paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1794b19a-12e9-4696-adab-e5aca07d6cb5 | feature-encoding-with-autoencoders-for-weakly | 2105.10500 | null | https://arxiv.org/abs/2105.10500v3 | https://arxiv.org/pdf/2105.10500v3.pdf | Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection | Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitti... | ['Lingqiao Liu', 'Ce Zhu', 'Fanxing Liu', 'Yanru Zhang', 'Xucheng Song', 'Yingjie Zhou'] | 2021-05-22 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 4.00327146e-02 -7.73631632e-02 -2.08570510e-02 -6.38135195e-01
-3.69524926e-01 -1.74166605e-01 3.29830408e-01 2.75885165e-02
-3.00678641e-01 4.14535016e-01 1.95616841e-01 -8.45851824e-02
1.39719009e-01 -6.70636415e-01 -3.93335938e-01 -7.60346889e-01
7.54358470e-02 2.31657490e-01 9.64156073e-03 1.15340650... | [7.627014636993408, 2.345529556274414] |
c74e0788-d3ec-4491-bc55-3ffe1298cea5 | exploring-structural-encoding-for-data-to | null | null | https://aclanthology.org/2021.inlg-1.44 | https://aclanthology.org/2021.inlg-1.44.pdf | Exploring Structural Encoding for Data-to-Text Generation | Due to efficient end-to-end training and fluency in generated texts, several encoder-decoder framework-based models are recently proposed for data-to-text generations. Appropriate encoding of input data is a crucial part of such encoder-decoder models. However, only a few research works have concentrated on proper enco... | ['Utpal Garain', 'Joy Mahapatra'] | null | null | null | null | inlg-acl-2021-8 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.93582213e-01 6.43176258e-01 3.17695737e-02 -5.08433521e-01
-9.11110818e-01 -3.14002752e-01 8.86497796e-01 -2.55056143e-01
-4.01622206e-01 1.08329129e+00 7.22266555e-01 -8.03412646e-02
2.68625528e-01 -7.46526003e-01 -7.86771774e-01 -2.51068920e-01
5.53353250e-01 7.95992374e-01 -2.03769058e-01 -4.46382135... | [11.84941291809082, 9.094732284545898] |
9e98cd66-619b-43a7-9c04-3bbe223469fe | recist-weakly-supervised-lesion-segmentation | 2303.00205 | null | https://arxiv.org/abs/2303.00205v1 | https://arxiv.org/pdf/2303.00205v1.pdf | RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training | As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline in CT slices. By marking each lesion with its longest axis and the longest perpendicular one, laborious pixel-wise manual annotation can be ... | ['Yefeng Zheng', 'Liansheng Wang', 'Wei Xue', 'Donghuan Lu', 'Dong Wei', 'Lianyu Zhou'] | 2023-03-01 | null | null | null | null | ['weakly-supervised-segmentation', 'lesion-segmentation'] | ['computer-vision', 'medical'] | [ 5.56987822e-01 1.05467595e-01 -8.47545326e-01 -4.19994235e-01
-1.05354416e+00 -4.24630642e-01 3.57525766e-01 3.25360030e-01
-5.42907715e-01 9.19837534e-01 2.74366885e-02 -4.30199534e-01
-5.82371801e-02 -6.67439997e-01 -3.63410980e-01 -1.21654236e+00
3.02295417e-01 3.38428289e-01 4.07157034e-01 3.67478132... | [14.750784873962402, -2.4250223636627197] |
af995005-c7d0-4140-9fa8-49358f1f4836 | the-growing-liberality-observed-in-primary | 2301.02433 | null | https://arxiv.org/abs/2301.02433v1 | https://arxiv.org/pdf/2301.02433v1.pdf | The Growing Liberality Observed in Primary Animal and Plant Cultures is Common to the Social Amoeba | Tissue culture environment liberates cells from ordinary laws of multi-cellular organisms. This liberation enables cells several behaviors, such as proliferation, dedifferentiation, acquisition of pluripotency, immortalization, and reprogramming. Recently, the quantitative value of cellular dedifferentiation and differ... | ['Norichika Ogata'] | 2023-01-06 | null | null | null | null | ['culture'] | ['speech'] | [ 2.64011145e-01 5.77774690e-03 4.33885381e-02 1.85303494e-01
-9.81082916e-02 -1.04749620e+00 6.67233646e-01 2.66885728e-01
-5.04315376e-01 1.51741254e+00 1.83419362e-01 -9.49131548e-02
6.72818348e-02 -8.62289906e-01 -2.46911615e-01 -1.20370388e+00
1.66088827e-02 4.47125703e-01 -1.70029640e-01 -5.16064018... | [5.625861167907715, 4.190752029418945] |
15963369-0900-430c-a4fa-ff2b57aee34f | evaluating-n-best-calibration-of-natural | null | null | https://aclanthology.org/2022.sigdial-1.54 | https://aclanthology.org/2022.sigdial-1.54.pdf | Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems | A Natural Language Understanding (NLU) component can be used in a dialogue system to perform intent classification, returning an N-best list of hypotheses with corresponding confidence estimates. We perform an in-depth evaluation of 5 NLUs, focusing on confidence estimation. We measure and visualize calibration for the... | ['Staffan Larsson', 'Alexander Berman', 'Ranim Khojah'] | null | null | null | null | sigdial-acl-2022-9 | ['intent-classification'] | ['natural-language-processing'] | [-3.27170819e-01 5.76814175e-01 -4.95403439e-01 -6.26273751e-01
-1.02873039e+00 -7.89585650e-01 8.29754949e-01 5.60510874e-01
-4.59308863e-01 9.13257778e-01 5.74986756e-01 -6.70575857e-01
-2.59589314e-01 -3.39271247e-01 -5.43837771e-02 2.28423346e-02
7.01401010e-02 1.01340425e+00 1.51267415e-02 -1.58263624... | [12.7022066116333, 8.016667366027832] |
8126f33e-b241-45cb-baf5-e9b79b45d2ab | simultaneous-indoor-and-outdoor-3d | 2207.05344 | null | https://arxiv.org/abs/2207.05344v2 | https://arxiv.org/pdf/2207.05344v2.pdf | Simultaneous Indoor and Outdoor 3D Localization with STAR-RIS-Assisted Millimeter Wave Systems | Simultaneously transmitting (refracting) and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been recently identified to improve the spectrum/energy efficiency and extend the communication range. However, their potential for enhanced concurrent indoor and outdoor localization has not yet been explored. ... | ['George C. Alexandropoulos', 'Aymen Fakhreddine', 'Jiguang He'] | 2022-07-12 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [ 3.34286451e-01 2.92643428e-01 1.80857420e-01 1.23801544e-01
-6.65680528e-01 -5.86127639e-01 1.40834346e-01 -3.75126123e-01
-1.08599916e-01 6.09684706e-01 8.94951459e-04 -6.70139790e-01
-7.14525819e-01 -7.66891181e-01 -5.08872151e-01 -1.13808298e+00
-5.28129339e-01 4.43601608e-02 -2.58734554e-01 -1.24746144... | [6.275763511657715, 1.2231241464614868] |
0ae83c79-d4ca-4ec1-9520-973764425ff0 | rtfnet-rgb-thermal-fusion-network-for | null | null | https://ieeexplore.ieee.org/abstract/document/8666745 | https://yuxiangsun.github.io/pub/RAL2019_rtfnet.pdf | RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes | Semantic segmentation is a fundamental capability for autonomous vehicles. With the advancements of deep learning technologies, many effective semantic segmentation networks have been proposed in recent years. However, most of them are designed using RGB images from visible cameras. The quality of RGB images is prone t... | ['Ming Liu', 'Weixun Zuo', 'Yuxiang Sun'] | 2019-03-13 | null | null | null | ieee-robotics-and-automation-letters-2019-3 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.01093990e-01 -1.56563789e-01 1.10305011e-01 -3.89810532e-01
8.34717900e-02 -2.28036985e-01 3.93603742e-01 -7.43816555e-01
-6.80909812e-01 4.78881001e-01 -2.49286860e-01 -1.13846950e-01
3.31304938e-01 -9.09266233e-01 -6.98878586e-01 -1.08453166e+00
8.80285084e-01 -1.99807167e-01 3.68889064e-01 -3.32971632... | [9.505574226379395, -1.4396198987960815] |
87966630-7aad-47fb-a48d-edc7af2ed163 | join-joint-gans-inversion-for-intrinsic-image | 2305.11321 | null | https://arxiv.org/abs/2305.11321v1 | https://arxiv.org/pdf/2305.11321v1.pdf | JoIN: Joint GANs Inversion for Intrinsic Image Decomposition | In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materials. Our method builds on the demonstrated success of GANs to capture complex image distributions. A... | ['Julien Philip', 'Svetlana Lazebnik', 'Viraj Shah'] | 2023-05-18 | null | null | null | null | ['intrinsic-image-decomposition', 'image-relighting'] | ['computer-vision', 'computer-vision'] | [ 8.69434118e-01 5.53121567e-01 2.16151476e-01 -1.71331353e-02
-7.59137332e-01 -8.23112607e-01 7.73998439e-01 -1.06333840e+00
1.23052619e-01 7.66425133e-01 2.86180258e-01 -2.35163659e-01
-4.05236967e-02 -9.05656755e-01 -9.59912241e-01 -8.53578985e-01
5.13947368e-01 5.59674263e-01 -4.27256078e-01 -2.43925080... | [11.699898719787598, -0.4202162027359009] |
38bdb963-3d94-4342-8822-7e8df13d4761 | csyngec-incorporating-constituent-based | 2211.08158 | null | https://arxiv.org/abs/2211.08158v1 | https://arxiv.org/pdf/2211.08158v1.pdf | CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser | Recently, Zhang et al. (2022) propose a syntax-aware grammatical error correction (GEC) approach, named SynGEC, showing that incorporating tailored dependency-based syntax of the input sentence is quite beneficial to GEC. This work considers another mainstream syntax formalism, i.e., constituent-based syntax. By drawin... | ['Zhenghua Li', 'Yue Zhang'] | 2022-11-15 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 4.30053890e-01 3.45060885e-01 4.35614765e-01 -5.73963165e-01
-7.15172291e-01 -5.09387493e-01 3.01872969e-01 4.87839371e-01
-4.97095942e-01 4.58065689e-01 3.62397730e-01 -6.78465009e-01
2.33742639e-01 -9.35602546e-01 -9.43921626e-01 -2.35875934e-01
3.34013104e-01 2.92702705e-01 2.24132225e-01 -5.94870508... | [11.087369918823242, 10.708354949951172] |
27932171-f025-402b-8ec9-0bdfaea561aa | neonatal-bowel-sound-detection-using | 2108.07467 | null | https://arxiv.org/abs/2108.07467v3 | https://arxiv.org/pdf/2108.07467v3.pdf | Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model | Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care. It helps early detection of neonatal bowel dysfunctions and allows timely intervention. This paper presents a neonatal bowel sound detection method to assist the auscultation. Specificall... | ['Faezeh Marzbanrad', 'Alistair McEwan', 'Anusha Withana', 'Murray Hinder', 'Omid Kavehei', 'Mark Tracy', 'Archana Priyadarshi', 'Jinyuan He', 'Chiranjibi Sitaula'] | 2021-08-17 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.43974026e-02 1.90394089e-01 -1.51498362e-01 -1.44537345e-01
-4.72669721e-01 -3.56183469e-01 -2.05406219e-01 6.48970842e-01
-3.79850864e-01 2.56256402e-01 1.79816619e-01 -8.13453615e-01
8.86301622e-02 -6.14713132e-01 -8.47168863e-01 -6.14893019e-01
-5.55117905e-01 1.64036065e-01 2.62362778e-01 3.43238950... | [14.072134971618652, -2.2439022064208984] |
abd23f5a-eaa6-48aa-a9a2-3bd7f48b4959 | multiobjective-programming-for-type-2 | 1705.05769 | null | http://arxiv.org/abs/1705.05769v1 | http://arxiv.org/pdf/1705.05769v1.pdf | Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees | This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An
HFIT produces an optimum treelike structure, i.e., a natural hierarchical
structure that accommodates simplicity by combining several low-dimensional
fuzzy inference systems (FISs). Such a natural hierarchical structure provides
a high degree ... | ['Ajith Abraham', 'Varun Kumar Ojha', 'Vaclav Snasel'] | 2017-05-16 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.76850140e-01 -2.13364020e-01 6.28579110e-02 -1.63946837e-01
1.17301986e-01 -1.47196740e-01 1.46743864e-01 2.52856277e-02
-1.91413999e-01 9.24185216e-01 -4.33677524e-01 -1.63012952e-01
-9.47126925e-01 -1.15620279e+00 -1.62998274e-01 -8.30628455e-01
-5.61439712e-03 7.10260451e-01 3.43180686e-01 -4.86272752... | [6.120675086975098, 3.5273244380950928] |
7603c037-3af7-43a9-9e6a-6401311e4aa4 | color-image-restoration-exploiting-inter | null | null | https://ieeexplore.ieee.org/document/9286520 | https://ieeexplore.ieee.org/document/9286520 | Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNN | Image restoration is a critical component of image processing pipelines and for low-level computer vision tasks. Conventional image restoration approaches are mostly based on hand-crafted image priors. The inter-channel correlation of color images is not fully exploited. Motivated by the special characteristics of the ... | ['Kai Cui; Atanas Boev; Elena Alshina; Eckehard Steinbach'] | 2020-12-08 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 4.06238616e-01 -6.51767135e-01 1.96309611e-01 -6.52301311e-02
-5.03818333e-01 -1.71896309e-01 1.94927543e-01 -2.54023373e-01
-5.64825416e-01 4.76006091e-01 1.32570546e-02 -3.69733155e-01
1.83898229e-02 -7.43075490e-01 -6.58507526e-01 -1.06357217e+00
1.06317565e-01 -4.11424965e-01 5.94190471e-02 -2.11198002... | [10.923559188842773, -2.111231565475464] |
d5da0414-8aa1-406c-bc90-2384342c765c | sidi-kws-a-large-scale-multilingual-dataset | null | null | https://www.isca-speech.org/archive/interspeech_2022/meneses22_interspeech.html | https://www.isca-speech.org/archive/pdfs/interspeech_2022/meneses22_interspeech.pdf | SiDi KWS: A Large-Scale Multilingual Dataset for Keyword Spotting | Keyword spotting (KWS) has become a hot topic in speech processing due to the rise of commercial applications based on voice command detection, such as voice assistants. Like tasks in computer vision, natural language processing, and even speech processing, most current successful approaches for KWS rely on deep learni... | ['Gabriela Dantas Rocha', 'Luis Vasconcelos Peres', 'Rafael Bérgamo Holanda', 'Michel Cardoso Meneses'] | 2022-09-22 | null | null | null | interspeech-2022-9 | ['keyword-extraction', 'keyword-spotting'] | ['natural-language-processing', 'speech'] | [ 1.46738723e-01 9.63351876e-03 -1.96561199e-02 -5.68901777e-01
-1.11474311e+00 -4.03833628e-01 6.04125321e-01 1.95958372e-02
-6.08806014e-01 4.14851338e-01 5.11600792e-01 -6.13106489e-01
1.06750773e-02 -2.37972692e-01 -4.99173731e-01 -3.86605501e-01
2.07855105e-01 6.36219203e-01 -1.18629104e-02 2.93314401... | [14.214993476867676, 6.767863750457764] |
0789fd8f-2ac9-4451-a9f8-8ecb8e0a89a9 | zero-day-ddos-attack-detection | 2208.14971 | null | https://arxiv.org/abs/2208.14971v1 | https://arxiv.org/pdf/2208.14971v1.pdf | Zero-day DDoS Attack Detection | The ability to detect zero-day (novel) attacks has become essential in the network security industry. Due to ever evolving attack signatures, existing network intrusion detection systems often fail to detect these threats. This project aims to solve the task of detecting zero-day DDoS (distributed denial-of-service) at... | ['Troy Januchowski', 'Cameron Boeder'] | 2022-08-31 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 0.02742825 -0.6965376 -0.20482226 -0.3060887 0.26681653 -0.9538455
0.63704973 0.05727097 -0.1252752 0.53742677 -0.54692006 -1.1157335
-0.13024506 -1.028467 0.3563434 -0.35056445 -0.5670662 0.50457066
0.6084674 -0.41747504 0.5749666 1.6527933 -0.9889158 0.18209064
-0.07714088 0.9836185 -0.85... | [5.26679801940918, 7.2314629554748535] |
a4fe9297-c052-4801-bbb8-dc5f2668a1df | deepref-a-framework-for-optimized-deep | null | null | https://aclanthology.org/2022.lrec-1.480 | https://aclanthology.org/2022.lrec-1.480.pdf | DeepREF: A Framework for Optimized Deep Learning-based Relation Classification | The Relation Extraction (RE) is an important basic Natural Language Processing (NLP) for many applications, such as search engines, recommender systems, question-answering systems and others. There are many studies in this subarea of NLP that continue to be explored, such as SemEval campaigns (2010 to 2018), or DDI Ext... | ['Sébastien Fournier', 'Bernard Espinasse', 'Adrian-Gabriel Chifu', 'Rinaldo Lima', 'Igor Nascimento'] | null | null | null | null | lrec-2022-6 | ['relation-classification'] | ['natural-language-processing'] | [-2.73268014e-01 3.79478514e-01 -4.90186036e-01 -3.02878261e-01
-3.19284022e-01 -4.08579081e-01 9.64584410e-01 3.61352473e-01
-6.04721248e-01 8.18602860e-01 1.53880715e-01 -5.63935101e-01
-3.83120120e-01 -1.14432287e+00 -4.54070628e-01 -2.99648792e-01
-2.18832240e-01 7.41670370e-01 2.39249781e-01 -3.61848027... | [9.358119010925293, 8.781594276428223] |
de7f7084-c770-4110-bd3b-c28f4839e7f7 | sparse-graphical-representation-based | 1607.00137 | null | http://arxiv.org/abs/1607.00137v1 | http://arxiv.org/pdf/1607.00137v1.pdf | Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition | Face images captured in heterogeneous environments, e.g., sketches generated
by the artists or composite-generation software, photos taken by common cameras
and infrared images captured by corresponding infrared imaging devices, usually
subject to large texture (i.e., style) differences. This results in heavily
degrade... | ['Chunlei Peng', 'Nannan Wang', 'Jie Li', 'Xinbo Gao'] | 2016-07-01 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 6.57004297e-01 -5.18165052e-01 7.52678700e-03 -3.48710239e-01
-5.54864705e-01 -3.17436904e-01 5.17587543e-01 -6.76364183e-01
2.48302639e-01 4.48900342e-01 -3.21911153e-04 2.93622375e-01
-2.97781110e-01 -5.65428376e-01 -3.22295338e-01 -9.44319010e-01
2.37721741e-01 1.94480985e-01 -1.47041723e-01 8.72909650... | [12.849709510803223, 0.3983350396156311] |
f8007c49-5a36-4052-99fb-63e56a189f4d | a-comprehensive-review-of-sign-language | 2204.03328 | null | https://arxiv.org/abs/2204.03328v1 | https://arxiv.org/pdf/2204.03328v1.pdf | A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets | A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increa... | ['Prof. Partha Pratim Roy', 'Dr. M. Madhiarasan'] | 2022-04-07 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.03927773e-01 -4.06255037e-01 -7.60131896e-01 -4.13906664e-01
-4.46455330e-01 -3.67904425e-01 5.80354393e-01 -8.42449129e-01
-5.50497651e-01 3.98190469e-01 3.11928451e-01 -3.28654379e-01
1.57650143e-01 -1.10485166e-01 4.66359966e-02 -5.75862408e-01
1.26237437e-01 -1.93254724e-01 3.55972797e-01 -2.32838374... | [9.119780540466309, -6.429074764251709] |
fac9c796-85e2-44da-9618-61447b6095ca | let-the-flows-tell-solving-graph | 2305.17010 | null | https://arxiv.org/abs/2305.17010v1 | https://arxiv.org/pdf/2305.17010v1.pdf | Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets | Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to apply machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowN... | ['Ling Pan', 'Yoshua Bengio', 'Aaron Courville', 'Nikolay Malkin', 'Hanjun Dai', 'Dinghuai Zhang'] | 2023-05-26 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 1.76069841e-01 -2.89012585e-02 -3.11308622e-01 -2.55946070e-01
-1.26977515e+00 -4.44486290e-01 5.74720740e-01 8.30021277e-02
-6.87812865e-01 1.29221940e+00 -1.47122517e-01 -3.61902207e-01
-3.38026792e-01 -8.21944475e-01 -7.32376277e-01 -7.64824927e-01
-5.63187227e-02 1.28614056e+00 -1.26183331e-01 3.36889595... | [6.822367191314697, 3.9349334239959717] |
c03a072e-f99f-4491-9fe9-5fc950acd9ab | sc6d-symmetry-agnostic-and-correspondence | 2208.02129 | null | https://arxiv.org/abs/2208.02129v3 | https://arxiv.org/pdf/2208.02129v3.pdf | SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation | This paper presents an efficient symmetry-agnostic and correspondence-free framework, referred to as SC6D, for 6D object pose estimation from a single monocular RGB image. SC6D requires neither the 3D CAD model of the object nor any prior knowledge of the symmetries. The pose estimation is decomposed into three sub-tas... | ['Esa Rahtu', 'Janne Heikkilä', 'Dingding Cai'] | 2022-08-03 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-2.26628140e-01 -3.04033309e-01 -3.13902825e-01 -2.99452811e-01
-7.73854733e-01 -5.94663501e-01 6.15941107e-01 -3.19780171e-01
-1.62328050e-01 -6.33314066e-03 -1.02533519e-01 -4.08492275e-02
1.40457034e-01 -3.27270865e-01 -8.18732560e-01 -4.66030329e-01
1.42664328e-01 9.26259100e-01 3.27552140e-01 2.54318982... | [7.404983997344971, -2.6434547901153564] |
318c22fb-2ce0-4dab-a311-fc4e434c0dc3 | acquiring-annotated-data-with-cross-lingual | 1808.10290 | null | http://arxiv.org/abs/1808.10290v2 | http://arxiv.org/pdf/1808.10290v2.pdf | Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification | Implicit discourse relation classification is one of the most challenging and
important tasks in discourse parsing, due to the lack of connective as strong
linguistic cues. A principle bottleneck to further improvement is the shortage
of training data (ca.~16k instances in the PDTB). Shi et al. (2017) proposed to
acqui... | ['Frances Yung', 'Wei Shi', 'Vera Demberg'] | 2018-08-30 | acquiring-annotated-data-with-cross-lingual-1 | https://aclanthology.org/W19-2703 | https://aclanthology.org/W19-2703.pdf | ws-2019-6 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 4.32208896e-01 8.68218482e-01 -6.34210825e-01 -2.80944705e-01
-1.01158655e+00 -1.04729462e+00 7.00370073e-01 3.79984260e-01
-3.82186085e-01 1.17167509e+00 5.99706888e-01 -9.33081865e-01
7.57181272e-02 -5.81664383e-01 -6.99318707e-01 -2.41726086e-01
2.86550105e-01 6.67293608e-01 1.88139305e-01 -6.25255704... | [10.740315437316895, 9.265174865722656] |
46fa9854-bc08-4249-b802-8d5a7bcdc04b | ambipun-generating-humorous-puns-with | null | null | https://openreview.net/forum?id=RhhZo7CpksR | https://openreview.net/pdf?id=RhhZo7CpksR | $AmbiPun$ : Generating Humorous Puns with Ambiguous Context | Computational humor has garnered interest of the natural language processing community due to its wide applications to real world scenarios. One way to express humor is via the use of puns. A homographic pun plays on words that are spelled the same way but have different meanings. In this paper, we propose a simple yet... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 2.07358271e-01 -2.43819907e-01 3.09922099e-01 -5.31884562e-03
-6.20010555e-01 -7.00810373e-01 6.36853814e-01 1.09368026e-01
-4.58334982e-01 8.59514832e-01 7.71762013e-01 -3.17911282e-02
4.62330997e-01 -1.04786944e+00 -4.28575218e-01 -4.78253484e-01
7.60873079e-01 4.56309915e-01 5.17691299e-03 -9.07311022... | [11.337682723999023, 9.07463264465332] |
2d5ee83e-8b03-4d9d-9d06-9c42983e6325 | span-based-joint-entity-and-relation-2 | 2210.12720 | null | https://arxiv.org/abs/2210.12720v1 | https://arxiv.org/pdf/2210.12720v1.pdf | Span-based joint entity and relation extraction augmented with sequence tagging mechanism | Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. However, since previous span-based models rely on span-level classifications, they cannot benefit from token-level label information, which has been proven advantageous for the task. In thi... | ['Jing Yang', 'Huijun Liu', 'Jun Ma', 'Jie Yu', 'Hao Xu', 'Shasha Li', 'Bin Ji'] | 2022-10-23 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-6.13655262e-02 3.81476171e-02 -4.14637476e-01 -2.15466410e-01
-8.98535728e-01 -4.37448084e-01 3.46003562e-01 -4.45676642e-03
-6.51687384e-01 9.18754399e-01 4.44409966e-01 -4.29076821e-01
1.88895300e-01 -7.76461780e-01 -6.22492015e-01 -5.20900488e-01
1.74241066e-02 1.38969451e-01 5.20053618e-02 -6.33380562... | [9.500022888183594, 9.38221549987793] |
08a28078-7396-4246-8f6e-ab40672eefd8 | gecko-a-grammatical-and-discourse-error | null | null | https://aclanthology.org/2021.jeptalnrecital-demo.3 | https://aclanthology.org/2021.jeptalnrecital-demo.3.pdf | GECko+: a Grammatical and Discourse Error Correction Tool | GECko+ : a Grammatical and Discourse Error Correction Tool We introduce GECko+, a web-based writing assistance tool for English that corrects errors both at the sentence and at the discourse level. It is based on two state-of-the-art models for grammar error correction and sentence ordering. GECko+ is available online ... | ['Ajinkya Kulkarni', 'Miguel Couceiro', 'Maxime Amblard', 'Thibo Rosemplatt', 'Léo Jacqmin', 'Eduardo Calò'] | null | null | null | null | jep-taln-recital-2021-6 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.14961059e-02 8.42210114e-01 1.99474782e-01 -3.35742503e-01
-8.33688974e-01 -2.58062065e-01 2.47284696e-01 8.85261059e-01
-1.53590098e-01 7.98955381e-01 6.49226129e-01 -4.60459888e-01
-1.98075414e-01 -6.07917666e-01 -4.79132712e-01 5.84254563e-01
5.02840579e-01 7.12824404e-01 4.21471804e-01 -8.11422706... | [11.084371566772461, 10.705199241638184] |
ed305093-db09-413d-bec3-4be2e8b043fd | a-coupling-enhancement-algorithm-for-zro2 | 2205.11145 | null | https://arxiv.org/abs/2205.11145v2 | https://arxiv.org/pdf/2205.11145v2.pdf | A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method | This study aimed to improve the surface defect detection accuracy of ZrO2 ceramic bearing balls. Combined with the noise damage of the image samples, a surface defect detection method for ZrO2 ceramic bearing balls based on cartoon-texture decomposition model was proposed. Building a ZrO2 ceramic bearing ball surface d... | ['Zhenhong Li', 'Wenjie Li', 'Xianqi Liao', 'Jiaqi Yi', 'Xin Zhang', 'Wei Wang'] | 2022-05-23 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-6.28623515e-02 -3.42850626e-01 5.83143651e-01 5.81111491e-01
-3.05829167e-01 5.19254446e-01 -2.90977895e-01 -1.21263433e-02
-1.02683745e-01 1.59807310e-01 -2.11024791e-01 1.07604556e-01
2.21437626e-02 -1.21994257e+00 1.06808748e-02 -1.03519392e+00
-1.98490284e-02 1.53583258e-01 8.67517292e-01 -6.73194468... | [7.491899490356445, 1.6142016649246216] |
a92990ed-e9bf-4eec-8eb9-7c08fa3605f4 | sam-da-uav-tracks-anything-at-night-with-sam | 2307.01024 | null | https://arxiv.org/abs/2307.01024v1 | https://arxiv.org/pdf/2307.01024v1.pdf | SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation | Domain adaptation (DA) has demonstrated significant promise for real-time nighttime unmanned aerial vehicle (UAV) tracking. However, the state-of-the-art (SOTA) DA still lacks the potential object with accurate pixel-level location and boundary to generate the high-quality target domain training sample. This key issue ... | ['Jia Pan', 'Changhong Fu', 'Guangze Zheng', 'Haobo Zuo', 'Liangliang Yao'] | 2023-07-03 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [-1.54378965e-01 -6.55308902e-01 -1.78534776e-01 -2.31721420e-02
-4.17939216e-01 -7.55749285e-01 3.92879426e-01 -6.31162345e-01
-2.44824901e-01 8.25768471e-01 -6.38947248e-01 -2.01386586e-01
-3.10465842e-01 -6.16848528e-01 -4.89190429e-01 -1.13363171e+00
-1.57019254e-02 2.89210916e-01 3.36703658e-01 -2.58593172... | [6.921438694000244, -1.861168622970581] |
d9e71850-0f15-4720-87c6-18797b17f4a1 | modularized-textual-grounding-for | 1904.03589 | null | https://arxiv.org/abs/1904.03589v2 | https://arxiv.org/pdf/1904.03589v2.pdf | Modularized Textual Grounding for Counterfactual Resilience | Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries. To achieve high grounding precision, current textual grounding methods heavily rely on large-scale training data with manual annotations at the pixel level. Such annot... | ['Shu Kong', 'Charless Fowlkes', 'Zhiyuan Fang', 'Yezhou Yang'] | 2019-04-07 | modularized-textual-grounding-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Fang_Modularized_Textual_Grounding_for_Counterfactual_Resilience_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Fang_Modularized_Textual_Grounding_for_Counterfactual_Resilience_CVPR_2019_paper.pdf | cvpr-2019-6 | ['phrase-grounding', 'natural-language-visual-grounding'] | ['natural-language-processing', 'reasoning'] | [ 4.08350736e-01 4.06108797e-01 -4.76947546e-01 -3.15981269e-01
-6.76736116e-01 -6.20611787e-01 8.90995741e-01 1.88707963e-01
-1.93749711e-01 8.61339688e-01 1.32539093e-01 -5.70229709e-01
3.59419361e-02 -7.54289806e-01 -9.26553607e-01 -4.21209514e-01
7.66354725e-02 3.21732908e-01 2.16198012e-01 -2.40752131... | [10.355557441711426, 1.7494498491287231] |
2b820469-52cb-4cc2-9214-daf9a4be7bec | ecological-semantics-programming-environments | 2003.04567 | null | https://arxiv.org/abs/2003.04567v2 | https://arxiv.org/pdf/2003.04567v2.pdf | Ecological Semantics: Programming Environments for Situated Language Understanding | Large-scale natural language understanding (NLU) systems have made impressive progress: they can be applied flexibly across a variety of tasks, and employ minimal structural assumptions. However, extensive empirical research has shown this to be a double-edged sword, coming at the cost of shallow understanding: inferio... | ['Gabriel Stanovsky', 'Reut Tsarfaty', 'Ronen Tamari', 'Dafna Shahaf'] | 2020-03-10 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 3.94204915e-01 4.91650909e-01 -2.06232950e-01 -3.58391613e-01
-2.69446254e-01 -8.41851652e-01 8.00461590e-01 4.63448286e-01
-2.01788962e-01 4.28773940e-01 9.33074236e-01 -6.50513470e-01
-5.97086251e-01 -9.97836351e-01 -8.28802347e-01 -2.24721462e-01
-1.97442189e-01 6.27491772e-01 -1.56573094e-02 -7.16600597... | [9.31600284576416, 6.813937187194824] |
4631c50f-cb27-4b83-9b58-940c2cdc6b60 | language-models-as-knowledge-embeddings | 2206.12617 | null | https://arxiv.org/abs/2206.12617v3 | https://arxiv.org/pdf/2206.12617v3.pdf | Language Models as Knowledge Embeddings | Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and relations into continuous vector spaces. Existing methods are mainly structure-based or description-based. Structure-based methods learn representations that preserve the inherent structure of KGs. They cannot well represent abundant l... | ['Yanghua Xiao', 'Jiaqing Liang', 'Qianyu He', 'Xintao Wang'] | 2022-06-25 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-5.49167395e-01 2.87793130e-01 -9.93561268e-01 -1.47397012e-01
-3.48492026e-01 -4.64612097e-01 6.20951653e-01 5.79985023e-01
-3.17427546e-01 8.31113875e-01 4.65217710e-01 -2.22640947e-01
-3.84129047e-01 -1.25540173e+00 -6.52011573e-01 -2.92407811e-01
-4.21965361e-01 7.39410639e-01 3.26150715e-01 -3.93590927... | [8.784525871276855, 7.944107532501221] |
b4881bae-394c-4f50-a2f1-ee847609be3e | 190807836 | 1908.07836 | null | https://arxiv.org/abs/1908.07836v1 | https://arxiv.org/pdf/1908.07836v1.pdf | PubLayNet: largest dataset ever for document layout analysis | Recognizing the layout of unstructured digital documents is an important step when parsing the documents into structured machine-readable format for downstream applications. Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images. Howev... | ['Antonio Jimeno Yepes', 'Xu Zhong', 'Jianbin Tang'] | 2019-08-16 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 1.97128132e-01 -1.24843322e-01 -2.23599404e-01 -3.88696790e-01
-7.86843121e-01 -1.10122335e+00 4.29195881e-01 5.52772164e-01
-4.79653180e-01 3.59068006e-01 3.14836770e-01 -6.63761318e-01
-1.87596492e-02 -5.83028436e-01 -1.06191707e+00 -4.05683428e-01
2.53140748e-01 6.94760144e-01 -1.54396966e-01 4.35872972... | [11.631739616394043, 2.756769895553589] |
0d907530-4b7b-48df-a888-ed9292bce7d5 | utilizing-multimodal-feature-consistency-to | null | null | https://aclanthology.org/2020.clinicalnlp-1.29 | https://aclanthology.org/2020.clinicalnlp-1.29.pdf | Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries | Recent studies have shown that adversarial examples can be generated by applying small perturbations to the inputs such that the well- trained deep learning models will misclassify. With the increasing number of safety and security-sensitive applications of deep learn- ing models, the robustness of deep learning models... | ['Li Xiong', 'Pengfei Tang', 'Ian Molloy', 'Taesung Lee', 'Youngja Park', 'Wenjie Wang'] | null | null | null | null | emnlp-clinicalnlp-2020-11 | ['readmission-prediction'] | ['medical'] | [ 1.60755783e-01 3.07428330e-01 1.20652348e-01 -3.33798677e-01
-1.02199221e+00 -9.67976093e-01 5.23376763e-01 5.23974717e-01
-2.32315689e-01 5.56755722e-01 4.39588606e-01 -6.09414458e-01
-1.83669284e-01 -8.41530144e-01 -9.57359612e-01 -6.57711685e-01
-2.73943901e-01 1.66075751e-01 -3.42502803e-01 -1.59965247... | [5.771636009216309, 7.690955638885498] |
d1f7f469-d000-4640-9fdb-78c44ed12955 | summarizing-opinions-aspect-extraction-meets | 1808.08858 | null | http://arxiv.org/abs/1808.08858v1 | http://arxiv.org/pdf/1808.08858v1.pdf | Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised | We present a neural framework for opinion summarization from online product
reviews which is knowledge-lean and only requires light supervision (e.g., in
the form of product domain labels and user-provided ratings). Our method
combines two weakly supervised components to identify salient opinions and form
extractive su... | ['Stefanos Angelidis', 'Mirella Lapata'] | 2018-08-27 | summarizing-opinions-aspect-extraction-meets-1 | https://aclanthology.org/D18-1403 | https://aclanthology.org/D18-1403.pdf | emnlp-2018-10 | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.99686325e-01 6.17715299e-01 -9.32442844e-01 -7.40548372e-01
-1.31771791e+00 -7.53586769e-01 5.46978891e-01 8.04256201e-01
-2.38418967e-01 6.89652801e-01 9.09321189e-01 -4.86234799e-02
2.28993088e-01 -4.91733998e-01 -5.05803883e-01 -1.79763243e-01
4.14105952e-01 5.14040411e-01 -2.08176434e-01 -3.74506652... | [11.43282413482666, 6.756292343139648] |
76b2d38e-bddd-4e0a-b826-63d77c03e9c0 | forecasting-algorithms-for-causal-inference | 2208.03489 | null | https://arxiv.org/abs/2208.03489v1 | https://arxiv.org/pdf/2208.03489v1.pdf | Forecasting Algorithms for Causal Inference with Panel Data | Conducting causal inference with panel data is a core challenge in social science research. Advances in forecasting methods can facilitate this task by more accurately predicting the counterfactual evolution of a treated unit had treatment not occurred. In this paper, we draw on a newly developed deep neural architectu... | ['Justin Young', 'Julian Nyarko', 'Jacob Goldin'] | 2022-08-06 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.20763111e-01 1.93326265e-01 -5.45409620e-01 -4.98606980e-01
-5.09765685e-01 -6.18384719e-01 1.10297751e+00 -5.60446270e-02
-2.66811214e-02 1.31942689e+00 1.21184027e+00 -7.27405429e-01
-1.90601975e-01 -8.48257840e-01 -9.73389208e-01 -6.33843005e-01
-2.71830291e-01 1.90604970e-01 -8.78207386e-01 2.52521373... | [8.01328182220459, 5.390271186828613] |
2e087ca9-3ea1-44cf-8586-027eb657ae93 | category-query-learning-for-human-object | 2303.14005 | null | https://arxiv.org/abs/2303.14005v1 | https://arxiv.org/pdf/2303.14005v1.pdf | Category Query Learning for Human-Object Interaction Classification | Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction categories, converted to image specific category representation via a transformer decoder, and learnt ... | ['Yichen Wei', 'Shuang Liang', 'Yue Hu', 'Fangao Zeng', 'Chi Xie'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_Category_Query_Learning_for_Human-Object_Interaction_Classification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_Category_Query_Learning_for_Human-Object_Interaction_Classification_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 8.56310606e-01 2.90139616e-01 -5.61151326e-01 -6.62483513e-01
-9.47627187e-01 -3.16386133e-01 7.68016100e-01 2.75517434e-01
-3.11099768e-01 5.79961121e-01 2.77044773e-01 -4.14618477e-02
-1.16531104e-01 -4.57063705e-01 -7.88210809e-01 -4.76373136e-01
2.97210902e-01 7.51168966e-01 3.63838494e-01 4.70527709... | [10.120262145996094, 1.989902138710022] |
c75e4e54-e707-4196-9e5e-3955a4b85ae3 | h-denseformer-an-efficient-hybrid-densely | 2307.01486 | null | https://arxiv.org/abs/2307.01486v1 | https://arxiv.org/pdf/2307.01486v1.pdf | H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation | Recently, deep learning methods have been widely used for tumor segmentation of multimodal medical images with promising results. However, most existing methods are limited by insufficient representational ability, specific modality number and high computational complexity. In this paper, we propose a hybrid densely co... | ['Xudong Xue', 'Hong An', 'Zhaohui Wang', 'Liang Qiao', 'Minfan Zhao', 'Ziqi Zhu', 'Shulan Ruan', 'Hongyu Kan', 'Jun Shi'] | 2023-07-04 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 1.30647197e-01 -1.06081508e-01 -4.28398639e-01 -3.02423537e-01
-1.13206828e+00 -1.84805036e-01 3.03561270e-01 -8.82597193e-02
-4.05549377e-01 4.89310890e-01 4.32492852e-01 -2.09933460e-01
1.80149805e-02 -8.51069152e-01 -3.24474841e-01 -1.02904606e+00
3.23395342e-01 -6.87793270e-02 2.26837561e-01 -1.29959330... | [14.564372062683105, -2.414703607559204] |
c5994f6f-2cae-4481-8703-eedf3fd00992 | a-unified-model-for-reverse-dictionary-and | 2205.04602 | null | https://arxiv.org/abs/2205.04602v2 | https://arxiv.org/pdf/2205.04602v2.pdf | A Unified Model for Reverse Dictionary and Definition Modelling | We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other ... | ['Zheng Zhao', 'Pinzhen Chen'] | 2022-05-09 | null | null | null | null | ['definition-modelling', 'reverse-dictionary'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.33459151e-01 -1.70537755e-02 -6.86980128e-01 -5.31394422e-01
-1.44257033e+00 -1.03085470e+00 8.49272609e-01 6.31734580e-02
-9.08328354e-01 8.25089216e-01 4.90208030e-01 -3.53565335e-01
-4.24396209e-02 -8.35881174e-01 -4.40393329e-01 -5.38238525e-01
4.00538325e-01 7.40421534e-01 -2.93219864e-01 -4.33611006... | [10.57789421081543, 8.823745727539062] |
dad80328-3124-43ce-98c8-07b195846c6b | rank-aware-negative-training-for-semi | 2306.07621 | null | https://arxiv.org/abs/2306.07621v1 | https://arxiv.org/pdf/2306.07621v1.pdf | Rank-Aware Negative Training for Semi-Supervised Text Classification | Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and then iteratively predict the unlabeled texts as their pseudo-labels for further training. However, the performance is largely affected by the a... | ['Yunfeng Liu', 'Wenze Zhang', 'Xinxin Cao', 'Jianlin Su', 'Wen Bo', 'Shengfeng Pan', 'Ahmed Murtadha'] | 2023-06-13 | null | null | null | null | ['text-classification', 'semi-supervised-text-classification-1'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.72269189e-01 1.97017133e-01 -3.99356276e-01 -7.07518995e-01
-1.00506830e+00 -5.31810939e-01 5.78454196e-01 4.17778045e-01
-4.90936399e-01 8.19095910e-01 -7.01425225e-02 -3.28476071e-01
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4.05007184e-01 8.62677932e-01 -1.07037704e-02 -1.28672391... | [9.50970458984375, 3.9967451095581055] |
fdbbceed-c111-457c-b605-67ae442fa9e1 | handwritten-digit-recognition-using-machine | 2106.12614 | null | https://arxiv.org/abs/2106.12614v1 | https://arxiv.org/pdf/2106.12614v1.pdf | Handwritten Digit Recognition using Machine and Deep Learning Algorithms | The reliance of humans over machines has never been so high such that from object classification in photographs to adding sound to silent movies everything can be performed with the help of deep learning and machine learning algorithms. Likewise, Handwritten text recognition is one of the significant areas of research ... | ['Rishika Kushwah', 'Ritik Dixit', 'Samay Pashine'] | 2021-06-23 | null | null | null | null | ['handwriting-recognition', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.14936870e-01 -6.84541687e-02 6.27031550e-02 -3.54871780e-01
9.35405940e-02 -6.83215499e-01 8.76365125e-01 -1.21995896e-01
-3.89936954e-01 6.36280417e-01 -1.00142375e-01 -6.93123043e-01
-6.89495057e-02 -5.39058805e-01 -3.57853383e-01 -3.98815751e-01
3.50578517e-01 3.26101810e-01 1.05350576e-01 -1.20824419... | [11.831443786621094, 2.648480176925659] |
afb78020-764e-4338-adf4-c0a15cfd9603 | a-new-neuromorphic-computing-approach-for | 2102.12773 | null | https://arxiv.org/abs/2102.12773v1 | https://arxiv.org/pdf/2102.12773v1.pdf | A New Neuromorphic Computing Approach for Epileptic Seizure Prediction | Several high specificity and sensitivity seizure prediction methods with convolutional neural networks (CNNs) are reported. However, CNNs are computationally expensive and power hungry. These inconveniences make CNN-based methods hard to be implemented on wearable devices. Motivated by the energy-efficient spiking neur... | ['Mohamad Sawan', 'Shiqi Zhao', 'Jie Yang', 'Fengshi Tian'] | 2021-02-25 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 3.35069597e-01 -2.18299851e-01 2.63382137e-01 -1.48351148e-01
-5.28989658e-02 -2.11173460e-01 9.75782946e-02 1.08557511e-02
-4.02880698e-01 1.21998370e+00 -2.79354364e-01 1.31943524e-01
5.18999137e-02 -7.40209162e-01 -5.05765915e-01 -7.20966101e-01
-1.85003936e-01 -3.81804705e-01 4.14347291e-01 1.08027428... | [8.320710182189941, 2.5108392238616943] |
da4d2220-1112-4667-8c0e-604ee912159d | markerless-outdoor-human-motion-capture-using | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Saini_Markerless_Outdoor_Human_Motion_Capture_Using_Multiple_Autonomous_Micro_Aerial_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Saini_Markerless_Outdoor_Human_Motion_Capture_Using_Multiple_Autonomous_Micro_Aerial_ICCV_2019_paper.pdf | Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles | Capturing human motion in natural scenarios means moving motion capture out of the lab and into the wild. Typical approaches rely on fixed, calibrated, cameras and reflective markers on the body, significantly limiting the motions that can be captured. To make motion capture truly unconstrained, we describe the first f... | [' Michael J. Black', ' Aamir Ahmad', ' Igor Martinovic', ' Roman Ludwig', ' Raffi Enficiaud', ' Rahul Tallamraju', ' Eric Price', 'Nitin Saini'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['markerless-motion-capture'] | ['computer-vision'] | [-5.58433346e-02 -1.58723205e-01 1.37598449e-02 6.41288385e-02
-3.88294071e-01 -7.43108928e-01 2.62574464e-01 -6.74230039e-01
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5.60133979e-02 6.58712506e-01 6.10240996e-01 -1.15082517... | [7.258089065551758, -1.3411588668823242] |
3904dab4-85cf-4bb4-84a6-3482a4ad2eda | tell-me-what-you-see-a-zero-shot-action | 2112.09976 | null | https://arxiv.org/abs/2112.09976v1 | https://arxiv.org/pdf/2112.09976v1.pdf | Tell me what you see: A zero-shot action recognition method based on natural language descriptions | Recently, several approaches have explored the detection and classification of objects in videos to perform Zero-Shot Action Recognition with remarkable results. In these methods, class-object relationships are used to associate visual patterns with the semantic side information because these relationships also tend to... | ['Helio Pedrini', 'David Menotti', 'Rayson Laroca', 'Valter Estevam'] | 2021-12-18 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 1.73865139e-01 -5.78927882e-02 -2.88985521e-01 -3.44545871e-01
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1.22091651e-01 2.27856025e-01 4.26937878e-01 -2.13115528... | [8.8910493850708, 0.9901512861251831] |
1db40ddd-6480-4338-a8a3-c3fb8401e8dc | on-device-detection-of-sentence-completion | null | null | https://aclanthology.org/2020.icon-main.53 | https://aclanthology.org/2020.icon-main.53.pdf | On-Device detection of sentence completion for voice assistants with low-memory footprint | Sentence completion detection (SCD) is an important task for various downstream Natural Language Processing (NLP) based applications. For NLP based applications, which use the Automatic Speech Recognition (ASR) from third parties as a service, SCD is essential to prevent unnecessary processing. Conventional approaches ... | ['Ranjan Samal', 'Anmol Bhasin', 'Priyadarshini Pai', 'Godawari Sudhakar Rao', 'Chandan Pandey', 'Vijeta Gour', 'Rahul Kumar'] | null | null | null | null | icon-2020-12 | ['boundary-detection'] | ['computer-vision'] | [ 5.91802180e-01 -1.21118240e-01 -4.20009084e-02 -4.30852950e-01
-1.18614078e+00 -4.95865613e-01 1.87451884e-01 5.09882510e-01
-5.89692533e-01 4.99723792e-01 4.87658709e-01 -9.71004963e-01
3.07233870e-01 -3.59554350e-01 -2.04527780e-01 -1.75379619e-01
9.33792666e-02 1.80070624e-02 3.28603178e-01 -1.94218546... | [14.4154052734375, 6.902316093444824] |
13e72faf-d5ed-4a02-81e0-4bd8a616b379 | beyond-tabula-rasa-reincarnating | 2206.01626 | null | https://arxiv.org/abs/2206.01626v2 | https://arxiv.org/pdf/2206.01626v2.pdf | Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress | Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo multiple design or algorithmic changes during their development cycle and ... | ['Marc G. Bellemare', 'Aaron Courville', 'Pablo Samuel Castro', 'Max Schwarzer', 'Rishabh Agarwal'] | 2022-06-03 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-1.18195839e-01 2.02671885e-02 -3.42759460e-01 5.43850735e-02
-6.84850216e-01 -1.08095467e+00 5.57434738e-01 -3.02635580e-01
-7.05159187e-01 1.20815694e+00 -1.59192130e-01 -9.90621507e-01
-2.37768129e-01 -7.66221225e-01 -9.46337402e-01 -7.60727167e-01
-2.77546048e-01 7.97366917e-01 -2.74304077e-02 -6.85450554... | [3.9503321647644043, 1.6331194639205933] |
e9227383-d97f-4934-904d-31456ecbf530 | grounding-hindsight-instructions-in-multi | 2204.04308 | null | https://arxiv.org/abs/2204.04308v2 | https://arxiv.org/pdf/2204.04308v2.pdf | Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics | This paper focuses on robotic reinforcement learning with sparse rewards for natural language goal representations. An open problem is the sample-inefficiency that stems from the compositionality of natural language, and from the grounding of language in sensory data and actions. We address these issues with three cont... | ['Stefan Wermter', 'Manfred Eppe', 'Frank Röder'] | 2022-04-08 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 2.34184787e-01 6.94221973e-01 -7.29179978e-02 -3.74034792e-01
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-6.77350402e-01 1.08087015e+00 6.93173289e-01 -3.22944969e-01
-5.03525436e-02 -6.15722001e-01 -1.01140702e+00 -6.36856019e-01
-1.74491927e-01 3.26527625e-01 -1.17105350e-01 -5.76760709... | [4.095640182495117, 1.5034393072128296] |
4f3377bc-4499-4d22-9e9a-fd5c0b55ccbe | joint-neural-architecture-and-hyperparameter | 2211.16126 | null | https://arxiv.org/abs/2211.16126v2 | https://arxiv.org/pdf/2211.16126v2.pdf | Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting | Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep lear... | ['Christian S. Jensen', 'Bin Yang', 'Chenjuan Guo', 'Miao Zhang', 'Dalin Zhang', 'Xinle Wu'] | 2022-11-29 | null | null | null | null | ['correlated-time-series-forecasting'] | ['time-series'] | [-1.43650351e-02 -3.68232548e-01 -8.75373855e-02 -1.54929325e-01
-6.77591085e-01 -8.29138696e-01 6.42395377e-01 1.49726138e-01
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-6.74199104e-01 -7.56972134e-01 -3.64501774e-01 -7.09056199e-01
-6.79824412e-01 3.81106913e-01 1.70401827e-01 -1.23837993... | [6.989395618438721, 2.947340726852417] |
d96a36ea-6e22-4d27-8ac4-53e04fdb6199 | unified-extraction-of-health-condition | null | null | https://aclanthology.org/N12-2005 | https://aclanthology.org/N12-2005.pdf | Unified Extraction of Health Condition Descriptions | null | ['Ivelina Nikolova'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['negation-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... | [-7.265807628631592, 3.6932621002197266] |
0bef1567-5318-4c13-8c31-035e906f35f9 | localized-vision-language-matching-for-open | 2205.06160 | null | https://arxiv.org/abs/2205.06160v2 | https://arxiv.org/pdf/2205.06160v2.pdf | Localized Vision-Language Matching for Open-vocabulary Object Detection | In this work, we propose an open-vocabulary object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes. It is a two-stage training approach that first uses a location-guided image-caption matching technique to learn class labels for both nov... | ['Thomas Brox', 'Sudhanshu Mittal', 'Maria A. Bravo'] | 2022-05-12 | null | null | null | null | ['open-vocabulary-object-detection', 'open-vocabulary-attribute-detection', 'open-world-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.66366839e-01 2.02783018e-01 -4.55329627e-01 -4.26600724e-01
-1.46940005e+00 -8.55232179e-01 6.92652166e-01 4.18699354e-01
-5.38550913e-01 4.57260489e-01 3.38888355e-02 -1.58829749e-01
2.52164215e-01 -4.90595043e-01 -1.08145130e+00 -4.56548095e-01
1.37006059e-01 7.25388169e-01 5.00945628e-01 2.03781545... | [9.750372886657715, 1.4800716638565063] |
b5d65aa0-0356-4f4d-8f72-a6ac5b65fb6a | xy-network-for-nuclear-segmentation-in-multi | 1812.06499 | null | https://arxiv.org/abs/1812.06499v5 | https://arxiv.org/pdf/1812.06499v5.pdf | HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images | Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-... | ['Nasir Rajpoot', 'Ayesha Azam', 'Simon Graham', 'Yee Wah Tsang', 'Quoc Dang Vu', 'Shan E Ahmed Raza', 'Jin Tae Kwak'] | 2018-12-16 | null | null | null | null | ['multi-tissue-nucleus-segmentation', 'nuclear-segmentation'] | ['medical', 'medical'] | [ 4.65814471e-01 4.77395877e-02 -7.64134079e-02 -1.40385062e-01
-1.08613718e+00 -7.50886858e-01 4.86432433e-01 9.37692046e-01
-9.11638975e-01 5.20927489e-01 -1.39310556e-02 -4.43856120e-02
-1.13273777e-01 -8.83409441e-01 -2.81112790e-01 -1.45048964e+00
-1.75034791e-01 8.05322111e-01 3.71559024e-01 7.72499815... | [15.0261812210083, -3.117263078689575] |
212eed41-58f1-4f7f-87ce-ac70755bf7d2 | comparison-of-single-and-multi-objective | 2306.17038 | null | https://arxiv.org/abs/2306.17038v1 | https://arxiv.org/pdf/2306.17038v1.pdf | Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery | Evolutionary differential equation discovery proved to be a tool to obtain equations with less a priori assumptions than conventional approaches, such as sparse symbolic regression over the complete possible terms library. The equation discovery field contains two independent directions. The first one is purely mathema... | ['Alexander Hvatov', 'Mikhail Maslyaev'] | 2023-06-29 | null | null | null | null | ['symbolic-regression'] | ['knowledge-base'] | [ 8.00772682e-02 7.86121935e-02 1.52554289e-01 -1.49112567e-01
2.12263986e-02 -3.15172404e-01 3.66810292e-01 2.56117642e-01
-6.90088928e-01 1.13421667e+00 -3.51593107e-01 -2.46897891e-01
-7.39253104e-01 -6.12627864e-01 -1.48275018e-01 -8.33089828e-01
-2.10159384e-02 7.55798638e-01 -1.24089278e-01 -6.13680124... | [6.005269527435303, 3.5255823135375977] |
871f8762-c836-4a53-a4db-9d386561b2ed | panoptic-segmentation-of-satellite-image-time | 2107.07933 | null | https://arxiv.org/abs/2107.07933v4 | https://arxiv.org/pdf/2107.07933v4.pdf | Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks | Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we arg... | ['Loic Landrieu', 'Vivien Sainte Fare Garnot'] | 2021-07-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Garnot_Panoptic_Segmentation_of_Satellite_Image_Time_Series_With_Convolutional_Temporal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Garnot_Panoptic_Segmentation_of_Satellite_Image_Time_Series_With_Convolutional_Temporal_ICCV_2021_paper.pdf | iccv-2021-1 | ['cloud-removal'] | ['computer-vision'] | [ 4.81520474e-01 -4.41432089e-01 -3.63978118e-01 -6.02463365e-01
-4.62452650e-01 -9.35839057e-01 4.66538757e-01 2.21124720e-02
-5.25511324e-01 4.94627923e-01 4.11515012e-02 -7.33645737e-01
-1.77538797e-01 -9.31352139e-01 -6.44442379e-01 -5.95275879e-01
-6.49892807e-01 3.28571498e-01 2.57944047e-01 -4.35647845... | [9.486859321594238, -1.461868166923523] |
a7bd8f60-fc94-4513-8d76-f72d1b258906 | privacy-enhancement-for-cloud-based-few-shot | 2205.07864 | null | https://arxiv.org/abs/2205.07864v2 | https://arxiv.org/pdf/2205.07864v2.pdf | Privacy Enhancement for Cloud-Based Few-Shot Learning | Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains. However, deploying few-shot models in untrusted environments may inflict privacy concerns, e.g., attacks or adversaries that may breach the privacy of user-supplied data. This paper studies the pr... | ['Minwoo Lee', 'Liyue Fan', 'Muhammad Usama', 'Archit Parnami'] | 2022-05-10 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.11716120e-02 6.05643764e-02 -6.52331933e-02 -6.73993289e-01
-9.01620686e-01 -5.70408881e-01 3.66309077e-01 -1.76057011e-01
-3.51532429e-01 4.90298420e-01 -2.68901773e-02 -5.40964454e-02
-6.26791865e-02 -7.60249019e-01 -7.64391303e-01 -9.90817606e-01
5.19862585e-03 -5.26217043e-01 -2.45101243e-01 2.16783062... | [5.903683662414551, 6.811548233032227] |
1ce120f4-bf66-4db9-818e-11820baa33b6 | creating-annotated-dialogue-resources-cross | null | null | https://aclanthology.org/L16-1017 | https://aclanthology.org/L16-1017.pdf | Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification | This paper describes a method to automatically create dialogue resources annotated with dialogue act information by reusing existing dialogue corpora. Numerous dialogue corpora are available for research purposes and many of them are annotated with dialogue act information that captures the intentions encoded in user u... | ['Volha Petukhova', 'Dilafruz Amanova', 'Dietrich Klakow'] | 2016-05-01 | creating-annotated-dialogue-resources-cross-1 | https://aclanthology.org/L16-1017 | https://aclanthology.org/L16-1017.pdf | lrec-2016-5 | ['sound-classification', 'dialogue-act-classification'] | ['audio', 'natural-language-processing'] | [ 5.75181484e-01 6.06645703e-01 4.46598511e-03 -7.71834552e-01
-6.34862304e-01 -7.75094867e-01 1.30970061e+00 3.45436424e-01
-5.97217143e-01 1.24389744e+00 6.57693386e-01 -4.23276844e-03
-1.38846934e-01 -3.97383124e-01 3.28929693e-01 -4.97665554e-01
2.70690054e-01 1.03675771e+00 8.49958956e-02 -5.86962461... | [12.899742126464844, 7.927119731903076] |
b787ae5f-1184-469d-bedc-f29602af4313 | deep-coevolutionary-network-embedding-user | 1609.03675 | null | http://arxiv.org/abs/1609.03675v4 | http://arxiv.org/pdf/1609.03675v4.pdf | Deep Coevolutionary Network: Embedding User and Item Features for Recommendation | Recommender systems often use latent features to explain the behaviors of
users and capture the properties of items. As users interact with different
items over time, user and item features can influence each other, evolve and
co-evolve over time. The compatibility of user and item's feature further
influence the futur... | ['Yichen Wang', 'Hanjun Dai', 'Rakshit Trivedi', 'Le Song'] | 2016-09-13 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [-4.54403430e-01 -5.25216043e-01 -3.07336032e-01 -1.32505581e-01
6.24504566e-01 -6.16301656e-01 7.58791089e-01 -1.16115108e-01
8.96823332e-02 3.58970761e-01 3.56475890e-01 1.58667043e-01
-5.57283163e-01 -1.03860688e+00 -5.80221474e-01 -6.51295722e-01
-6.45951152e-01 5.20425737e-01 -6.80853333e-03 -4.89537448... | [10.159870147705078, 5.604224681854248] |
4946403d-17ee-4679-8b2c-bce42483866d | pq-net-a-generative-part-seq2seq-network-for | 1911.10949 | null | https://arxiv.org/abs/1911.10949v3 | https://arxiv.org/pdf/1911.10949v3.pdf | PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes | We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly. The input to our network is a 3D shape segmented into parts, where each part is first encoded into a feature representation using a part autoencoder. The core component of PQ-NET is a sequence-to-sequence o... | ['Rundi Wu', 'Hao Zhang', 'Yixin Zhuang', 'Kai Xu', 'Baoquan Chen'] | 2019-11-25 | pq-net-a-generative-part-seq2seq-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wu_PQ-NET_A_Generative_Part_Seq2Seq_Network_for_3D_Shapes_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wu_PQ-NET_A_Generative_Part_Seq2Seq_Network_for_3D_Shapes_CVPR_2020_paper.pdf | cvpr-2020-6 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.91497493e-01 5.10427415e-01 2.76009083e-01 -2.77776271e-01
-6.03891671e-01 -7.24865913e-01 4.37119514e-01 -5.17444611e-01
5.19768357e-01 5.53367078e-01 5.73559403e-01 6.79577366e-02
4.08576697e-01 -1.46894884e+00 -1.15998006e+00 -7.30558515e-01
1.59298062e-01 9.22701657e-01 -2.10301936e-01 -3.64047326... | [8.992735862731934, -3.6404168605804443] |
acee7c0d-10a8-4151-b47a-06d735eddec2 | neurohsmd-neuromorphic-hybrid-spiking-motion | 2112.06102 | null | https://arxiv.org/abs/2112.06102v5 | https://arxiv.org/pdf/2112.06102v5.pdf | NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector | Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, yet a complex challenges for modern computers. In vertebrates, the detection of object motion is performed by specialised retinal cells named Object Motion Sensitive Ganglion Cells (OMS-GC). OMS-GC process conti... | ['T. M. McGinnity', 'Andreas Oikonomou', 'Joao Filipe Ferreira', 'Pedro Machado'] | 2021-12-12 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.52138335e-01 -7.20025778e-01 7.97703266e-01 2.05760926e-01
2.25127578e-01 -5.10761499e-01 4.90162134e-01 -7.31963515e-02
-1.29966557e+00 6.21272862e-01 -4.83977586e-01 -2.47437954e-01
5.09953678e-01 -6.27628684e-01 -8.04948509e-01 -8.48711491e-01
1.56434271e-02 -4.18053061e-01 1.38254631e+00 4.31894623... | [8.650334358215332, -1.142829179763794] |
665ce979-c632-4d01-bb44-5e5054d8225e | degraphcs-embedding-variable-based-flow-graph | 2103.13020 | null | https://arxiv.org/abs/2103.13020v3 | https://arxiv.org/pdf/2103.13020v3.pdf | deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search | With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning based approaches(e.g., DeepCS and MMAN) have provided the end-to-end solutions (i.e., accepts natural language as queries and ... | ['Mingyang Geng', 'Xiangke Liao', 'Wei Dong', 'Bailin Xiao', 'Zhiming Wang', 'Xin Xia', 'Shanshan Li', 'Yue Yu', 'Chen Zeng'] | 2021-03-24 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.55917031e-01 -1.78633109e-01 -5.85405886e-01 -1.07893080e-01
-6.87638640e-01 -6.86577380e-01 3.46947908e-02 2.37838253e-01
6.20447919e-02 3.55613269e-02 9.67370644e-02 -7.11703420e-01
-2.16630816e-01 -9.20864940e-01 -7.32259631e-01 1.12110248e-03
-1.78506947e-03 6.15601540e-02 3.07636976e-01 -1.64460957... | [7.497451305389404, 8.050570487976074] |
cccf1726-49ca-43df-9201-00e104596005 | self-taught-cross-domain-few-shot-learning | 2109.01302 | null | https://arxiv.org/abs/2109.01302v1 | https://arxiv.org/pdf/2109.01302v1.pdf | Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition | The domain shift between the source and target domain is the main challenge in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is absolutely unknown during the training on the source domain, which results in lacking directed guidance for target tasks. We observe that since there are similar backgrou... | ['Zhongfei Zhang', 'Yanwei Pang', 'Zhong Ji', 'Xiyao Liu'] | 2021-09-03 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.12445223e-01 -4.13775742e-01 -3.90699714e-01 -4.46327686e-01
-9.14849877e-01 -5.14932752e-01 6.54418826e-01 -3.63342971e-01
-1.31620690e-01 6.96494341e-01 1.17417276e-01 2.00324044e-01
-1.50810376e-01 -5.80465615e-01 -5.33794582e-01 -7.44108856e-01
2.86720872e-01 3.87393057e-01 5.14856577e-01 -4.07142788... | [10.314334869384766, 2.9253389835357666] |
02039c4e-e672-4b6e-ba6a-9a4aa10edc9d | data-centric-domain-adaptation-for-historical | 2107.00927 | null | https://arxiv.org/abs/2107.00927v1 | https://arxiv.org/pdf/2107.00927v1.pdf | Data Centric Domain Adaptation for Historical Text with OCR Errors | We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by integrating unsupervised in-domain data via contextualized string embeddings; and OCR errors by injecting synthetic OCR errors into the sour... | ['Hinrich Schütze', 'Benjamin Roth', 'Nina Poerner', 'Stefan Schweter', 'Luisa März'] | 2021-07-02 | null | null | null | null | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 7.78241903e-02 -2.26751402e-01 -1.01193562e-01 -6.09027803e-01
-1.40656400e+00 -1.42418277e+00 6.62890732e-01 2.29515880e-01
-1.07459748e+00 9.89751041e-01 4.10689116e-01 -2.21481323e-01
6.95090443e-02 -5.12882113e-01 -7.58658171e-01 -1.09590232e-01
2.93783814e-01 6.99282825e-01 2.85535574e-01 -2.89567292... | [9.837955474853516, 9.620129585266113] |
72c91543-ffc7-4117-bfa3-14491492eb21 | nonnegative-matrix-factorization-with | 1705.04193 | null | http://arxiv.org/abs/1705.04193v2 | http://arxiv.org/pdf/1705.04193v2.pdf | Nonnegative Matrix Factorization with Transform Learning | Traditional NMF-based signal decomposition relies on the factorization of
spectral data, which is typically computed by means of short-time frequency
transform. In this paper we propose to relax the choice of a pre-fixed
transform and learn a short-time orthogonal transform together with the
factorization. To this end,... | ['Cédric Févotte', 'Dylan Fagot', 'Herwig Wendt'] | 2017-05-11 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.08296442e-01 -1.28179535e-01 1.57304779e-01 -1.29900917e-01
-9.20753002e-01 -6.25369728e-01 3.97053480e-01 7.17703328e-02
-6.41381443e-01 6.31990075e-01 3.38475794e-01 -2.38979891e-01
-5.08928418e-01 -2.38124996e-01 -6.41457200e-01 -8.72071385e-01
-9.67263803e-02 3.09456773e-02 -3.68429214e-01 -2.40657419... | [15.463371276855469, 5.564912796020508] |
61052d57-bc9d-4ebe-a1c0-f974d08b8d61 | speech-driven-talking-face-generation-from-a | 2008.03592 | null | https://arxiv.org/abs/2008.03592v2 | https://arxiv.org/pdf/2008.03592v2.pdf | Speech Driven Talking Face Generation from a Single Image and an Emotion Condition | Visual emotion expression plays an important role in audiovisual speech communication. In this work, we propose a novel approach to rendering visual emotion expression in speech-driven talking face generation. Specifically, we design an end-to-end talking face generation system that takes a speech utterance, a single f... | ['Sefik Emre Eskimez', 'You Zhang', 'Zhiyao Duan'] | 2020-08-08 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 1.25505194e-01 2.75395423e-01 2.40339756e-01 -6.23730361e-01
-6.94341838e-01 -4.36365664e-01 7.39699602e-01 -6.40822768e-01
-8.17857236e-02 4.98555809e-01 5.00604331e-01 2.37688079e-01
6.47748947e-01 -1.29061863e-01 -6.19092107e-01 -7.19458759e-01
8.43434185e-02 -1.59082457e-01 -4.02073801e-01 -1.55358076... | [13.274232864379883, -0.40248703956604004] |
4d1add68-8d74-4e5d-8ae3-151af7451928 | continual-dialogue-state-tracking-via-example | 2305.13721 | null | https://arxiv.org/abs/2305.13721v1 | https://arxiv.org/pdf/2305.13721v1.pdf | Continual Dialogue State Tracking via Example-Guided Question Answering | Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates ... | ['Chinnadhurai Sankar', 'Jonathan May', 'Jing Xu', 'Satwik Kottur', 'Khyathi Raghavi Chandu', 'Zhaojiang Lin', 'Andrea Madotto', 'Hyundong Cho'] | 2023-05-23 | null | null | null | null | ['memorization', 'dialogue-state-tracking'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97071695e-01 6.72237933e-01 -3.17101210e-01 -6.86290205e-01
-1.03348768e+00 -6.62735939e-01 1.00592339e+00 1.96025431e-01
-3.66299272e-01 7.99978852e-01 6.72136664e-01 -6.07466936e-01
1.25811964e-01 -1.56273961e-01 -3.71971101e-01 -1.78056359e-01
-2.41067000e-02 1.20375156e+00 3.76500726e-01 -9.79584157... | [12.863096237182617, 7.931657791137695] |
37299ea0-4f90-4c13-9fc6-8add64c52f74 | formnet-structural-encoding-beyond-sequential | 2203.08411 | null | https://arxiv.org/abs/2203.08411v2 | https://arxiv.org/pdf/2203.08411v2.pdf | FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction | Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety of layout patterns. We propose FormNet, a structure-aware sequence model to mitigate the ... | ['Tomas Pfister', 'Yasuhisa Fujii', 'Renshen Wang', 'Joshua Ainslie', 'Nan Hua', 'Guolong Su', 'Vincent Perot', 'Timothy Dozat', 'Chun-Liang Li', 'Chen-Yu Lee'] | 2022-03-16 | null | https://aclanthology.org/2022.acl-long.260 | https://aclanthology.org/2022.acl-long.260.pdf | acl-2022-5 | ['document-ai'] | ['natural-language-processing'] | [ 2.15392843e-01 -2.38465250e-01 -2.37743050e-01 -2.58049935e-01
-5.66882908e-01 -9.37833726e-01 6.56149328e-01 5.77658653e-01
-4.26431626e-01 4.71120149e-01 6.80032730e-01 -7.02500999e-01
5.71104623e-02 -7.18606412e-01 -7.91107357e-01 -8.92617777e-02
3.78015311e-03 3.04783911e-01 -5.78645170e-02 -1.77509129... | [10.855698585510254, 7.7542877197265625] |
5ed03802-ae5b-4864-a7fb-beb94140dfba | semantic-specialization-for-knowledge-based | 2304.11340 | null | https://arxiv.org/abs/2304.11340v1 | https://arxiv.org/pdf/2304.11340v1.pdf | Semantic Specialization for Knowledge-based Word Sense Disambiguation | A promising approach for knowledge-based Word Sense Disambiguation (WSD) is to select the sense whose contextualized embeddings computed for its definition sentence are closest to those computed for a target word in a given sentence. This approach relies on the similarity of the \textit{sense} and \textit{context} embe... | ['Naoaki Okazaki', 'Sakae Mizuki'] | 2023-04-22 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.08282901e-01 -1.48388088e-01 -1.21968150e-01 -4.35367197e-01
-5.28826475e-01 -7.40589619e-01 7.54452884e-01 1.01691341e+00
-1.17678177e+00 4.53743219e-01 6.37168944e-01 -6.44877627e-02
-4.67140496e-01 -9.06797409e-01 -1.64444000e-02 -4.91081417e-01
1.31027400e-01 3.95569503e-01 4.77170229e-01 -8.57234836... | [10.314053535461426, 8.962039947509766] |
950a0cbe-328a-4b70-aa7f-84dfb453626d | mixture-model-for-designs-in-high-dimensional | 1210.4762 | null | https://arxiv.org/abs/1210.4762v2 | https://arxiv.org/pdf/1210.4762v2.pdf | Mixture model for designs in high dimensional regression and the LASSO | The LASSO is a recent technique for variable selection in the regression model \bean y & = & X\beta + z, \eean where $X\in \R^{n\times p}$ and $z$ is a centered gaussian i.i.d. noise vector $\mathcal N(0,\sigma^2I)$. The LASSO has been proved to achieve remarkable properties such as exact support recovery of sparse vec... | ['Stéphane Chrétien', 'Mohamed Ibrahim Assoweh'] | 2012-10-17 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.30232185e-01 -5.88835850e-02 -1.07596606e-01 3.77277546e-02
-4.80402857e-01 -4.04257238e-01 2.84131825e-01 -3.74660641e-01
-2.13387191e-01 1.08014131e+00 -2.17945307e-01 -1.38597399e-01
-6.59159303e-01 -5.95898807e-01 -6.79427385e-01 -1.35413957e+00
-1.94825277e-01 4.67450738e-01 -5.88688791e-01 -1.33520141... | [7.003347396850586, 4.49307107925415] |
848d7ae5-68c2-4242-a29f-81103f39869a | projection-free-online-convex-optimization | 2305.01333 | null | https://arxiv.org/abs/2305.01333v2 | https://arxiv.org/pdf/2305.01333v2.pdf | Projection-Free Online Convex Optimization with Stochastic Constraints | This paper develops projection-free algorithms for online convex optimization with stochastic constraints. We design an online primal-dual projection-free framework that can take any projection-free algorithms developed for online convex optimization with no long-term constraint. With this general template, we deduce s... | ['Dabeen Lee', 'Nam Ho-Nguyen', 'Duksang Lee'] | 2023-05-02 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.00140134e-01 1.92623675e-01 -3.17108363e-01 -4.09516126e-01
-1.19531357e+00 -7.56765366e-01 -2.52524912e-01 9.58394781e-02
-5.74288309e-01 9.75551605e-01 1.13350525e-01 -6.80260420e-01
-4.53632474e-01 -5.22289276e-01 -1.01143599e+00 -7.34350502e-01
-3.05113465e-01 3.00157875e-01 -3.47144574e-01 -1.06257223... | [6.152957439422607, 4.283018112182617] |
a5ed8bea-29b3-4e50-85d5-b9fe4ad4df91 | machine-learning-for-mention-head-detection | null | null | https://aclanthology.org/R13-1097 | https://aclanthology.org/R13-1097.pdf | Machine Learning for Mention Head Detection in Multilingual Coreference Resolution | null | ['ra', 'S K{\\"u}bler', 'Desislava Zhekova'] | 2013-09-01 | machine-learning-for-mention-head-detection-1 | https://aclanthology.org/R13-1097 | https://aclanthology.org/R13-1097.pdf | ranlp-2013-9 | ['head-detection'] | ['computer-vision'] | [-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... | [-7.250907897949219, 3.764009952545166] |
c9c88ed8-f75f-4a9e-a22a-bc802d3845ce | structured-scene-memory-for-vision-language | 2103.03454 | null | https://arxiv.org/abs/2103.03454v1 | https://arxiv.org/pdf/2103.03454v1.pdf | Structured Scene Memory for Vision-Language Navigation | Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks... | ['Jianbing Shen', 'Caiming Xiong', 'Wei Liang', 'Wenguan Wang', 'Hanqing Wang'] | 2021-03-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['vision-language-navigation'] | ['computer-vision'] | [ 1.02890216e-01 -1.02452040e-01 -1.20201051e-01 -4.35744673e-01
-6.96303621e-02 -4.22754824e-01 8.61225843e-01 4.64842059e-02
-5.49626291e-01 3.12877566e-01 4.98929024e-01 -5.00942349e-01
-2.64667064e-01 -1.05958521e+00 -6.81975961e-01 -5.76107919e-01
-1.76824555e-01 5.39942086e-01 2.23270401e-01 -4.91307914... | [4.5190958976745605, 0.5307801365852356] |
60b5ae3e-6b8b-4287-88dc-ed05acde9e6c | preservation-of-the-global-knowledge-by-not | 2106.03097 | null | https://arxiv.org/abs/2106.03097v5 | https://arxiv.org/pdf/2106.03097v5.pdf | Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models. Although this precludes the need to access clients' data directly, the global model's convergence often suffers from data heterogeneity. This study starts from an analogy to continual learning and sug... | ['Sangmin Bae', 'Yongjin Shin', 'Se-Young Yun', 'Minchan Jeong', 'Gihun Lee'] | 2021-06-06 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-4.88339424e-01 2.67325699e-01 -3.62654239e-01 -6.02196813e-01
-9.38736141e-01 -7.18425393e-01 4.78487343e-01 -1.02232508e-01
-3.54145736e-01 1.01543069e+00 2.49676019e-01 -2.28671193e-01
-2.75222421e-01 -7.87158310e-01 -8.54542971e-01 -1.08185017e+00
-3.91889885e-02 6.46114707e-01 5.81462309e-02 4.10999656... | [5.86270809173584, 6.384459018707275] |
0b0e3b84-c2dc-45d2-b891-4f02329f7f74 | comprehensive-movie-recommendation-system | 2112.12463 | null | https://arxiv.org/abs/2112.12463v1 | https://arxiv.org/pdf/2112.12463v1.pdf | Comprehensive Movie Recommendation System | A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item. This article designs and implements a complete movie recommendation system prototype based on the Genre, Pearson Correlation Coefficient, Cosine Sim... | ['Jaydip Sen', 'Ananda Chatterjee', 'Hrisav Bhowmick'] | 2021-12-23 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-5.40214658e-01 -4.86218095e-01 -3.87942731e-01 -4.61463898e-01
2.04053119e-01 -8.61320436e-01 6.51757061e-01 2.78374702e-01
-4.29816425e-01 3.24679255e-01 8.76423836e-01 -2.50303209e-01
-1.03768432e+00 -7.88400471e-01 -9.06568244e-02 -2.77733803e-01
-1.98449403e-01 2.56229609e-01 3.45718741e-01 -5.23014247... | [10.017128944396973, 5.887871265411377] |
52b8edf8-29bc-4841-953c-af4594cda075 | logical-message-passing-networks-with-one-hop | 2301.08859 | null | https://arxiv.org/abs/2301.08859v3 | https://arxiv.org/pdf/2301.08859v3.pdf | Logical Message Passing Networks with One-hop Inference on Atomic Formulas | Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural models are proposed to answer the logical queries by parameterizing set operators with complex neural networks. However, such methods usually... | ['Simon See', 'Ginny Y. Wong', 'Yangqiu Song', 'ZiHao Wang'] | 2023-01-21 | null | null | null | null | ['complex-query-answering', 'logical-reasoning'] | ['knowledge-base', 'reasoning'] | [-9.74450856e-02 5.51235259e-01 -3.82147402e-01 -5.73916078e-01
-4.01767880e-01 -3.36626202e-01 5.01510501e-01 5.40534258e-01
-2.47854963e-01 3.57070446e-01 1.56256542e-01 -6.42784119e-01
-4.70237523e-01 -1.77767694e+00 -9.84812796e-01 -1.20815292e-01
-7.05495328e-02 8.20392132e-01 4.99480963e-01 -5.41289270... | [9.222637176513672, 7.684553623199463] |
f3cdb86c-609a-49fe-9cf3-ef2da552ad02 | input-convex-neural-networks | 1609.07152 | null | http://arxiv.org/abs/1609.07152v3 | http://arxiv.org/pdf/1609.07152v3.pdf | Input Convex Neural Networks | This paper presents the input convex neural network architecture. These are
scalar-valued (potentially deep) neural networks with constraints on the
network parameters such that the output of the network is a convex function of
(some of) the inputs. The networks allow for efficient inference via
optimization over some ... | ['Brandon Amos', 'Lei Xu', 'J. Zico Kolter'] | 2016-09-22 | input-convex-neural-networks-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=835 | http://proceedings.mlr.press/v70/amos17b/amos17b.pdf | icml-2017-8 | ['inference-optimization'] | ['audio'] | [ 7.44121492e-01 5.50635099e-01 -6.36142313e-01 -7.39347339e-01
-6.92185819e-01 -2.32405350e-01 2.19697535e-01 -2.02270657e-01
-4.72817838e-01 9.77938831e-01 3.02699506e-01 -3.26459169e-01
-5.57658792e-01 -6.37962282e-01 -1.29147494e+00 -9.11156952e-01
-2.80962251e-02 7.44176030e-01 -8.07252944e-01 8.55286792... | [8.266666412353516, 4.3725810050964355] |
09e8ea34-5ffd-4bee-93f3-86866a1a35ac | detecting-causes-of-stock-price-rise-and | null | null | https://aclanthology.org/2022.fnp-1.4 | https://aclanthology.org/2022.fnp-1.4.pdf | Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT | In this paper, we focused on news reported when stock prices fluctuate significantly. The news reported when stock prices change is a very useful source of information on what factors cause stock prices to change. However, because it is manually produced, not all events that cause stock prices to change are necessarily... | ['Takehito Utsuro', 'Gakuto Tsutsumi'] | null | null | null | null | fnp-lrec-2022-6 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 1.01683557e-01 2.17519283e-01 -3.25019777e-01 -3.89500320e-01
-7.12463796e-01 -7.60800302e-01 6.64221227e-01 9.45886314e-01
-2.84773201e-01 8.02339733e-01 3.70453835e-01 -7.57673919e-01
7.46323839e-02 -1.41121078e+00 -8.08518589e-01 -1.17598839e-01
4.85952407e-01 3.22906435e-01 3.47383142e-01 -6.58486307... | [4.500942230224609, 4.357499122619629] |
1a2c24be-a3c7-47fd-aedc-abaa46413c42 | mastering-the-game-of-stratego-with-model | 2206.15378 | null | https://arxiv.org/abs/2206.15378v1 | https://arxiv.org/pdf/2206.15378v1.pdf | Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning | We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. This popular game has an enormous game tree on the order of $10^{... | ['Karl Tuyls', 'Demis Hassabis', 'Satinder Singh', 'David Silver', 'Remi Munos', 'Nathalie Beauguerlange', 'Laurent SIfre', 'Edward Lockhart', 'Shayegan Omidshafiei', 'Bilal Piot', 'Jean-Baptiste Lespiau', 'Marc Lanctot', 'Mark Rowland', 'Tom Eccles', 'Toby Pohlen', 'Finbarr Timbers', 'Sherjil Ozair', 'Mina Khan', 'Ale... | 2022-06-30 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.46059015e-01 3.85604203e-01 5.85264973e-02 4.87237751e-01
-6.15256846e-01 -8.04991663e-01 3.15533400e-01 -6.65296987e-02
-7.43839920e-01 1.02858818e+00 -2.54879683e-01 -6.10075235e-01
-5.97384632e-01 -1.21467936e+00 -5.47786415e-01 -6.14912331e-01
-5.32964230e-01 1.11694312e+00 5.40671051e-01 -1.17247331... | [3.525688886642456, 1.5252134799957275] |
7f8ee8b8-0c55-4d12-9686-f0744bd6ce24 | a-neural-comprehensive-ranker-ncr-for-open | 1709.10204 | null | http://arxiv.org/abs/1709.10204v2 | http://arxiv.org/pdf/1709.10204v2.pdf | A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering | This paper proposes a novel neural machine reading model for open-domain
question answering at scale. Existing machine comprehension models typically
assume that a short piece of relevant text containing answers is already
identified and given to the models, from which the models are designed to
extract answers. This a... | ['Hao Ma', 'Bin Bi'] | 2017-09-29 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.46499866e-01 4.20514822e-01 1.56757593e-01 -4.03747052e-01
-1.53643799e+00 -6.98891044e-01 4.07386214e-01 8.30000520e-01
-6.47407889e-01 6.74889266e-01 4.27488178e-01 -5.74897408e-01
-4.32295173e-01 -1.04096127e+00 -6.42387629e-01 2.42145240e-01
4.16827261e-01 8.62454355e-01 6.58071041e-01 -7.69003868... | [11.33544635772705, 7.987379550933838] |
ea33b670-4125-4e17-b7a5-97fab85195da | learning-to-compose-with-professional | 1702.00503 | null | http://arxiv.org/abs/1702.00503v2 | http://arxiv.org/pdf/1702.00503v2.pdf | Learning to Compose with Professional Photographs on the Web | Photo composition is an important factor affecting the aesthetics in
photography. However, it is a highly challenging task to model the aesthetic
properties of good compositions due to the lack of globally applicable rules to
the wide variety of photographic styles. Inspired by the thinking process of
photo taking, we ... | ['Kwan-Liu Ma', 'Shao-Yi Chien', 'Yi-Ling Chen', 'Jan Klopp', 'Min Sun'] | 2017-02-01 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.57503444e-01 -1.75530314e-01 -4.92068604e-02 -7.33709216e-01
-5.84458888e-01 -7.68443465e-01 4.72810686e-01 -3.63415152e-01
2.04560161e-01 2.30326634e-02 4.43589658e-01 -7.95245990e-02
-4.57154453e-01 -7.48615623e-01 -8.78864050e-01 -4.29735392e-01
3.28857780e-01 2.27375194e-01 -3.63208711e-01 -3.67289573... | [11.500914573669434, -0.9595103859901428] |
c64d51cd-38bf-4944-9d30-1f04271c5bc0 | convolutional-neural-networks-based-focal | 2001.03329 | null | https://arxiv.org/abs/2001.03329v1 | https://arxiv.org/pdf/2001.03329v1.pdf | Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification | Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. ... | ['Supawit Vatathanavaro', 'Suchat Tungjitnob', 'Kitsuchart Pasupa'] | 2020-01-10 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-2.86770552e-01 -2.71162577e-02 1.04144901e-01 -4.99036700e-01
-5.38826548e-02 -1.49096893e-02 -1.72043428e-01 5.03327072e-01
-5.15551329e-01 8.86605382e-01 -3.16318363e-01 -8.65890533e-02
-1.34290263e-01 -1.10570133e+00 -3.94946128e-01 -9.83612061e-01
-8.71122107e-02 5.71399152e-01 -1.36597678e-01 -2.14747995... | [15.115007400512695, -2.8220016956329346] |
75d11110-54ce-49db-8255-444705dcb1a1 | generative-domain-migration-hashing-for | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Jingyi_Zhang_Generative_Domain-Migration_Hashing_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jingyi_Zhang_Generative_Domain-Migration_Hashing_ECCV_2018_paper.pdf | Generative Domain-Migration Hashing for Sketch-to-Image Retrieval | Due to the succinct nature of free-hand sketch drawings, sketch-based image retrieval (SBIR) has abundant practical use cases in consumer electronics. However, SBIR remains a long-standing unsolved problem mainly due to the significant discrepancy between the sketch domain and the image domain. In this work, we propose... | ['Luc van Gool', 'Heng Tao Shen', 'Mengyang Yu', 'Fan Zhu', 'Li Liu', 'Jingyi Zhang', 'Fumin Shen', 'Ling Shao'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.22512549e-01 -4.20754075e-01 -2.41747439e-01 -3.09616655e-01
-1.15278459e+00 -8.29469323e-01 5.18465459e-01 -3.55965108e-01
-7.67416283e-02 5.44816613e-01 -3.84798087e-02 1.48730427e-01
-2.46358022e-01 -7.67714083e-01 -6.92007840e-01 -8.83280396e-01
1.92480922e-01 6.31345093e-01 1.66377276e-01 -1.01673126... | [11.626060485839844, 0.6967300176620483] |
6eeb9fbf-96f1-4897-a8d3-b144cd386936 | transifc-invariant-cues-aware-feature | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10023961 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10023961 | TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification | Fine-grained bird image classification (FBIC) is not
only meaningful for endangered bird observation and protection
but also a prevalent task for image classification in multimedia
processing and computer vision. However, FBIC suffers from
several challenges, such as bird molting, complex background, and
arbitrary... | ['You-Fu Li', 'Zhaoli Zhang', 'Tingting Liu', 'Bochen Xie', 'Yongjian Deng', 'Cheng Zhang', 'Hai Liu'] | 2022-12-31 | null | null | null | tip-2022-12 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 4.25770506e-02 -6.57570720e-01 1.94283992e-01 -3.82677704e-01
2.45724842e-02 -5.85100472e-01 4.52686340e-01 1.33429572e-01
-4.42616433e-01 3.07121962e-01 1.17659517e-01 2.54483908e-01
-2.14114457e-01 -8.19955647e-01 -5.95775902e-01 -8.17490518e-01
-4.25710529e-01 -4.71622765e-01 3.10006410e-01 -4.68311846... | [9.625448226928711, 2.027846574783325] |
46299c88-ee63-4aef-b4a2-849fd756c717 | compositional-generalization-by-factorizing | null | null | https://aclanthology.org/2020.acl-srw.42 | https://aclanthology.org/2020.acl-srw.42.pdf | Compositional Generalization by Factorizing Alignment and Translation | Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words.... | ['all', "R O{'}Reilly", 'Jason Jo', 'Jacob Russin', 'Yoshua Bengio'] | 2020-07-01 | null | null | null | acl-2020-6 | ['systematic-generalization'] | ['reasoning'] | [ 6.18594229e-01 5.08366823e-01 -1.95868060e-01 -8.04086983e-01
-3.50032836e-01 -8.87746334e-01 9.54917908e-01 2.41410151e-01
-4.29262280e-01 5.12037933e-01 5.54069221e-01 -7.87003994e-01
1.88531622e-01 -9.11312044e-01 -1.01382494e+00 -3.48081738e-01
2.02015266e-01 7.76635587e-01 -2.20160764e-02 -5.72907269... | [10.673141479492188, 9.047115325927734] |
aac1fc0b-3b40-42a5-b914-3388ee60e516 | memory-time-span-in-lstms-for-multi-speaker | 1808.08097 | null | http://arxiv.org/abs/1808.08097v1 | http://arxiv.org/pdf/1808.08097v1.pdf | Memory Time Span in LSTMs for Multi-Speaker Source Separation | With deep learning approaches becoming state-of-the-art in many speech (as
well as non-speech) related machine learning tasks, efforts are being taken to
delve into the neural networks which are often considered as a black box. In
this paper it is analyzed how recurrent neural network (RNNs) cope with
temporal dependen... | ['Hugo Van hamme', 'Jeroen Zegers'] | 2018-08-24 | null | null | null | null | ['multi-speaker-source-separation'] | ['speech'] | [ 2.07149506e-01 1.31851122e-01 -1.01891518e-01 -3.12944144e-01
-5.14895856e-01 -3.99067372e-01 8.49342346e-01 2.67374758e-02
-6.04717791e-01 6.21308744e-01 2.56416887e-01 -5.89180887e-01
-1.23883724e-01 -2.92150557e-01 -4.60118026e-01 -1.01175547e+00
-2.04382285e-01 4.83803540e-01 2.17319742e-01 -3.66908237... | [14.513855934143066, 6.205336570739746] |
b3b18038-4903-4d69-83a7-c3dda8d19d07 | video-moment-retrieval-from-text-queries-via | 2204.09409 | null | https://arxiv.org/abs/2204.09409v3 | https://arxiv.org/pdf/2204.09409v3.pdf | Video Moment Retrieval from Text Queries via Single Frame Annotation | Video moment retrieval aims at finding the start and end timestamps of a moment (part of a video) described by a given natural language query. Fully supervised methods need complete temporal boundary annotations to achieve promising results, which is costly since the annotator needs to watch the whole moment. Weakly su... | ['Xiaowei Guo', 'Yu-Gang Jiang', 'Huyang Sun', 'Jingjing Chen', 'Elena Daskalaki', 'Pai Peng', 'Tianwen Qian', 'Ran Cui'] | 2022-04-20 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-5.08265495e-02 -4.46783490e-02 -5.17847180e-01 -3.81271005e-01
-1.19946623e+00 -7.42685795e-01 8.10671985e-01 2.61977106e-01
-6.57310069e-01 3.47326487e-01 2.93737650e-01 2.62274027e-01
3.05136796e-02 -1.90825090e-01 -8.45291793e-01 -7.75376439e-01
-3.55663806e-01 9.77757052e-02 4.22017902e-01 2.08268419... | [10.105945587158203, 0.696228563785553] |
365e8643-5830-438f-8840-5a599ccaeeed | domain-embedded-multi-model-generative | 2002.02909 | null | https://arxiv.org/abs/2002.02909v2 | https://arxiv.org/pdf/2002.02909v2.pdf | Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting | Prior knowledge of face shape and structure plays an important role in face inpainting. However, traditional face inpainting methods mainly focus on the generated image resolution of the missing portion without consideration of the special particularities of the human face explicitly and generally produce discordant fa... | ['Qi Song', 'Bin Kong', 'Siwei Lyu', 'Jiancheng Lv', 'Canghong Shi', 'Youbing Yin', 'Xian Zhang', 'Xin Wang', 'Xiaojie Li'] | 2020-02-05 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.82731801e-01 3.28281343e-01 4.95438762e-02 -3.65241289e-01
-7.03413904e-01 -4.70762014e-01 2.94561177e-01 -9.91242766e-01
2.39655226e-01 1.09445739e+00 8.96079689e-02 2.94066936e-01
3.42100769e-01 -8.42124701e-01 -1.00495434e+00 -8.59366059e-01
4.80410039e-01 5.54538727e-01 -3.55392456e-01 -2.01411992... | [12.628503799438477, -0.17896310985088348] |
71206cb6-afb3-4b19-bdbc-865a6edf492f | comparison-analysis-of-tree-based-and | 2010.14921 | null | https://arxiv.org/abs/2010.14921v1 | https://arxiv.org/pdf/2010.14921v1.pdf | Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction | Rapid increase of traffic volume on urban roads over time has changed the traffic scenario globally. It has also increased the ratio of road accidents that can be severe and fatal in the worst case. To improve traffic safety and its management on urban roads, there is a need for prediction of severity level of accident... | ['Hamza Ahmad Madni', 'Najia Saher', 'Saleem Ullah', 'Abid Ishaq', 'Saima Sadiq', 'Muhammad Umer'] | 2020-10-27 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-2.29819536e-01 -2.67049432e-01 -2.60369450e-01 -3.46766323e-01
-2.70220041e-01 -1.11636661e-01 5.35485864e-01 2.28753150e-01
-6.18733823e-01 1.19105136e+00 3.74871224e-01 -9.66174304e-01
-4.75162894e-01 -1.13137686e+00 -1.31971896e-01 -6.93600237e-01
2.59588748e-01 3.28476936e-01 5.85380852e-01 -4.68075514... | [6.830014228820801, 2.0767405033111572] |
dc600694-e99d-4545-9fde-cf04eac23fff | bert-based-distractor-generation-for-swedish | 2108.03973 | null | https://arxiv.org/abs/2108.03973v1 | https://arxiv.org/pdf/2108.03973v1.pdf | BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset | An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options. In this paper, we present a new BERT-based method for automatically generating distractors using only a small-scale dataset. We also relea... | ['Johan Boye', 'Dmytro Kalpakchi'] | 2021-08-09 | null | https://aclanthology.org/2021.inlg-1.43 | https://aclanthology.org/2021.inlg-1.43.pdf | inlg-acl-2021-8 | ['distractor-generation'] | ['natural-language-processing'] | [-1.37978822e-01 6.33911431e-01 2.12075785e-01 -4.86209422e-01
-1.25066674e+00 -9.50662732e-01 2.98546731e-01 4.62717861e-01
-4.64184344e-01 1.03609824e+00 4.03815925e-01 -8.17905486e-01
-2.89154142e-01 -4.65338171e-01 -4.16772127e-01 -1.17492154e-01
8.10222447e-01 6.44289374e-01 6.74246550e-01 -4.12007600... | [11.48919677734375, 8.199422836303711] |
44f83efa-a644-4ceb-b4a1-472187adb171 | deim-an-effective-deep-encoding-and | 2203.10482 | null | https://arxiv.org/abs/2203.10482v1 | https://arxiv.org/pdf/2203.10482v1.pdf | DEIM: An effective deep encoding and interaction model for sentence matching | Natural language sentence matching is the task of comparing two sentences and identifying the relationship between them.It has a wide range of applications in natural language processing tasks such as reading comprehension, question and answer systems. The main approach is to compute the interaction between text repres... | ['Zhenguo Zhang', 'Rongyi Cui', 'Yahui Zhao', 'Kexin Jiang'] | 2022-03-20 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 5.59627831e-01 -2.47357965e-01 1.94312632e-01 -6.78698003e-01
-5.54097295e-01 -1.96569577e-01 4.54807192e-01 3.76349449e-01
-7.29352176e-01 2.82481551e-01 3.63130033e-01 -4.62536216e-01
-1.26891464e-01 -1.04222822e+00 -6.04057670e-01 -3.09206694e-01
7.32414484e-01 1.56846419e-01 3.46010774e-01 -3.18048775... | [11.070534706115723, 8.27021312713623] |
e67d052b-c805-47bf-8131-6d500eedb64d | a-large-scale-evaluation-of-neural-machine | null | null | https://aclanthology.org/2021.eacl-main.303 | https://aclanthology.org/2021.eacl-main.303.pdf | A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages | We take up the task of large-scale evaluation of neural machine transliteration between English and Indic languages, with a focus on multilingual transliteration to utilize orthographic similarity between Indian languages. We create a corpus of 600K word pairs mined from parallel translation corpora and monolingual cor... | ['Rahul Kejriwal', 'Siddharth Jain', 'Anoop Kunchukuttan'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['transliteration'] | ['natural-language-processing'] | [-1.89870466e-02 -5.30849338e-01 -6.76990867e-01 -4.58029211e-01
-9.24050033e-01 -1.06982660e+00 3.86799335e-01 -2.37329841e-01
-7.60193229e-01 9.81229246e-01 4.69022989e-01 -1.30649662e+00
2.87828475e-01 -5.13893843e-01 -8.53362322e-01 1.67907663e-02
6.25408232e-01 8.04662168e-01 -6.71169698e-01 -6.13426149... | [11.518038749694824, 10.375542640686035] |
4942f881-ff3c-4a8a-ad6d-50445169cc15 | cross-platform-and-cross-domain-abusive | 2211.06452 | null | https://arxiv.org/abs/2211.06452v1 | https://arxiv.org/pdf/2211.06452v1.pdf | Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning | The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we... | ['Laks V. S. Lakshmanan', 'Muhammad Abdul-Mageed', 'Md Tawkat Islam Khondaker'] | 2022-11-11 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [-1.56396821e-01 -5.31471014e-01 -5.67297578e-01 -1.23047754e-01
-1.40018618e+00 -7.89131641e-01 5.35134852e-01 1.59713224e-01
-4.11324173e-01 5.24882793e-01 7.59932175e-02 -2.59339005e-01
1.83346838e-01 -1.49380907e-01 -4.16155517e-01 -2.21549228e-01
-5.12759201e-02 1.94231957e-01 -1.58948693e-02 -3.04972172... | [8.813624382019043, 10.530917167663574] |
55b990d4-9a31-44bb-aaac-69ecf67b7c55 | parkinsons-disease-emg-signal-prediction | null | null | https://ieeexplore.ieee.org/abstract/document/8914553 | https://ieeexplore.ieee.org/abstract/document/8914553 | Parkinson’s Disease EMG Signal Prediction Using Neural Networks | This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns. The experimental results indicate that the proposed ... | ['Maria Claudia Ferrari de Castro', 'Esther Luna Colombini', 'Rafael Anicet Zanini'] | 2019-10-06 | null | null | null | null | ['emg-signal-prediction', 'electromyography-emg'] | ['medical', 'medical'] | [ 6.38018131e-01 3.20714951e-01 -4.78675455e-01 -8.80862251e-02
-3.69713083e-02 3.49538386e-01 6.26069382e-02 -8.74164045e-01
-3.20965052e-01 1.00777912e+00 7.29999840e-01 -2.73167580e-01
-7.80238271e-01 -3.60553890e-01 -6.69177920e-02 -3.96645963e-01
-2.40102097e-01 3.09044719e-01 2.52893772e-02 -2.79542178... | [6.8661370277404785, 0.2295713722705841] |
04da1f79-0bfd-4e91-9f11-71d589b0a5a9 | medical-image-harmonization-using-deep | 2010.05355 | null | https://arxiv.org/abs/2010.05355v1 | https://arxiv.org/pdf/2010.05355v1.pdf | Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging | Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine. However, these methods have yet to see widespread clinical adoption, in part due to limited generaliz... | ['Christos Davatzikos', 'Ilya M. Nasrallah', 'Haochang Shou', 'David A. Wolk', 'R. Nick Bryan', 'Susan M. Resnick', 'Hans J. Grabe', 'Marilyn S. Albert', 'John C. Morris', 'Ruben C. Gur', 'Raquel E. Gur', 'Daniel H. Wolf', 'Theodore D. Satterthwaite', 'Nikolaos Koutsouleris', 'Jurgen Fripp', 'Sterling C. Johnson', 'Hen... | 2020-10-11 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 4.85642731e-01 -8.73113573e-02 -7.23161697e-02 -8.28301549e-01
-9.79742646e-01 -1.88230574e-01 6.19402945e-01 2.66337335e-01
-6.27287686e-01 6.58821881e-01 3.50756794e-01 -6.08589128e-02
-4.32161808e-01 -4.98025537e-01 -3.79423618e-01 -7.19289422e-01
-1.47499725e-01 8.76901329e-01 -1.82813704e-01 4.04853404... | [14.251005172729492, -1.7838460206985474] |
aa517278-4f1f-4f50-8c5a-a83ee674260f | chinese-grammatical-error-diagnosis-using-2 | null | null | https://aclanthology.org/W16-4920 | https://aclanthology.org/W16-4920.pdf | Chinese Grammatical Error Diagnosis Using Single Word Embedding | Abstract Automatic grammatical error detection for Chinese has been a big challenge for NLP researchers. Due to the formal and strict grammar rules in Chinese, it is hard for foreign students to master Chinese. A computer-assisted learning tool which can automatically detect and correct Chinese grammatical errors is ne... | ['Xue-jie Zhang', 'Bo Peng', 'Jixian Zhang', 'Jinnan Yang', 'Jin Wang'] | 2016-12-01 | null | null | null | ws-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.37467504e-01 -7.52599388e-02 3.56365323e-01 -4.89612192e-01
-6.37395024e-01 -1.34977445e-01 -1.73790097e-01 6.92714214e-01
-7.97125518e-01 6.87431574e-01 9.84607041e-02 -5.76421797e-01
1.50785118e-01 -8.87105048e-01 -6.68099463e-01 -1.95907205e-01
1.62167341e-01 2.40866795e-01 1.27138823e-01 -3.06607336... | [11.046547889709473, 10.792856216430664] |
eabb4373-5a00-4a39-b046-495b4baff3a5 | fakeretouch-evading-deepfakes-detection-via | 2009.09213 | null | https://arxiv.org/abs/2009.09213v4 | https://arxiv.org/pdf/2009.09213v4.pdf | Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering | The current high-fidelity generation and high-precision detection of DeepFake images are at an arms race. We believe that producing DeepFakes that are highly realistic and 'detection evasive' can serve the ultimate goal of improving future generation DeepFake detection capabilities. In this paper, we propose a simple y... | ['Geguang Pu', 'Yang Liu', 'Felix Juefei-Xu', 'Yihao Huang', 'Qing Guo'] | 2020-09-19 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 3.36881459e-01 -4.50153127e-02 2.89262980e-01 8.61930996e-02
-8.12240005e-01 -7.69611716e-01 6.85104489e-01 -5.15621662e-01
-3.14047545e-01 4.26166296e-01 3.40851098e-02 -4.69875187e-01
1.70347571e-01 -7.63878465e-01 -9.56693411e-01 -7.22897947e-01
1.95763618e-01 -1.61371186e-01 5.05988955e-01 -5.41957617... | [12.46097183227539, 1.0605998039245605] |
fd3c6d48-f149-4434-98d1-107d285cf9b6 | unsupervised-collaborative-learning-of | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Sheng_Unsupervised_Collaborative_Learning_of_Keyframe_Detection_and_Visual_Odometry_Towards_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Sheng_Unsupervised_Collaborative_Learning_of_Keyframe_Detection_and_Visual_Odometry_Towards_ICCV_2019_paper.pdf | Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM | In this paper we tackle the joint learning problem of keyframe detection and visual odometry towards monocular visual SLAM systems. As an important task in visual SLAM, keyframe selection helps efficient camera relocalization and effective augmentation of visual odometry. To benefit from it, we first present a deep net... | [' Xiaogang Wang', ' Wanli Ouyang', ' Dan Xu', 'Lu Sheng'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['camera-relocalization'] | ['computer-vision'] | [-2.88159758e-01 -2.38625444e-02 -3.53206307e-01 -3.06745082e-01
-4.74391818e-01 -2.46055052e-01 5.64469099e-01 -1.50202528e-01
-6.36375844e-01 7.05741942e-01 1.24191016e-01 4.00890261e-02
5.84610552e-02 -2.75839299e-01 -9.23275888e-01 -5.45153737e-01
-1.18739247e-01 5.07606626e-01 9.54501778e-02 7.85715356... | [8.019814491271973, -2.17936635017395] |
a71325ef-932b-4acd-b0fc-586bcf9e05cb | generating-image-descriptions-using | null | null | https://aclanthology.org/W17-4750 | https://aclanthology.org/W17-4750.pdf | Generating Image Descriptions using Multilingual Data | null | ['Alan Jaffe'] | 2017-09-01 | null | null | null | ws-2017-9 | ['multimodal-machine-translation'] | ['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... | [-7.255665302276611, 3.7011587619781494] |
9455420e-0aa0-4676-a787-0640e7e243a1 | lrelu-piece-wise-linear-activation-functions | 1910.12259 | null | https://arxiv.org/abs/1910.12259v1 | https://arxiv.org/pdf/1910.12259v1.pdf | L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization | Deep neural networks paved the way for significant improvements in image visual categorization during the last years. However, even though the tasks are highly varying, differing in complexity and difficulty, existing solutions mostly build on the same architectural decisions. This also applies to the selection of acti... | ['Peter M. Roth', 'Mina Basirat'] | 2019-10-27 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.81581363e-01 -3.37833256e-01 -5.94005994e-02 -4.32019234e-01
-4.97738980e-02 -7.06432998e-01 8.47012758e-01 3.51431847e-01
-5.39044023e-01 5.12352526e-01 -7.48491287e-03 -1.96280554e-01
-5.09368539e-01 -7.70758629e-01 -6.51716650e-01 -8.74478459e-01
-3.23501788e-02 -3.67189124e-02 3.87164772e-01 -2.96656281... | [9.518637657165527, 2.2033040523529053] |
5ae1750f-b891-4c74-9636-57514789996f | an-encryption-method-of-convmixer-models | 2207.11939 | null | https://arxiv.org/abs/2207.11939v1 | https://arxiv.org/pdf/2207.11939v1.pdf | An Encryption Method of ConvMixer Models without Performance Degradation | In this paper, we propose an encryption method for ConvMixer models with a secret key. Encryption methods for DNN models have been studied to achieve adversarial defense, model protection and privacy-preserving image classification. However, the use of conventional encryption methods degrades the performance of models ... | ['Hitoshi Kiya', 'Ryota Iijima'] | 2022-07-25 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.58870298e-01 -9.96551514e-02 1.99341372e-01 -4.10839885e-01
-1.16220564e-01 -7.04674959e-01 7.57443190e-01 -1.84591994e-01
-8.80529881e-01 5.70856571e-01 -3.59117270e-01 -2.80410737e-01
8.95761028e-02 -7.20504701e-01 -6.06744945e-01 -9.14783955e-01
3.45493108e-01 -4.98459525e-02 -1.43079847e-01 4.11289521... | [5.525876522064209, 7.369512557983398] |
fe26acbc-c9a1-48d7-a704-9a9859682ed4 | online-nonnegative-tensor-factorization-and | 2009.07612 | null | https://arxiv.org/abs/2009.07612v4 | https://arxiv.org/pdf/2009.07612v4.pdf | Online nonnegative CP-dictionary learning for Markovian data | Online Tensor Factorization (OTF) is a fundamental tool in learning low-dimensional interpretable features from streaming multi-modal data. While various algorithmic and theoretical aspects of OTF have been investigated recently, a general convergence guarantee to stationary points of the objective function without any... | ['Christopher Strohmeier', 'Deanna Needell', 'Hanbaek Lyu'] | 2020-09-16 | null | null | null | null | ['online-nonnegative-cp-decomposition'] | ['methodology'] | [ 3.40470336e-02 -3.89914781e-01 -1.33969411e-01 -1.12150922e-01
-4.75793749e-01 -8.62076283e-01 3.27153891e-01 2.69216318e-02
-1.12483203e-01 4.10248101e-01 3.99501026e-01 -2.67977148e-01
-5.87729394e-01 -2.95499295e-01 -8.25686276e-01 -1.10992730e+00
-5.90364575e-01 6.53854132e-01 -4.95937169e-01 -1.73051387... | [7.177590370178223, 4.660567760467529] |
33b026be-9af6-4b9e-b367-0a7e1c9fb6f9 | local-post-hoc-explanations-for-predictive | 2009.10513 | null | https://arxiv.org/abs/2009.10513v2 | https://arxiv.org/pdf/2009.10513v2.pdf | Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing | This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and ... | ['Nijat Mehdiyev', 'Peter Fettke'] | 2020-09-22 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.05493972e-01 7.28678346e-01 -4.04886782e-01 -5.49217522e-01
-5.65809682e-02 4.52913940e-02 4.52617556e-01 5.91628850e-01
4.02331382e-01 7.82548308e-01 1.57474771e-01 -8.53757381e-01
-1.10018206e+00 -1.01850927e+00 -3.31462204e-01 -3.48559588e-01
-3.40775192e-01 6.22437418e-01 -1.11259747e+00 -6.84000924... | [8.662771224975586, 5.939888000488281] |
db57d7b3-d428-4351-bd10-73a099aa7b9e | automl-in-the-wild-obstacles-workarounds-and | 2302.10827 | null | https://arxiv.org/abs/2302.10827v1 | https://arxiv.org/pdf/2302.10827v1.pdf | AutoML in The Wild: Obstacles, Workarounds, and Expectations | Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users. Recent work has investigated the role of humans in enhancing AutoML functionality throughout a standard ML workflow. However, it is also critical to understand how users adopt existing AutoML solutions in complex, real... | ['Ting Wang', 'Fenglong Ma', 'Xinning Gui', 'Qiurong Song', 'Yuan Sun'] | 2023-02-21 | null | null | null | null | ['automl'] | ['methodology'] | [ 1.51764760e-02 5.86665213e-01 -1.08835243e-01 -7.28012681e-01
-6.99974000e-01 -9.41628039e-01 3.56121451e-01 6.26956522e-01
-6.71701252e-01 4.06054765e-01 6.09799743e-01 -8.83219719e-01
3.80472057e-02 -9.53190494e-03 -1.19430512e-01 9.59517956e-02
7.53744900e-01 1.11235254e-01 -6.22742236e-01 3.19235355... | [9.707473754882812, 7.109508991241455] |
21fc7e68-aba2-4b01-8570-f4011ae9561c | sawu-net-spatial-attention-weighted-unmixing | 2304.11320 | null | https://arxiv.org/abs/2304.11320v1 | https://arxiv.org/pdf/2304.11320v1.pdf | SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images | Hyperspectral unmixing is a critical yet challenging task in hyperspectral image interpretation. Recently, great efforts have been made to solve the hyperspectral unmixing task via deep autoencoders. However, existing networks mainly focus on extracting spectral features from mixed pixels, and the employment of spatial... | ['Xinbo Gao', 'Junyu Dong', 'Feng Gao', 'Xuewen Qin', 'Lin Qi'] | 2023-04-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.26300371e-01 -4.44344312e-01 1.06710389e-01 -1.18072525e-01
-2.80788779e-01 -1.34773880e-01 4.28473532e-01 -1.14210851e-01
-3.04215044e-01 4.90467936e-01 2.85253108e-01 -1.43699810e-01
-4.23463643e-01 -9.60131824e-01 -5.92720628e-01 -1.26996505e+00
2.64776260e-01 -1.71547934e-01 -9.29885507e-02 -1.82341650... | [10.004490852355957, -1.741968035697937] |
0566a5bd-45e4-4a4a-9656-8878d50a92af | view-n-gram-network-for-3d-object-retrieval | 1908.01958 | null | https://arxiv.org/abs/1908.01958v2 | https://arxiv.org/pdf/1908.01958v2.pdf | View N-gram Network for 3D Object Retrieval | How to aggregate multi-view representations of a 3D object into an informative and discriminative one remains a key challenge for multi-view 3D object retrieval. Existing methods either use view-wise pooling strategies which neglect the spatial information across different views or employ recurrent neural networks whic... | ['Tengteng Huang', 'Song Bai', 'Xinwei He', 'Xiang Bai'] | 2019-08-06 | view-n-gram-network-for-3d-object-retrieval-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/He_View_N-Gram_Network_for_3D_Object_Retrieval_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/He_View_N-Gram_Network_for_3D_Object_Retrieval_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision'] | [-3.24335247e-01 -7.04453111e-01 -2.80594021e-01 -4.86759514e-01
-8.73274386e-01 -8.37417364e-01 6.99913204e-01 -1.20821245e-01
1.21603526e-01 -4.40450162e-02 7.84886479e-01 7.22937435e-02
-5.98912537e-02 -8.33126783e-01 -3.09387714e-01 -8.06065083e-01
2.57262528e-01 2.15910867e-01 -1.39346523e-02 1.44094825... | [8.140265464782715, -3.8591115474700928] |
f023ae73-8889-4815-90fb-11a3130ecef2 | spatiotemporal-multi-scale-bilateral-motion | 2209.12364 | null | https://arxiv.org/abs/2209.12364v1 | https://arxiv.org/pdf/2209.12364v1.pdf | Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition | The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented feature... | ['Kejun Wang', 'Yu Zhang', 'Shan Du', 'Xinnan Ding'] | 2022-09-26 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-3.63202430e-02 -7.69471943e-01 -6.42010793e-02 -3.54640573e-01
-9.16593298e-02 1.94735564e-02 3.17174017e-01 -2.69304663e-01
-1.94011152e-01 7.40969479e-01 2.93060690e-01 4.11417723e-01
-1.02312215e-01 -9.16664898e-01 -1.77319780e-01 -9.36066806e-01
-4.87650901e-01 -4.90559429e-01 3.68672788e-01 -3.05287689... | [14.283258438110352, 1.4251500368118286] |
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