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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e888e6e5-e1d5-4fed-9649-06d43dab2091 | accented-speech-recognition-a-survey | 2104.10747 | null | https://arxiv.org/abs/2104.10747v2 | https://arxiv.org/pdf/2104.10747v2.pdf | Accented Speech Recognition: A Survey | Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and provid... | ['Miguel Jette', 'Nishchal Bhandari', 'Ilya Pirkin', 'Jennifer Drexler', 'Joseph Palakapilly', 'Michelle Huang', 'Ryan Westerman', 'Joshua Dong', 'Quinten McNamara', 'Miguel Del Rio', 'Natalie Delworth', 'Arthur Hinsvark'] | 2021-04-21 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 1.56672329e-01 -1.52194977e-01 -3.78611058e-01 -7.32529461e-01
-1.04737568e+00 -7.80666709e-01 4.42663223e-01 -1.36973664e-01
-3.79888088e-01 5.93988359e-01 3.74616444e-01 -4.65662003e-01
2.46736571e-01 7.44691640e-02 -2.65192896e-01 -5.43729126e-01
1.28179848e-01 6.02626443e-01 -2.00874493e-01 -6.62737250... | [14.354887962341309, 6.717148780822754] |
4cf7d560-f0a6-496e-a3f3-ea1c10f56d14 | using-super-resolution-for-enhancing-visual | 2306.11848 | null | https://arxiv.org/abs/2306.11848v1 | https://arxiv.org/pdf/2306.11848v1.pdf | Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology | The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental r... | ['Kazimierz Wiatr', 'Marcin Pietroń', 'Agnieszka Dąbrowska-Boruch', 'Sebastian Koryciak', 'Ernest Jamro', 'Anna Śmiech', 'Rafał Frączek', 'Szymon Mazurek', 'Michał Karwatowski', 'Jakub Grzeszczyk', 'Paweł Russek', 'Daria Łukasik', 'Maciej Wielgosz', 'Jakub Caputa'] | 2023-06-20 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 9.17545140e-01 3.97966534e-01 2.11421341e-01 -4.48319793e-01
-1.35683429e+00 -2.59458184e-01 3.77962708e-01 2.81422347e-01
-2.68867880e-01 3.90137762e-01 -9.44696441e-02 -2.53208131e-01
-7.91234449e-02 -6.84921980e-01 -5.25134206e-01 -7.33903766e-01
3.23728532e-01 3.35749567e-01 7.46338010e-01 -9.05153304... | [15.027816772460938, -2.551020860671997] |
933061a7-d657-4bc7-b5dc-795d10cb3189 | learning-intrinsic-images-for-clothing | 2111.08521 | null | https://arxiv.org/abs/2111.08521v1 | https://arxiv.org/pdf/2111.08521v1.pdf | Learning Intrinsic Images for Clothing | Reconstruction of human clothing is an important task and often relies on intrinsic image decomposition. With a lack of domain-specific data and coarse evaluation metrics, existing models failed to produce satisfying results for graphics applications. In this paper, we focus on intrinsic image decomposition for clothin... | ['Xiaodong Yang', 'Zian Wang', 'Kuo Jiang'] | 2021-11-16 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.70987642e-01 -1.50152057e-01 1.87710971e-01 -5.37565351e-01
-8.40854883e-01 -4.75374579e-01 2.33559728e-01 -3.65995109e-01
-8.50568861e-02 7.50548601e-01 -3.18132974e-02 1.16012551e-01
3.74449790e-01 -6.20530486e-01 -9.08786058e-01 -7.33422458e-01
2.08817020e-01 1.69469431e-01 3.91948909e-01 -2.14139089... | [11.950610160827637, -0.8882811665534973] |
e14508fe-f4f7-409d-ba0d-37c6d2cc3e63 | differential-angular-imaging-for-material | 1612.02372 | null | http://arxiv.org/abs/1612.02372v2 | http://arxiv.org/pdf/1612.02372v2.pdf | Differential Angular Imaging for Material Recognition | Material recognition for real-world outdoor surfaces has become increasingly
important for computer vision to support its operation "in the wild."
Computational surface modeling that underlies material recognition has
transitioned from reflectance modeling using in-lab controlled radiometric
measurements to image-based... | ['Ko Nishino', 'Jia Xue', 'Hang Zhang', 'Kristin Dana'] | 2016-12-07 | differential-angular-imaging-for-material-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Xue_Differential_Angular_Imaging_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Xue_Differential_Angular_Imaging_CVPR_2017_paper.pdf | cvpr-2017-7 | ['material-recognition'] | ['computer-vision'] | [ 8.30466807e-01 -4.01649863e-01 2.94261187e-01 -3.96699965e-01
-7.40759850e-01 -6.30652010e-01 5.64684808e-01 -3.72007638e-01
-7.11266920e-02 3.96981090e-01 -2.61400640e-01 -8.35397169e-02
-2.18620077e-01 -1.11354995e+00 -9.37039256e-01 -6.26725316e-01
1.41632864e-02 4.68064547e-01 2.74711370e-01 -4.55749005... | [9.597152709960938, -2.800967216491699] |
a0e9ea56-8689-420c-aacf-247ffb1cfd14 | progressive-training-of-a-two-stage-framework | 2204.09924 | null | https://arxiv.org/abs/2204.09924v2 | https://arxiv.org/pdf/2204.09924v2.pdf | Progressive Training of A Two-Stage Framework for Video Restoration | As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts. Among video restorations, compressed video quality enhancement and video super-resolution are two of the main tacks with significant values in prac... | ['Ying Chen', 'Huaida Liu', 'Lai Jiang', 'Mai Xu', 'Minglang Qiao', 'Qunliang Xing', 'Meisong Zheng'] | 2022-04-21 | null | null | null | null | ['video-super-resolution', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 3.29304159e-01 -3.93088400e-01 -1.52249068e-01 -1.90314233e-01
-8.94611180e-01 1.06468305e-01 1.09773621e-01 -4.79560465e-01
-2.66323984e-02 7.16340423e-01 4.54026699e-01 -1.69351436e-02
-1.80268034e-01 -5.20073891e-01 -6.69350207e-01 -5.85360110e-01
-3.03524230e-02 -2.43339524e-01 1.85338527e-01 -3.02867144... | [11.159753799438477, -1.8987257480621338] |
0134ba87-7a4c-476c-863b-2b18d050cab6 | permutation-invariant-strategy-using | null | null | https://aclanthology.org/2022.findings-naacl.59 | https://aclanthology.org/2022.findings-naacl.59.pdf | Permutation Invariant Strategy Using Transformer Encoders for Table Understanding | Representing text in tables is essential for many business intelligence tasks such as semantic retrieval, data exploration and visualization, and question answering. Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or c... | ['Alfio Gliozzo', 'Nandana Mihindukulasooriya', 'Sugato Bagchi', 'Sarthak Dash'] | null | null | null | null | findings-naacl-2022-7 | ['semantic-retrieval', 'column-type-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.93827587e-01 4.62721467e-01 -5.00037074e-01 -5.48458397e-01
-8.91115725e-01 -7.64028132e-01 5.67624986e-01 8.78394902e-01
-6.99596033e-02 9.30484772e-01 3.98886800e-01 -7.43404865e-01
-2.99902469e-01 -1.13843942e+00 -1.23099542e+00 8.00518319e-02
1.14155710e-01 7.20785677e-01 2.71880597e-01 -3.24878812... | [9.574139595031738, 7.852672576904297] |
c0af878f-1e22-4931-af8e-fea1f93f740a | ultra-high-definition-image-hdr | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zheng_Ultra-High-Definition_Image_HDR_Reconstruction_via_Collaborative_Bilateral_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zheng_Ultra-High-Definition_Image_HDR_Reconstruction_via_Collaborative_Bilateral_Learning_ICCV_2021_paper.pdf | Ultra-High-Definition Image HDR Reconstruction via Collaborative Bilateral Learning | Existing single image high dynamic range (HDR) reconstruction attempt to expand the range of luminance. They are not effective to generate plausible textures and colors in the reconstructed results, especially for high-density pixels in ultra-high-definition (UHD) images.To address these problems, we propose a new ... | ['Xiuyi Jia', 'Tao Wang', 'Xiaochun Cao', 'Wenqi Ren', 'Zhuoran Zheng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['hdr-reconstruction'] | ['computer-vision'] | [ 2.05794826e-01 -3.21032465e-01 1.29884690e-01 -1.69249758e-01
-6.96650088e-01 8.83072615e-02 2.92900056e-01 -6.03781521e-01
-2.01981962e-01 8.18587422e-01 3.43349755e-01 -5.35985231e-02
9.03209448e-02 -1.28422773e+00 -8.05219710e-01 -8.71406198e-01
1.48792788e-01 -2.18669310e-01 2.33830795e-01 -3.62167597... | [10.815096855163574, -2.1235055923461914] |
1dcc415b-c4f9-4127-a12b-b906d4b41ec8 | a-generative-model-for-relation-extraction | 2202.13229 | null | https://arxiv.org/abs/2202.13229v1 | https://arxiv.org/pdf/2202.13229v1.pdf | A Generative Model for Relation Extraction and Classification | Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering. In this paper, we present a novel generative model for relation extraction and classification (which we call GREC), where RE is mod... | ['Radu Florian', 'Alfio Gliozzo', 'Gaetano Rossiello', 'Jian Ni'] | 2022-02-26 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [ 6.16942346e-01 2.89676368e-01 -4.03096408e-01 -2.89042115e-01
-1.07436895e+00 -6.94012284e-01 5.94152331e-01 4.42412317e-01
-3.22535574e-01 9.89739776e-01 3.55014689e-02 -7.24839330e-01
4.48678993e-02 -1.20953798e+00 -7.68065155e-01 -4.52655345e-01
-1.03865072e-01 6.23592377e-01 4.76317257e-01 -3.27443421... | [9.391840934753418, 8.696677207946777] |
40243fd5-6d6a-4756-b37e-9613e1f0d5a2 | ambicoref-evaluating-human-and-model | 2302.00762 | null | https://arxiv.org/abs/2302.00762v2 | https://arxiv.org/pdf/2302.00762v2.pdf | AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference | Given a sentence "Abby told Brittney that she upset Courtney", one would struggle to understand who "she" refers to, and ask for clarification. However, if the word "upset" were replaced with "hugged", "she" unambiguously refers to Abby. We study if modern coreference resolution models are sensitive to such pronominal ... | ['Mark Yatskar', 'Chaitanya Malaviya', 'Yuewei Yuan'] | 2023-02-01 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 2.65053242e-01 3.68180007e-01 1.58876285e-01 -7.37752318e-01
-7.25282550e-01 -1.17109692e+00 7.14886189e-01 4.93039042e-01
-5.32643795e-01 8.06730449e-01 5.57039499e-01 -7.35870838e-01
-1.55436590e-01 -5.60347021e-01 -3.85202259e-01 -1.86038300e-01
5.23361683e-01 9.38503325e-01 1.84119284e-01 -8.08498621... | [10.148037910461426, 9.2455472946167] |
2d3f3d3c-022f-460e-989c-e8b65ff8c7ef | probing-inter-modality-visual-parsing-with-1 | null | null | https://openreview.net/forum?id=e0nZIFEpmYh | https://openreview.net/pdf?id=e0nZIFEpmYh | Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training | Vision-Language Pre-training (VLP) aims to learn multi-modal representations from image-text pairs and serves for downstream vision-language tasks in a fine-tuning fashion. The dominant VLP models adopt a CNN-Transformer architecture, which embeds images with a CNN, and then aligns images and text with a Transformer. ... | ['Jiebo Luo', 'Houqiang Li', 'Jianlong Fu', 'Houwen Peng', 'Bei Liu', 'Yupan Huang', 'Hongwei Xue'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['visual-entailment'] | ['reasoning'] | [ 6.37896582e-02 -1.46590490e-02 -3.46511960e-01 -4.09686923e-01
-4.96661872e-01 -5.23040473e-01 8.64919424e-01 -1.28778622e-01
-4.11171913e-01 1.62563279e-01 3.82440418e-01 -4.12683547e-01
9.17263702e-02 -7.90882647e-01 -9.37947631e-01 -6.11427486e-01
5.15501380e-01 -2.42345911e-02 2.14197025e-01 -2.21800923... | [10.746261596679688, 1.496458888053894] |
0509bf57-56e6-4962-9b34-dfaab6e0d312 | implementing-measurement-error-models-in-a | 2307.01539 | null | https://arxiv.org/abs/2307.01539v1 | https://arxiv.org/pdf/2307.01539v1.pdf | Implementing measurement error models in a likelihood-based framework for estimation, identifiability analysis, and prediction in the life sciences | Throughout the life sciences we routinely seek to interpret measurements and observations using parameterised mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achie... | ['Matthew J. Simpson', 'Oliver J. Maclaren', 'Ryan J. Murphy'] | 2023-07-04 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 5.62220991e-01 -1.68308467e-01 4.50247154e-02 -2.09561363e-01
-4.32522863e-01 -5.85375249e-01 6.63455427e-01 2.66824752e-01
-2.78428406e-01 1.15630126e+00 -2.99778711e-02 -4.82734561e-01
-7.29937136e-01 -6.84714496e-01 -6.85378015e-01 -8.73301208e-01
1.58211187e-01 5.54070354e-01 2.56877225e-02 5.76914549... | [6.560003280639648, 3.9903030395507812] |
17be832d-fb35-4411-b383-f1aef56e4efe | exploiting-explicit-paths-for-multi-hop | 1811.01127 | null | https://arxiv.org/abs/1811.01127v2 | https://arxiv.org/pdf/1811.01127v2.pdf | Exploiting Explicit Paths for Multi-hop Reading Comprehension | We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates p... | ['Souvik Kundu', 'Ashish Sabharwal', 'Tushar Khot', 'Peter Clark'] | 2018-11-02 | exploiting-explicit-paths-for-multi-hop-1 | https://aclanthology.org/P19-1263 | https://aclanthology.org/P19-1263.pdf | acl-2019-7 | ['multi-hop-reading-comprehension', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.69072852e-01 9.45839882e-01 -3.06324333e-01 -3.58612299e-01
-9.84996319e-01 -6.52188778e-01 7.10931718e-01 1.02515376e+00
-1.98451459e-01 8.57348442e-01 5.90114772e-01 -7.44611681e-01
-7.50110686e-01 -1.45442390e+00 -1.01340759e+00 2.80238420e-01
-2.64805313e-02 9.22720671e-01 8.85588586e-01 -6.92678154... | [10.784125328063965, 7.925074100494385] |
728fe9f9-d6ca-4a59-9770-4fd634ba9e80 | anisotropic-diffusion-for-details-enhancement | 1307.2818 | null | http://arxiv.org/abs/1307.2818v1 | http://arxiv.org/pdf/1307.2818v1.pdf | Anisotropic Diffusion for Details Enhancement in Multi-Exposure Image Fusion | We develop a multiexposure image fusion method based on texture features,
which exploits the edge preserving and intraregion smoothing property of
nonlinear diffusion filters based on partial differential equations (PDE). With
the captured multiexposure image series, we first decompose images into base
layers and detai... | ['Vinay Kumar', 'Harbinder Singh', 'Sunil Bhooshan'] | 2013-07-10 | null | null | null | null | ['multi-exposure-image-fusion', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 7.15326488e-01 -4.68359947e-01 2.91661263e-01 -2.22308323e-01
-5.73010921e-01 -6.28389418e-01 2.58781463e-01 -2.90245235e-01
-3.19810718e-01 8.53810966e-01 1.48491815e-01 2.51169980e-01
-1.96002662e-01 -6.90454006e-01 -4.15733844e-01 -1.12067950e+00
4.57881391e-01 -5.87345600e-01 6.22885704e-01 -1.91784531... | [10.919646263122559, -2.4125216007232666] |
04a5062c-746a-4292-8463-e3c5577c1f33 | autonomous-vision-based-rapid-aerial-grasping | 2211.13093 | null | https://arxiv.org/abs/2211.13093v2 | https://arxiv.org/pdf/2211.13093v2.pdf | Autonomous Marker-less Rapid Aerial Grasping | In a future with autonomous robots, visual and spatial perception is of utmost importance for robotic systems. Particularly for aerial robotics, there are many applications where utilizing visual perception is necessary for any real-world scenarios. Robotic aerial grasping using drones promises fast pick-and-place solu... | ['Robert K. Katzschmann', 'Barnabas Gavin Cangan', 'Erik Bauer'] | 2022-11-23 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.88439656e-02 -1.87418148e-01 3.34163725e-01 -2.88864374e-01
-8.82700160e-02 -1.17207336e+00 1.57534644e-01 -3.35464552e-02
-3.67693990e-01 1.87303841e-01 -6.25276804e-01 -1.39420062e-01
-3.14366430e-01 -8.11855078e-01 -8.91987562e-01 -4.33655381e-01
-5.21200836e-01 6.45700991e-01 6.85426235e-01 -6.43243849... | [5.776187896728516, -0.8430099487304688] |
063dcac9-9e8d-4ed7-ada8-ffafe09f5a92 | unprocessing-images-for-learned-raw-denoising | 1811.11127 | null | http://arxiv.org/abs/1811.11127v1 | http://arxiv.org/pdf/1811.11127v1.pdf | Unprocessing Images for Learned Raw Denoising | Machine learning techniques work best when the data used for training
resembles the data used for evaluation. This holds true for learned
single-image denoising algorithms, which are applied to real raw camera sensor
readings but, due to practical constraints, are often trained on synthetic
image data. Though it is und... | ['Jonathan T. Barron', 'Ben Mildenhall', 'Dillon Sharlet', 'Tim Brooks', 'Jiawen Chen', 'Tianfan Xue'] | 2018-11-27 | unprocessing-images-for-learned-raw-denoising-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Brooks_Unprocessing_Images_for_Learned_Raw_Denoising_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Brooks_Unprocessing_Images_for_Learned_Raw_Denoising_CVPR_2019_paper.pdf | cvpr-2019-6 | ['tone-mapping'] | ['computer-vision'] | [ 8.42851520e-01 -1.64976269e-01 6.81316137e-01 -6.24334455e-01
-7.97636211e-01 -6.67564511e-01 4.55067337e-01 1.12861529e-01
-8.41972649e-01 3.15796643e-01 -5.95443249e-02 -3.12364668e-01
1.04325645e-01 -9.65475440e-01 -1.20399094e+00 -7.70245135e-01
4.38562632e-02 1.51430834e-02 1.75306529e-01 -2.76740223... | [11.003206253051758, -2.3672523498535156] |
96ba781b-f468-407c-aa3a-d1caabd89903 | language-identification-using-deep | 1708.04811 | null | http://arxiv.org/abs/1708.04811v1 | http://arxiv.org/pdf/1708.04811v1.pdf | Language Identification Using Deep Convolutional Recurrent Neural Networks | Language Identification (LID) systems are used to classify the spoken
language from a given audio sample and are typically the first step for many
spoken language processing tasks, such as Automatic Speech Recognition (ASR)
systems. Without automatic language detection, speech utterances cannot be
parsed correctly and ... | ['Tom Herold', 'Christian Bartz', 'Haojin Yang', 'Christoph Meinel'] | 2017-08-16 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 3.30451012e-01 -2.73923993e-01 4.88194339e-02 -4.50391471e-01
-1.20620787e+00 -8.04949284e-01 4.30996597e-01 -2.17226535e-01
-4.56678063e-01 3.78157765e-01 2.04841062e-01 -6.00497842e-01
3.10102493e-01 -2.93326139e-01 -3.45892936e-01 -4.66758132e-01
-1.99429076e-02 4.82865304e-01 -1.81069579e-02 -4.67106178... | [14.179137229919434, 6.537304878234863] |
c0e43291-50a7-4209-af92-73f227619d3d | bayesian-neural-network-language-modeling-for | 2208.13259 | null | https://arxiv.org/abs/2208.13259v1 | https://arxiv.org/pdf/2208.13259v1.pdf | Bayesian Neural Network Language Modeling for Speech Recognition | State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. They are prone to overfitting and poor generalization when given limited training data. To this end, an overarching full Bayesian learning fra... | ['Helen Meng', 'Xunying Liu', 'Mengzhe Geng', 'Junhao Xu', 'Shoukang Hu', 'Boyang Xue'] | 2022-08-28 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.19956696e-01 4.25525457e-01 -2.83850972e-02 -4.61850911e-01
-1.40326786e+00 -1.52293444e-01 6.40381575e-01 -5.25591135e-01
-6.21730030e-01 7.75238395e-01 4.60713267e-01 -5.21296620e-01
-7.21139833e-02 1.86913814e-02 -7.33665168e-01 -9.91123855e-01
3.10621619e-01 7.60766268e-01 -4.09049951e-02 2.06731901... | [14.554274559020996, 6.313554286956787] |
877df515-930a-4018-8c6e-5a45b742b1a7 | generating-textual-explanations-for-machine | null | null | https://aclanthology.org/2022.lrec-1.379 | https://aclanthology.org/2022.lrec-1.379.pdf | Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task | Numerical tables are widely employed to communicate or report the classification performance of machine learning (ML) models with respect to a set of evaluation metrics. For non-experts, domain knowledge is required to fully understand and interpret the information presented by numerical tables. This paper proposes a n... | ['Noura Al Moubayed', 'Amir Enshaei', 'James Burton', 'Isaac Ampomah'] | null | null | null | null | lrec-2022-6 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.25831759e-01 8.99955869e-01 -1.35657638e-02 -6.64779067e-01
-1.01483059e+00 -5.26551425e-01 9.41442907e-01 6.09643757e-01
1.50335252e-01 9.83957648e-01 6.00192249e-01 -6.05589807e-01
-6.37073116e-03 -9.48439300e-01 -6.98317945e-01 -3.11722662e-02
1.62183255e-01 7.29790211e-01 -5.59287429e-01 -2.08825737... | [11.495943069458008, 8.80200481414795] |
13b78ab2-e67e-485f-a324-2670066b715f | iterative-thresholded-bi-histogram | 1508.05704 | null | http://arxiv.org/abs/1508.05704v1 | http://arxiv.org/pdf/1508.05704v1.pdf | Iterative Thresholded Bi-Histogram Equalization for Medical Image Enhancement | Enhancement of human vision to get an insight to information content is of
vital importance. The traditional histogram equalization methods have been
suffering from amplified contrast with the addition of artifacts and a
surprising unnatural visibility of the processed images. In order to overcome
these drawbacks, this... | ['Muhammad Ali Qadar', 'Li Hua', 'Yan Zhaowen'] | 2015-08-24 | null | null | null | null | ['medical-image-enhancement'] | ['computer-vision'] | [ 4.08436835e-01 -2.69666582e-01 4.15652066e-01 -2.66447067e-01
-2.44866908e-01 -3.31265211e-01 3.70216161e-01 3.97645414e-01
-6.90877318e-01 6.45658553e-01 -1.79520790e-02 -9.36548188e-02
-7.55154788e-02 -7.60951042e-01 -1.82268098e-01 -1.20993400e+00
4.05469202e-02 -3.91518742e-01 6.99239969e-01 -1.54341415... | [10.91064167022705, -2.3980486392974854] |
94ccbfcf-7341-4a72-9f2f-7971ebe2b837 | prediction-of-adverse-biological-effects-of | 2112.04605 | null | https://arxiv.org/abs/2112.04605v2 | https://arxiv.org/pdf/2112.04605v2.pdf | Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings | We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, a... | ['Knut Erik Tollefsen', 'Raoul Wolf', 'Jiaoyan Chen', 'Ernesto Jiménez-Ruiz', 'Erik B. Myklebust'] | 2021-12-08 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 4.89725955e-02 2.12904945e-01 -1.80922985e-01 -2.12820247e-02
-1.86774075e-01 -6.30951643e-01 5.11265874e-01 8.63822937e-01
-3.53926688e-01 4.21671212e-01 6.48540914e-01 -4.46927100e-01
-6.09694600e-01 -1.25103426e+00 -7.82837391e-01 -3.79726797e-01
-4.06851321e-01 1.98716670e-01 3.74777108e-01 -3.08740735... | [7.879035949707031, 7.340205192565918] |
88b7ba53-0350-4c22-a499-107ed7e3d22b | melt-mutual-enhancement-of-long-tailed-user | 2304.08382 | null | https://arxiv.org/abs/2304.08382v1 | https://arxiv.org/pdf/2304.08382v1.pdf | MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation | The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studies address the long-tailed problem in SRS, they only focus on either the user or item perspective. However, we discover that the long-tailed... | ['Chanyoung Park', 'Sukwon Yun', 'Dongmin Hyun', 'Kibum Kim'] | 2023-04-17 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-1.17326498e-01 -2.67405689e-01 -3.76317888e-01 -2.26456180e-01
-3.68785083e-01 -5.68152428e-01 4.72464822e-02 -1.57312810e-01
-2.21312895e-01 5.46159089e-01 2.32395187e-01 -5.21271229e-01
-4.10397112e-01 -6.08445466e-01 -5.62775016e-01 -6.65277302e-01
1.45466477e-01 4.52332377e-01 4.29396778e-01 -5.27129233... | [10.142111778259277, 5.550002574920654] |
a25612e9-0211-46a4-9d0a-7cac7830d33c | diabetic-retinopathy-diagnosis-based-on | 2008.00148 | null | https://arxiv.org/abs/2008.00148v1 | https://arxiv.org/pdf/2008.00148v1.pdf | Diabetic Retinopathy Diagnosis based on Convolutional Neural Network | Diabetic Retinopathy DR is a popular disease for many people as a result of age or the diabetic, as a result, it can cause blindness. therefore, diagnosis of this disease especially in the early time can prevent its effect for a lot of patients. To achieve this diagnosis, eye retina must be examined continuously. There... | ['Lamia Abed Noor Muhammed', 'Mohammed hamzah abed', 'Sarah Hussein Toman'] | 2020-08-01 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-2.13622138e-01 -1.52334034e-01 2.58007854e-01 -2.06954464e-01
2.86925554e-01 1.11605786e-01 2.58017838e-01 -2.08168790e-01
-3.40953231e-01 7.26708055e-01 2.89097995e-01 -2.79540122e-01
-1.37262791e-01 -8.86114299e-01 -1.80581748e-01 -8.26854765e-01
6.73011988e-02 8.38974491e-02 3.16420794e-01 -4.61558178... | [15.836468696594238, -3.9965524673461914] |
b9d60398-9883-4248-aaed-6667e46a2457 | know-what-and-know-where-an-object-and-room | 2104.04167 | null | https://arxiv.org/abs/2104.04167v2 | https://arxiv.org/pdf/2104.04167v2.pdf | The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation | Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take the words in the instructions and the discrete views of each panorama as the minimal unit of encoding. However, this... | ['Qi Wu', 'Anton Van Den Hengel', 'Ming-Hsuan Yang', 'Yicong Hong', 'Zizheng Pan', 'Yuankai Qi'] | 2021-04-09 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Qi_The_Road_To_Know-Where_An_Object-and-Room_Informed_Sequential_BERT_for_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Qi_The_Road_To_Know-Where_An_Object-and-Room_Informed_Sequential_BERT_for_ICCV_2021_paper.pdf | iccv-2021-1 | ['vision-language-navigation'] | ['computer-vision'] | [-6.88534603e-02 -4.97990519e-01 -4.69124643e-03 -6.44202828e-01
-1.96967512e-01 -7.26428509e-01 6.29551053e-01 -3.25354785e-02
-4.14152980e-01 4.67513382e-01 3.88029367e-01 -5.55518389e-01
-6.21298403e-02 -8.61996651e-01 -8.43586326e-01 -4.21657532e-01
1.71760187e-01 3.16175193e-01 1.67977527e-01 -5.03501058... | [4.499335289001465, 0.4764355421066284] |
964828ac-5db8-4437-a372-dba60cd3c5ad | convolutional-neural-networks-for-automatic | 1902.09600 | null | http://arxiv.org/abs/1902.09600v1 | http://arxiv.org/pdf/1902.09600v1.pdf | Convolutional Neural Networks for Automatic Meter Reading | In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high
capability of Convolutional Neural Networks (CNNs). We design a two-stage
approach that employs the Fast-YOLO object detector for counter detection and
evaluates three different CNN-based approaches for counter recognition. In the
AMR literat... | ['Gabriel R. Gonçalves', 'Rayson Laroca', 'William Robson Schwartz', 'Victor Barroso', 'David Menotti', 'Matheus A. Diniz'] | 2019-02-25 | null | null | null | null | ['image-based-automatic-meter-reading'] | ['computer-vision'] | [ 1.56231940e-01 -2.50757784e-01 -3.74871105e-01 -1.44503757e-01
-8.64998639e-01 -2.04307497e-01 6.92971408e-01 2.45799154e-01
-5.93156099e-01 7.38286495e-01 6.64699590e-03 -4.32545960e-01
2.40610018e-01 -7.92897940e-01 -5.32251596e-01 -4.00001317e-01
4.76860553e-02 3.08992058e-01 -5.04479259e-02 -1.88220948... | [11.343738555908203, 2.625261068344116] |
dfe635be-d35a-4ef1-95b7-c0a950444332 | gam-changer-editing-generalized-additive | 2112.03245 | null | https://arxiv.org/abs/2112.03245v1 | https://arxiv.org/pdf/2112.03245v1.pdf | GAM Changer: Editing Generalized Additive Models with Interactive Visualization | Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in deployment. However, it is unclear how we can fix these models. We present our ongoing work, GAM Changer, an open-source interactive system to... | ['Rich Caruana', 'Jennifer Wortman Vaughan', 'Mihaela Vorvoreanu', 'Duen Horng Chau', 'Mark Nunnally', 'Peter Stella', 'Harsha Nori', 'Alex Kale', 'Zijie J. Wang'] | 2021-12-06 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.34111613e-01 5.49510121e-01 -2.73853183e-01 -5.42821050e-01
-3.29782873e-01 -7.14130819e-01 1.67642906e-01 1.91053480e-01
-3.44265178e-02 2.72778481e-01 1.37447178e-01 -1.05880296e+00
-8.94372389e-02 -5.36939144e-01 -4.82578993e-01 -8.07846244e-03
7.83202350e-02 4.14758146e-01 -2.39008084e-01 -4.30785380... | [8.761786460876465, 5.924224853515625] |
eeb91fdd-1f35-451a-ace8-8b6062343b85 | joint-estimation-of-clustered-user-activity | 2212.00116 | null | https://arxiv.org/abs/2212.00116v1 | https://arxiv.org/pdf/2212.00116v1.pdf | Joint Estimation of Clustered User Activity and Correlated Channels with Unknown Covariance in mMTC | This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a \emph{clustered} user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. ... | ['Markku Juntti', 'Markus Leinonen', 'Hamza Djelouat'] | 2022-11-30 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.40860540e-01 2.64025956e-01 -1.47381693e-01 3.01348478e-01
-8.79295468e-01 -5.95094040e-02 1.26377150e-01 -3.42299908e-01
-4.63029504e-01 1.22814071e+00 -9.34309214e-02 -7.35153198e-01
-3.87089342e-01 -2.99334407e-01 -3.44569117e-01 -1.12116456e+00
-6.19815767e-01 2.08593115e-01 -5.48722446e-01 1.42569602... | [6.205172061920166, 1.4123210906982422] |
97ec728f-d4fa-4a5b-8318-8a063b3cd01b | automated-essay-scoring-for-swedish | null | null | https://aclanthology.org/W13-1705 | https://aclanthology.org/W13-1705.pdf | Automated Essay Scoring for Swedish | null | ['Erik H{\\"o}glin', 'Bj{\\"o}rn Tyrefors Hinnerich', 'Robert {\\"O}stling', 'Andre Smolentzov'] | 2013-06-01 | null | null | null | ws-2013-6 | ['automated-essay-scoring'] | ['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... | [-7.332584381103516, 3.5991404056549072] |
14d96d7f-d8c7-46bf-96c2-ba6d3f3dd3f4 | comparing-machines-and-children-using | 2305.11243 | null | https://arxiv.org/abs/2305.11243v1 | https://arxiv.org/pdf/2305.11243v1.pdf | Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses | Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capacities. Moreover, experimental techniques in developmental psychology have been carefully designed to discriminate the cognitive capacities t... | ['Danielle Krettek Cobb', 'Alison Gopnik', 'Leslie Lai', 'Emily Rose Reagan', 'Eliza Kosoy'] | 2023-05-18 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [-6.14565797e-02 1.40067816e-01 1.12968564e-01 -2.67131627e-01
3.49975824e-01 -5.38712382e-01 7.35746026e-01 5.34793854e-01
-5.65086663e-01 2.83626795e-01 5.30322194e-02 -4.18246925e-01
-3.30066383e-01 -1.12321723e+00 -8.44652236e-01 -3.92952055e-01
-1.95161909e-01 5.17182708e-01 4.11481738e-01 -3.40182483... | [10.241897583007812, 8.604592323303223] |
b2914663-215f-4ec9-99ad-a433ceb84351 | metric-oriented-speech-enhancement-using | 2302.11989 | null | https://arxiv.org/abs/2302.11989v1 | https://arxiv.org/pdf/2302.11989v1.pdf | Metric-oriented Speech Enhancement using Diffusion Probabilistic Model | Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data. However, the task-specific evaluation metric (e.g., PESQ) is usually non-differentiable and can not be directly constructed in the training criteria. This mismatch between the t... | ['Eng Siong Chng', 'Weiwei Weng', 'Yuchen Hu', 'Chen Chen'] | 2023-02-23 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.47410214e-01 2.00976692e-02 1.58160239e-01 -5.67587018e-01
-1.17118239e+00 -2.02901945e-01 6.93571687e-01 -3.06694210e-01
-4.87197310e-01 6.95080042e-01 5.87036133e-01 -3.64595056e-01
-2.55004764e-01 -5.62885642e-01 -4.99231070e-01 -8.79363418e-01
2.75023937e-01 3.95374522e-02 -5.52319130e-03 -1.80800021... | [14.833250045776367, 5.9426960945129395] |
c92c6f4a-b9d4-46e5-9f8e-3cbd18be0f47 | explainability-of-the-implications-of | 2112.04827 | null | https://arxiv.org/abs/2112.04827v1 | https://arxiv.org/pdf/2112.04827v1.pdf | Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models | It is challenging to derive explainability for unsupervised or statistical-based face image quality assessment (FIQA) methods. In this work, we propose a novel set of explainability tools to derive reasoning for different FIQA decisions and their face recognition (FR) performance implications. We avoid limiting the dep... | ['Naser Damer', 'Biying Fu'] | 2021-12-09 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.22974932e-01 4.39340770e-01 3.39638591e-01 -8.29533875e-01
-1.26812026e-01 -4.13215578e-01 5.50058067e-01 -2.89507538e-01
9.47158784e-03 4.03932244e-01 1.78341120e-01 1.06400132e-01
-6.85878277e-01 -7.49500573e-01 -5.35212219e-01 -6.95812762e-01
-4.05142978e-02 1.96926326e-01 -2.12031469e-01 -3.89618635... | [12.964550018310547, 0.9538121819496155] |
f513f2f4-f8e5-450d-9638-14e76140f66a | data-driven-grammatical-error-detection-in | null | null | https://aclanthology.org/D14-1106 | https://aclanthology.org/D14-1106.pdf | Data Driven Grammatical Error Detection in Transcripts of Children's Speech | null | ['Anna Eva Hallin', 'Eric Morley', 'Brian Roark'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['grammatical-error-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... | [-7.22912073135376, 3.7041378021240234] |
b7c077f8-1256-48d5-b7c6-4f0e636eb8e1 | recurrent-instance-segmentation | 1511.08250 | null | http://arxiv.org/abs/1511.08250v3 | http://arxiv.org/pdf/1511.08250v3.pdf | Recurrent Instance Segmentation | Instance segmentation is the problem of detecting and delineating each
distinct object of interest appearing in an image. Current instance
segmentation approaches consist of ensembles of modules that are trained
independently of each other, thus missing opportunities for joint learning.
Here we propose a new instance s... | ['Bernardino Romera-Paredes', 'Philip H. S. Torr'] | 2015-11-25 | null | null | null | null | ['plant-phenotyping', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [ 5.99768043e-01 5.10125995e-01 -2.97349155e-01 -4.95559305e-01
-7.02870548e-01 -6.26917899e-01 3.04242104e-01 2.90359944e-01
-1.92482740e-01 4.40541416e-01 -6.04953110e-01 -3.54321778e-01
-1.19290784e-01 -8.53866994e-01 -1.01788259e+00 -6.86074257e-01
-9.60759595e-02 9.23110902e-01 3.12097400e-01 4.66244161... | [9.480244636535645, 0.213429257273674] |
2a3510b1-555f-4e6f-953c-75c52673e078 | seeing-out-of-the-box-end-to-end-pre-training | 2104.03135 | null | https://arxiv.org/abs/2104.03135v2 | https://arxiv.org/pdf/2104.03135v2.pdf | Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning | We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based visual ... | ['Jianlong Fu', 'Dongmei Fu', 'Bei Liu', 'Yupan Huang', 'Zhaoyang Zeng', 'Zhicheng Huang'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Huang_Seeing_Out_of_the_Box_End-to-End_Pre-Training_for_Vision-Language_Representation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['visual-entailment'] | ['reasoning'] | [-9.63592250e-03 -1.30711824e-01 -3.75944465e-01 -4.27637398e-01
-1.09070623e+00 -6.07124507e-01 8.46835375e-01 2.56912529e-01
-8.11627686e-01 3.44345033e-01 2.12754071e-01 -2.87334323e-01
4.51208532e-01 -4.60847586e-01 -1.20649087e+00 -2.99629152e-01
3.79698217e-01 3.67880225e-01 1.30974308e-01 -2.02430680... | [10.552867889404297, 1.5991672277450562] |
d7f1deab-8a2e-49e6-9105-36c7691c9011 | aco-tagger-a-novel-method-for-part-of-speech | 2303.16760 | null | https://arxiv.org/abs/2303.16760v1 | https://arxiv.org/pdf/2303.16760v1.pdf | ACO-tagger: A Novel Method for Part-of-Speech Tagging using Ant Colony Optimization | Swarm Intelligence algorithms have gained significant attention in recent years as a means of solving complex and non-deterministic problems. These algorithms are inspired by the collective behavior of natural creatures, and they simulate this behavior to develop intelligent agents for computational tasks. One such alg... | ['Mohammad bahrani', 'Sara Hajiaghajani', 'Amirhossein Mohammadi'] | 2023-03-27 | null | null | null | null | ['part-of-speech-tagging'] | ['natural-language-processing'] | [ 1.49883389e-01 -2.56840706e-01 7.23658726e-02 1.58126447e-02
2.24969491e-01 -4.16237801e-01 5.70504785e-01 6.21991634e-01
-8.54788840e-01 8.22844684e-01 -1.38991028e-02 1.32408245e-02
-1.13899902e-01 -8.67050111e-01 5.56998327e-02 -9.30522382e-01
-2.22535014e-01 7.97960103e-01 5.09148538e-01 -4.37424034... | [5.701745510101318, 3.4989864826202393] |
c3ae2641-ef4c-4c54-bb80-dc1b123992d0 | image-cropping-on-twitter-fairness-metrics | 2105.08667 | null | https://arxiv.org/abs/2105.08667v2 | https://arxiv.org/pdf/2105.08667v2.pdf | Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency | Twitter uses machine learning to crop images, where crops are centered around the part predicted to be the most salient. In fall 2020, Twitter users raised concerns that the automated image cropping system on Twitter favored light-skinned over dark-skinned individuals, as well as concerns that the system favored croppi... | ['Shubhanshu Mishra', 'Uthaipon Tantipongpipat', 'Kyra Yee'] | 2021-05-18 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 2.57201403e-01 7.69213676e-01 -5.40871441e-01 -4.08377826e-01
-5.06762445e-01 -5.46677351e-01 7.22179174e-01 6.31871045e-01
-5.40970862e-01 5.52639484e-01 1.04661322e+00 -4.23932016e-01
2.73957461e-01 -5.75051367e-01 -4.88940448e-01 -6.51258007e-02
5.23146868e-01 -3.12769949e-01 -3.08921248e-01 -2.70549119... | [12.678815841674805, 1.344490885734558] |
36ee1943-fb0a-4eae-855d-13c5798a291d | hierarchical-latent-structure-for-multi-modal | 2207.04624 | null | https://arxiv.org/abs/2207.04624v1 | https://arxiv.org/pdf/2207.04624v1.pdf | Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting | Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and has nice manifold representations. However, when applied to image reconstruction and synthesis tasks, VAE shows the limitation that the generated sample tends to be blurry. We o... | ['Kyoungwook Min', 'Dooseop Choi'] | 2022-07-11 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-2.78299928e-01 -1.78484693e-01 -3.30615968e-01 -2.29196936e-01
-5.28599262e-01 -2.32229397e-01 8.57339323e-01 -5.20057440e-01
2.32327208e-01 6.27018929e-01 4.09541398e-01 -4.63231802e-01
1.40916288e-01 -8.78794134e-01 -8.87952030e-01 -1.12714028e+00
7.49745741e-02 4.45233166e-01 2.80407183e-02 -1.22685425... | [6.544538974761963, 1.0588490962982178] |
eaa66539-ffb0-4814-97e3-0ea91ebad00e | source-free-domain-adaptation-via-1 | 2101.10842 | null | https://arxiv.org/abs/2101.10842v1 | https://arxiv.org/pdf/2101.10842v1.pdf | Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics | In this paper, we propose a novel domain adaptation method for the source-free setting. In this setting, we cannot access source data during adaptation, while unlabeled target data and a model pretrained with source data are given. Due to lack of source data, we cannot directly match the data distributions between doma... | ['Masashi Sugiyama', 'Masato Ishii'] | 2021-01-19 | source-free-domain-adaptation-via | https://openreview.net/forum?id=HWqv5Pm3E3 | https://openreview.net/pdf?id=HWqv5Pm3E3 | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.80196100e-01 3.84364463e-03 -6.04349315e-01 -7.65170932e-01
-7.30464101e-01 -6.94638252e-01 4.86060739e-01 9.65723321e-02
-5.55010736e-01 8.87722254e-01 6.04763627e-02 9.93406326e-02
1.54504091e-01 -8.12191129e-01 -8.51719916e-01 -6.84602976e-01
3.55228215e-01 5.72871506e-01 1.86169192e-01 -3.72292362... | [10.407031059265137, 3.118748664855957] |
a985bbd3-ee0e-4761-875b-c7397ddc6404 | bias-against-93-stigmatized-groups-in-masked | 2306.05550 | null | https://arxiv.org/abs/2306.05550v1 | https://arxiv.org/pdf/2306.05550v1.pdf | Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks | The rapid deployment of artificial intelligence (AI) models demands a thorough investigation of biases and risks inherent in these models to understand their impact on individuals and society. This study extends the focus of bias evaluation in extant work by examining bias against social stigmas on a large scale. It fo... | ['Aylin Caliskan', 'Sonia Fereidooni', 'Katelyn X. Mei'] | 2023-06-08 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 4.11823481e-01 4.56824660e-01 -6.30531251e-01 -7.51965523e-01
-2.46287197e-01 -5.33521950e-01 6.69817984e-01 6.69598937e-01
-7.49553800e-01 8.04950356e-01 9.75608349e-01 -5.22002935e-01
-1.77993011e-02 -7.79873252e-01 -3.75091702e-01 -3.43470454e-01
1.91700071e-01 2.39610210e-01 -5.37316322e-01 -5.57230055... | [9.242762565612793, 10.213057518005371] |
b10b9d87-e34b-4b42-b0c3-d6a15cd7cd50 | sim2real-3d-object-classification-using | 2103.06134 | null | https://arxiv.org/abs/2103.06134v1 | https://arxiv.org/pdf/2103.06134v1.pdf | Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme | While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of annotation necessary to approach this problem, but most methods still struggle with the differences existing between artificial an... | ['Markus Vincze', 'Timothy Patten', 'Jean-Baptiste Weibel'] | 2021-03-10 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-4.61599045e-02 5.85819602e-01 -2.94606239e-02 -4.31370199e-01
-3.34315747e-01 -6.59873843e-01 6.25317097e-01 1.81018099e-01
-2.49203101e-01 2.89930552e-01 -3.14081699e-01 -1.57371476e-01
1.24368794e-01 -6.93646371e-01 -8.69152844e-01 -6.01018667e-01
1.25782713e-01 8.19263816e-01 7.23297894e-01 1.20621197... | [7.973675727844238, -2.944370746612549] |
b2336a7b-b7fe-4ff6-a9d0-de0c5b52b745 | quantum-state-tomography-with-conditional | 2008.03240 | null | https://arxiv.org/abs/2008.03240v2 | https://arxiv.org/pdf/2008.03240v2.pdf | Quantum State Tomography with Conditional Generative Adversarial Networks | Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two duelling neural networks, a generator and a discriminator, learn multi-modal models from data. We augment a CGAN with custom n... | ['Anton Frisk Kockum', 'Carlos Sánchez Muñoz', 'Shahnawaz Ahmed', 'Franco Nori'] | 2020-08-07 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 6.82842374e-01 2.95343250e-01 2.17225596e-01 -1.94026947e-01
-1.33432603e+00 -6.20587170e-01 6.02859020e-01 -4.99514759e-01
-4.90164548e-01 1.06337142e+00 -1.69789597e-01 -5.54469705e-01
2.88254976e-01 -1.28274739e+00 -1.09379232e+00 -1.01477599e+00
2.14554489e-01 8.55447650e-01 -7.65532628e-02 -1.97651282... | [5.608818531036377, 4.960163116455078] |
bdac3244-8b3c-496a-93e3-8cf760c17f45 | improve-chit-chat-and-qa-sentence | null | null | https://aclanthology.org/2021.rocling-1.19 | https://aclanthology.org/2021.rocling-1.19.pdf | Improve Chit-Chat and QA Sentence Classification in User Messages of Dialogue System using Dialogue Act Embedding | In recent years, dialogue system is booming and widely used in customer service system, and has achieved good results. Viewing the conversation records between users and real customer service, we can see that the user’s sentences are mixed with questions about products and services, and chat with customer service. Acco... | ['Yu Ching Chiu', 'Xi Jie Hou', 'Chi Hsiang Chao'] | null | null | null | null | rocling-2021-10 | ['sentence-classification'] | ['natural-language-processing'] | [-1.51315406e-01 2.40059510e-01 6.44637570e-02 -7.39793301e-01
-4.87337738e-01 -5.48782647e-01 6.51340604e-01 -5.60663566e-02
-1.75445721e-01 5.97396135e-01 8.53886306e-01 -5.89851856e-01
2.48737112e-01 -7.08297849e-01 2.34992757e-01 -4.02299374e-01
4.83062744e-01 6.70817614e-01 2.20778719e-01 -9.97187316... | [12.761570930480957, 7.736016750335693] |
806ece4d-837b-4bb6-9b6a-20b96f0990b1 | subgraph-neighboring-relations-infomax-for | 2208.00850 | null | https://arxiv.org/abs/2208.00850v3 | https://arxiv.org/pdf/2208.00850v3.pdf | Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs | Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage. Most previous works learn entity-specific embeddings of entities, which cannot handle unseen entities. Recent several methods utilize enclosing subgraph to obtain inductive ability.... | ['Chaoyang Yan', 'Chengpeng Chao', 'Yongquan He', 'Peng Zhang', 'Xiaohan Xu'] | 2022-07-28 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-3.27453136e-01 7.20468283e-01 -7.64866889e-01 -3.18553567e-01
2.30102614e-02 -4.47316349e-01 4.31772679e-01 3.88826758e-01
3.46156470e-02 9.20039892e-01 3.87823254e-01 -1.37965500e-01
-5.77713847e-01 -1.33398342e+00 -7.01336324e-01 -3.71602297e-01
-4.79566455e-01 3.51105779e-01 3.29566628e-01 -1.59086093... | [8.776371002197266, 7.952267646789551] |
ef87eb7f-071a-4da4-97b3-3dd382e0bcd6 | toward-sensor-based-sleep-monitoring-with | 1901.11440 | null | http://arxiv.org/abs/1901.11440v1 | http://arxiv.org/pdf/1901.11440v1.pdf | Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures | We use self-report and electrodermal activity (EDA) wearable sensor data from
77 nights of sleep on six participants to test the efficacy of EDA data for
sleep monitoring. We used factor analysis to find latent factors in the EDA
data, and causal model search to find the most probable graphical model
accounting for sel... | ['Tanvi Banerjee', 'Garrett Goodman', 'William Romine'] | 2019-01-31 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-1.19769223e-01 -1.20427810e-01 -2.29228809e-01 -6.46590829e-01
-2.94873536e-01 -3.37644041e-01 -1.17157940e-02 4.41988140e-01
-4.29154336e-01 5.31265199e-01 7.73692071e-01 -5.81696510e-01
-5.00167191e-01 -5.57680726e-01 -1.76584497e-01 -2.99492478e-01
-6.08436346e-01 -1.87327608e-01 -1.28431901e-01 1.91217382... | [13.555607795715332, 3.4138896465301514] |
9c8599d6-a66d-4b7b-bfc9-cc8c37001dec | semi-supervised-skin-lesion-segmentation-via | 1808.03887 | null | http://arxiv.org/abs/1808.03887v1 | http://arxiv.org/pdf/1808.03887v1.pdf | Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model | Automatic skin lesion segmentation on dermoscopic images is an essential
component in computer-aided diagnosis of melanoma. Recently, many fully
supervised deep learning based methods have been proposed for automatic skin
lesion segmentation. However, these approaches require massive pixel-wise
annotation from experien... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Lequan Yu', 'Hao Chen', 'Xiaomeng Li'] | 2018-08-12 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.61536455e-01 3.75028282e-01 -6.23772144e-01 -4.94759858e-01
-9.22850966e-01 -4.76102084e-01 2.22534835e-01 4.03952822e-02
-6.98702395e-01 5.51500618e-01 -2.84006685e-01 -2.39504695e-01
2.79500544e-01 -6.68344617e-01 -5.77385128e-01 -9.18449938e-01
4.59116638e-01 1.41943246e-01 3.40038627e-01 1.75072789... | [15.441121101379395, -2.772311210632324] |
4f56cf04-adff-4b5a-9e8a-39f3b77e8386 | dorabella-cipher-as-musical-inspiration | null | null | https://aclanthology.org/2021.smp-1.5 | https://aclanthology.org/2021.smp-1.5.pdf | Dorabella Cipher as Musical Inspiration | The Dorabella cipher is an encrypted note of English composer Edward Elgar, which has defied decipherment attempts for more than a century. While most proposed solutions are English texts, we investigate the hypothe- sis that Dorabella represents enciphered music. We weigh the evidence in favor of and against the hypot... | ['Grzegorz Kondrak', 'Scott Smallwood', 'Abram Hindle', 'Colin Choi', 'Bradley Hauer'] | null | null | null | null | smp-icon-2021-12 | ['decipherment'] | ['natural-language-processing'] | [ 4.57040727e-01 -1.27084017e-01 2.85436571e-01 2.26218447e-01
-7.11700499e-01 -1.22126913e+00 6.51659429e-01 -2.40200367e-02
-5.13318717e-01 7.92289972e-01 6.05610371e-01 -5.82790852e-01
-8.69497508e-02 -7.28170395e-01 -3.74532908e-01 -6.24303341e-01
-1.92623921e-02 4.48421508e-01 -4.38139021e-01 -1.75255626... | [15.997786521911621, 5.450129508972168] |
b41e5459-2b6d-4c52-a61e-6ea2b77ae4d2 | transformer-and-snowball-graph-convolution | 2303.16132 | null | https://arxiv.org/abs/2303.16132v2 | https://arxiv.org/pdf/2303.16132v2.pdf | Transformer and Snowball Graph Convolution Learning for Brain functional network Classification | Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and identify the functional connections between brain disorder and health. In this paper, we proposed a novel Transformer and snowball encoding networks (TSEN) for brain functio... | ['Shoubin Dong', 'Yangmin Huang', 'Jinlong Hu'] | 2023-03-28 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 2.46340148e-02 1.96630836e-01 2.37069011e-01 -3.67002726e-01
4.90365267e-01 -1.27776578e-01 5.07281482e-01 -2.24615987e-02
-6.47385865e-02 5.81752896e-01 3.02179277e-01 -1.34557173e-01
-6.32260323e-01 -1.28732026e+00 -6.11430466e-01 -5.22304714e-01
-5.69177806e-01 4.77414668e-01 4.99440253e-01 -4.00156856... | [12.371113777160645, 3.3952908515930176] |
64a1b618-cb49-407e-98ae-09007e04ed98 | boosting-weakly-supervised-temporal-action | 2305.00607 | null | https://arxiv.org/abs/2305.00607v1 | https://arxiv.org/pdf/2305.00607v1.pdf | Boosting Weakly-Supervised Temporal Action Localization with Text Information | Due to the lack of temporal annotation, current Weakly-supervised Temporal Action Localization (WTAL) methods are generally stuck into over-complete or incomplete localization. In this paper, we aim to leverage the text information to boost WTAL from two aspects, i.e., (a) the discriminative objective to enlarge the in... | ['Xinbo Gao', 'Xiaoyu Wang', 'Nannan Wang', 'Xinpeng Ding', 'De Cheng', 'Guozhang Li'] | 2023-05-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Boosting_Weakly-Supervised_Temporal_Action_Localization_With_Text_Information_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Boosting_Weakly-Supervised_Temporal_Action_Localization_With_Text_Information_CVPR_2023_paper.pdf | cvpr-2023-1 | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.91619688e-01 -1.01996221e-01 -5.71364999e-01 -3.03269297e-01
-9.46536303e-01 -3.30647171e-01 5.07293046e-01 -9.01803225e-02
-4.83545691e-01 5.08596718e-01 4.50777739e-01 7.04759061e-02
1.06010824e-01 -4.23510104e-01 -5.73842108e-01 -8.48643005e-01
1.92387760e-01 5.04921526e-02 7.77446687e-01 8.46464187... | [8.852846145629883, 0.6098569631576538] |
b541927d-cd9b-4831-ac01-881124ef27c9 | cross-view-action-recognition-understanding | 2305.15699 | null | https://arxiv.org/abs/2305.15699v1 | https://arxiv.org/pdf/2305.15699v1.pdf | Cross-view Action Recognition Understanding From Exocentric to Egocentric Perspective | Understanding action recognition in egocentric videos has emerged as a vital research topic with numerous practical applications. With the limitation in the scale of egocentric data collection, learning robust deep learning-based action recognition models remains difficult. Transferring knowledge learned from the large... | ['Khoa Luu', 'Thanh-Dat Truong'] | 2023-05-25 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.89892871e-03 -3.03397775e-01 -2.58545876e-01 -3.70639086e-01
-4.34910595e-01 -3.50082457e-01 5.45752406e-01 -7.81706810e-01
-2.86381930e-01 4.28051561e-01 7.21949577e-01 4.51241434e-01
-2.45759696e-01 -4.11827058e-01 -8.72112155e-01 -8.13096285e-01
1.10263281e-01 -4.23700847e-02 2.86457598e-01 -5.18447980... | [8.34745979309082, 0.5540667176246643] |
0932f41e-d589-4efa-9b7f-a4fe2bb5b901 | nprf-a-neural-pseudo-relevance-feedback | 1810.12936 | null | http://arxiv.org/abs/1810.12936v1 | http://arxiv.org/pdf/1810.12936v1.pdf | NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval | Pseudo-relevance feedback (PRF) is commonly used to boost the performance of
traditional information retrieval (IR) models by using top-ranked documents to
identify and weight new query terms, thereby reducing the effect of
query-document vocabulary mismatches. While neural retrieval models have
recently demonstrated s... | ['Kai Hui', 'Andrew Yates', 'Canjia Li', 'Ben He', 'Yingfei Sun', 'Le Wang', 'Jungang Xu', 'Le Sun'] | 2018-10-30 | nprf-a-neural-pseudo-relevance-feedback-1 | https://aclanthology.org/D18-1478 | https://aclanthology.org/D18-1478.pdf | emnlp-2018-10 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 2.24603072e-01 -1.33752733e-01 -6.54217303e-01 -3.07498872e-01
-9.67365682e-01 -4.36765611e-01 8.32391381e-01 2.34834045e-01
-6.64387345e-01 3.31022590e-01 4.08208400e-01 -3.04228872e-01
-4.94540840e-01 -5.53238928e-01 -5.96593797e-01 -1.12748798e-02
8.46326128e-02 6.09399259e-01 2.67782092e-01 -7.78073490... | [11.46475601196289, 7.6197614669799805] |
11e7525e-96de-4240-a71c-192bba24af31 | cross-modal-contrastive-attention-model-for | null | null | https://aclanthology.org/2022.coling-1.210 | https://aclanthology.org/2022.coling-1.210.pdf | Cross-modal Contrastive Attention Model for Medical Report Generation | Medical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently. However, this image-to-text task is rather challenging due to the typical data biases: 1) Normal physiological structures dominate the images, with only tiny abnormalities; 2) Normal... | ['Pengxu Wei', 'Ying Liu', 'Junzhong Ji', 'Xiaodan Zhang', 'Xiao Song'] | null | null | null | null | coling-2022-10 | ['medical-report-generation'] | ['medical'] | [ 3.01499069e-01 2.38876358e-01 -2.81417847e-01 -1.91299349e-01
-1.33892596e+00 -1.97919533e-01 5.88788509e-01 3.84270966e-01
-1.11648843e-01 8.22127759e-01 6.20867670e-01 -2.22883865e-01
-1.70903787e-01 -3.71186137e-01 -4.68901724e-01 -6.62127435e-01
5.33798188e-02 3.97291899e-01 2.35546321e-01 4.31776270... | [15.030667304992676, -1.4361791610717773] |
ec1e83a2-89bf-4541-b4f5-2117e7f7aca6 | cadge-context-aware-dialogue-generation | 2305.06294 | null | https://arxiv.org/abs/2305.06294v2 | https://arxiv.org/pdf/2305.06294v2.pdf | CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation | Commonsense knowledge is crucial to many natural language processing tasks. Existing works usually incorporate graph knowledge with conventional graph neural networks (GNNs), leading to the text and graph knowledge encoding processes being separated in a serial pipeline. We argue that these separate representation lear... | ['Chenghua Lin', 'Tyler Loakman', 'Hongbo Zhang', 'Stefan Goetze', 'Chen Tang'] | 2023-05-10 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 6.58812225e-01 7.66323924e-01 -1.78350478e-01 -2.28276521e-01
-5.17810345e-01 -5.39992452e-01 9.32449758e-01 7.48528302e-01
-2.79562443e-01 5.42590976e-01 7.47815728e-01 -3.87991101e-01
4.35967594e-02 -1.09744859e+00 -5.01356781e-01 -1.64182663e-01
1.65200770e-01 5.38808882e-01 1.23995498e-01 -6.52505934... | [10.422201156616211, 8.08612060546875] |
98c0008b-7bb1-4818-b2ca-ecacf65e385b | automatic-generation-of-personalized-comment | 1907.10371 | null | https://arxiv.org/abs/1907.10371v1 | https://arxiv.org/pdf/1907.10371v1.pdf | Automatic Generation of Personalized Comment Based on User Profile | Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks. Besides, since different user has different expression habits, it is necessary to take the user's profile into considera... | ['Lei LI', 'Wenhuan Zeng', 'Pengcheng Yang', 'Abulikemu Abuduweili'] | 2019-07-24 | automatic-generation-of-personalized-comment-1 | https://aclanthology.org/P19-2032 | https://aclanthology.org/P19-2032.pdf | acl-2019-7 | ['comment-generation'] | ['natural-language-processing'] | [ 3.30480514e-03 2.77605623e-01 2.56940365e-01 -5.72139144e-01
-2.45112285e-01 -3.12456131e-01 6.27592146e-01 1.38200685e-01
-1.46175325e-01 7.79781759e-01 8.18167686e-01 -9.38447267e-02
6.57236636e-01 -9.12251413e-01 -2.41450131e-01 -3.86926740e-01
2.23710358e-01 4.26603913e-01 -1.41429320e-01 -7.79673636... | [12.113656997680664, 9.024527549743652] |
8ddba1e6-4705-4c40-86cd-c453c2a42d43 | amigos-a-dataset-for-affect-personality-and | 1702.02510 | null | http://arxiv.org/abs/1702.02510v3 | http://arxiv.org/pdf/1702.02510v3.pdf | AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups | We present AMIGOS-- A dataset for Multimodal research of affect, personality
traits and mood on Individuals and GrOupS. Different to other databases, we
elicited affect using both short and long videos in two social contexts, one
with individual viewers and one with groups of viewers. The database allows the
multimodal... | [] | 2017-04-13 | null | null | null | null | ['continuous-affect-estimation'] | ['computer-vision'] | [-2.39429519e-01 -1.28505349e-01 2.63833642e-01 -6.15608871e-01
-1.49292260e-01 -7.56223381e-01 3.77180040e-01 4.59917367e-01
-3.96806002e-01 6.12351179e-01 3.98125470e-01 7.88849473e-01
9.97073427e-02 -3.38524252e-01 -1.84650913e-01 -7.36666858e-01
-5.12755275e-01 -4.83312696e-01 -4.72704142e-01 -1.54601678... | [13.463116645812988, 2.602109432220459] |
d24cd5e2-6328-43d8-8029-29f6dcb1db44 | gaze-estimation-using-transformer | 2105.14424 | null | https://arxiv.org/abs/2105.14424v1 | https://arxiv.org/pdf/2105.14424v1.pdf | Gaze Estimation using Transformer | Recent work has proven the effectiveness of transformers in many computer vision tasks. However, the performance of transformers in gaze estimation is still unexplored. In this paper, we employ transformers and assess their effectiveness for gaze estimation. We consider two forms of vision transformer which are pure tr... | ['Feng Lu', 'Yihua Cheng'] | 2021-05-30 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-2.58564293e-01 -1.67385023e-02 7.59432539e-02 -3.27146262e-01
-2.31579289e-01 -3.41443598e-01 5.04450560e-01 -5.94524980e-01
-3.75767976e-01 3.62067163e-01 9.57749113e-02 -2.82352895e-01
2.88158298e-01 -4.19384748e-01 -8.26368213e-01 -8.26731980e-01
4.37007129e-01 -1.88507006e-01 5.28581142e-01 -1.60020396... | [14.112004280090332, 0.06112677603960037] |
a2ba5dc0-8cdf-4660-8f0a-64bf53969ef6 | a-variational-inequality-perspective-on | 1802.10551 | null | https://arxiv.org/abs/1802.10551v5 | https://arxiv.org/pdf/1802.10551v5.pdf | A Variational Inequality Perspective on Generative Adversarial Networks | Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods desi... | ['Simon Lacoste-Julien', 'Gaëtan Vignoud', 'Pascal Vincent', 'Gauthier Gidel', 'Hugo Berard'] | 2018-02-28 | a-variational-inequality-perspective-on-1 | https://openreview.net/forum?id=r1laEnA5Ym | https://openreview.net/pdf?id=r1laEnA5Ym | iclr-2019-5 | ['misconceptions'] | ['miscellaneous'] | [ 1.69065148e-01 4.77021903e-01 1.97028130e-01 -2.09406719e-01
-8.25712562e-01 -5.70381045e-01 7.54007816e-01 -5.08188903e-01
-7.18405321e-02 1.18925321e+00 1.17082335e-01 -3.77289951e-01
-6.67128712e-02 -9.62443888e-01 -8.44840825e-01 -8.94039989e-01
5.52901685e-01 4.91882116e-01 -4.89440709e-01 -4.43395972... | [11.606589317321777, -0.043271370232105255] |
782bbfda-3ed4-4be2-929e-5b0186d28650 | pose-aware-attention-network-for-flexible | 2306.08006 | null | https://arxiv.org/abs/2306.08006v1 | https://arxiv.org/pdf/2306.08006v1.pdf | Pose-aware Attention Network for Flexible Motion Retargeting by Body Part | Motion retargeting is a fundamental problem in computer graphics and computer vision. Existing approaches usually have many strict requirements, such as the source-target skeletons needing to have the same number of joints or share the same topology. To tackle this problem, we note that skeletons with different structu... | ['Shihong Xia', 'Boyuan Jiang', 'Chongyang Zhong', 'Zihao Zhang', 'Lei Hu'] | 2023-06-13 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-3.73051316e-02 1.37403443e-01 -4.01964217e-01 -1.98289119e-02
-2.39864811e-01 -4.31685925e-01 4.01414812e-01 -5.77474356e-01
-2.64300883e-01 6.06150091e-01 5.73645949e-01 1.44328430e-01
1.94906682e-01 -8.31282854e-01 -7.93610990e-01 -7.19232500e-01
3.47606391e-01 1.66435465e-01 7.19913304e-01 -4.36725438... | [7.454733848571777, -0.4104195535182953] |
cf73176f-5389-4da5-bf7d-2c84b567105c | towards-computational-architecture-of-liberty | 2305.00510 | null | https://arxiv.org/abs/2305.00510v2 | https://arxiv.org/pdf/2305.00510v2.pdf | Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse | 3D shape generation techniques utilizing deep learning are increasing attention from both computer vision and architectural design. This survey focuses on investigating and comparing the current latest approaches to 3D object generation with deep generative models (DGMs), including Generative Adversarial Networks (GANs... | ['Pan Hui', 'Lik-Hang Lee', 'Jiachuan Shen', 'Jiahua Dong', 'Anqi Wang'] | 2023-04-30 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [-1.17665000e-01 5.97622871e-01 4.53315854e-01 2.06469506e-01
-4.89144325e-01 -4.88020182e-01 5.59439898e-01 -8.62285852e-01
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1.69510826e-01 -1.32818472e+00 -8.10922086e-01 -5.89043379e-01
-3.25606577e-02 4.79184061e-01 -3.36526275e-01 -6.84022129... | [5.834673881530762, 3.234663724899292] |
40ac1c25-404e-4480-a8ad-f8661bf8c619 | dual-gan-joint-bvp-and-noise-modeling-for | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lu_Dual-GAN_Joint_BVP_and_Noise_Modeling_for_Remote_Physiological_Measurement_CVPR_2021_paper.pdf | Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement | Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc. Existing methods mainly focus on how to enhance or extract the very weak blood volume pulse (BVP) signals from face videos, but seldom explicitly model the noises that dominat... | ['S. Kevin Zhou', 'Hu Han', 'Hao Lu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['heart-rate-variability'] | ['medical'] | [-1.11821508e-02 -5.68603575e-02 2.29394883e-01 -4.03933436e-01
-6.50949717e-01 -1.01238512e-01 1.53821886e-01 -6.20835900e-01
-1.02079287e-01 9.02353406e-01 3.85313272e-01 2.80113965e-01
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2.99863011e-01 -2.75047421e-01 -3.79539877e-01 -5.51528633... | [13.896013259887695, 2.7274270057678223] |
88d30329-4f7c-4689-842f-e3661d4186b4 | ft-tdr-frequency-guided-transformer-and-top | 2108.04424 | null | https://arxiv.org/abs/2108.04424v2 | https://arxiv.org/pdf/2108.04424v2.pdf | FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting | Blind face inpainting refers to the task of reconstructing visual contents without explicitly indicating the corrupted regions in a face image. Inherently, this task faces two challenges: (1) how to detect various mask patterns of different shapes and contents; (2) how to restore visually plausible and pleasing content... | ['Yu-Gang Jiang', 'Zuxuan Wu', 'Shaoxiang Chen', 'Junke Wang'] | 2021-08-10 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 4.67502117e-01 -9.24976543e-02 1.02905020e-01 -1.58726051e-01
-6.46292508e-01 -2.88624614e-01 2.72531241e-01 -6.93953931e-01
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2.51775570e-02 -5.59486151e-01 -7.37070501e-01 -6.84509575e-01
3.90839964e-01 -1.00983478e-01 1.25638127e-01 -2.57557303... | [12.721569061279297, -0.14921171963214874] |
f4475734-0276-4d5d-9d04-a465a54f8607 | deep-neural-review-text-interaction-for | 2003.07051 | null | https://arxiv.org/abs/2003.07051v1 | https://arxiv.org/pdf/2003.07051v1.pdf | Deep Neural Review Text Interaction for Recommendation Systems | Users' reviews contain valuable information which are not taken into account in most recommender systems. According to the latest studies in this field, using review texts could not only improve the performance of recommendation, but it can also alleviate the impact of data sparsity and help to tackle the cold start pr... | ['Saeedeh Momtazi', 'Parisa Abolfath Beygi Dezfouli', 'Mehdi Dehghan'] | 2020-03-16 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.36775091e-01 -2.42684826e-01 -3.12936187e-01 -4.53565001e-01
-3.20299149e-01 -2.40838468e-01 5.17001987e-01 2.07726613e-01
-4.25750643e-01 1.97821423e-01 3.66134703e-01 -1.84136301e-01
-3.02580774e-01 -1.10069537e+00 -4.02493507e-01 -3.45612884e-01
3.74722421e-01 1.45289034e-01 -8.45461860e-02 -5.00832498... | [10.170783996582031, 5.659460067749023] |
d7a948e4-6897-4202-b127-c99de153ee4d | plant-species-classification-using-transfer | 2209.03076 | null | https://arxiv.org/abs/2209.03076v1 | https://arxiv.org/pdf/2209.03076v1.pdf | Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19 | Deep learning is currently the most important branch of machine learning, with applications in speech recognition, computer vision, image classification, and medical imaging analysis. Plant recognition is one of the areas where image classification can be used to identify plant species through their leaves. Botanists d... | ['Dheeraj Kumar Agrawal', 'Bhupendra Singh Kirar', 'Thiru Siddharth'] | 2022-09-07 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.91042233e-01 2.31174864e-02 -2.37007231e-01 -2.29642883e-01
-6.12074733e-02 -8.56982410e-01 3.35300803e-01 3.56351942e-01
-1.36926353e-01 2.80686110e-01 -4.32507664e-01 -6.42227113e-01
2.66116671e-02 -1.12826610e+00 -2.25151762e-01 -8.65743458e-01
-6.55569807e-02 2.95154095e-01 8.53936821e-02 1.41906455... | [9.186015129089355, -1.5209834575653076] |
185f468a-4f55-4dbf-93f9-ec32124abb63 | exploiting-unlabeled-data-with-vision-and | 2207.08954 | null | https://arxiv.org/abs/2207.08954v1 | https://arxiv.org/pdf/2207.08954v1.pdf | Exploiting Unlabeled Data with Vision and Language Models for Object Detection | Building robust and generic object detection frameworks requires scaling to larger label spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations for thousands of categories at a large scale. We propose a novel method that leverages the rich semantics available in recent vision an... | ['Dimitris Metaxas', 'Manmohan Chandraker', 'Anastasis Stathopoulos', 'Vijay Kumar B. G', 'Long Zhao', 'Samuel Schulter', 'Zhixing Zhang', 'Shiyu Zhao'] | 2022-07-18 | null | null | null | null | ['open-vocabulary-object-detection', 'semi-supervised-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.34343386e-01 -3.49050108e-03 -2.07759514e-01 -4.93074417e-01
-1.10288203e+00 -9.72464979e-01 6.63429141e-01 1.53415695e-01
-4.99871224e-01 3.57400715e-01 -9.32619870e-02 -2.72657752e-01
6.16959751e-01 -5.02986014e-01 -8.05842578e-01 -4.53866035e-01
2.31769785e-01 5.90028763e-01 6.05294406e-01 2.03118265... | [9.625821113586426, 1.469839334487915] |
dce2b4e4-45e6-4f7d-9353-9172e44ba0f0 | learning-to-stop-a-simple-yet-effective | 2009.13112 | null | https://arxiv.org/abs/2009.13112v3 | https://arxiv.org/pdf/2009.13112v3.pdf | Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation | Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in real-world environments. A key challenge is to recognize and stop at the correct location, especially for complicated outdoor environments. Existing m... | ['William Yang Wang', 'Xin Eric Wang', 'Jiannan Xiang'] | 2020-09-28 | null | https://aclanthology.org/2020.findings-emnlp.62 | https://aclanthology.org/2020.findings-emnlp.62.pdf | findings-of-the-association-for-computational | ['vision-language-navigation'] | ['computer-vision'] | [ 1.82227567e-02 -2.72497535e-01 -1.37505203e-01 -3.81071478e-01
-5.63358426e-01 -9.40276146e-01 9.06152487e-01 4.68067918e-03
-1.02414417e+00 7.96620488e-01 2.31352210e-01 -6.96472704e-01
2.67798215e-01 -6.24069989e-01 -6.10885978e-01 -4.54694688e-01
-4.02385928e-02 5.53902447e-01 4.53218520e-01 -5.61579943... | [4.5236945152282715, 0.5720837116241455] |
e1832e77-0488-4a6a-9e35-c6943237bcb0 | transvpr-transformer-based-place-recognition | 2201.02001 | null | https://arxiv.org/abs/2201.02001v4 | https://arxiv.org/pdf/2201.02001v4.pdf | TransVPR: Transformer-based place recognition with multi-level attention aggregation | Visual place recognition is a challenging task for applications such as autonomous driving navigation and mobile robot localization. Distracting elements presenting in complex scenes often lead to deviations in the perception of visual place. To address this problem, it is crucial to integrate information from only tas... | ['Nanning Zheng', 'Sanping Zhou', 'Weiliang Zuo', 'Yanqing Shen', 'Ruotong Wang'] | 2022-01-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_TransVPR_Transformer-Based_Place_Recognition_With_Multi-Level_Attention_Aggregation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_TransVPR_Transformer-Based_Place_Recognition_With_Multi-Level_Attention_Aggregation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-place-recognition'] | ['computer-vision'] | [ 2.21264154e-01 -2.51110822e-01 -1.47470713e-01 -4.08189565e-01
-7.91417122e-01 -1.31831810e-01 6.25826061e-01 3.56443286e-01
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2.79782712e-01 4.10097912e-02 5.55942297e-01 -1.93626717... | [9.813495635986328, -0.19518662989139557] |
1f1feda4-74c4-4d4d-a894-1534c2bd3dc6 | scamps-synthetics-for-camera-measurement-of | 2206.04197 | null | https://arxiv.org/abs/2206.04197v1 | https://arxiv.org/pdf/2206.04197v1.pdf | SCAMPS: Synthetics for Camera Measurement of Physiological Signals | The use of cameras and computational algorithms for noninvasive, low-cost and scalable measurement of physiological (e.g., cardiac and pulmonary) vital signs is very attractive. However, diverse data representing a range of environments, body motions, illumination conditions and physiological states is laborious, time ... | ['Tadas Baltrusaitis', 'Jonathan Lester', 'Javier Hernandez', 'Brian L. Hill', 'Xin Liu', 'Miah Wander', 'Daniel McDuff'] | 2022-06-08 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.33134085e-01 -4.00564730e-01 4.20219004e-02 -5.07637560e-01
-4.81851816e-01 -6.84137106e-01 1.81240126e-01 1.42461546e-02
-1.35630578e-01 8.02884579e-01 1.74972899e-02 2.33081996e-01
2.46954896e-02 -1.58823878e-01 -2.44959176e-01 -1.06342196e+00
-3.17134112e-01 -5.80692366e-02 -2.83576220e-01 2.85314679... | [13.89770793914795, 2.829139232635498] |
b9675a45-af14-441b-8777-47597db2714b | memen-multi-layer-embedding-with-memory | 1707.09098 | null | http://arxiv.org/abs/1707.09098v1 | http://arxiv.org/pdf/1707.09098v1.pdf | MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension | Machine comprehension(MC) style question answering is a representative
problem in natural language processing. Previous methods rarely spend time on
the improvement of encoding layer, especially the embedding of syntactic
information and name entity of the words, which are very crucial to the quality
of encoding. Moreo... | ['Boyuan Pan', 'Zhou Zhao', 'Deng Cai', 'Bin Cao', 'Xiaofei He', 'Hao Li'] | 2017-07-28 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 2.62092669e-02 -4.36978154e-02 -2.81340722e-02 -4.97153640e-01
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3.88577193e-01 3.94363880e-01 5.75930417e-01 -6.35683417... | [11.134993553161621, 8.080790519714355] |
171b887c-f79f-49b4-9cd8-1acab1c10c8a | learning-to-communicate-using-contrastive | 2307.01403 | null | https://arxiv.org/abs/2307.01403v1 | https://arxiv.org/pdf/2307.01403v1.pdf | Learning to Communicate using Contrastive Learning | Communication is a powerful tool for coordination in multi-agent RL. But inducing an effective, common language is a difficult challenge, particularly in the decentralized setting. In this work, we introduce an alternative perspective where communicative messages sent between agents are considered as different incomple... | ['Michael Noukhovitch', 'Jakob Foerster', 'Biswa Sengupta', 'Yat Long Lo'] | 2023-07-03 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 1.82456877e-02 2.15073302e-01 -2.34581023e-01 -2.52381593e-01
-1.07973158e+00 -9.07828808e-01 9.95364130e-01 4.51895356e-01
-5.68013072e-01 9.95766759e-01 7.19419062e-01 -2.55234796e-03
-2.33330712e-01 -6.88602746e-01 -7.04148352e-01 -7.43195891e-01
-6.75926268e-01 5.50764143e-01 -3.91920894e-01 -4.36163783... | [3.876816987991333, 1.9944801330566406] |
41eeff84-a2b1-4d89-8edf-cb408f169e05 | stableface-analyzing-and-improving-motion | 2208.13717 | null | https://arxiv.org/abs/2208.13717v1 | https://arxiv.org/pdf/2208.13717v1.pdf | StableFace: Analyzing and Improving Motion Stability for Talking Face Generation | While previous speech-driven talking face generation methods have made significant progress in improving the visual quality and lip-sync quality of the synthesized videos, they pay less attention to lip motion jitters which greatly undermine the realness of talking face videos. What causes motion jitters, and how to mi... | ['Li Song', 'Sheng Zhao', 'Yuchao Zhang', 'Runnan Li', 'Liyang Chen', 'Xu Tan', 'Jun Ling'] | 2022-08-29 | null | null | null | null | ['video-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [-2.30402611e-02 -2.66249239e-01 8.36660936e-02 -2.49991640e-01
-6.75661683e-01 -4.03825909e-01 3.50718141e-01 -7.91062295e-01
4.35517021e-02 4.11071688e-01 3.32379788e-01 -1.25236601e-01
1.19443461e-02 -4.10032123e-01 -7.08926737e-01 -7.47528374e-01
2.34284326e-01 -2.86861658e-01 3.69899184e-01 -9.71477628... | [13.275999069213867, -0.4048531651496887] |
7366e7a5-3882-4536-85df-2c407230ba80 | state-of-the-art-models-for-fake-news | null | null | https://ieeexplore.ieee.org/document/9089487 | https://www.researchgate.net/publication/340478553_State_of_the_Art_Models_for_Fake_News_Detection_Tasks | State of the Art Models for Fake News Detection Tasks | This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets. The proposed solutions achieved first place in a recent international competition on fake news. For fake news detection, ... | ['Wissam Antoun ; Fady Baly ; Rim Achour ; Amir Hussein ; Hazem Hajj'] | 2020-05-11 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.11312458e-01 -6.51979372e-02 -5.42098463e-01 5.68137318e-02
-4.80493933e-01 -5.88480234e-01 1.22671866e+00 4.71832663e-01
-4.77338493e-01 5.13424933e-01 4.15437818e-01 -1.92952767e-01
1.79917619e-01 -6.70825422e-01 -3.73930484e-01 -2.20691338e-01
1.13059863e-01 4.70300555e-01 4.37478960e-01 -5.68398893... | [8.202781677246094, 10.235111236572266] |
d8dbaf02-e664-4aba-99e7-89614f481339 | reliable-and-efficient-image-cropping-a-grid | 1904.04441 | null | http://arxiv.org/abs/1904.04441v1 | http://arxiv.org/pdf/1904.04441v1.pdf | Reliable and Efficient Image Cropping: A Grid Anchor based Approach | Image cropping aims to improve the composition as well as aesthetic quality
of an image by removing extraneous content from it. Existing image cropping
databases provide only one or several human-annotated bounding boxes as the
groundtruth, which cannot reflect the non-uniqueness and flexibility of image
cropping in pr... | ['Hui Zeng', 'Lida Li', 'Lei Zhang', 'Zisheng Cao'] | 2019-04-09 | reliable-and-efficient-image-cropping-a-grid-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zeng_Reliable_and_Efficient_Image_Cropping_A_Grid_Anchor_Based_Approach_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zeng_Reliable_and_Efficient_Image_Cropping_A_Grid_Anchor_Based_Approach_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-cropping'] | ['computer-vision'] | [ 2.17735901e-01 -9.54109579e-02 -1.54596820e-01 -1.17571220e-01
-5.02278566e-01 -7.80246198e-01 1.15211971e-01 3.98947477e-01
-1.13512628e-01 4.51002240e-01 -3.72779638e-01 -2.02987164e-01
1.98890746e-01 -1.14436662e+00 -9.23377752e-01 -6.54751480e-01
1.20766750e-02 -3.38171303e-01 3.73332053e-01 -2.70897299... | [11.22376823425293, -1.1091156005859375] |
b12f3386-581a-4b9a-a931-acda15dd53ae | parameters-or-privacy-a-provable-tradeoff | 2202.01243 | null | https://arxiv.org/abs/2202.01243v2 | https://arxiv.org/pdf/2202.01243v2.pdf | Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference | A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to memorize the training data (zero error on the training data). This has led to an arms race towards increasingly overparameterized models (c.f... | ['Richard G. Baraniuk', 'Hamid Javadi', 'Blake Mason', 'Jasper Tan'] | 2022-02-02 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.25517100e-01 4.20598030e-01 -1.84034705e-01 -4.30621028e-01
-7.68726885e-01 -8.95275772e-01 3.80400360e-01 8.84264484e-02
-5.72507679e-01 9.60053921e-01 -2.92315781e-01 -6.58659637e-01
-1.25022069e-01 -7.09114969e-01 -9.57927227e-01 -9.46732461e-01
-2.48105273e-01 3.58738005e-01 -1.80272967e-01 4.14514206... | [5.963038921356201, 6.969730377197266] |
8b910fd1-ebc3-42f5-af64-da332a07a100 | hirevae-an-online-and-adaptive-factor-model | 2306.02848 | null | https://arxiv.org/abs/2306.02848v1 | https://arxiv.org/pdf/2306.02848v1.pdf | HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE | Factor model is a fundamental investment tool in quantitative investment, which can be empowered by deep learning to become more flexible and efficient in practical complicated investing situations. However, it is still an open question to build a factor model that can conduct stock prediction in an online and adaptive... | ['Dahua Lin', 'Bo Dai', 'Anyi Rao', 'Zikai Wei'] | 2023-06-05 | null | null | null | null | ['open-question', 'stock-prediction'] | ['natural-language-processing', 'time-series'] | [-8.25968683e-01 -3.63191843e-01 -5.60710132e-01 -9.79225338e-02
-2.56045789e-01 -8.06571782e-01 6.38853848e-01 -3.47385764e-01
-1.37519464e-01 3.69898707e-01 4.67307180e-01 -6.45553589e-01
-4.93535370e-01 -1.03868771e+00 -3.11586887e-01 -3.91221017e-01
-1.87015221e-01 5.75563431e-01 1.67074695e-01 -2.88760304... | [4.442282676696777, 4.191076755523682] |
76b80aaa-c9e8-4383-ac3f-2b33723343fa | deep-inverse-tone-mapping-using-ldr-based | 1903.01277 | null | http://arxiv.org/abs/1903.01277v1 | http://arxiv.org/pdf/1903.01277v1.pdf | Deep Inverse Tone Mapping Using LDR Based Learning for Estimating HDR Images with Absolute Luminance | In this paper, a novel inverse tone mapping method using a convolutional
neural network (CNN) with LDR based learning is proposed. In conventional
inverse tone mapping with CNNs, generated HDR images cannot have absolute
luminance, although relative luminance can. Moreover, loss functions suitable
for learning HDR imag... | [] | 2019-02-28 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 4.44219828e-01 -1.74707264e-01 -7.91815072e-02 -2.28994310e-01
-4.90372211e-01 -4.26479317e-02 3.56768489e-01 -5.33345938e-01
-3.30495059e-01 1.21522009e+00 -1.80985361e-01 -1.65186465e-01
2.13844076e-01 -1.28876460e+00 -8.14959228e-01 -7.31391490e-01
3.03756535e-01 -4.45974506e-02 1.04016736e-01 -5.54588854... | [10.97602367401123, -2.2164177894592285] |
92775850-3293-40f0-b033-bc7761967b0e | an-analysis-of-svd-for-deep-rotation | 2006.14616 | null | https://arxiv.org/abs/2006.14616v1 | https://arxiv.org/pdf/2006.14616v1.pdf | An Analysis of SVD for Deep Rotation Estimation | Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$. These tools have long been used for applications in computer vision, for example optimal 3D alignment problems solved by orthogonal Procrustes, rotation averaging, or Essential ... | ['Noah Snavely', 'Jake Levinson', 'Afshin Rostamizadeh', 'Angjoo Kanazawa', 'Ameesh Makadia', 'Kefan Chen', 'Carlos Esteves'] | 2020-06-25 | null | http://proceedings.neurips.cc/paper/2020/hash/fec3392b0dc073244d38eba1feb8e6b7-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/fec3392b0dc073244d38eba1feb8e6b7-Paper.pdf | neurips-2020-12 | ['3d-rotation-estimation'] | ['computer-vision'] | [-2.23362640e-01 -1.00593030e-01 -3.81935328e-01 -1.31562456e-01
1.88621700e-01 -6.16812110e-01 6.57991230e-01 -2.48006344e-01
-7.55461872e-01 2.97375977e-01 3.91658634e-01 -5.11958301e-01
-3.13102715e-02 -2.84369677e-01 -7.27650285e-01 -8.02353323e-01
-2.63978213e-01 3.73495728e-01 -6.85208380e-01 -5.00292242... | [8.8942289352417, 2.3297197818756104] |
95d89543-161c-4228-8017-bcd132ac939d | tencent-avs-a-holistic-ads-video-dataset-for | 2212.04700 | null | https://arxiv.org/abs/2212.04700v1 | https://arxiv.org/pdf/2212.04700v1.pdf | Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation | Temporal video segmentation and classification have been advanced greatly by public benchmarks in recent years. However, such research still mainly focuses on human actions, failing to describe videos in a holistic view. In addition, previous research tends to pay much attention to visual information yet ignores the mu... | ['Wei Liu', 'Qinglin Lu', 'Rongwei Quan', 'Jiangfeng Xiong', 'Zhimin Li', 'Jie Jiang'] | 2022-12-09 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 7.96537474e-02 -4.93078828e-01 -8.20413530e-01 -4.44228321e-01
-1.00139832e+00 -9.47537422e-01 3.69407892e-01 -1.99444238e-02
9.55649931e-03 1.49004027e-01 4.28922713e-01 -2.24354789e-02
9.41829383e-02 -4.23042417e-01 -5.59978724e-01 -6.63316548e-01
-3.18065006e-03 9.12278816e-02 5.62283933e-01 -1.71663873... | [9.539041519165039, 0.485289990901947] |
9c5d7e27-8ccd-4cc1-b9aa-8e7b74ea56da | represent-compare-and-learn-a-similarity | 2203.08354 | null | https://arxiv.org/abs/2203.08354v1 | https://arxiv.org/pdf/2203.08354v1.pdf | Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting | Class-agnostic counting (CAC) aims to count all instances in a query image given few exemplars. A standard pipeline is to extract visual features from exemplars and match them with query images to infer object counts. Two essential components in this pipeline are feature representation and similarity metric. Existing m... | ['Zhiguo Cao', 'Chengxin Liu', 'Chen Feng', 'Hao Lu', 'Min Shi'] | 2022-03-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_Represent_Compare_and_Learn_A_Similarity-Aware_Framework_for_Class-Agnostic_Counting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_Represent_Compare_and_Learn_A_Similarity-Aware_Framework_for_Class-Agnostic_Counting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['object-counting'] | ['computer-vision'] | [ 1.48543835e-01 -7.29114294e-01 -1.22975931e-01 -5.63478231e-01
-8.13908756e-01 -5.92448711e-01 8.11115921e-01 3.80237222e-01
-7.85272002e-01 1.63453788e-01 7.34512657e-02 2.91267280e-02
9.37263295e-02 -9.73262489e-01 -7.49995708e-01 -3.24850023e-01
-1.13537265e-02 4.70422059e-01 6.36814177e-01 7.28620514... | [8.99155044555664, 0.5052405595779419] |
c376f60b-b308-4dbf-96c4-ad0137425fd0 | cross-lingual-language-model-pretraining | 1901.07291 | null | http://arxiv.org/abs/1901.07291v1 | http://arxiv.org/pdf/1901.07291v1.pdf | Cross-lingual Language Model Pretraining | Recent studies have demonstrated the efficiency of generative pretraining for
English natural language understanding. In this work, we extend this approach
to multiple languages and show the effectiveness of cross-lingual pretraining.
We propose two methods to learn cross-lingual language models (XLMs): one
unsupervise... | ['Guillaume Lample', 'Alexis Conneau'] | 2019-01-22 | cross-lingual-language-model-pretraining-1 | http://papers.nips.cc/paper/8928-cross-lingual-language-model-pretraining | http://papers.nips.cc/paper/8928-cross-lingual-language-model-pretraining.pdf | neurips-2019-12 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.76609527e-02 6.48458228e-02 -6.11039698e-01 -4.50400054e-01
-1.66124749e+00 -7.99472809e-01 8.24287474e-01 -7.04176202e-02
-6.06782913e-01 1.01690638e+00 1.76304340e-01 -8.33716691e-01
3.21290374e-01 -4.90068674e-01 -1.07333958e+00 -3.33654165e-01
2.79555976e-01 9.08767164e-01 -2.70413905e-01 -3.40069681... | [11.430704116821289, 10.212129592895508] |
f0194cdf-efd7-471f-8c41-301fea8ea400 | prediction-of-good-reaction-coordinates-and | 2208.10962 | null | https://arxiv.org/abs/2208.10962v1 | https://arxiv.org/pdf/2208.10962v1.pdf | Prediction of good reaction coordinates and future evolution of MD trajectories using Regularized Sparse Autoencoders: A novel deep learning approach | Identifying reaction coordinates(RCs) is an active area of research, given the crucial role RCs play in determining the progress of a chemical reaction. The choice of the reaction coordinate is often based on heuristic knowledge. However, an essential criterion for the choice is that the coordinate should capture both ... | ['Arnab Mukherjee', 'Abhijit Gupta'] | 2022-08-22 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.12751493e-01 -3.74449730e-01 -2.23987609e-01 6.63554519e-02
-2.77741909e-01 -6.03652537e-01 7.51796961e-01 4.19050485e-01
-4.52659488e-01 9.56595600e-01 1.84488580e-01 -4.03314620e-01
-1.70332342e-01 -8.03574562e-01 -8.55723977e-01 -1.51007187e+00
-1.95978492e-01 6.39029443e-01 -4.25772294e-02 -2.97175229... | [5.050365447998047, 5.2684831619262695] |
25f97cf0-5d79-40bc-a87c-08a886cff2b4 | assessing-the-severity-of-health-states-based | 2009.09600 | null | https://arxiv.org/abs/2009.09600v1 | https://arxiv.org/pdf/2009.09600v1.pdf | Assessing the Severity of Health States based on Social Media Posts | The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert inter... | ['Joy Prakash Sain', 'Sriparna Saha', 'Amit Sheth', 'Asif Ekbal', 'Shweta Yadav', 'Pushpak Bhattacharyya'] | 2020-09-21 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 5.22535928e-02 3.39629710e-01 -7.73287475e-01 -3.67008150e-01
-4.62994635e-01 -2.80891001e-01 2.98634857e-01 1.11583447e+00
1.05413154e-01 6.02973819e-01 9.48922932e-01 -2.02602804e-01
-3.25850368e-01 -4.93489474e-01 2.80688763e-01 -4.79673535e-01
-4.59364615e-02 3.03669989e-01 -2.95958459e-01 -2.60843843... | [8.657608032226562, 8.86437702178955] |
41fabb8e-b51e-4053-a147-c39a65d9b282 | open-domain-question-answering-via-chain-of | 2210.12338 | null | https://arxiv.org/abs/2210.12338v1 | https://arxiv.org/pdf/2210.12338v1.pdf | Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge | We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is the introduction of the intermediary modules into the current retriever-reader pipeline. Unlike previous methods that solely rely on the re... | ['Jianfeng Gao', 'Eric Nyberg', 'Xiaodong Liu', 'Hao Cheng', 'Kaixin Ma'] | 2022-10-22 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-4.16445166e-01 4.90163773e-01 -2.75960058e-01 7.63892159e-02
-1.83310902e+00 -1.07133222e+00 6.00655317e-01 8.31929505e-01
-4.28013146e-01 8.44783306e-01 6.49214208e-01 -5.36190271e-01
-3.99042070e-01 -1.15209174e+00 -9.62402165e-01 1.26316756e-01
5.45976497e-03 1.03094327e+00 1.27440894e+00 -7.34861255... | [10.757257461547852, 7.910121917724609] |
587deed2-62e3-4678-843f-579030e9337d | aio-p-expanding-neural-performance-predictors | 2211.17228 | null | https://arxiv.org/abs/2211.17228v2 | https://arxiv.org/pdf/2211.17228v2.pdf | AIO-P: Expanding Neural Performance Predictors Beyond Image Classification | Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. However, existing predictors are task-dependent, predominantly estimati... | ['Shangling Jui', 'Wei Lu', 'Jialin Zhang', 'Puyuan Liu', 'Fred X. Han', 'Weichen Qiu', 'Mohammad Salameh', 'Di Niu', 'Keith G. Mills'] | 2022-11-30 | aio-p-expanding-neural-performance-predictors | https://arxiv.org/abs/2211.17228 | https://arxiv.org/pdf/2211.17228.pdf | null | ['panoptic-segmentation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.91028917e-01 -6.70279488e-02 -2.65149176e-01 -5.55532753e-01
-6.91420376e-01 -6.46900594e-01 4.10377651e-01 -2.26591099e-02
-3.79633904e-01 4.75866646e-01 -2.91833967e-01 -4.92134839e-01
-2.54134208e-01 -4.74937171e-01 -1.07954109e+00 -4.59494948e-01
-2.02535093e-01 7.16513693e-01 4.23387885e-01 -3.10881902... | [8.736870765686035, 3.313368320465088] |
f809cf12-de23-4a0a-a911-a467e0d73b66 | late-multimodal-fusion-for-image-and-audio | 2204.03063 | null | https://arxiv.org/abs/2204.03063v3 | https://arxiv.org/pdf/2204.03063v3.pdf | Late multimodal fusion for image and audio music transcription | Music transcription, which deals with the conversion of music sources into a structured digital format, is a key problem for Music Information Retrieval (MIR). When addressing this challenge in computational terms, the MIR community follows two lines of research: music documents, which is the case of Optical Music Reco... | ['Jorge Calvo-Zaragoza', 'José M. Iñesta', 'Jose J. Valero-Mas', 'María Alfaro-Contreras'] | 2022-04-06 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 8.53213906e-01 -2.98067510e-01 -7.99115002e-02 1.00524187e-01
-1.22063875e+00 -8.36998701e-01 8.42544854e-01 7.08337501e-02
-3.52235496e-01 4.40403849e-01 3.20622295e-01 6.77593872e-02
-6.91218257e-01 -3.79163772e-01 -2.40450844e-01 -8.38649154e-01
3.29990983e-01 4.27404106e-01 -1.70627534e-01 -1.98872268... | [15.78703498840332, 5.310118675231934] |
b7825602-ccd2-4114-80bd-e9864ac19fb7 | reinforcement-learning-with-reward-machines | 2305.17372 | null | https://arxiv.org/abs/2305.17372v1 | https://arxiv.org/pdf/2305.17372v1.pdf | Reinforcement Learning With Reward Machines in Stochastic Games | We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate high-level knowledge of complex tasks. We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn... | ['Yongming Liu', 'Ufuk Topcu', 'Zhe Xu', 'Yanze Wang', 'Jean-Raphaël Gaglione', 'Jueming Hu'] | 2023-05-27 | null | null | null | null | ['q-learning', 'multi-agent-reinforcement-learning'] | ['methodology', 'methodology'] | [-4.53484207e-01 1.70708403e-01 -2.00355023e-01 3.93271625e-01
-1.07474303e+00 -5.85628629e-01 8.31561387e-02 -1.63919225e-01
-8.62181127e-01 1.25125706e+00 -3.55531499e-02 -3.51198584e-01
-7.13929713e-01 -5.61893106e-01 -5.38296759e-01 -9.26627517e-01
-4.68708843e-01 7.33598232e-01 1.83539286e-01 -4.34869826... | [4.167448043823242, 2.534403085708618] |
ae95d78c-3f2f-46b7-9c4f-31a2f13f79e2 | multisum-a-dataset-for-multimodal | 2306.04216 | null | https://arxiv.org/abs/2306.04216v1 | https://arxiv.org/pdf/2306.04216v1.pdf | MultiSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos | Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless, numerous limitations exist within existing public MSMO datasets, including insufficient upkeep, data inaccessibility, limited size, and the absence of proper categorization, which pose significant challeng... | ['Lijuan Wang', 'Ding Zhao', 'Bo Li', 'JianFeng Wang', 'Linjie Li', 'Zhengyuan Yang', 'Claire Jin', 'Karthik Mittal', 'Aditesh Kumar', 'William Han', 'Jiacheng Zhu', 'JieLin Qiu'] | 2023-06-07 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 2.80061990e-01 -1.07383892e-01 -4.51930910e-01 -8.79531503e-02
-1.07937002e+00 -7.23880768e-01 5.53146541e-01 2.26553977e-01
-2.85355330e-01 6.58696830e-01 5.89579284e-01 -1.48240358e-01
-2.01489069e-02 -2.17506468e-01 -4.31679815e-01 -4.54367965e-01
1.30555615e-01 1.21679768e-01 1.27545267e-01 -1.06764689... | [10.685943603515625, 0.6552039980888367] |
881e8487-5736-489b-80f9-798dc4d3ce96 | evaluation-of-a-canonical-image | 2304.09243 | null | https://arxiv.org/abs/2304.09243v1 | https://arxiv.org/pdf/2304.09243v1.pdf | Evaluation of a Canonical Image Representation for Sidescan Sonar | Acoustic sensors play an important role in autonomous underwater vehicles (AUVs). Sidescan sonar (SSS) detects a wide range and provides photo-realistic images in high resolution. However, SSS projects the 3D seafloor to 2D images, which are distorted by the AUV's altitude, target's range and sensor's resolution. As a ... | ['John Folkesson', 'Jun Zhang', 'Yiping Xie', 'Li Ling', 'Weiqi Xu'] | 2023-04-18 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.73192739e-01 -3.63075256e-01 6.56558514e-01 -5.86084366e-01
-3.83634001e-01 -5.51073611e-01 3.64720047e-01 -3.26435976e-02
-9.03265774e-01 3.47593963e-01 -2.84248102e-03 2.58816838e-01
-3.26286778e-02 -1.15428197e+00 -7.53724158e-01 -6.84757590e-01
5.31551391e-02 2.73477495e-01 7.04129875e-01 -6.93088531... | [7.540106773376465, -1.8375636339187622] |
e2bcf2a5-c2ba-4579-86ac-e544b4e985e3 | a-survey-on-csi-based-human-behavior | null | null | https://doi.org/10.1109/ACCESS.2019.2922244 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8735849 | A Survey on CSI-Based Human Behavior Recognition in Through-the-Wall Scenario | Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to t... | ['Yinjing Guo', 'Yushan Hou', 'Wenwen Dou', 'Chengming Zhang', 'Kangkang Jiang', 'Zhengjie Wang', 'Zehua Huang'] | 2019-06-12 | null | null | null | ieee-access-volume-7-2019-6 | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 4.36037034e-01 -5.29013872e-01 -3.19318444e-01 -2.58391738e-01
-5.61273694e-01 -3.10840219e-01 1.05677515e-01 -2.76191652e-01
-5.83576076e-02 6.55071259e-01 5.03277183e-01 -1.26135200e-01
-2.32666478e-01 -6.23141885e-01 -4.03101683e-01 -8.34031940e-01
-4.56005752e-01 -3.37685019e-01 -2.00276226e-01 2.65837321... | [6.6919779777526855, 0.722235381603241] |
274194af-8dcc-4103-a267-558e46a29ca7 | escnet-an-end-to-end-superpixel-enhanced | null | null | https://ieeexplore.ieee.org/document/9474911 | https://ieeexplore.ieee.org/document/9474911 | ESCNet: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images | Change detection (CD), as one of the central problems in Earth observation, has attracted a lot of research interest over recent decades. Due to the rapid development of satellite sensors in recent years, we have witnessed an enrichment of the CD source data with the availability of very-high-resolution (VHR) multispec... | ['Liangpei Zhang', 'Guangyi Yang', 'Manhui Lin', 'Hongyan zhang'] | 2021-07-05 | null | null | null | ieee-transactions-on-neural-networks-and-9 | ['change-detection', 'superpixels', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.33676964e-01 -3.24418008e-01 -8.61779694e-03 -4.21238661e-01
-7.23385930e-01 -1.73884258e-01 5.73197842e-01 -2.54975319e-01
-6.64993942e-01 6.18005753e-01 6.86725378e-02 -2.74687782e-02
-1.15596540e-01 -1.04588437e+00 -6.18187904e-01 -9.18211937e-01
-1.07729912e-01 -6.74808994e-02 3.41470957e-01 -3.73705089... | [9.697587966918945, -1.3918957710266113] |
da800672-f4b1-43ff-bf79-9eb0cf3a1f8b | intelligent-home-3d-automatic-3d-house-design | 2003.00397 | null | https://arxiv.org/abs/2003.00397v1 | https://arxiv.org/pdf/2003.00397v1.pdf | Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only | Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language.... | ['Yu-Han Wang', 'Shuai Wang', 'Rui Tang', 'Qi Wu', 'Qi Chen', 'Mingkui Tan'] | 2020-03-01 | intelligent-home-3d-automatic-3d-house-design-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Intelligent_Home_3D_Automatic_3D-House_Design_From_Linguistic_Descriptions_Only_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Intelligent_Home_3D_Automatic_3D-House_Design_From_Linguistic_Descriptions_Only_CVPR_2020_paper.pdf | cvpr-2020-6 | ['text-to-3d'] | ['computer-vision'] | [ 4.24984068e-01 5.13609469e-01 6.64894819e-01 -5.54496586e-01
-2.37261266e-01 -4.45100576e-01 7.84083366e-01 -3.01560223e-01
5.06804049e-01 5.78879714e-01 5.74357092e-01 -3.64997238e-01
3.61763656e-01 -1.58378851e+00 -8.00761700e-01 -4.21395123e-01
3.02688777e-01 6.43910885e-01 -4.21175033e-01 -3.78544480... | [9.378976821899414, -2.948323965072632] |
2a0187dd-a7cf-4289-8cd1-b27230f29a87 | advancing-biomedicine-with-graph | 2306.10456 | null | https://arxiv.org/abs/2306.10456v2 | https://arxiv.org/pdf/2306.10456v2.pdf | Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions | Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challeng... | ['Cui Tao', 'Zenan Sun', 'Yi Nian', 'Fang Li'] | 2023-06-18 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [ 5.02355576e-01 1.64444819e-01 -3.03750932e-01 -1.56534463e-01
-6.38591051e-01 -1.54126987e-01 2.26149216e-01 8.18545341e-01
2.65691094e-02 7.50715911e-01 1.52430698e-01 -4.54290181e-01
-3.02784085e-01 -6.69948280e-01 -1.48406878e-01 -9.89244461e-01
-5.13623476e-01 2.63047487e-01 -2.60555241e-02 -1.62080377... | [7.12399959564209, 6.395130634307861] |
577565aa-9ad8-4c72-b8f7-981c00626be1 | automatic-keyphrase-generation-by | null | null | https://aclanthology.org/2022.coling-1.204 | https://aclanthology.org/2022.coling-1.204.pdf | Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning | The keyphrase generation task is a challenging work that aims to generate a set of keyphrases for a piece of text. Many previous studies based on the sequence-to-sequence model were used to generate keyphrases, and they introduce a copy mechanism to achieve good results. However, we observed that most of the keyphrases... | ['Yin Wang', 'Yao Huang', 'Jianhui Jiang', 'Siyu Wang'] | null | null | null | null | coling-2022-10 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.30636299e-01 -1.47961944e-01 -4.30287659e-01 2.36102924e-01
-8.66838396e-01 -9.01049972e-01 9.94700909e-01 3.38539124e-01
-4.14406478e-01 8.61947179e-01 8.11515212e-01 -4.14440662e-01
5.02375722e-01 -8.97962928e-01 -6.26944304e-01 -4.37641501e-01
2.64340043e-01 4.21653353e-02 6.13671720e-01 -6.36666059... | [12.308065414428711, 8.889082908630371] |
59680308-574d-401c-80a9-d7dfd9180e5d | bayesian-optimization-meets-self-distillation | 2304.12666 | null | https://arxiv.org/abs/2304.12666v1 | https://arxiv.org/pdf/2304.12666v1.pdf | Bayesian Optimization Meets Self-Distillation | Bayesian optimization (BO) has contributed greatly to improving model performance by suggesting promising hyperparameter configurations iteratively based on observations from multiple training trials. However, only partial knowledge (i.e., the measured performances of trained models and their hyperparameter configurati... | ['Donggeun Yoo', 'Suyeong Park', 'Gi-hyeon Lee', 'Hyeonsoo Lee', 'Heon Song', 'Hyunjae Lee'] | 2023-04-25 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.01202407e-01 -9.68312398e-02 -4.54578251e-01 -5.39350688e-01
-1.08212566e+00 -2.11996436e-01 5.35165906e-01 6.80893734e-02
-6.09699070e-01 8.62615883e-01 1.14783451e-01 3.17230076e-02
-2.57256359e-01 -9.12585706e-02 -5.47050714e-01 -1.01716220e+00
3.36152554e-01 5.20435333e-01 9.64025706e-02 2.51112789... | [9.337357521057129, 3.571859359741211] |
19ad9ea8-af7c-4fd1-a747-45bcb4ed6a28 | rgb-t-tracking-based-on-mixed-attention | 2304.04264 | null | https://arxiv.org/abs/2304.04264v4 | https://arxiv.org/pdf/2304.04264v4.pdf | RGB-T Tracking Based on Mixed Attention | RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on mixed attention mechanism to achieve ... | ['Jin Yu', 'Xiqing Guo', 'Mingtao Dong', 'Yang Luo'] | 2023-04-09 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [ 1.11224249e-01 -1.89653620e-01 -8.98256674e-02 -1.72098801e-01
-9.21197712e-01 -5.44091821e-01 5.64058721e-01 -2.65141368e-01
-3.36471915e-01 2.41210938e-01 3.38079482e-01 3.03656280e-01
-2.37502679e-02 -2.04880714e-01 -5.67685902e-01 -1.10602462e+00
4.16225344e-01 -1.83001444e-01 3.54730725e-01 -9.00589004... | [6.339169502258301, -2.199431896209717] |
bae35e43-af4c-4d83-bb99-4b61c525b3dd | mm-diffusion-learning-multi-modal-diffusion | 2212.09478 | null | https://arxiv.org/abs/2212.09478v2 | https://arxiv.org/pdf/2212.09478v2.pdf | MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation | We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In... | ['Baining Guo', 'Qin Jin', 'Nicholas Jing Yuan', 'Jianlong Fu', 'Bei Liu', 'Huiguo He', 'Huan Yang', 'Yiyang Ma', 'Ludan Ruan'] | 2022-12-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ruan_MM-Diffusion_Learning_Multi-Modal_Diffusion_Models_for_Joint_Audio_and_Video_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ruan_MM-Diffusion_Learning_Multi-Modal_Diffusion_Models_for_Joint_Audio_and_Video_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation'] | ['computer-vision'] | [ 3.10704522e-02 1.23745963e-01 1.23932809e-01 -2.07409680e-01
-1.57346749e+00 -5.88823915e-01 6.32710218e-01 -6.28143966e-01
-3.61627489e-02 5.19324899e-01 6.42618060e-01 9.87000912e-02
1.12405956e-01 -6.75106645e-01 -1.10893297e+00 -7.68075407e-01
2.32380062e-01 5.80643676e-02 3.76028195e-02 -1.87974453... | [15.365753173828125, 5.240102767944336] |
8516432d-8237-4b43-a2ee-8b4050e349d5 | scopeit-scoping-task-relevant-sentences-in | 2003.04988 | null | https://arxiv.org/abs/2003.04988v2 | https://arxiv.org/pdf/2003.04988v2.pdf | ScopeIt: Scoping Task Relevant Sentences in Documents | Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are trained to parse information when the conversation is synchronous and short; however, for email-based conversational agents, the communication is asynchronous, and often contains information irrelevant to the assistant. This makes it harder for ... | ['Charles Lee', 'Pamela Bhattacharya', 'Chala Fufa', 'Barun Patra', 'Vishwas Suryanarayanan'] | 2020-02-23 | null | https://aclanthology.org/2020.coling-industry.20 | https://aclanthology.org/2020.coling-industry.20.pdf | coling-2020-8 | ['entity-extraction'] | ['natural-language-processing'] | [ 3.72438878e-01 6.19189918e-01 -9.55555961e-03 -3.93009216e-01
-8.19639146e-01 -6.08623743e-01 6.36628628e-01 4.35921639e-01
-6.26241565e-01 6.49996042e-01 5.06360233e-01 -5.75932026e-01
-1.55750245e-01 -5.45238376e-01 -2.76652813e-01 -3.18551272e-01
-1.50973007e-01 1.00114620e+00 1.41146734e-01 -2.18070984... | [12.497527122497559, 7.8071208000183105] |
824c7faf-1d6a-440d-9c74-c1026e090f65 | novelty-detection-in-network-traffic-using | 2301.06229 | null | https://arxiv.org/abs/2301.06229v1 | https://arxiv.org/pdf/2301.06229v1.pdf | Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification | Intrusion Detection Systems are an important component of many organizations' cyber defense and resiliency strategies. However, one downside of these systems is their reliance on known attack signatures for detection of malicious network events. When it comes to unknown attack types and zero-day exploits, modern Intrus... | ['Nathaniel Bastian', 'Elie Alhajjar', 'Taylor Bradley'] | 2023-01-16 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 1.31597653e-01 -4.14593697e-01 -6.77191734e-01 -5.74104451e-02
-1.57957405e-01 -6.74112380e-01 7.38073587e-01 7.80401647e-01
-4.23327446e-01 7.81038642e-01 -3.92644018e-01 -1.24396515e+00
-3.81761074e-01 -8.63916159e-01 7.63159096e-02 -4.94410306e-01
-4.93319988e-01 3.51332188e-01 4.36802685e-01 6.72672363... | [5.277563571929932, 7.2189531326293945] |
89fbf220-bd99-42a9-bc0e-7d401906bda2 | on-hallucinating-context-and-background | 1811.07104 | null | https://arxiv.org/abs/1811.07104v3 | https://arxiv.org/pdf/1811.07104v3.pdf | On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs | We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask. Instead of swapping a face on to an existing picture, our model directly generates realistic context and background pixels based on the features of the p... | ['Patrick J. Flynn', 'Kevin W. Bowyer', 'Walter J. Scheirer', 'Sandipan Banerjee'] | 2018-11-17 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 7.91757464e-01 5.21411598e-01 4.90736455e-01 -4.19552207e-01
-7.77697802e-01 -5.32161117e-01 5.13394773e-01 -1.01160991e+00
1.33254513e-01 9.48570609e-01 4.16917562e-01 3.27018350e-01
5.96857548e-01 -5.74334085e-01 -8.90112460e-01 -6.47300422e-01
5.39870322e-01 3.35639179e-01 -3.41519654e-01 -2.38244608... | [12.566381454467773, -0.27086618542671204] |
7d52f6d5-9d93-4525-b8db-bf1ff269a0a8 | icon-interactive-conversational-memory | null | null | https://aclanthology.org/D18-1280 | https://aclanthology.org/D18-1280.pdf | ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection | Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detec... | ['Devamanyu Hazarika', 'Soujanya Poria', 'Roger Zimmermann', 'Erik Cambria', 'Rada Mihalcea'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['multimodal-emotion-recognition', 'emotion-recognition-in-conversation', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [-3.23652983e-01 -7.73652866e-02 -3.42920154e-01 -6.39128268e-01
-3.15611988e-01 -3.68402898e-01 6.96755171e-01 -5.69413938e-02
-7.52659440e-02 6.18228972e-01 1.02144814e+00 4.59813714e-01
2.91165859e-01 -5.30893266e-01 -2.58038193e-01 -5.04058659e-01
-1.46083370e-01 -5.84467612e-02 -4.13912266e-01 -4.70537156... | [13.0523681640625, 5.950223922729492] |
073e600a-1a4a-44a0-9581-daf0170d5209 | security-of-distributed-parameter-cyber | 2107.14159 | null | https://arxiv.org/abs/2107.14159v2 | https://arxiv.org/pdf/2107.14159v2.pdf | Security of Distributed Parameter Cyber-Physical Systems: Cyber-Attack Detection in Linear Parabolic PDEs | Security of Distributed Parameter Cyber-Physical Systems (DPCPSs) is of critical importance in the face of cyber-attack threats. Although security aspects of Cyber-Physical Systems (CPSs) modelled by Ordinary differential Equations (ODEs) have been extensively explored during the past decade, security of DPCPSs has not... | ['Satadru Dey', 'Tanushree Roy'] | 2021-07-29 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 3.34913135e-01 2.57186234e-01 1.50218934e-01 4.79362369e-01
-3.59151065e-02 -1.02974319e+00 6.71627283e-01 2.50046402e-01
-7.41151273e-02 7.13874102e-01 -5.36542237e-01 -6.50512278e-01
-6.35521352e-01 -4.47080940e-01 -4.29152787e-01 -9.44815159e-01
-3.51546705e-01 -3.87487471e-01 3.03383112e-01 -1.80121541... | [5.327722072601318, 2.6192643642425537] |
3a0a7409-509b-43b2-9d4f-02e486f35ac1 | contrastive-learning-with-stronger-1 | 2104.07713 | null | https://arxiv.org/abs/2104.07713v2 | https://arxiv.org/pdf/2104.07713v2.pdf | Contrastive Learning with Stronger Augmentations | Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that are carefully designated to maintain their identities so that the images transformed from the same instance can still be retrieved. However... | ['Guo-Jun Qi', 'Xiao Wang'] | 2021-04-15 | contrastive-learning-with-stronger | https://openreview.net/forum?id=KJSC_AsN14 | https://openreview.net/pdf?id=KJSC_AsN14 | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.58004135e-01 -1.73976898e-01 -2.67713398e-01 -4.34626609e-01
-7.98848808e-01 -4.58171248e-01 9.25189853e-01 -2.94067740e-01
-6.03831351e-01 5.21028996e-01 1.72454566e-01 -4.32752930e-02
-2.26663742e-02 -6.83147252e-01 -8.38894129e-01 -8.65656912e-01
2.05569431e-01 3.69881183e-01 1.53942496e-01 -5.32459438... | [9.864394187927246, 2.1261606216430664] |
38820c74-f30c-436a-a569-23a227ad8e33 | red-psm-regularization-by-denoising-of | 2304.03483 | null | https://arxiv.org/abs/2304.03483v1 | https://arxiv.org/pdf/2304.03483v1.pdf | RED-PSM: Regularization by Denoising of Partially Separable Models for Dynamic Imaging | Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed. In this work, we propose... | ['Yoram Bresler', 'Marc L. Klasky', 'Berk Iskender'] | 2023-04-07 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 4.45618510e-01 -1.92486286e-01 5.31339943e-01 -1.57017112e-01
-9.82157052e-01 -1.90399960e-01 4.76721346e-01 -3.78267735e-01
-5.38217306e-01 6.99256897e-01 1.26468599e-01 1.31134048e-01
-6.38672709e-01 -4.79602724e-01 -4.78212357e-01 -1.45451379e+00
1.84068620e-01 6.97076499e-01 7.88365025e-03 -9.31717977... | [11.78738021850586, -2.4414846897125244] |
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