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4b398a0d-1a75-476b-b518-0c607f82c031 | dialo-ap-a-dependency-parsing-based-argument | null | null | https://aclanthology.org/2022.coling-1.74 | https://aclanthology.org/2022.coling-1.74.pdf | Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues | While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. Th... | ['Rohini K. Srihari', 'Souvik Das', 'Sougata Saha'] | null | null | null | null | coling-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 4.96203929e-01 1.25419414e+00 -3.01092416e-01 -5.52521288e-01
-1.20247149e+00 -7.69702852e-01 1.17350984e+00 6.20223463e-01
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2.22766086e-01 -8.81515920e-01 -5.89822173e-01 -3.34520452e-02
9.66975838e-02 9.70094800e-01 -8.08712617e-02 -6.57951057... | [10.603743553161621, 9.483752250671387] |
45be4b1c-c461-4791-9e5b-7d8d204120cd | pseudo-labels-for-single-positive-multi-label | 2306.01034 | null | https://arxiv.org/abs/2306.01034v1 | https://arxiv.org/pdf/2306.01034v1.pdf | Pseudo Labels for Single Positive Multi-Label Learning | The cost of data annotation is a substantial impediment for multi-label image classification: in every image, every category must be labeled as present or absent. Single positive multi-label (SPML) learning is a cost-effective solution, where models are trained on a single positive label per image. Thus, SPML is a more... | ['Julio Arroyo'] | 2023-06-01 | null | null | null | null | ['multi-label-image-classification', 'multi-label-learning'] | ['computer-vision', 'methodology'] | [ 6.99524522e-01 2.44647324e-01 -7.26843894e-01 -6.72345698e-01
-9.91966009e-01 -6.20120704e-01 3.10256362e-01 1.49235040e-01
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6.21744275e-01 6.46418989e-01 3.39902878e-01 3.51329535... | [9.589408874511719, 4.051997184753418] |
bee1e2d7-e58c-4fec-b80d-b98f06ed340c | ct2-colorization-transformer-via-color-tokens | null | null | https://dl.acm.org/doi/abs/10.1007/978-3-031-20071-7_1 | https://ci.idm.pku.edu.cn/Weng_ECCV22b.pdf | CT2: Colorization Transformer via Color Tokens | Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation. In this paper, we propose an end-to-end transformer-based model to overcome these challenges. Benefited from the long-range cont... | ['Boxin Shi', 'Si Li', 'Yu Li', 'Jimeng Sun', 'Shuchen Weng'] | 2022-10-23 | null | null | null | eccv-2022-10 | ['colorization'] | ['computer-vision'] | [ 5.18438630e-02 -2.58570015e-01 1.98379196e-02 -3.94986928e-01
-5.85398972e-01 -6.53388202e-01 4.00146484e-01 -3.86457205e-01
-2.82644868e-01 3.72289717e-01 9.91544724e-02 -1.86770663e-01
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5.40324450e-01 -2.03269586e-01 5.73839962e-01 -1.07776307... | [11.39420223236084, -1.030258297920227] |
2a6f7097-c0d8-4ff9-864b-0a2c220f3a08 | how-to-learn-from-unlabeled-volume-data-self | null | null | https://openreview.net/forum?id=rJlYmEE46N | https://openreview.net/pdf?id=rJlYmEE46N | How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning | The vast majority of 3D medical images lacks detailed image-based expert annotations. The ongoing advances of deep convolutional neural networks clearly demonstrate the benefit of supervised learning to successfully extract relevant anatomical information and aid image-based analysis and interventions, but it heavily r... | ['Anonymous'] | 2019-05-23 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 4.94905800e-01 4.28692073e-01 -5.37273169e-01 -6.71509147e-01
-1.16924381e+00 -4.61158037e-01 4.08265859e-01 4.91116345e-01
-6.53968930e-01 5.77239513e-01 1.92494810e-01 -6.03507906e-02
-4.78979558e-01 -5.39917767e-01 -5.03493071e-01 -8.25376332e-01
-3.23247671e-01 3.38771582e-01 2.67267942e-01 -1.36793360... | [14.758092880249023, -2.3271780014038086] |
ffccc4b4-ebfe-4b1f-ad7a-ead6c67b4d4f | offboard-3d-object-detection-from-point-cloud | 2103.05073 | null | https://arxiv.org/abs/2103.05073v1 | https://arxiv.org/pdf/2103.05073v1.pdf | Offboard 3D Object Detection from Point Cloud Sequences | While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels. Existing 3D object detectors fail to satisfy the high-quality requirem... | ['Dragomir Anguelov', 'Boyang Deng', 'Khoa Vo', 'Pei Sun', 'Mahyar Najibi', 'Yin Zhou', 'Charles R. Qi'] | 2021-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-object-recognition'] | ['computer-vision'] | [ 1.03296556e-01 5.27823716e-02 -1.12663664e-01 -5.13045490e-01
-7.75825918e-01 -8.86609733e-01 7.86204398e-01 1.49193287e-01
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1.52500972e-01 9.22526479e-01 9.34398711e-01 -4.49965596... | [7.605356216430664, -2.62255597114563] |
e1b27e96-aafd-4717-899e-288ecf5493f0 | unsupervised-detection-of-lung-nodules-in | 2108.02233 | null | https://arxiv.org/abs/2108.02233v1 | https://arxiv.org/pdf/2108.02233v1.pdf | Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks | Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-e... | ['H. R. Tizhoosh', 'Christos Karanassios', 'Nedim Hodzic', 'David Ramon Prados', 'Nitish Bhatt'] | 2021-08-04 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 5.26121259e-01 5.44032454e-01 1.64419860e-01 -1.35849100e-02
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4.75397967e-02 7.44439542e-01 5.73586345e-01 4.31487441... | [15.061203956604004, -2.041186571121216] |
81c70b04-6efa-423d-90dd-cac9948e317e | revisiting-inferential-benchmarks-for | 2306.04814 | null | https://arxiv.org/abs/2306.04814v1 | https://arxiv.org/pdf/2306.04814v1.pdf | Revisiting Inferential Benchmarks for Knowledge Graph Completion | Knowledge Graph (KG) completion is the problem of extending an incomplete KG with missing facts. A key feature of Machine Learning approaches for KG completion is their ability to learn inference patterns, so that the predicted facts are the results of applying these patterns to the KG. Standard completion benchmarks, ... | ['Egor V. Kostylev', 'Ian Horrocks', 'Bernardo Cuenca Grau', 'Shuwen Liu'] | 2023-06-07 | null | null | null | null | ['knowledge-graph-completion'] | ['knowledge-base'] | [ 3.62959951e-01 8.31600428e-01 -4.82786417e-01 -4.46077079e-01
-5.87942488e-02 -4.09549415e-01 6.83297932e-01 1.60580680e-01
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-4.56035048e-01 -1.38209319e+00 -1.25060022e+00 -2.75537759e-01
-2.81614542e-01 8.09633911e-01 4.80684608e-01 -1.66216016... | [9.046649932861328, 7.1761651039123535] |
e0e31223-dda9-441c-9f1a-4270b49cf6f1 | multi-scale-guided-attention-for-medical | 1906.02849 | null | https://arxiv.org/abs/1906.02849v3 | https://arxiv.org/pdf/1906.02849v3.pdf | Multi-scale self-guided attention for medical image segmentation | Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple ... | ['Jose Dolz', 'Ashish Sinha'] | 2019-06-07 | multi-scale-guided-attention-for-medical-1 | null | null | arxiv-preprint-2019-6 | ['deep-attention', 'attentive-segmentation-networks', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.39694774e-01 3.26500028e-01 -1.28260642e-01 -4.02666390e-01
-6.82416022e-01 -3.10374022e-01 3.43551010e-01 5.10423660e-01
-6.79023266e-01 6.79805160e-01 1.12340949e-01 7.42426738e-02
-1.64944813e-01 -7.00792551e-01 -6.57987058e-01 -6.93215251e-01
1.04991250e-01 3.22275907e-01 4.50869203e-01 -1.78429782... | [14.625965118408203, -2.570199728012085] |
a957018d-043c-4322-8860-e4dffec05d57 | speech-denoising-in-the-waveform-domain-with | 2202.07790 | null | https://arxiv.org/abs/2202.07790v3 | https://arxiv.org/pdf/2202.07790v3.pdf | Speech Denoising in the Waveform Domain with Self-Attention | In this work, we present CleanUNet, a causal speech denoising model on the raw waveform. The proposed model is based on an encoder-decoder architecture combined with several self-attention blocks to refine its bottleneck representations, which is crucial to obtain good results. The model is optimized through a set of l... | ['Bryan Catanzaro', 'Ambrish Dantrey', 'Wei Ping', 'Zhifeng Kong'] | 2022-02-15 | null | null | null | null | ['speech-denoising'] | ['speech'] | [-1.14039898e-01 -2.76414514e-01 2.71344692e-01 -4.42123055e-01
-1.01818383e+00 -1.22116074e-01 3.73059034e-01 -1.04991153e-01
-2.23916709e-01 6.12325013e-01 6.10990226e-01 -1.57285377e-01
1.34510666e-01 -3.85273457e-01 -6.24250889e-01 -6.10539496e-01
-5.91880903e-02 -2.14266405e-01 2.33067319e-01 -3.89047503... | [14.978015899658203, 5.9712748527526855] |
b5a5a284-8fa3-49f6-9837-7a9f26fb7143 | mv6d-multi-view-6d-pose-estimation-on-rgb-d | 2208.01172 | null | https://arxiv.org/abs/2208.01172v1 | https://arxiv.org/pdf/2208.01172v1.pdf | MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network | Estimating 6D poses of objects is an essential computer vision task. However, most conventional approaches rely on camera data from a single perspective and therefore suffer from occlusions. We overcome this issue with our novel multi-view 6D pose estimation method called MV6D which accurately predicts the 6D poses of ... | ['Gerhard Neumann', 'Tobias Demmler', 'Fabian Duffhauss'] | 2022-08-01 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [-5.09974249e-02 -5.68747744e-02 1.14545777e-01 -3.23681772e-01
-7.45346844e-01 -9.68335509e-01 4.89109755e-01 -1.17422521e-01
-4.27210718e-01 -3.70372459e-02 -1.89914063e-01 1.05241172e-01
1.36438861e-01 -5.25197268e-01 -1.03504324e+00 -4.71929222e-01
5.40442705e-01 1.06582952e+00 7.55427063e-01 -3.40410769... | [7.581632137298584, -2.6093719005584717] |
187d3a6d-33c5-412f-ba53-d12f07796bf8 | few-shot-table-to-text-generation-with-prefix | 2208.10709 | null | https://arxiv.org/abs/2208.10709v1 | https://arxiv.org/pdf/2208.10709v1.pdf | Few-Shot Table-to-Text Generation with Prefix-Controlled Generator | Neural table-to-text generation approaches are data-hungry, limiting their adaptation for low-resource real-world applications. Previous works mostly resort to Pre-trained Language Models (PLMs) to generate fluent summaries of a table. However, they often contain hallucinated contents due to the uncontrolled nature of ... | ['Shilin Wang', 'Gongshen Liu', 'Menghua Lu', 'Yutao Luo'] | 2022-08-23 | null | https://aclanthology.org/2022.coling-1.565 | https://aclanthology.org/2022.coling-1.565.pdf | coling-2022-10 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.54843348e-01 9.71968099e-02 4.46127914e-02 -6.82830438e-02
-9.30867910e-01 -6.98177159e-01 8.78206670e-01 3.06279510e-01
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4.35649604e-01 6.27844632e-01 1.20704174e-01 -3.99714410... | [11.729345321655273, 8.878050804138184] |
9a40fff1-cc65-4a88-96d7-14440fe055f2 | low-rank-extended-kalman-filtering-for-online | 2305.19535 | null | https://arxiv.org/abs/2305.19535v3 | https://arxiv.org/pdf/2305.19535v3.pdf | Low-rank extended Kalman filtering for online learning of neural networks from streaming data | We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The method is based on the extended Kalman filter (EKF), but uses a novel low-rank plus diagonal decomposition of the posterior precision matrix, w... | ['Gerardo Durán-Martín', 'Peter G. Chang', 'Kevin Murphy', 'Matt Jones', 'Alexander Y Shestopaloff'] | 2023-05-31 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-5.24403788e-02 -2.86354274e-01 -5.91660082e-01 -1.57449499e-01
-1.11529946e+00 -6.55051053e-01 7.12679744e-01 -6.57625571e-02
-7.86493897e-01 1.23859179e+00 2.00398937e-01 -4.96066600e-01
-4.98118073e-01 -4.43985224e-01 -9.31957066e-01 -6.45042181e-01
5.33352382e-02 8.16788375e-01 2.88883239e-01 4.13809210... | [4.809502601623535, 3.1281702518463135] |
283bb83d-cde0-479c-9f25-d299f5b6a817 | unsupervised-multi-target-domain-adaptation | 1810.11547 | null | http://arxiv.org/abs/1810.11547v1 | http://arxiv.org/pdf/1810.11547v1.pdf | Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach | Unsupervised domain adaptation (uDA) models focus on pairwise adaptation
settings where there is a single, labeled, source and a single target domain.
However, in many real-world settings one seeks to adapt to multiple, but
somewhat similar, target domains. Applying pairwise adaptation approaches to
this setting may be... | ['Vladimir Pavlovic', 'Pritish Sahu', 'Behnam Gholami', 'Ognjen Rudovic', 'Konstantinos Bousmalis'] | 2018-10-26 | unsupervised-multi-target-domain-adaptation-1 | https://openreview.net/forum?id=BJxLH2AcYX | https://openreview.net/pdf?id=BJxLH2AcYX | iclr-2019-5 | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.71677870e-01 -1.40903309e-01 -6.02391660e-01 -4.55573082e-01
-1.09045482e+00 -9.21912968e-01 7.12209046e-01 6.50886670e-02
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2.92823344e-01 9.45580423e-01 2.47370135e-02 7.07659498... | [10.345138549804688, 3.1325161457061768] |
3ac0b527-625d-4647-aefd-6513aa82ce97 | real-time-model-free-deep-reinforcement | 2304.04911 | null | https://arxiv.org/abs/2304.04911v1 | https://arxiv.org/pdf/2304.04911v1.pdf | Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator | Many state-of-the art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation. Model-free PID control methods are more prone to instability due to nonlinearities in the SEA where cascaded model-based robust controlle... | ['Alexander Leonessa', 'Connor W. Herron', 'Stephen Welch', 'Aydin Gokce', 'Ruturaj Sambhus'] | 2023-04-11 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.36176646e-01 5.03435969e-01 -2.91237175e-01 4.92848456e-01
-1.48313537e-01 -5.16289830e-01 9.79160815e-02 -2.66221166e-01
-5.51570117e-01 1.02176297e+00 -3.29532534e-01 -3.26784313e-01
-5.89451611e-01 -2.37530589e-01 -1.10396695e+00 -8.31710279e-01
-5.10694504e-01 2.79865742e-01 2.88075417e-01 -1.04289639... | [4.931324005126953, 1.7596080303192139] |
7a5c947e-4949-488d-af5d-65696cbf94f7 | fast-abc-with-joint-generative-modelling-and | 2104.08156 | null | https://arxiv.org/abs/2104.08156v1 | https://arxiv.org/pdf/2104.08156v1.pdf | Fast ABC with joint generative modelling and subset simulation | We propose a novel approach for solving inverse-problems with high-dimensional inputs and an expensive forward mapping. It leverages joint deep generative modelling to transfer the original problem spaces to a lower dimensional latent space. By jointly modelling input and output variables and endowing the latent with a... | ['Niklas Linde', 'David Ginsbourger', 'Eliane Maalouf'] | 2021-04-16 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 1.62128076e-01 3.63077432e-01 3.38227302e-01 -2.39822373e-01
-1.13158274e+00 -4.17810708e-01 9.20726299e-01 -4.50198144e-01
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-5.67305982e-01 -7.44208097e-01 -6.12586617e-01 -9.05248702e-01
1.44500034e-02 9.52509284e-01 -1.67297140e-01 2.19318539... | [6.860017776489258, 3.7596347332000732] |
bbcb7cbf-6b7a-422d-ab66-95aeee135ed1 | mitigating-artifacts-in-real-world-video | 2212.07339 | null | https://arxiv.org/abs/2212.07339v1 | https://arxiv.org/pdf/2212.07339v1.pdf | Mitigating Artifacts in Real-World Video Super-Resolution Models | The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states tend to contain unpleasant artifacts and propagate them to restored fr... | ['Ying Shan', 'Chao Dong', 'Jinjin Gu', 'Shuwei Shi', 'Xintao Wang', 'Liangbin Xie'] | 2022-12-14 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 2.47897908e-01 -2.53384650e-01 -4.89425808e-02 -8.83972794e-02
-6.08395815e-01 -1.14844233e-01 4.99168783e-01 -4.71442908e-01
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1.74617380e-01 -3.84859920e-01 2.92599708e-01 -3.51739824... | [11.073149681091309, -1.9170470237731934] |
49b6ce64-a128-45fd-b62d-e20efcc4073b | deep-learning-for-clustering-of-multivariate | null | null | https://academic.oup.com/gigascience/article/8/11/giz134/5626377 | https://academic.oup.com/gigascience/article-pdf/8/11/giz134/30797160/giz134.pdf | Deep learning for clustering of multivariate clinical patient trajectories with missing values | Background
Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivari... | ['Holger Fröhlich', 'Martin Hofmann-Apitius', 'Henri Vrooman', 'Ashar Ahmad', 'Patrice Godard', 'Meemansa Sood', 'Reagon Karki', 'Ping Wu', 'Mohammad Asif Emon', 'Johann de Jong'] | 2019-11-15 | null | null | null | gigascience-2019-11 | ['time-series-clustering'] | ['time-series'] | [ 6.01450875e-02 -3.30319762e-01 -2.73404658e-01 -3.76081407e-01
-8.99361253e-01 -4.23646361e-01 3.93904805e-01 2.51307458e-01
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-3.69992286e-01 1.10265410e+00 -3.90487731e-01 2.66619958... | [7.228837013244629, 5.38394832611084] |
a4fd702c-8c26-4388-bfc0-45b232fab9f0 | expressive-variable-and-controllable-duration | 2206.14165 | null | https://arxiv.org/abs/2206.14165v1 | https://arxiv.org/pdf/2206.14165v1.pdf | Expressive, Variable, and Controllable Duration Modelling in TTS | Duration modelling has become an important research problem once more with the rise of non-attention neural text-to-speech systems. The current approaches largely fall back to relying on previous statistical parametric speech synthesis technology for duration prediction, which poorly models the expressiveness and varia... | ['Thomas Drugman', 'Elia Gatti', 'Simon Slangen', 'Ewa Muszynska', 'Sri Karlapati', 'Alexis Moinet', 'Thomas Merritt', 'Ammar Abbas'] | 2022-06-28 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-1.99421169e-03 3.02636445e-01 -3.10400188e-01 -4.04280484e-01
-5.60550332e-01 -6.77285790e-01 7.90202856e-01 -6.80956291e-03
-2.39062294e-01 5.08544505e-01 6.36002958e-01 -4.22034353e-01
5.37601560e-02 -3.47275645e-01 -1.86577141e-01 -5.18077135e-01
6.66016191e-02 3.86939824e-01 2.71853179e-01 -1.98160186... | [14.909103393554688, 6.57694149017334] |
307cf0b0-35fc-443f-bf64-c61aa7f5a04f | autism-spectrum-disorder-classification-in | 2307.00976 | null | https://arxiv.org/abs/2307.00976v1 | https://arxiv.org/pdf/2307.00976v1.pdf | Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder | Autism spectrum disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted ... | ['Yi Pan', 'Wenhui Xi', 'Yanjie Wei', 'Jintao Meng', 'Yanlin Wang', 'Ruitao Xie', 'Ruimin Ma'] | 2023-07-03 | null | null | null | null | ['classification-1', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [ 1.40080135e-02 1.56163782e-01 6.16399571e-02 -3.53929788e-01
-1.64502844e-01 5.30858636e-02 5.85329421e-02 2.90900975e-01
-2.83300906e-01 4.76450890e-01 1.36708289e-01 1.26128271e-01
-3.49325418e-01 -5.47630608e-01 -2.05888405e-01 -5.20912290e-01
-2.91913867e-01 5.36988080e-01 -4.34340090e-02 9.59374309... | [12.706745147705078, 3.0255184173583984] |
87d4d71d-09bd-4e60-bae5-7172a2f9f054 | watermarking-text-generated-by-black-box | 2305.08883 | null | https://arxiv.org/abs/2305.08883v1 | https://arxiv.org/pdf/2305.08883v1.pdf | Watermarking Text Generated by Black-Box Language Models | LLMs now exhibit human-like skills in various fields, leading to worries about misuse. Thus, detecting generated text is crucial. However, passive detection methods are stuck in domain specificity and limited adversarial robustness. To achieve reliable detection, a watermark-based method was proposed for white-box LLMs... | ['Nenghai Yu', 'Han Fang', 'Jie Zhang', 'Yuang Qi', 'Chang Liu', 'Weiming Zhang', 'Kejiang Chen', 'Xi Yang'] | 2023-05-14 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 7.68407643e-01 -9.71669983e-03 -8.61390114e-01 2.52250999e-01
-6.50405288e-01 -1.20966208e+00 4.12147492e-01 2.60332286e-01
-3.77257645e-01 4.67479944e-01 -1.80751100e-01 -7.82170475e-01
6.01464093e-01 -9.04448390e-01 -6.66590154e-01 -3.98611248e-01
-3.77979502e-03 -3.33589375e-01 4.48594719e-01 -6.70675486... | [5.903992176055908, 7.767019271850586] |
fa456c7b-041d-4d8d-b158-37f8d1bb736b | speaker-conditioning-single-channel-target | 2205.13851 | null | https://arxiv.org/abs/2205.13851v1 | https://arxiv.org/pdf/2205.13851v1.pdf | Speaker-conditioning Single-channel Target Speaker Extraction using Conformer-based Architectures | Target speaker extraction aims at extracting the target speaker from a mixture of multiple speakers exploiting auxiliary information about the target speaker. In this paper, we consider a complete time-domain target speaker extraction system consisting of a speaker embedder network and a speaker separator network which... | ['Simon Doclo', 'Christian Rollwage', 'Marvin Tammen', 'Ragini Sinha'] | 2022-05-27 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.97320879e-01 3.34834099e-01 1.22812413e-01 -6.72985196e-01
-1.17685616e+00 -3.34108293e-01 8.23922276e-01 -3.41504544e-01
-1.99435845e-01 -2.44032685e-02 3.16814899e-01 -3.55129808e-01
1.74080208e-01 -7.84157515e-02 -4.72870499e-01 -8.30538452e-01
-2.51370102e-01 4.51660633e-01 2.08197609e-01 -2.09816307... | [14.652992248535156, 5.973742485046387] |
18d4d60c-30bf-423e-a852-4c9c409f263a | cvsnet-a-computer-implementation-for-central | 2305.19492 | null | https://arxiv.org/abs/2305.19492v1 | https://arxiv.org/pdf/2305.19492v1.pdf | CVSNet: A Computer Implementation for Central Visual System of The Brain | In computer vision, different basic blocks are created around different matrix operations, and models based on different basic blocks have achieved good results. Good results achieved in vision tasks grants them rationality. However, these experimental-based models also make deep learning long criticized for principle ... | ['Zhekai Duan', 'Hao Zou', 'Ruimin Gao'] | 2023-05-31 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-2.73625195e-01 2.89725006e-01 1.87493131e-01 -7.27467239e-02
7.58973718e-01 -2.49935403e-01 7.75432825e-01 -6.15595520e-01
-6.38684511e-01 5.54696858e-01 1.05914041e-01 -1.01148210e-01
-6.53493702e-02 -8.58539164e-01 -7.26704299e-01 -9.35023963e-01
2.38532111e-01 1.62384063e-02 4.46693867e-01 -3.05916935... | [9.58764362335205, 2.2519516944885254] |
e6c79336-5c81-4966-9fcd-11f83f715578 | embedding-open-domain-common-sense-knowledge | null | null | https://aclanthology.org/L16-1732 | https://aclanthology.org/L16-1732.pdf | Embedding Open-domain Common-sense Knowledge from Text | Our ability to understand language often relies on common-sense knowledge ― background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can... | ['a', 'Travis Goodwin', 'S Harabagiu'] | 2016-05-01 | embedding-open-domain-common-sense-knowledge-1 | https://aclanthology.org/L16-1732 | https://aclanthology.org/L16-1732.pdf | lrec-2016-5 | ['open-information-extraction'] | ['natural-language-processing'] | [-8.64297003e-02 6.74907982e-01 -4.01991934e-01 -2.53052115e-01
-2.45498285e-01 -7.52244711e-01 6.14740968e-01 9.26930726e-01
-7.31465697e-01 6.26865745e-01 7.55220413e-01 -3.73615503e-01
-4.95653808e-01 -1.04952478e+00 -5.06988943e-01 -2.84780234e-01
7.85883293e-02 4.88500446e-01 -9.57928672e-02 -6.31701171... | [10.114314079284668, 8.718711853027344] |
647cd26d-ca23-48c7-acd0-790e2d2259f4 | tanet-robust-3d-object-detection-from-point | 1912.05163 | null | https://arxiv.org/abs/1912.05163v1 | https://arxiv.org/pdf/1912.05163v1.pdf | TANet: Robust 3D Object Detection from Point Clouds with Triple Attention | In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the perfo... | ['Xin Zhao', 'Yu Zhou', 'Zhe Liu', 'Ruolan Hu', 'Xiang Bai', 'Tengteng Huang'] | 2019-12-11 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [-1.41036719e-01 -3.22428912e-01 3.41993511e-01 3.77535168e-03
-7.21813858e-01 -2.73697108e-01 5.75057924e-01 3.82355064e-01
-5.48723578e-01 4.18147117e-01 -4.18001711e-01 -9.49866176e-02
8.78744423e-02 -6.58643126e-01 -9.41317260e-01 -9.89483654e-01
4.92636934e-02 2.65331924e-01 7.08859622e-01 -2.37467214... | [7.798068523406982, -1.1503320932388306] |
5a2286b1-1880-4626-a3a4-02d99135adc1 | ssit-saliency-guided-self-supervised-image | 2210.10969 | null | https://arxiv.org/abs/2210.10969v4 | https://arxiv.org/pdf/2210.10969v4.pdf | SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading | Self-supervised learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, we propose Saliency-guided Self-Supervised image Transformer (SSiT) for diabetic retinopathy (DR) grading ... | ['Xiaoying Tang', 'Roger Tam', 'Pujin Cheng', 'Junyan Lyu', 'Yijin Huang'] | 2022-10-20 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 6.00088537e-01 2.73694813e-01 -6.82442009e-01 -5.29781282e-01
-7.28539944e-01 1.18676677e-01 2.66358793e-01 1.30427647e-02
-2.73840338e-01 7.16721714e-01 6.24123752e-01 6.91237226e-02
-2.27086470e-01 -3.70262772e-01 -7.23081172e-01 -8.45947623e-01
2.21620411e-01 -1.63876206e-01 3.81850213e-01 -6.76142052... | [9.776597023010254, -0.28591957688331604] |
84ff6e33-ab42-46d3-a502-99145f9fc593 | uncertainty-based-biological-age-estimation | 2103.08491 | null | https://arxiv.org/abs/2103.08491v1 | https://arxiv.org/pdf/2103.08491v1.pdf | Uncertainty-Based Biological Age Estimation of Brain MRI Scans | Age is an essential factor in modern diagnostic procedures. However, assessment of the true biological age (BA) remains a daunting task due to the lack of reference ground-truth labels. Current BA estimation approaches are either restricted to skeletal images or rely on non-imaging modalities that yield a whole-body BA... | ['Bin Yang', 'Sergios Gatidis', 'Tobias Hepp', 'Wenbin Shi', 'Sherif Abdulatif', 'Karim Armanious'] | 2021-03-15 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 5.13155796e-02 2.83131033e-01 -7.19382763e-02 -6.54346526e-01
-5.77691734e-01 1.35994405e-01 4.00970608e-01 2.72108048e-01
-3.64525527e-01 1.04404604e+00 9.41125974e-02 -4.42907922e-02
-1.09753564e-01 -5.92160046e-01 -4.58001107e-01 -6.65306032e-01
-3.02302808e-01 1.07877493e+00 1.80426270e-01 2.14306638... | [14.111309051513672, -1.5970097780227661] |
d06d2487-48f1-46a2-985a-23ef00e94d90 | pausespeech-natural-speech-synthesis-via-pre | 2306.07489 | null | https://arxiv.org/abs/2306.07489v1 | https://arxiv.org/pdf/2306.07489v1.pdf | PauseSpeech: Natural Speech Synthesis via Pre-trained Language Model and Pause-based Prosody Modeling | Although text-to-speech (TTS) systems have significantly improved, most TTS systems still have limitations in synthesizing speech with appropriate phrasing. For natural speech synthesis, it is important to synthesize the speech with a phrasing structure that groups words into phrases based on semantic information. In t... | ['Seong-Whan Lee', 'Sang-Hoon Lee', 'Ji-Sang Hwang'] | 2023-06-13 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 2.24013001e-01 2.33821779e-01 -4.22962457e-01 -4.95005071e-01
-1.13207674e+00 -4.77111489e-01 2.58001328e-01 -2.16323718e-01
5.66715561e-02 5.71607888e-01 9.07172501e-01 -6.04890823e-01
5.63201368e-01 -5.72398722e-01 -6.22913122e-01 -4.59406585e-01
5.14649451e-01 1.50516450e-01 1.52340859e-01 -2.09006518... | [14.796871185302734, 6.770724773406982] |
22fde00f-08b3-4266-8e11-daa426aeb10c | grounded-human-object-interaction-hotspots | 1812.04558 | null | http://arxiv.org/abs/1812.04558v2 | http://arxiv.org/pdf/1812.04558v2.pdf | Grounded Human-Object Interaction Hotspots from Video | Learning how to interact with objects is an important step towards embodied
visual intelligence, but existing techniques suffer from heavy supervision or
sensing requirements. We propose an approach to learn human-object interaction
"hotspots" directly from video. Rather than treat affordances as a manually
supervised ... | ['Tushar Nagarajan', 'Kristen Grauman', 'Christoph Feichtenhofer'] | 2018-12-11 | grounded-human-object-interaction-hotspots-2 | http://openaccess.thecvf.com/content_ICCV_2019/html/Nagarajan_Grounded_Human-Object_Interaction_Hotspots_From_Video_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Nagarajan_Grounded_Human-Object_Interaction_Hotspots_From_Video_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-to-image-affordance-grounding'] | ['computer-vision'] | [ 5.17093420e-01 5.14787138e-01 -2.75435895e-01 -4.70122218e-01
-1.44500867e-01 -6.79039359e-01 6.07217371e-01 -4.37745042e-02
-4.43613350e-01 5.40706933e-01 4.50214088e-01 9.30204019e-02
-2.37437547e-03 -4.61948097e-01 -1.04131007e+00 -1.99025437e-01
-4.56081957e-01 5.45599401e-01 3.66381049e-01 -9.96330082... | [5.120748519897461, 0.18832671642303467] |
403e4163-0831-487f-9ddb-1a743ce33437 | generative-language-models-for-paragraph | 2210.03992 | null | https://arxiv.org/abs/2210.03992v3 | https://arxiv.org/pdf/2210.03992v3.pdf | Generative Language Models for Paragraph-Level Question Generation | Powerful generative models have led to recent progress in question generation (QG). However, it is difficult to measure advances in QG research since there are no standardized resources that allow a uniform comparison among approaches. In this paper, we introduce QG-Bench, a multilingual and multidomain benchmark for Q... | ['Jose Camacho-Collados', 'Fernando Alva-Manchego', 'Asahi Ushio'] | 2022-10-08 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-2.43798614e-01 5.94853237e-02 1.03340290e-01 -2.37565473e-01
-1.70739317e+00 -1.02524400e+00 8.95935953e-01 -2.74743959e-02
-3.74554545e-01 1.01471150e+00 5.25187254e-01 -5.11231363e-01
-1.23807006e-01 -8.32609057e-01 -5.79256773e-01 -2.35681668e-01
4.56953913e-01 8.54005873e-01 1.58536494e-01 -7.22324371... | [11.349650382995605, 8.328505516052246] |
d2a46971-4fd8-44f2-bdb9-c10d60aa98cc | artificial-intelligence-to-advance-earth | 2305.08413 | null | https://arxiv.org/abs/2305.08413v1 | https://arxiv.org/pdf/2305.08413v1.pdf | Artificial intelligence to advance Earth observation: a perspective | Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based infor... | ['Rochelle Schneider', 'Bertrand Le Saux', 'Volker Markl', 'Jorge-Arnulfo Quiané-Ruiz', 'Mihai Datcu', 'Fabio Del Frate', 'Holger H. Hoos', 'Jan N. van Rijn', 'Sašo Džeroski', 'Mrinalini Kochupillai', 'Xiao Xiang Zhu', 'Gustau Camps-Valls', 'Begüm Demir', 'Konrad Schindler', 'Devis Tuia'] | 2023-05-15 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.02089414e-01 9.95334908e-02 -2.23157510e-01 4.10540439e-02
2.02159379e-02 -3.16476911e-01 8.73126209e-01 4.44624782e-01
-1.93319932e-01 5.76336563e-01 3.53447884e-01 -9.68038619e-01
-4.08823192e-01 -8.69845748e-01 -5.85110247e-01 -7.89058566e-01
-5.29805303e-01 4.56077367e-01 -3.09786975e-01 -2.31434673... | [6.551549911499023, 3.287259578704834] |
d3e71da1-b31e-4084-9daf-cbbdc906d243 | mixing-signals-data-augmentation-approach-for | 2204.03737 | null | https://arxiv.org/abs/2204.03737v1 | https://arxiv.org/pdf/2204.03737v1.pdf | Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition | With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic classification by deep learning technology. However, deep learning models are data-driven methods, w... | ['Xiaoniu Yang', 'Qi Xuan', 'Shilian Zheng', 'Shanqing Yu', 'Huaji Zhou', 'Dongwei Xu', 'Zhuangzhi Chen', 'Xinjie Xu'] | 2022-04-05 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 2.36389145e-01 -3.27601999e-01 -1.45453110e-01 -3.49315614e-01
-7.23919749e-01 -1.58688538e-02 7.02771068e-01 -3.96899909e-01
-3.27416748e-01 7.79960275e-01 2.08200395e-01 -7.59211063e-01
-2.91507155e-01 -8.83235097e-01 -2.75979757e-01 -8.20080280e-01
2.14295294e-02 6.74232692e-02 -3.73643875e-01 -3.94086748... | [6.488638877868652, 1.4801249504089355] |
16c926d1-9500-4e72-80c7-c37e40df9cf5 | chatgpt-steered-editing-instructor-for | 2305.02483 | null | https://arxiv.org/abs/2305.02483v1 | https://arxiv.org/pdf/2305.02483v1.pdf | ChatGPT-steered Editing Instructor for Customization of Abstractive Summarization | Tailoring outputs of large language models, such as ChatGPT, to specific user needs remains a challenge despite their impressive generation quality. In this paper, we propose a tri-agent generation pipeline consisting of a generator, an instructor, and an editor to enhance the customization of generated outputs. The ge... | ['Pengcheng He', 'Giuseppe Carenini', 'Yujia Xie', 'Wen Xiao'] | 2023-05-04 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.56595480e-01 4.60131824e-01 -1.95158526e-01 -4.48309720e-01
-1.10719836e+00 -7.38664985e-01 4.59448576e-01 2.93500423e-01
-1.79295808e-01 8.04371297e-01 6.56977594e-01 -4.62596118e-01
4.24106479e-01 -8.05101335e-01 -7.05466151e-01 -1.34999484e-01
4.97237533e-01 8.65717947e-01 5.59220910e-02 -4.78784382... | [11.949604034423828, 8.779806137084961] |
f4b2b3fd-d7a4-4fdc-adfb-62877d853801 | learning-to-solve-nlp-tasks-in-an-incremental | null | null | https://aclanthology.org/2021.acl-short.106 | https://aclanthology.org/2021.acl-short.106.pdf | Learning to Solve NLP Tasks in an Incremental Number of Languages | In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially when the number of ... | ['Roberto Basili', 'Danilo Croce', 'Simone Filice', 'Giuseppe Castellucci'] | 2021-08-01 | null | null | null | acl-2021-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 3.24451715e-01 1.21774532e-01 -4.86595303e-01 -6.51644289e-01
-7.80732810e-01 -1.00530756e+00 6.42138198e-02 7.06732690e-01
-7.64302790e-01 1.39642560e+00 -2.37715438e-01 -5.47640562e-01
3.24362189e-01 -7.20664680e-01 -9.45622921e-01 -3.16603899e-01
1.67608429e-02 7.79479682e-01 4.62585449e-01 -1.84871733... | [10.379491806030273, 9.391749382019043] |
e5c58118-f834-4aaf-895c-b7eb2c5a696f | adaptive-prototypical-networks-with-label | 2101.03526 | null | https://arxiv.org/abs/2101.03526v1 | https://arxiv.org/pdf/2101.03526v1.pdf | Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification | Relation classification (RC) task is one of fundamental tasks of information extraction, aiming to detect the relation information between entity pairs in unstructured natural language text and generate structured data in the form of entity-relation triple. Although distant supervision methods can effectively alleviate... | ['Kuangrong Hao', 'Yaochu Jin', 'Yan Xiao'] | 2021-01-10 | null | null | null | null | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 3.61682504e-01 3.97738785e-01 -4.52037424e-01 -6.72196269e-01
-3.26763988e-01 4.34412882e-02 5.04331708e-01 4.12545472e-01
-3.26055825e-01 8.55170548e-01 -2.30925474e-02 -4.67644483e-02
-3.73226345e-01 -1.19078660e+00 -4.70409542e-01 -7.76405454e-01
3.73735763e-02 6.66565299e-01 3.26804668e-01 -3.38809848... | [9.22569751739502, 8.469858169555664] |
fcdfeeeb-a080-410f-b79b-ec58361e0e9c | coupled-training-of-sequence-to-sequence | null | null | https://ieeexplore.ieee.org/abstract/document/9052912 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9052912&casa_token=7z2KnkcdEkIAAAAA:VuhPm22dVjjwhCKWpAQpJnKUkmYHZHAqxd6w89UA-eecNPtW0U05m4KmPpn69GlcmejMc1c9OOZc&tag=1 | Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition | Accented speech poses significant challenges for state-of-the-art automatic speech recognition (ASR) systems. Accent is a property of speech that lasts throughout an utterance in varying degrees of strength. This makes it hard to isolate the influence of accent on individual speech sounds. We propose coupled training f... | ['Preethi Jyothi', 'Nitish Joshi', 'Vinit Unni'] | 2020-05-14 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 3.76140386e-01 2.67675042e-01 -5.82389534e-02 -7.35703468e-01
-9.76819456e-01 -6.34161353e-01 4.71516639e-01 -2.20744491e-01
-5.18942714e-01 4.80145752e-01 7.87832201e-01 -3.41203511e-01
3.61113191e-01 -1.77354753e-01 -7.08913743e-01 -7.12463617e-01
1.34646505e-01 5.57697177e-01 -2.57724255e-01 -5.03040254... | [14.357266426086426, 6.695898056030273] |
01a867b6-b509-4e3f-8a21-4abeac731a07 | semantic-role-labeling-guided-out-of | 2305.18026 | null | https://arxiv.org/abs/2305.18026v1 | https://arxiv.org/pdf/2305.18026v1.pdf | Semantic Role Labeling Guided Out-of-distribution Detection | Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications. Previous works identify the OOD instance by leveraging a single global feature embedding to represent the sentence, which cannot characterize subtle OOD patterns well. Another major challenge current OO... | ['Javen Qinfeng Shi', 'Ehsan Abbasnejad', 'Lingqiao Liu', 'Haiyao Cao', 'YuHao Lin', 'Yu Tian', 'Maihao Guo', 'Jinan Zou'] | 2023-05-29 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.34974343e-01 4.27889675e-01 -5.41266024e-01 -7.29710162e-01
-8.69453013e-01 -6.92927778e-01 7.59104252e-01 8.44070494e-01
1.90575402e-02 3.93552363e-01 8.16463351e-01 1.80430889e-01
-2.93247938e-01 -4.88323867e-01 -3.48852277e-01 -5.82922518e-01
-1.44577354e-01 4.16363031e-01 2.21764058e-01 -2.32464567... | [12.159680366516113, 7.499963283538818] |
b06a2c7f-3bf0-4568-9ef5-0b90dbf6208d | constrained-pure-exploration-multi-armed | 2211.14768 | null | https://arxiv.org/abs/2211.14768v1 | https://arxiv.org/pdf/2211.14768v1.pdf | Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget | We consider a constrained, pure exploration, stochastic multi-armed bandit formulation under a fixed budget. Each arm is associated with an unknown, possibly multi-dimensional distribution and is described by multiple attributes that are a function of this distribution. The aim is to optimize a particular attribute sub... | ['Jayakrishnan Nair', 'Fathima Zarin Faizal'] | 2022-11-27 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 2.96639919e-01 2.51332283e-01 -8.07443082e-01 -3.38768780e-01
-1.19444215e+00 -8.87818992e-01 2.18780324e-01 1.86028987e-01
-5.25885463e-01 9.53543544e-01 -1.14046685e-01 -5.48711717e-01
-9.43995178e-01 -8.03095639e-01 -7.36204088e-01 -8.98617923e-01
-8.55306089e-02 1.21463430e+00 -3.52167040e-01 3.21086466... | [4.551568984985352, 3.3016715049743652] |
68cb2912-adce-4784-83db-1bfda72818ca | end-to-end-learning-of-multi-scale | 1906.10399 | null | https://arxiv.org/abs/1906.10399v1 | https://arxiv.org/pdf/1906.10399v1.pdf | End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching | Deep neural networks have shown excellent performance in stereo matching task. Recently CNN-based methods have shown that stereo matching can be formulated as a supervised learning task. However, less attention is paid on the fusion of contextual semantic information and details. To tackle this problem, we propose a ne... | ['Quanhong Wang', 'Li Zhang', 'Yong Zhao', 'Haihua Lu'] | 2019-06-25 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 6.22184426e-02 -4.68035311e-01 7.06323907e-02 -7.86707401e-01
-3.60700577e-01 7.88329393e-02 4.93472844e-01 -1.59488484e-01
-5.28398156e-01 7.04192162e-01 5.40442109e-01 1.03886411e-01
-1.46216035e-01 -1.01446748e+00 -5.83752453e-01 -7.15117753e-01
3.96204084e-01 -1.09263696e-01 5.54656148e-01 -3.55537236... | [8.935369491577148, -2.2934250831604004] |
aa68c2d4-aa30-4491-80a3-763d5f715de6 | merlot-multimodal-neural-script-knowledge | 2106.02636 | null | https://arxiv.org/abs/2106.02636v3 | https://arxiv.org/pdf/2106.02636v3.pdf | MERLOT: Multimodal Neural Script Knowledge Models | As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future. We introduce MERLOT, a model that learns multimodal script knowledge by watching millions of YouTube videos with transcribed speech -- in an entirely labe... | ['Yejin Choi', 'Ali Farhadi', 'Jize Cao', 'Jae Sung Park', 'Youngjae Yu', 'Jack Hessel', 'Ximing Lu', 'Rowan Zellers'] | 2021-06-04 | null | http://proceedings.neurips.cc/paper/2021/hash/c6d4eb15f1e84a36eff58eca3627c82e-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c6d4eb15f1e84a36eff58eca3627c82e-Paper.pdf | neurips-2021-12 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 9.88898277e-02 -6.19471632e-02 -2.00185925e-01 -4.67952073e-01
-7.26872087e-01 -9.61534381e-01 9.63844478e-01 -6.64291531e-02
-4.50533241e-01 5.00582039e-01 6.75458550e-01 -2.91358620e-01
1.92610204e-01 -5.12995303e-01 -9.77058053e-01 -2.73997694e-01
-8.14441442e-02 3.08811367e-01 3.16783726e-01 -2.80429929... | [10.505581855773926, 1.1220802068710327] |
a189452d-e3a5-4dd7-97b8-ae50a66efe29 | full-body-awareness-from-partial-observations | 2008.06046 | null | https://arxiv.org/abs/2008.06046v1 | https://arxiv.org/pdf/2008.06046v1.pdf | Full-Body Awareness from Partial Observations | There has been great progress in human 3D mesh recovery and great interest in learning about the world from consumer video data. Unfortunately current methods for 3D human mesh recovery work rather poorly on consumer video data, since on the Internet, unusual camera viewpoints and aggressive truncations are the norm ra... | ['Chris Rockwell', 'David F. Fouhey'] | 2020-08-13 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2820_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620511.pdf | eccv-2020-8 | ['human-mesh-recovery'] | ['computer-vision'] | [-3.78924161e-02 -2.26695508e-01 -3.68419766e-01 -1.05313919e-01
-1.11715853e+00 -4.77003247e-01 2.62581080e-01 -2.97214866e-01
-3.54078799e-01 3.46176416e-01 5.64697742e-01 1.55793540e-02
2.53542274e-01 -1.38498753e-01 -1.09876323e+00 -2.72760183e-01
-4.04428899e-01 6.95990503e-01 5.04236937e-01 -2.80999452... | [7.172818660736084, -0.8580766320228577] |
4a2770ac-bf51-45f2-9b7a-ee99a62826a8 | towards-targeted-change-detection-with | 2203.00049 | null | https://arxiv.org/abs/2203.00049v2 | https://arxiv.org/pdf/2203.00049v2.pdf | Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping | Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems. To resolve this pr... | ['Jane Uhd Jepsen', 'Stian Normann Anfinsen', 'Luigi T. Luppino', 'Jørgen A. Agersborg'] | 2022-02-28 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 9.41661060e-01 -4.42039043e-01 1.48655698e-01 -3.68972152e-01
-5.85546136e-01 -7.11446404e-01 1.05467451e+00 -4.26725037e-02
-6.14624918e-01 9.31735456e-01 -3.98643203e-02 -3.35347563e-01
-1.73109204e-01 -1.14197123e+00 -6.84964001e-01 -9.87392664e-01
-4.91645426e-01 4.05883878e-01 1.78524390e-01 -5.01469195... | [9.662151336669922, -1.6545783281326294] |
e67bf2e0-733f-4bbb-8ac4-d6d80752a6dd | twitter-bot-detection-using-diversity | null | null | https://aclanthology.org/W19-7401 | https://aclanthology.org/W19-7401.pdf | Twitter Bot Detection using Diversity Measures | null | ['Dijana Kosmajac', 'Vlado Keselj'] | 2019-09-01 | null | null | null | ws-2019-9 | ['twitter-bot-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.326053142547607, 3.56752347946167] |
363bbd52-1190-443c-bceb-a408dcc023fa | learning-a-probabilistic-latent-space-of | 1610.07584 | null | http://arxiv.org/abs/1610.07584v2 | http://arxiv.org/pdf/1610.07584v2.pdf | Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling | We study the problem of 3D object generation. We propose a novel framework,
namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects
from a probabilistic space by leveraging recent advances in volumetric
convolutional networks and generative adversarial nets. The benefits of our
model are three-fol... | ['Chengkai Zhang', 'Jiajun Wu', 'William T. Freeman', 'Tianfan Xue', 'Joshua B. Tenenbaum'] | 2016-10-24 | learning-a-probabilistic-latent-space-of-1 | http://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling | http://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling.pdf | neurips-2016-12 | ['3d-point-cloud-linear-classification', '3d-object-recognition', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.76430254e-03 3.36083382e-01 1.39632933e-02 -1.37088194e-01
-7.58712471e-01 -7.15640128e-01 8.45518291e-01 -6.72299206e-01
3.26408327e-01 5.51095247e-01 1.32398427e-01 -5.74953035e-02
1.42452091e-01 -1.17385876e+00 -9.87425864e-01 -6.97061956e-01
1.69410303e-01 9.17224646e-01 1.16797522e-01 1.83907434... | [8.879555702209473, -3.653654098510742] |
68ad000e-91c5-4b7f-9859-91cc2b74135e | key-instance-selection-for-unsupervised-video | 1906.07851 | null | https://arxiv.org/abs/1906.07851v2 | https://arxiv.org/pdf/1906.07851v2.pdf | Key Instance Selection for Unsupervised Video Object Segmentation | This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for each frame. We link objects according to their similarity until the M-th ... | ['Donghyeon Cho', 'Sungil Kang', 'Sungeun Hong', 'Jiwon Kim'] | 2019-06-18 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.28217468e-01 -4.74569723e-02 -7.85573781e-01 3.35632786e-02
-2.34000549e-01 -6.42722785e-01 3.53961915e-01 3.53211671e-01
-5.75383782e-01 5.16537368e-01 1.21720187e-01 3.77405405e-01
-1.72828436e-01 -2.89582670e-01 -6.63826704e-01 -3.99044156e-01
-4.42146510e-01 2.21250653e-01 1.06965232e+00 1.65851519... | [9.305869102478027, -0.2390822321176529] |
622625e1-edcc-44ec-a2a0-8854361b5abb | graph-convolutional-policy-for-solving-tree | 1910.08371 | null | https://arxiv.org/abs/1910.08371v2 | https://arxiv.org/pdf/1910.08371v2.pdf | Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics | We propose a Reinforcement Learning based approach to approximately solve the Tree Decomposition (TD) problem. TD is a combinatorial problem, which is central to the analysis of graph minor structure and computational complexity, as well as in the algorithms of probabilistic inference, register allocation, and other pr... | ['Taras Khakhulin', 'Ivan Oseledets', 'Roman Schutski'] | 2019-10-18 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-2.78801261e-03 8.04248452e-01 -3.35891128e-01 -2.18878705e-02
-8.18802536e-01 -5.37159801e-01 3.81354451e-01 3.60120296e-01
-2.52712429e-01 9.69920278e-01 -2.61007398e-01 -6.98659956e-01
-5.44618547e-01 -1.20223927e+00 -1.01217341e+00 -7.65484869e-01
-3.27522755e-01 1.15256011e+00 9.68656614e-02 -9.33172256... | [5.229701519012451, 3.0222702026367188] |
664e3525-63d5-41cc-a0f0-2721026f1e76 | temporal-modulation-network-for-controllable | 2104.10642 | null | https://arxiv.org/abs/2104.10642v2 | https://arxiv.org/pdf/2104.10642v2.pdf | Temporal Modulation Network for Controllable Space-Time Video Super-Resolution | Space-time video super-resolution (STVSR) aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. Besid... | ['Ming-Ming Cheng', 'Xing Sun', 'Liang Wang', 'Zhen Li', 'Jun Xu', 'Gang Xu'] | 2021-04-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xu_Temporal_Modulation_Network_for_Controllable_Space-Time_Video_Super-Resolution_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xu_Temporal_Modulation_Network_for_Controllable_Space-Time_Video_Super-Resolution_CVPR_2021_paper.pdf | cvpr-2021-1 | ['space-time-video-super-resolution'] | ['computer-vision'] | [ 1.78115919e-01 -5.34125686e-01 -2.22344577e-01 -4.02440667e-01
-7.21699178e-01 -1.52577132e-01 4.70517039e-01 -7.13973701e-01
-3.40576112e-01 7.41831243e-01 3.48133832e-01 1.49351984e-01
-1.07381061e-01 -7.40182579e-01 -7.82461107e-01 -6.63661778e-01
-3.43385972e-02 -3.87258828e-01 5.49549699e-01 -2.21945718... | [11.019201278686523, -1.8642851114273071] |
80b813ac-52c2-4f9f-9ce7-df1606d2475e | mixsim-a-hierarchical-framework-for-mixed | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Suo_MixSim_A_Hierarchical_Framework_for_Mixed_Reality_Traffic_Simulation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Suo_MixSim_A_Hierarchical_Framework_for_Mixed_Reality_Traffic_Simulation_CVPR_2023_paper.pdf | MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation | The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the real world, we need to evaluate them in closed-loop. Towards this goal, we propose to leverage the wealth of interesting scenarios capt... | ['Raquel Urtasun', 'Sergio Casas', 'Alexander Cui', 'James Tu', 'Justin Xu', 'Kelvin Wong', 'Simon Suo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['mixed-reality'] | ['computer-vision'] | [-4.03368682e-01 1.59698352e-01 1.30654782e-01 -3.50557566e-01
-2.41851121e-01 -8.12740922e-01 9.28157806e-01 -2.97920793e-01
-2.26730928e-01 8.75166774e-01 -2.02109829e-01 -6.83344185e-01
1.79026738e-01 -1.12652063e+00 -7.43349612e-01 -2.69615948e-01
-3.94467443e-01 9.78149176e-01 7.71250725e-01 -8.40924621... | [5.071260929107666, 1.325390338897705] |
ac6e41e4-e7cb-4ed6-84a3-101f830e71d5 | dynamic-path-controllable-deep-unfolding | 2306.16060 | null | https://arxiv.org/abs/2306.16060v1 | https://arxiv.org/pdf/2306.16060v1.pdf | Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing | Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural network has achieved great success in compressive sensing (CS) due to its good interpretability and high performance. Each stage in DUN corresponds to one iteration in optimization. At the test time, all the sampling images generall... | ['Jian Zhang', 'Bin Chen', 'Jiechong Song'] | 2023-06-28 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 2.43364081e-01 -3.24617475e-01 -8.35633501e-02 -2.78034836e-01
-5.22086978e-01 -3.33560079e-01 1.02976747e-01 -2.21441120e-01
-1.51974946e-01 3.31480801e-01 2.40887821e-01 -4.39051062e-01
-3.01545322e-01 -7.68966556e-01 -6.08022273e-01 -8.52861583e-01
-3.18102598e-01 -1.60108283e-02 1.26291983e-04 -1.41838983... | [11.127436637878418, -1.9788933992385864] |
b34204b6-ba9a-4a42-a123-d75fce151744 | exploring-in-context-learning-capabilities-of | 2305.08804 | null | https://arxiv.org/abs/2305.08804v1 | https://arxiv.org/pdf/2305.08804v1.pdf | Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text | Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstream applications such as question-answering, recommendation systems, semantic search, and advanced analytics. However, at the moment, buildi... | ['Sven Groppe', 'Jinghua Groppe', 'Sanju Tiwari', 'Nandana Mihindukulasooriya', 'Hanieh Khorashadizadeh'] | 2023-05-15 | null | null | null | null | ['graph-construction', 'relation-extraction'] | ['graphs', 'natural-language-processing'] | [ 9.26083550e-02 5.40797234e-01 -2.77343243e-01 -3.05203080e-01
-3.13934743e-01 -7.02851653e-01 6.97764516e-01 8.06136370e-01
-2.35012382e-01 7.43964136e-01 3.79558913e-02 -5.81340909e-01
-1.98478311e-01 -1.27392757e+00 -5.20294428e-01 -7.52034411e-02
-1.17655862e-02 6.81536138e-01 3.89814973e-01 -4.29259747... | [9.394291877746582, 8.246756553649902] |
fd7e6914-7c56-4483-9658-1bd3d764f164 | building-blocks-for-complex-tasks-robust | 2306.09544 | null | https://arxiv.org/abs/2306.09544v1 | https://arxiv.org/pdf/2306.09544v1.pdf | Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts | This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better generalization across exam modalities compared to approaches that employ B... | ['Mari Ostendorf', 'Meliha Yetisgen', 'Sitong Zhou'] | 2023-06-15 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 6.32318854e-01 5.82831681e-01 -3.26753378e-01 -7.85032988e-01
-1.73222351e+00 -5.57859242e-01 4.29721594e-01 3.00969362e-01
-3.55846316e-01 8.38665426e-01 6.14200592e-01 -7.11275518e-01
-2.78060913e-01 -6.34028792e-01 -7.36919403e-01 -2.90558130e-01
1.95318416e-01 6.93661153e-01 2.18969196e-01 2.16960177... | [8.71165657043457, 8.632440567016602] |
35aea6cc-485b-4de5-82ef-ec9a23747c59 | genius-sketch-based-language-model-pre | 2211.10330 | null | https://arxiv.org/abs/2211.10330v1 | https://arxiv.org/pdf/2211.10330v1.pdf | GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation | We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large-scale textual corpus with a novel reconstruction fro... | ['Weizhu Chen', 'Nan Duan', 'Hailiang Huang', 'Songqiao Han', 'Yelong Shen', 'Yeyun Gong', 'Biyang Guo'] | 2022-11-18 | null | null | null | null | ['conditional-text-generation', 'sketch-to-text-generation', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.64218265e-01 1.90832570e-01 -2.43658587e-01 -2.22826913e-01
-1.15382636e+00 -6.67833507e-01 8.76242280e-01 -1.03828050e-01
-3.77911478e-01 7.66209126e-01 6.26118481e-01 -6.22466624e-01
3.80159169e-01 -8.02576244e-01 -8.10718596e-01 -4.46433097e-01
4.52489197e-01 6.31466448e-01 -2.48309866e-01 -1.44507766... | [11.65408992767334, 8.884458541870117] |
e2543179-a76a-49fc-bbd1-acae9b037de4 | magnitude-attention-based-dynamic-pruning | 2306.05056 | null | https://arxiv.org/abs/2306.05056v1 | https://arxiv.org/pdf/2306.05056v1.pdf | Magnitude Attention-based Dynamic Pruning | Existing pruning methods utilize the importance of each weight based on specified criteria only when searching for a sparse structure but do not utilize it during training. In this work, we propose a novel approach - \textbf{M}agnitude \textbf{A}ttention-based Dynamic \textbf{P}runing (MAP) method, which applies the im... | ['Jangho Kim', 'Namhyuk Ahn', 'Jihye Back'] | 2023-06-08 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 2.95856953e-01 2.40416154e-01 -3.66146773e-01 -3.99891198e-01
-1.22466087e-01 -5.57663068e-02 1.13383316e-01 1.92565992e-01
-6.73869491e-01 7.57660449e-01 1.70539439e-01 -2.19408795e-01
-5.05900264e-01 -8.64856601e-01 -5.78165054e-01 -5.97635448e-01
-3.12488377e-01 4.24442977e-01 3.49371880e-01 -1.33334249... | [8.593972206115723, 3.208749532699585] |
38d7aa4b-583a-45f6-96ce-c3d45812f379 | interlayer-ferromagnetism-and-high | 2101.07684 | null | https://arxiv.org/abs/2101.07684v1 | https://arxiv.org/pdf/2101.07684v1.pdf | Interlayer Ferromagnetism and High-Temperature Quantum Anomalous Hall Effect in \textit{p}-Doped MnBi$_2$Te$_4$ Multilayers | The interlayer antiferromagnetic coupling hinders the observation of quantum anomalous Hall effect in magnetic topological insulator MnBi$_2$Te$_4$. We demonstrate that interlayer \textit{ferromagnetism} can be established by utilizing the \textit{p}-doping method in MnBi$_2$Te$_4$ multilayers. In two septuple-layers s... | ['Zhenhua Qiao', 'Xiaohong Xu', 'Shifei Qi', 'Shiyang Sun', 'Yulei Han'] | 2021-01-19 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 3.77196670e-01 1.90543197e-02 -2.73624241e-01 1.24610122e-02
5.17006256e-02 2.81174421e-01 4.83095795e-01 -3.92688841e-01
-2.74243802e-01 1.09406459e+00 -1.96798474e-01 -2.19223246e-01
-2.02857137e-01 -9.50003743e-01 -7.03043103e-01 -1.50400019e+00
1.42456636e-01 4.47153687e-01 5.63253045e-01 -5.63630342... | [5.438369274139404, 4.814970016479492] |
2fd6761a-c628-4ab9-b1d7-80f59b2e0ad5 | ai-planning-annotation-for-sample-efficient | 2203.00669 | null | https://arxiv.org/abs/2203.00669v2 | https://arxiv.org/pdf/2203.00669v2.pdf | Hierarchical Reinforcement Learning with AI Planning Models | Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrate with symbolic knowledge, and often efficient, but requires an up-front logical domain specification and is sensitive to noise; RL only req... | ['Tim Klinger', 'Geraud Nangue Tasse', 'Shirin Sohrabi', 'Miao Liu', 'Don Joven Agravante', 'Michael Katz', 'JunKyu Lee'] | 2022-03-01 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.67591631e-01 6.43367589e-01 -2.69033343e-01 -2.45891765e-01
-6.16482258e-01 -5.35845339e-01 7.72085309e-01 3.64789099e-01
-2.92551458e-01 9.85948503e-01 3.77967030e-01 -4.20711190e-01
-6.33353949e-01 -9.56192493e-01 -6.66136861e-01 -4.59209085e-01
-4.69271332e-01 9.67812121e-01 5.39325953e-01 -3.49247873... | [4.190720558166504, 1.4619699716567993] |
1d93a4ca-ae50-4961-b233-fd5b8b956399 | towards-hate-speech-detection-in-low-resource | 2306.00410 | null | https://arxiv.org/abs/2306.00410v1 | https://arxiv.org/pdf/2306.00410v1.pdf | Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili | We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition (ASR) system for the target low-resource language. We compare this to using acoustic word embedding (AWE) models that map speech segments to a space where matching words have similar ... | ['Herman Kamper', 'Bruce A. Bassett', 'Everlyn Asiko Chimoto', 'Nathanaël Carraz Rakotonirina', 'Christiaan Jacobs'] | 2023-06-01 | null | null | null | null | ['word-embeddings', 'hate-speech-detection', 'keyword-spotting', 'automatic-speech-recognition'] | ['methodology', 'natural-language-processing', 'speech', 'speech'] | [ 1.69225127e-01 2.94158578e-01 2.20599949e-01 -2.33553007e-01
-1.33653641e+00 -5.74129283e-01 9.42444265e-01 -2.98909862e-02
-7.95189619e-01 1.94485769e-01 5.20520329e-01 -4.46111053e-01
2.82602221e-01 -2.55164921e-01 -5.03586233e-01 -5.06198049e-01
7.35635730e-03 3.38481396e-01 2.27955893e-01 -5.11430323... | [14.317459106445312, 6.841878890991211] |
89d76270-8f35-4b78-b891-72ee52692f1c | attend-and-interact-higher-order-object | 1711.06330 | null | http://arxiv.org/abs/1711.06330v2 | http://arxiv.org/pdf/1711.06330v2.pdf | Attend and Interact: Higher-Order Object Interactions for Video Understanding | Human actions often involve complex interactions across several inter-related
objects in the scene. However, existing approaches to fine-grained video
understanding or visual relationship detection often rely on single object
representation or pairwise object relationships. Furthermore, learning
interactions across mul... | ['Chih-Yao Ma', 'Zsolt Kira', 'Hans Peter Graf', 'Ghassan AlRegib', 'Iain Melvin', 'Asim Kadav'] | 2017-11-16 | attend-and-interact-higher-order-object-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Ma_Attend_and_Interact_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_Attend_and_Interact_CVPR_2018_paper.pdf | cvpr-2018-6 | ['video-description', 'visual-relationship-detection'] | ['computer-vision', 'computer-vision'] | [ 3.10761362e-01 -1.35249481e-01 -3.15136552e-01 -3.63301933e-01
-7.59708166e-01 -6.68543518e-01 5.19433796e-01 2.38080040e-01
-3.14664334e-01 7.83173025e-01 2.25742728e-01 9.66720656e-02
-2.57118195e-01 -1.75625622e-01 -1.31095254e+00 -4.19009358e-01
-3.69632185e-01 7.32542098e-01 6.27798259e-01 1.82340607... | [8.9428129196167, 0.4784051477909088] |
711abb67-26d2-4b46-95ef-89b5414238bb | relational-context-learning-for-human-object | 2304.04997 | null | https://arxiv.org/abs/2304.04997v1 | https://arxiv.org/pdf/2304.04997v1.pdf | Relational Context Learning for Human-Object Interaction Detection | Recent state-of-the-art methods for HOI detection typically build on transformer architectures with two decoder branches, one for human-object pair detection and the other for interaction classification. Such disentangled transformers, however, may suffer from insufficient context exchange between the branches and lead... | ['Minsu Cho', 'Deunsol Jung', 'Sanghyun Kim'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Relational_Context_Learning_for_Human-Object_Interaction_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Relational_Context_Learning_for_Human-Object_Interaction_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 1.50648594e-01 5.61686456e-01 -4.15239781e-01 -3.50398198e-02
-4.57050920e-01 -3.06475937e-01 8.10863733e-01 2.72269309e-01
1.89017132e-01 5.65484881e-01 3.14159364e-01 -2.14973062e-01
-5.20105243e-01 -1.03071463e+00 -6.43499970e-01 -4.35291469e-01
-1.57622814e-01 9.01007771e-01 4.22256559e-01 -5.10866791... | [8.886168479919434, 7.884591102600098] |
3db4e5c8-6332-4472-b316-3dd763d17a69 | line-a-line-a-tool-for-annotating-word | null | null | https://aclanthology.org/2020.bucc-1.1 | https://aclanthology.org/2020.bucc-1.1.pdf | Line-a-line: A Tool for Annotating Word-Alignments | We here describe line-a-line, a web-based tool for manual annotation of word-alignments in sentence-aligned parallel corpora. The graphical user interface, which builds on a design template from the Jigsaw system for investigative analysis, displays the words from each sentence pair that is to be annotated as elements ... | ['Rickard Domeij', 'Maria Skeppstedt', 'Gunnar Eriksson', 'Magnus Ahltorp'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['multilingual-word-embeddings'] | ['methodology'] | [ 2.50187159e-01 1.17580183e-01 -9.03557017e-02 -3.04922879e-01
-9.00469840e-01 -6.62790477e-01 4.11960751e-01 8.82932782e-01
-8.53558660e-01 5.98932266e-01 4.31299806e-01 -7.13214517e-01
-6.58022845e-03 -3.62085938e-01 -7.02302158e-02 -4.54364181e-01
2.65468620e-02 8.41594338e-01 3.81031245e-01 -4.49289888... | [10.341002464294434, 10.031517028808594] |
2902ee6f-6a87-42e5-84b1-9213aed190b4 | bert-enhanced-relational-sentence-ordering | null | null | https://aclanthology.org/2020.emnlp-main.511 | https://aclanthology.org/2020.emnlp-main.511.pdf | BERT-enhanced Relational Sentence Ordering Network | In this paper, we introduce a novel BERT-enhanced Relational Sentence Ordering Network (referred to as BRSON) by leveraging BERT for capturing better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph. In particular, we develop a new Relational Pointer Decoder (referred a... | ['Zhongfei Zhang', 'Yingming Li', 'Baiyun Cui'] | null | null | null | null | emnlp-2020-11 | ['sentence-ordering'] | ['natural-language-processing'] | [-2.36924127e-01 2.68009812e-01 -3.24887186e-01 -7.73540676e-01
-5.51267624e-01 -2.42448643e-01 4.32283223e-01 1.67399079e-01
-1.62030503e-01 4.75120991e-01 9.01247919e-01 -2.80684501e-01
-2.70018667e-01 -8.19186151e-01 -7.55734324e-01 -2.48779088e-01
-2.53325328e-02 7.44329169e-02 6.04016840e-01 -5.70416212... | [10.931355476379395, 8.680809020996094] |
7e1b556b-cdcb-49d3-aba1-3731d0e5b641 | large-scale-domain-specific-pretraining-for | 2303.00915 | null | https://arxiv.org/abs/2303.00915v1 | https://arxiv.org/pdf/2303.00915v1.pdf | Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing | Contrastive pretraining on parallel image-text data has attained great success in vision-language processing (VLP), as exemplified by CLIP and related methods. However, prior explorations tend to focus on general domains in the web. Biomedical images and text are rather different, but publicly available datasets are sm... | ['Hoifung Poon', 'Tristan Naumann', 'Matthew P. Lungren', 'Cliff Wong', 'Naveen Valluri', 'Mu Wei', 'Rajesh Rao', 'Sam Preston', 'Robert Tinn', 'Jaspreet Bagga', 'Naoto Usuyama', 'Yanbo Xu', 'Sheng Zhang'] | 2023-03-02 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 4.57112968e-01 -1.26574878e-02 -4.52057689e-01 -1.34568870e-01
-1.58490992e+00 -6.30644381e-01 4.93022949e-01 3.79611671e-01
-7.08071589e-01 7.17126846e-01 5.08416295e-01 -6.95084572e-01
9.87922624e-02 -1.96091175e-01 -8.25830042e-01 -4.76779550e-01
1.03786148e-01 6.02523744e-01 5.80491535e-02 2.06210703... | [14.98039436340332, -1.8184908628463745] |
40466585-e527-4163-9aa0-648ecd925c0b | virtual-bike-emulation-in-a-series-parallel | 2305.05569 | null | https://arxiv.org/abs/2305.05569v1 | https://arxiv.org/pdf/2305.05569v1.pdf | Virtual-bike emulation in a series-parallel human-powered electric bike | Combining the advantages of standard bicycles and electrified vehicles, electric bikes (e-Bikes) are promising vehicles to reduce emission and traffic. The current literature on e-Bikes ranges from works on the energy management to the vehicle control to properly govern the human-vehicle interaction. This last point is... | ['Sergio M. Savaresi', 'Giulio Panzani', 'Stefano Radrizzani'] | 2023-05-09 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.95153534e-01 1.25219837e-01 -5.90299964e-01 1.28626004e-01
3.89268786e-01 -3.49447280e-01 6.76943779e-01 -6.34788930e-01
-3.48024182e-02 9.07725453e-01 -5.46273589e-01 -5.49171746e-01
-7.14694381e-01 -1.09540725e+00 -6.07708752e-01 -8.92198384e-01
1.82728887e-01 3.32090527e-01 5.45947015e-01 -7.80002475... | [5.595942974090576, 2.100679397583008] |
87238330-a8e5-440a-9216-b4de491b827f | distributional-reinforcement-learning-for-4 | 2110.13578 | null | https://arxiv.org/abs/2110.13578v1 | https://arxiv.org/pdf/2110.13578v1.pdf | Distributional Reinforcement Learning for Multi-Dimensional Reward Functions | A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known methods in this branch is distributional RL, which models return distribution instead of scalar value. In another line of work, hybrid reward... | ['Tie-Yan Liu', 'Tao Qin', 'Wei Xiong', 'Li Zhao', 'Xiaoyu Chen', 'Pushi Zhang'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/0b9e57c46de934cee33b0e8d1839bfc2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/0b9e57c46de934cee33b0e8d1839bfc2-Paper.pdf | neurips-2021-12 | ['distributional-reinforcement-learning'] | ['methodology'] | [-6.15680277e-01 -1.44339055e-01 -5.26651204e-01 -8.71506780e-02
-7.72368014e-01 -4.88979459e-01 4.31344122e-01 2.09038213e-01
-5.24607122e-01 1.06024015e+00 2.92884678e-01 5.85506074e-02
-6.47370815e-01 -9.14772451e-01 -5.30759931e-01 -9.17156577e-01
-5.80689490e-01 5.22203445e-01 -1.11431256e-01 -7.18340933... | [4.059291362762451, 2.4915127754211426] |
2064cde2-a9b3-4f7d-bc95-a580234d4353 | introduction-to-presentation-attack-detection | 2111.11794 | null | https://arxiv.org/abs/2111.11794v2 | https://arxiv.org/pdf/2111.11794v2.pdf | Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances | The main scope of this chapter is to serve as an introduction to face presentation attack detection, including key resources and advances in the field in the last few years. The next pages present the different presentation attacks that a face recognition system can confront, in which an attacker presents to the sensor... | ['Javier Galbally', 'Aythami Morales', 'Julian Fierrez', 'Javier Hernandez-Ortega'] | 2021-11-23 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 6.63740695e-01 -1.31455049e-01 1.28959775e-01 -1.34669825e-01
-3.13682407e-01 -1.01235533e+00 6.90765500e-01 -3.49167347e-01
4.69436459e-02 2.04024449e-01 -2.90347666e-01 -3.66563469e-01
-5.70935942e-02 -7.16655433e-01 -3.59540999e-01 -9.36858177e-01
-2.68488854e-01 -2.43561894e-01 -8.55619907e-02 -1.83581993... | [13.080181121826172, 1.107970952987671] |
d115175c-b109-4cfd-a64e-4183284d21e6 | automatic-dialect-detection-in-arabic | 1509.06928 | null | http://arxiv.org/abs/1509.06928v2 | http://arxiv.org/pdf/1509.06928v2.pdf | Automatic Dialect Detection in Arabic Broadcast Speech | We investigate different approaches for dialect identification in Arabic
broadcast speech, using phonetic, lexical features obtained from a speech
recognition system, and acoustic features using the i-vector framework. We
studied both generative and discriminate classifiers, and we combined these
features using a multi... | ['Ahmed Ali', 'Sree Harsha Yella', 'Sameer Khurana', 'Patrick Cardinal', 'Steve Renals', 'James Glass', 'Peter Bell', 'Najim Dehak'] | 2015-09-23 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 1.44336984e-01 -2.49989837e-01 3.56283814e-01 -6.99346721e-01
-9.93108630e-01 -1.07414913e+00 7.49854445e-01 9.17305350e-02
-4.30448920e-01 4.55228657e-01 -1.96106709e-03 -5.17655551e-01
-6.26621619e-02 -7.30046809e-01 -5.24837151e-02 -9.42120373e-01
-3.85832079e-02 9.31792080e-01 4.37626578e-02 -7.99123228... | [10.20938491821289, 10.689129829406738] |
7fd680a5-12f3-4964-89c9-14ed4b707315 | towards-robust-video-object-segmentation-with | 2207.00887 | null | https://arxiv.org/abs/2207.00887v1 | https://arxiv.org/pdf/2207.00887v1.pdf | Towards Robust Video Object Segmentation with Adaptive Object Calibration | In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting objects in all target frames of a video, given annotated object masks of reference frames. Most existing methods build pixel-wise reference-... | ['Yan Lu', 'Xiang Ming', 'Jinglu Wang', 'Xiaohao Xu'] | 2022-07-02 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 3.09663862e-01 -1.38715714e-01 -4.14691955e-01 -2.83194065e-01
-6.79818451e-01 -5.95029056e-01 3.90151113e-01 -2.23470718e-01
-2.66197443e-01 4.22927797e-01 -2.66451929e-02 1.02497332e-01
1.78584028e-02 -4.61060315e-01 -8.96185219e-01 -7.66902089e-01
3.46567817e-02 2.78576463e-01 9.03103113e-01 3.22744506... | [9.26196002960205, -0.08121488988399506] |
33e7e880-3dfd-497e-925c-69d76524ea6b | automated-mining-of-leaderboards-for | 2109.13089 | null | https://arxiv.org/abs/2109.13089v1 | https://arxiv.org/pdf/2109.13089v1.pdf | Automated Mining of Leaderboards for Empirical AI Research | With the rapid growth of research publications, empowering scientists to keep oversight over the scientific progress is of paramount importance. In this regard, the Leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressin... | ['Sören Auer', "Jennifer D'Souza", 'Salomon Kabongo'] | 2021-08-31 | null | null | null | null | ['scientific-results-extraction'] | ['natural-language-processing'] | [-3.51463646e-01 5.04136622e-01 -8.45569849e-01 1.58758894e-01
-8.91548097e-01 -9.97014940e-01 8.53948116e-01 6.66926086e-01
-2.25989912e-02 8.85024548e-01 2.83948630e-01 -4.81687039e-01
-4.44743007e-01 -1.04004455e+00 -8.54583263e-01 -1.08044684e-01
1.37262195e-01 6.93276167e-01 4.03481424e-02 -8.84592086... | [9.6219482421875, 8.24588394165039] |
665a5995-1ce6-45d6-9926-816c1c226381 | towards-zero-resource-cross-lingual-entity | 1909.13180 | null | https://arxiv.org/abs/1909.13180v2 | https://arxiv.org/pdf/1909.13180v2.pdf | Towards Zero-resource Cross-lingual Entity Linking | Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, much previous work on XEL has been on simulated settings that actually use significa... | ['Shuyan Zhou', 'Shruti Rijhwani', 'Graham Neubig'] | 2019-09-29 | towards-zero-resource-cross-lingual-entity-1 | https://aclanthology.org/D19-6127 | https://aclanthology.org/D19-6127.pdf | ws-2019-11 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-6.91704392e-01 1.44111648e-01 -5.46739638e-01 -9.19135064e-02
-1.20991194e+00 -8.77442956e-01 5.92493296e-01 3.33031178e-01
-9.51357126e-01 1.46456552e+00 4.75436687e-01 -3.43688756e-01
1.03018589e-01 -8.23115706e-01 -8.36055100e-01 3.80394220e-01
-1.09212846e-01 7.47540474e-01 4.13457751e-01 -4.72682029... | [9.565641403198242, 9.020813941955566] |
41f3133d-811b-45b5-ad56-50a2226b7fd8 | generativere-incorporating-a-novel-copy | null | null | https://aclanthology.org/2021.findings-emnlp.182 | https://aclanthology.org/2021.findings-emnlp.182.pdf | GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction | Previous neural Seq2Seq models have shown the effectiveness for jointly extracting relation triplets. However, most of these models suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. To tackle these problems, we propose a generative, multi-task learning framework... | ['Sophia Ananiadou', 'Jiarun Cao'] | null | null | null | null | findings-emnlp-2021-11 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 3.90973955e-01 5.40128887e-01 -1.93792731e-02 -4.15506840e-01
-9.74290669e-01 -3.72229904e-01 3.90128076e-01 -2.17640460e-01
-5.46897650e-01 1.12039411e+00 1.84551850e-01 -1.99287519e-01
1.95440531e-01 -7.67044127e-01 -9.10135806e-01 -4.60304260e-01
2.78204530e-01 7.46634364e-01 1.48856565e-01 -1.13305077... | [9.439807891845703, 8.811860084533691] |
f804afc7-ae90-4199-b048-4935bb08fb45 | disruption-precursor-onset-time-study-based | 2303.14965 | null | https://arxiv.org/abs/2303.14965v1 | https://arxiv.org/pdf/2303.14965v1.pdf | Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection | The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption pre... | ['J-TEXT team', 'Yuan Pan', 'Yonghua Ding', 'Zhongyong Chen', 'Zhipeng Chen', 'Zhoujun Yang', 'Nengchao Wang', 'Yu Zhong', 'Bingjia Xiao', 'Bihao Guo', 'Chengshuo Shen', 'Dalong Chen', 'Ming Zhang', 'Wei Zheng', 'Xinkun Ai'] | 2023-03-27 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-3.26606423e-01 -2.32093528e-01 -9.42775160e-02 -4.29533392e-01
-6.81655169e-01 -5.07264197e-01 5.15939236e-01 7.10952461e-01
1.37433559e-01 5.11378586e-01 -1.06768692e-02 -3.18078846e-01
-2.34389350e-01 -4.98483360e-01 -2.01952875e-01 -9.02570486e-01
-3.00135940e-01 8.22299898e-01 3.75140399e-01 -3.20094496... | [7.397780418395996, 2.5938074588775635] |
e9b721b1-52da-4290-8108-37cfacbcbe9d | self-supervised-contrastive-learning-for-6 | null | null | https://ieeexplore.ieee.org/document/9763018 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9763018 | Self-Supervised Contrastive Learning for Singing Voices | This study introduces self-supervised contrastive learning to acquire feature representations of singing voices. To acquire robust representations in an unsupervised manner, regular self-supervised contrastive learning trains neural networks to make the feature representation of a sample close to those of its computati... | ['Masataka Goto', 'Kento Watanabe', 'Hiromu Yakura'] | 2022-04-26 | null | null | null | ieee-acm-transactions-on-audio-speech-and-7 | ['singer-identification', 'vocal-technique-classification'] | ['music', 'music'] | [ 4.22270656e-01 7.42234811e-02 1.09352954e-01 -2.79461652e-01
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-2.22099081e-01 1.34197339e-01 -1.00519516e-01 -4.23155665... | [15.613308906555176, 5.732864856719971] |
bac66a7e-953f-4e27-ba80-f7de91cca8aa | implementation-of-tiny-machine-learning | 2207.12866 | null | https://arxiv.org/abs/2207.12866v1 | https://arxiv.org/pdf/2207.12866v1.pdf | Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition | In this article gesture recognition and speech recognition applications are implemented on embedded systems with Tiny Machine Learning (TinyML). It features 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. The gesture recognition,provides an innovative approach nonverbal communication. It has wide applic... | ['Nishant Mohan', 'Viveka Simha PJ', 'Prem Chowdary Kakarla', 'Raghavendra Prasanna', 'Ramachandra A. C', 'Viswanatha V'] | 2022-07-23 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.97181904e-01 -3.77855569e-01 -4.63066921e-02 -2.41627350e-01
1.89867571e-01 -4.68547314e-01 6.98743641e-01 -8.28392267e-01
-6.95400953e-01 3.83116305e-01 3.17358762e-01 -6.13877833e-01
1.09974548e-01 -4.23396468e-01 -2.22180024e-01 -7.62658834e-01
2.19546899e-01 1.71902597e-01 -5.98057322e-02 -1.30261183... | [6.517053604125977, -0.21946744620800018] |
b205444b-8691-400a-a482-28270aa42cc9 | probabilistic-deep-learning-for-instance | 2008.10678 | null | https://arxiv.org/abs/2008.10678v2 | https://arxiv.org/pdf/2008.10678v2.pdf | Probabilistic Deep Learning for Instance Segmentation | Probabilistic convolutional neural networks, which predict distributions of predictions instead of point estimates, led to recent advances in many areas of computer vision, from image reconstruction to semantic segmentation. Besides state of the art benchmark results, these networks made it possible to quantify local u... | ['Dagmar Kainmueller', 'Lisa Mais', 'Josef Lorenz Rumberger'] | 2020-08-24 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 5.50849020e-01 7.72131920e-01 -1.89053953e-01 -8.67041707e-01
-1.18813443e+00 -6.41278267e-01 6.35637879e-01 5.76455653e-01
-7.39351451e-01 9.63275969e-01 -3.06297719e-01 -1.60898462e-01
-2.01902136e-01 -7.08924055e-01 -1.21256101e+00 -5.04065037e-01
-3.78730968e-02 8.82900417e-01 6.82095587e-01 3.15038115... | [7.868232727050781, 3.1213345527648926] |
c87699cd-9e3d-4703-909c-9f8aab0f762f | online-boosting-based-target-identification | null | null | https://www.mdpi.com/1424-8220/22/21/8422 | https://www.mdpi.com/1424-8220/22/21/8422/pdf | Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots | It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features... | ['Redhwan Algabri'] | 2022-11-02 | null | null | null | sensors-2022-11 | ['person-identification'] | ['computer-vision'] | [-1.20856222e-02 -4.32833225e-01 2.30886832e-01 -3.79217684e-01
-2.42692977e-01 -3.56843591e-01 4.61254328e-01 1.34227172e-01
-8.82661462e-01 6.47905767e-01 -2.23971397e-01 4.44949836e-01
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-1.32639512e-01 3.23051721e-01 4.90460753e-01 9.42635760... | [6.82118034362793, -1.614709734916687] |
f8ecfee0-9470-47c7-8b9b-f2c7954fc60a | artificial-life-in-game-mods-for-intuitive | 2007.03787 | null | https://arxiv.org/abs/2007.03787v1 | https://arxiv.org/pdf/2007.03787v1.pdf | Artificial Life in Game Mods for Intuitive Evolution Education | The understanding and acceptance of evolution by natural selection has become a difficult issue in many parts of the world, particularly the United States of America. The use of games to improve intuition about evolution via natural selection is promising but can be challenging. We propose the use of modifications to c... | ['Barbara Z. Johnson', 'Kevin Connors', 'Anya E. Vostinar'] | 2020-07-07 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-2.37469316e-01 1.85323343e-01 3.61233205e-01 8.20659548e-02
3.05897892e-01 -7.60864437e-01 6.58142984e-01 -7.70541560e-03
-6.98427200e-01 1.04555249e+00 -9.72424224e-02 -8.10496688e-01
-3.33565660e-02 -9.09490049e-01 -4.79467303e-01 -3.76743466e-01
-3.25079471e-01 4.92401719e-01 5.54822385e-01 -1.24802268... | [5.491997718811035, 4.015431880950928] |
0ae75577-32ce-4e1a-9386-8644af94b1cf | peek-into-the-future-camera-based-occupant | 2212.11950 | null | https://arxiv.org/abs/2212.11950v1 | https://arxiv.org/pdf/2212.11950v1.pdf | Peek into the Future Camera-based Occupant Sensing in Configurable Cabins for Autonomous Vehicles | The development of fully autonomous vehicles (AVs) can potentially eliminate drivers and introduce unprecedented seating design. However, highly flexible seat configurations may lead to occupants' unconventional poses and actions. Understanding occupant behaviors and prioritize safety features become eye-catching topic... | ['Saeed Barbat', 'Srinivasan Sundararajan', 'Jialiang Le', 'Lingxi Li', 'Renran Tian', 'Avinash Prabu'] | 2022-12-22 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [-1.00994088e-01 3.56552415e-02 -7.83872530e-02 -3.89218479e-01
-3.17042410e-01 -6.54745877e-01 1.44692779e-01 -4.33674514e-01
-2.42675871e-01 5.36907434e-01 1.07011020e-01 -4.61939067e-01
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4.72647190e-01 2.59932905e-01 5.98018527e-01 -4.13698912... | [7.859551429748535, -1.043502926826477] |
81e49ff7-4035-4aa0-86cd-e0ff8600488e | learning-a-universal-human-prior-for | 2304.04602 | null | https://arxiv.org/abs/2304.04602v1 | https://arxiv.org/pdf/2304.04602v1.pdf | Learning a Universal Human Prior for Dexterous Manipulation from Human Preference | Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands. Even in simulation with no sample constraints, scripting controllers is intractable due to high degrees of freedom, and manual reward engineering can also be hard and lead to non-realistic motion... | ['Chi Jin', 'Hao Dong', 'Shixiang Shane Gu', 'Allen Z. Ren', 'Yuanpei Chen', 'Zihan Ding'] | 2023-04-10 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.72001421e-02 2.16884881e-01 -1.40008211e-01 -3.08302715e-02
-3.57491851e-01 -6.29469573e-01 3.53118479e-01 -7.14920402e-01
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-2.54052788e-01 -9.35636386e-02 -7.41008699e-01 -6.25834107e-01
-4.78899986e-01 8.37552845e-01 2.11707175e-01 -4.58318442... | [4.586612701416016, 0.8007894158363342] |
23bca87b-16c3-4980-a9a7-317865623a61 | misinformation-as-information-pollution | 2306.12466 | null | https://arxiv.org/abs/2306.12466v1 | https://arxiv.org/pdf/2306.12466v1.pdf | Misinformation as Information Pollution | Social media feed algorithms are designed to optimize online social engagements for the purpose of maximizing advertising profits, and therefore have an incentive to promote controversial posts including misinformation. By thinking about misinformation as information pollution, we can draw parallels with environmental ... | ['Rada Mihalcea', 'Ashkan Kazemi'] | 2023-06-21 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 1.44128025e-01 7.69446254e-01 -9.83774304e-01 -1.71230972e-01
-4.19462204e-01 -6.13955140e-01 7.40873218e-01 4.81800377e-01
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-5.87453917e-02 -1.15598822e+00 -6.40586257e-01 -1.68152779e-01
2.82034785e-01 1.85173422e-01 4.71489727e-02 -4.37594950... | [9.066611289978027, 5.8225579261779785] |
f54ed931-aebb-4991-95c9-f9383457c82c | stock-index-prediction-using-cointegration | 2109.15045 | null | https://arxiv.org/abs/2109.15045v1 | https://arxiv.org/pdf/2109.15045v1.pdf | Stock Index Prediction using Cointegration test and Quantile Loss | Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundament... | ['Minjung Kang', 'Heejoon Lee', 'Jaeyoung Cheong'] | 2021-09-29 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.34338021e-01 -6.25337720e-01 -1.89030007e-01 -2.98058897e-01
-5.29640377e-01 -5.18466115e-01 4.90391374e-01 -5.72196953e-02
-5.90001285e-01 8.17168832e-01 1.69108197e-01 -5.69404721e-01
-2.21555963e-01 -1.08998883e+00 -7.73131073e-01 -8.02756250e-01
-2.24710748e-01 3.54048342e-01 2.59820700e-01 -4.17832762... | [4.439486026763916, 4.224885940551758] |
a7d74a7d-a2c0-438e-ad83-850b09ff2297 | few-shot-class-incremental-learning-via-class | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Few-Shot_Class-Incremental_Learning_via_Class-Aware_Bilateral_Distillation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Few-Shot_Class-Incremental_Learning_via_Class-Aware_Bilateral_Distillation_CVPR_2023_paper.pdf | Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation | Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel classes based on only few training samples, which poses a more challenging task than the well-studied Class-Incremental Learning (CIL) due to data scarcity. While knowledge distillation, a prevailing technique in CIL, can alleviate the cata... | ['Xiangzhong Fang', 'Yi Niu', 'Dashan Guo', 'Zhanzhan Cheng', 'Yunlu Xu', 'Jing Lu', 'Linglan Zhao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning', 'general-knowledge', 'novel-concepts'] | ['computer-vision', 'methodology', 'methodology', 'miscellaneous', 'reasoning'] | [ 3.16978455e-01 2.65790790e-01 -2.48004213e-01 -1.69461682e-01
-1.72748163e-01 -1.29644334e-01 3.42816651e-01 3.51013154e-01
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2.75572211e-01 1.28737643e-01 7.45230675e-01 -2.89267927... | [9.81306266784668, 3.3963847160339355] |
22d7f4b0-f19c-471c-a202-a4b98a452a6b | vptr-efficient-transformers-for-video | 2203.15836 | null | https://arxiv.org/abs/2203.15836v1 | https://arxiv.org/pdf/2203.15836v1.pdf | VPTR: Efficient Transformers for Video Prediction | In this paper, we propose a new Transformer block for video future frames prediction based on an efficient local spatial-temporal separation attention mechanism. Based on this new Transformer block, a fully autoregressive video future frames prediction Transformer is proposed. In addition, a non-autoregressive video pr... | ['Guillaume-Alexandre Bilodeau', 'Xi Ye'] | 2022-03-29 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 9.26415846e-02 2.18456641e-01 -1.68559954e-01 -4.65775728e-01
-5.29616177e-01 1.01729311e-01 5.65728486e-01 -3.19467336e-01
-2.07024977e-01 5.77403009e-01 2.81553447e-01 -1.42458498e-01
1.33987382e-01 -4.67599481e-01 -7.35024869e-01 -6.07047200e-01
8.38230848e-02 2.73893718e-02 4.66219425e-01 -1.84543803... | [8.787407875061035, 0.2459997534751892] |
f003a3ff-390f-49d4-bd72-40a37f1b5d0a | keyphrase-generation-a-text-summarization-1 | null | null | https://aclanthology.org/N19-1070 | https://aclanthology.org/N19-1070.pdf | Keyphrase Generation: A Text Summarization Struggle | Authors{'} keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of consi... | ['Ond{\\v{r}}ej Bojar', 'Erion {\\c{C}}ano'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.68751174e-01 3.62022340e-01 -1.96112171e-01 1.70770347e-01
-8.64916205e-01 -7.41216540e-01 1.08493364e+00 9.78551090e-01
-4.21296597e-01 1.19145751e+00 8.73137534e-01 -2.93285608e-01
-3.04752409e-01 -7.62936056e-01 -8.52926016e-01 -5.38738966e-01
-9.66483578e-02 3.35962385e-01 -2.07414269e-01 1.56289443... | [12.485389709472656, 9.380666732788086] |
87003252-bcad-4ff8-9dab-0ce2c975557e | generalization-bounds-for-magnitude-based | 2305.18789 | null | https://arxiv.org/abs/2305.18789v2 | https://arxiv.org/pdf/2305.18789v2.pdf | Generalization Bounds for Magnitude-Based Pruning via Sparse Matrix Sketching | In this paper, we derive a novel bound on the generalization error of Magnitude-Based pruning of overparameterized neural networks. Our work builds on the bounds in Arora et al. [2018] where the error depends on one, the approximation induced by pruning, and two, the number of parameters in the pruned model, and improv... | ['Xiaoming Huo', 'Prasanjit Dubey', 'Etash Kumar Guha'] | 2023-05-30 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 2.79676855e-01 3.41951519e-01 -2.37284943e-01 -2.63146251e-01
-3.33188593e-01 -4.89564955e-01 6.32481873e-02 1.74642280e-01
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-6.51308239e-01 -7.67019451e-01 -1.02884042e+00 -7.59409547e-01
-3.26519340e-01 4.30709720e-01 2.93890357e-01 2.26357318... | [8.396909713745117, 3.372965097427368] |
8f317bd5-02cc-457b-82f3-c24ef20388af | towards-overcoming-false-positives-in-visual | 2012.12510 | null | https://arxiv.org/abs/2012.12510v2 | https://arxiv.org/pdf/2012.12510v2.pdf | Towards Overcoming False Positives in Visual Relationship Detection | In this paper, we investigate the cause of the high false positive rate in Visual Relationship Detection (VRD). We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e.g., the inaccurate object detection, which lea... | ['Xianglong Liu', 'Hongsheng Li', 'Zhoujun Li', 'Shuai Yi', 'Haiyu Zhao', 'Mingyuan Zhang', 'Jiashu Tao', 'Yizhuo Zhou', 'Chongzhi Zhang', 'Xiao Ma', 'Daisheng Jin'] | 2020-12-23 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-1.47705674e-01 1.03436805e-01 -2.69436598e-01 -2.52020746e-01
-5.05631745e-01 -2.99651265e-01 3.37937921e-01 -5.61460368e-02
-2.91486770e-01 5.34584701e-01 3.14118788e-02 -2.82857060e-01
-7.36147165e-02 -7.51389980e-01 -7.36553967e-01 -6.63196385e-01
1.08835876e-01 7.93855965e-01 8.62994432e-01 -1.75260007... | [9.1362886428833, 0.625058650970459] |
e771ea64-aa29-4083-ba70-e35b61793bfe | academic-writing-with-gpt-3-5-reflections-on | 2304.11079 | null | https://arxiv.org/abs/2304.11079v1 | https://arxiv.org/pdf/2304.11079v1.pdf | Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency | The debate around the use of GPT 3.5 has been a popular topic among academics since the release of ChatGPT. Whilst some have argued for the advantages of GPT 3.5 in enhancing academic writing, others have raised concerns such as plagiarism, the spread of false information, and ecological issues. The need for finding wa... | ["Oğuz 'Oz' Buruk"] | 2023-02-12 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-3.59280020e-01 5.60352445e-01 -2.33921006e-01 2.51401991e-01
-3.36032152e-01 -8.43372345e-01 6.49490535e-01 3.94863069e-01
-2.24639252e-01 6.89850867e-01 9.69612598e-01 -8.40129852e-01
-3.47586900e-01 -3.60196143e-01 -3.00022632e-01 -3.85724232e-02
6.78538442e-01 1.46820650e-01 1.17962426e-02 -2.22376227... | [10.209488868713379, 7.400301456451416] |
ad9976fc-58dd-4d86-8398-a1242a7b8c9f | towards-the-semantic-weak-generalization | 2204.11280 | null | https://arxiv.org/abs/2204.11280v3 | https://arxiv.org/pdf/2204.11280v3.pdf | Deconstructed Generation-Based Zero-Shot Model | Recent research on Generalized Zero-Shot Learning (GZSL) has focused primarily on generation-based methods. However, current literature has overlooked the fundamental principles of these methods and has made limited progress in a complex manner. In this paper, we aim to deconstruct the generator-classifier framework an... | ['Philip H. S. Torr', 'Haofeng Zhang', 'Yuming Shen', 'Dubing Chen'] | 2022-04-24 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 3.60532343e-01 2.96629697e-01 -1.23628944e-01 -3.61669600e-01
-9.82465088e-01 -5.58145940e-01 7.98765838e-01 9.96146873e-02
1.02095686e-01 8.68440628e-01 5.75838871e-02 -3.35475028e-01
-4.07884836e-01 -1.05019724e+00 -4.20628607e-01 -8.88058782e-01
1.19239822e-01 5.01879096e-01 5.55041321e-02 -2.14514211... | [9.862812042236328, 2.9193367958068848] |
140066fe-97b1-4c03-b0a7-16cd788c8816 | animepose-multi-person-3d-pose-estimation-and | 2002.02792 | null | https://arxiv.org/abs/2002.02792v1 | https://arxiv.org/pdf/2002.02792v1.pdf | AnimePose: Multi-person 3D pose estimation and animation | 3D animation of humans in action is quite challenging as it involves using a huge setup with several motion trackers all over the person's body to track the movements of every limb. This is time-consuming and may cause the person discomfort in wearing exoskeleton body suits with motion sensors. In this work, we present... | ['Laxman Kumarapu', 'Prerana Mukherjee'] | 2020-02-06 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-1.80905327e-01 -6.17977679e-02 2.06046835e-01 8.68652761e-02
-5.53781450e-01 -3.73171896e-01 3.23807150e-01 -3.97900939e-01
-7.26791859e-01 6.90254569e-01 3.92336190e-01 4.14740086e-01
3.75064939e-01 -3.84797096e-01 -5.42574227e-01 -4.84132886e-01
-6.14930950e-02 1.10357451e+00 3.04460675e-01 -2.81368822... | [7.031610488891602, -0.8457633852958679] |
083b0279-f262-46b8-abb0-8cb228e7589b | minimum-divergence-vs-maximum-margin-an | null | null | https://openreview.net/forum?id=H1xD9sR5Fm | https://openreview.net/pdf?id=H1xD9sR5Fm | Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models | Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction. While traditional seq2seq models are trained by Maximum Likelihood Estimation (MLE), much recent work has made various attempts to optimize evaluation scores directly to solve the mismatch between training and evaluati... | ['Huan Zhang', 'Hai Zhao'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 6.21213615e-01 1.74406990e-01 -6.21563137e-01 -6.52280331e-01
-1.16833854e+00 -5.99850953e-01 6.28527641e-01 9.34436619e-02
-6.17435932e-01 1.21522963e+00 3.86243939e-01 -4.30165082e-01
2.95077622e-01 -3.14887017e-01 -5.06957829e-01 -4.19750988e-01
3.56037199e-01 5.19884646e-01 2.57668346e-01 -2.46978730... | [11.9368896484375, 9.34886646270752] |
c617b679-b1ef-4223-b61f-37e6c0a99829 | when-evolutionary-computation-meets-privacy | 2304.01205 | null | https://arxiv.org/abs/2304.01205v1 | https://arxiv.org/pdf/2304.01205v1.pdf | When Evolutionary Computation Meets Privacy | Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also ... | ['Jun Zhang', 'Qingqi Pei', 'Ximeng Liu', 'Xiaoguo Li', 'Wei-neng Chen', 'Bowen Zhao'] | 2023-03-22 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.66940475e-01 -4.10194069e-01 -3.06634102e-02 -4.50882524e-01
-4.11922544e-01 -8.77085328e-01 2.44786724e-01 1.06118195e-01
-4.85323846e-01 8.34040463e-01 5.06746322e-02 -2.87449472e-02
-4.72401768e-01 -1.06208885e+00 -4.30032969e-01 -1.12503147e+00
9.69667956e-02 1.27488196e-01 -4.17136341e-01 1.94596145... | [5.890294551849365, 6.517660140991211] |
fcbff802-89fe-41dd-b35c-efcb1654d1d7 | auto-absa-automatic-detection-of-aspects-in | 2202.00484 | null | https://arxiv.org/abs/2202.00484v2 | https://arxiv.org/pdf/2202.00484v2.pdf | Auto-ABSA: Automatic Detection of Aspects in Aspect-Based Sentiment Analysis | After transformer is proposed, lots of pre-trained language models have been come up with and sentiment analysis (SA) task has been improved. In this paper, we proposed a method that uses an auxiliary sentence about aspects that the sentence contains to help sentiment prediction. The first is aspect detection, which us... | ['Yijie Tong', 'Bolun Sun', 'Teng Wang'] | 2022-01-05 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.08641475e-01 3.45435977e-01 -1.74127564e-01 -8.76790226e-01
-5.45849621e-01 -6.76176846e-01 7.86067069e-01 2.60808766e-01
-4.17701811e-01 5.13497770e-01 5.25537133e-01 -2.61784822e-01
6.59017265e-01 -1.01010501e+00 -4.30400461e-01 -3.45413983e-01
5.55038452e-01 4.61021155e-01 3.38115633e-01 -8.35109711... | [11.402005195617676, 6.700481414794922] |
91b81324-e99e-46d0-8fc2-8f25c20e0d5d | covid-19-and-misinformation-a-large-scale | null | null | https://aclanthology.org/2021.acl-srw.13 | https://aclanthology.org/2021.acl-srw.13.pdf | COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter | Social media is often used by individuals and organisations as a platform to spread misinformation. With the recent coronavirus pandemic we have seen a surge of misinformation on Twitter, posing a danger to public health. In this paper, we compile a large COVID-19 Twitter misinformation corpus and perform an analysis t... | ['David Rogers', 'Alun Preece', 'Jose Camacho-Collados', 'Dimosthenis Antypas'] | 2021-08-01 | null | null | null | acl-2021-5 | ['lexical-analysis'] | ['natural-language-processing'] | [ 4.67609614e-02 1.18432581e-01 -5.81042886e-01 -3.29356007e-02
-4.85204518e-01 -7.47824728e-01 1.19454467e+00 9.88876343e-01
-6.78086877e-01 7.17885196e-01 9.19119298e-01 -5.27139723e-01
2.27678478e-01 -8.79485130e-01 -2.85189509e-01 -3.96881014e-01
-1.28558651e-01 3.69160622e-01 1.26056090e-01 -6.88730538... | [8.460530281066895, 9.820080757141113] |
84810460-02d9-412f-88b7-e0427d97d52e | artificial-intelligence-distinguishes-covid | null | null | https://pubs.rsna.org/doi/10.1148/radiol.2020200905 | https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020200905 | Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT | Background
Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT.
Purpose
To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances.
Materials and M... | ['Shiqin Zhang', 'Daliang Liu', 'Kunlin Cao', 'Yi Lu', 'Qi Song', 'Lixin Qin', 'Jun Xia', 'Bin Kong', 'Qizhong Xu', 'Guisheng Wang', 'Youbing Yin', 'Lin Li', 'Junjie Bai', 'Zhenghan Fang', 'Zeguo Xu', 'Xisheng Fang', 'Xin Wang', 'Juan Xia'] | 2020-02-19 | null | null | null | radiology-2020-2 | ['covid-19-image-segmentation'] | ['computer-vision'] | [-8.48772451e-02 -3.82039666e-01 -1.14027917e-01 1.09120579e-02
-6.79344833e-01 -6.73056006e-01 1.61764815e-01 4.82963592e-01
-7.33435094e-01 7.06217170e-01 -1.64642045e-03 -6.99766934e-01
-1.40728250e-01 -7.76121199e-01 -5.49536586e-01 -7.35080540e-01
-3.05241674e-01 8.10423434e-01 2.51691520e-01 7.98367739... | [15.503130912780762, -1.7930952310562134] |
9080c0f3-fdcf-44a7-8e7b-3282cd2301f6 | cdnet-contrastive-disentangled-network-for | 2206.08524 | null | https://arxiv.org/abs/2206.08524v1 | https://arxiv.org/pdf/2206.08524v1.pdf | CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound | Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still challenges experienced ophthalmologists. Thus a novel contrastive disentangled network (CDNet) is developed in this work, aiming to tack... | ['Yaqi Wang', 'Qun Jin', 'Juan Ye', 'Ruiquan Ge', 'Gangyong Jia', 'Yijie Wang', 'Yunxiang Li', 'Ruilong Dan'] | 2022-06-17 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 1.14661641e-01 -2.04299510e-01 -1.36050209e-01 -1.22339413e-01
-8.12333584e-01 -3.38108987e-01 4.42956924e-01 -1.99137270e-01
2.05009580e-02 4.50512916e-01 1.30514011e-01 -2.21264064e-01
-7.11643279e-01 -2.99813658e-01 -1.60145551e-01 -1.06382656e+00
9.06903967e-02 2.81766474e-01 -4.98420447e-02 2.66119808... | [15.762152671813965, -3.926283359527588] |
0ec2d3a2-9007-4adb-8ef3-c9dcda8176f1 | aggression-identification-in-english-hindi | null | null | https://aclanthology.org/2020.trac-1.12 | https://aclanthology.org/2020.trac-1.12.pdf | Aggression Identification in English, Hindi and Bangla Text using BERT, RoBERTa and SVM | This paper presents the results of the classifiers we developed for the shared tasks in aggression identification and misogynistic aggression identification. These two shared tasks were held as part of the second workshop on Trolling, Aggression and Cyberbullying (TRAC). Both the subtasks were held for English, Hindi a... | ['Kuntal Dey', 'Kaushik Das', 'Ferdous Barbhuiya', 'Arup Baruah'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['misogynistic-aggression-identification', 'aggression-identification'] | ['natural-language-processing', 'natural-language-processing'] | [-7.99898982e-01 -1.64653897e-01 7.58360252e-02 -2.82179087e-01
-6.10604107e-01 -2.99621671e-01 6.19480848e-01 2.66568244e-01
-9.67619956e-01 1.04395020e+00 1.97510064e-01 -3.50632340e-01
-5.95194876e-01 -5.00404775e-01 5.34271263e-02 -5.66015244e-01
-4.89902981e-02 6.55510604e-01 5.14230371e-01 -9.45677161... | [8.80667781829834, 10.769123077392578] |
b9189938-2e9d-43a0-9817-5b15fcd2daea | poseur-direct-human-pose-regression-with | 2201.07412 | null | https://arxiv.org/abs/2201.07412v2 | https://arxiv.org/pdf/2201.07412v2.pdf | Poseur: Direct Human Pose Regression with Transformers | We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transformer network. This network directly learns a regression mapping from images to the keypoint coordinates, without resorting to intermediate ... | ['Anton Van Den Hengel', 'Zhibin Wang', 'Xinlong Wang', 'Zhi Tian', 'Chunhua Shen', 'Yongtao Ge', 'Weian Mao'] | 2022-01-19 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 3.72917540e-02 8.14889849e-04 -1.97461098e-01 -2.63767660e-01
-1.03231442e+00 -3.37110847e-01 5.43878078e-01 -1.08416229e-01
-5.77530026e-01 5.89967728e-01 3.22238535e-01 2.15028226e-01
4.42569777e-02 -2.84221679e-01 -9.82164979e-01 -3.26850146e-01
-2.62950957e-01 8.29471231e-01 1.16953170e-02 -3.65195602... | [6.98205041885376, -0.9448651671409607] |
c1fd8626-e7fe-48f3-aee4-60550aebc2eb | multi-step-joint-modality-attention-network | 2001.06206 | null | https://arxiv.org/abs/2001.06206v1 | https://arxiv.org/pdf/2001.06206v1.pdf | Multi-step Joint-Modality Attention Network for Scene-Aware Dialogue System | Understanding dynamic scenes and dialogue contexts in order to converse with users has been challenging for multimodal dialogue systems. The 8-th Dialog System Technology Challenge (DSTC8) proposed an Audio Visual Scene-Aware Dialog (AVSD) task, which contains multiple modalities including audio, vision, and language, ... | ['Lun-Wei Ku', 'Chao-Chun Hsu', 'Kuan-Yen Lin', 'Yun-Wei Chu'] | 2020-01-17 | null | null | null | null | ['scene-aware-dialogue'] | ['computer-vision'] | [-2.10566580e-01 7.84765184e-03 1.17354147e-01 -4.14514363e-01
-8.72601449e-01 -7.35632241e-01 1.04104745e+00 -3.69028330e-01
-4.40667838e-01 4.58352983e-01 1.15048075e+00 -5.50416932e-02
5.55056930e-01 -1.05490245e-01 -1.24457352e-01 -1.71973035e-01
4.93178219e-01 6.70754611e-01 -3.81600996e-03 -6.45810723... | [10.887042045593262, 1.3133264780044556] |
13463e49-5d30-40c9-8d27-a7501716b347 | context-aware-image-denoising-with-auto | 2101.05833 | null | https://arxiv.org/abs/2101.05833v1 | https://arxiv.org/pdf/2101.05833v1.pdf | Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation | This paper presents a novel context-aware image denoising algorithm that combines an adaptive image smoothing technique and color reduction techniques to remove perturbation from adversarial images. Adaptive image smoothing is achieved using auto-threshold canny edge detection to produce an accurate edge map used to pr... | ['Wu-chi Feng', 'Yeganeh Jalalpour', 'Li-Yun Wang'] | 2021-01-14 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 4.59607840e-01 -4.28733081e-01 6.96045816e-01 -1.29496187e-01
-3.04551065e-01 -8.61469269e-01 3.70443612e-01 -1.82419077e-01
-7.71220803e-01 6.45937324e-01 -2.21156534e-02 -2.03699559e-01
1.39472649e-01 -8.53191614e-01 -8.23642969e-01 -8.73112798e-01
1.35997370e-01 -6.39203370e-01 4.00205404e-01 -4.42130357... | [5.51540470123291, 7.953231334686279] |
166720ad-5f63-4977-a5b1-93b08c6f55fb | a-two-step-approach-for-handling-zero | 2302.09887 | null | https://arxiv.org/abs/2302.09887v1 | https://arxiv.org/pdf/2302.09887v1.pdf | A Two-step Approach for Handling Zero-Cardinality in Relation Extraction | Relation tuple extraction from text is an important task for building knowledge bases. Recently, joint entity and relation extraction models have achieved very high F1 scores in this task. However, the experimental settings used by these models are restrictive and the datasets used in the experiments are not realistic.... | ['Indrajit Bhattacharya', 'Samiran Pal', 'Tapas Nayak', 'Pratik Saini'] | 2023-02-20 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.53942287e-01 4.80173469e-01 -3.60716134e-01 -5.47535896e-01
-8.83971810e-01 -2.75759697e-01 6.73512757e-01 6.47598147e-01
-5.28423369e-01 1.08989918e+00 -8.92323703e-02 -3.68683636e-01
-1.69249669e-01 -1.16099298e+00 -8.53704691e-01 -1.45018071e-01
-2.87922800e-01 7.82013357e-01 6.17970407e-01 -3.24420542... | [9.331939697265625, 8.680254936218262] |
e5f6d893-5743-484d-b231-50a112cc24b4 | blindspotnet-seeing-where-we-cannot-see | 2207.03870 | null | https://arxiv.org/abs/2207.03870v1 | https://arxiv.org/pdf/2207.03870v1.pdf | BlindSpotNet: Seeing Where We Cannot See | We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual driver or a self-driving system to potential causes of accidents (e.g., draw attention to a road reg... | ['Ko Nishino', 'Shohei Nobuhara', 'Shinya Ishizaki', 'Kotaro Hasegawa', 'Taichi Fukuda'] | 2022-07-08 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 1.05284497e-01 2.76206315e-01 -1.66881382e-01 -4.71395850e-01
-6.64493024e-01 -7.04668522e-01 4.53137994e-01 -5.43881416e-01
-3.63991261e-01 4.52845275e-01 1.36863470e-01 -7.56594658e-01
1.96146935e-01 -7.29413986e-01 -1.00198889e+00 -2.41933361e-01
4.35235471e-01 4.75928605e-01 6.89950049e-01 1.64831564... | [8.092247009277344, -2.334517002105713] |
08ccaf1f-8c89-48d0-a2b7-e16c7060f30b | robustl2s-speaker-specific-lip-to-speech | 2307.01233 | null | https://arxiv.org/abs/2307.01233v1 | https://arxiv.org/pdf/2307.01233v1.pdf | RobustL2S: Speaker-Specific Lip-to-Speech Synthesis exploiting Self-Supervised Representations | Significant progress has been made in speaker dependent Lip-to-Speech synthesis, which aims to generate speech from silent videos of talking faces. Current state-of-the-art approaches primarily employ non-autoregressive sequence-to-sequence architectures to directly predict mel-spectrograms or audio waveforms from lip ... | ['Vineet Gandhi', 'Vishal Tambrahalli', 'Neil Shah', 'Neha Sahipjohn'] | 2023-07-03 | null | null | null | null | ['speaker-specific-lip-to-speech-synthesis', 'lip-to-speech-synthesis', 'speech-synthesis'] | ['computer-vision', 'computer-vision', 'speech'] | [ 2.07120880e-01 1.16948970e-01 -3.66045535e-01 -3.66264999e-01
-1.43372941e+00 -4.05675501e-01 7.60106444e-01 -8.29050958e-01
3.61213446e-01 5.73421836e-01 7.95112967e-01 -1.56920433e-01
5.15733778e-01 -8.64120051e-02 -6.45388484e-01 -7.84053683e-01
2.11258948e-01 1.32609993e-01 -2.24838629e-01 1.15262233... | [14.361948013305664, 5.0094709396362305] |
14adc557-9f70-4e05-b096-e163d43f69cd | a-simple-baseline-for-audio-visual-scene-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Schwartz_A_Simple_Baseline_for_Audio-Visual_Scene-Aware_Dialog_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Schwartz_A_Simple_Baseline_for_Audio-Visual_Scene-Aware_Dialog_CVPR_2019_paper.pdf | A Simple Baseline for Audio-Visual Scene-Aware Dialog | The recently proposed audio-visual scene-aware dialog task paves the way to a more data-driven way of learning virtual assistants, smart speakers and car navigation systems. However, very little is known to date about how to effectively extract meaningful information from a plethora of sensors that pound the computati... | [' Tamir Hazan', ' Alexander G. Schwing', 'Idan Schwartz'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['scene-aware-dialogue'] | ['computer-vision'] | [ 9.51396972e-02 7.63815418e-02 1.80651113e-01 -5.88408232e-01
-1.07941854e+00 -6.71756268e-01 1.01431215e+00 -1.04449429e-02
-5.19669652e-01 5.08053243e-01 5.68458319e-01 -1.60764784e-01
1.83514680e-03 -2.32292518e-01 -3.57071310e-01 -4.56393331e-01
2.43220866e-01 5.79329133e-01 4.44174379e-01 -6.55078769... | [14.556314468383789, 5.050055980682373] |
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