdf | Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition | We introduce SynSE, a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence em... | ['Ravi Kiran Sarvadevabhatla', 'Divyanshu Sharma', 'Pranay Gupta'] | 2021-01-27 | syntactically-guided-generative-embeddings | https://arxiv.org/pdf/2101.11530.pdf | https://arxiv.org/pdf/2101.11530.pdf | null | ['zero-shot-skeletal-action-recognition'] | ['computer-vision'] | [ 5.36520898e-01 1.27458900e-01 -5.48021019e-01 -2.79342651e-01
-1.26001143e+00 -2.31440097e-01 7.44456172e-01 -4.08296019e-01
-2.99950808e-01 4.87911105e-01 8.63854766e-01 3.26926082e-01
1.31153837e-01 -5.66298842e-01 -6.29596949e-01 -6.85933352e-01
-3.13806050e-02 3.96101505e-01 3.15562874e-01 -3.05511933... | [8.733853340148926, 0.9293658137321472] |
ee35cf5d-4142-4ccc-8f23-7570ad3a2b30 | adaptive-speech-quality-aware-complex-neural | 2210.16791 | null | https://arxiv.org/abs/2210.16791v3 | https://arxiv.org/pdf/2210.16791v3.pdf | Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning | Acoustic echo cancellation (AEC) is designed to remove echoes, reverberation, and unwanted added sounds from the microphone signal while maintaining the quality of the near-end speaker's speech. This paper proposes adaptive speech quality complex neural networks to focus on specific tasks for real-time acoustic echo ca... | ['Hantao Huang', 'Xiaoxi Yu', 'Bozhong Liu'] | 2022-10-30 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.10849598e-02 -3.80286336e-01 7.72272587e-01 -4.64894354e-01
-1.05965042e+00 -1.53548762e-01 1.20687686e-01 -3.32297474e-01
-7.31020629e-01 2.51022905e-01 5.25341809e-01 -3.52096081e-01
2.52573919e-02 -1.82617947e-01 -4.82573122e-01 -5.73080420e-01
-2.21386135e-01 -4.02491003e-01 1.07588530e-01 -2.23033518... | [14.999635696411133, 5.944546699523926] |
16a55983-f9d5-4372-998d-de9880e5ef26 | who-wins-the-game-of-thrones-how-sentiments | 2003.07683 | null | https://arxiv.org/abs/2003.07683v1 | https://arxiv.org/pdf/2003.07683v1.pdf | Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice | This paper analyzes how candidate choice prediction improves by different psychological predictors. To investigate this question, it collected an original survey dataset featuring the popular TV series "Game of Thrones". The respondents answered which character they anticipated to win in the final episode of the series... | ['Chaehan So'] | 2020-02-29 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [-1.19153477e-01 3.64738017e-01 -6.05957210e-01 -6.95303202e-01
-7.62339175e-01 -3.58414829e-01 3.99256259e-01 5.37829161e-01
-4.92201895e-01 8.00611019e-01 3.28442067e-01 2.14974016e-01
-2.24415809e-01 -8.31865489e-01 -1.35813370e-01 -3.08064282e-01
1.98348284e-01 4.12188649e-01 -3.37669790e-01 -7.63217270... | [9.39883804321289, 10.198674201965332] |
37841ffa-a5a3-451a-ae8f-df7eff8273f8 | csvc-net-code-switched-voice-command | null | null | https://ieeexplore.ieee.org/document/9564183 | https://ieeexplore.ieee.org/document/9564183 | CSVC-Net: Code-Switched Voice Command Classification using Deep CNN-LSTM Network | Colloquial Bengali has adopted many English words due to colonial influence. In conversational Bengali, it is quite common to speak in a mixture of English and Bengali, a phenomenon termed Code-switching (CS). To build a Voice Command Classifier in this era, when the usage of CS is ever-increasing, it is often necessar... | ['Md. Hasanul Kabir', 'Sabbir Ahmed', 'Fariha Ishrat Rahman', 'Arowa Yasmeen'] | 2021-08-17 | null | null | null | international-conference-on-informatics-1 | ['voice-query-recognition'] | ['speech'] | [ 1.22919366e-01 -2.36937612e-01 6.37811542e-01 -4.73526746e-01
-6.69433236e-01 -5.04493713e-01 5.98484874e-01 -3.07228994e-02
-5.09909332e-01 4.48448747e-01 1.30810663e-01 -4.50592101e-01
1.03617184e-01 -4.75135207e-01 -3.58697355e-01 -5.96906543e-01
-1.10297903e-01 4.82175857e-01 3.28881405e-02 -4.68653262... | [14.153274536132812, 6.523498058319092] |
dd81ad46-9407-493d-a9e1-ca24a5a5aa4d | post-processing-independent-evaluation-of | 2306.15440 | null | https://arxiv.org/abs/2306.15440v1 | https://arxiv.org/pdf/2306.15440v1.pdf | Post-Processing Independent Evaluation of Sound Event Detection Systems | Due to the high variation in the application requirements of sound event detection (SED) systems, it is not sufficient to evaluate systems only in a single operating mode. Therefore, the community recently adopted the polyphonic sound detection score (PSDS) as an evaluation metric, which is the normalized area under th... | ['Romain Serizel', 'Reinhold Haeb-Umbach', 'Janek Ebbers'] | 2023-06-27 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 1.24705113e-01 -5.13924062e-01 4.71467227e-01 -3.62104535e-01
-1.06966031e+00 -8.78535628e-01 3.99488032e-01 4.62645918e-01
-5.49234092e-01 1.44436777e-01 -2.73871068e-02 -4.38515037e-01
-2.96584696e-01 -5.39426088e-01 -3.35590780e-01 -6.73003078e-01
-3.10260326e-01 -1.00919761e-01 9.01973486e-01 5.06423265... | [15.278253555297852, 5.371995449066162] |
e3ff1804-931f-463f-9b63-78d74cf57ce1 | filtered-guided-diffusion-fast-filter | 2306.17141 | null | https://arxiv.org/abs/2306.17141v1 | https://arxiv.org/pdf/2306.17141v1.pdf | Filtered-Guided Diffusion: Fast Filter Guidance for Black-Box Diffusion Models | Recent advances in diffusion-based generative models have shown incredible promise for Image-to-Image translation and editing. Most recent work in this space relies on additional training or architecture-specific adjustments to the diffusion process. In this work, we show that much of this low-level control can be achi... | ['Abe Davis', 'Zeqi Gu'] | 2023-06-29 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 3.37879330e-01 1.64331332e-01 -6.06861338e-03 -1.88156605e-01
-5.83129466e-01 -5.81558585e-01 1.11375034e+00 -9.04397517e-02
-5.14061570e-01 3.97480339e-01 3.24499398e-01 -4.20524627e-01
1.90207869e-01 -7.12671638e-01 -6.95793509e-01 -7.58657157e-01
2.63304889e-01 6.35835528e-01 5.96424401e-01 -4.57445711... | [11.350852966308594, -0.22509580850601196] |
a8302d7a-b8c9-4ee2-a4cf-e8d85717b6d0 | co-learning-with-pre-trained-networks | 2212.07585 | null | https://arxiv.org/abs/2212.07585v1 | https://arxiv.org/pdf/2212.07585v1.pdf | Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation | Source-free domain adaptation aims to adapt a source model trained on fully-labeled source domain data to a target domain with unlabeled target domain data. Source data is assumed inaccessible due to proprietary or privacy reasons. Existing works use the source model to pseudolabel target data, but the pseudolabels are... | ['Chuan-Sheng Foo', 'Li Shen', 'Wenyu Zhang'] | 2022-12-15 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.11152446e-01 2.49827534e-01 -8.25363338e-01 -7.71528661e-01
-1.02634597e+00 -9.49918568e-01 7.51746595e-01 2.00113486e-02
-4.82352257e-01 1.13645136e+00 1.93378270e-01 -4.90261912e-02
2.80183941e-01 -7.88940549e-01 -8.72347891e-01 -5.52779973e-01
4.97163922e-01 5.75398684e-01 3.78933288e-02 -6.74329624... | [10.374053955078125, 3.10821533203125] |
320ea123-ba48-4e33-84d2-98c4d5200aff | exploiting-sentence-level-representations-for | 2106.07316 | null | https://arxiv.org/abs/2106.07316v2 | https://arxiv.org/pdf/2106.07316v2.pdf | Exploiting Sentence-Level Representations for Passage Ranking | Recently, pre-trained contextual models, such as BERT, have shown to perform well in language related tasks. We revisit the design decisions that govern the applicability of these models for the passage re-ranking task in open-domain question answering. We find that common approaches in the literature rely on fine-tuni... | ['Avishek Anand', 'Fabian Beringer', 'Jurek Leonhardt'] | 2021-06-14 | null | null | null | null | ['passage-ranking', 'passage-re-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.95556924e-01 1.98178515e-01 1.08874738e-01 -3.86380792e-01
-1.26069105e+00 -7.29412913e-01 7.25906134e-01 6.27384245e-01
-8.26296210e-01 7.52735376e-01 7.05090582e-01 -4.93249714e-01
-1.70675293e-01 -6.65748119e-01 -7.18976676e-01 -3.74495506e-01
2.03307951e-03 6.45353854e-01 6.82056189e-01 -7.53701091... | [11.326737403869629, 8.003156661987305] |
15a0236d-586c-4fb1-9c67-e7b9438a83b4 | test-time-adaptation-with-clip-reward-for | 2305.18010 | null | https://arxiv.org/abs/2305.18010v1 | https://arxiv.org/pdf/2305.18010v1.pdf | Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models | Misalignment between the outputs of a vision-language (VL) model and task goal hinders its deployment. This issue can worsen when there are distribution shifts between the training and test data. To address this problem, prevailing fully test-time adaptation~(TTA) methods bootstrap themselves through entropy minimizati... | ['Yi Yang', 'Linchao Zhu', 'Xiaohan Wang', 'Shuai Zhao'] | 2023-05-29 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 2.41763040e-01 -1.55813187e-01 -3.88237357e-01 -5.71873069e-01
-9.52837706e-01 -4.36904043e-01 3.53182375e-01 -4.00905907e-01
-4.42905605e-01 7.47503340e-01 -1.01032436e-01 -2.94716299e-01
2.90587038e-01 -4.05389965e-01 -8.89562666e-01 -6.63075268e-01
3.66925210e-01 4.01333392e-01 1.51465714e-01 3.03226173... | [9.837273597717285, 2.8782670497894287] |
4d568dfb-cccb-4cfe-8e8d-1504d54ac712 | multi-view-priors-for-learning-detectors-from | 1312.6095 | null | http://arxiv.org/abs/1312.6095v2 | http://arxiv.org/pdf/1312.6095v2.pdf | Multi-View Priors for Learning Detectors from Sparse Viewpoint Data | While the majority of today's object class models provide only 2D bounding
boxes, far richer output hypotheses are desirable including viewpoint,
fine-grained category, and 3D geometry estimate. However, models trained to
provide richer output require larger amounts of training data, preferably well
covering the releva... | ['Michael Stark', 'Peter Gehler', 'Bojan Pepik', 'Bernt Schiele'] | 2013-12-20 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 7.30781704e-02 1.24980807e-01 -3.14241141e-01 -4.78576660e-01
-7.57943630e-01 -7.89415777e-01 7.52672970e-01 3.32052588e-01
-1.44719079e-01 2.42844149e-01 2.75976032e-01 -1.24947220e-01
2.54157543e-01 -8.06663215e-01 -8.69827867e-01 -4.29100871e-01
1.01585209e-01 6.00204110e-01 6.83007717e-01 5.02992533... | [7.789819240570068, -2.8108577728271484] |
df138635-6249-483d-b1c9-a311d927f982 | decouple-learning-for-parameterized-image | 1807.08186 | null | http://arxiv.org/abs/1807.08186v2 | http://arxiv.org/pdf/1807.08186v2.pdf | Decouple Learning for Parameterized Image Operators | Many different deep networks have been used to approximate, accelerate or
improve traditional image operators, such as image smoothing, super-resolution
and denoising. Among these traditional operators, many contain parameters which
need to be tweaked to obtain the satisfactory results, which we refer to as
"parameteri... | ['Dong-Dong Chen', 'Baoquan Chen', 'Qingnan Fan', 'Nenghai Yu', 'Lu Yuan', 'Gang Hua'] | 2018-07-21 | decouple-learning-for-parameterized-image-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Qingnan_Fan_Learning_to_Learn_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Qingnan_Fan_Learning_to_Learn_ECCV_2018_paper.pdf | eccv-2018-9 | ['image-smoothing'] | ['computer-vision'] | [ 2.26240039e-01 -3.26219112e-01 -1.02365069e-01 -4.47461963e-01
-1.22161463e-01 -2.06423119e-01 1.08364560e-01 -2.86531538e-01
-5.02311885e-01 2.66601413e-01 -3.60258482e-02 -1.71899080e-01
-5.55305555e-02 -6.26191676e-01 -6.21147335e-01 -9.41596150e-01
2.35149115e-01 2.38053612e-02 3.37303340e-01 -1.25657916... | [11.057214736938477, -1.3483364582061768] |
6dfcc9e6-6bfa-4bfb-991d-2bdc10f4f0e7 | multiple-human-3d-pose-estimation-from | null | null | https://link.springer.com/article/10.1007/s11042-017-5133-8 | https://link.springer.com/article/10.1007/s11042-017-5133-8 | Multiple human 3d pose estimation from multiview images | Multiple human 3D pose estimation is a challenging task. It is mainly because of large variations in the scale and pose of humans, fast motions, multiple persons in the scene, and arbitrary number of visible body parts due to occlusion or truncation. Some of these ambiguities can be resolved by using multiview images. ... | ['Shohreh Kasaei', 'Sara Ershadi-Nasab', 'Esmaeil Sanaei', 'Erfan Noury'] | 2017-09-04 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-1.59402505e-01 -1.19943105e-01 2.42180433e-02 -1.54896557e-01
-5.63942790e-01 -2.54451782e-01 4.14818406e-01 -9.72801968e-02
-4.54267889e-01 5.97895563e-01 2.03128412e-01 6.79563046e-01
7.01772347e-02 -2.76859730e-01 -7.53145754e-01 -6.13775969e-01
-5.88061772e-02 1.01352370e+00 5.92706561e-01 -1.35362640... | [7.035489559173584, -1.0003266334533691] |
2992d0e9-08dd-4c93-aa0d-76eb000eecde | adaptive-estimators-show-information | 1902.09037 | null | https://arxiv.org/abs/1902.09037v2 | https://arxiv.org/pdf/1902.09037v2.pdf | Adaptive Estimators Show Information Compression in Deep Neural Networks | To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task. However, empirical evid... | ["Cian O'Donnell", 'Ivan Chelombiev', 'Conor Houghton'] | 2019-02-24 | adaptive-estimators-show-information-1 | https://openreview.net/forum?id=SkeZisA5t7 | https://openreview.net/pdf?id=SkeZisA5t7 | iclr-2019-5 | ['mutual-information-estimation', 'l2-regularization'] | ['methodology', 'methodology'] | [ 3.25799406e-01 1.30631268e-01 1.04459755e-01 -4.86433953e-01
-2.12976903e-01 -5.30282259e-01 4.84771192e-01 2.37396374e-01
-9.68453586e-01 7.87925124e-01 1.54992312e-01 -3.80460292e-01
-5.84668517e-01 -6.69353604e-01 -7.18597651e-01 -7.37666905e-01
-2.91085809e-01 3.04018766e-01 2.28818133e-01 -8.99378285... | [8.22033977508545, 3.4473588466644287] |
0d22f2ca-a496-4523-b8ca-b364f7e230d7 | topological-deep-learning-a-review-of-an | 2302.03836 | null | https://arxiv.org/abs/2302.03836v1 | https://arxiv.org/pdf/2302.03836v1.pdf | Topological Deep Learning: A Review of an Emerging Paradigm | Topological data analysis (TDA) provides insight into data shape. The summaries obtained by these methods are principled global descriptions of multi-dimensional data whilst exhibiting stable properties such as robustness to deformation and noise. Such properties are desirable in deep learning pipelines but they are ty... | ['Lars Petersson', 'Vivien Rolland', 'Zeeshan Hayder', 'James Nichols', 'Abdelwahed Khamis', 'Ali Zia'] | 2023-02-08 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-6.41759932e-01 1.73297487e-02 1.70278717e-02 -2.17507124e-01
-3.17765743e-01 -8.03719819e-01 9.89815474e-01 4.17531669e-01
1.40662074e-01 3.63886327e-01 2.90785968e-01 -4.88151371e-01
-6.74634635e-01 -1.05168378e+00 -8.53193462e-01 -7.19982445e-01
-1.00748825e+00 6.66250288e-01 3.30187500e-01 -4.06466961... | [6.773507118225098, 5.882699489593506] |
faa6fee9-5676-49c3-b19a-018e490860f2 | differential-viewpoints-for-ground-terrain | 2009.11072 | null | https://arxiv.org/abs/2009.11072v1 | https://arxiv.org/pdf/2009.11072v1.pdf | Differential Viewpoints for Ground Terrain Material Recognition | Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined single-view images captured in the scene. We take a middle-ground approach for material recognition that t... | ['Ko Nishino', 'Jia Xue', 'Hang Zhang', 'Kristin J. Dana'] | 2020-09-22 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 7.21363783e-01 -3.32152873e-01 1.60500661e-01 -5.95260918e-01
-8.12860966e-01 -6.64533377e-01 3.02185655e-01 -3.23352456e-01
-7.76126087e-02 3.16072822e-01 -1.22767337e-01 -1.10752776e-01
-3.87145072e-01 -1.41436625e+00 -1.01568711e+00 -5.48830450e-01
1.17942113e-02 3.55033726e-01 1.80888936e-01 -5.17491102... | [9.47161865234375, -2.7694756984710693] |
f6a20fbb-1e6b-4d74-87ed-da87de16d6b4 | counter-gap-counterfactual-bias-evaluation | 2302.05674 | null | https://arxiv.org/abs/2302.05674v1 | https://arxiv.org/pdf/2302.05674v1.pdf | Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns | Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods. In this work, we focus on evaluating gender bias through coreference resolution, where previous datasets are either hand-crafted or fail to reliably measure an explicitly d... | ['Oana-Maria Camburu', 'Thomas Lukasiewicz', 'Vid Kocijan', 'Zhongbin Xie'] | 2023-02-11 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 9.30919200e-02 2.40161121e-01 -7.09667146e-01 -8.52588892e-01
-7.55084872e-01 -6.67789280e-01 1.18055260e+00 3.83189976e-01
-6.84806049e-01 1.26509082e+00 8.46181333e-01 -2.73819417e-01
8.72185733e-03 -8.29388380e-01 -7.06802070e-01 -2.51347691e-01
2.26359978e-01 5.58115423e-01 -3.55975509e-01 -3.72571349... | [9.362992286682129, 10.242742538452148] |
c79284a9-32cd-4c24-9cc7-ca83852dceeb | order-disorder-imitation-adversarial-attacks | 2209.06506 | null | https://arxiv.org/abs/2209.06506v2 | https://arxiv.org/pdf/2209.06506v2.pdf | Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models | Neural text ranking models have witnessed significant advancement and are increasingly being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of general neural models, which have been detected but remain underexplored by prior studies. Moreover, the inherit adversarial vulnerabilities ... | ['Xiaozhong Liu', 'Wei Lu', 'XiaoFeng Wang', 'Changlong Sun', 'Kaisong Song', 'Di Tang', 'Yangyang Kang', 'Jiawei Liu'] | 2022-09-14 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 3.68342996e-01 -6.13960624e-02 -9.48894322e-02 -2.14869559e-01
-8.95882726e-01 -1.03845513e+00 7.36496389e-01 -3.72039944e-01
-4.91147637e-01 5.97450554e-01 2.63677090e-01 -4.62428302e-01
-2.56417006e-01 -7.89531410e-01 -9.11888361e-01 -4.81204510e-01
-3.89973223e-02 6.32864684e-02 3.04019302e-01 -6.59241319... | [6.040886878967285, 8.112648010253906] |
57ce1c13-4b3c-4778-af63-ddad2efcb31a | 2d-human-pose-estimation-with-explicit | 2212.02163 | null | https://arxiv.org/abs/2212.02163v1 | https://arxiv.org/pdf/2212.02163v1.pdf | 2D Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints | Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process. However, we found that some human anatomi... | ['Yuhua Qian', 'Yapeng Chen', 'Ming Zhang', 'Zilong Wang', 'Zhangjian Ji'] | 2022-12-05 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-4.12713110e-01 1.48477331e-01 -2.74618685e-01 -8.68275538e-02
-4.50279295e-01 -2.03796700e-01 1.74852327e-01 6.96083680e-02
-7.53300071e-01 5.77079535e-01 2.30837047e-01 3.05993229e-01
-2.65973687e-01 -4.82630700e-01 -6.05225086e-01 -5.00518262e-01
-2.44059071e-01 4.26318496e-01 6.19013727e-01 -3.73214841... | [7.133866310119629, -0.7379562258720398] |
c9b94a00-7a9e-45e5-ae93-56f2f025a7c4 | visual-composite-set-detection-using-part-and | 2105.02170 | null | https://arxiv.org/abs/2105.02170v2 | https://arxiv.org/pdf/2105.02170v2.pdf | Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries | Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both the parts (subject, object, and predicate) and the sum (triplet as a whole) are to be detected in a hierarchical fashion. In this paper, we pr... | ['Stefano Soatto', 'Vijay Mahadevan', 'Yuting Zhang', 'Haofu Liao', 'Zhuowen Tu', 'Qi Dong'] | 2021-05-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dong_Visual_Relationship_Detection_Using_Part-and-Sum_Transformers_With_Composite_Queries_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dong_Visual_Relationship_Detection_Using_Part-and-Sum_Transformers_With_Composite_Queries_ICCV_2021_paper.pdf | iccv-2021-1 | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.97814757e-01 1.29502654e-01 1.11415938e-01 -3.62825274e-01
-4.98534232e-01 -6.21442139e-01 8.95906270e-01 3.51516396e-01
-2.29526371e-01 -1.76065508e-02 2.48720441e-02 -1.91818058e-01
6.22421838e-02 -2.87896901e-01 -6.57817423e-01 -3.04656476e-01
-1.29562232e-03 8.39748204e-01 9.37757969e-01 -1.66240662... | [10.058732986450195, 1.557834267616272] |
03a3cbe8-0916-4b0d-add7-ddb199a1b68f | recurrent-models-of-visual-attention | 1406.6247 | null | http://arxiv.org/abs/1406.6247v1 | http://arxiv.org/pdf/1406.6247v1.pdf | Recurrent Models of Visual Attention | Applying convolutional neural networks to large images is computationally
expensive because the amount of computation scales linearly with the number of
image pixels. We present a novel recurrent neural network model that is capable
of extracting information from an image or video by adaptively selecting a
sequence of ... | ['Koray Kavukcuoglu', 'Volodymyr Mnih', 'Nicolas Heess', 'Alex Graves'] | 2014-06-24 | recurrent-models-of-visual-attention-1 | http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention | http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf | neurips-2014-12 | ['hard-attention'] | ['methodology'] | [ 6.61603153e-01 8.87480974e-02 -4.00718212e-01 -2.24940702e-01
-5.31830072e-01 -6.40706956e-01 6.29491329e-01 -2.04684481e-01
-8.77071381e-01 4.33447242e-01 -8.62268955e-02 -4.01294917e-01
1.61638007e-01 -6.83223605e-01 -1.06301725e+00 -7.49844849e-01
-1.06207319e-01 3.20304871e-01 3.97943676e-01 -7.53692016... | [9.347434997558594, 1.082413911819458] |
70b8a279-7b33-4b3f-83f3-9e91705af174 | text2facegan-face-generation-from-fine | 1911.11378 | null | https://arxiv.org/abs/1911.11378v1 | https://arxiv.org/pdf/1911.11378v1.pdf | Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions | Powerful generative adversarial networks (GAN) have been developed to automatically synthesize realistic images from text. However, most existing tasks are limited to generating simple images such as flowers from captions. In this work, we extend this problem to the less addressed domain of face generation from fine-gr... | ['Yi Yu', 'Shailesh Kumar Jha', 'Rajiv Ratn Shah', 'Manraj Singh Grover', 'Ajit Kumar', 'Osaid Rehman Nasir'] | 2019-11-26 | null | null | null | null | ['text-to-face-generation'] | ['computer-vision'] | [ 5.45457661e-01 2.63083100e-01 3.45307082e-01 -6.73352182e-01
-1.01244116e+00 -8.24448764e-01 9.56093132e-01 -8.69019330e-01
6.55930340e-02 1.21985567e+00 1.31233245e-01 7.29456097e-02
3.72542351e-01 -9.10224617e-01 -1.06343114e+00 -7.67421901e-01
4.90664035e-01 9.35566366e-01 -5.64054847e-01 -1.13277085... | [12.24974250793457, -0.09776004403829575] |
6bca4c5c-63d6-408c-a2d9-b8abd5f3a4e1 | inspecting-state-of-the-art-performance-and | 2011.09257 | null | https://arxiv.org/abs/2011.09257v3 | https://arxiv.org/pdf/2011.09257v3.pdf | Inspecting state of the art performance and NLP metrics in image-based medical report generation | Several deep learning architectures have been proposed over the last years to deal with the problem of generating a written report given an imaging exam as input. Most works evaluate the generated reports using standard Natural Language Processing (NLP) metrics (e.g. BLEU, ROUGE), reporting significant progress. In thi... | ['Sergio Uribe', 'Cecilia Besa', 'Pablo Messina', 'Denis Parra', 'Pablo Pino'] | 2020-11-18 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 3.10243309e-01 8.09164286e-01 -2.82737434e-01 -5.97746551e-01
-1.53158116e+00 -5.63395381e-01 7.01577961e-01 6.83509886e-01
-5.68803430e-01 1.00026584e+00 7.18839288e-01 -4.84588742e-01
-8.59631822e-02 -6.92396998e-01 -5.46554565e-01 -3.37952942e-01
6.73611555e-03 7.12091982e-01 -4.07898836e-02 9.60959494... | [15.035369873046875, -1.3583585023880005] |
4f253181-ed76-459d-8f78-2c1c243840a3 | reinforced-self-attention-network-a-hybrid-of | 1801.10296 | null | http://arxiv.org/abs/1801.10296v2 | http://arxiv.org/pdf/1801.10296v2.pdf | Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling | Many natural language processing tasks solely rely on sparse dependencies
between a few tokens in a sentence. Soft attention mechanisms show promising
performance in modeling local/global dependencies by soft probabilities between
every two tokens, but they are not effective and efficient when applied to long
sentences... | ['Tao Shen', 'Sen Wang', 'Jing Jiang', 'Chengqi Zhang', 'Tianyi Zhou', 'Guodong Long'] | 2018-01-31 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.03709298e-01 1.12337060e-01 -1.05147921e-02 -6.64007664e-01
-8.76913548e-01 -2.97793984e-01 6.20834887e-01 2.28132412e-01
-7.34867036e-01 9.52881634e-01 3.05280030e-01 -2.98654288e-01
2.86679268e-01 -7.77746379e-01 -9.50752139e-01 -6.82321787e-01
2.41570577e-01 3.59981984e-01 1.50831595e-01 -4.82911199... | [10.9798583984375, 8.706693649291992] |
cac80aec-b136-4789-b650-0b0c07f1db55 | heterogeneous-tri-stream-clustering-network | 2301.04451 | null | https://arxiv.org/abs/2301.04451v1 | https://arxiv.org/pdf/2301.04451v1.pdf | Heterogeneous Tri-stream Clustering Network | Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks. Despite the rapid progress, previous works mostly require both positive and negative sample pairs for contrastive clustering, which rely on a relative large batch... | ['Chang-Dong Wang', 'Dong Huang', 'Xiaozhi Deng'] | 2023-01-11 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-1.77895263e-01 -2.58820206e-01 7.32863322e-02 -3.47306341e-01
-6.37229323e-01 -3.06407273e-01 6.63029909e-01 -6.87201843e-02
-3.88825208e-01 7.43614789e-03 -5.37082180e-02 1.21227257e-01
-3.86929326e-03 -4.75214928e-01 -7.16341794e-01 -9.70337987e-01
-1.75712958e-01 5.92851341e-01 1.70271978e-01 8.74946937... | [9.117911338806152, 3.327848196029663] |
343cd165-93b4-486b-b976-0ced44932ed1 | author-profiling-for-abuse-detection | null | null | https://aclanthology.org/C18-1093 | https://aclanthology.org/C18-1093.pdf | Author Profiling for Abuse Detection | The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet. Previous research suggests that such abusive content tends to come from users who share a set of common stereotypes and form communities around them. T... | ['Ekaterina Shutova', 'Helen Yannakoudakis', 'Marco del Tredici', 'Pushkar Mishra'] | 2018-08-01 | author-profiling-for-abuse-detection-1 | https://aclanthology.org/C18-1093 | https://aclanthology.org/C18-1093.pdf | coling-2018-8 | ['abuse-detection'] | ['natural-language-processing'] | [-2.71973073e-01 -2.98759133e-01 -5.46506763e-01 -1.70739457e-01
-5.03183484e-01 -7.74796128e-01 7.44606078e-01 7.39152193e-01
-4.68768895e-01 4.96701598e-01 5.92502713e-01 -4.28136677e-01
2.70591229e-01 -7.52243698e-01 -8.46738145e-02 -5.10104410e-02
-1.70552462e-01 8.94652456e-02 2.15094239e-01 -4.57937002... | [8.625264167785645, 10.456000328063965] |
a666747a-c4a0-46bd-b33f-8ca4cabbd2c1 | copula-based-conformal-prediction-for-multi | 2101.12002 | null | https://arxiv.org/abs/2101.12002v1 | https://arxiv.org/pdf/2101.12002v1.pdf | Copula-based conformal prediction for Multi-Target Regression | There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions applied to... | ['Sylvain Rousseau', 'Sébastien Destercke', 'Soundouss Messoudi'] | 2021-01-28 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 6.28801212e-02 -5.21973856e-02 -2.82693267e-01 -5.31530678e-01
-1.20921183e+00 -2.85608619e-01 3.53467613e-01 9.88920927e-02
-2.12033242e-01 1.02256036e+00 -6.89369291e-02 -1.29718244e-01
-5.82278669e-01 -8.56640875e-01 -8.00946176e-01 -7.15093017e-01
1.07570719e-02 5.75032830e-01 -7.87838101e-02 9.36713591... | [7.8377604484558105, 4.040994644165039] |
17659bf9-ab7f-4b71-9fed-ca4fa3279707 | to-compute-or-not-to-compute-adaptive-smart | 2209.02166 | null | https://arxiv.org/abs/2209.02166v2 | https://arxiv.org/pdf/2209.02166v2.pdf | To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing | We consider a network of smart sensors for edge computing application that sample a signal of interest and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at ... | ['Paolo Dini', 'Francesco Zanini', 'Giovanni Peserico', 'Luca Ballotta'] | 2022-09-05 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 6.79667592e-01 2.74286360e-01 -3.68652642e-01 -3.05528283e-01
-7.01905489e-01 -2.29571730e-01 1.06410064e-01 3.66004348e-01
-5.90805531e-01 7.16910958e-01 -3.64575356e-01 -1.49128333e-01
-2.77489066e-01 -1.24755800e+00 -6.30741835e-01 -6.45680070e-01
-5.41103482e-01 1.84054911e-01 1.91715732e-01 7.36035034... | [5.9357686042785645, 1.5816491842269897] |
3eef10a4-dd42-4c70-8426-3988b45619d1 | stochastic-shield-a-probabilistic-approach | 2105.06512 | null | https://arxiv.org/abs/2105.06512v1 | https://arxiv.org/pdf/2105.06512v1.pdf | Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs | Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerable to adversarial attacks as the full-precision models. With the proliferation of neural networks on small devices that we carry or surround ... | ['Partha Maji', 'René de Jong', 'Sangwon Ha', 'Lorena Qendro'] | 2021-05-13 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.61715552e-01 1.97099805e-01 -9.65751559e-02 -2.64292717e-01
-8.06867540e-01 -8.92410338e-01 5.60535967e-01 -1.39050409e-01
-5.85880756e-01 6.67599976e-01 -6.20916337e-02 -7.42169797e-01
-7.91739381e-04 -8.29282701e-01 -1.21058953e+00 -5.96612275e-01
-8.75594616e-02 -3.65830660e-02 2.84751326e-01 -2.11484924... | [5.706996917724609, 7.7302374839782715] |
dbb92058-f5da-4615-80c4-0f5c664a0bae | item-tagging-for-information-retrieval-a | 2008.11567 | null | https://arxiv.org/abs/2008.11567v1 | https://arxiv.org/pdf/2008.11567v1.pdf | Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach | Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions. A lot of research has been done for either multi-label text categorization or image annotation. However, there is a lack of published work that targets a... | ['Ruiming Tang', 'Jieming Zhu', 'Xi Xiao', 'Biao Lu', 'Xiuqiang He', 'Kelong Mao'] | 2020-08-26 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 3.86350334e-01 4.90930341e-02 -8.83775771e-01 -4.01363015e-01
-1.07512295e+00 -3.91735733e-01 2.99638301e-01 3.64121974e-01
-4.15162921e-01 5.83148658e-01 1.80672914e-01 -4.52433713e-02
-3.22050571e-01 -5.27365923e-01 -4.62888092e-01 -4.66022670e-01
1.65389642e-01 5.65279663e-01 2.27426603e-01 -4.47671860... | [9.6884765625, 4.430361270904541] |
baacf6d5-4d22-4839-a04a-7a8592bc614b | leveraging-video-coding-knowledge-for-deep | 2302.13594 | null | https://arxiv.org/abs/2302.13594v1 | https://arxiv.org/pdf/2302.13594v1.pdf | Leveraging Video Coding Knowledge for Deep Video Enhancement | Recent advancements in deep learning techniques have significantly improved the quality of compressed videos. However, previous approaches have not fully exploited the motion characteristics of compressed videos, such as the drastic change in motion between video contents and the hierarchical coding structure of the co... | ['Van-Quang Nguyen', 'Thuong Nguyen Canh', 'Thong Bach'] | 2023-02-27 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 2.16198027e-01 -7.44802892e-01 -2.07091942e-01 -1.94425315e-01
-6.47920310e-01 -1.95909932e-01 3.68337393e-01 1.95319988e-02
-3.45761836e-01 4.85753059e-01 7.61507630e-01 -1.91051275e-01
-8.34448040e-02 -4.67839122e-01 -7.45502770e-01 -4.79614794e-01
-5.32629728e-01 -4.47491437e-01 2.38561705e-01 -2.52140939... | [11.362659454345703, -1.711758017539978] |
25bce792-2d15-476b-b3ea-89c40cc3a5d6 | optimizing-fiducial-marker-placement-for | 2211.01513 | null | https://arxiv.org/abs/2211.01513v2 | https://arxiv.org/pdf/2211.01513v2.pdf | Optimizing Fiducial Marker Placement for Improved Visual Localization | Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This paper explores the problem of automatic marker placement within a scene. Specifically... | ['John J. Leonard', 'Sudipta N. Sinha', 'Victor Fragoso', 'Joseph DeGol', 'Qiangqiang Huang'] | 2022-11-02 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 1.75529588e-02 -3.47050726e-01 -1.99892551e-01 -2.47061431e-01
-7.47483730e-01 -9.54621613e-01 4.54914898e-01 2.80856073e-01
-4.20862466e-01 4.04014677e-01 -2.16543674e-01 -3.67644131e-01
2.85116434e-01 -2.65089184e-01 -8.49191785e-01 -1.09746419e-01
-3.75086606e-01 1.28369927e-01 3.75652105e-01 4.32501733... | [7.6637468338012695, -2.137322187423706] |
e8643cf2-5a8e-4ffb-ad52-741fff1b0519 | network-traffic-analysis-based-iot-device | 2009.04682 | null | https://arxiv.org/abs/2009.04682v1 | https://arxiv.org/pdf/2009.04682v1.pdf | Network Traffic Analysis based IoT Device Identification | Device identification is the process of identifying a device on Internet without using its assigned network or other credentials. The sharp rise of usage in Internet of Things (IoT) devices has imposed new challenges in device identification due to a wide variety of devices, protocols and control interfaces. In a netwo... | ['Emeroylariffion Abas', 'Sandhya Aneja', 'Nagender Aneja', 'Rajarshi Roy Chowdhury'] | 2020-09-10 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 6.09066129e-01 -8.95905793e-02 -7.27535069e-01 -1.91337153e-01
-1.25121266e-01 -1.09803951e+00 4.78309840e-01 2.49901682e-01
-2.73417145e-01 6.96435630e-01 -7.35392496e-02 -5.05453229e-01
-2.90136456e-01 -9.01520908e-01 -1.21341564e-01 -5.14627099e-01
1.26161993e-01 2.46484220e-01 3.77098918e-01 5.17099023... | [5.18638277053833, 7.133514881134033] |
22c61720-c31d-4d49-be11-ba71cef036d6 | data-augmentation-for-leaf-segmentation-and | 1903.08583 | null | http://arxiv.org/abs/1903.08583v1 | http://arxiv.org/pdf/1903.08583v1.pdf | Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants | Deep learning techniques involving image processing and data analysis are
constantly evolving. Many domains adapt these techniques for object
segmentation, instantiation and classification. Recently, agricultural
industries adopted those techniques in order to bring automation to farmers
around the globe. One analysis ... | ['Alon Zvirin', 'Yaron Honen', 'Ron Kimmel', 'Dmitry Kuznichov'] | 2019-03-20 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 5.69762170e-01 -5.15739806e-03 -4.36469018e-02 -3.13733160e-01
6.53492212e-02 -9.20040011e-01 7.84679428e-02 5.53526461e-01
-4.98317704e-02 4.19415027e-01 -6.32389784e-01 -3.06129187e-01
-1.23229600e-01 -1.17413008e+00 -6.61009133e-01 -7.45990098e-01
1.04746707e-01 6.78464115e-01 1.99249133e-01 -1.45185769... | [9.139488220214844, -1.5344096422195435] |
ae9631d5-c954-4d71-92c5-03435fe977cc | image-blind-denoising-using-dual | 2304.01620 | null | https://arxiv.org/abs/2304.01620v1 | https://arxiv.org/pdf/2304.01620v1.pdf | Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection | In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied with large numbers of model parameters, leading to high training cost and long inference time, which limits their application in practical d... | ['Yungang Zhang', 'Peng Liang', 'Guannan Lv', 'Shicheng Liao', 'Wencong Wu'] | 2023-04-04 | null | null | null | null | ['noise-estimation'] | ['medical'] | [-2.73146462e-02 -4.60880965e-01 5.57020128e-01 -2.36336976e-01
-3.37605715e-01 -8.67557451e-02 3.62944752e-01 -1.56118363e-01
-6.95801139e-01 4.23684537e-01 -5.88951930e-02 -1.76416442e-01
9.81867611e-02 -9.05166388e-01 -4.93135959e-01 -1.24969077e+00
2.46208459e-01 -4.44355339e-01 5.73226929e-01 -3.06560785... | [11.40324878692627, -2.376570224761963] |
ad022e2c-9cb5-4ac6-993c-4bdc90ddd65b | urbangiraffe-representing-urban-scenes-as | 2303.14167 | null | https://arxiv.org/abs/2303.14167v2 | https://arxiv.org/pdf/2303.14167v2.pdf | UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields | Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation. Despite the fact that rapid progress has been made in 3D-aware generative models, most existing methods focus on object-centric images and are not applicable to generating... | ['Yiyi Liao', 'Yue Wang', 'Rong Xiong', 'Hanlei Guo', 'Yifei Yang', 'Yuanbo Yang'] | 2023-03-24 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 3.13278824e-01 -3.78305279e-02 2.55007923e-01 -1.18801571e-01
-3.72220844e-01 -8.57661009e-01 9.08636570e-01 -4.81341183e-01
1.27038300e-01 4.96353656e-01 2.32638061e-01 1.16234468e-02
2.28963066e-02 -1.01904535e+00 -1.03349781e+00 -6.18137240e-01
5.96546710e-01 7.61339903e-01 2.18014926e-01 -2.66586155... | [9.223445892333984, -3.1164424419403076] |
8ab2a445-bd34-4baf-b644-802d63fc3416 | dadmatools-natural-language-processing | null | null | https://aclanthology.org/2022.naacl-demo.13 | https://aclanthology.org/2022.naacl-demo.13.pdf | DadmaTools: Natural Language Processing Toolkit for Persian Language | We introduce DadmaTools, an open-source Python Natural Language Processing toolkit for the Persian language. The toolkit is a neural pipeline based on spaCy for several text processing tasks, including normalization, tokenization, lemmatization, part-of-speech, dependency parsing, constituency parsing, chunking, and ez... | ['Mohammad Taher Pilehvar', 'Mohamad Bagher Sajadi', 'Najmeh Zare', 'Mohammad Karrabi', 'Romina Etezadi'] | null | null | null | null | naacl-acl-2022-7 | ['constituency-parsing'] | ['natural-language-processing'] | [-6.57246828e-01 1.94393814e-01 -1.65438384e-01 -7.30574548e-01
-7.61093676e-01 -9.08444881e-01 5.14640749e-01 5.99940181e-01
-9.30337965e-01 5.69130838e-01 6.86648607e-01 -2.62169927e-01
4.30756897e-01 -7.65027344e-01 -2.08474308e-01 -2.91022450e-01
1.20641664e-01 7.79227912e-01 1.13372758e-01 -3.21081072... | [10.466817855834961, 9.992916107177734] |
7d6623cd-9714-4c73-b46e-2102f12d8327 | egocentric-audio-visual-noise-suppression | 2211.03643 | null | https://arxiv.org/abs/2211.03643v2 | https://arxiv.org/pdf/2211.03643v2.pdf | Egocentric Audio-Visual Noise Suppression | This paper studies audio-visual noise suppression for egocentric videos -- where the speaker is not captured in the video. Instead, potential noise sources are visible on screen with the camera emulating the off-screen speaker's view of the outside world. This setting is different from prior work in audio-visual speech... | ['Kaustubh Kalgaonkar', 'Yang Liu', 'Egor Lakomkin', 'Ju Lin', 'Weipeng He', 'Roshan Sharma'] | 2022-11-07 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 2.89787829e-01 -4.71364170e-01 1.29415244e-01 -9.10207629e-02
-1.37182498e+00 -5.00229299e-01 3.85806739e-01 -2.09247187e-01
-4.96358842e-01 2.33967438e-01 8.33213329e-01 1.81374714e-01
4.59709689e-02 -1.72493346e-02 -6.60525382e-01 -1.00756788e+00
1.45456314e-01 -6.61496341e-01 1.46022558e-01 -5.08574024... | [14.439260482788086, 5.1395134925842285] |
336c69e2-9e3d-4e42-a9f8-88a18541990b | metric-type-identification-for-multi-level | 2102.00819 | null | https://arxiv.org/abs/2102.00819v1 | https://arxiv.org/pdf/2102.00819v1.pdf | Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers | Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. We introduce a new information extraction task, metric-type identification from multi-level header numerical tables, and provide a dataset ext... | ['Hiroya Takamura', 'Manabu Okumura', 'Hidetaka Kamigaito', 'Lya Hulliyyatus Suadaa'] | 2021-02-01 | null | https://aclanthology.org/2021.eacl-main.267 | https://aclanthology.org/2021.eacl-main.267.pdf | eacl-2021-2 | ['metric-type-identification'] | ['natural-language-processing'] | [-5.47726192e-02 -1.89096898e-01 -6.18441880e-01 -3.93876940e-01
-1.04306126e+00 -9.21951175e-01 4.27380592e-01 8.02701354e-01
8.18806961e-02 1.09826517e+00 3.36924009e-02 -9.53103602e-01
-3.01836580e-01 -1.23811936e+00 -9.41225767e-01 6.96088374e-02
-1.60949603e-01 4.99797195e-01 -2.77809322e-01 2.38551006... | [11.647747039794922, 3.107576847076416] |
44f58288-f3e4-4c43-b011-f609a9746a49 | stereo-matching-with-cost-volume-based-sparse | 2201.11937 | null | https://arxiv.org/abs/2201.11937v1 | https://arxiv.org/pdf/2201.11937v1.pdf | Stereo Matching with Cost Volume based Sparse Disparity Propagation | Stereo matching is crucial for binocular stereo vision. Existing methods mainly focus on simple disparity map fusion to improve stereo matching, which require multiple dense or sparse disparity maps. In this paper, we propose a simple yet novel scheme, termed feature disparity propagation, to improve general stereo mat... | ['Xiaojiang Peng', 'Wei Xue'] | 2022-01-28 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.96434543e-01 -5.41361034e-01 -6.42390251e-02 -5.17956197e-01
-6.56974792e-01 7.50509789e-03 4.31663692e-01 2.21338533e-02
-3.23724270e-01 6.76007688e-01 3.29991192e-01 9.98270363e-02
6.21009544e-02 -1.01200187e+00 -6.40321970e-01 -5.41392982e-01
3.58947337e-01 -2.66671814e-02 6.39927089e-01 -1.68817028... | [9.067461013793945, -2.3990566730499268] |
d1a59858-dccb-4dfd-acf0-d960134f9d75 | unsupervised-domain-adaptation-with-2 | 2106.08752 | null | https://arxiv.org/abs/2106.08752v1 | https://arxiv.org/pdf/2106.08752v1.pdf | Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation | Unsupervised domain adaptation is useful in medical image segmentation. Particularly, when ground truths of the target images are not available, domain adaptation can train a target-specific model by utilizing the existing labeled images from other modalities. Most of the reported works mapped images of both the source... | ['Xiahai Zhuang', 'Fuping Wu'] | 2021-06-16 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.22318399e-01 3.38551253e-01 -2.61825919e-01 -4.60608304e-01
-1.01230991e+00 -5.22287786e-01 3.93743098e-01 -1.27418667e-01
-4.74967122e-01 8.87751937e-01 -1.35899216e-01 -6.56162053e-02
1.77078143e-01 -7.01013863e-01 -7.56025732e-01 -1.03686452e+00
2.36052766e-01 7.58841574e-01 3.30243409e-01 2.21673757... | [14.536225318908691, -2.008660078048706] |
5df4bbfe-2b34-4834-b62e-770517390871 | syntactic-structure-processing-in-the-brain | 2302.08589 | null | https://arxiv.org/abs/2302.08589v1 | https://arxiv.org/pdf/2302.08589v1.pdf | Syntactic Structure Processing in the Brain while Listening | Syntactic parsing is the task of assigning a syntactic structure to a sentence. There are two popular syntactic parsing methods: constituency and dependency parsing. Recent works have used syntactic embeddings based on constituency trees, incremental top-down parsing, and other word syntactic features for brain activit... | ['Bapi Raju Surampud', 'Manish Gupta', 'Mounika Marreddy', 'Subba Reddy Oota'] | 2023-02-16 | null | null | null | null | ['activity-prediction', 'dependency-parsing', 'activity-prediction'] | ['computer-vision', 'natural-language-processing', 'time-series'] | [-9.36116129e-02 2.69931048e-01 -1.11006707e-01 -5.96547604e-01
-2.89219797e-01 -5.80486298e-01 6.37838900e-01 4.75757003e-01
-5.79121828e-01 2.26063311e-01 1.06308675e+00 -2.88173199e-01
-2.93237656e-01 -8.05739462e-01 -4.42938089e-01 -6.70388579e-01
-4.62294698e-01 1.29742637e-01 8.12421516e-02 3.19137834... | [10.35354995727539, 8.50393009185791] |
15fb337c-ddf6-4d8c-a6e4-252daf36893d | yolox-exceeding-yolo-series-in-2021 | 2107.08430 | null | https://arxiv.org/abs/2107.08430v2 | https://arxiv.org/pdf/2107.08430v2.pdf | YOLOX: Exceeding YOLO Series in 2021 | In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the... | ['Jian Sun', 'Zeming Li', 'Feng Wang', 'Songtao Liu', 'Zheng Ge'] | 2021-07-18 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-4.47813034e-01 -2.44215727e-01 -2.62371421e-01 -5.87958694e-02
-8.06369007e-01 -5.29133320e-01 -1.59650408e-02 -7.40127414e-02
-6.16299093e-01 2.41565511e-01 -3.73703629e-01 -3.67224663e-01
6.06945634e-01 -3.25302690e-01 -8.42114627e-01 -4.93583381e-01
-3.70124578e-01 2.91676279e-02 1.01267314e+00 -2.92139202... | [8.5990629196167, -0.3257101774215698] |
0047f076-eddf-4164-bb25-952146632337 | ill-posed-image-reconstruction-without-an | 2304.05589 | null | https://arxiv.org/abs/2304.05589v1 | https://arxiv.org/pdf/2304.05589v1.pdf | Ill-Posed Image Reconstruction Without an Image Prior | We consider solving ill-posed imaging inverse problems without access to an image prior or ground-truth examples. An overarching challenge in these inverse problems is that an infinite number of images, including many that are implausible, are consistent with the observed measurements. Thus, image priors are required t... | ['Katherine L. Bouman', 'He Sun', 'Angela F. Gao', 'Oscar Leong'] | 2023-04-12 | null | null | null | null | ['image-reconstruction', 'video-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.16414046e-01 3.44702512e-01 2.33206257e-01 -2.16395333e-01
-9.32379127e-01 -6.47175968e-01 4.75264847e-01 -6.32883310e-01
-3.81907284e-01 6.89926565e-01 2.06439450e-01 -1.32420287e-02
-5.42720139e-01 -4.79358763e-01 -8.05049300e-01 -9.90380704e-01
2.79650867e-01 5.45758188e-01 -2.72664189e-01 -2.95213535... | [11.623950958251953, -2.3373100757598877] |
849d2925-efc5-47ea-bf45-eaa1d7a5f116 | image-based-3d-object-reconstruction-state-of | 1906.06543 | null | https://arxiv.org/abs/1906.06543v3 | https://arxiv.org/pdf/1906.06543v3.pdf | Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era | 3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive per... | ['Xian-Feng Han', 'Mohammed Bennamoun', 'Hamid Laga'] | 2019-06-15 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.49254629e-01 1.16092004e-01 -3.50200161e-02 -3.79909217e-01
-1.24891922e-01 -1.91173673e-01 3.15391302e-01 -3.80061001e-01
-1.27917811e-01 2.55142808e-01 5.97323589e-02 2.89982539e-02
-4.63200584e-02 -8.16838801e-01 -6.14138126e-01 -6.10429347e-01
-1.37414530e-01 4.96660203e-01 -9.95683074e-02 1.93247944... | [8.442591667175293, -3.488193988800049] |
b18817a8-1f7e-4bdc-b8e5-c9b870109c1f | gaittake-gait-recognition-by-temporal | 2207.03608 | null | https://arxiv.org/abs/2207.03608v2 | https://arxiv.org/pdf/2207.03608v2.pdf | GaitTAKE: Gait Recognition by Temporal Attention and Keypoint-guided Embedding | Gait recognition, which refers to the recognition or identification of a person based on their body shape and walking styles, derived from video data captured from a distance, is widely used in crime prevention, forensic identification, and social security. However, to the best of our knowledge, most of the existing me... | ['Kwang-Ju Kim', 'Hoang Le Uyen Thuc', 'Jenq-Neng Hwang', 'Cheng-Yen Yang', 'Yizhou Wang', 'Hung-Min Hsu'] | 2022-07-07 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.29578546e-01 -6.15119755e-01 -2.13983171e-02 -1.72774538e-01
-6.26789451e-01 -4.37821187e-02 2.75933981e-01 -3.09363417e-02
-4.10489142e-01 4.41325665e-01 2.48412341e-01 3.29230338e-01
-1.74367666e-01 -5.81828654e-01 -1.07280195e-01 -8.55300903e-01
-3.75145465e-01 3.05640101e-01 8.03376958e-02 -3.54231633... | [14.291805267333984, 1.4306927919387817] |
32ebd2ed-4571-416f-ab66-9a0a53a5c7d3 | leveraging-inpainting-for-single-image-shadow | 2302.05361 | null | https://arxiv.org/abs/2302.05361v2 | https://arxiv.org/pdf/2302.05361v2.pdf | Leveraging Inpainting for Single-Image Shadow Removal | Fully-supervised shadow removal methods achieve the best restoration qualities on public datasets but still generate some shadow remnants. One of the reasons is the lack of large-scale shadow & shadow-free image pairs. Unsupervised methods can alleviate the issue but their restoration qualities are much lower than thos... | ['Song Wang', 'Ivor Tsang', 'Wei Feng', 'Di Lin', 'Rabab Abdelfattah', 'Qing Guo', 'Xiaoguang Li'] | 2023-02-10 | null | null | null | null | ['shadow-removal', 'image-inpainting', 'image-shadow-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.13236749e-01 6.25912324e-02 3.01666260e-02 -5.15172780e-01
-7.21176624e-01 5.56991063e-02 2.26193041e-01 -6.70379639e-01
-2.15487510e-01 8.94259155e-01 4.10223544e-01 -1.71391830e-01
1.62646249e-01 -7.38390625e-01 -1.02261388e+00 -1.12493038e+00
2.84249246e-01 4.63743024e-02 2.89565802e-01 -3.55440378... | [10.846075057983398, -4.108641147613525] |
9cc21522-4fa2-44a9-9b2e-a9cf08776002 | neuromorphic-computing-with-deeply-scaled | 2103.13302 | null | https://arxiv.org/abs/2103.13302v2 | https://arxiv.org/pdf/2103.13302v2.pdf | Neuromorphic Computing with Ferroelectric FinFETs in the Presence of Temperature, Process Variation, Device Aging and Flicker Noise | This paper reports a comprehensive study on the impacts of temperature-change, process variation, flicker noise and device aging on the inference accuracy of pre-trained all-ferroelectric (FE) FinFET deep neural networks. Multiple-level-cell (MLC) operation with a novel adaptive-program-and-read algorithm with 100ns wr... | ['Darsen Lu', 'Yao-Jen Lee', 'Chung-Jun Su', 'Md. Aftab Baig', 'Wei-Xuan Bu', 'Bo-Han Qiu', 'Sourav De'] | 2021-03-05 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [ 3.27967912e-01 -3.56991142e-01 -2.55080909e-01 -1.94581375e-01
-4.39168932e-03 -2.46594712e-01 9.34972912e-02 2.68481106e-01
-7.59408057e-01 1.20183241e+00 -4.70397443e-01 -3.90707046e-01
-5.97165786e-02 -7.98816144e-01 -7.26701558e-01 -1.04314125e+00
1.65211067e-01 2.06684172e-01 2.38881186e-01 -2.44132951... | [8.264960289001465, 2.5404210090637207] |
d6785268-d63e-4229-b873-6f5cdf747b71 | associative-embedding-end-to-end-learning-for | 1611.05424 | null | http://arxiv.org/abs/1611.05424v2 | http://arxiv.org/pdf/1611.05424v2.pdf | Associative Embedding: End-to-End Learning for Joint Detection and Grouping | We introduce associative embedding, a novel method for supervising
convolutional neural networks for the task of detection and grouping. A number
of computer vision problems can be framed in this manner including multi-person
pose estimation, instance segmentation, and multi-object tracking. Usually the
grouping of det... | ['Alejandro Newell', 'Zhiao Huang', 'Jia Deng'] | 2016-11-16 | associative-embedding-end-to-end-learning-for-1 | http://papers.nips.cc/paper/6822-associative-embedding-end-to-end-learning-for-joint-detection-and-grouping | http://papers.nips.cc/paper/6822-associative-embedding-end-to-end-learning-for-joint-detection-and-grouping.pdf | neurips-2017-12 | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.12777054e-01 2.91979492e-01 2.43114427e-01 -6.41731739e-01
-5.01612186e-01 -4.25300360e-01 5.32215893e-01 2.46391013e-01
-8.74855459e-01 2.25978345e-01 -8.07712078e-02 2.84589261e-01
3.72640081e-02 -6.12807691e-01 -8.67657840e-01 -2.66195387e-01
-3.80703390e-01 1.03443825e+00 6.36944830e-01 -7.55736977... | [7.214658737182617, -0.7910486459732056] |
69a5be27-d33c-4547-8c54-ea8d0a48bc0c | ct-icp-real-time-elastic-lidar-odometry-with | 2109.12979 | null | https://arxiv.org/abs/2109.12979v2 | https://arxiv.org/pdf/2109.12979v2.pdf | CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure | Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment. For this, we propose a new real-time LiDAR-only odometry method called CT-ICP (for Continuous-Time ICP), completed i... | ['François Goulette', 'Bastien Jacquet', 'Jean-Emmanuel Deschaud', 'Pierre Dellenbach'] | 2021-09-27 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-5.93560226e-02 -3.74131687e-02 1.47569761e-01 -4.25246447e-01
-5.82449913e-01 -3.74783576e-01 6.76325023e-01 2.22560927e-01
-7.05012500e-01 4.76084650e-01 -4.70595688e-01 -3.42062384e-01
-2.10149124e-01 -9.92305875e-01 -9.57579434e-01 -2.60932982e-01
-1.10641897e-01 1.25568676e+00 7.51763225e-01 -4.49101031... | [7.37793493270874, -2.183598518371582] |
2d7fced2-180c-47e2-9e25-f499a982a33e | addressing-the-rank-degeneration-in | 2306.11986 | null | https://arxiv.org/abs/2306.11986v1 | https://arxiv.org/pdf/2306.11986v1.pdf | Addressing the Rank Degeneration in Sequential Recommendation via Singular Spectrum Smoothing | Sequential recommendation (SR) investigates the dynamic user preferences modeling and generates the next-item prediction. The next item preference is typically generated by the affinity between the sequence and item representations. However, both sequence and item representations suffer from the rank degeneration issue... | ['Philip S. Yu', 'Hao Peng', 'Zhiwei Liu', 'Ziwei Fan'] | 2023-06-21 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-4.99766767e-02 -8.16023648e-01 -3.02089542e-01 -2.20043510e-01
-2.65025675e-01 -6.24955058e-01 3.31749737e-01 2.70300936e-02
-1.75185338e-01 2.65006244e-01 8.14546585e-01 -1.17400758e-01
-4.30183589e-01 -5.23492873e-01 -4.15143669e-01 -7.91270912e-01
-6.11115023e-02 -1.02081433e-01 2.66430259e-01 -5.00966311... | [10.104257583618164, 5.567850589752197] |
aab895ef-c26d-4fb3-8479-2743537ac9e4 | markerless-3d-human-pose-tracking-through | 2303.18119 | null | https://arxiv.org/abs/2303.18119v1 | https://arxiv.org/pdf/2303.18119v1.pdf | Markerless 3D human pose tracking through multiple cameras and AI: Enabling high accuracy, robustness, and real-time performance | Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intel... | ['Arash Ajoudani', 'Elena De Momi', 'Juan M. Gandarias', 'Mattia Leonori', 'Luca Fortini'] | 2023-03-31 | null | null | null | null | ['pose-tracking', '3d-human-pose-tracking'] | ['computer-vision', 'computer-vision'] | [-0.05514497 -0.25025064 -0.15244909 0.20987605 -0.32146654 -0.5213676
0.6187867 -0.16022551 -0.6487709 0.468544 -0.02291054 -0.15729976
0.19232412 -0.33902574 -0.19392845 -0.30752617 -0.2722912 0.47858772
0.66712666 -0.22509179 0.032602 0.90185523 -1.6144843 -0.3087157
0.4470013 0.7932389 0.03... | [7.272627353668213, -0.9229164719581604] |
5e9479b2-d4d9-415c-a285-9ae915ce9185 | deep-multi-view-learning-for-tire | 2203.12451 | null | https://arxiv.org/abs/2203.12451v1 | https://arxiv.org/pdf/2203.12451v1.pdf | Deep Multi-View Learning for Tire Recommendation | We are constantly using recommender systems, often without even noticing. They build a profile of our person in order to recommend the content we will most likely be interested in. The data representing the users, their interactions with the system or the products may come from different sources and be of a various nat... | ['Bruno Canitia', 'Khalid Benabdeslem', 'Kilian Bourhis', 'Thomas Ranvier'] | 2022-03-23 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.00534123e-01 -2.08855629e-01 -2.36645162e-01 -5.96096814e-01
-2.19887376e-01 -6.92476869e-01 6.82747722e-01 2.79150102e-02
2.74491370e-01 2.35270619e-01 6.33190334e-01 2.52105165e-02
-4.54598904e-01 -8.21131229e-01 -1.92706391e-01 -1.57875076e-01
1.40211850e-01 9.64977086e-01 3.31051111e-01 -8.05644155... | [10.042545318603516, 5.776186943054199] |
c69f19be-7617-400d-98ca-92337d60722e | tinto-multisensor-benchmark-for-3d | 2305.09928 | null | https://arxiv.org/abs/2305.09928v1 | https://arxiv.org/pdf/2305.09928v1.pdf | Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences | The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geolo... | ['Michael Heizmann', 'Richard Gloaguen', 'Moritz Kirsch', 'Raimon Tolosana-Delgado', 'Pedram Ghamisi', 'Sandra Lorenz', 'Samuel T. Thiele', 'Ahmed J. Afifi'] | 2023-05-17 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-1.67265415e-01 1.11760855e-01 2.75097042e-01 -3.33479226e-01
-1.03686821e+00 -5.23200750e-01 6.15417182e-01 4.45785820e-01
-3.09873372e-01 7.67333210e-01 1.27856791e-01 -2.57238805e-01
-1.36564165e-01 -1.46484768e+00 -9.02052164e-01 -7.09020376e-01
-4.66574967e-01 9.02105689e-01 1.32884860e-01 -3.73962194... | [9.393776893615723, -1.4091452360153198] |
30870e5b-8b3a-4f70-98bb-f80a9151ff1d | materials-representation-and-transfer | 2106.02225 | null | https://arxiv.org/abs/2106.02225v3 | https://arxiv.org/pdf/2106.02225v3.pdf | Materials Representation and Transfer Learning for Multi-Property Prediction | The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships ... | ['John M. Gregoire', 'Carla P. Gomes', 'Dan Guevarra', 'Shufeng Kong'] | 2021-06-04 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 6.54572606e-01 -1.70995116e-01 -2.52226412e-01 -5.20969555e-02
-1.04207754e+00 -2.41811797e-01 5.36160767e-01 2.46469557e-01
-2.57302020e-02 8.93809557e-01 1.06616765e-01 -3.06984186e-01
-5.95719457e-01 -1.12516797e+00 -8.06612313e-01 -1.15904868e+00
8.31164122e-02 6.00068152e-01 1.16785668e-01 -2.93834299... | [5.209752082824707, 5.43380069732666] |
82b548ca-0e9b-4dfc-8f71-e1ef63fdfc7a | where-is-my-uri | null | null | https://www.researchgate.net/publication/325529570_Where_is_My_URI | https://svn.aksw.org/papers/2018/ESWC_WIMU/public.pdf | Where is my URI? | One of the Semantic Web foundations is the possibility to dereference URIs to let applications negotiate their semantic content. However, this exploitation is often infeasible as the availability of such information depends on the reliability of networks, services, and human factors. Moreover, it has been shown that ar... | ['Axel-Cyrille Ngonga Ngomo', 'Andre Valdestilhas', 'Markus Nentwig', 'Edgard Marx', 'Tommaso Soru', 'Muhammad Saleem'] | 2018-06-15 | null | null | null | european-semantic-web-conference-2018-6 | ['rdf-dataset-discovery'] | ['knowledge-base'] | [-1.26011714e-01 5.92679977e-01 -4.58237648e-01 -3.81394863e-01
-4.76606756e-01 -9.68571842e-01 5.72053671e-01 6.80924237e-01
-3.09443116e-01 8.25649738e-01 1.52018905e-01 -1.59457505e-01
-5.53870499e-01 -1.18093789e+00 -5.73686719e-01 -3.64999734e-02
1.89950407e-01 5.78011572e-01 8.41668069e-01 -3.80501151... | [9.106501579284668, 7.748978614807129] |
39e8201b-e9ba-4873-b868-551cad668dcd | optical-flow-for-video-super-resolution-a | 2203.10462 | null | https://arxiv.org/abs/2203.10462v1 | https://arxiv.org/pdf/2203.10462v1.pdf | Optical Flow for Video Super-Resolution: A Survey | Video super-resolution is currently one of the most active research topics in computer vision as it plays an important role in many visual applications. Generally, video super-resolution contains a significant component, i.e., motion compensation, which is used to estimate the displacement between successive video fram... | ['Junsong Yuan', 'Baoxin Li', 'Shifu Zhang', 'Yuanzhong Liu', 'Wei Xie', 'Hongyan Li', 'Zhigang Tu'] | 2022-03-20 | null | null | null | null | ['video-super-resolution', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [ 3.96380097e-01 -5.70791781e-01 -3.86329859e-01 -1.29917106e-02
-4.37708437e-01 -1.64565057e-01 2.55736232e-01 -5.96414745e-01
-2.38462433e-01 1.02243721e+00 3.76780242e-01 3.40217531e-01
4.45367815e-03 -6.34235561e-01 -5.09839594e-01 -8.43025744e-01
-9.14405808e-02 -3.59304428e-01 5.15642047e-01 -3.34903568... | [11.003952026367188, -1.9156601428985596] |
83c8a1b4-7786-4fc6-98cd-313ae6e8d6ec | high-resolution-depth-maps-imaging-via | 2104.01530 | null | https://arxiv.org/abs/2104.01530v3 | https://arxiv.org/pdf/2104.01530v3.pdf | High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion | Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grade RGB-D cameras suffers from low spatial resolution. Guided depth map super-resolution (DSR) is a popular approach to address this problem, ... | ['Xiangyang Ji', 'Zhiwen Chen', 'Debin Zhao', 'Junjun Jiang', 'Xianming Liu', 'Zhiwei Zhong'] | 2021-04-04 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 2.56709397e-01 -5.01160085e-01 8.40568170e-02 -5.43459535e-01
-1.17106366e+00 -8.68482292e-02 3.06374401e-01 -6.07298315e-02
-2.16820672e-01 3.86113316e-01 4.50701863e-01 4.08218414e-01
-4.79195058e-01 -1.00108552e+00 -4.48767573e-01 -7.49218345e-01
4.58318800e-01 -2.38231872e-03 4.76575911e-01 -3.61378044... | [9.802844047546387, -2.298464298248291] |
3cd69128-86d5-421b-a11f-eb18f7406419 | learning-geometry-image-representation-for-3d | 2011.14289 | null | https://arxiv.org/abs/2011.14289v1 | https://arxiv.org/pdf/2011.14289v1.pdf | Learning geometry-image representation for 3D point cloud generation | We study the problem of generating point clouds of 3D objects. Instead of discretizing the object into 3D voxels with huge computational cost and resolution limitations, we propose a novel geometry image based generator (GIG) to convert the 3D point cloud generation problem to a 2D geometry image generation problem. Si... | ['Yuxuan Liu', 'Yaolin Hou', 'Pengjie Tao', 'Yuchun Huang', 'Lei Wang'] | 2020-11-29 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 5.10825552e-02 4.72973108e-01 3.53447616e-01 -6.81241751e-02
-5.90869069e-01 -6.92282140e-01 9.45117235e-01 -2.59495318e-01
1.95494026e-01 4.19075131e-01 -4.24643680e-02 -1.72406092e-01
3.50508392e-02 -1.31960607e+00 -1.06525064e+00 -6.05955422e-01
3.93048748e-02 1.00641131e+00 7.56736025e-02 3.55503820... | [8.720512390136719, -3.6427557468414307] |
04aa2270-dcd2-45ef-8074-56d52bb48424 | mv-fcos3d-multi-view-camera-only-4d-object | 2207.12716 | null | https://arxiv.org/abs/2207.12716v1 | https://arxiv.org/pdf/2207.12716v1.pdf | MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones | In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022. For multi-view camera-only 3D detection, methods based on bird-eye-view or 3D geometric representations can leverage the stereo cues from overlapped regions between adjacen... | ['Wenwei Zhang', 'Xinge Zhu', 'Chenming Zhu', 'Qing Lian', 'Tai Wang'] | 2022-07-26 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-2.20387325e-01 -6.67370409e-02 -2.89462924e-01 -3.31463218e-01
-8.33753049e-01 -7.39898443e-01 5.41234553e-01 -4.67597485e-01
-3.99593651e-01 -4.01476286e-02 -4.01657522e-02 -1.99337855e-01
3.79449815e-01 -7.77431965e-01 -9.07938421e-01 -2.88511068e-01
3.69118869e-01 5.21525681e-01 9.15304244e-01 -3.21366400... | [7.836630821228027, -2.6108896732330322] |
1ba901bb-f115-4bc3-a6eb-7f67d96b9b18 | application-of-knowledge-distillation-to | 2210.16611 | null | https://arxiv.org/abs/2210.16611v2 | https://arxiv.org/pdf/2210.16611v2.pdf | Application of Knowledge Distillation to Multi-task Speech Representation Learning | Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a l... | ['Erik Visser', 'Shuhua Zhang', 'Van Nguyen', 'Mine Kerpicci'] | 2022-10-29 | null | null | null | null | ['keyword-spotting', 'speaker-verification'] | ['speech', 'speech'] | [ 2.90424198e-01 2.76457250e-01 -1.50285572e-01 -2.94780016e-01
-1.30056274e+00 -6.28874123e-01 6.08712614e-01 -1.48214456e-02
-3.79426479e-01 5.55662334e-01 4.82760787e-01 -5.04716337e-01
7.95070678e-02 -2.48804212e-01 -6.09988093e-01 -3.89736623e-01
-7.28642866e-02 4.52315122e-01 8.74102786e-02 -1.30476370... | [14.354606628417969, 6.377509117126465] |
de81caa9-0a95-4afd-97aa-06ab44f93b43 | velocity-continuation-with-fourier-neural | 2203.14386 | null | https://arxiv.org/abs/2203.14386v1 | https://arxiv.org/pdf/2203.14386v1.pdf | Velocity continuation with Fourier neural operators for accelerated uncertainty quantification | Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty quantification is essential for determining how variability in the background models affects seismic imaging. Due to the costs associated with... | ['Felix J. Herrmann', 'Mathias Louboutin', 'Ali Siahkoohi'] | 2022-03-27 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 7.32420087e-01 6.86861202e-03 8.08675110e-01 -2.26450071e-01
-1.38491273e+00 -3.95800561e-01 5.72549105e-01 -2.25951701e-01
-6.54355884e-01 6.24164164e-01 4.26139235e-01 -1.30256370e-01
-3.93818289e-01 -6.89892292e-01 -8.20064008e-01 -8.66059780e-01
-2.08039418e-01 4.33700114e-01 4.40187901e-01 1.06043413... | [6.843433380126953, 3.366564989089966] |
a6edad6a-43f2-45ac-b066-6a16370f4e91 | studenteval-a-benchmark-of-student-written | 2306.04556 | null | https://arxiv.org/abs/2306.04556v1 | https://arxiv.org/pdf/2306.04556v1.pdf | StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code | Code LLMs are being rapidly deployed and there is evidence that they can make professional programmers more productive. Current benchmarks for code generation measure whether models generate correct programs given an expert prompt. In this paper, we present a new benchmark containing multiple prompts per problem, writt... | ['Carolyn Jane Anderson', 'Molly Q Feldman', 'Arjun Guha', 'Yangtian Zi', 'Sydney Nguyen', 'Hannah McLean Babe'] | 2023-06-07 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-5.12286695e-03 2.62527347e-01 -8.83890241e-02 -5.30538857e-01
-9.09673393e-01 -1.14609528e+00 3.66548806e-01 5.30027688e-01
-1.72636226e-01 4.40019161e-01 -2.67853178e-02 -1.11313879e+00
9.24756154e-02 -6.96678519e-01 -8.75670493e-01 -1.02634378e-01
2.43860438e-01 2.44406879e-01 3.09680045e-01 -1.14051871... | [9.26189136505127, 7.439583778381348] |
56dbe940-9934-49c2-8d8c-8aaf655299d7 | partial-auc-optimization-based-deep-speaker | 1911.08077 | null | https://arxiv.org/abs/1911.08077v1 | https://arxiv.org/pdf/1911.08077v1.pdf | Partial AUC optimization based deep speaker embeddings with class-center learning for text-independent speaker verification | Deep embedding based text-independent speaker verification has demonstrated superior performance to traditional methods in many challenging scenarios. Its loss functions can be generally categorized into two classes, i.e., verification and identification. The verification loss functions match the pipeline of speaker ve... | ['Xiao-Lei Zhang', 'Zhongxin Bai', 'Jingdong Chen'] | 2019-11-19 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.32661453e-02 -3.26645344e-01 2.24642269e-02 -9.30444479e-01
-1.13145244e+00 -5.71075261e-01 3.99900138e-01 -1.17978361e-02
-5.57336509e-01 2.76692927e-01 1.07192539e-01 -5.92365801e-01
2.80807950e-02 -2.43612975e-01 -5.09347916e-01 -8.27663481e-01
6.74291998e-02 1.65682286e-01 -8.76644030e-02 5.10535873... | [14.275091171264648, 6.060482978820801] |
a1746700-0c95-4d0c-8d87-486105e58267 | survey-of-aspect-based-sentiment-analysis | 2204.05232 | null | https://arxiv.org/abs/2204.05232v4 | https://arxiv.org/pdf/2204.05232v4.pdf | Survey of Aspect-based Sentiment Analysis Datasets | Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects. Numerous yet scattered corpor... | ['Thamar Solorio', 'Nedim Lipka', 'Franck Dernoncourt', 'Siva Uday Sampreeth Chebolu'] | 2022-04-11 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 8.96861702e-02 9.23705474e-02 -4.16061997e-01 -8.46834123e-01
-7.98173070e-01 -9.66051817e-01 9.21140373e-01 6.92297637e-01
-4.13662910e-01 5.23075163e-01 2.79127836e-01 -4.46412534e-01
2.03773201e-01 -6.90887392e-01 -4.17458385e-01 -3.82178515e-01
2.46450230e-01 6.70045674e-01 7.94163346e-02 -7.04943776... | [11.280167579650879, 6.8393354415893555] |
e30fccdb-1a2c-4ee3-a857-e8b266c162a8 | latent-predictor-networks-for-code-generation | 1603.06744 | null | http://arxiv.org/abs/1603.06744v2 | http://arxiv.org/pdf/1603.06744v2.pdf | Latent Predictor Networks for Code Generation | Many language generation tasks require the production of text conditioned on
both structured and unstructured inputs. We present a novel neural network
architecture which generates an output sequence conditioned on an arbitrary
number of input functions. Crucially, our approach allows both the choice of
conditioning co... | ['Fumin Wang', 'Tomáš Kočiský', 'Edward Grefenstette', 'Wang Ling', 'Karl Moritz Hermann', 'Phil Blunsom', 'Andrew Senior'] | 2016-03-22 | latent-predictor-networks-for-code-generation-1 | https://aclanthology.org/P16-1057 | https://aclanthology.org/P16-1057.pdf | acl-2016-8 | ['card-games'] | ['playing-games'] | [ 5.09039104e-01 2.67505765e-01 -3.51139344e-02 -3.79718035e-01
-8.02445889e-01 -8.66674781e-01 9.40632105e-01 -7.71253780e-02
-3.40035498e-01 9.54423368e-01 1.86329201e-01 -7.49790609e-01
2.67272651e-01 -1.07883465e+00 -8.85345340e-01 -1.38743848e-01
-7.62952715e-02 6.52687252e-01 -5.97157851e-02 -3.95341843... | [8.194466590881348, 7.5525407791137695] |
5af2891d-be61-42e3-b2f8-b7d9e03bea66 | investigating-the-contribution-of | null | null | https://aclanthology.org/W14-1505 | https://aclanthology.org/W14-1505.pdf | Investigating the Contribution of Distributional Semantic Information for Dialogue Act Classification | null | ['Matthew Purver', 'Dmitrijs Milajevs'] | 2014-04-01 | null | null | null | ws-2014-4 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.379645824432373, 3.7125189304351807] |
e394f459-36b9-4548-9c57-fb789cea9833 | mcscset-a-specialist-annotated-dataset-for | 2210.11720 | null | https://arxiv.org/abs/2210.11720v1 | https://arxiv.org/pdf/2210.11720v1.pdf | MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction | Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensive use in many applications, like search engines and optical character recognition systems, little has been explored in medical scenarios in... | ['Yefeng Zheng', 'Yujiu Yang', 'Bang Liu', 'Siheng Li', 'Yi Liu', 'Jianguang Zheng', 'Ruihui Zhao', 'Zijing Ou', 'Zhihao Ye', 'Wangjie Jiang'] | 2022-10-21 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 7.32681751e-01 -1.63637176e-01 5.76442247e-03 -1.86010107e-01
-1.21366704e+00 -5.19630194e-01 3.31663668e-01 6.39104068e-01
-8.34203780e-01 8.29442441e-01 3.12717497e-01 -4.72111344e-01
1.13769583e-01 -3.37459385e-01 -2.89786696e-01 -4.99870956e-01
3.89057308e-01 7.32889533e-01 3.23603511e-01 -6.89118579... | [10.839962005615234, 10.572408676147461] |
7fff9aa4-d5ba-4832-ad9e-9239af031653 | a-nuclear-norm-model-for-multi-frame-super | 1704.06196 | null | http://arxiv.org/abs/1704.06196v1 | http://arxiv.org/pdf/1704.06196v1.pdf | A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips | We propose a variational approach to obtain super-resolution images from
multiple low-resolution frames extracted from video clips. First the
displacement between the low-resolution frames and the reference frame are
computed by an optical flow algorithm. Then a low-rank model is used to
construct the reference frame i... | ['Rui Zhao', 'Raymond H. Chan'] | 2017-04-17 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 1.67694926e-01 -1.95197657e-01 -1.27743557e-01 -1.39360055e-01
-8.90125453e-01 -1.73898265e-01 4.16665465e-01 -6.88651741e-01
-3.98220390e-01 1.07173789e+00 5.57202280e-01 7.11578250e-01
-7.83404261e-02 -4.15313423e-01 -4.91335809e-01 -6.85496092e-01
-7.06734732e-02 -1.68775663e-01 4.17978585e-01 -4.35196385... | [10.999505996704102, -2.008357048034668] |
d12a270a-4a8b-44ba-9b94-0e2da38729ce | taxonomy-of-aisecops-threat-modeling-for | 2305.11189 | null | https://arxiv.org/abs/2305.11189v1 | https://arxiv.org/pdf/2305.11189v1.pdf | Taxonomy of AISecOps Threat Modeling for Cloud Based Medical Chatbots | Artificial Intelligence (AI) is playing a vital role in all aspects of technology including cyber security. Application of Conversational AI like the chatbots are also becoming very popular in the medical field to provide timely and immediate medical assistance to patients in need. As medical chatbots deal with a lot o... | ['Subash Chandran', 'Sharon Priya S', 'Aisha Banu', 'Ruby Annette J'] | 2023-05-18 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-3.49827707e-02 3.14237148e-01 2.12405041e-01 2.98210651e-01
-2.39803717e-01 -6.76647604e-01 5.63473463e-01 7.04725325e-01
-3.58050823e-01 3.65800828e-01 5.12938723e-02 -6.44680798e-01
-7.15260386e-01 -7.81356752e-01 2.65935093e-01 -6.60177946e-01
1.12425879e-01 6.55546188e-01 3.04249227e-01 -6.51294827... | [5.402652740478516, 7.151310443878174] |
15a2c47b-2198-44f8-9072-2fb2456bb5bc | optimization-of-rule-based-energy-management | 2207.06450 | null | https://arxiv.org/abs/2207.06450v1 | https://arxiv.org/pdf/2207.06450v1.pdf | Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming | Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the veh... | ['Yang Xu', 'Vivek Kumar', 'Sumanth Reddy Dadam', 'Ewan Pritchard', 'Di Zhu'] | 2022-07-08 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.80445653e-01 1.84646294e-01 -5.82635641e-01 -1.07839577e-01
-3.51377428e-02 -3.01135063e-01 5.08059025e-01 1.81643158e-01
-1.77253723e-01 6.64111912e-01 -3.16039532e-01 -7.40142465e-01
-2.60073841e-01 -1.04686642e+00 -4.59347546e-01 -8.23880196e-01
3.69454354e-01 1.34776086e-01 1.31561771e-01 -1.82294428... | [5.5767903327941895, 2.160926580429077] |
39b2bebd-c375-4366-bbb5-8c9aaff4f06d | a-trigger-sense-memory-flow-framework-for | 2101.10213 | null | https://arxiv.org/abs/2101.10213v3 | https://arxiv.org/pdf/2101.10213v3.pdf | A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction | Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model. Current efforts on joint entity and relation ex... | ['Weiming Lu', 'Yechun Tang', 'Xinyin Ma', 'Yongliang Shen'] | 2021-01-25 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 2.12849766e-01 6.36811078e-01 -2.02409044e-01 -4.59956676e-01
-5.46585858e-01 -3.62456232e-01 5.23097694e-01 3.16060454e-01
-6.62909150e-01 6.78187728e-01 2.34749556e-01 -5.12901127e-01
5.76259494e-02 -1.05038130e+00 -8.72939467e-01 -1.60451338e-01
-8.09645001e-03 2.93881118e-01 3.42889220e-01 -2.65666276... | [9.288496017456055, 8.734658241271973] |
aa0afb0d-58ee-452b-95b8-7edb3bb26aa0 | implicit-anatomical-rendering-for-medical | 2304.03209 | null | https://arxiv.org/abs/2304.03209v1 | https://arxiv.org/pdf/2304.03209v1.pdf | Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts | Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for med... | ['James S. Duncan', 'Lawrence Staib', 'Yifei Min', 'Weicheng Dai', 'Chenyu You'] | 2023-04-06 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 4.01478946e-01 3.13423276e-01 -6.02460876e-02 -4.52292174e-01
-1.20953310e+00 -1.68861255e-01 1.96155459e-01 2.49410853e-01
-4.06244606e-01 3.29804927e-01 6.20193146e-02 -1.44507438e-01
-1.36972338e-01 -7.93982804e-01 -4.90997821e-01 -7.88448513e-01
-9.44381356e-02 5.16446292e-01 2.68665344e-01 -8.29508826... | [14.41737174987793, -2.3663878440856934] |
902402e6-d081-4e29-ae1e-86653fe6e04c | cleanclip-mitigating-data-poisoning-attacks | 2303.03323 | null | https://arxiv.org/abs/2303.03323v2 | https://arxiv.org/pdf/2303.03323v2.pdf | CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning | Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data. However, previous studies have revealed that such models are vulnerable to backdoor attacks. Specifically, when trained on backdoored examples, CLIP learns spurious corre... | ['Kai-Wei Chang', 'Aditya Grover', 'Fan Yin', 'Yu Yang', 'Nishad Singhi', 'Hritik Bansal'] | 2023-03-06 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.09595931e-01 7.23074079e-02 -3.39176953e-01 -2.68149406e-01
-1.02831531e+00 -1.23714983e+00 8.16432655e-01 -1.32017151e-01
-4.55837935e-01 4.81805116e-01 1.65806621e-01 -1.70023710e-01
1.90816715e-01 -3.08967829e-01 -1.42945373e+00 -6.78281009e-01
-9.13432389e-02 7.15484023e-02 -1.46944925e-01 -3.20088118... | [5.855416774749756, 7.896878719329834] |
d9287634-f55e-453b-be13-e068e25e7a03 | neural-multigrid-memory-for-computational | 2306.12545 | null | https://arxiv.org/abs/2306.12545v2 | https://arxiv.org/pdf/2306.12545v2.pdf | Neural Multigrid Memory For Computational Fluid Dynamics | Turbulent flow simulation plays a crucial role in various applications, including aircraft and ship design, industrial process optimization, and weather prediction. In this paper, we propose an advanced data-driven method for simulating turbulent flow, representing a significant improvement over existing approaches. Ou... | ['Truong Son Hy', 'Nguyen Tri Nguyen', 'Tri Huynh', 'Tuan Anh Nguyen', 'Minh Chau Vu', 'Duc Minh Nguyen'] | 2023-06-21 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-5.68332195e-01 -1.15204239e+00 1.93012685e-01 2.35192537e-01
-1.15275159e-01 -5.37918210e-01 6.06107116e-01 3.91788557e-02
1.54613823e-01 7.93504000e-01 4.16044623e-01 -6.13108933e-01
-2.06385449e-01 -8.08877110e-01 -1.36676744e-01 -8.24677825e-01
-1.98799074e-01 -9.46509019e-02 1.97750032e-01 -2.15305299... | [6.588656902313232, 3.1986076831817627] |
7de93ba1-ac9e-4666-adf4-9630980a04b2 | how-important-are-activation-functions-in | 2209.02681 | null | https://arxiv.org/abs/2209.02681v6 | https://arxiv.org/pdf/2209.02681v6.pdf | How important are activation functions in regression and classification? A survey, performance comparison, and future directions | Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network commonly used in many real-world problems. Various activation functions have been proposed in the literature for classification as well as regression tasks. In this work, we survey th... | ['George Em Karniadakis', 'Ameya D. Jagtap'] | 2022-09-06 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-2.56758898e-01 -3.55590075e-01 8.23708624e-02 -4.81593490e-01
9.20061022e-02 -2.97925025e-01 4.79658157e-01 1.90489307e-01
-8.75847995e-01 9.47677076e-01 -2.89449662e-01 -1.48106650e-01
-4.37748611e-01 -1.02518582e+00 -4.49932545e-01 -1.10340726e+00
-2.05182955e-01 2.10650459e-01 2.05635838e-02 -3.60689014... | [8.30443000793457, 3.202078342437744] |
3e21da68-fb69-45b9-bad3-ed2711ed92ca | item-graph-convolution-collaborative | 2303.15946 | null | https://arxiv.org/abs/2303.15946v1 | https://arxiv.org/pdf/2303.15946v1.pdf | Item Graph Convolution Collaborative Filtering for Inductive Recommendations | Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side information, the majority of existing models adopt an approach of randomly initial... | ['Aonghus Lawlor', 'Neil Hurley', 'Barry Smyth', 'Elias Tragos', 'Khalil Muhammad', "Edoardo D'Amico"] | 2023-03-28 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 1.77021310e-01 4.63619381e-01 -1.54467180e-01 -3.12388450e-01
2.97605097e-01 -7.97969341e-01 8.78673434e-01 2.65307397e-01
-4.18195873e-01 4.37593788e-01 5.65251470e-01 -5.80542982e-01
-3.06223541e-01 -1.25673139e+00 -7.62238622e-01 -5.07554114e-01
-1.11589447e-01 7.22173572e-01 -7.63727427e-02 -6.55804038... | [10.014036178588867, 5.672024726867676] |
ae1044a3-1314-4872-a544-57e1c0dea211 | on-matrix-factorizations-in-subspace | 2106.12016 | null | https://arxiv.org/abs/2106.12016v1 | https://arxiv.org/pdf/2106.12016v1.pdf | On Matrix Factorizations in Subspace Clustering | This article explores subspace clustering algorithms using CUR decompositions, and examines the effect of various hyperparameters in these algorithms on clustering performance on two real-world benchmark datasets, the Hopkins155 motion segmentation dataset and the Yale face dataset. Extensive experiments are done for a... | ['Keaton Hamm', 'Reeshad Arian'] | 2021-06-22 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-2.31587991e-01 -7.53486812e-01 -5.48268318e-01 -4.52196777e-01
-8.90782058e-01 -5.28552890e-01 4.75638747e-01 -3.93384427e-01
-7.33363748e-01 4.72733885e-01 1.73173442e-01 -1.27375588e-01
-1.60371661e-01 -1.04050092e-01 1.21931210e-01 -1.15181577e+00
-7.35762060e-01 4.95674938e-01 2.52853751e-01 3.47310692... | [7.675237655639648, 4.489449501037598] |
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