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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
42e05efa-5507-430c-9a9d-27e1772499d7 | weakly-supervised-action-transition-learning | 2205.15608 | null | https://arxiv.org/abs/2205.15608v1 | https://arxiv.org/pdf/2205.15608v1.pdf | Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction | We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history. This differs from existing works, which predict motions that either do not respect any specific action category, or follow a si... | ['Mathieu Salzmann', 'Miaomiao Liu', 'Wei Mao'] | 2022-05-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stochastic-human-motion-prediction'] | ['computer-vision'] | [ 6.01007998e-01 1.27639860e-01 -5.00276983e-01 -1.63647115e-01
-8.27272952e-01 -4.41129565e-01 9.55064237e-01 -6.08986616e-01
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-2.31452864e-02 4.98177767e-01 6.28020883e-01 -6.30777776... | [7.330733776092529, -0.13229529559612274] |
6e3150b7-d251-4455-a09c-2428270f4cab | wesinger-data-augmented-singing-voice | 2203.1075 | null | https://arxiv.org/abs/2203.10750v5 | https://arxiv.org/pdf/2203.10750v5.pdf | WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses | In this paper, we develop a new multi-singer Chinese neural singing voice synthesis (SVS) system named WeSinger. To improve the accuracy and naturalness of synthesized singing voice, we design several specifical modules and techniques: 1) A deep bi-directional LSTM-based duration model with multi-scale rhythm loss and ... | ['Li Lu', 'Xinhui Li', 'Yibin Zheng', 'Zewang Zhang'] | 2022-03-21 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-2.50974447e-01 -3.99410933e-01 7.49765188e-02 1.09846242e-01
-1.44803429e+00 -5.26150465e-01 6.09258451e-02 -5.74564159e-01
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7.40651786e-02 5.60298711e-02 -1.43225780e-02 -3.47856790... | [15.498756408691406, 6.173089027404785] |
eca398bf-d277-46a6-b45d-28db4dd9ca88 | hierarchical-clustering-guided-re-id-with | 1910.12278 | null | https://arxiv.org/abs/1910.12278v2 | https://arxiv.org/pdf/1910.12278v2.pdf | Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification | For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate on the target dataset, which often focuses on learning differences between the source dataset and the target dataset to improve the generaliz... | ['Kaiwei Zeng'] | 2019-10-27 | hierarchical-clustering-with-hard-batch | http://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.pdf | cvpr-2020-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.06853597e-01 -4.33894731e-02 -1.35442942e-01 -6.71885490e-01
-4.94852304e-01 -2.35401377e-01 7.58657575e-01 -1.14446811e-01
-7.48151898e-01 7.44367421e-01 2.75490582e-01 8.88652503e-02
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1.18736289e-01 9.98196244e-01 2.64328979e-02 1.07168958... | [14.825767517089844, 1.1026968955993652] |
8f096f49-fb2c-41fb-85a5-e35a7ce99e61 | differentiable-inductive-logic-programming-in | 2208.06652 | null | https://arxiv.org/abs/2208.06652v2 | https://arxiv.org/pdf/2208.06652v2.pdf | Differentiable Inductive Logic Programming in High-Dimensional Space | Synthesizing large logic programs through symbolic Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates typically degrades performance. In contrast, gradient descent provides an efficient way to find solutions within such hig... | ['Cezary Kaliszyk', 'David M. Cerna', 'Stanisław J. Purgał'] | 2022-08-13 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 1.27390325e-01 4.92513627e-01 -5.71188390e-01 -2.95164675e-01
-5.08435786e-01 -7.21816063e-01 4.89863724e-01 -7.22458065e-02
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-2.14858353e-01 -1.13690460e+00 -1.08295119e+00 -2.23491430e-01
-4.07449901e-01 6.70401871e-01 -9.64082628e-02 -4.45475042... | [8.783377647399902, 7.174227237701416] |
65fcee45-4de7-4f69-b367-3577e5cc709b | large-capacity-image-steganography-based-on | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lu_Large-Capacity_Image_Steganography_Based_on_Invertible_Neural_Networks_CVPR_2021_paper.pdf | Large-Capacity Image Steganography Based on Invertible Neural Networks | Many attempts have been made to hide information in images, where the main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganog... | ['Paul L. Rosin', 'Tao Zhong', 'Rong Wang', 'Shao-Ping Lu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['image-steganography'] | ['computer-vision'] | [ 1.17098415e+00 6.85163438e-01 3.88346352e-02 4.17784333e-01
-3.24971616e-01 -6.42965496e-01 5.44186473e-01 -7.68770695e-01
-2.57382005e-01 2.85611272e-01 -5.62142767e-02 -7.96071589e-01
3.93726856e-01 -9.52511072e-01 -7.74273992e-01 -9.13134933e-01
-3.21933895e-01 -3.27317476e-01 3.09958845e-01 -3.38455439... | [4.343638896942139, 8.041733741760254] |
fb66b885-d3a1-47fd-81b5-1da68ac4ad4d | generating-adversarial-examples-with-an | 2007.00146 | null | https://arxiv.org/abs/2007.00146v1 | https://arxiv.org/pdf/2007.00146v1.pdf | Generating Adversarial Examples with an Optimized Quality | Deep learning models are widely used in a range of application areas, such as computer vision, computer security, etc. However, deep learning models are vulnerable to Adversarial Examples (AEs),carefully crafted samples to deceive those models. Recent studies have introduced new adversarial attack methods, but, to the ... | ['David Mohaisen', 'Aminollah Khormali', 'DaeHun Nyang'] | 2020-06-30 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.37159723e-01 -2.41594106e-01 2.12016985e-01 -2.05540895e-01
-5.84154129e-01 -8.21465909e-01 5.93824208e-01 9.43781063e-02
-6.82941675e-01 6.99818134e-01 -2.94610620e-01 -2.77828664e-01
-3.05760354e-01 -9.15111482e-01 -7.64784694e-01 -7.95844197e-01
-1.29404619e-01 -2.15916753e-01 -1.46218777e-01 -2.10756525... | [5.499810218811035, 7.858671188354492] |
65e02393-84a3-4f99-a32d-8ef6416e13f2 | diffpack-a-torsional-diffusion-model-for | 2306.01794 | null | https://arxiv.org/abs/2306.01794v1 | https://arxiv.org/pdf/2306.01794v1.pdf | DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing | Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interact... | ['Jian Tang', 'Sanchit Misra', 'Bozitao Zhong', 'Zuobai Zhang', 'Yangtian Zhan'] | 2023-06-01 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 1.70745760e-01 -6.73645213e-02 -3.07781935e-01 -2.32555434e-01
-3.80206972e-01 -5.55060983e-01 1.31092936e-01 3.81023407e-01
-4.20483440e-01 1.21529734e+00 2.27704227e-01 -6.77603543e-01
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-3.67833406e-01 5.86794794e-01 3.09335321e-01 -1.66375026... | [4.804259300231934, 5.537286281585693] |
02741630-db45-445b-93bc-5d855ae51deb | pack-together-entity-and-relation-extraction | 2109.06067 | null | https://arxiv.org/abs/2109.06067v5 | https://arxiv.org/pdf/2109.06067v5.pdf | Packed Levitated Marker for Entity and Relation Extraction | Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In this work, we propose a novel span representation approach, named Pack... | ['Maosong Sun', 'Peng Li', 'Yankai Lin', 'Deming Ye'] | 2021-09-13 | null | https://aclanthology.org/2022.acl-long.337 | https://aclanthology.org/2022.acl-long.337.pdf | acl-2022-5 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-5.16652279e-02 3.44938785e-01 -6.05993927e-01 -2.93870419e-01
-5.89360356e-01 -3.68090719e-01 1.69266969e-01 3.79674464e-01
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-1.52131766e-01 -8.83866727e-01 -8.24272275e-01 -3.40595424e-01
-3.19269925e-01 3.02201867e-01 4.63746667e-01 -1.67902380... | [9.406478881835938, 8.96382999420166] |
c199e93f-f70b-4463-9b6d-72ba4eaabb31 | sscu-net-spatial-spectral-collaborative | 2203.06375 | null | https://arxiv.org/abs/2203.06375v2 | https://arxiv.org/pdf/2203.06375v2.pdf | SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images | Linear spectral unmixing is an essential technique in hyperspectral image processing and interpretation. In recent years, deep learning-based approaches have shown great promise in hyperspectral unmixing, in particular, unsupervised unmixing methods based on autoencoder networks are a recent trend. The autoencoder mode... | ['Lin Qi', 'Qian Du', 'Xinbo Gao', 'Junyu Dong', 'Feng Gao'] | 2022-03-12 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.41877371e-01 -6.45642102e-01 8.84432867e-02 6.35320023e-02
-2.11254358e-01 -3.74848545e-01 4.72017080e-01 -3.21780354e-01
-1.99392378e-01 5.91928244e-01 2.65270263e-01 -2.33424455e-02
-3.47981155e-01 -9.58589613e-01 -5.79652071e-01 -1.37516344e+00
1.39875993e-01 1.94301143e-01 -5.66574037e-01 -2.09238231... | [10.085527420043945, -1.9552838802337646] |
40a9c9f3-73ee-4676-837c-89aec430340b | surgical-video-motion-magnification-with | 2009.07432 | null | https://arxiv.org/abs/2009.07432v1 | https://arxiv.org/pdf/2009.07432v1.pdf | Surgical Video Motion Magnification with Suppression of Instrument Artefacts | Video motion magnification could directly highlight subsurface blood vessels in endoscopic video in order to prevent inadvertent damage and bleeding. Applying motion filters to the full surgical image is however sensitive to residual motion from the surgical instruments and can impede practical application due to aberr... | ['Neil L. Dorward', 'Danail Stoyanov', 'Mirek Janatka', 'Hani J. Marcus'] | 2020-09-16 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 1.94980815e-01 8.06605890e-02 8.93633366e-02 1.89511567e-01
-1.13478631e-01 -8.26488674e-01 3.04734319e-01 1.02716111e-01
-7.74322152e-01 3.18024099e-01 5.64879775e-01 -2.43878603e-01
-2.28625506e-01 -2.47829497e-01 -4.62140322e-01 -8.29995453e-01
-3.23583931e-01 -3.59828174e-01 4.64121014e-01 -5.48985414... | [13.826096534729004, -3.0549190044403076] |
fc71477c-5f1e-4d39-82b2-72a21c391520 | to-find-waldo-you-need-contextual-cues-1 | null | null | https://aclanthology.org/2022.acl-short.39 | https://aclanthology.org/2022.acl-short.39.pdf | To Find Waldo You Need Contextual Cues: Debiasing Who’s Waldo | We present a debiased dataset for the Person-centric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who’s Waldo dataset. Given an image and a caption, PCVG requires pairing up a person’s name mentioned in a caption with a bounding box that points to the person in the image. We find that the ori... | ['Chitta Baral', 'Yezhou Yang', 'Tejas Gokhale', 'Pratyay Banerjee', 'Yiran Luo'] | null | null | null | null | acl-2022-5 | ['person-centric-visual-grounding'] | ['computer-vision'] | [ 2.13498518e-01 3.12110543e-01 -2.87019640e-01 -3.62592131e-01
-9.47869062e-01 -9.09996331e-01 7.44058549e-01 -1.00263841e-01
-4.51952338e-01 9.28064048e-01 4.58996207e-01 -4.24365997e-01
-4.84471060e-02 -3.96755368e-01 -9.28586900e-01 -3.97651821e-01
3.30206573e-01 9.13489819e-01 1.37868956e-01 -1.26293629... | [10.809090614318848, 1.5545026063919067] |
32ed1531-5c82-4c8a-9947-a175c291b030 | semi-supervised-learning-for-few-shot-audio | 2102.08074 | null | https://arxiv.org/abs/2102.08074v1 | https://arxiv.org/pdf/2102.08074v1.pdf | Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining | Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the form of a mean vector of the embedded support points within a class. The performance of prototypical ne... | ['Sunil Kumar Kopparapu', 'Rupayan Chakraborty', 'Swapnil Bhosale'] | 2021-02-16 | null | null | null | null | ['few-shot-audio-classification'] | ['audio'] | [ 2.49748409e-01 7.08429217e-02 -2.17240080e-02 -5.71583927e-01
-8.80833387e-01 7.87225924e-03 4.79167998e-01 1.11854345e-01
-4.81587648e-01 8.70864511e-01 -1.17365621e-01 1.75867021e-01
-4.37881589e-01 -6.82774723e-01 -6.20671630e-01 -8.07307899e-01
-2.00417787e-01 4.63816017e-01 2.70445943e-01 -1.15736574... | [9.954097747802734, 3.199692964553833] |
84ddb4a0-7821-4216-b7f6-7cfeed58a09d | voicefilter-targeted-voice-separation-by | 1810.04826 | null | https://arxiv.org/abs/1810.04826v6 | https://arxiv.org/pdf/1810.04826v6.pdf | VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking | In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) ... | ['Zelin Wu', 'Hannah Muckenhirn', 'Ye Jia', 'Ron J. Weiss', 'Rif A. Saurous', 'John Hershey', 'Prashant Sridhar', 'Kevin Wilson', 'Ignacio Lopez Moreno', 'Quan Wang'] | 2018-10-11 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 5.28933585e-01 2.28644073e-01 1.92843482e-01 -6.78439200e-01
-1.19338131e+00 -3.99032742e-01 3.73298019e-01 -3.44712824e-01
-3.93677980e-01 2.42907479e-01 3.65298450e-01 -4.98139739e-01
5.96165717e-01 -2.24444076e-01 -4.15910333e-01 -6.00390077e-01
7.51290545e-02 -8.04486200e-02 2.60095447e-01 -1.51680321... | [14.568918228149414, 6.269887447357178] |
9dfe7dbf-f15c-42a5-8eba-4093fe867ed4 | deep-cross-modality-adaptation-via-semantics | 1807.01806 | null | http://arxiv.org/abs/1807.01806v1 | http://arxiv.org/pdf/1807.01806v1.pdf | Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval | Due to the large cross-modality discrepancy between 2D sketches and 3D
shapes, retrieving 3D shapes by sketches is a significantly challenging task.
To address this problem, we propose a novel framework to learn a discriminative
deep cross-modality adaptation model in this paper. Specifically, we first
separately adopt... | ['Yi Fang', 'Jiaxin Chen'] | 2018-07-04 | deep-cross-modality-adaptation-via-semantics-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiaxin_Chen_Deep_Cross-modality_Adaptation_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 1.73380390e-01 -4.18759584e-01 4.35933992e-02 -4.46962416e-01
-9.69931006e-01 -7.58117616e-01 7.62735486e-01 -2.55730093e-01
-1.37515947e-01 3.02580774e-01 2.56820738e-01 1.08587705e-01
-3.20943773e-01 -8.32912982e-01 -7.26072907e-01 -6.06185973e-01
3.56129557e-01 3.00198108e-01 -2.11209834e-01 -9.12555084... | [11.613027572631836, 0.6806143522262573] |
dbbd90af-a179-4bdf-8b81-7ae0de896f41 | promptunet-toward-interactive-medical-image | 2305.103 | null | https://arxiv.org/abs/2305.10300v1 | https://arxiv.org/pdf/2305.10300v1.pdf | PromptUNet: Toward Interactive Medical Image Segmentation | Prompt-based segmentation, also known as interactive segmentation, has recently become a popular approach in image segmentation. A well-designed prompt-based model called Segment Anything Model (SAM) has demonstrated its ability to segment a wide range of natural images, which has sparked a lot of discussion in the com... | ['Junde Wu'] | 2023-05-17 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 5.03047705e-01 3.08353305e-01 -3.89386922e-01 -5.18489242e-01
-8.69202077e-01 -7.29880512e-01 2.65319854e-01 1.43520281e-01
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-1.05047479e-01 -3.42602581e-01 -3.14670682e-01 -5.47264159e-01
1.48560151e-01 6.93974853e-01 6.23655975e-01 2.42118128... | [14.682249069213867, -2.263587236404419] |
1b0a96ce-d11e-4d1f-8744-14d946868cbc | graph-augmentation-clustering-network | 2211.10627 | null | https://arxiv.org/abs/2211.10627v1 | https://arxiv.org/pdf/2211.10627v1.pdf | Graph Augmentation Clustering Network | Existing graph clustering networks heavily rely on a predefined graph and may fail if the initial graph is of low quality. To tackle this issue, we propose a novel graph augmentation clustering network capable of adaptively enhancing the initial graph to achieve better clustering performance. Specifically, we first int... | ['Junhui Hou', 'Yuheng Jia', 'Hui Liu', 'Zhihao Peng'] | 2022-11-19 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 3.18584777e-02 2.06015989e-01 -2.88834333e-01 -4.04686064e-01
-5.88082731e-01 -6.01581812e-01 4.37262893e-01 4.00990635e-01
-2.05307469e-01 4.54496622e-01 8.72731283e-02 -1.49125814e-01
-3.28681260e-01 -8.28782916e-01 -6.32853925e-01 -8.60765338e-01
-3.42050433e-01 5.56348741e-01 1.60083055e-01 6.50258735... | [7.253442764282227, 5.995439052581787] |
9d2d9b4c-7f74-43c2-95f8-65c4c8fb6bc8 | revisiting-unsupervised-meta-learning | 2011.14663 | null | https://arxiv.org/abs/2011.14663v3 | https://arxiv.org/pdf/2011.14663v3.pdf | Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks | Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Le... | ['De-Chuan Zhan', 'Lu Han', 'Han-Jia Ye'] | 2020-11-30 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.40567052e-01 -1.25721306e-01 -5.94976604e-01 -6.13284886e-01
-9.06583905e-01 -2.16964841e-01 8.14564645e-01 1.30973026e-01
-5.08452356e-01 5.56068242e-01 2.63561487e-01 2.02106044e-01
2.44046431e-02 -7.93283582e-01 -6.34011567e-01 -8.53770018e-01
2.26816103e-01 1.77739114e-01 4.07529563e-01 -1.79663017... | [10.045866012573242, 3.0908405780792236] |
4ba91db2-e20d-457d-93ed-7aa8c413514c | reinforcement-federated-learning-method-based | 2306.12859 | null | https://arxiv.org/abs/2306.12859v2 | https://arxiv.org/pdf/2306.12859v2.pdf | Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering | Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into g... | ['Zeli Guan', 'Yingxia Shao', 'Junping Du', 'Tianyu Zhao'] | 2023-06-22 | null | null | null | null | ['clustering'] | ['methodology'] | [-0.61336094 -0.18388158 0.20785786 -0.5316895 -0.29938743 -0.61234534
-0.07541193 -0.3088176 -0.34080487 0.3630086 -0.16352154 -0.08554724
-0.36906573 -0.7382259 -0.50330067 -1.2635117 0.08180067 0.32204694
-0.13122715 0.37441903 0.01546424 0.5482175 -1.6572423 0.44010377
0.9552528 1.1417804 0.... | [5.839359283447266, 6.35646915435791] |
161debf9-195c-482e-b804-ce57f5b29a27 | residual-gated-graph-convnets | 1711.07553 | null | http://arxiv.org/abs/1711.07553v2 | http://arxiv.org/pdf/1711.07553v2.pdf | Residual Gated Graph ConvNets | Graph-structured data such as social networks, functional brain networks,
gene regulatory networks, communications networks have brought the interest in
generalizing deep learning techniques to graph domains. In this paper, we are
interested to design neural networks for graphs with variable length in order
to solve le... | ['Thomas Laurent', 'Xavier Bresson'] | 2017-11-20 | residual-gated-graph-convnets-1 | https://openreview.net/forum?id=HyXBcYg0b | https://openreview.net/pdf?id=HyXBcYg0b | iclr-2018-1 | ['graph-regression'] | ['graphs'] | [-1.00619551e-02 5.08450508e-01 -4.56452221e-02 -2.52493083e-01
9.53359604e-02 -2.28303716e-01 3.47443044e-01 1.03089556e-01
-1.95072100e-01 7.66234577e-01 -1.54600456e-01 -5.64322531e-01
-2.14341730e-01 -1.22279620e+00 -7.85670340e-01 -6.14153504e-01
-6.18453503e-01 4.92037266e-01 -2.27650888e-02 -3.85807663... | [6.9434614181518555, 6.201667785644531] |
8b52265a-2bb4-4504-ad49-12606833d163 | codet-a-benchmark-for-contrastive-dialectal | 2305.17267 | null | https://arxiv.org/abs/2305.17267v1 | https://arxiv.org/pdf/2305.17267v1.pdf | CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation | Neural machine translation (NMT) systems exhibit limited robustness in handling source-side linguistic variations. Their performance tends to degrade when faced with even slight deviations in language usage, such as different domains or variations introduced by second-language speakers. It is intuitive to extend this o... | ['Antonios Anastasopoulos', 'Sina Ahmadi', 'Md Mahfuz ibn Alam'] | 2023-05-26 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.13220707e-01 -2.94147432e-01 -4.91400450e-01 -4.17186350e-01
-1.15176034e+00 -1.05633628e+00 8.67142737e-01 -4.23271537e-01
-4.61631924e-01 9.51088190e-01 2.83207625e-01 -7.41050124e-01
3.92517954e-01 -3.19807500e-01 -7.76033580e-01 -2.54054189e-01
2.23385558e-01 7.40075409e-01 -7.83123374e-02 -6.62395000... | [11.47451400756836, 10.248960494995117] |
f6cc8dcf-16ee-42db-b38a-64310cb39c8d | ai-generated-characters-for-supporting | null | null | https://www.nature.com/articles/s42256-021-00417-9 | https://www.nature.com/articles/s42256-021-00417-9.pdf | AI-generated characters for supporting personalized learning and well-being | Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspir... | ['Pattie Maes & Misha Sra', 'Dan Novy', 'Parinya Punpongsanon', 'Joanne Leong', 'Valdemar Danry', 'Pat Pataranutaporn'] | 2021-12-15 | null | null | null | nature-machine-intelligence-2021-12 | ['talking-head-generation', 'text-to-face-generation', 'face-reenactment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.51493490e-01 9.30597007e-01 3.73913169e-01 -2.25127250e-01
-5.07049382e-01 -5.25811493e-01 1.08526671e+00 1.96011752e-01
-5.83494529e-02 7.73364127e-01 7.93789268e-01 1.05681727e-02
4.48834360e-01 -7.71097183e-01 -5.89184642e-01 -2.78828919e-01
4.12276052e-02 2.56997764e-01 -4.00854409e-01 -3.91744047... | [9.36655330657959, 6.33167839050293] |
000fb91a-5c4a-46d5-9b94-69335dc706c2 | eaml-ensemble-self-attention-based-mutual | 2305.06923 | null | https://arxiv.org/abs/2305.06923v1 | https://arxiv.org/pdf/2305.06923v1.pdf | EAML: Ensemble Self-Attention-based Mutual Learning Network for Document Image Classification | In the recent past, complex deep neural networks have received huge interest in various document understanding tasks such as document image classification and document retrieval. As many document types have a distinct visual style, learning only visual features with deep CNNs to classify document images have encountere... | ['Marçal Rusiñol', 'Mickael Coustaty', 'Ziheng Ming', 'Souhail Bakkali'] | 2023-05-11 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 2.59136558e-01 -3.40106398e-01 -3.67035508e-01 -4.57413226e-01
-7.25901008e-01 -4.57330972e-01 1.01071084e+00 2.20359832e-01
-4.24814582e-01 4.73311573e-01 -7.19294995e-02 -1.35838062e-01
-2.56161660e-01 -5.91663301e-01 -5.48310280e-01 -9.75953162e-01
5.25377929e-01 1.92523196e-01 -1.56953067e-01 1.61424994... | [11.228654861450195, 2.176584005355835] |
d2886cc5-9535-46d0-a952-441db0058480 | satimnet-structured-and-harmonised-training | 2006.10623 | null | https://arxiv.org/abs/2006.10623v2 | https://arxiv.org/pdf/2006.10623v2.pdf | SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification | Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets. While there exists a plethora of data sets that can be used for this purpose, they are usually very heterogeneous and not interoperable. In this context, the present wo... | ['Vasileios Syrris', 'Pierre Soille', 'Ondrej Pesek'] | 2020-06-18 | null | null | null | null | ['satellite-image-classification', 'remote-sensing-image-classification'] | ['computer-vision', 'miscellaneous'] | [ 6.12004064e-02 -1.71666831e-01 1.79888114e-01 -5.94987392e-01
-3.40095431e-01 -3.94616872e-01 5.74781597e-01 2.66634285e-01
-5.96280158e-01 7.19147384e-01 -3.42113316e-01 -4.61878031e-01
-6.48986399e-01 -1.24412262e+00 -2.92446017e-01 -7.73156762e-01
-1.76978454e-01 7.74317384e-01 8.31093732e-03 -4.06387448... | [9.670825958251953, -1.5302832126617432] |
cf94574a-3130-4841-afa4-5fde28738470 | a-multiresolution-3d-morphable-face-model-and | null | null | https://www.scitepress.org/Link.aspx?doi=10.5220%2f0005669500790086 | https://www.scitepress.org/Link.aspx?doi=10.5220%2f0005669500790086 | A Multiresolution 3D Morphable Face Model and Fitting Framework | 3D Morphable Face Models are a powerful tool in computer vision. They consists of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmar... | ['Josef Kittler', 'Matthias Rätsch', 'William Christmas', 'Willem P. Koppen', 'Pouria Mortazavian', 'Rafael Tena', 'Guosheng Hu', 'Patrik Huber'] | 2016-02-01 | null | null | null | null | ['head-pose-estimation', 'face-model'] | ['computer-vision', 'computer-vision'] | [-1.35111421e-01 2.27249116e-01 9.01286379e-02 -3.35419148e-01
-6.43766046e-01 -3.34366560e-01 3.10366601e-01 -2.41231933e-01
-2.08179131e-01 2.22054645e-01 -1.57740023e-02 -7.82331731e-03
8.66640806e-02 -5.89179993e-01 -3.81855637e-01 -5.83347142e-01
-1.25372306e-01 8.85917306e-01 2.54062235e-01 -8.25392306... | [13.36933708190918, 0.08758172392845154] |
abebccb5-54d0-4966-b145-908c7876bdb7 | cross-modal-local-shortest-path-and-global | 2206.04401 | null | https://arxiv.org/abs/2206.04401v1 | https://arxiv.org/pdf/2206.04401v1.pdf | Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification | In addition to considering the recognition difficulty caused by human posture and occlusion, it is also necessary to solve the modal differences caused by different imaging systems in the Visible-Thermal cross-modal person re-identification (VT-ReID) task. In this paper,we propose the Cross-modal Local Shortest Path an... | ['Xiangcai Ma', 'Chaoqi Li', 'XiaoHong Wang'] | 2022-06-09 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [-1.37281641e-01 -5.84916353e-01 1.35448322e-01 -4.13014919e-01
-7.32403994e-01 -2.91735865e-02 3.68157893e-01 -2.74719298e-01
-6.49210453e-01 4.33712810e-01 4.38696682e-01 4.88084853e-01
-3.21250021e-01 -6.62890315e-01 -3.08800071e-01 -8.09797049e-01
1.78452522e-01 2.20546961e-01 1.30757149e-02 -3.94942909... | [14.722939491271973, 0.9174274802207947] |
af04e08f-6d29-4fa6-88b7-d3e4717f68ed | meta-learning-triplet-network-with-adaptive | 2302.07739 | null | https://arxiv.org/abs/2302.07739v1 | https://arxiv.org/pdf/2302.07739v1.pdf | Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition | Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the ... | ['Wei Wu', 'Xuezhi Cao', 'Ming Gao', 'Xiang Li', 'FengJiao Chen', 'Jun Kuang', 'Renyu Zhu', 'Chengcheng Han'] | 2023-02-14 | null | null | null | null | ['miscellaneous', 'few-shot-ner'] | ['miscellaneous', 'natural-language-processing'] | [-3.26855779e-01 -1.88335672e-01 -5.26128471e-01 -4.77559894e-01
-6.82177424e-01 -3.06705654e-01 3.23622674e-01 1.90960929e-01
-6.57568038e-01 6.08558118e-01 9.95044857e-02 1.97562039e-01
-7.96720386e-02 -1.01961219e+00 -4.90028918e-01 -5.20912290e-01
2.29888827e-01 5.53675890e-01 3.32156032e-01 -2.16889128... | [9.628957748413086, 9.34502124786377] |
84031d2f-c664-4eb8-b3b3-560a6c4044f0 | perceiving-and-modeling-density-is-all-you | 2111.09733 | null | https://arxiv.org/abs/2111.09733v1 | https://arxiv.org/pdf/2111.09733v1.pdf | Perceiving and Modeling Density is All You Need for Image Dehazing | In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze is varied from image to image. Recent methods adopt deep neural networks to recover clean scenes from hazy images directly. However, due to the paradox caused by the variation of real captured haze... | ['Zhiyong Lu', 'Pen Chen', 'ErKang Chen', 'Liang Chen', 'Yunchen Zhang', 'Mingchao Jiang', 'Tian Ye'] | 2021-11-18 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.79176177e-02 -5.80192924e-01 4.53958213e-01 -3.45939487e-01
-4.42704797e-01 -1.16360977e-01 2.44853824e-01 -4.55595940e-01
-1.42573193e-01 6.95465386e-01 2.99292743e-01 -3.21121030e-02
-2.84698635e-01 -9.16852057e-01 -7.73408234e-01 -1.44072449e+00
-3.09422221e-02 2.84625590e-02 3.29044253e-01 -4.99945045... | [10.947150230407715, -3.1516191959381104] |
8f6b1473-ed3b-48bb-baad-96904a577470 | global-and-local-interpretation-of-black-box | 2109.05087 | null | https://arxiv.org/abs/2109.05087v1 | https://arxiv.org/pdf/2109.05087v1.pdf | Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data | The COVID-19 corona virus has claimed 4.1 million lives, as of July 24, 2021. A variety of machine learning models have been applied to related data to predict important factors such as the severity of the disease, infection rate and discover important prognostic factors. Often the usefulness of the findings from the u... | ['Dimitris Metaxas', 'Vinod Rustgi', 'Carlos D. Minacapelli', 'Ananya Jana'] | 2021-09-10 | null | null | null | null | ['explainable-models', 'severity-prediction'] | ['computer-vision', 'computer-vision'] | [ 1.66682169e-01 1.57326102e-01 -9.27771404e-02 -5.21549881e-01
5.13687283e-02 -3.54372859e-01 3.10086995e-01 5.28659165e-01
1.93166956e-01 8.88565004e-01 5.90749260e-04 -8.45309973e-01
-7.70604849e-01 -5.35321116e-01 -5.21701574e-01 -3.78909260e-01
-5.63230693e-01 9.50437129e-01 -4.22680259e-01 -2.58929193... | [8.26749038696289, 5.846005916595459] |
9c20e4c9-34d0-4673-8463-facdfd0845a9 | playgol-learning-programs-through-play | 1904.08993 | null | https://arxiv.org/abs/1904.08993v2 | https://arxiv.org/pdf/1904.08993v2.pdf | Playgol: learning programs through play | Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge. Before solving the tasks, the learner enters an unsupervised playing stage where it creates its own tasks to... | ['Andrew Cropper'] | 2019-04-18 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.75652158e-01 7.35696852e-01 -2.62866437e-01 -2.71517336e-01
-5.62726378e-01 -8.25726986e-01 3.04140449e-01 2.70025045e-01
-2.08239064e-01 7.20401406e-01 -1.79766312e-01 -6.11162424e-01
-1.60642549e-01 -1.44997597e+00 -1.07824469e+00 -5.03883898e-01
-3.94661307e-01 8.80118430e-01 7.69870937e-01 -3.00037593... | [8.75359058380127, 7.138139247894287] |
1695bff1-0bff-4c78-925a-2dd44fa475b2 | cost-splitting-for-multi-objective-conflict | 2211.12885 | null | https://arxiv.org/abs/2211.12885v1 | https://arxiv.org/pdf/2211.12885v1.pdf | Cost Splitting for Multi-Objective Conflict-Based Search | The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such cost metrics include arrival times, travel distances, and energy consumption.In this paper, we focus ... | ['Sven Koenig', 'Jiaoyang Li', 'Han Zhang', 'Cheng Ge'] | 2022-11-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.31436259e-01 -1.05320282e-01 -3.85056674e-01 2.65403628e-01
-6.37950122e-01 -7.93381631e-01 4.33676168e-02 4.55531806e-01
-4.13879812e-01 1.03706491e+00 -3.87525350e-01 -3.68631124e-01
-8.45092475e-01 -8.62906992e-01 -3.13622802e-01 -6.37552619e-01
-7.06997335e-01 8.80289614e-01 8.24800432e-01 -3.91065031... | [4.980977535247803, 1.8696259260177612] |
f4cc5d0e-5388-4160-961a-e94574acbe53 | deep-boosting-for-image-denoising | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.pdf | Deep Boosting for Image Denoising | Boosting is a classic algorithm which has been successfully applied to diverse computer vision tasks. In the scenario of image denoising, however, the existing boosting algorithms are surpassed by the emerging learning-based models. In this paper, we propose a novel deep boosting framework (DBF) for denoising, which in... | ['Chang Chen', 'Xinmei Tian', 'Feng Wu', 'Zhiwei Xiong'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [-6.65538991e-03 -5.22789657e-01 2.54960507e-01 -5.93805790e-01
-3.89021397e-01 3.20176631e-02 6.74385548e-01 -1.68473888e-02
-5.17270863e-01 5.97217858e-01 2.24730462e-01 -2.71247387e-01
9.99496654e-02 -8.91713500e-01 -7.42863715e-01 -1.03076255e+00
3.24828506e-01 -5.10278583e-01 1.39143080e-01 -6.58561230... | [11.389479637145996, -2.3851065635681152] |
852ebe96-ccb0-4f27-878f-049d8c8450b8 | protnn-fast-and-accurate-nearest-neighbor | 1511.00736 | null | http://arxiv.org/abs/1511.00736v2 | http://arxiv.org/pdf/1511.00736v2.pdf | ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space | Studying the function of proteins is important for understanding the
molecular mechanisms of life. The number of publicly available protein
structures has increasingly become extremely large. Still, the determination of
the function of a protein structure remains a difficult, costly, and time
consuming task. The diffic... | ['Abdoulaye Baniré Diallo', 'Wajdi Dhifli'] | 2015-11-02 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 1.77068591e-01 -1.88789606e-01 -3.28672044e-02 -3.07590783e-01
-4.81416285e-01 -7.26057589e-01 1.34193778e-01 7.79004276e-01
-3.51910412e-01 8.36501896e-01 -1.13813832e-01 -3.16641837e-01
-3.23721170e-01 -6.73501670e-01 -7.39105284e-01 -8.73676896e-01
-1.26597166e-01 9.39458549e-01 5.97259879e-01 -4.97500338... | [4.793422698974609, 5.4698262214660645] |
e293f01f-5e7c-46ef-b89c-dfa837715b20 | causal-aware-safe-policy-improvement-for-task | 2103.0637 | null | https://arxiv.org/abs/2103.06370v1 | https://arxiv.org/pdf/2103.06370v1.pdf | Causal-aware Safe Policy Improvement for Task-oriented dialogue | The recent success of reinforcement learning's (RL) in solving complex tasks is most often attributed to its capacity to explore and exploit an environment where it has been trained. Sample efficiency is usually not an issue since cheap simulators are available to sample data on-policy. On the other hand, task oriented... | ['Caiming Xiong', 'Kazuma Hashimoto', 'Govardana Sachithanandam Ramachandran'] | 2021-03-10 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 8.36096630e-02 5.91338813e-01 -1.81732357e-01 -3.52868378e-01
-9.03366506e-01 -5.91220081e-01 1.00867248e+00 -6.30722344e-02
-6.93507612e-01 1.33390808e+00 2.96313494e-01 -2.46578142e-01
3.51727419e-02 -3.57876569e-01 -5.69721878e-01 -4.50760424e-01
-1.53238848e-01 1.01701188e+00 9.69798341e-02 -6.31519198... | [13.02002239227295, 8.060592651367188] |
6aba92cf-15ad-4fb7-84a1-4b2ef4bb6269 | a-universally-deployable-asr-frontend-for | 2209.0641 | null | https://arxiv.org/abs/2209.06410v1 | https://arxiv.org/pdf/2209.06410v1.pdf | A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation | Recent work has shown that it is possible to train a single model to perform joint acoustic echo cancellation (AEC), speech enhancement, and voice separation, thereby serving as a unified frontend for robust automatic speech recognition (ASR). The joint model uses contextual information, such as a reference of the play... | ['Quan Wang', 'Arun Narayanan', "Tom O'Malley"] | 2022-09-14 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 4.99762088e-01 -6.50776103e-02 3.95253330e-01 -3.24568152e-01
-1.31894052e+00 -3.97762179e-01 4.20264691e-01 -6.71710074e-02
-6.61982000e-01 3.11897725e-01 5.09656370e-01 -5.25997698e-01
2.88706899e-01 -2.51012370e-02 -8.35303962e-01 -6.40056431e-01
2.21125588e-01 -2.17981204e-01 3.91156077e-01 -3.61356527... | [14.767834663391113, 6.161407470703125] |
73f811b4-206c-42e9-8fcf-1655f911ca28 | augmenting-robot-knowledge-consultants-with | 1811.10229 | null | http://arxiv.org/abs/1811.10229v1 | http://arxiv.org/pdf/1811.10229v1.pdf | Augmenting Robot Knowledge Consultants with Distributed Short Term Memory | Human-robot communication in situated environments involves a complex
interplay between knowledge representations across a wide variety of
modalities. Crucially, linguistic information must be associated with
representations of objects, locations, people, and goals, which may be
represented in very different ways. In p... | ['Matthias Scheutz', 'Bradley Oosterveld', 'Evan Krause', 'Ravenna Thielstrom', 'Tom Williams'] | 2018-11-26 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 2.99185038e-01 2.90099651e-01 1.99955016e-01 -3.86674285e-01
-6.71177924e-01 -6.84720755e-01 8.70033860e-01 4.58763331e-01
-3.21175307e-01 7.59469569e-01 1.04489517e+00 -1.59969941e-01
-2.94339687e-01 -1.02614522e+00 -3.48834068e-01 -1.01169147e-01
2.34397538e-02 3.63052249e-01 2.52945453e-01 -5.71270585... | [9.238121032714844, 6.730945110321045] |
83c6bf87-8254-4992-89fe-accb30b89e8b | autoexp-a-multidisciplinary-multi-sensor | 2306.03115 | null | https://arxiv.org/abs/2306.03115v1 | https://arxiv.org/pdf/2306.03115v1.pdf | AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars | The adoption of self-driving cars will certainly revolutionize our lives, even though they may take more time to become fully autonomous than initially predicted. The first vehicles are already present in certain cities of the world, as part of experimental robot-taxi services. However, most existing studies focus on t... | ['Laure Tougne Rodet', 'Stephanie Souche-Le Corvec', 'Florent Laroche', 'Christophe Jallais', 'Romain Guesdon', 'Carlos Crispim-Junior'] | 2023-06-05 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [-3.41136813e-01 1.07849903e-01 -2.92851534e-02 -4.74283636e-01
6.90972954e-02 -2.21074969e-01 7.42952585e-01 -2.14878842e-01
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1.65792197e-01 6.05226696e-01 4.11885858e-01 -5.76715767... | [5.7157087326049805, 1.0884253978729248] |
b7929047-6f79-41e3-9635-874161211923 | dialog2api-task-oriented-dialogue-with-api | 2212.09946 | null | https://arxiv.org/abs/2212.09946v1 | https://arxiv.org/pdf/2212.09946v1.pdf | Dialog2API: Task-Oriented Dialogue with API Description and Example Programs | Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradig... | ['Dan Roth', 'Yi Zhang', 'Saab Mansour', 'Arshit Gupta', 'Salvatore Romeo', 'Nikolaos Pappas', 'Tamer Alkhouli', 'Elman Mansimov', 'Raphael Shu'] | 2022-12-20 | null | null | null | null | ['semantic-parsing', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.41151920e-01 8.44686329e-01 3.40914540e-02 -7.67022848e-01
-6.03913665e-01 -1.05866146e+00 9.81028378e-01 -6.74973428e-02
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3.38805318e-01 9.06983793e-01 4.89337295e-01 -9.53334093... | [12.8721284866333, 7.939924240112305] |
41f9c8cb-7bf4-4172-9064-4a9592490f45 | hate-a-little-less-love-a-little-more | null | null | https://openreview.net/forum?id=KSvkXL6bRU7 | https://openreview.net/pdf?id=KSvkXL6bRU7 | Hate a Little Less, Love a Little More! Proactively Curbing Online Hatred via Hate Speech Normalization | Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible due to several political, geographical, and cultural reasons. To reduce the severity of the problem, in this paper, we introduce a novel task, hate speech normalization – weakening the intensity of hatred... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['hate-speech-normalization'] | ['natural-language-processing'] | [ 1.03695750e-01 9.39786783e-04 1.10693552e-01 3.31084244e-02
-5.37455499e-01 -7.99711883e-01 5.41055143e-01 1.26937300e-01
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2.92818844e-01 -2.57131577e-01 6.48258394e-03 -3.65305275... | [8.743552207946777, 10.565234184265137] |
9102d190-bcb3-4099-a321-d350955911f4 | sent2span-span-detection-for-pico-extraction | 2109.02254 | null | https://arxiv.org/abs/2109.02254v1 | https://arxiv.org/pdf/2109.02254v1.pdf | Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations | The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Populat... | ['Adam G. Dunn', 'Florence T. Bourgeois', 'Wei Wang', 'Bing Li', 'Yifang Sun', 'Shifeng Liu'] | 2021-09-06 | null | https://aclanthology.org/2021.findings-emnlp.147 | https://aclanthology.org/2021.findings-emnlp.147.pdf | findings-emnlp-2021-11 | ['pico'] | ['natural-language-processing'] | [ 2.89363682e-01 2.86813408e-01 -6.63810909e-01 -3.05391252e-01
-1.39292228e+00 -6.29827440e-01 3.01579654e-01 1.07080829e+00
-7.56057620e-01 8.52522969e-01 4.65822428e-01 -7.43516505e-01
-4.54680622e-02 -4.42172587e-01 -5.61838925e-01 -1.95655301e-01
3.11283946e-01 4.20069665e-01 2.72218287e-01 2.56434321... | [8.426085472106934, 8.724601745605469] |
0504b42d-3c66-41f2-baf1-1ac078965827 | 190807888 | 1908.07888 | null | https://arxiv.org/abs/1908.07888v1 | https://arxiv.org/pdf/1908.07888v1.pdf | Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition | In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework. Intent recognition is a powerful technique for dialog flow management in turn-oriented, human-machine dialogs. This technique can also be very useful in the con... | ['Łukasz Augustyniak', 'Piotr Szymański', 'Mikołaj Morzy', 'Piotr Żelasko', 'Yishay Carmiel', 'Jan Mizgajski', 'Adrian Szymczak'] | 2019-08-21 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 5.55569351e-01 6.23585045e-01 -1.26714140e-01 -5.14249802e-01
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1.22253977e-01 9.12349105e-01 3.21317941e-01 -9.46169138... | [12.756237030029297, 7.813971042633057] |
0c10e6b3-1cda-474b-b1fd-b79892445759 | tdeer-an-efficient-translating-decoding | null | null | https://aclanthology.org/2021.emnlp-main.635 | https://aclanthology.org/2021.emnlp-main.635.pdf | TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations | Joint extraction of entities and relations from unstructured texts to form factual triples is a fundamental task of constructing a Knowledge Base (KB). A common method is to decode triples by predicting entity pairs to obtain the corresponding relation. However, it is still challenging to handle this task efficiently, ... | ['Zhen He', 'Beidi Luan', 'Daichuan Yang', 'Chenghao Dong', 'Xiaotian Luo', 'Xianming Li'] | null | null | null | null | emnlp-2021-11 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.12133317e-01 4.87190396e-01 -5.00657141e-01 -2.44381130e-01
-9.82349455e-01 -5.22975802e-01 4.23506677e-01 8.90851170e-02
-1.04077205e-01 9.11608279e-01 3.01177531e-01 -4.49393600e-01
2.14179918e-01 -1.06727278e+00 -9.88747895e-01 -3.30771267e-01
3.91256034e-01 8.03276598e-01 2.37765789e-01 -3.92357558... | [9.232139587402344, 8.569710731506348] |
57a6271c-8c34-4d09-b64a-0b2438b53c7e | parallel-data-augmentation-for-formality | 2005.07522 | null | https://arxiv.org/abs/2005.07522v1 | https://arxiv.org/pdf/2005.07522v1.pdf | Parallel Data Augmentation for Formality Style Transfer | The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demons... | ['Xu sun', 'Tao Ge', 'Yi Zhang'] | 2020-05-14 | parallel-data-augmentation-for-formality-1 | https://aclanthology.org/2020.acl-main.294 | https://aclanthology.org/2020.acl-main.294.pdf | acl-2020-6 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 4.16039646e-01 3.11857730e-01 -9.00361910e-02 -5.38941562e-01
-7.68842638e-01 -5.19085050e-01 7.58272350e-01 -2.38280203e-02
-7.76978910e-01 1.10887408e+00 2.97233403e-01 -5.22067130e-01
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2.27432892e-01 7.48220682e-01 -3.35897096e-02 -1.02304125... | [11.468095779418945, 9.582596778869629] |
b305b605-78d2-4a60-a362-500e0b1762c3 | a-faithful-deep-sensitivity-estimation-for | 2210.12723 | null | https://arxiv.org/abs/2210.12723v1 | https://arxiv.org/pdf/2210.12723v1.pdf | A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging | Recent deep learning is superior in providing high-quality images and ultra-fast reconstructions in accelerated magnetic resonance imaging (MRI). Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccu... | ['Xiaobo Qu', 'Di Guo', 'Jianzhong Lin', 'Wenping Wei', 'Jianjun Zhou', 'Liuhong Zhu', 'Lijun Bao', 'Boxuan Shi', 'Chen Qian', 'Haoming Fang', 'Zi Wang'] | 2022-10-23 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 8.72251857e-03 -1.09003089e-01 1.51381284e-01 -3.80511761e-01
-5.89146972e-01 -1.19336203e-01 9.35586244e-02 -1.34550080e-01
-3.67265463e-01 6.97736740e-01 2.10867018e-01 -9.53967571e-02
-4.61729616e-01 -2.93164611e-01 -7.42179930e-01 -9.53405023e-01
-4.59026754e-01 1.21277705e-01 3.51497591e-01 -1.74335361... | [13.628854751586914, -2.4136414527893066] |
489746af-147b-4642-b361-29d283f3ba51 | multiwave-multiresolution-deep-architectures | 2306.10164 | null | https://arxiv.org/abs/2306.10164v1 | https://arxiv.org/pdf/2306.10164v1.pdf | MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction | The analysis of multivariate time series data is challenging due to the various frequencies of signal changes that can occur over both short and long terms. Furthermore, standard deep learning models are often unsuitable for such datasets, as signals are typically sampled at different rates. To address these issues, we... | ['Madalina Fiterau', 'Iman Deznabi'] | 2023-06-16 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'mortality-prediction', 'human-activity-recognition', 'time-series-prediction'] | ['computer-vision', 'computer-vision', 'medical', 'time-series', 'time-series'] | [-3.62318754e-02 -4.48405892e-01 -3.28654557e-01 -2.64141828e-01
-7.63438046e-01 -2.84763813e-01 -2.30128076e-02 4.81636792e-01
-1.73355386e-01 5.35260737e-01 5.83769739e-01 -1.74306761e-02
-6.46027923e-02 -7.16729522e-01 -5.95173776e-01 -6.06325746e-01
-6.58392310e-01 -1.16700873e-01 -2.39589438e-01 -8.95514414... | [13.694453239440918, 3.3457751274108887] |
07400cf0-57bf-4b7a-bc0f-7c0fc28cc04c | ttan-two-stage-temporal-alignment-network-for | 2107.04782 | null | https://arxiv.org/abs/2107.04782v4 | https://arxiv.org/pdf/2107.04782v4.pdf | TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition | Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not idea... | ['Weiyao Lin', 'Xiaoyuan Yu', 'Mengjuan Fei', 'John See', 'Yuxi Li', 'Rui Qian', 'Huabin Liu', 'Shuyuan Li'] | 2021-07-10 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.45713484e-01 -4.48545694e-01 -4.21778172e-01 -4.50517446e-01
-8.25818658e-01 -4.44470853e-01 6.33653760e-01 -1.71107620e-01
-3.46674740e-01 4.47006702e-01 3.10878307e-01 2.49699101e-01
-1.07569635e-01 -3.06814939e-01 -5.06146550e-01 -8.48205328e-01
-1.01863153e-01 -3.01795099e-02 6.26958311e-01 1.11760244... | [8.454169273376465, 0.7487806081771851] |
9bed4ad0-a7c6-4095-8d9c-c8f002229957 | re2tal-rewiring-pretrained-video-backbones | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Re2TAL_Rewiring_Pretrained_Video_Backbones_for_Reversible_Temporal_Action_Localization_CVPR_2023_paper.pdf | Re2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization | Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challenge. Most methods can only train on pre-extracted features without o... | ['Bernard Ghanem', 'Karttikeya Mangalam', 'Shuming Liu', 'Chen Zhao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.69372991e-01 -1.72349159e-02 -4.79947686e-01 -1.58998400e-01
-4.99480695e-01 -6.05791748e-01 3.88494432e-01 -5.45868874e-01
-6.00898504e-01 6.59468234e-01 3.07124883e-01 -3.82255018e-02
2.79311717e-01 -5.90113819e-01 -1.22844326e+00 -5.51624000e-01
-4.50840220e-02 1.32383600e-01 5.68299830e-01 4.80400324... | [8.91839599609375, 0.5209043622016907] |
98d68d95-f49f-4afa-bdd0-e8247c1dd4e3 | impact-of-visual-assistance-for-automated | 2211.10539 | null | https://arxiv.org/abs/2211.10539v2 | https://arxiv.org/pdf/2211.10539v2.pdf | Impact of visual assistance for automated audio captioning | We study the impact of visual assistance for automated audio captioning. Utilizing multi-encoder transformer architectures, which have previously been employed to introduce vision-related information in the context of sound event detection, we analyze the usefulness of incorporating a variety of pretrained features. We... | ['Hugo Van hamme', 'Wim Boes'] | 2022-11-18 | null | null | null | null | ['sound-event-detection', 'audio-captioning'] | ['audio', 'audio'] | [ 2.35605955e-01 2.31970288e-02 1.54169887e-01 -2.08515614e-01
-6.89467609e-01 -5.27378023e-01 1.03103042e+00 7.30180085e-01
-8.66333723e-01 4.26238507e-01 6.90521836e-01 -1.02050833e-01
-2.40212932e-01 -5.09001493e-01 -6.26482964e-01 -6.34580433e-01
2.94421017e-02 1.85243994e-01 3.11084360e-01 -2.81765968... | [15.180192947387695, 5.005707740783691] |
ae15daed-856b-49e3-ab0a-5e1a40c47713 | multi-domain-learning-for-accurate-and-few | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Multi-Domain_Learning_for_Accurate_and_Few-Shot_Color_Constancy_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xiao_Multi-Domain_Learning_for_Accurate_and_Few-Shot_Color_Constancy_CVPR_2020_paper.pdf | Multi-Domain Learning for Accurate and Few-Shot Color Constancy | Color constancy is an important process in camera pipeline to remove the color bias of captured image caused by scene illumination. Recently, significant improvements in color constancy accuracy have been achieved by using deep neural networks (DNNs). However, existing DNNbased color constancy methods learn distinct ma... | [' Lei Zhang', ' Shuhang Gu', 'Jin Xiao'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['color-constancy'] | ['computer-vision'] | [ 9.68774706e-02 -8.04562211e-01 -1.77434444e-01 -4.88870412e-01
-4.77887571e-01 -7.48872995e-01 3.72720391e-01 -3.90766203e-01
-5.01387417e-01 4.83031273e-01 -3.11034411e-01 -8.91576633e-02
3.61755610e-01 -4.50853854e-01 -8.39093089e-01 -7.78411925e-01
5.73245525e-01 -2.14532882e-01 2.50072479e-01 -1.51476651... | [10.502601623535156, -2.5569045543670654] |
ba096bb5-2462-4036-9c6c-73e9ebdae712 | cbnet-a-plug-and-play-network-for | 2212.0234 | null | https://arxiv.org/abs/2212.02340v2 | https://arxiv.org/pdf/2212.02340v2.pdf | CBNet: A Plug-and-Play Network for Segmentation-based Scene Text Detection | Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion. However, the segmentation process only considers each pixel independently, and the expansion process is difficult to achieve a favorable accuracy-speed trade-off. In th... | ['Jingping Shao', 'Jinghe Hu', 'Zhangang Lin', 'Xin Zhu', 'Jingjing Lv', 'Zheng Zhang', 'Wei Feng', 'Xi Zhao'] | 2022-12-05 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.40469655e-01 -4.60128725e-01 -1.03880875e-01 -2.38176182e-01
-4.33137923e-01 -1.18733704e-01 2.51342058e-01 1.42214894e-01
-5.37240684e-01 1.01535683e-02 -3.97834219e-02 -1.44250467e-01
3.67672443e-01 -9.90426958e-01 -4.07197773e-01 -6.51988864e-01
6.55723870e-01 2.05555409e-01 1.00424063e+00 8.10928717... | [12.107073783874512, 2.207473039627075] |
a1aff1a1-6df5-4979-ad32-7f73a690fe8e | learning-syntactic-and-dynamic-selective | 2003.11173 | null | https://arxiv.org/abs/2003.11173v1 | https://arxiv.org/pdf/2003.11173v1.pdf | Learning Syntactic and Dynamic Selective Encoding for Document Summarization | Text summarization aims to generate a headline or a short summary consisting of the major information of the source text. Recent studies employ the sequence-to-sequence framework to encode the input with a neural network and generate abstractive summary. However, most studies feed the encoder with the semantic word emb... | ['Haiyang Xu', 'Xiangang Li', 'Yahao He', 'Kun Han', 'Junwen Chen'] | 2020-03-25 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 5.96389174e-01 2.66205579e-01 -2.48899102e-01 -5.30028880e-01
-7.51780927e-01 -3.47119391e-01 4.47743833e-01 3.14183652e-01
-2.75370628e-01 6.94181979e-01 1.23692822e+00 -4.72748448e-04
4.50190902e-01 -7.65558898e-01 -6.63244724e-01 -3.98975343e-01
5.24613798e-01 1.43820018e-01 2.46561214e-01 -3.73014003... | [12.4804105758667, 9.439338684082031] |
448dd757-6537-43f2-b11c-73905f677cb1 | prediction-of-prognosis-and-survival-of | null | null | https://doi.org/10.5114/aoms/135594 | https://www.archivesofmedicalscience.com/pdf-135594-63895?filename=Prediction%20of%20Prognosis.pdf | Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model | Introduction:
It’s very necessary to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random Forest is an excellent machine learning algorithm even without any modification. We propose a new Random Forest weighting method and apply it to th... | ['Fan Ye', 'Yue Cao', 'TianLong Zheng', 'Jing Wang', 'Cheng Xu'] | 2021-04-10 | null | null | null | archives-of-medical-science-2021-4 | ['epidemiology'] | ['medical'] | [ 6.20980971e-02 2.81460192e-02 -9.01125312e-01 -4.76081192e-01
-6.91149354e-01 4.36129458e-02 3.36968243e-01 3.57156277e-01
-6.60255551e-01 1.07304394e+00 3.58366251e-01 -6.81425273e-01
-3.33817780e-01 -1.22587216e+00 -5.36351046e-03 -9.81334567e-01
-3.15251797e-01 5.74645460e-01 1.70613855e-01 7.35649467... | [8.391419410705566, 4.92259407043457] |
06a3ef2e-3c98-4861-af2a-5e32d8525613 | overview-and-evaluation-of-sound-event | 2009.02792 | null | https://arxiv.org/abs/2009.02792v2 | https://arxiv.org/pdf/2009.02792v2.pdf | Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019 | Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal activity of sounds of interest. This paper presents an overview of the first international evaluation on sound event localization and detection,... | ['Tuomas Virtanen', 'Toni Heittola', 'Sharath Adavanne', 'Annamaria Mesaros', 'Archontis Politis'] | 2020-09-06 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-2.46420186e-02 -4.69350666e-01 7.10126638e-01 -2.04479843e-01
-1.63162541e+00 -9.48646367e-01 4.88720357e-01 6.82799876e-01
-7.64473498e-01 3.52495372e-01 3.52809608e-01 6.67929649e-03
-3.00861746e-01 -3.02190930e-01 -4.85104322e-01 -6.42963111e-01
-5.01080692e-01 3.05690039e-02 7.82968938e-01 1.78103924... | [15.120392799377441, 5.16060733795166] |
3fbd9332-d9a3-4787-a16e-79c2e7afc9b6 | l3das21-challenge-machine-learning-for-3d | 2104.05499 | null | https://arxiv.org/abs/2104.05499v3 | https://arxiv.org/pdf/2104.05499v3.pdf | L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing | The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio cor... | ['Danilo Comminiello', 'Aurelio Uncini', 'Enrico Rocchi', 'Sveva Pepe', 'Marco Pennese', 'Ludovica Paglialunga', 'Leonardo Nucciarelli', 'Giuseppe Nachira', 'Claudia Medaglia', 'Edoardo Massaro', 'Christian Marinoni', 'Saeid Jamili', 'Riccardo F. Gramaccioni', 'Eric Guizzo'] | 2021-04-12 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [-1.74326077e-01 -3.23482394e-01 7.02403486e-01 -7.79503840e-04
-1.37887120e+00 -5.83244920e-01 4.63024974e-01 -9.73306298e-02
-2.89687127e-01 -5.34335561e-02 5.99709392e-01 -9.74242613e-02
-5.23483716e-02 -1.62041172e-01 -7.14275420e-01 -7.07307339e-01
-4.54544544e-01 1.40313581e-01 3.83443050e-02 -5.94732426... | [15.03058910369873, 5.6143012046813965] |
c9536c10-3bd8-4c1c-9a44-26195bebcbc2 | complex-a-new-corpus-for-lexical-complexity-1 | null | null | https://aclanthology.org/2020.readi-1.9 | https://aclanthology.org/2020.readi-1.9.pdf | CompLex --- A New Corpus for Lexical Complexity Prediction from Likert Scale Data | Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studieshave approached the task as a binary classification t... | ['Michael Cooper', 'Matthew Shardlow', 'Marcos Zampieri'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['lexical-complexity-prediction', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.04783946e-02 3.85791928e-01 -4.52540964e-01 -5.56390166e-01
-8.55955362e-01 -7.88102806e-01 7.06873715e-01 9.12710428e-01
-1.12722170e+00 1.04950428e+00 6.56039774e-01 -2.86231399e-01
-1.35804281e-01 -5.34380734e-01 -7.27065578e-02 -1.53810307e-01
4.85799849e-01 7.95490980e-01 -1.68474585e-01 -2.33206391... | [10.678643226623535, 10.400710105895996] |
dc6d93f4-7fb1-496b-be1a-e60a861af777 | feature-compression-for-rate-constrained | 2204.07314 | null | https://arxiv.org/abs/2204.07314v1 | https://arxiv.org/pdf/2204.07314v1.pdf | Feature Compression for Rate Constrained Object Detection on the Edge | Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural networks. An emerging approach to solve this problem is to offload th... | ['Yao Wang', 'Elza Erkip', 'Siddharth Garg', 'Samyak Rawlekar', 'Zhongzheng Yuan'] | 2022-04-15 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 2.64376700e-01 -2.59540915e-01 -3.08389455e-01 -2.14400128e-01
-1.55681387e-01 -9.23775882e-02 -2.57132966e-02 -4.67080288e-02
-6.40272975e-01 -1.15856223e-01 -3.06516975e-01 -1.76297188e-01
1.55482784e-01 -8.20169091e-01 -7.70691037e-01 -5.96812963e-01
2.34076768e-01 3.48620385e-01 3.41638893e-01 2.76163995... | [8.487523078918457, 2.760322093963623] |
ac265321-1442-4f84-bfde-e2f7d43a29d3 | a-dataset-of-multi-illumination-images-in-the | 1910.08131 | null | https://arxiv.org/abs/1910.08131v1 | https://arxiv.org/pdf/1910.08131v1.pdf | A Dataset of Multi-Illumination Images in the Wild | Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed t... | ['Miika Aittala', 'Lukas Murmann', 'Michael Gharbi', 'Fredo Durand'] | 2019-10-17 | a-dataset-of-multi-illumination-images-in-the-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Murmann_A_Dataset_of_Multi-Illumination_Images_in_the_Wild_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Murmann_A_Dataset_of_Multi-Illumination_Images_in_the_Wild_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-relighting'] | ['computer-vision'] | [ 7.63624072e-01 -3.98651689e-01 -1.26218628e-02 -3.72455388e-01
-7.49947965e-01 -6.50859475e-01 6.12978637e-01 -4.45217907e-01
-2.58069158e-01 7.43920267e-01 -2.79175639e-01 -2.73753792e-01
2.62881905e-01 -2.34084368e-01 -7.43427038e-01 -7.07070291e-01
4.19659317e-01 2.63403058e-01 -8.22516754e-02 -4.63634208... | [9.98889446258545, -2.764709949493408] |
9754cfbc-1dd2-4809-a57a-8cc31fcfe541 | planar-object-tracking-in-the-wild-a | 1703.07938 | null | http://arxiv.org/abs/1703.07938v2 | http://arxiv.org/pdf/1703.07938v2.pdf | Planar Object Tracking in the Wild: A Benchmark | Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a ca... | ['Haibin Ling', 'Chunyuan Liao', 'Liming Wang', 'Hu Lu', 'Yifan Wu', 'Pengpeng Liang'] | 2017-03-23 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-3.29725333e-02 -5.26251674e-01 -1.20044351e-01 -1.32426977e-01
-4.00762081e-01 -9.50564921e-01 5.94992578e-01 -2.90651888e-01
-3.22916746e-01 4.91481006e-01 -2.17689380e-01 2.90709198e-01
6.58618100e-03 -1.08734556e-01 -8.65767539e-01 -6.75685167e-01
-4.57028985e-01 3.54170620e-01 1.10017765e+00 2.46653765... | [6.650651454925537, -2.0493946075439453] |
3538462f-7c21-446b-bf9d-d67b4f1e46b9 | diachronic-embedding-for-temporal-knowledge | 1907.03143 | null | https://arxiv.org/abs/1907.03143v1 | https://arxiv.org/pdf/1907.03143v1.pdf | Diachronic Embedding for Temporal Knowledge Graph Completion | Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective fo... | ['Rishab Goel', 'Marcus Brubaker', 'Pascal Poupart', 'Seyed Mehran Kazemi'] | 2019-07-06 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.35743690e-01 6.18001461e-01 -4.21711147e-01 -7.30530992e-02
-3.70832413e-01 -6.45301044e-01 8.38425756e-01 7.24836528e-01
-3.32628548e-01 6.97031200e-01 4.06959832e-01 -1.12983644e-01
-4.12629575e-01 -1.16400540e+00 -7.18928993e-01 -4.23368871e-01
-6.98861361e-01 5.13504922e-01 6.39179409e-01 -2.07334712... | [8.58368968963623, 7.845951557159424] |
6bfda603-949f-44d0-908f-67dd64193135 | iranis-a-large-scale-dataset-of-farsi-license | 2101.00295 | null | https://arxiv.org/abs/2101.00295v1 | https://arxiv.org/pdf/2101.00295v1.pdf | Iranis: A Large-scale Dataset of Farsi License Plate Characters | Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In th... | ['Alireza Akoushideh', 'Asadollah Shahbahrami', 'Sajjad Soroori', 'Ali Tourani'] | 2021-01-01 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [-4.28480245e-02 -7.56163061e-01 3.59808207e-02 -1.84497282e-01
-2.41454914e-01 -7.07771838e-01 5.67020774e-01 -4.37840462e-01
-5.40291488e-01 6.39033616e-01 -3.11053395e-01 -1.35852143e-01
1.53019696e-01 -7.90333211e-01 -7.63140202e-01 -8.61020505e-01
3.36581916e-01 3.25787485e-01 3.78507078e-01 -1.81560665... | [9.825026512145996, -4.93101692199707] |
1d2b870e-e182-46f1-9fae-1c7edb25f1a5 | 3d-dual-fusion-dual-domain-dual-query-camera-1 | 2211.13529 | null | https://arxiv.org/abs/2211.13529v2 | https://arxiv.org/pdf/2211.13529v2.pdf | 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection | Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a nove... | ['Jun Won Choi', 'Dongsuk Kum', 'Minwook Kim', 'Konyul Park', 'Yecheol Kim'] | 2022-11-24 | 3d-dual-fusion-dual-domain-dual-query-camera | https://arxiv.org/abs/2211.13529 | https://arxiv.org/abs/2211.13529 | null | ['robust-3d-object-detection'] | ['computer-vision'] | [ 8.06602985e-02 -4.25500602e-01 1.84174255e-02 -5.54664671e-01
-1.42608535e+00 -7.17516541e-01 5.86658537e-01 -3.73521373e-02
-2.41200656e-01 1.95994973e-02 4.61336784e-02 -1.21133529e-01
1.65572062e-01 -6.19651973e-01 -9.99303043e-01 -5.23087800e-01
4.53954875e-01 4.28543150e-01 6.31400347e-01 -1.16093829... | [7.749001502990723, -2.668344259262085] |
da05a02e-48b7-49fd-b03a-c063f39d19ee | implementation-and-comparative-quantitative | 1511.04659 | null | http://arxiv.org/abs/1511.04659v1 | http://arxiv.org/pdf/1511.04659v1.pdf | Implementation and comparative quantitative assessment of different multispectral image pansharpening approches | In remote sensing, images acquired by various earth observation satellites
tend to have either a high spatial and low spectral resolution or vice versa.
Pansharpening is a technique which aims to improve spatial resolution of
multispectral image. The challenges involve in the pansharpening are not only
to improve the s... | ['Shailesh Panchal', 'Rajesh Thakker'] | 2015-11-15 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 7.95682669e-01 -5.76338112e-01 8.38127732e-02 -6.12240145e-03
-6.32802129e-01 -6.56443059e-01 3.79221141e-01 4.61819842e-02
-2.97618866e-01 8.73535097e-01 -8.20330009e-02 -1.36358276e-01
-6.95546389e-01 -1.05725288e+00 9.66000929e-03 -1.02560318e+00
-4.56834920e-02 -4.29580569e-01 2.68637002e-01 -4.57328349... | [10.146171569824219, -2.130579948425293] |
5594e2a2-9850-463f-bf56-e7d401089ccd | improving-contextualized-topic-models-with | 2303.14951 | null | https://arxiv.org/abs/2303.14951v1 | https://arxiv.org/pdf/2303.14951v1.pdf | Improving Contextualized Topic Models with Negative Sampling | Topic modeling has emerged as a dominant method for exploring large document collections. Recent approaches to topic modeling use large contextualized language models and variational autoencoders. In this paper, we propose a negative sampling mechanism for a contextualized topic model to improve the quality of the gene... | ['Partha Pratim Das', 'Debarshi Kumar Sanyal', 'Avishek Lahiri', 'Suman Adhya'] | 2023-03-27 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.21659353e-01 3.44806492e-01 -4.01051790e-01 -2.31317371e-01
-1.14857638e+00 -4.27607536e-01 1.09328794e+00 2.15502217e-01
-9.68897156e-03 8.10566604e-01 7.34853923e-01 -6.53776079e-02
1.55782342e-01 -7.28716791e-01 -8.18613112e-01 -9.30832803e-01
1.56019941e-01 8.97979319e-01 1.66275054e-01 -5.18496186... | [10.378274917602539, 6.942861080169678] |
20ba4b9b-5a06-4fe9-8774-202b2ef54b8d | std-net-search-of-image-steganalytic-deep | 2206.05651 | null | https://arxiv.org/abs/2206.05651v1 | https://arxiv.org/pdf/2206.05651v1.pdf | STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition | Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The existing model compression method cannot flexibly compress the convolutional layer in residual shortcut block so that a satisfactory shrinkin... | ['Jiwu Huang', 'Bin Li', 'Laiyuan Li', 'Qiushi Li', 'Shunquan Tan'] | 2022-06-12 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 5.47017455e-01 -2.28222311e-02 4.41628508e-02 9.71627906e-02
3.17693949e-01 -3.25870246e-01 4.19543475e-01 -4.73927319e-01
-4.50106561e-01 3.19211572e-01 -1.44156724e-01 -5.31449676e-01
-3.67539860e-02 -9.82028544e-01 -5.36804080e-01 -1.03430390e+00
6.55706003e-02 -1.59851357e-01 5.95778286e-01 -3.80998224... | [4.316509246826172, 8.054389953613281] |
5091e908-01fc-4193-9ac1-097513378708 | a-cooperation-aware-lane-change-method-for-1 | null | null | https://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=A%20Cooperation-Aware%20Lane%20Change%20Method%20for%20Automated%20Vehicles | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9971784 | A Cooperation-Aware Lane Change Method for Automated Vehicles | Lane change for automated vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guaranteeing safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a... | ['Shibei Xue', 'Lin Liu', 'Zihao Sheng'] | 2023-03-01 | null | null | null | ieee-transactions-on-intelligent-16 | ['trajectory-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [-3.64135891e-01 3.35781455e-01 -2.94283241e-01 -2.99439669e-01
2.66781193e-03 -3.49659592e-01 4.32604611e-01 -1.49083257e-01
-4.71865773e-01 8.05360198e-01 -1.36221156e-01 -7.98787892e-01
-1.57965139e-01 -9.67638969e-01 -5.01674056e-01 -8.72411072e-01
-4.37849686e-02 -2.11415254e-03 7.97559023e-01 -4.99558002... | [5.543769836425781, 1.5516884326934814] |
32c5f3d0-8191-463d-b3b7-026c7f49fb44 | disfluencyfixer-a-tool-to-enhance-language | 2305.16957 | null | https://arxiv.org/abs/2305.16957v1 | https://arxiv.org/pdf/2305.16957v1.pdf | DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction | Conversational speech often consists of deviations from the speech plan, producing disfluent utterances that affect downstream NLP tasks. Removing these disfluencies is necessary to create fluent and coherent speech. This paper presents DisfluencyFixer, a tool that performs speech-to-speech disfluency correction in Eng... | ['Pushpak Bhattacharyya', 'Preethi Jyothi', 'Vineet Bhat'] | 2023-05-26 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-1.50625721e-01 2.70319968e-01 2.74946958e-01 -5.03004670e-01
-8.48980248e-01 -9.64599192e-01 3.50917369e-01 -1.37570411e-01
-2.47287378e-01 9.46216345e-01 9.79909360e-01 -6.96116388e-01
3.06476653e-01 -1.33003145e-01 -4.13965493e-01 -2.55082428e-01
4.43946213e-01 4.43179190e-01 1.00362211e-01 -5.15489280... | [14.402436256408691, 6.85552453994751] |
4135c74a-a9ed-4b2d-8ab4-8859c422ed45 | faithful-learning-with-sure-data-for-lung | 2202.12515 | null | https://arxiv.org/abs/2202.12515v1 | https://arxiv.org/pdf/2202.12515v1.pdf | Faithful learning with sure data for lung nodule diagnosis | Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic dia... | ['Guang-Zhong Yang', 'Yun Gu', 'Zhexin Wang', 'Feng Yao', 'Yulei Qin', 'Minghui Zhang', 'Xiao Gu', 'Liang Chen', 'Hanxiao Zhang'] | 2022-02-25 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 1.25561342e-01 7.29072511e-01 -6.45296514e-01 -6.69189692e-01
-1.16462052e+00 -4.28543419e-01 9.83700827e-02 -1.91473663e-01
1.24033637e-01 4.94709879e-01 8.31550136e-02 -5.47605574e-01
-6.16148174e-01 -7.65647411e-01 -4.72179562e-01 -6.99229836e-01
4.14036177e-02 9.18847322e-01 4.22789425e-01 2.15391234... | [15.28981876373291, -2.1584012508392334] |
44080de4-1e85-48d1-9fff-94574d5a28ae | multilingual-few-shot-learning-via-language | 2306.10964 | null | https://arxiv.org/abs/2306.10964v1 | https://arxiv.org/pdf/2306.10964v1.pdf | Multilingual Few-Shot Learning via Language Model Retrieval | Transformer-based language models have achieved remarkable success in few-shot in-context learning and drawn a lot of research interest. However, these models' performance greatly depends on the choice of the example prompts and also has high variability depending on how samples are chosen. In this paper, we conduct a ... | ['Yash Chandarana', 'Soumya Vadlamannati', 'Liang-Kang Huang', 'Genta Indra Winata'] | 2023-06-19 | null | null | null | null | ['few-shot-learning', 'sentiment-analysis', 'intent-detection'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.72616113e-02 -6.61402762e-01 -5.25477231e-01 -6.77490413e-01
-1.25909507e+00 -5.04283905e-01 9.39185143e-01 5.14391482e-01
-8.85743022e-01 4.84884769e-01 4.20979261e-01 -1.50000334e-01
2.17637226e-01 -5.50007761e-01 -3.06717396e-01 -3.71320307e-01
5.18980205e-01 5.35874665e-01 4.05180722e-01 -2.85839856... | [10.775749206542969, 7.647121429443359] |
8f90c4b6-a7e5-41d6-b8c1-38ac937ede5c | x-maps-direct-depth-lookup-for-event-based | null | null | https://fraunhoferhhi.github.io/X-maps/ | https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_X-Maps_Direct_Depth_Lookup_for_Event-based_Structured_Light_Systems.pdf | X-maps: Direct Depth Lookup for Event-based Structured Light Systems | We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras. These dynamic vision sensors are a great fit to be paired with laser projectors for depth estimation in a structured light approach. Our key contributions involve a conversion of the projector time... | ['Peter Eisert', 'Anna Hilsmann', 'Simon Baumann', 'Niklas Gard', 'Wieland Morgenstern'] | 2023-06-19 | null | null | null | cvpr-workshop-on-event-based-vision-2023-6 | ['depth-estimation', 'disparity-estimation'] | ['computer-vision', 'computer-vision'] | [ 4.18842643e-01 -2.31668070e-01 3.40615928e-01 -5.09905577e-01
-5.54564416e-01 -5.01756072e-01 5.25922954e-01 3.66130397e-02
-7.63413668e-01 6.16921067e-01 3.32066640e-02 -1.51805043e-01
1.93141058e-01 -1.14366341e+00 -6.51431084e-01 -3.15643638e-01
2.54436940e-01 6.27245843e-01 7.81220496e-01 -2.03529894... | [8.986989974975586, -2.4647202491760254] |
edfec7e0-8f98-46a1-95c6-c2142a4bbfc1 | efficient-exploration-with-self-imitation | 1907.10247 | null | https://arxiv.org/abs/1907.10247v3 | https://arxiv.org/pdf/1907.10247v3.pdf | Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards | Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can be incremental and slow. Recent work demonstrated that using a memory buffer of previous successful trajectories can result in more effective... | ['Honglak Lee', 'Mohammad Norouzi', 'Samy Bengio', 'Shengyu Feng', 'Jongwook Choi', 'Yijie Guo', 'Marcin Moczulski'] | 2019-07-24 | memory-based-trajectory-conditioned-policies | http://proceedings.neurips.cc/paper/2020/hash/2df45244f09369e16ea3f9117ca45157-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/2df45244f09369e16ea3f9117ca45157-Paper.pdf | neurips-2020-12 | ['montezumas-revenge'] | ['playing-games'] | [-2.96264201e-01 -1.53312802e-01 -4.20450389e-01 1.71321407e-01
-7.46869981e-01 -5.09736419e-01 6.70904875e-01 -2.12136298e-01
-8.92146647e-01 1.49723768e+00 7.06923604e-02 -1.66078985e-01
-1.30160809e-01 -5.15274704e-01 -9.11563456e-01 -7.91067183e-01
-6.59576476e-01 5.09867728e-01 2.77871817e-01 -3.11236620... | [4.066275596618652, 1.8833699226379395] |
506f9b41-1671-44ef-995d-45e7d0ef31f2 | a-birds-eye-view-on-knowledge-graph | 2205.09088 | null | https://arxiv.org/abs/2205.09088v1 | https://arxiv.org/pdf/2205.09088v1.pdf | A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges | In recent years, Knowledge Graph (KG) development has attracted significant researches considering the applications in web search, relation prediction, natural language processing, information retrieval, question answering to name a few. However, often KGs are incomplete due to which Knowledge Graph Completion (KGC) ha... | ['Dwaipayan Roy', 'Satvik Garg'] | 2022-05-18 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.46876842e-01 4.39511269e-01 -5.96289694e-01 -1.33202761e-01
-2.39094749e-01 -4.20707405e-01 4.36863810e-01 9.04151618e-01
-1.80893868e-01 7.12656200e-01 1.69568151e-01 -3.75314802e-01
-6.40618801e-01 -1.07331729e+00 -5.60494483e-01 -2.59353667e-01
-4.47817266e-01 5.43575644e-01 2.78134584e-01 -3.70481491... | [8.797784805297852, 7.898748874664307] |
0520c5dd-fd6c-4db2-abce-fa5b7d550257 | end-to-end-music-source-separation-is-it | 1810.12187 | null | https://arxiv.org/abs/1810.12187v2 | https://arxiv.org/pdf/1810.12187v2.pdf | End-to-end music source separation: is it possible in the waveform domain? | Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into a... | ['Francesc Lluís', 'Xavier Serra', 'Jordi Pons'] | 2018-10-29 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 7.24923089e-02 -3.30447674e-01 2.46040002e-01 1.15319163e-01
-1.00373292e+00 -8.67019951e-01 5.16523123e-01 1.82039753e-01
-3.31323296e-01 5.86011112e-01 5.77105463e-01 -1.33239940e-01
-5.05101025e-01 -4.83367056e-01 -3.64744633e-01 -8.35567236e-01
-4.02989805e-01 4.91577052e-02 -1.99138243e-02 -2.94442713... | [15.423227310180664, 5.546756267547607] |
4d5ed394-596f-4204-b992-e2b2da10f9a3 | a-low-rank-tensor-regularization-strategy-for | 1803.06355 | null | http://arxiv.org/abs/1803.06355v1 | http://arxiv.org/pdf/1803.06355v1.pdf | A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing | Tensor-based methods have recently emerged as a more natural and effective
formulation to address many problems in hyperspectral imaging. In hyperspectral
unmixing (HU), low-rank constraints on the abundance maps have been shown to
act as a regularization which adequately accounts for the multidimensional
structure of ... | ['José Carlos Moreira Bermudez', 'Tales Imbiriba', 'Ricardo Augusto Borsoi'] | 2018-03-16 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.08491874e-01 -5.42389095e-01 1.03725299e-01 -1.32861644e-01
-1.29633456e-01 -5.15017450e-01 4.39021230e-01 -1.77364379e-01
-8.51376727e-02 6.66769028e-01 2.72701651e-01 7.35765919e-02
-7.19186604e-01 -4.74841058e-01 -1.69290349e-01 -1.23444724e+00
-1.37677789e-01 6.34205565e-02 -2.50244349e-01 -3.00691277... | [10.080864906311035, -2.025794506072998] |
ca6bea22-8352-4f40-8779-435136cd427a | efficient-relation-aware-neighborhood | 2212.05581 | null | https://arxiv.org/abs/2212.05581v3 | https://arxiv.org/pdf/2212.05581v3.pdf | Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition | Many Graph Neural Networks (GNNs) are proposed for Knowledge Graph Embedding (KGE). However, lots of these methods neglect the importance of the information of relations and combine it with the information of entities inefficiently, leading to low expressiveness. To address this issue, we introduce a general knowledge ... | ['Hadi Moradi', 'Reshad Hosseini', 'Peyman Baghershahi'] | 2022-12-11 | null | null | null | null | ['knowledge-graph-embedding', 'general-knowledge'] | ['graphs', 'miscellaneous'] | [-3.30801785e-01 4.21809137e-01 -3.60301673e-01 -2.06825018e-01
4.02002595e-02 -5.31289220e-01 3.19600403e-01 9.86411050e-02
-4.12021339e-01 5.17664015e-01 5.06984413e-01 -4.24847901e-01
-4.57118690e-01 -1.25505972e+00 -8.35522115e-01 -4.57402468e-01
-3.86462927e-01 4.39934283e-01 7.49744335e-03 -5.15119851... | [8.741929054260254, 7.870265960693359] |
92e10089-6ad9-422c-a6e3-522ee65b7293 | training-custom-modality-specific-u-net | 2102.10607 | null | https://arxiv.org/abs/2102.10607v3 | https://arxiv.org/pdf/2102.10607v3.pdf | Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations | Deep learning (DL) has drawn tremendous attention in object localization and recognition for both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional handcrafted feature-based methods. Medical image modality-specific DL models are better at transferring ... | ['Sameer Antani', 'Philip Alderson', 'Jane Dimperio', 'Les Folio', 'Sivaramakrishnan Rajaraman'] | 2021-02-21 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 6.34393394e-01 -1.97600976e-01 -5.09015679e-01 -4.81090486e-01
-1.27752709e+00 -5.47021329e-01 3.37836623e-01 1.60640150e-01
-5.65751076e-01 8.41136515e-01 1.03384525e-01 -8.41890037e-01
-2.91544586e-01 -8.67195070e-01 -8.24702919e-01 -5.05907953e-01
2.16520071e-01 7.54266620e-01 3.43015254e-01 4.76615518... | [15.171575546264648, -2.0236711502075195] |
305de6ea-730b-4086-b270-7f11855d036f | deep-neural-models-for-medical-concept | 1907.07972 | null | https://arxiv.org/abs/1907.07972v1 | https://arxiv.org/pdf/1907.07972v1.pdf | Deep Neural Models for Medical Concept Normalization in User-Generated Texts | In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical termino... | ['Elena Tutubalina', 'Zulfat Miftahutdinov'] | 2019-07-18 | deep-neural-models-for-medical-concept-1 | https://aclanthology.org/P19-2055 | https://aclanthology.org/P19-2055.pdf | acl-2019-7 | ['medical-concept-normalization'] | ['medical'] | [ 7.95039654e-01 5.17133057e-01 -5.28756022e-01 -2.55207717e-01
-6.09798551e-01 -3.77336890e-02 3.19003075e-01 8.65367055e-01
-1.06643856e+00 7.36513674e-01 7.95169115e-01 -3.55960935e-01
-1.44844074e-02 -8.94567728e-01 -3.27145427e-01 -4.16252226e-01
1.36307091e-01 5.83458364e-01 3.78441502e-04 -7.02914953... | [8.521759033203125, 8.659845352172852] |
8feabe72-526e-404f-bd65-5283bc98e756 | improving-replay-based-continual-semantic | 2209.09839 | null | https://arxiv.org/abs/2209.09839v1 | https://arxiv.org/pdf/2209.09839v1.pdf | Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection | Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains. A central challenge in Continual Learning is overcoming the effects of catastrophic forgetting, which refers to the sudde... | ['Jürgen Beyerer', 'Björn Mauthe', 'Tobias Kalb'] | 2022-09-20 | null | null | null | null | ['continual-semantic-segmentation'] | ['computer-vision'] | [ 5.62078297e-01 -8.68523270e-02 -8.44886526e-02 -2.90567160e-01
-6.11353040e-01 -6.25240386e-01 5.44835091e-01 4.26516622e-01
-9.58918273e-01 9.71032977e-01 1.18964417e-02 -1.49750160e-02
-1.42022669e-01 -6.63298011e-01 -8.72411966e-01 -7.45037436e-01
2.40148395e-01 5.84748864e-01 6.90759420e-01 3.15720178... | [9.757970809936523, 3.2733848094940186] |
cb8b4723-ef51-4d9d-8370-63f3041203aa | clinet-joint-detection-of-road-network | 2302.02259 | null | https://arxiv.org/abs/2302.02259v1 | https://arxiv.org/pdf/2302.02259v1.pdf | CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D | This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. In contrast to existing work that focuses on detection of visual cues, we explore feature extraction methods that are directly amenable to the urban driving task. To develop and ev... | ['Henrik I. Christensen', 'Yunchao Yao', 'Srinidhi Kalgundi Srinivas', 'David Paz'] | 2023-02-04 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [-2.37089366e-01 2.79091001e-02 -1.71113923e-01 -6.97918534e-01
-8.84897947e-01 -7.81978548e-01 9.03953135e-01 1.32297024e-01
-3.79722893e-01 2.74422854e-01 1.17954753e-01 -5.90087771e-01
2.73691088e-01 -8.14085603e-01 -5.31174064e-01 -2.10869804e-01
-8.10981467e-02 1.50615320e-01 5.32280624e-01 -3.02688509... | [7.940277099609375, -1.9386234283447266] |
f1e8ca11-1676-42ee-bd27-e6721fa323b3 | ns3d-neuro-symbolic-grounding-of-3d-objects | 2303.13483 | null | https://arxiv.org/abs/2303.13483v1 | https://arxiv.org/pdf/2303.13483v1.pdf | NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations | Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D ... | ['Jiajun Wu', 'Jiayuan Mao', 'Joy Hsu'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['referring-expression', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [-3.17863747e-02 3.50835323e-01 -1.48938164e-01 -4.52192456e-01
-3.96982819e-01 -8.64073753e-01 6.75868273e-01 2.36680180e-01
9.32748020e-02 -3.17821242e-02 3.05984199e-01 -5.78970551e-01
-7.80809000e-02 -1.07812083e+00 -8.81997168e-01 -1.46326557e-01
-3.43742184e-02 6.50213420e-01 1.30171776e-01 -6.04839444... | [10.533061981201172, 1.8335076570510864] |
d0e695aa-2aa1-4475-b834-cf799cb37166 | intel-tut-dataset-for-camera-invariant-color | 1703.09778 | null | http://arxiv.org/abs/1703.09778v2 | http://arxiv.org/pdf/1703.09778v2.pdf | INTEL-TUT Dataset for Camera Invariant Color Constancy Research | In this paper, we provide a novel dataset designed for camera invariant color
constancy research. Camera invariance corresponds to the robustness of an
algorithm's performance when run on images of the same scene taken by different
cameras. Accordingly, images in the database correspond to several lab and
field scenes ... | ['Moncef Gabbouj', 'Jarno Nikkanen', 'Caglar Aytekin'] | 2017-03-21 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.38910663e-01 -7.24628389e-01 1.57780394e-01 -5.84164798e-01
-1.81366578e-01 -8.62986684e-01 2.96498865e-01 -1.60476208e-01
-3.23616743e-01 5.38441956e-01 -2.08831310e-01 -2.08231747e-01
3.99469197e-01 -4.06247765e-01 -6.39069915e-01 -7.68171430e-01
2.42677078e-01 -3.50180238e-01 7.68195763e-02 -2.11636890... | [10.387214660644531, -2.530217170715332] |
3bc0d6d3-ac55-460d-bc28-800e2c3c7f6b | instance-conditioned-gan | 2109.0507 | null | https://arxiv.org/abs/2109.05070v2 | https://arxiv.org/pdf/2109.05070v2.pdf | Instance-Conditioned GAN | Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging in unconditional settings. In this paper, we take inspiration from kernel density estimation techniqu... | ['Adriana Romero-Soriano', 'Michal Drozdzal', 'Jakob Verbeek', 'Marlène Careil', 'Arantxa Casanova'] | 2021-09-10 | null | http://proceedings.neurips.cc/paper/2021/hash/e7ac288b0f2d41445904d071ba37aaff-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/e7ac288b0f2d41445904d071ba37aaff-Paper.pdf | neurips-2021-12 | ['conditional-image-generation'] | ['computer-vision'] | [ 8.40327740e-02 3.97200376e-01 -9.83319804e-02 -3.75064135e-01
-9.59577084e-01 -6.82710588e-01 7.19083011e-01 -7.89331734e-01
2.77963467e-02 9.36774313e-01 2.14867219e-01 -1.22517638e-01
3.56125683e-01 -9.03158247e-01 -1.14240503e+00 -7.49506116e-01
3.83626759e-01 8.84453118e-01 -5.13927698e-01 2.27005988... | [11.629429817199707, -0.2803264558315277] |
6beff70c-d996-4d86-bb46-8722297f7969 | differentiating-concepts-and-instances-for | 1811.04588 | null | http://arxiv.org/abs/1811.04588v1 | http://arxiv.org/pdf/1811.04588v1.pdf | Differentiating Concepts and Instances for Knowledge Graph Embedding | Concepts, which represent a group of different instances sharing common
properties, are essential information in knowledge representation. Most
conventional knowledge embedding methods encode both entities (concepts and
instances) and relations as vectors in a low dimensional semantic space
equally, ignoring the differ... | ['Zhiyuan Liu', 'Xin Lv', 'Lei Hou', 'Juanzi Li'] | 2018-11-12 | differentiating-concepts-and-instances-for-1 | https://aclanthology.org/D18-1222 | https://aclanthology.org/D18-1222.pdf | emnlp-2018-10 | ['triple-classification'] | ['graphs'] | [-4.56304818e-01 1.79212511e-01 -6.02238894e-01 -2.99338877e-01
2.54267812e-01 -6.09433234e-01 5.97863972e-01 5.45683622e-01
-1.55316219e-01 6.18357956e-01 3.92302364e-01 -1.59603357e-03
-5.36905885e-01 -1.34863949e+00 -4.83726740e-01 -5.12518108e-01
-1.70103595e-01 5.12240827e-01 2.57461518e-01 -3.75818521... | [8.729610443115234, 7.91467809677124] |
0d16c622-82fd-42d8-9c51-e4ce6781920f | assessing-neural-referential-form-selectors | 2210.04828 | null | https://arxiv.org/abs/2210.04828v2 | https://arxiv.org/pdf/2210.04828v2.pdf | Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset | Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) use. To tackle this issue, we build a dataset based on the OntoNotes corpus that contains a broader range of RE use in both English and Chine... | ['Kees Van Deemter', 'Fahime Same', 'Guanyi Chen'] | 2022-10-10 | null | null | null | null | ['referring-expression-generation', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 1.74189970e-01 4.35044616e-01 -5.50625920e-01 -4.07578617e-01
-9.18646276e-01 -8.87382984e-01 9.98807609e-01 -1.05978534e-01
-6.53959095e-01 1.10636163e+00 1.27990365e+00 -4.97385204e-01
1.68489423e-02 -9.41282392e-01 -5.08231401e-01 6.05173036e-02
4.62764293e-01 2.63089240e-01 -2.47586712e-01 -6.39049530... | [10.762638092041016, 9.196441650390625] |
c819865b-6571-4548-8730-c9c35bf3f39a | tsrformer-table-structure-recognition-with | 2208.04921 | null | https://arxiv.org/abs/2208.04921v1 | https://arxiv.org/pdf/2208.04921v1.pdf | TSRFormer: Table Structure Recognition with Transformers | We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognizing the structures of complex tables with geometrical distortions from various table images. Unlike previous methods, we formulate table separation line prediction as a line regression problem instead of an image segmenta... | ['Qiang Huo', 'Lei Sun', 'Jiawei Wang', 'Mingze Li', 'Chixiang Ma', 'Zheng Sun', 'WeiHong Lin'] | 2022-08-09 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 2.69103885e-01 -9.57971066e-02 -5.18529527e-02 -3.31165940e-01
-1.06644571e+00 -8.21741402e-01 3.41356546e-01 4.96511728e-01
-9.25173908e-02 7.56480098e-01 -2.25278035e-01 -3.75053495e-01
4.29328904e-02 -9.10238802e-01 -1.18583059e+00 -3.20822954e-01
2.94803381e-01 9.51106966e-01 3.54167223e-01 -2.63402551... | [11.711536407470703, 3.0497686862945557] |
5a9c0049-d665-4eaa-b86e-5497220c05f0 | nicer-slam-neural-implicit-scene-encoding-for | 2302.03594 | null | https://arxiv.org/abs/2302.03594v1 | https://arxiv.org/pdf/2302.03594v1.pdf | NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM | Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, previous works in this direction either rely on RGB-D sensors, or require a separate monocular SLAM approach for camera tracking and do not produce high-fidelity dense ... | ['Marc Pollefeys', 'Andreas Geiger', 'Martin R. Oswald', 'Zhaopeng Cui', 'Viktor Larsson', 'Songyou Peng', 'Zihan Zhu'] | 2023-02-07 | null | null | null | null | ['simultaneous-localization-and-mapping', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 7.48516470e-02 -2.50138193e-01 -2.77160201e-02 -5.63012958e-01
-5.25020242e-01 -6.10484898e-01 6.70324624e-01 -1.83349580e-01
-3.29981267e-01 7.57044971e-01 7.75873438e-02 -7.77267590e-02
-2.43546683e-02 -7.74905562e-01 -8.98553014e-01 -3.60660642e-01
3.21699947e-01 6.33993506e-01 -2.99039427e-02 -9.48206708... | [8.03956127166748, -2.423236131668091] |
d16340a4-3f90-47b9-975e-ce8faf2d1a61 | exploiting-class-activation-value-for-partial | null | null | https://openreview.net/forum?id=qqdXHUGec9h | https://openreview.net/pdf?id=qqdXHUGec9h | Exploiting Class Activation Value for Partial-Label Learning | Partial-label learning (PLL) solves the multi-class classification problem, where each training instance is assigned a set of candidate labels that include the true label. Recent advances showed that PLL can be compatible with deep neural networks, which achieved state-of-the-art performance. However, most of the exist... | ['Masashi Sugiyama', 'Tao Qin', 'Gang Niu', 'Tongliang Liu', 'Bo Han', 'Lei Feng', 'Fei Zhang'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['partial-label-learning'] | ['methodology'] | [ 4.56629753e-01 -1.28132086e-02 -6.08835697e-01 -4.71776187e-01
-6.44563198e-01 -6.10533237e-01 5.15241683e-01 3.31375152e-02
-3.54457587e-01 4.79585826e-01 -4.91248161e-01 -2.92565465e-01
-2.27715313e-01 -6.90896392e-01 -6.64398849e-01 -8.43291581e-01
2.70411253e-01 2.90282339e-01 2.15197101e-01 2.07083538... | [9.50214672088623, 3.3253560066223145] |
ed2e50ed-5bc0-452e-9434-3866f26efabd | representing-videos-as-discriminative-sub-1 | 2201.04027 | null | https://arxiv.org/abs/2201.04027v1 | https://arxiv.org/pdf/2201.04027v1.pdf | Representing Videos as Discriminative Sub-graphs for Action Recognition | Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-gra... | ['Tao Mei', 'Houqiang Li', 'Ting Yao', 'Yingwei Pan', 'Zhaofan Qiu', 'Dong Li'] | 2022-01-11 | representing-videos-as-discriminative-sub | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Representing_Videos_As_Discriminative_Sub-Graphs_for_Action_Recognition_CVPR_2021_paper.pdf | cvpr-2021-1 | ['online-clustering'] | ['computer-vision'] | [-2.34292746e-01 -1.03313506e-01 -3.56103778e-01 -6.42632693e-02
-1.68809414e-01 -5.30231297e-01 6.20779812e-01 1.41854554e-01
6.88276961e-02 1.20313451e-01 3.74722272e-01 2.23942176e-01
-2.75333107e-01 -4.13789898e-01 -6.70710862e-01 -6.66319013e-01
-5.86605728e-01 5.43694854e-01 5.37955344e-01 2.62296647... | [8.248259544372559, 0.5519829988479614] |
bc52332a-d884-4409-8844-52122253890b | assemblyhands-towards-egocentric-activity | 2304.12301 | null | https://arxiv.org/abs/2304.12301v1 | https://arxiv.org/pdf/2304.12301v1.pdf | AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation | We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object interactions. The dataset includes synchronized egocentric and exocentric images sampled from the recent Assembly101 dataset, in which participa... | ['Cem Keskin', 'Luan Tran', 'Tomas Hodan', 'Fadime Sener', 'Kun He', 'Takehiko Ohkawa'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ohkawa_AssemblyHands_Towards_Egocentric_Activity_Understanding_via_3D_Hand_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-hand-pose-estimation', 'action-classification', 'hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [-1.53434828e-01 -1.85947210e-01 -2.05486804e-01 -9.81194079e-02
-8.27771544e-01 -9.31123674e-01 4.77573901e-01 -5.74007750e-01
-4.44553971e-01 4.32820559e-01 7.19246924e-01 3.44825625e-01
1.72799528e-01 5.96890040e-02 -7.79967606e-01 -5.61613500e-01
8.36112499e-02 1.01284909e+00 1.81907550e-01 -2.75699422... | [6.649822235107422, -0.8266847729682922] |
8406554a-1ba4-4a57-96dc-a895db6a7929 | dual-networks-based-3d-multi-person-pose | 2205.00748 | null | https://arxiv.org/abs/2205.00748v3 | https://arxiv.org/pdf/2205.00748v3.pdf | Dual networks based 3D Multi-Person Pose Estimation from Monocular Video | Monocular 3D human pose estimation has made progress in recent years. Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i.e., the coordinates based on the center of the target person. Hence, these methods are inapplicable for multi-person 3D pose estimation, where ... | ['Robby T. Tan', 'Bo wang', 'Yu Cheng'] | 2022-05-02 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', 'monocular-3d-human-pose-estimation', '3d-multi-person-pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.87128329e-01 -2.16045752e-01 1.07514165e-01 -7.87975788e-02
-5.43746471e-01 -3.05522084e-01 1.62593752e-01 -2.03922868e-01
-6.10715687e-01 5.14394760e-01 2.26713538e-01 4.81739670e-01
1.55091956e-01 -7.16225326e-01 -6.60199761e-01 -5.70474684e-01
8.45975429e-02 6.77403152e-01 4.80145723e-01 -1.72088534... | [7.08331823348999, -0.8555943965911865] |
eed054a9-7c45-494c-bc4f-0a92a6891428 | rotate-and-render-unsupervised-photorealistic | 2003.08124 | null | https://arxiv.org/abs/2003.08124v1 | https://arxiv.org/pdf/2003.08124v1.pdf | Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images | Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods. The current generative models heavily rely on datasets with multi-view images of the same person. Thus, their generated results are restricted by the scale and dom... | ['Yu Liu', 'Jihao Liu', 'Hang Zhou', 'Ziwei Liu', 'Xiaogang Wang'] | 2020-03-18 | rotate-and-render-unsupervised-photorealistic-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Rotate-and-Render_Unsupervised_Photorealistic_Face_Rotation_From_Single-View_Images_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-face-modeling'] | ['computer-vision'] | [ 1.03840545e-01 -1.98891923e-01 -2.03701228e-01 -5.10373831e-01
-6.35572910e-01 -6.28977895e-01 9.28957462e-01 -1.02052724e+00
9.14067030e-02 5.28875232e-01 2.27278367e-01 4.68387157e-02
3.20805728e-01 -7.44804561e-01 -7.34988391e-01 -7.24240303e-01
4.53724295e-01 6.09999180e-01 -3.64723414e-01 -4.16384935... | [12.954380989074707, 0.051384154707193375] |
3b615e6d-e1fa-4242-811f-f2d0fd4e01dd | goalienet-a-multi-stage-network-for-joint | 2306.15853 | null | https://arxiv.org/abs/2306.15853v1 | https://arxiv.org/pdf/2306.15853v1.pdf | GoalieNet: A Multi-Stage Network for Joint Goalie, Equipment, and Net Pose Estimation in Ice Hockey | In the field of computer vision-driven ice hockey analytics, one of the most challenging and least studied tasks is goalie pose estimation. Unlike general human pose estimation, goalie pose estimation is much more complex as it involves not only the detection of keypoints corresponding to the joints of the goalie conce... | ['Alexander Wong', 'David Clausi', 'Marjan Shahi'] | 2023-06-28 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-2.71773994e-01 4.79764938e-02 2.04973876e-01 3.30391169e-01
-6.15798533e-01 -5.88711023e-01 2.70052105e-02 -1.09862737e-01
-5.06324410e-01 1.92012906e-01 -1.89443439e-01 4.77631062e-01
-2.20168501e-01 -3.21312994e-01 -1.00324297e+00 -3.38167995e-01
-1.93638399e-01 4.95261729e-01 3.03232461e-01 -3.28089833... | [7.088001251220703, -0.9338282346725464] |
77c9c5bc-b6cb-4892-8b9d-3cc2f12e7bbe | grid-tagging-scheme-for-aspect-oriented-fine | 2010.0464 | null | https://arxiv.org/abs/2010.04640v2 | https://arxiv.org/pdf/2010.04640v2.pdf | Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction | Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided... | ['Rui Xia', 'Xinyu Dai', 'Zhifang Fan', 'Fei Zhao', 'Chengcan Ying', 'Zhen Wu'] | 2020-10-09 | null | https://aclanthology.org/2020.findings-emnlp.234 | https://aclanthology.org/2020.findings-emnlp.234.pdf | findings-of-the-association-for-computational | ['aspect-sentiment-opinion-triplet-extraction', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-3.89313325e-02 -2.69041155e-02 -9.16108266e-02 -5.83150208e-01
-1.00315320e+00 -6.61509216e-01 5.14514685e-01 1.92849301e-02
-1.69985637e-01 5.73576570e-01 3.47356111e-01 -4.15338427e-01
2.65742302e-01 -7.69270003e-01 -4.17840421e-01 -6.53765500e-01
2.47528002e-01 2.32824042e-01 -1.82545781e-02 -1.71378881... | [11.495271682739258, 6.589450836181641] |
3ff18420-b7b6-49fa-84d7-869c472e22bb | deep-learning-for-spatio-temporal-forecasting | 2205.03571 | null | https://arxiv.org/abs/2205.03571v1 | https://arxiv.org/pdf/2205.03571v1.pdf | Deep learning for spatio-temporal forecasting -- application to solar energy | This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge... | ['Vincent Le Guen'] | 2022-05-07 | null | null | null | null | ['video-prediction', 'spatio-temporal-forecasting'] | ['computer-vision', 'time-series'] | [ 3.07923090e-02 -2.39186183e-01 -4.19050068e-01 -3.18020105e-01
-2.21504167e-01 -3.84160876e-01 1.01957798e+00 -4.76974338e-01
2.33827546e-01 8.92297983e-01 9.18448344e-02 -3.67232472e-01
-5.65933883e-01 -9.32127833e-01 -9.35982168e-01 -1.37688208e+00
-5.25339618e-02 -6.69892132e-03 -1.49975438e-02 -1.70547113... | [6.559482097625732, 3.3089616298675537] |
e723e2e4-48b1-4648-8ffc-0ca630530fae | personalized-automatic-sleep-staging-with | 2004.11349 | null | https://arxiv.org/abs/2004.11349v2 | https://arxiv.org/pdf/2004.11349v2.pdf | Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization | Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a ... | ['Preben Kidmose', 'Oliver Y. Chén', 'Alfred Mertins', 'Philipp Koch', 'Kaare Mikkelsen', 'Huy Phan', 'Maarten De Vos'] | 2020-04-23 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-9.41505432e-02 1.85471967e-01 -2.40588598e-02 -7.27319300e-01
-5.66557884e-01 -6.95307180e-02 5.34103028e-02 -1.00479955e-02
-1.03867686e+00 1.04214954e+00 6.19917884e-02 7.57182762e-02
-2.23416835e-01 -4.58132386e-01 -4.73949611e-01 -8.63742292e-01
4.02507037e-02 4.59022850e-01 2.54946142e-01 6.91721365... | [13.450603485107422, 3.5114126205444336] |
a90b59b5-1214-493d-bbf8-ccabbb0e16ad | swim-a-general-purpose-high-performing-and | 2303.0264 | null | https://arxiv.org/abs/2303.02640v1 | https://arxiv.org/pdf/2303.02640v1.pdf | Swim: A General-Purpose, High-Performing, and Efficient Activation Function for Locomotion Control Tasks | Activation functions play a significant role in the performance of deep learning algorithms. In particular, the Swish activation function tends to outperform ReLU on deeper models, including deep reinforcement learning models, across challenging tasks. Despite this progress, ReLU is the preferred function partly becaus... | ['Tony Dear', 'Maryam Abdool'] | 2023-03-05 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-3.39265764e-01 -2.62679905e-01 -1.39835507e-01 7.35818818e-02
-2.64555030e-03 -3.12740952e-01 6.89474463e-01 -1.03295773e-01
-9.36151981e-01 1.05346036e+00 9.60328430e-02 -1.81308404e-01
-5.53070068e-01 -8.60857725e-01 -5.65240264e-01 -7.82691061e-01
-2.75516063e-01 5.50689220e-01 2.54580021e-01 -7.83987701... | [4.070342063903809, 1.4033290147781372] |
387537bf-3aee-4b50-9757-8d9f6f560c0d | attention-based-clinical-note-summarization | 2104.08942 | null | https://arxiv.org/abs/2104.08942v3 | https://arxiv.org/pdf/2104.08942v3.pdf | Attention-based Clinical Note Summarization | In recent years, the trend of deploying digital systems in numerous industries has hiked. The health sector has observed an extensive adoption of digital systems and services that generate significant medical records. Electronic health records contain valuable information for prospective and retrospective analysis that... | ['Giuseppe Rizzo', 'Neel Kanwal'] | 2021-04-18 | null | null | null | null | ['clinical-information-retreival'] | ['natural-language-processing'] | [ 5.19114077e-01 4.23749626e-01 -2.12415248e-01 -2.34187752e-01
-1.11116445e+00 -3.26096326e-01 2.40478635e-01 1.31123590e+00
-3.47501695e-01 6.31152630e-01 1.27010858e+00 -3.55603844e-01
-3.14826101e-01 -5.49315453e-01 -1.72714040e-01 -6.43577993e-01
-2.98116356e-01 6.10524416e-01 -2.93369651e-01 -3.20881084... | [8.56306266784668, 8.560612678527832] |
7f86d0cd-967d-4408-a6c7-1a4ba4da39c1 | representation-learning-over-dynamic-graphs | 1803.04051 | null | http://arxiv.org/abs/1803.04051v2 | http://arxiv.org/pdf/1803.04051v2.pdf | Representation Learning over Dynamic Graphs | How can we effectively encode evolving information over dynamic graphs into
low-dimensional representations? In this paper, we propose DyRep, an inductive
deep representation learning framework that learns a set of functions to
efficiently produce low-dimensional node embeddings that evolves over time. The
learned embe... | ['Hongyuan Zha', 'Rakshit Trivedi', 'Prasenjeet Biswal', 'Mehrdad Farajtabar'] | 2018-03-11 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-2.99317509e-01 1.46782398e-01 -2.75776714e-01 -1.82435453e-01
-8.35407674e-02 -6.36077046e-01 9.77131128e-01 6.04788065e-01
-5.60151115e-02 2.96098202e-01 4.98756260e-01 -3.65045100e-01
-4.92916703e-01 -1.30101562e+00 -5.59467793e-01 -4.12088364e-01
-9.86741364e-01 9.86726284e-01 1.57404855e-01 -2.91269422... | [7.228328704833984, 6.026055812835693] |
65403034-5fbe-4c87-af4d-492e4f504962 | visual-slam-what-are-the-current-trends-and | 2210.10491 | null | https://arxiv.org/abs/2210.10491v2 | https://arxiv.org/pdf/2210.10491v2.pdf | Visual SLAM: What are the Current Trends and What to Expect? | Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map genera... | ['Holger Voos', 'Jose Luis Sanchez-Lopez', 'Hriday Bavle', 'Ali Tourani'] | 2022-10-19 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.97510682e-02 -6.56644583e-01 -1.33376688e-01 -4.52836096e-01
-1.73559338e-01 -7.75308609e-01 6.73938274e-01 2.57154703e-02
-4.97078627e-01 9.78678763e-01 -1.41955256e-01 -2.53171660e-02
-4.80067655e-02 -7.56214261e-01 -6.20957077e-01 -4.81948167e-01
6.32998347e-02 2.64760554e-01 3.24929625e-01 -2.63886720... | [7.36745023727417, -2.1522836685180664] |
343bd28d-d51a-4f23-9cc1-79aa44025eb1 | simple-and-effective-unsupervised-speech | 2204.02524 | null | https://arxiv.org/abs/2204.02524v3 | https://arxiv.org/pdf/2204.02524v3.pdf | Simple and Effective Unsupervised Speech Synthesis | We introduce the first unsupervised speech synthesis system based on a simple, yet effective recipe. The framework leverages recent work in unsupervised speech recognition as well as existing neural-based speech synthesis. Using only unlabeled speech audio and unlabeled text as well as a lexicon, our method enables spe... | ['Alexei Baevski', 'James Glass', 'Michael Auli', 'Wei-Ning Hsu', 'Cheng-I Jeff Lai', 'Alexander H. Liu'] | 2022-04-06 | null | null | null | null | ['unsupervised-speech-recognition'] | ['speech'] | [ 3.81245255e-01 6.44170821e-01 -1.45821452e-01 -5.44083178e-01
-9.55848157e-01 -6.17979765e-01 9.10713196e-01 -1.06879517e-01
-1.43977642e-01 6.20167077e-01 7.63004243e-01 -5.21149099e-01
4.46972668e-01 -4.76314336e-01 -4.28416252e-01 -3.74502599e-01
3.77042860e-01 3.76881242e-01 -9.45689082e-02 -3.93864274... | [14.692024230957031, 6.82486629486084] |
17b4fb8a-bb0f-46ce-a3c1-834bc496d91f | confidence-guided-semi-supervised-learning-in | 2305.10344 | null | https://arxiv.org/abs/2305.10344v2 | https://arxiv.org/pdf/2305.10344v2.pdf | Confidence-Guided Semi-supervised Learning in Land Cover Classification | Semi-supervised learning has been well developed to help reduce the cost of manual labelling by exploiting a large quantity of unlabelled data. Especially in the application of land cover classification, pixel-level manual labelling in large-scale imagery is labour-intensive, time-consuming and expensive. However, exis... | ['Paul L. Rosin', 'Oktay Karakus', 'Wanli Ma'] | 2023-05-17 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 6.06630802e-01 2.49290213e-01 -6.28413618e-01 -7.00971127e-01
-7.32878745e-01 -4.09063101e-01 5.45674086e-01 4.56861585e-01
-6.10001206e-01 1.04947186e+00 -1.12654261e-01 -5.48329175e-01
-2.68281251e-01 -1.05616879e+00 -5.53297400e-01 -7.62851238e-01
-1.06262952e-01 3.60275120e-01 1.74608484e-01 -2.12247044... | [9.670392990112305, -1.4026988744735718] |
cc76acc6-68b5-4c3b-984a-2e8d67bdd693 | application-of-information-spectrum-method-on | 1907.02713 | null | http://arxiv.org/abs/1907.02713v3 | http://arxiv.org/pdf/1907.02713v3.pdf | Application of Information Spectrum Method on Small Molecules and Target Recognition | Current methods for investigation of receptor - ligand interactions in drug
discovery are based on three-dimensional complementarity of receptor and ligand
surfaces, and they include pharmacophore modelling, QSAR, molecular docking
etc. Those methods only consider short-range molecular interactions (distances
<5A), and... | [] | 2020-04-15 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 2.41145864e-01 -7.82723501e-02 -2.79199332e-01 -4.08508033e-01
-1.58711568e-01 -6.15574181e-01 3.90826434e-01 5.74140549e-01
-5.09106815e-01 1.48088193e+00 1.95764020e-01 -5.06602526e-01
-2.35849530e-01 -7.98834920e-01 -6.90481246e-01 -8.67239892e-01
-3.54766548e-01 7.72352338e-01 3.68233711e-01 -3.66915971... | [4.766985893249512, 5.340245246887207] |
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