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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d530a53c-a87a-44e5-b829-874ad185ddef | chart-question-answering-state-of-the-art-and | 2205.03966 | null | https://arxiv.org/abs/2205.03966v2 | https://arxiv.org/pdf/2205.03966v2.pdf | Chart Question Answering: State of the Art and Future Directions | Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights. Often people analyze charts to answer questions that they have in mind. Answering such questions can be challenging as they often require a significant amount of perceptual and cognitive e... | ['Ahmed Masry', 'Parsa Kavehzadeh', 'Enamul Hoque'] | 2022-05-08 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [-4.31724405e-03 2.46931121e-01 1.46151215e-01 -3.69989783e-01
-7.52168179e-01 -1.01688409e+00 4.67935950e-01 9.68389094e-01
2.66236812e-01 3.40775400e-01 4.22300309e-01 -1.03934360e+00
-1.02273278e-01 -6.04343593e-01 -1.34157762e-01 -1.35697350e-01
-1.46337613e-01 2.34347105e-01 2.24805072e-01 -1.09463014... | [11.207382202148438, 2.0307939052581787] |
3f99eed1-f230-450e-a2c2-5832644c9f52 | deepmcat-large-scale-deep-clustering-for | 2110.00109 | null | https://arxiv.org/abs/2110.00109v1 | https://arxiv.org/pdf/2110.00109v1.pdf | DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization | In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due to the fact that ground-truth labels are time-consuming and expensive to obtain manually. Generating labels from patient metadata might be ... | ['Daniel Rueckert', 'Ben Glocker', 'Wenjia Bai', 'Turkay Kart'] | 2021-09-30 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.67480046e-01 3.35019499e-01 -3.34970132e-02 -6.82280123e-01
-1.09639692e+00 -5.43957770e-01 2.99937893e-02 9.01557744e-01
-5.07271409e-01 6.22384548e-01 3.25335823e-02 -1.18204184e-01
-3.83739501e-01 -5.02249122e-01 -2.99166024e-01 -9.26051855e-01
-5.42370714e-02 1.04396212e+00 -4.14989218e-02 5.94682455... | [14.800623893737793, -2.351714849472046] |
559297fe-caef-45b3-8d04-707055d34f91 | enhance-the-motion-cues-for-face-anti | 1901.05635 | null | http://arxiv.org/abs/1901.05635v1 | http://arxiv.org/pdf/1901.05635v1.pdf | Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture | Spatio-temporal information is very important to capture the discriminative
cues between genuine and fake faces from video sequences. To explore such a
temporal feature, the fine-grained motions (e.g., eye blinking, mouth movements
and head swing) across video frames are very critical. In this paper, we
propose a joint... | ['Zheng Ma', 'Xiaoguang Tu', 'Mei Xie', 'Yuefei Zhang', 'Yao Luo', 'Hengsheng Zhang'] | 2019-01-17 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [-1.27534121e-01 -6.01200581e-01 -2.94701219e-01 -3.01484764e-01
-1.69444203e-01 -1.59089744e-01 4.51021671e-01 -7.29960144e-01
-2.04128906e-01 4.20705944e-01 2.75703549e-01 8.86336565e-02
3.27937417e-02 -3.04811954e-01 -7.22774744e-01 -1.09515679e+00
-3.96343112e-01 -7.01295257e-01 1.57020129e-02 -1.34527817... | [13.209188461303711, 1.336948275566101] |
5a2a10ba-0123-4bbd-bef7-898729a6259a | end-to-end-learning-to-index-and-search-in | 2210.08410 | null | https://arxiv.org/abs/2210.08410v2 | https://arxiv.org/pdf/2210.08410v2.pdf | ELIAS: End-to-End Learning to Index and Search in Large Output Spaces | Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an... | ['Inderjit S Dhillon', 'Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Patrick H. Chen', 'Nilesh Gupta'] | 2022-10-16 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.57877181e-02 -5.62187731e-02 -5.71037412e-01 -5.95583737e-01
-1.09544432e+00 -6.01272464e-01 1.65750176e-01 3.58967215e-01
-4.17769790e-01 4.63965982e-01 -2.24525705e-01 -1.88608095e-01
-3.45208704e-01 -6.59389913e-01 -5.26515543e-01 -8.73175085e-01
1.55775622e-01 1.17538774e+00 6.28742352e-02 2.40668312... | [9.498454093933105, 4.357867240905762] |
068a0ae8-40ed-470b-b6ed-ff7a7abba3f0 | on-counterfactual-inference-with-unobserved | 2211.08209 | null | https://arxiv.org/abs/2211.08209v2 | https://arxiv.org/pdf/2211.08209v2.pdf | On counterfactual inference with unobserved confounding | Given an observational study with $n$ independent but heterogeneous units, our goal is to learn the counterfactual distribution for each unit using only one $p$-dimensional sample per unit containing covariates, interventions, and outcomes. Specifically, we allow for unobserved confounding that introduces statistical b... | ['Gregory W. Wornell', 'Devavrat Shah', 'Raaz Dwivedi', 'Abhin Shah'] | 2022-11-14 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.39730957e-02 3.20263952e-01 -8.30069721e-01 -5.51816642e-01
-1.15913486e+00 -3.30512077e-01 -1.97667748e-01 1.34248123e-01
-5.45332015e-01 1.21180785e+00 4.49389189e-01 -2.15245932e-01
-3.21017891e-01 -8.89862001e-01 -1.30303979e+00 -8.58180940e-01
-6.44054472e-01 2.37947062e-01 -8.73372614e-01 5.45045435... | [7.95032262802124, 5.169031143188477] |
73c5ac56-73df-4d01-ac5c-509b9537b2ea | improving-video-instance-segmentation-by | 2012.07504 | null | https://arxiv.org/abs/2012.07504v2 | https://arxiv.org/pdf/2012.07504v2.pdf | Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates | Instance segmentation with neural networks is an essential task in environment perception. In many works, it has been observed that neural networks can predict false positive instances with high confidence values and true positives with low ones. Thus, it is important to accurately model the uncertainties of neural net... | ['Hanno Gottschalk', 'Fabian Hueger', 'Serin Varghese', 'Peter Schlicht', 'Matthias Rottmann', 'Kira Maag'] | 2020-12-14 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 6.44873798e-01 3.70982230e-01 1.39998337e-02 -8.29584181e-01
-8.06795120e-01 -3.71163130e-01 4.61925238e-01 4.16387677e-01
-5.90817392e-01 9.82930183e-01 -6.84306622e-01 -2.83518791e-01
-4.50356543e-01 -8.84064734e-01 -1.20821238e+00 -5.41199386e-01
-2.17610762e-01 4.33352172e-01 6.74211800e-01 1.51241690... | [7.875953674316406, -0.8416284322738647] |
e9e87080-4f90-4bc1-acae-86e796e1d909 | fast-facial-landmark-detection-and | 2101.10808 | null | https://arxiv.org/abs/2101.10808v3 | https://arxiv.org/pdf/2101.10808v3.pdf | Fast Facial Landmark Detection and Applications: A Survey | Dense facial landmark detection is one of the key elements of face processing pipeline. It is used in virtual face reenactment, emotion recognition, driver status tracking, etc. Early approaches were suitable for facial landmark detection in controlled environments only, which is clearly insufficient. Neural networks h... | ['Larysa Koriashkina', 'Kostiantyn Khabarlak'] | 2021-01-12 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 7.03698322e-02 6.58879131e-02 -1.41869962e-01 -6.38304651e-01
-3.79262596e-01 -4.61357027e-01 5.09439826e-01 -3.91923517e-01
-5.33932865e-01 3.83728355e-01 -2.30698973e-01 -6.57545775e-02
-1.63704827e-01 -3.95596236e-01 -4.52071577e-01 -7.72239029e-01
-1.80183128e-01 2.66953290e-01 3.03212609e-02 -3.68475139... | [13.335107803344727, 0.730558454990387] |
4be95811-f26a-4999-be1f-59299bda6a48 | katsum-knowledge-aware-abstractive-text | 2212.03371 | null | https://arxiv.org/abs/2212.03371v1 | https://arxiv.org/pdf/2212.03371v1.pdf | KATSum: Knowledge-aware Abstractive Text Summarization | Text Summarization is recognised as one of the NLP downstream tasks and it has been extensively investigated in recent years. It can assist people with perceiving the information rapidly from the Internet, including news articles, social posts, videos, etc. Most existing research works attempt to develop summarization ... | ['Jianhua Jiang', 'Edmund Lai', 'Weihua Li', 'Guan Wang'] | 2022-12-06 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 5.86340725e-01 4.47930157e-01 -3.10044497e-01 4.12355177e-03
-7.90820181e-01 -4.56976950e-01 6.55998766e-01 6.00365877e-01
-1.94619760e-01 9.88385618e-01 1.15567541e+00 1.78555265e-01
-2.93461293e-01 -5.01090288e-01 -4.10829246e-01 -3.28515857e-01
2.47133315e-01 1.13521941e-01 1.17428787e-01 -4.44311172... | [12.586559295654297, 9.519729614257812] |
f78f45fa-17a1-4d03-b111-73db819b2715 | does-crowdfunding-really-foster-innovation | 2101.02683 | null | https://arxiv.org/abs/2101.02683v2 | https://arxiv.org/pdf/2101.02683v2.pdf | Does Crowdfunding Really Foster Innovation? Evidence from the Board Game Industry | Crowdfunding offers inventors and entrepreneurs alternative access to resources with which they can develop and realize their ideas. Besides helping to secure capital, crowdfunding also connects creators with engaged early supporters who provide public feedback. But does this process foster truly innovative outcomes? D... | ['Balazs Vedres', 'Johannes Wachs'] | 2021-01-07 | null | null | null | null | ['board-games'] | ['playing-games'] | [-8.42260778e-01 4.97996330e-01 -6.08873606e-01 3.76574278e-01
-2.02362344e-01 -8.06687176e-01 4.48485225e-01 -6.03779964e-02
-3.25156808e-01 5.98199844e-01 7.61262298e-01 -8.71697009e-01
-1.75369456e-01 -9.74655986e-01 -6.62097275e-01 2.41464123e-01
2.23365217e-01 9.06491205e-02 -1.20568626e-01 -4.28385973... | [9.134956359863281, 6.195166110992432] |
d96e354b-e18d-4b9f-9f02-6741bf3551e0 | hierarchical-boundary-aware-neural-encoder | 1611.09312 | null | http://arxiv.org/abs/1611.09312v3 | http://arxiv.org/pdf/1611.09312v3.pdf | Hierarchical Boundary-Aware Neural Encoder for Video Captioning | The use of Recurrent Neural Networks for video captioning has recently gained
a lot of attention, since they can be used both to encode the input video and
to generate the corresponding description. In this paper, we present a
recurrent video encoding scheme which can discover and leverage the
hierarchical structure of... | ['Lorenzo Baraldi', 'Rita Cucchiara', 'Costantino Grana'] | 2016-11-28 | hierarchical-boundary-aware-neural-encoder-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Baraldi_Hierarchical_Boundary-Aware_Neural_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Baraldi_Hierarchical_Boundary-Aware_Neural_CVPR_2017_paper.pdf | cvpr-2017-7 | ['video-description'] | ['computer-vision'] | [ 4.95230079e-01 2.91507822e-02 -4.86457914e-01 -3.34662884e-01
-6.91055179e-01 -5.24973333e-01 6.69878900e-01 -7.53329545e-02
-1.10161655e-01 6.98408127e-01 7.05036044e-01 -1.18310342e-03
2.60961086e-01 -4.93024796e-01 -1.08542728e+00 -5.22355974e-01
-7.59928152e-02 8.28947946e-02 3.17506224e-01 5.22110499... | [10.58216381072998, 0.6662557125091553] |
0f7a680d-1a46-41c1-9377-244253d51459 | incremental-learning-for-animal-pose | 2110.13598 | null | https://arxiv.org/abs/2110.13598v1 | https://arxiv.org/pdf/2110.13598v1.pdf | Incremental Learning for Animal Pose Estimation using RBF k-DPP | Pose estimation is the task of locating keypoints for an object of interest in an image. Animal Pose estimation is more challenging than estimating human pose due to high inter and intra class variability in animals. Existing works solve this problem for a fixed set of predefined animal categories. Models trained on su... | ['Anirban Chakraborty', 'Het Shah', 'Gaurav Kumar Nayak'] | 2021-10-26 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 2.06671268e-01 -2.13041410e-01 2.72792391e-02 -2.19982564e-01
-4.27217454e-01 -5.07959604e-01 3.49500656e-01 1.53708160e-01
-5.40664554e-01 7.31708229e-01 -1.87940776e-01 3.19338620e-01
-8.62873867e-02 -5.47810793e-01 -1.11799073e+00 -5.98275661e-01
-1.06436260e-01 4.67436463e-01 5.12900949e-01 -1.27097175... | [7.518299102783203, -0.9246379733085632] |
60601b67-b2a0-46d8-ab75-0c93dfb196f1 | neural-color-operators-for-sequential-image | 2207.08080 | null | https://arxiv.org/abs/2207.08080v2 | https://arxiv.org/pdf/2207.08080v2.pdf | Neural Color Operators for Sequential Image Retouching | We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar... | ['Errui Ding', 'Fu Li', 'Qi Zhang', 'Dongliang He', 'Kun Xu', 'Xin Li', 'Yili Wang'] | 2022-07-17 | null | null | null | null | ['image-retouching'] | ['computer-vision'] | [ 4.17544320e-02 -1.43202797e-01 -2.83657044e-01 -3.03838462e-01
-5.20870209e-01 -6.97487712e-01 5.87091744e-01 -4.90237236e-01
-3.70715171e-01 4.35947567e-01 1.81985900e-01 -2.41394445e-01
4.10650849e-01 -6.19473815e-01 -1.08152103e+00 -7.47225046e-01
1.02397151e-01 -1.33756265e-01 2.09730670e-01 -2.61103719... | [11.404088020324707, -0.9918814301490784] |
0617cfd2-b76b-4801-9d95-f980fbb644af | data-augmentation-for-low-resource-dialogue-1 | null | null | https://aclanthology.org/2022.findings-naacl.53 | https://aclanthology.org/2022.findings-naacl.53.pdf | Data Augmentation for Low-Resource Dialogue Summarization | We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization. Our method generates synthetic examples by replacing sections of text from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue. We utilize pre... | ['Shashi Narayan', 'Gonçalo Simões', 'Joshua Maynez', 'Yongtai Liu'] | null | null | null | null | findings-naacl-2022-7 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.10931671e-01 9.76717293e-01 -2.70963442e-02 -3.34236294e-01
-1.39770997e+00 -7.01232553e-01 1.00017679e+00 3.94852191e-01
-2.97462910e-01 1.36898041e+00 1.27839696e+00 -9.11939610e-03
3.62259060e-01 -3.54960293e-01 -1.75332278e-01 -1.29662275e-01
3.89196903e-01 9.33597982e-01 -2.63667673e-01 -4.33092117... | [12.39822006225586, 9.134276390075684] |
e6805ac1-93b2-48de-b205-a2e7a23dcb78 | efficient-adaptation-for-end-to-end-vision-1 | null | null | https://openreview.net/forum?id=CVN5cZBFFFG | https://openreview.net/pdf?id=CVN5cZBFFFG | Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation | One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments, but most robot learning systems today are deployed as fixed policies which do not adapt after deployment. Can we efficiently adapt previously learned behavior... | ['Karol Hausman', 'Chelsea Finn', 'Sergey Levine', 'Gaurav S. Sukhatme', 'Benjamin Swanson', 'Ryan Julian'] | 2020-06-12 | null | null | null | icml-workshop-lifelongml-2020-7 | ['robotic-grasping'] | ['robots'] | [ 4.00702916e-02 7.10721686e-02 -5.93137182e-02 -2.56740451e-01
-2.67584264e-01 -8.21932495e-01 5.31554222e-01 -1.10941090e-01
-9.01620686e-01 1.13440824e+00 -9.87101719e-02 -1.06218174e-01
-2.75605321e-01 -6.17406428e-01 -1.21873510e+00 -5.30499578e-01
-4.21333671e-01 9.08328831e-01 6.56438649e-01 -6.13257766... | [4.394259452819824, 1.0371134281158447] |
642d2f6a-b44c-4e6a-acf7-4fd210d9e66e | egocentric-audio-visual-object-localization | 2303.13471 | null | https://arxiv.org/abs/2303.13471v1 | https://arxiv.org/pdf/2303.13471v1.pdf | Egocentric Audio-Visual Object Localization | Humans naturally perceive surrounding scenes by unifying sound and sight in a first-person view. Likewise, machines are advanced to approach human intelligence by learning with multisensory inputs from an egocentric perspective. In this paper, we explore the challenging egocentric audio-visual object localization task ... | ['Chenliang Xu', 'Anurag Kumar', 'Yapeng Tian', 'Chao Huang'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Egocentric_Audio-Visual_Object_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Egocentric_Audio-Visual_Object_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-localization'] | ['computer-vision'] | [-2.49400940e-02 -3.43069553e-01 6.07085116e-02 -2.63391227e-01
-7.25019336e-01 -6.26897931e-01 4.21813637e-01 -1.99784294e-01
-2.69577146e-01 2.44650498e-01 5.69826961e-01 6.66964412e-01
-1.20863318e-02 -1.50065497e-01 -7.85078943e-01 -5.97120821e-01
3.41318250e-02 -2.28789419e-01 1.00680172e-01 8.71429592... | [14.762530326843262, 4.944378852844238] |
cdf38deb-0ee5-4125-94e4-d3153c69c9a0 | document-image-cleaning-using-budget-aware | 2306.13236 | null | https://arxiv.org/abs/2306.13236v1 | https://arxiv.org/pdf/2306.13236v1.pdf | Document Image Cleaning using Budget-Aware Black-Box Approximation | Recent work has shown that by approximating the behaviour of a non-differentiable black-box function using a neural network, the black-box can be integrated into a differentiable training pipeline for end-to-end training. This methodology is termed "differentiable bypass,'' and a successful application of this method i... | ['Nilanjan Ray', 'Eric Van Oeveren', 'Katyani Singh', 'Ganesh Tata'] | 2023-06-22 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.40815216e-01 -4.38552059e-04 -1.70018837e-01 -2.65597284e-01
-1.17310202e+00 -1.02850068e+00 3.78323793e-01 1.30697027e-01
-5.75780809e-01 3.15021843e-01 -5.63799322e-01 -8.42755437e-01
2.20756024e-01 -6.62826002e-01 -1.12353194e+00 -3.28082323e-01
2.77032822e-01 2.38618150e-01 1.82556763e-01 -2.48161316... | [5.894392490386963, 8.029037475585938] |
9c6a7c25-a721-4e44-9530-8f75806d6194 | deeply-interleaved-two-stream-encoder-for | 2203.15969 | null | https://arxiv.org/abs/2203.15969v1 | https://arxiv.org/pdf/2203.15969v1.pdf | Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation | Referring video segmentation aims to segment the corresponding video object described by the language expression. To address this task, we first design a two-stream encoder to extract CNN-based visual features and transformer-based linguistic features hierarchically, and a vision-language mutual guidance (VLMG) module ... | ['Huchuan Lu', 'Zhiwei Hu', 'Lihe Zhang', 'Guang Feng'] | 2022-03-30 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 2.14317609e-02 -2.42464855e-01 -2.68515646e-01 -5.02619624e-01
-5.66662669e-01 -1.01482503e-01 6.40358567e-01 -7.06065744e-02
-5.51464736e-01 3.13543439e-01 5.19790590e-01 2.15467662e-02
-3.31138335e-02 -9.25903320e-01 -5.32251239e-01 -7.09091485e-01
3.86623174e-01 -2.17800051e-01 7.80076206e-01 -1.56166881... | [9.524349212646484, 0.06990666687488556] |
9d27506c-26f5-42ca-9364-34c27112ed7f | text-to-sql-error-correction-with-language | 2305.13073 | null | https://arxiv.org/abs/2305.13073v2 | https://arxiv.org/pdf/2305.13073v2.pdf | Text-to-SQL Error Correction with Language Models of Code | Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level... | ['Huan Sun', 'Yu Su', 'Jayanth Srinivasa', 'Ali Payani', 'Raymond Mooney', 'Michael White', 'Shijie Chen', 'Ziru Chen'] | 2023-05-22 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 2.98994817e-02 1.23373955e-01 -1.63285583e-01 -9.47629154e-01
-1.23455119e+00 -7.56397188e-01 1.93855554e-01 8.53279471e-01
-2.66574353e-01 3.03497314e-01 2.71556079e-01 -6.51635826e-01
4.44789261e-01 -9.32921767e-01 -1.19573820e+00 4.60185498e-01
3.36912751e-01 3.82020354e-01 4.76788521e-01 -2.30950847... | [7.947274684906006, 7.867146015167236] |
e446c51a-7df3-4ab0-9f68-83ec0c36e515 | from-private-to-public-benchmarking-gans-in | 2303.15916 | null | https://arxiv.org/abs/2303.15916v2 | https://arxiv.org/pdf/2303.15916v2.pdf | From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification | Deep learning has proven to be successful in various domains and for different tasks. However, when it comes to private data several restrictions are making it difficult to use deep learning approaches in these application fields. Recent approaches try to generate data privately instead of applying a privacy-preserving... | ['Sheraz Ahmed', 'Andreas Dengel', 'Dominique Mercier'] | 2023-03-28 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 2.84239948e-01 1.88645557e-01 1.48685396e-01 -2.69914210e-01
-8.33257258e-01 -6.98505580e-01 9.64637816e-01 -1.84491612e-02
-3.33516210e-01 1.00096655e+00 1.32328764e-01 -1.82563543e-01
3.15314010e-02 -1.14724839e+00 -6.09403551e-01 -1.00915611e+00
7.49391541e-02 1.28271833e-01 -3.81612688e-01 -2.47434899... | [6.156399250030518, 6.85683012008667] |
d6d5f9fa-3325-49e8-8877-f1dde7cad282 | keyphrase-generation-a-text-summarization | 1904.00110 | null | http://arxiv.org/abs/1904.00110v2 | http://arxiv.org/pdf/1904.00110v2.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 conside... | ['Ondřej Bojar', 'Erion Çano'] | 2019-03-29 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.52821833e-01 3.72204036e-01 -2.43698359e-01 1.72535822e-01
-8.92603815e-01 -7.44069219e-01 1.06100500e+00 9.76241231e-01
-4.30160016e-01 1.18686390e+00 8.96992147e-01 -2.98821598e-01
-2.72700995e-01 -7.58429825e-01 -8.27791870e-01 -4.79728103e-01
-4.55349013e-02 3.50210279e-01 -1.54886425e-01 1.50828257... | [12.490280151367188, 9.343859672546387] |
2826c839-23f0-440e-8d28-1ca20524f0e4 | rank-1-matrix-completion-with-gradient | 2212.09396 | null | https://arxiv.org/abs/2212.09396v2 | https://arxiv.org/pdf/2212.09396v2.pdf | Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization | The nonconvex formulation of matrix completion problem has received significant attention in recent years due to its affordable complexity compared to the convex formulation. Gradient descent (GD) is the simplest yet efficient baseline algorithm for solving nonconvex optimization problems. The success of GD has been wi... | ['Hye Won Chung', 'Daesung Kim'] | 2022-12-19 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [-8.18518102e-02 -1.12748884e-01 -1.92684934e-01 6.68558897e-03
-8.21225047e-01 -6.57869339e-01 1.24360994e-01 9.22029391e-02
-3.97594333e-01 7.55960405e-01 3.42328936e-01 -3.35933059e-01
-2.64549434e-01 -4.40705687e-01 -8.78929555e-01 -9.78827655e-01
-2.49630809e-01 3.58367711e-01 -1.61955342e-01 -3.72515500... | [7.006585121154785, 4.5205183029174805] |
da953a83-25eb-4636-b0f4-ba946c173b07 | multi-modal-multi-correlation-learning-for | 2207.01197 | null | https://arxiv.org/abs/2207.01197v1 | https://arxiv.org/pdf/2207.01197v1.pdf | Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation | In this paper we propose a multi-modal multi-correlation learning framework targeting at the task of audio-visual speech separation. Although previous efforts have been extensively put on combining audio and visual modalities, most of them solely adopt a straightforward concatenation of audio and visual features. To ex... | ['Yan Lu', 'Xiulian Peng', 'Xiangyu Kong', 'Xiaoyu Wang'] | 2022-07-04 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.11001365e-01 -1.57818899e-01 -1.53961703e-01 -2.39207342e-01
-1.07392180e+00 -5.43320775e-01 8.09558511e-01 -1.42946452e-01
-2.87686795e-01 5.69642365e-01 2.82587260e-01 1.25324847e-02
-1.30480319e-01 -1.71533450e-01 -2.11491257e-01 -1.00855482e+00
-9.79471877e-02 -8.95705223e-02 3.09492815e-02 -1.51137978... | [14.411968231201172, 5.107040882110596] |
786bfd49-a47c-4ed3-be35-f5315db1fc07 | inference-with-hybrid-bio-hardware-neural | 1905.11594 | null | https://arxiv.org/abs/1905.11594v2 | https://arxiv.org/pdf/1905.11594v2.pdf | Inference with Hybrid Bio-hardware Neural Networks | To understand the learning process in brains, biologically plausible algorithms have been explored by modeling the detailed neuron properties and dynamics. On the other hand, simplified multi-layer models of neural networks have shown great success on computational tasks such as image classification and speech recognit... | ['Zubayer Ibne Ferdous', 'Zhiyuan Yan', 'Yuan Zeng', 'Weixiang Zhang', 'Xiaochen Guo', 'Drew Patel', 'Yevgeny Berdichevsky', 'Mufan Xu', 'Anlan Yu'] | 2019-05-28 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.21419448e-01 1.46616399e-01 9.13819596e-02 -2.14603081e-01
4.58858818e-01 1.85710490e-01 4.96858478e-01 -9.53527689e-02
-5.73153079e-01 8.14217329e-01 -4.95974511e-01 -7.46768340e-02
-2.38242179e-01 -6.58938408e-01 -1.05252707e+00 -1.18854761e+00
1.05262913e-01 4.46453631e-01 4.50187773e-01 -6.09227791... | [8.157161712646484, 2.7445082664489746] |
5bb058a2-2001-46ed-94f8-9fc46cb9a3bc | renderdiffusion-text-generation-as-image | 2304.12519 | null | https://arxiv.org/abs/2304.12519v2 | https://arxiv.org/pdf/2304.12519v2.pdf | GlyphDiffusion: Text Generation as Image Generation | Diffusion models have become a new generative paradigm for text generation. Considering the discrete categorical nature of text, in this paper, we propose GlyphDiffusion, a novel diffusion approach for text generation via text-guided image generation. Our key idea is to render the target text as a glyph image containin... | ['Ji-Rong Wen', 'Jian-Yun Nie', 'Wayne Xin Zhao', 'Junyi Li'] | 2023-04-25 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 5.36697268e-01 2.30190694e-01 2.32091650e-01 -1.59777731e-01
-7.45849609e-01 -4.75472003e-01 1.28658724e+00 -1.67521805e-01
-5.54398634e-02 7.44663060e-01 7.10460663e-01 -1.85737759e-01
4.39300895e-01 -1.21589255e+00 -6.29179895e-01 -6.34453833e-01
6.26702964e-01 5.56452274e-01 9.22613367e-02 -3.16274881... | [11.31615924835205, -0.07377715408802032] |
1c45e4d2-9c66-4408-ad91-0a77b6b551eb | from-behavioral-theories-to-econometrics | 2112.15151 | null | https://arxiv.org/abs/2112.15151v1 | https://arxiv.org/pdf/2112.15151v1.pdf | From Behavioral Theories to Econometrics: Inferring Preferences of Human Agents from Data on Repeated Interactions | We consider the problem of estimating preferences of human agents from data of strategic systems where the agents repeatedly interact. Recently, it was demonstrated that a new estimation method called "quantal regret" produces more accurate estimates for human agents than the classic approach that assumes that agents a... | ['Gali Noti'] | 2021-12-30 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-5.44061661e-01 1.06275424e-01 -3.01017076e-01 -1.83443606e-01
-4.89194185e-01 -7.26818442e-01 3.47553819e-01 -2.07740054e-01
-8.94801676e-01 1.10904360e+00 4.96780388e-02 -5.51990449e-01
-6.50820076e-01 -5.49582183e-01 -2.85514891e-01 -4.41631496e-01
-2.49422804e-01 6.45563126e-01 -3.77107388e-03 -2.78031528... | [4.256002426147461, 2.945192337036133] |
1f1593e9-3850-4b36-9b3d-bccaec02a67c | geometric-processing-for-image-based-3d | 2106.14307 | null | https://arxiv.org/abs/2106.14307v1 | https://arxiv.org/pdf/2106.14307v1.pdf | Geometric Processing for Image-based 3D Object Modeling | Image-based 3D object modeling refers to the process of converting raw optical images to 3D digital representations of the objects. Very often, such models are desired to be dimensionally true, semantically labeled with photorealistic appearance (reality-based modeling). Laser scanning was deemed as the standard (and d... | ['Xu Huang', 'Rongjun Qin'] | 2021-06-27 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.76404577e-01 -3.92634347e-02 4.47625428e-01 -3.26406121e-01
-4.33372587e-01 -3.31214130e-01 8.21743131e-01 1.88224241e-01
-1.22233495e-01 2.68489033e-01 -3.63506198e-01 -5.53265698e-02
-4.82796848e-01 -1.09765005e+00 -5.83902657e-01 -3.13416690e-01
-2.02778005e-03 1.21203864e+00 3.38320166e-01 -1.40959084... | [8.43966007232666, -2.7125673294067383] |
169e0e27-64a6-4fca-b0d7-8ab6690f4f30 | art-authentication-with-vision-transformers | 2307.03039 | null | https://arxiv.org/abs/2307.03039v2 | https://arxiv.org/pdf/2307.03039v2.pdf | Art Authentication with Vision Transformers | In recent years, Transformers, initially developed for language, have been successfully applied to visual tasks. Vision Transformers have been shown to push the state-of-the-art in a wide range of tasks, including image classification, object detection, and semantic segmentation. While ample research has shown promisin... | ['Eric Postma', 'Carina Popovici', 'Ludovica Schaerf'] | 2023-07-06 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 2.04571754e-01 -1.53630599e-01 7.55965859e-02 -9.08828992e-03
-3.29015225e-01 -8.68613005e-01 1.09840202e+00 -9.69351903e-02
-5.41735172e-01 2.53028393e-01 -2.88261294e-01 -2.20992371e-01
-1.04833916e-01 -7.53544629e-01 -4.88783449e-01 -3.95240605e-01
3.47094774e-01 6.77871466e-01 3.70923221e-01 -2.05406740... | [11.473394393920898, 0.3791159391403198] |
588a92b1-842c-4748-96f3-988b9ce00dbd | multiple-people-tracking-by-lifted-multicut | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Tang_Multiple_People_Tracking_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf | Multiple People Tracking by Lifted Multicut and Person Re-Identification | Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work... | ['Bernt Schiele', 'Bjoern Andres', 'Siyu Tang', 'Mykhaylo Andriluka'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['multiple-people-tracking'] | ['computer-vision'] | [-9.13393423e-02 3.26883569e-02 3.54119807e-01 -3.22797805e-01
-1.95517763e-01 -6.74569190e-01 2.32284650e-01 1.42451465e-01
-7.48290479e-01 6.23919487e-01 1.64537311e-01 3.76191616e-01
-2.39891768e-01 -5.12587547e-01 -9.66006875e-01 -2.65612006e-01
-3.51512879e-01 9.12541091e-01 4.65165615e-01 -1.96498394... | [6.902027130126953, -1.0674060583114624] |
a6eee3b4-9183-4520-86a0-c8d3599e17e6 | real-time-human-motion-capture-with-multiple | 1605.08068 | null | http://arxiv.org/abs/1605.08068v1 | http://arxiv.org/pdf/1605.08068v1.pdf | Real-Time Human Motion Capture with Multiple Depth Cameras | Commonly used human motion capture systems require intrusive attachment of
markers that are visually tracked with multiple cameras. In this work we
present an efficient and inexpensive solution to markerless motion capture
using only a few Kinect sensors. Unlike the previous work on 3d pose estimation
using a single de... | ['James J. Little', 'Alireza Shafaei'] | 2016-05-25 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [-3.89718427e-03 7.59928599e-02 -2.15499759e-01 -1.22740977e-01
-9.44237769e-01 -6.46557629e-01 3.62415969e-01 -2.74702102e-01
-9.24251497e-01 6.48884535e-01 -1.70805439e-01 1.25896499e-01
6.36826217e-01 -2.71086544e-01 -8.40386093e-01 -3.22048962e-01
1.36200175e-01 8.01852703e-01 6.41179800e-01 2.14787960... | [7.1447954177856445, -1.0938127040863037] |
00266f35-6678-4e16-8fe7-e22f280da80d | conda-continual-unsupervised-domain-1 | 2212.00621 | null | https://arxiv.org/abs/2212.00621v1 | https://arxiv.org/pdf/2212.00621v1.pdf | CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars | Although unsupervised domain adaptation methods have achieved remarkable performance in semantic scene segmentation in visual perception for self-driving cars, these approaches remain impractical in real-world use cases. In practice, the segmentation models may encounter new data that have not been seen yet. Also, the ... | ['Khoa Luu', 'Jackson David Cothren', 'Ahmed Moustafa', 'Pierce Helton', 'Thanh-Dat Truong'] | 2022-12-01 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.37605411e-01 -1.27441408e-02 -5.04173972e-02 -5.97726464e-01
-2.41573259e-01 -3.91553789e-01 3.90466779e-01 1.61616534e-01
-7.23164678e-01 9.24721956e-01 -3.30081403e-01 -1.13580503e-01
8.71669874e-02 -8.04529428e-01 -6.55798197e-01 -7.62683749e-01
4.73859280e-01 4.90326732e-01 7.56658614e-01 8.81157815... | [9.996642112731934, 2.298212766647339] |
24855a54-eb33-4d23-aa0f-40a939006d01 | model-reduction-of-consensus-network-systems | 2203.14377 | null | https://arxiv.org/abs/2203.14377v1 | https://arxiv.org/pdf/2203.14377v1.pdf | Model Reduction of Consensus Network Systems via Selection of Optimal Edge Weights and Nodal Time-Scales | This paper proposes model reduction approaches for consensus network systems based on a given clustering of the underlying graph. Namely, given a consensus network system of time-scaled agents evolving over a weighted undirected graph and a graph clustering, a parameterized reduced consensus network system is construct... | ['Dany Abou Jaoude', 'Ralph Sabbagh'] | 2022-03-27 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.40878424e-01 4.63975787e-01 1.45909578e-01 8.85708481e-02
-1.93365574e-01 -5.34972608e-01 2.10755527e-01 1.34995744e-01
1.33567452e-01 5.90897262e-01 -3.94719839e-01 -5.98471053e-02
-8.85894299e-01 -4.76985574e-01 -1.65646508e-01 -9.99149024e-01
-7.41879821e-01 2.19199494e-01 -1.26309022e-01 -3.80983412... | [5.176692008972168, 2.697511672973633] |
ba106849-1899-44ee-bfd0-8663aff1fe0c | how-well-do-self-supervised-methods-perform | 2202.09014 | null | https://arxiv.org/abs/2202.09014v1 | https://arxiv.org/pdf/2202.09014v1.pdf | How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning? | Cross-domain few-shot learning (CDFSL) remains a largely unsolved problem in the area of computer vision, while self-supervised learning presents a promising solution. Both learning methods attempt to alleviate the dependency of deep networks on the requirement of large-scale labeled data. Although self-supervised meth... | ['Jun Wang', 'Xiaogang Xu', 'Ying Zheng', 'Yiyi Zhang'] | 2022-02-18 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 7.78529886e-03 3.44438702e-02 -5.01120627e-01 -6.72999859e-01
-8.72096181e-01 -4.68604475e-01 9.20729637e-01 1.66226208e-01
-4.22914505e-01 7.89040983e-01 1.98038548e-01 -2.59548016e-02
-2.58918196e-01 -7.03110814e-01 -5.14236867e-01 -6.79036736e-01
1.33396819e-01 4.62357938e-01 3.26196551e-01 -3.62564415... | [9.931191444396973, 2.9676365852355957] |
f6af1f0a-554e-48ba-86cf-edeeaf31e771 | headline-generation-learning-from-decomposed | 1904.08455 | null | https://arxiv.org/abs/1904.08455v3 | https://arxiv.org/pdf/1904.08455v3.pdf | Headline Generation: Learning from Decomposable Document Titles | We propose a novel method for generating titles for unstructured text documents. We reframe the problem as a sequential question-answering task. A deep neural network is trained on document-title pairs with decomposable titles, meaning that the vocabulary of the title is a subset of the vocabulary of the document. To t... | ['Oleg Vasilyev', 'John Bohannon', 'Tom Grek'] | 2019-04-17 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 1.86399326e-01 5.66442847e-01 -3.37314129e-01 -4.98766452e-01
-1.05694020e+00 -6.40935838e-01 1.04595983e+00 2.92806298e-01
-6.61334157e-01 9.64078009e-01 9.05260324e-01 -1.79098800e-01
1.67952761e-01 -8.64356041e-01 -1.06512010e+00 -2.05193162e-02
2.87565231e-01 8.91283631e-01 -1.67261641e-02 -2.77780563... | [12.295337677001953, 9.359468460083008] |
adda9c4c-4635-4fb4-9bd1-704d3bc36e95 | improving-road-signs-detection-performance-by | 2010.06453 | null | https://arxiv.org/abs/2010.06453v1 | https://arxiv.org/pdf/2010.06453v1.pdf | Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture | With the large uses of the intelligent systems in different domains, and in order to increase the drivers and pedestrians safety, the road and traffic sign recognition system has been a challenging issue and an important task for many years. But studies, done in this field of detection and recognition of traffic signs ... | ['Abdellah Amghar', 'Karim Afdel', 'Mourad Boussaid', 'Tarik Ayaou'] | 2020-10-13 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 1.60761610e-01 -4.26661670e-01 2.22886335e-02 -3.40678394e-01
-7.64560550e-02 -3.37739825e-01 7.73083627e-01 1.57045685e-02
-5.53257465e-01 5.99683166e-01 -2.22930416e-01 -5.38356304e-01
-4.43791837e-01 -7.91920424e-01 -1.42964751e-01 -8.66860271e-01
1.55853540e-01 4.27392244e-01 6.46521389e-01 -2.89520591... | [9.789229393005371, -4.990457057952881] |
016e57b7-8b26-4615-8065-16c099b40884 | low-resource-cross-lingual-adaptive-training | 2307.00382 | null | https://arxiv.org/abs/2307.00382v1 | https://arxiv.org/pdf/2307.00382v1.pdf | Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin | Developing effective spoken language processing systems for low-resource languages poses several challenges due to the lack of parallel data and limited resources for fine-tuning models. In this work, we target on improving upon both text classification and translation of Nigerian Pidgin (Naija) by collecting a large-s... | ['Merel Scholman', 'Ernie Chang', 'Muhammed Saeed', 'Pin-Jie Lin'] | 2023-07-01 | null | null | null | null | ['text-classification'] | ['natural-language-processing'] | [-3.43152136e-02 -2.18002692e-01 -1.06273517e-01 -7.97822118e-01
-1.33635378e+00 -7.24125862e-01 6.21947348e-01 1.50612488e-01
-8.83391142e-01 7.42938638e-01 6.00646377e-01 -6.09656036e-01
2.15924129e-01 -4.39083248e-01 -5.64697802e-01 -1.18132299e-02
1.97362334e-01 9.98345673e-01 3.95343406e-03 -6.00564122... | [10.957368850708008, 10.002338409423828] |
0cde69ed-26a9-4ebe-a53e-91a1937d7e3b | adaptive-event-detection-for-representative | 2107.11287 | null | https://arxiv.org/abs/2107.11287v1 | https://arxiv.org/pdf/2107.11287v1.pdf | Adaptive Event Detection for Representative Load Signature Extraction | Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameters may fail in case of unpredictable and complicated residential load changes such as high fluctuatio... | ['Zuyi Li', 'Jiayu Han', 'Wei Tian', 'Lei Yan'] | 2021-07-23 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.18244359e-01 -4.76763248e-01 -2.09291965e-01 -1.99189439e-01
-5.96885562e-01 -2.50225574e-01 2.86704302e-01 4.06882703e-01
1.65188178e-01 8.33723903e-01 1.21520974e-01 -9.20173973e-02
-3.82970273e-01 -7.98176050e-01 1.33801447e-02 -6.14328265e-01
-3.88676941e-01 3.40661466e-01 4.81467426e-01 -1.86065212... | [6.021787166595459, 2.5990068912506104] |
5344accc-e36f-4fb6-88d6-0a244f053111 | compressing-low-precision-deep-neural | 1709.06262 | null | http://arxiv.org/abs/1709.06262v2 | http://arxiv.org/pdf/1709.06262v2.pdf | Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks | A low precision deep neural network training technique for producing sparse,
ternary neural networks is presented. The technique incorporates hard- ware
implementation costs during training to achieve significant model compression
for inference. Training involves three stages: network training using L2
regularization a... | ['Giulio Gambardella', 'Michaela Blott', 'Julian Faraone', 'Nicholas Fraser', 'Philip H. W. Leong'] | 2017-09-19 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 4.75701421e-01 3.33782107e-01 -6.53325737e-01 -7.94982553e-01
-3.43027800e-01 -3.75919268e-02 1.61450937e-01 2.47828677e-01
-7.67956376e-01 8.97395194e-01 -7.64848143e-02 -6.92115843e-01
-1.17779955e-01 -9.13186908e-01 -8.22392702e-01 -3.21078241e-01
-8.18861574e-02 3.52211565e-01 1.49278715e-02 3.12118709... | [8.533129692077637, 3.041383981704712] |
abcb38d5-c9e9-4753-9942-712327298dff | dual-stream-computer-generated-image | 2207.03205 | null | https://arxiv.org/abs/2207.03205v1 | https://arxiv.org/pdf/2207.03205v1.pdf | Dual Stream Computer-Generated Image Detection Network Based On Channel Joint And Softpool | With the development of computer graphics technology, the images synthesized by computer software become more and more closer to the photographs. While computer graphics technology brings us a grand visual feast in the field of games and movies, it may also be utilized by someone with bad intentions to guide public opi... | ['Weiqi Luo', 'Hao Lin', 'Ziyi Xi'] | 2022-07-07 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.14425191e-01 -3.56513023e-01 1.44945653e-02 -1.38780624e-01
-5.05793691e-01 -2.15473324e-01 3.67357254e-01 -1.74205303e-01
-4.68559295e-01 4.03295875e-01 2.44389161e-01 -4.09774512e-01
3.85749549e-01 -1.08594823e+00 -4.12878036e-01 -8.20078194e-01
4.14317071e-01 -5.54977000e-01 5.91400206e-01 -6.98696589... | [12.31151294708252, 0.829243540763855] |
ea09db61-b42e-47cb-b298-6effa9100cbc | learning-grounded-vision-language | 2303.06378 | null | https://arxiv.org/abs/2303.06378v2 | https://arxiv.org/pdf/2303.06378v2.pdf | Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos | Joint video-language learning has received increasing attention in recent years. However, existing works mainly focus on single or multiple trimmed video clips (events), which makes human-annotated event boundaries necessary during inference. To break away from the ties, we propose a grounded vision-language learning f... | ['Ping Luo', 'Ran Cheng', 'Wenhao Jiang', 'Feng Zheng', 'Jinrui Zhang', 'Teng Wang'] | 2023-03-11 | null | null | null | null | ['dense-video-captioning', 'natural-language-moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 2.75193542e-01 2.47211590e-01 -3.30714762e-01 -4.96771783e-01
-1.41137409e+00 -4.56639141e-01 7.38586903e-01 -1.86156452e-01
-2.92908877e-01 7.65940309e-01 7.89921880e-01 -2.02997066e-02
2.69786239e-01 -5.34263492e-01 -1.08000302e+00 -5.05002797e-01
5.66227585e-02 5.44363678e-01 2.67094165e-01 3.54886353... | [10.41744613647461, 0.6920532584190369] |
0e842a33-4475-4d7b-97a3-7421c514359a | real-image-denoising-with-feature-attention | 1904.07396 | null | https://arxiv.org/abs/1904.07396v2 | https://arxiv.org/pdf/1904.07396v2.pdf | Real Image Denoising with Feature Attention | Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stag... | ['Nick Barnes', 'Saeed Anwar'] | 2019-04-16 | real-image-denoising-with-feature-attention-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Anwar_Real_Image_Denoising_With_Feature_Attention_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Anwar_Real_Image_Denoising_With_Feature_Attention_ICCV_2019_paper.pdf | iccv-2019-10 | ['color-image-denoising'] | ['computer-vision'] | [ 1.16305657e-01 -4.63817656e-01 4.40665394e-01 -3.51622641e-01
-6.47773683e-01 -1.67024285e-01 4.98552740e-01 -6.00121975e-01
-7.46671081e-01 6.46625876e-01 3.83032173e-01 -9.14335772e-02
-6.51478991e-02 -5.89549780e-01 -5.00499129e-01 -9.90283787e-01
-1.36468515e-01 -7.41506100e-01 2.14976504e-01 -4.24754620... | [11.44696044921875, -2.3405251502990723] |
fcdb9f8e-8ec1-48ae-8692-7a18274425e0 | conditioning-autoencoder-latent-spaces-for | 2001.11296 | null | https://arxiv.org/abs/2001.11296v1 | https://arxiv.org/pdf/2001.11296v1.pdf | Conditioning Autoencoder Latent Spaces for Real-Time Timbre Interpolation and Synthesis | We compare standard autoencoder topologies' performances for timbre generation. We demonstrate how different activation functions used in the autoencoder's bottleneck distributes a training corpus's embedding. We show that the choice of sigmoid activation in the bottleneck produces a more bounded and uniformly distribu... | ['Joseph T Colonel', 'Sam Keene'] | 2020-01-30 | null | null | null | null | ['timbre-interpolation'] | ['audio'] | [ 1.42280513e-03 1.61677480e-01 -3.05824075e-02 -3.11810613e-01
-5.88160634e-01 -5.89167118e-01 6.34018958e-01 -1.48621142e-01
-5.40711939e-01 6.84012890e-01 8.84639561e-01 -1.94212839e-01
2.12562054e-01 -8.17781389e-01 -6.53071046e-01 -9.72715259e-01
7.51670897e-02 6.15632348e-02 -4.31273460e-01 -5.14568985... | [15.45969009399414, 5.720325946807861] |
d5434072-6034-45c8-8c7a-8a9c436796b0 | answering-questions-about-data-visualizations | 1908.01801 | null | https://arxiv.org/abs/1908.01801v2 | https://arxiv.org/pdf/1908.01801v2.pdf | Answering Questions about Data Visualizations using Efficient Bimodal Fusion | Chart question answering (CQA) is a newly proposed visual question answering (VQA) task where an algorithm must answer questions about data visualizations, e.g. bar charts, pie charts, and line graphs. CQA requires capabilities that natural-image VQA algorithms lack: fine-grained measurements, optical character recogni... | ['Brian Price', 'Kushal Kafle', 'Scott Cohen', 'Robik Shrestha', 'Christopher Kanan'] | 2019-08-05 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [-6.79462329e-02 5.40975332e-02 2.17770725e-01 -3.33698750e-01
-1.21240103e+00 -1.12062955e+00 7.37364531e-01 5.30882478e-01
4.60169539e-02 1.53846189e-01 4.15904373e-01 -9.16880786e-01
1.44268245e-01 -7.68947423e-01 -8.51590931e-01 -2.25069210e-01
2.13030517e-01 6.15132570e-01 2.15636060e-01 -2.61321902... | [11.112095832824707, 2.0152506828308105] |
12fb1034-6102-4c5e-9c03-5d2dc3b05140 | an-application-of-deep-reinforcement-learning | 2004.06627 | null | https://arxiv.org/abs/2004.06627v3 | https://arxiv.org/pdf/2004.06627v3.pdf | An Application of Deep Reinforcement Learning to Algorithmic Trading | This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the r... | ['Thibaut Théate', 'Damien Ernst'] | 2020-04-07 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-4.89914387e-01 -1.79670483e-01 -6.67853281e-03 1.68414816e-01
-4.18196112e-01 -6.49917960e-01 7.98121035e-01 1.71014126e-02
-5.76441526e-01 1.04788768e+00 -1.95249513e-01 -4.93213326e-01
-6.41062200e-01 -1.26106358e+00 -4.45721477e-01 -6.32174253e-01
-1.79428309e-01 6.69551611e-01 3.78117301e-02 -4.62893903... | [4.451768398284912, 3.921389102935791] |
f99e7c13-259d-4bf1-88f6-f7a6bbabf91e | a-weighting-scheme-for-open-information | null | null | https://aclanthology.org/N12-2011 | https://aclanthology.org/N12-2011.pdf | A Weighting Scheme for Open Information Extraction | null | ['Yuval Merhav'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.379974365234375, 3.756701946258545] |
8f0deb00-62e6-452d-8b01-db24f2486e24 | 190503678 | 1905.03678 | null | https://arxiv.org/abs/1905.03678v1 | https://arxiv.org/pdf/1905.03678v1.pdf | What Do Single-view 3D Reconstruction Networks Learn? | Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by the idea of having an encoder-decoder network that performs non-trivial reasoning about the 3D structure of the output space. In this work, ... | ['René Ranftl', 'Zhuwen Li', 'Vladlen Koltun', 'Thomas Brox', 'Maxim Tatarchenko', 'Stephan R. Richter'] | 2019-05-09 | what-do-single-view-3d-reconstruction | http://openaccess.thecvf.com/content_CVPR_2019/html/Tatarchenko_What_Do_Single-View_3D_Reconstruction_Networks_Learn_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tatarchenko_What_Do_Single-View_3D_Reconstruction_Networks_Learn_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-8.78283195e-03 -6.90271556e-02 -1.63154036e-01 -6.03817046e-01
-8.44851792e-01 -6.18676424e-01 9.34599042e-01 -3.06164950e-01
-2.18525514e-01 3.44825178e-01 4.82956529e-01 -2.72645652e-01
1.23587780e-01 -7.45334804e-01 -1.16897440e+00 -5.21048486e-01
1.10900573e-01 5.85969388e-01 1.35327712e-01 -7.19635189... | [8.526097297668457, -3.0632779598236084] |
4bf4659b-4abf-4f4a-85f1-04e6daa79ecc | semi-supervised-temporal-action-detection | 2207.07059 | null | https://arxiv.org/abs/2207.07059v1 | https://arxiv.org/pdf/2207.07059v1.pdf | Semi-Supervised Temporal Action Detection with Proposal-Free Masking | Existing temporal action detection (TAD) methods rely on a large number of training data with segment-level annotations. Collecting and annotating such a training set is thus highly expensive and unscalable. Semi-supervised TAD (SS-TAD) alleviates this problem by leveraging unlabeled videos freely available at scale. H... | ['Tao Xiang', 'Yi-Zhe Song', 'Xiatian Zhu', 'Sauradip Nag'] | 2022-07-14 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.43930691e-01 -1.51821971e-01 -5.61723471e-01 -3.49921167e-01
-9.69308555e-01 -4.88340199e-01 5.83075106e-01 -1.26146600e-02
-3.79532009e-01 5.53391516e-01 8.72656927e-02 -7.69917965e-02
2.89555281e-01 -4.21833724e-01 -5.18818498e-01 -7.09452748e-01
-1.16791189e-01 3.52774441e-01 1.01314390e+00 6.89860731... | [8.429756164550781, 0.5927621722221375] |
ed37d7a9-cae4-46a9-b400-de6e20706899 | electrode-clustering-and-bandpass-analysis-of | 2302.12710 | null | https://arxiv.org/abs/2302.12710v1 | https://arxiv.org/pdf/2302.12710v1.pdf | Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation | In this study, we validate the findings of previously published papers, showing the feasibility of an Electroencephalography (EEG) based gaze estimation. Moreover, we extend previous research by demonstrating that with only a slight drop in model performance, we can significantly reduce the number of electrodes, indica... | ['Roger Wattenhofer', 'Nicolas Langer', 'Joël Küchler', 'Martyna Beata Plomecka', 'Ard Kastrati'] | 2023-02-19 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.23049663e-02 4.25884388e-02 3.86695087e-01 -1.82190001e-01
-1.50066197e-01 -3.61800909e-01 1.52737126e-01 3.19525987e-01
-7.33791828e-01 8.48958433e-01 -1.16404193e-02 -3.74824464e-01
-4.18304801e-01 -1.68950796e-01 -7.69607663e-01 -4.30613577e-01
1.91582646e-02 -2.61496276e-01 -5.98835014e-03 1.06533863... | [13.231764793395996, 3.255540132522583] |
72e165aa-a164-410a-990d-57fc5fb17270 | deep-curiosity-loops-in-social-environments | 1806.03645 | null | http://arxiv.org/abs/1806.03645v1 | http://arxiv.org/pdf/1806.03645v1.pdf | Deep Curiosity Loops in Social Environments | Inspired by infants' intrinsic motivation to learn, which values informative
sensory channels contingent on their immediate social environment, we developed
a deep curiosity loop (DCL) architecture. The DCL is composed of a learner,
which attempts to learn a forward model of the agent's state-action transition,
and a n... | ['Jonatan Barkan', 'Goren Gordon'] | 2018-06-10 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-1.60769835e-01 5.84425032e-01 2.31434837e-01 -3.57754439e-01
3.53597581e-01 -1.29155010e-01 6.02729201e-01 -5.04491106e-02
-5.45996249e-01 6.31700337e-01 2.16085374e-01 3.84665400e-01
-9.45289806e-03 -8.29440892e-01 -1.19543731e+00 -9.74316001e-01
-5.20583808e-01 -8.08289126e-02 2.29014009e-01 -2.11489096... | [4.211451530456543, 1.4380632638931274] |
cbf81895-9bed-404f-a570-7afde56651cb | expert-concept-modeling-ground-truth | null | null | https://aclanthology.org/2020.coling-main.586 | https://aclanthology.org/2020.coling-main.586.pdf | Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains | We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings. In particular, our method is designed to evaluate the application of word and paragraph embeddings in concept-focused textual domains, ... | ['Jelke Bloem', 'Andrew Salway', 'Yvette Oortwijn', 'Thijs Ossenkoppele', 'Martin Reynaert', 'Arianna Betti'] | 2020-12-01 | null | null | null | coling-2020-8 | ['embeddings-evaluation'] | ['natural-language-processing'] | [ 3.28980014e-02 4.81018633e-01 3.74706425e-02 -9.07808095e-02
-5.26712716e-01 -6.80101156e-01 1.02781928e+00 8.20369363e-01
-8.48013878e-01 3.46893370e-01 6.52771533e-01 -6.23761356e-01
-6.16986811e-01 -6.83330178e-01 -4.26774412e-01 -2.69647330e-01
2.24657148e-01 7.43446589e-01 -1.23124093e-01 -6.36417627... | [10.275096893310547, 8.877296447753906] |
7a221578-1882-406a-91e7-d37578a0825e | line-as-object-datasets-and-framework-for | 1909.06591 | null | https://arxiv.org/abs/1909.06591v2 | https://arxiv.org/pdf/1909.06591v2.pdf | Sem-LSD: A Learning-based Semantic Line Segment Detector | In this paper, we introduces a new type of line-shaped image representation, named semantic line segment (Sem-LS) and focus on solving its detection problem. Sem-LS contains high-level semantics and is a compact scene representation where only visually salient line segments with stable semantics are preserved. Combined... | ['Mingyang Li', 'Boren Li', 'Yi Sun', 'Kai Sun', 'Yongjiang Chen', 'Xushen Han'] | 2019-09-14 | null | null | null | null | ['line-segment-detection', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 1.08078077e-01 -7.67748728e-02 -1.57448635e-01 -2.98473656e-01
-5.09680629e-01 -3.22191298e-01 6.03918672e-01 4.45391268e-01
-3.14653724e-01 2.75555074e-01 -1.90169550e-02 -2.47403756e-01
-5.38319983e-02 -6.39906406e-01 -8.22747707e-01 -1.58248097e-01
-3.51531029e-01 8.36093649e-02 9.26118255e-01 -2.46393174... | [7.7646613121032715, -1.941810965538025] |
98f8bb64-7c4a-4a68-ae35-73c84d5d05d1 | multimodal-prototype-enhanced-network-for-few | 2212.04873 | null | https://arxiv.org/abs/2212.04873v1 | https://arxiv.org/pdf/2212.04873v1.pdf | Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition | Current methods for few-shot action recognition mainly fall into the metric learning framework following ProtoNet. However, they either ignore the effect of representative prototypes or fail to enhance the prototypes with multimodal information adequately. In this work, we propose a novel Multimodal Prototype-Enhanced ... | ['Yujiu Yang', 'Yatai Ji', 'Yong liu', 'Hao Wen', 'Xinzhe Ni'] | 2022-12-09 | null | null | null | null | ['few-shot-action-recognition', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.35873944e-01 -5.39341390e-01 -3.50121170e-01 -3.94979954e-01
-7.92157769e-01 -2.30002627e-01 8.77424002e-01 1.24755176e-02
-6.99018359e-01 3.72393191e-01 3.99354607e-01 2.80028611e-01
-2.59129882e-01 -4.61767405e-01 -2.90596396e-01 -5.75599194e-01
2.09321752e-01 2.61661083e-01 4.08813357e-01 -1.78296149... | [8.622791290283203, 0.7958241105079651] |
270977e9-0fd2-4ed3-ad14-490b11eea889 | visual-enhanced-3d-point-cloud-reconstruction | 2108.07685 | null | https://arxiv.org/abs/2108.07685v1 | https://arxiv.org/pdf/2108.07685v1.pdf | Visual Enhanced 3D Point Cloud Reconstruction from A Single Image | Solving the challenging problem of 3D object reconstruction from a single image appropriately gives existing technologies the ability to perform with a single monocular camera rather than requiring depth sensors. In recent years, thanks to the development of deep learning, 3D reconstruction of a single image has demons... | ['Han Wang', 'Mahdi Abolfazli Esfahani', 'Guiju Ping'] | 2021-08-17 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction', '3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.40897874e-02 -7.21731782e-02 -2.72161309e-02 -3.74406606e-01
-4.13395166e-01 -1.99073419e-01 4.51396972e-01 -5.30255167e-03
-3.89539599e-01 3.37426454e-01 -4.09079373e-01 -1.05654575e-01
6.72314391e-02 -8.48459721e-01 -9.13452804e-01 -4.13705230e-01
-1.91182103e-02 6.29473865e-01 3.92110199e-01 2.01347202... | [8.428468704223633, -3.1254990100860596] |
557835d8-fcc8-4d2c-86d8-002067b56dca | astronomical-image-time-series-classification | 2304.01236 | null | https://arxiv.org/abs/2304.01236v1 | https://arxiv.org/pdf/2304.01236v1.pdf | Astronomical image time series classification using CONVolutional attENTION (ConvEntion) | Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of the... | ['Julian Bautista', 'Frédéric Comby', 'Jerome Paquet', 'Dominique Fouchez', 'Marc Chaumont', 'Anass Bairouk'] | 2023-04-03 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [-1.13103837e-01 -7.35798120e-01 2.54519075e-01 -5.41642308e-02
6.98256269e-02 -6.19830728e-01 1.18811154e+00 -1.20049767e-01
-7.68948019e-01 3.93495142e-01 -3.65354955e-01 -4.90595490e-01
-3.53825182e-01 -9.41214859e-01 -3.32825601e-01 -9.57402885e-01
-2.61157393e-01 4.28781450e-01 3.91280562e-01 -4.00334388... | [7.607814311981201, 3.0508198738098145] |
ec870853-ddb1-4683-a799-b2da22238058 | difftalk-crafting-diffusion-models-for | 2301.03786 | null | https://arxiv.org/abs/2301.03786v2 | https://arxiv.org/pdf/2301.03786v2.pdf | DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation | Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practi... | ['Jiwen Lu', 'Jie zhou', 'Zheng Zhu', 'Wanhua Li', 'Zibin Meng', 'Wenliang Zhao', 'Shuai Shen'] | 2023-01-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_DiffTalk_Crafting_Diffusion_Models_for_Generalized_Audio-Driven_Portraits_Animation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_DiffTalk_Crafting_Diffusion_Models_for_Generalized_Audio-Driven_Portraits_Animation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-head-generation'] | ['computer-vision'] | [ 3.58583103e-03 1.61669105e-02 1.42137364e-01 -4.23793525e-01
-7.43955970e-01 -3.09790015e-01 5.49650192e-01 -5.44028938e-01
2.61381358e-01 3.95997465e-01 4.69717115e-01 2.96202868e-01
-4.06812541e-02 -4.82448637e-01 -5.23464561e-01 -1.03299522e+00
2.78687716e-01 -1.95985317e-01 -2.31154803e-02 -9.09530744... | [13.220559120178223, -0.35609808564186096] |
8c9e3a28-d06a-4d8e-8f0a-4f2a64380811 | cross-view-image-matching-for-geo | 1703.07815 | null | http://arxiv.org/abs/1703.07815v1 | http://arxiv.org/pdf/1703.07815v1.pdf | Cross-View Image Matching for Geo-localization in Urban Environments | In this paper, we address the problem of cross-view image geo-localization.
Specifically, we aim to estimate the GPS location of a query street view image
by finding the matching images in a reference database of geo-tagged bird's eye
view images, or vice versa. To this end, we present a new framework for
cross-view im... | ['Yicong Tian', 'Chen Chen', 'Mubarak Shah'] | 2017-03-22 | cross-view-image-matching-for-geo-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Tian_Cross-View_Image_Matching_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tian_Cross-View_Image_Matching_CVPR_2017_paper.pdf | cvpr-2017-7 | ['cross-view-image-to-image-translation'] | ['computer-vision'] | [-2.34921709e-01 -6.18723214e-01 1.30876273e-01 -4.06243503e-01
-1.07564306e+00 -6.87126994e-01 3.82124543e-01 3.75639684e-02
-5.29876888e-01 9.86807048e-02 -1.85516730e-01 1.47953540e-01
6.26457334e-02 -1.20724416e+00 -9.92661119e-01 -5.81419647e-01
6.14757687e-02 2.11484358e-01 4.51173961e-01 -1.02380194... | [7.707897186279297, -1.9389103651046753] |
9474f4ff-3333-40bd-9c6c-e66ebce70c32 | adaptive-bernstein-change-detector-for-high | 2306.12974 | null | https://arxiv.org/abs/2306.12974v1 | https://arxiv.org/pdf/2306.12974v1.pdf | Adaptive Bernstein Change Detector for High-Dimensional Data Streams | Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In... | ['Klemens Böhm', 'Florian Kalinke', 'Tanja Fenn', 'Vadim Arzamasov', 'Edouard Fouché', 'Marco Heyden'] | 2023-06-22 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 8.44260305e-02 -3.83806407e-01 -2.08838522e-01 -2.96774387e-01
-6.95737839e-01 -7.93743908e-01 5.42205215e-01 8.33247900e-01
-2.87667006e-01 4.70070571e-01 1.67380497e-01 -2.53210533e-02
2.78297700e-02 -8.84067059e-01 -8.33166301e-01 -4.57954675e-01
-4.02486473e-01 3.41598660e-01 3.81968111e-01 9.77887362... | [7.43136739730835, 3.0040297508239746] |
430c7afa-62e9-4014-bffe-7237b9bffbab | fednoisy-federated-noisy-label-learning | 2306.11650 | null | https://arxiv.org/abs/2306.11650v1 | https://arxiv.org/pdf/2306.11650v1.pdf | FedNoisy: Federated Noisy Label Learning Benchmark | Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolation may be complicated by data quality, making it more vulnerable to noisy labels. Many efforts exist to defend against the negative impacts... | ['Zenglin Xu', 'Jiayu Zhou', 'Junyuan Hong', 'Dun Zeng', 'Jintao Huang', 'Siqi Liang'] | 2023-06-20 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-2.92315245e-01 -5.18681407e-01 -8.39232374e-03 -5.77605784e-01
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-4.67760116e-02 7.78023243e-01 -7.44568184e-03 -3.68790984e-01
-2.49353364e-01 -4.63288695e-01 -4.75792617e-01 -9.38115954e-01
-2.39414588e-01 2.04324543e-01 -1.94921583e-01 1.58548698... | [5.8401570320129395, 6.433576583862305] |
978010a2-240d-437c-9514-d36a97eee06a | portfolio-optimization-using-a-consistent | 2204.05611 | null | https://arxiv.org/abs/2204.05611v1 | https://arxiv.org/pdf/2204.05611v1.pdf | Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach | This paper is concerned with optimizing the global minimum-variance portfolio's (GMVP) weights in high-dimensional settings where both observation and population dimensions grow at a bounded ratio. Optimizing the GMVP weights is highly influenced by the data covariance matrix estimation. In a high-dimensional setting, ... | ['Ubaid Al-Saggaf', 'Tareq Y. Al-Naffouri', 'Muhammad Moinuddin', 'Tarig Ballal', 'Maaz Mahadi'] | 2022-04-12 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 3.85142607e-03 7.39462823e-02 -2.32290804e-01 -3.14214565e-02
-8.92999470e-01 -1.52181387e-01 4.11497056e-02 -1.08182937e-01
-6.33518696e-01 7.99879730e-01 7.34155923e-02 -3.07532042e-01
-6.53000474e-01 -4.09893245e-01 -4.80924398e-01 -1.17404902e+00
-5.11791445e-02 2.81461895e-01 -3.20397258e-01 1.75841004... | [7.06342077255249, 4.344584941864014] |
d66c8b66-0b46-4b49-a2f7-a3f6d380b136 | codesc-a-large-code-description-parallel | 2105.14220 | null | https://arxiv.org/abs/2105.14220v1 | https://arxiv.org/pdf/2105.14220v1.pdf | CoDesc: A Large Code-Description Parallel Dataset | Translation between natural language and source code can help software development by enabling developers to comprehend, ideate, search, and write computer programs in natural language. Despite growing interest from the industry and the research community, this task is often difficult due to the lack of large standard ... | ['Rifat Shahriyar', 'Anindya Iqbal', 'Wasi Uddin Ahmad', 'Tahmid Hasan', 'Md. Mahim Anjum Haque', 'Kazi Sajeed Mehrab', 'Abdullah Al Ishtiaq', 'Tanveer Muttaqueen', 'Masum Hasan'] | 2021-05-29 | null | null | null | null | ['code-summarization', 'code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-vision'] | [ 1.58770978e-01 -1.12534262e-01 -4.48062778e-01 -3.56973052e-01
-1.01236403e+00 -5.87511063e-01 2.54997939e-01 3.87187630e-01
-2.03675941e-01 3.21558356e-01 3.65419954e-01 -4.46402669e-01
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4.76397248e-03 5.79182841e-02 -8.81655794e-03 -1.43716633... | [7.58885383605957, 7.944717884063721] |
abcbb7c5-1a18-469f-a914-5c1a72d4ac27 | sampling-from-large-graphs | null | null | https://dl.acm.org/doi/10.1145/1150402.1150479 | https://cs.stanford.edu/people/jure/pubs/sampling-kdd06.pdf | Sampling From Large Graphs | Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for large graphs. Thus graph sampling is essential.The natural questions to ask are (a) which sampli... | ['Christos Faloutsos', 'Jure Leskovec'] | 2006-08-21 | null | null | null | kdd-2006-8 | ['graph-sampling'] | ['graphs'] | [ 1.41018778e-01 1.29873767e-01 -2.77187914e-01 6.41128197e-02
-4.53250259e-01 -7.90784180e-01 5.00630438e-01 3.56525660e-01
-2.66565561e-01 9.80978310e-01 -8.99240226e-02 -4.05973345e-01
-4.99912918e-01 -1.26028895e+00 -5.00785708e-01 -7.41338551e-01
-4.65674818e-01 8.68906021e-01 7.87267089e-01 -3.63707930... | [6.946131229400635, 5.416998386383057] |
92d90d9b-b94f-411c-9594-b4ed6ee887df | end-to-end-2d-3d-registration-between-image | 2306.11346 | null | https://arxiv.org/abs/2306.11346v1 | https://arxiv.org/pdf/2306.11346v1.pdf | End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization | Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based ... | ['Hesheng Wang', 'Wolfram Burgard', 'Yixiang Zhu', 'Zhe Liu', 'Yanfeng Guo', 'Yu Zheng', 'Guangming Wang'] | 2023-06-20 | null | null | null | null | ['point-cloud-registration', 'image-based-localization', 'image-to-point-cloud-registration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.23655745e-01 -2.84578383e-01 -4.06349227e-02 -4.24625099e-01
-9.49615479e-01 -3.82569015e-01 4.03374851e-01 -4.94287871e-02
-7.87438810e-01 2.28670835e-01 -6.00430369e-01 6.10634275e-02
-7.54233524e-02 -8.30586612e-01 -1.01956880e+00 -4.16230381e-01
8.88789892e-02 1.11461961e+00 5.32866120e-01 -3.57204556... | [7.3972883224487305, -2.2886905670166016] |
fcd2497e-5616-4233-8291-f2134d7d4029 | open-eye-an-open-platform-to-study-human | 2205.06680 | null | https://arxiv.org/abs/2205.06680v1 | https://arxiv.org/pdf/2205.06680v1.pdf | Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces | AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts. Although progress has been made in developing automatic methods to detect AI-synthesized faces, there is no open platform to study t... | ['Siwei Lyu', 'Ming-Ching Chang', 'Xin Wang', 'Shu Hu', 'Hui Guo'] | 2022-05-13 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 1.66549464e-03 3.58588755e-01 2.00491250e-01 -1.19119547e-01
-1.41190186e-01 -4.49494451e-01 6.91395402e-01 -3.40183318e-01
-3.80526460e-03 4.32185918e-01 -1.12689331e-01 2.19063442e-02
6.56894684e-01 -4.15843189e-01 -3.37308437e-01 -4.41438764e-01
1.07059233e-01 1.06979318e-01 2.08082423e-01 -2.71804154... | [12.651317596435547, 1.0952308177947998] |
db66dbaf-ae9d-43c0-920f-195865f63586 | towards-contextual-spelling-correction-for | 2203.00888 | null | https://arxiv.org/abs/2203.00888v2 | https://arxiv.org/pdf/2203.00888v2.pdf | Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems | Contextual biasing is an important and challenging task for end-to-end automatic speech recognition (ASR) systems, which aims to achieve better recognition performance by biasing the ASR system to particular context phrases such as person names, music list, proper nouns, etc. Existing methods mainly include contextual ... | ['Hosam Khalil', 'Sheng Zhao', 'Veljko Miljanic', 'Jinyu Li', 'Yanqing Liu', 'Xiaoqiang Wang'] | 2022-03-02 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 4.23393756e-01 -2.94761956e-01 -2.77080655e-01 -6.96265578e-01
-1.07293725e+00 -4.25855100e-01 5.70508718e-01 -2.01826379e-01
-8.19556653e-01 6.01258337e-01 7.64548779e-01 -8.23240817e-01
2.50473768e-01 -2.99380302e-01 -6.32772505e-01 -5.54219186e-01
5.76065600e-01 4.01472300e-01 2.91242659e-01 -5.32009780... | [14.393120765686035, 6.755378246307373] |
d8db907c-d27f-4ae7-a779-8a9ee1cc110a | sandwich-batch-normalization-1 | 2102.11382 | null | https://arxiv.org/abs/2102.11382v2 | https://arxiv.org/pdf/2102.11382v2.pdf | Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity | We present Sandwich Batch Normalization (SaBN), a frustratingly easy improvement of Batch Normalization (BN) with only a few lines of code changes. SaBN is motivated by addressing the inherent feature distribution heterogeneity that one can be identified in many tasks, which can arise from data heterogeneity (multiple ... | ['Zhangyang Wang', 'Tianlong Chen', 'Wuyang Chen', 'Xinyu Gong'] | 2021-02-22 | sandwich-batch-normalization | https://openreview.net/forum?id=A-Sp6CR9-AA | https://openreview.net/pdf?id=A-Sp6CR9-AA | null | ['conditional-image-generation'] | ['computer-vision'] | [ 1.46006152e-01 -1.74161494e-01 -1.09899357e-01 -4.84071016e-01
-9.74781990e-01 -8.71430874e-01 8.78616929e-01 -7.64479280e-01
-4.95162666e-01 7.19707966e-01 2.74870783e-01 -4.93808538e-01
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1.90455735e-01 3.53647023e-01 -1.15854494e-01 -5.07694364... | [10.306394577026367, 2.164268970489502] |
31a3cacc-32e4-461a-af6a-8066deb2e2aa | deep-reinforcement-learning-for-dexterous | 1709.06977 | null | http://arxiv.org/abs/1709.06977v1 | http://arxiv.org/pdf/1709.06977v1.pdf | Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks | Deep reinforcement learning yields great results for a large array of
problems, but models are generally retrained anew for each new problem to be
solved. Prior learning and knowledge are difficult to incorporate when training
new models, requiring increasingly longer training as problems become more
complex. This is e... | ['Marcos Campos', 'Victor Shnayder', 'Ruofan Kong', 'Ross Story', 'Matthew Brown', 'Aditya Gudimella', 'Matineh Shaker'] | 2017-09-20 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-6.37117177e-02 2.08998352e-01 2.17509791e-01 2.39290148e-02
-1.91042349e-01 -7.16487348e-01 1.33095175e-01 2.00635210e-01
-6.43300474e-01 9.30408835e-01 -2.64251858e-01 -6.36397749e-02
-5.66740870e-01 -9.08983886e-01 -9.15704727e-01 -6.11334562e-01
-5.53018510e-01 7.84766614e-01 2.95801282e-01 -6.71810389... | [4.068114280700684, 1.4252097606658936] |
f5e257cc-7843-4bb1-a9da-6f908f5de24c | gaussian-hermite-moment-invariants-of-general | 2201.00877 | null | https://arxiv.org/abs/2201.00877v2 | https://arxiv.org/pdf/2201.00877v2.pdf | Gaussian-Hermite Moment Invariants of General Multi-Channel Functions | With the development of data acquisition technology, large amounts of multi-channel data are collected and widely used in many fields. Most of them, such as RGB images and vector fields, can be expressed as different types of multi-channel functions. Feature extraction of multi-channel data for identifying interest pat... | ['Guoying Zhao', 'Hua Li', 'Hanlin Mo'] | 2022-01-03 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.86295766e-02 -8.76409292e-01 2.56147236e-02 -1.51236281e-01
-4.88981158e-01 -8.08606207e-01 3.24338078e-01 -4.31360416e-02
-2.90330797e-01 3.94983798e-01 -3.08916986e-01 -3.14429142e-02
-5.20011127e-01 -6.93148553e-01 -3.75239968e-01 -7.20846474e-01
-3.01064134e-01 3.78220640e-02 2.58048952e-01 -2.63119459... | [10.18285083770752, -1.2646727561950684] |
6d105c1d-530d-440b-9604-fc15f047499d | did-the-models-understand-documents | 2306.11386 | null | https://arxiv.org/abs/2306.11386v1 | https://arxiv.org/pdf/2306.11386v1.pdf | Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction | Document-level relation extraction (DocRE) attracts more research interest recently. While models achieve consistent performance gains in DocRE, their underlying decision rules are still understudied: Do they make the right predictions according to rationales? In this paper, we take the first step toward answering this... | ['Xiangdong Zhou', 'Bingsheng Chen', 'Haotian Chen'] | 2023-06-20 | null | null | null | null | ['benchmarking', 'document-level-relation-extraction', 'relation-extraction', 'benchmarking'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing', 'robots'] | [ 1.78026244e-01 5.69788277e-01 -3.04200649e-01 -2.86464840e-01
-5.81162274e-01 -7.95164645e-01 7.88729131e-01 2.48170286e-01
-1.06185064e-01 5.76624632e-01 5.90751655e-02 -5.32146811e-01
-2.40826532e-01 -7.02488780e-01 -4.75753725e-01 3.25705372e-02
1.72536999e-01 4.75505263e-01 3.26994151e-01 -3.13500613... | [9.362767219543457, 8.648487091064453] |
4863607e-0146-49ec-a2e6-1793a20cb82a | combining-deep-neural-reranking-and | 2302.01148 | null | https://arxiv.org/abs/2302.01148v1 | https://arxiv.org/pdf/2302.01148v1.pdf | Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization | The CrisisFACTS Track aims to tackle challenges such as multi-stream fact-finding in the domain of event tracking; participants' systems extract important facts from several disaster-related events while incorporating the temporal order. We propose a combination of retrieval, reranking, and the well-known Integer Linea... | ['Korbinian Riedhammer', 'Philipp Seeberger'] | 2023-02-02 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 3.27236429e-02 1.89102754e-01 -4.42163497e-01 -1.24080427e-01
-1.69782770e+00 -6.87740803e-01 9.03679550e-01 1.03999841e+00
-8.85864675e-01 1.06782222e+00 1.24092162e+00 7.24881664e-02
-5.56471169e-01 -6.77659810e-01 -3.61689270e-01 -1.83810085e-01
-5.25388777e-01 6.58838987e-01 5.19742489e-01 -4.01645303... | [12.52050495147705, 9.487093925476074] |
f1a340ed-d769-4047-a208-ada753fcad39 | ego-pose-estimation-and-forecasting-as-real | 1906.03173 | null | https://arxiv.org/abs/1906.03173v2 | https://arxiv.org/pdf/1906.03173v2.pdf | Ego-Pose Estimation and Forecasting as Real-Time PD Control | We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos. The method learns directly from unsegmented egocentric videos and motion capture data consisting of various complex human motions (e.g., crouch... | ['Ye Yuan', 'Kris Kitani'] | 2019-06-07 | ego-pose-estimation-and-forecasting-as-real-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Yuan_Ego-Pose_Estimation_and_Forecasting_As_Real-Time_PD_Control_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yuan_Ego-Pose_Estimation_and_Forecasting_As_Real-Time_PD_Control_ICCV_2019_paper.pdf | iccv-2019-10 | ['human-pose-forecasting', 'egocentric-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.40687957e-01 1.66698024e-02 1.76188711e-03 1.36563517e-02
-6.60403728e-01 -5.96252441e-01 6.42163396e-01 -5.11789739e-01
-3.77025366e-01 6.95078611e-01 3.68798435e-01 4.97613512e-02
4.56675291e-02 -3.90882641e-01 -9.22184050e-01 -5.74864149e-01
-5.34402847e-01 4.80468303e-01 4.64288682e-01 -2.89630294... | [7.158624649047852, -0.5237789154052734] |
85db75ce-d935-470d-908d-40ba62ba8b14 | discourse-analysis-for-evaluating-coherence | 2201.06207 | null | https://arxiv.org/abs/2201.06207v1 | https://arxiv.org/pdf/2201.06207v1.pdf | Discourse Analysis for Evaluating Coherence in Video Paragraph Captions | Video paragraph captioning is the task of automatically generating a coherent paragraph description of the actions in a video. Previous linguistic studies have demonstrated that coherence of a natural language text is reflected by its discourse structure and relations. However, existing video captioning methods evaluat... | ['Song-Chun Zhu', 'Arjun R Akula'] | 2022-01-17 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 8.47777948e-02 6.37539029e-01 -3.73523742e-01 -3.65765363e-01
-4.98520494e-01 -6.11185789e-01 1.30083537e+00 1.99875563e-01
1.21708333e-01 9.81384456e-01 1.29535747e+00 -6.98265061e-02
4.14966106e-01 -3.57546955e-01 -8.29266191e-01 -3.91287863e-01
-8.67860243e-02 1.91181675e-01 1.47691533e-01 -7.08492696... | [10.851067543029785, 0.8148797750473022] |
951b42b4-4ed5-48d5-8b9d-808dffbf7a7b | handling-and-mining-linguistic-variation-in | null | null | https://aclanthology.org/W15-5402 | https://aclanthology.org/W15-5402.pdf | Handling and Mining Linguistic Variation in UGC | null | ['Leon Derczynski'] | 2015-09-01 | null | null | null | ws-2015-9 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.461014270782471, 3.6235029697418213] |
aee7032e-33e6-47df-baed-876dc863ddd2 | rsa-inr-riemannian-shape-autoencoding-via-4d | 2305.12854 | null | https://arxiv.org/abs/2305.12854v1 | https://arxiv.org/pdf/2305.12854v1.pdf | RSA-INR: Riemannian Shape Autoencoding via 4D Implicit Neural Representations | Shape encoding and shape analysis are valuable tools for comparing shapes and for dimensionality reduction. A specific framework for shape analysis is the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, which is capable of shape matching and dimensionality reduction. Researchers have recently introduc... | ['Christoph Brune', 'Nicola Strisciuglio', 'Sven Dummer'] | 2023-05-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-3.48798841e-01 5.82068972e-02 8.91082957e-02 -4.39048022e-01
-4.62569207e-01 -6.44272447e-01 6.88790798e-01 -2.65936911e-01
-1.25383779e-01 5.28653618e-03 2.22063258e-01 -1.36650398e-01
-4.70926344e-01 -1.00292921e+00 -6.09577835e-01 -7.11752534e-01
-3.37249562e-02 5.05438328e-01 -3.33503842e-01 -9.36539322... | [8.210700988769531, -3.2123796939849854] |
1b486c60-3c14-489f-bff7-84ef57cadfc3 | deep-discourse-analysis-for-generating | 2103.07785 | null | https://arxiv.org/abs/2103.07785v1 | https://arxiv.org/pdf/2103.07785v1.pdf | Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems | We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform s... | ['Jackie C. K. Cheung', 'François St-Hilaire', 'Iulian V. Serban', 'Ekaterina Kochmar', 'Robert Belfer', 'Matt Grenander'] | 2021-03-13 | null | null | null | null | ['misconceptions', 'discourse-segmentation'] | ['miscellaneous', 'natural-language-processing'] | [ 4.89832789e-01 9.70968008e-01 -3.89030576e-01 -3.82587820e-01
-8.47925007e-01 -8.84064853e-01 3.36025834e-01 9.38755751e-01
7.90085122e-02 9.51042533e-01 7.00044572e-01 -9.06425834e-01
-3.43304664e-01 -1.00111318e+00 -4.52581525e-01 1.07962534e-01
3.82992178e-01 6.41521335e-01 5.39410293e-01 -7.82272518... | [11.843454360961914, 8.032219886779785] |
efa2f8bb-0d03-4e6d-966f-74f00d48f123 | document-level-relation-extraction-as | 2106.03618 | null | https://arxiv.org/abs/2106.03618v2 | https://arxiv.org/pdf/2106.03618v2.pdf | Document-level Relation Extraction as Semantic Segmentation | Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-l... | ['Huajun Chen', 'Luo Si', 'Fei Huang', 'Mosha Chen', 'Chuanqi Tan', 'Shumin Deng', 'Xin Xie', 'Xiang Chen', 'Ningyu Zhang'] | 2021-06-07 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 8.27067196e-02 4.25574332e-01 -6.65453494e-01 -4.14305151e-01
-7.16206312e-01 -6.43373728e-01 7.78980970e-01 6.28481150e-01
-5.45064993e-02 3.99013937e-01 1.05477281e-01 -2.48706847e-01
-3.73836875e-01 -1.30477810e+00 -8.31597030e-01 -6.76937625e-02
-1.34658918e-01 5.43719947e-01 2.85714388e-01 -7.35700652... | [9.133729934692383, 8.28341007232666] |
74585c89-01ae-427f-9ed0-7cdef70e0a70 | forensic-license-plate-recognition-with | 2207.14686 | null | https://arxiv.org/abs/2207.14686v2 | https://arxiv.org/pdf/2207.14686v2.pdf | Forensic License Plate Recognition with Compression-Informed Transformers | Forensic license plate recognition (FLPR) remains an open challenge in legal contexts such as criminal investigations, where unreadable license plates (LPs) need to be deciphered from highly compressed and/or low resolution footage, e.g., from surveillance cameras. In this work, we propose a side-informed Transformer a... | ['Christian Riess', 'Jürgen Seiler', 'Andreas Spruck', 'Anatol Maier', 'Denise Moussa'] | 2022-07-29 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 7.15674877e-01 -3.76585960e-01 -2.27585226e-01 -9.40804854e-02
-1.45545030e+00 -9.61256146e-01 3.90687495e-01 -7.52205729e-01
-2.70216405e-01 5.14880598e-01 1.33320034e-01 -5.97487390e-01
2.28705090e-02 -4.96217698e-01 -9.63837504e-01 -5.78286171e-01
4.00294006e-01 2.39913732e-01 2.73161173e-01 2.10956335... | [9.892252922058105, -4.835448265075684] |
eb1b6c7c-a085-4e34-be80-677e52dd18b7 | product-information-extraction-using-chatgpt | 2306.14921 | null | https://arxiv.org/abs/2306.14921v1 | https://arxiv.org/pdf/2306.14921v1.pdf | Product Information Extraction using ChatGPT | Structured product data in the form of attribute/value pairs is the foundation of many e-commerce applications such as faceted product search, product comparison, and product recommendation. Product offers often only contain textual descriptions of the product attributes in the form of titles or free text. Hence, extra... | ['Christian Bizer', 'Reng Chiz Der', 'Roee Shraga', 'Alexander Brinkmann'] | 2023-06-23 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.84195969e-02 6.63192719e-02 -6.84396803e-01 -6.47830486e-01
-9.57602799e-01 -7.69106328e-01 5.25419354e-01 6.91864192e-01
-1.86812028e-01 4.42533374e-01 2.52340108e-01 -2.34355837e-01
-1.56884998e-01 -8.35130095e-01 -3.30513060e-01 -1.93077341e-01
-1.32004758e-02 9.75793123e-01 -5.75526012e-03 -5.67834735... | [10.001337051391602, 6.233855724334717] |
3eba0552-40e2-45d1-be94-1db42e50a63e | co-fusion-real-time-segmentation-tracking-and | 1706.06629 | null | http://arxiv.org/abs/1706.06629v1 | http://arxiv.org/pdf/1706.06629v1.pdf | Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects | In this paper we introduce Co-Fusion, a dense SLAM system that takes a live
stream of RGB-D images as input and segments the scene into different objects
(using either motion or semantic cues) while simultaneously tracking and
reconstructing their 3D shape in real time. We use a multiple model fitting
approach where ea... | ['Martin Rünz', 'Lourdes Agapito'] | 2017-06-20 | null | null | null | null | ['object-slam', 'semantic-slam'] | ['computer-vision', 'computer-vision'] | [ 2.72834837e-01 1.26677901e-01 1.21214271e-01 -3.05033803e-01
-4.83481348e-01 -7.73407638e-01 4.17109460e-01 2.49781728e-01
-4.38689142e-01 2.23902762e-01 -5.50131798e-01 9.19923633e-02
1.47250265e-01 -5.18154383e-01 -6.32904828e-01 -6.33466005e-01
1.28125072e-01 1.06715357e+00 9.27891910e-01 1.67516932... | [7.334436893463135, -2.3255414962768555] |
957b0224-73ec-4e2b-b78f-6a4913a4b61c | using-duck-net-for-polyp-image-segmentation | null | null | https://www.nature.com/articles/s41598-023-36940-5#Abs1 | https://www.nature.com/articles/s41598-023-36940-5.pdf | Using DUCK-Net for polyp image segmentation | This paper presents a novel supervised convolutional neural network architecture, “DUCK-Net”, capable of effectively learning and generalizing from small amounts of medical images to perform accurate segmentation tasks. Our model utilizes an encoder-decoder structure with a residual downsampling mechanism and a custom ... | ['Catalin Craciun', 'Darius Peteleaza', 'Razvan-Gabriel Dumitru'] | 2023-06-16 | null | null | null | nature-scientific-reports-2023-6 | ['polyp-segmentation'] | ['computer-vision'] | [ 3.37640822e-01 2.33364761e-01 -2.67903268e-01 -4.64719564e-01
-8.90821815e-01 -4.18675125e-01 1.00620776e-01 5.87893546e-01
-6.41499162e-01 4.69183981e-01 2.37183403e-02 -6.44080698e-01
5.90176657e-02 -7.47591317e-01 -7.51775444e-01 -4.87775922e-01
-3.32387716e-01 3.18012089e-01 3.40647489e-01 -9.75633189... | [14.587197303771973, -2.7563531398773193] |
64741661-135e-4adf-9067-d727b745ffcd | qualiassistant-extracting-qualia-structures | null | null | https://aclanthology.org/2022.argmining-1.19 | https://aclanthology.org/2022.argmining-1.19.pdf | QualiAssistant: Extracting Qualia Structures from Texts | In this paper, we present QualiAssistant, a free and open-source system written in Java for identification and extraction of Qualia structures from any natural language texts having many application scenarios such as argument mining or creating dictionaries. It answers the call for a Qualia bootstrapping tool with a re... | ['Ralf Schenkel', 'Björn Metzler', 'Markus Nilles', 'Lorik Dumani', 'Manuel Biertz'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 1.26475140e-01 4.80798930e-01 -4.51410353e-01 -2.67392933e-01
-6.40514374e-01 -1.08646154e+00 8.96617889e-01 8.38344991e-01
-5.61315894e-01 1.04107916e+00 5.61191320e-01 -6.34622157e-01
-4.44723278e-01 -8.80704403e-01 -2.85182983e-01 -2.24004954e-01
4.29977179e-01 1.22417974e+00 4.56103593e-01 -6.38201594... | [9.951732635498047, 9.560660362243652] |
514da470-319f-4d37-b531-55e69b8a396a | dual-encoder-decoder-based-generative | 1909.08797 | null | https://arxiv.org/abs/1909.08797v1 | https://arxiv.org/pdf/1909.08797v1.pdf | Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning | To learn disentangled representations of facial images, we present a Dual Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structu... | ['Xiao-Jun Wu', 'Zhen-Hua Feng', 'Cong Hu', 'Josef Kittler'] | 2019-09-19 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.35854346e-01 4.62840289e-01 1.43785134e-01 -4.47397411e-01
-8.49209845e-01 -5.70157111e-01 6.63575649e-01 -8.54319334e-01
2.23035365e-02 6.47192657e-01 7.96039030e-02 2.46179700e-01
3.23524833e-01 -6.97894156e-01 -8.35952103e-01 -1.00448048e+00
1.72084183e-01 4.34897691e-01 -5.86098194e-01 -2.34359019... | [12.900186538696289, 0.16244354844093323] |
08b3b2c3-113d-4ada-97a4-6a4aba11c1d7 | rico-regularizing-the-unobservable-for-indoor | 2303.08605 | null | https://arxiv.org/abs/2303.08605v1 | https://arxiv.org/pdf/2303.08605v1.pdf | RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction | Recently, neural implicit surfaces have become popular for multi-view reconstruction. To facilitate practical applications like scene editing and manipulation, some works extend the framework with semantic masks input for the object-compositional reconstruction rather than the holistic perspective. Though achieving pla... | ['Yong liu', 'Yiyi Liao', 'Mengmeng Wang', 'Yuanyuan Ding', 'Xiaoyang Lyu', 'Zizhang Li'] | 2023-03-15 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 4.94058847e-01 1.44791557e-02 1.55812621e-01 -3.04408878e-01
-2.06010580e-01 -4.21901047e-01 3.53191793e-01 -5.18351495e-01
8.07344168e-02 7.08735049e-01 3.94311339e-01 7.19177201e-02
-3.07805873e-02 -1.01330471e+00 -7.69693375e-01 -8.58010471e-01
4.69446033e-01 1.78377941e-01 3.83858591e-01 -6.67686239... | [9.098954200744629, -3.021789073944092] |
f45665ae-cf62-4730-b975-0ba01b1d1b6e | improving-the-numerical-reasoning-skills-of | 2205.06733 | null | https://arxiv.org/abs/2205.06733v2 | https://arxiv.org/pdf/2205.06733v2.pdf | Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models | State-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require understanding and working with numbers. Recent work suggests two main reasons for this: (1) popular tokenisation algorithms have limited expressiveness for numbers, and (2) common pretr... | ['Iryna Gurevych', 'Nafise Sadat Moosavi', 'Dominic Petrak'] | 2022-05-13 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.42618555e-01 9.70296934e-02 -2.63211787e-01 -3.91279280e-01
-7.55164564e-01 -5.37668467e-01 7.53658295e-01 7.32639611e-01
-8.27264607e-01 1.05220079e+00 2.53975540e-01 -7.50143170e-01
3.26248407e-02 -1.17535841e+00 -1.11126959e+00 1.54172936e-02
2.80355185e-01 7.08899260e-01 -6.19919859e-02 -4.37233359... | [9.672320365905762, 7.432043075561523] |
5491aac3-e854-4dec-8464-b452a384a08f | large-language-models-can-be-easily | 2302.00093 | null | https://arxiv.org/abs/2302.00093v3 | https://arxiv.org/pdf/2302.00093v3.pdf | Large Language Models Can Be Easily Distracted by Irrelevant Context | Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models... | ['Denny Zhou', 'Nathanael Schärli', 'Ed Chi', 'David Dohan', 'Nathan Scales', 'Kanishka Misra', 'Xinyun Chen', 'Freda Shi'] | 2023-01-31 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 1.27499759e-01 1.04963601e-01 -1.81773808e-02 -3.23561847e-01
-5.93519032e-01 -5.74705958e-01 4.24482524e-01 8.38582814e-01
-4.22074139e-01 3.54862809e-01 4.13053960e-01 -7.68820405e-01
-1.58886671e-01 -7.28860259e-01 -7.17181623e-01 -2.23836407e-01
4.26294744e-01 2.23192513e-01 3.68222862e-01 -4.37036484... | [9.855569839477539, 7.513607025146484] |
8076d460-6b01-4ef6-bbe7-899a717e6d4a | the-behavior-and-convergence-of-local | 2305.15572 | null | https://arxiv.org/abs/2305.15572v1 | https://arxiv.org/pdf/2305.15572v1.pdf | The Behavior and Convergence of Local Bayesian Optimization | A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to traditional global strategies. The "folk wisdom" in the literature is that the focus on local optimization sidesteps the curse of dimensional... | ['Jacob R. Gardner', 'Roman Garnett', 'Kyurae Kim', 'Kaiwen Wu'] | 2023-05-24 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.70236990e-01 -8.14921632e-02 1.65854543e-01 -3.36104065e-01
-1.31764674e+00 -3.99242967e-01 7.68456221e-01 -1.01468235e-01
-4.43800628e-01 9.41821873e-01 2.23203406e-01 -3.17709923e-01
-6.54544950e-01 -5.13653517e-01 -5.57997882e-01 -1.19688976e+00
-1.27668872e-01 6.11034751e-01 1.17326893e-01 3.93263638... | [6.876373767852783, 3.8305795192718506] |
c0761ef4-eb61-4a60-ac6a-c53b2ab01bbf | offline-reinforcement-learning-with-adaptive | 2211.08251 | null | https://arxiv.org/abs/2211.08251v1 | https://arxiv.org/pdf/2211.08251v1.pdf | Offline Reinforcement Learning with Adaptive Behavior Regularization | Offline reinforcement learning (RL) defines a sample-efficient learning paradigm, where a policy is learned from static and previously collected datasets without additional interaction with the environment. The major obstacle to offline RL is the estimation error arising from evaluating the value of out-of-distribution... | ['Qingyu Qu', 'Xijun Li', 'Yunfan Zhou'] | 2022-11-15 | null | null | null | null | ['d4rl'] | ['robots'] | [ 1.35862723e-01 -2.48815101e-02 -4.17661130e-01 -2.51489580e-01
-8.81738603e-01 -5.46735823e-01 4.59066123e-01 4.90481973e-01
-9.00532126e-01 1.12282264e+00 -1.25010550e-01 -1.09963492e-01
-2.76651472e-01 -7.73199141e-01 -8.91527712e-01 -9.38736796e-01
-1.52759477e-01 6.01844072e-01 2.18539149e-01 -5.29439636... | [4.136989116668701, 2.243880271911621] |
d5ade06b-3fb8-4cb3-8f0b-4866a0e1d15c | combining-domain-specific-meta-learners-in | 2011.00179 | null | https://arxiv.org/abs/2011.00179v1 | https://arxiv.org/pdf/2011.00179v1.pdf | Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification | The goal of few-shot classification is to learn a model that can classify novel classes using only a few training examples. Despite the promising results shown by existing meta-learning algorithms in solving the few-shot classification problem, there still remains an important challenge: how to generalize to unseen dom... | ['Martin Ester', 'Weilian Song', 'Shuman Peng'] | 2020-10-31 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 0.2649662 -0.22927329 -0.34258425 -0.6069214 -0.9491261 -0.14793411
0.606577 0.16013847 -0.48739862 0.7868103 -0.04563992 0.36543408
-0.20595272 -0.84173816 -0.5315592 -0.5546582 0.18737268 0.6448753
0.7286123 -0.40124276 0.17445986 0.03473043 -1.8661116 0.51267886
1.0734807 0.8986523 0.4... | [10.048171997070312, 3.1461851596832275] |
89b55108-6f75-4a57-9cfe-ab622569c055 | ciff-net-contextual-image-feature-fusion-for | 2303.03672 | null | https://arxiv.org/abs/2303.03672v1 | https://arxiv.org/pdf/2303.03672v1.pdf | CIFF-Net: Contextual Image Feature Fusion for Melanoma Diagnosis | Melanoma is considered to be the deadliest variant of skin cancer causing around 75\% of total skin cancer deaths. To diagnose Melanoma, clinicians assess and compare multiple skin lesions of the same patient concurrently to gather contextual information regarding the patterns, and abnormality of the skin. So far this ... | ['Shaikh Anowarul Fattah', 'Tanvir Mahmud', 'Bishmoy Paul', 'Md Awsafur Rahman'] | 2023-03-07 | null | null | null | null | ['melanoma-diagnosis', 'skin-cancer-classification'] | ['computer-vision', 'medical'] | [ 6.64720535e-01 -2.52240151e-01 -1.81246057e-01 -2.13966221e-01
-1.19057488e+00 6.69204369e-02 7.47547328e-01 5.30547261e-01
-5.94648838e-01 6.26866043e-01 4.50088829e-01 6.08892441e-02
-4.74538863e-01 -4.91674900e-01 -1.27091765e-01 -1.04965448e+00
3.51683259e-01 -3.61337990e-01 1.45578161e-01 -1.49097115... | [15.610678672790527, -2.9315478801727295] |
63cb838e-a5af-448f-9d45-602fe65adc78 | pretrained-encyclopedia-weakly-supervised-1 | 1912.09637 | null | https://arxiv.org/abs/1912.09637v1 | https://arxiv.org/pdf/1912.09637v1.pdf | Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model | Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition to standard syntactic and semantic NLP tasks, pretrained models achieve strong improvements on tasks that involve real-world knowledge, sug... | ['William Yang Wang', 'Wenhan Xiong', 'Veselin Stoyanov', 'Jingfei Du'] | 2019-12-20 | null | https://openreview.net/forum?id=BJlzm64tDH | https://openreview.net/pdf?id=BJlzm64tDH | iclr-2020-1 | ['triviaqa'] | ['miscellaneous'] | [-2.74800271e-01 5.26930630e-01 -4.08250004e-01 -5.20176649e-01
-1.18872619e+00 -8.07966352e-01 7.50613391e-01 4.79434162e-01
-9.26725626e-01 9.43096399e-01 4.19034749e-01 -3.88947636e-01
-9.27355662e-02 -8.97006750e-01 -1.24333704e+00 1.19863905e-01
5.74206226e-02 8.33294153e-01 4.26154912e-01 -6.04344666... | [10.582815170288086, 8.071761131286621] |
fc09ff9d-a2c8-488c-8527-42a25575687e | illiterate-dall-cdot-e-learns-to-compose-1 | 2110.11405 | null | https://arxiv.org/abs/2110.11405v3 | https://arxiv.org/pdf/2110.11405v3.pdf | Illiterate DALL-E Learns to Compose | Although DALL-E has shown an impressive ability of composition-based systematic generalization in image generation, it requires the dataset of text-image pairs and the compositionality is provided by the text. In contrast, object-centric representation models like the Slot Attention model learn composable representatio... | ['Sungjin Ahn', 'Fei Deng', 'Gautam Singh'] | 2021-10-17 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 4.51045513e-01 4.52522218e-01 2.84289774e-02 -2.30102479e-01
-8.82677078e-01 -2.12222695e-01 1.09703767e+00 -4.29950058e-01
-1.38347670e-01 6.54182553e-01 1.96861118e-01 -3.62839758e-01
5.77584803e-02 -9.25601959e-01 -1.06954396e+00 -7.67388940e-01
4.93627310e-01 7.32940316e-01 2.13159040e-01 -3.00375432... | [11.146866798400879, -0.04014776274561882] |
3500633d-9178-482c-ae6f-9928b792e32a | c-nn-fd-a-deep-learning-framework-for | 2306.05889 | null | https://arxiv.org/abs/2306.05889v1 | https://arxiv.org/pdf/2306.05889v1.pdf | C(NN)FD -- a deep learning framework for turbomachinery CFD analysis | Deep Learning methods have seen a wide range of successful applications across different industries. Up until now, applications to physical simulations such as CFD (Computational Fluid Dynamics), have been limited to simple test-cases of minor industrial relevance. This paper demonstrates the development of a novel dee... | ['Senthil K. Krishnababu', 'Sepehr Maleki', 'Giuseppe Bruni'] | 2023-06-09 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-3.26296866e-01 -3.11805725e-01 2.94323117e-01 -1.33295832e-02
-9.83656198e-02 -5.79054415e-01 5.41034877e-01 3.07705551e-01
-3.07130590e-02 6.88215733e-01 -4.27359760e-01 -7.21014738e-01
-6.97412550e-01 -9.22270000e-01 -4.91770506e-01 -9.36230242e-01
-4.17542607e-01 5.04288733e-01 -3.96243334e-01 -1.35687605... | [6.355125427246094, 3.174762010574341] |
a3450dbe-d390-46ad-b3f0-28a5d38cf5fe | 190409352 | 1904.09352 | null | http://arxiv.org/abs/1904.09352v1 | http://arxiv.org/pdf/1904.09352v1.pdf | Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding | Swarm Intelligence is a metaheuristic optimization approach that has become
very predominant over the last few decades. These algorithms are inspired by
animals' physical behaviors and their evolutionary perceptions. The simplicity
of these algorithms allows researchers to simulate different natural phenomena
to solve ... | ['Nawzad K. Al-Salihi', 'Rawan A. Al-Rashid Agha', 'Mokhtar Mohammadi', 'Ahmed S. Shamsaldin', 'Tarik A. Rashid'] | 2019-04-19 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-3.29868972e-01 -5.44373989e-01 -1.97125375e-01 6.33206889e-02
4.77567464e-01 -2.86695629e-01 5.96436739e-01 1.36517406e-01
-7.52901793e-01 9.66810346e-01 -2.72185504e-01 -7.82461166e-02
-7.01425374e-01 -1.06157517e+00 -4.25791554e-02 -8.56943309e-01
-5.90596557e-01 7.08424509e-01 3.83187771e-01 -9.19178486... | [5.653913974761963, 3.498018741607666] |
7cb2bd0d-b6d9-4478-bb02-165b72ade36a | robust-point-set-registration-using-gaussian | null | null | https://ieeexplore.ieee.org/document/5674050 | https://github.com/bing-jian/gmmreg/blob/master/gmmreg_PAMI_preprint.pdf | Robust Point Set Registration Using Gaussian Mixture Models | In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registratio... | ['Bing Jian', 'Baba C. Vemuri'] | 2010-12-23 | null | null | null | ieee-transactions-on-pattern-analysis-and-9 | ['3d-point-cloud-matching'] | ['computer-vision'] | [ 4.29710038e-02 5.19781597e-02 -5.10462262e-02 -2.86512017e-01
-8.58023465e-01 -5.51678300e-01 8.06750298e-01 4.43890505e-02
-3.09657156e-01 1.97207525e-01 -6.98224008e-02 8.43890235e-02
-4.52254087e-01 -4.50573772e-01 -4.23130453e-01 -8.76404047e-01
4.03701924e-02 8.65442455e-01 1.65586218e-01 -2.23044813... | [7.741795063018799, -2.855252742767334] |
a5c13b9e-34e1-4a4e-af0f-73604b5b495f | concept-to-text-generation-via-discriminative | null | null | https://aclanthology.org/P12-1039 | https://aclanthology.org/P12-1039.pdf | Concept-to-text Generation via Discriminative Reranking | null | ['Ioannis Konstas', 'Mirella Lapata'] | 2012-07-01 | null | null | null | acl-2012-7 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.408435344696045, 3.8702902793884277] |
d5ec9c19-2c7d-48c4-8d24-62d0698ba325 | fast-real-time-counterfactual-explanations | 2007.05684 | null | https://arxiv.org/abs/2007.05684v2 | https://arxiv.org/pdf/2007.05684v2.pdf | Fast Real-time Counterfactual Explanations | Counterfactual explanations are considered, which is to answer {\it why the prediction is class A but not B.} Different from previous optimization based methods, an optimization-free Fast ReAl-time Counterfactual Explanation (FRACE) algorithm is proposed benefiting from the development of multi-domain image to image tr... | ['Yunxia Zhao'] | 2020-07-11 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 7.58771360e-01 8.74888062e-01 -3.49929929e-01 -2.65972435e-01
-7.32671022e-01 -5.52812099e-01 8.48365724e-01 -6.02116704e-01
-2.69528721e-02 1.17442203e+00 1.00896634e-01 -4.51171845e-01
2.27073133e-01 -9.02042687e-01 -1.17868400e+00 -6.87808096e-01
3.69443655e-01 6.07693017e-01 -1.48158208e-01 -6.29199371... | [11.622353553771973, -0.32373806834220886] |
3142a695-8c56-4362-910d-5932c30766d2 | dynamic-scenario-representation-learning-for | 2303.04364 | null | https://arxiv.org/abs/2303.04364v1 | https://arxiv.org/pdf/2303.04364v1.pdf | Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks | Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving. Most existing works exploit static road graphs to characterize scenarios and are limited in modeling evolving spatio-temporal dependencies in dynamic scenarios. In this paper, we resort ... | ['Hongkai Xiong', 'Yikang Li', 'Xiaogang Jia', 'Xing Gao'] | 2023-03-08 | null | null | null | null | ['trajectory-prediction', 'motion-forecasting'] | ['computer-vision', 'computer-vision'] | [-1.94057047e-01 -2.02573299e-01 -1.94159284e-01 -4.34312969e-01
-4.95571457e-02 -4.98336136e-01 9.64250863e-01 -2.58450300e-01
-1.64500177e-01 7.38675296e-01 7.30406821e-01 -3.60587239e-01
-2.16916669e-02 -9.35600460e-01 -8.34800601e-01 -6.33332789e-01
-5.45422375e-01 4.33301389e-01 8.14064682e-01 -6.89771712... | [5.979978561401367, 0.9209823608398438] |
12ef1209-2cf6-4f0b-9446-1d5862bf0bf0 | the-naked-sun-malicious-cooperation-between | 1911.02423 | null | https://arxiv.org/abs/1911.02423v1 | https://arxiv.org/pdf/1911.02423v1.pdf | The Naked Sun: Malicious Cooperation Between Benign-Looking Processes | Recent progress in machine learning has generated promising results in behavioral malware detection. Behavioral modeling identifies malicious processes via features derived by their runtime behavior. Behavioral features hold great promise as they are intrinsically related to the functioning of each malware, and are the... | ['Giulio Pagnotta', 'Luigi V. Mancini', 'Dorjan Hitaj', 'Fabio De Gaspari', 'Lorenzo De Carli'] | 2019-11-06 | null | null | null | null | ['behavioral-malware-detection'] | ['miscellaneous'] | [ 1.53783888e-01 -3.95822108e-01 -2.85403341e-01 -6.04013130e-02
-1.47160947e-01 -9.80906129e-01 9.62750256e-01 1.63894653e-01
-1.98706731e-01 3.14734399e-01 -2.84409463e-01 -7.76503444e-01
5.37789017e-02 -5.69766998e-01 -3.45010102e-01 -8.20080876e-01
-4.10381138e-01 5.06128132e-01 3.76053572e-01 -1.40917867... | [14.388594627380371, 9.666810035705566] |
f775b21c-9f64-4041-8f82-e81b1aa126b0 | commonsense-knowledge-aware-concept-selection | 2102.02963 | null | https://arxiv.org/abs/2102.02963v1 | https://arxiv.org/pdf/2102.02963v1.pdf | Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling | Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity of the generated stories while preserving the informative content from the images. We propose to foster the diversity and informativeness of a generated story by using ... | ['Hideki Nakayama', 'Hiroya Takamura', 'Yifei HUANG', 'Hong Chen'] | 2021-02-05 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 3.61881196e-01 3.65279436e-01 -3.74505579e-01 -2.25174665e-01
-3.73944074e-01 -5.03473222e-01 9.07968342e-01 2.40347579e-01
-7.92248175e-02 6.90176308e-01 6.40309215e-01 2.33264685e-01
5.32066748e-02 -7.33356118e-01 -6.04255140e-01 -4.36927915e-01
6.66856617e-02 2.20834747e-01 3.25562716e-01 -1.93726957... | [11.119112014770508, 0.6785460114479065] |
1b9283bf-38cc-4aec-adcc-56514f62308f | improving-perceptual-quality-of-drum | 2004.00188 | null | https://arxiv.org/abs/2004.00188v5 | https://arxiv.org/pdf/2004.00188v5.pdf | Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset | We introduce the Expanded Groove MIDI dataset (E-GMD), an automatic drum transcription (ADT) dataset that contains 444 hours of audio from 43 drum kits, making it an order of magnitude larger than similar datasets, and the first with human-performed velocity annotations. We use E-GMD to optimize classifiers for use in ... | ['Jesse Engel', 'Lee Callender', 'Curtis Hawthorne'] | 2020-04-01 | null | null | null | null | ['drum-transcription'] | ['music'] | [ 3.93350199e-02 2.01788858e-01 2.32065842e-02 1.18915349e-01
-1.22909498e+00 -9.55032766e-01 5.63752174e-01 7.82195032e-02
-2.02613324e-01 4.61400628e-01 1.03323877e+00 -1.52317628e-01
-3.49975437e-01 -5.46663165e-01 -3.74544442e-01 -3.02976251e-01
-1.61291838e-01 3.36821198e-01 8.37918893e-02 -4.14937913... | [16.041061401367188, 5.580324172973633] |
aa8ecece-1bb4-4f59-a357-c24243282b08 | graph-based-thermal-inertial-slam-with | 2104.07196 | null | https://arxiv.org/abs/2104.07196v3 | https://arxiv.org/pdf/2104.07196v3.pdf | Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks | Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly depends on the ambient illumination conditions. In scenarios with adverse visibility or in the presence of airborne particulates (e.g. smoke, ... | ['Pedro P. B. de Gusmao', 'Chris Xiaoxuan Lu', 'Niki Trigoni', 'Andrew Markham', 'Bing Wang', 'Muhamad Risqi U. Saputra'] | 2021-04-15 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 2.63460696e-01 -4.02663648e-01 2.44057328e-01 -3.63918304e-01
-6.95165932e-01 -4.58249778e-01 6.95525229e-01 -1.16066955e-01
-7.69651294e-01 6.68816805e-01 -1.79110289e-01 -1.19199015e-01
-3.24698240e-01 -7.08266497e-01 -9.15307760e-01 -8.49708557e-01
-2.16240883e-02 6.41700268e-01 -1.27069205e-01 -6.04902394... | [7.486794471740723, -2.182952880859375] |
06ec1beb-f9f0-4d8d-93b2-471c890d303f | set-supervised-action-learning-in-procedural | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lu_Set-Supervised_Action_Learning_in_Procedural_Task_Videos_via_Pairwise_Order_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lu_Set-Supervised_Action_Learning_in_Procedural_Task_Videos_via_Pairwise_Order_CVPR_2022_paper.pdf | Set-Supervised Action Learning in Procedural Task Videos via Pairwise Order Consistency | We address the problem of set-supervised action learning, whose goal is to learn an action segmentation model using weak supervision in the form of sets of actions occurring in training videos. Our key observation is that videos within the same task have similar ordering of actions, which can be leveraged for effec... | ['Ehsan Elhamifar', 'Zijia Lu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['action-localization', 'weakly-supervised-action-segmentation-action', 'action-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.74946582e-01 -1.78540558e-01 -5.63403845e-01 -3.65287334e-01
-8.04030716e-01 -5.63092709e-01 4.30100620e-01 -2.54324198e-01
-4.99763727e-01 5.22633433e-01 4.40705955e-01 6.70696124e-02
-8.88342708e-02 -1.46358043e-01 -9.97069180e-01 -7.57022083e-01
-1.83779210e-01 2.98221916e-01 4.20000643e-01 2.49204636... | [8.464563369750977, 0.7136480808258057] |
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