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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4178f08e-5438-4717-a4d3-dc1e4f419672 | a-metal-artifact-reduction-scheme-for | 2202.00116 | null | https://arxiv.org/abs/2202.00116v1 | https://arxiv.org/pdf/2202.00116v1.pdf | A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms | CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping-power maps used in treatment planning. However, the presence of metal implants introduces severe streaking artifacts in the ... | ["Joseph A. O'Sullivan", 'Bruce R. Whiting', 'David G. Politte', 'Jeffrey F. Williamson', 'Rui Liao', 'Maria Medrano', 'Tao Ge'] | 2022-01-31 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.97139406e-01 -1.58644065e-01 3.50240320e-01 -2.96016838e-02
-1.00260806e+00 5.19423038e-02 1.70149416e-01 -3.62062119e-02
-3.70905399e-01 6.96557581e-01 4.36577171e-01 -2.48544231e-01
-5.77878833e-01 -4.44685668e-01 -3.21661502e-01 -1.03122485e+00
2.43409768e-01 8.55788946e-01 4.65396285e-01 2.31373474... | [13.23569107055664, -2.635951042175293] |
6ec62e25-07b9-41a2-8141-48e9ec32188b | coloc-conditioned-localizer-and-classifier | 2210.13932 | null | https://arxiv.org/abs/2210.13932v1 | https://arxiv.org/pdf/2210.13932v1.pdf | CoLoC: Conditioned Localizer and Classifier for Sound Event Localization and Detection | In this article, we describe Conditioned Localizer and Classifier (CoLoC) which is a novel solution for Sound Event Localization and Detection (SELD). The solution constitutes of two stages: the localization is done first and is followed by classification conditioned by the output of the localizer. In order to resolve ... | ['Jakub Tkaczuk', 'Sławomir Kapka'] | 2022-10-25 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 1.44973293e-01 1.50684521e-01 3.15330505e-01 -2.47075588e-01
-1.32081461e+00 -7.36385286e-01 4.95235860e-01 -9.13304836e-02
-4.23760891e-01 5.68173766e-01 2.14204729e-01 -3.26708883e-01
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-5.75677045e-02 2.64078323e-02 6.29913867e-01 -9.36129987... | [15.20635986328125, 5.2278265953063965] |
95042d31-09d6-44aa-8b3f-0eae2eb7fda6 | do-transformers-parse-while-predicting-the | 2303.08117 | null | https://arxiv.org/abs/2303.08117v1 | https://arxiv.org/pdf/2303.08117v1.pdf | Do Transformers Parse while Predicting the Masked Word? | Pre-trained language models have been shown to encode linguistic structures, e.g. dependency and constituency parse trees, in their embeddings while being trained on unsupervised loss functions like masked language modeling. Some doubts have been raised whether the models actually are doing parsing or only some computa... | ['Sanjeev Arora', 'Rong Ge', 'Abhishek Panigrahi', 'Haoyu Zhao'] | 2023-03-14 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.94096302e-02 1.11218226e+00 1.50743112e-01 -6.41443908e-01
-1.06930339e+00 -6.28759444e-01 2.12341458e-01 1.65140957e-01
-4.31445062e-01 6.17013514e-01 4.91351247e-01 -7.72547543e-01
3.93089503e-01 -1.19868398e+00 -1.06322908e+00 -5.48938155e-01
-3.79312515e-01 6.63965702e-01 8.37895423e-02 -6.41945824... | [10.395525932312012, 9.565067291259766] |
fbb35cdf-80b6-4691-b972-536e90d76f3c | icdar-2021-competition-on-historical-map | 2105.13265 | null | https://arxiv.org/abs/2105.13265v1 | https://arxiv.org/pdf/2105.13265v1.pdf | ICDAR 2021 Competition on Historical Map Segmentation | This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task~1 consists in detecting buildi... | ['Pavel Král', 'Ladislav Lenc', 'Josef Baloun', 'Nam Nguyen', 'Vincent Nguyen', 'Thierry Géraud', 'Clément Mallet', 'Bertrand Duménieu', 'Julien Perret', 'Yizi Chen', 'Edwin Carlinet', 'Joseph Chazalon'] | 2021-05-27 | null | null | null | null | ['document-layout-analysis', 'line-segment-detection', 'line-detection', 'contour-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.39759168e-01 2.48675108e-01 1.87040702e-01 -3.95691186e-01
-1.04572761e+00 -9.46132123e-01 9.24104691e-01 2.47100681e-01
-7.17555642e-01 4.76728827e-01 2.52030790e-01 -2.07560837e-01
8.00578296e-02 -1.12190199e+00 -8.69921863e-01 -1.87094286e-01
-8.09047520e-01 7.73641646e-01 7.09435999e-01 -5.17421663... | [8.707534790039062, -1.6334044933319092] |
5fca6b5f-4170-4339-ba6c-cde9956c8c0f | constructing-large-proposition-databases | null | null | https://aclanthology.org/L12-1238 | https://aclanthology.org/L12-1238.pdf | Constructing Large Proposition Databases | With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―a... | ['Peter Exner', 'Pierre Nugues'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-1.91155672e-01 6.17427528e-01 1.73385032e-02 -1.60533935e-01
-8.57131183e-01 -9.08958554e-01 9.61347938e-01 9.95939374e-01
-6.08915031e-01 1.06237459e+00 4.38460529e-01 -2.30578154e-01
-2.36707389e-01 -1.10283959e+00 -6.98022485e-01 1.23850904e-01
2.23240882e-01 5.82279265e-01 8.16742539e-01 -3.21029216... | [9.378386497497559, 8.464118003845215] |
1b36321f-17ad-4a9f-8838-02f9b7fa647c | deep-multimodal-learning-for-audio-visual | 1501.05396 | null | http://arxiv.org/abs/1501.05396v1 | http://arxiv.org/pdf/1501.05396v1.pdf | Deep Multimodal Learning for Audio-Visual Speech Recognition | In this paper, we present methods in deep multimodal learning for fusing
speech and visual modalities for Audio-Visual Automatic Speech Recognition
(AV-ASR). First, we study an approach where uni-modal deep networks are trained
separately and their final hidden layers fused to obtain a joint feature space
in which anot... | ['Youssef Mroueh', 'Etienne Marcheret', 'Vaibhava Goel'] | 2015-01-22 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.91242591e-01 1.13619283e-01 3.18412930e-01 -5.21758378e-01
-1.69924104e+00 -3.12684387e-01 4.56787795e-01 -1.46626845e-01
-4.37026381e-01 6.00857258e-01 2.24002182e-01 -2.88777918e-01
4.72358942e-01 -3.27856839e-01 -9.22865093e-01 -7.65610278e-01
-1.26464535e-02 7.05028921e-02 -2.63613522e-01 9.34454501... | [14.369234085083008, 5.167525768280029] |
10277e3c-ec93-4077-8e8e-5d09ae4f40ae | group-activity-recognition-via-dynamic | 2305.05583 | null | https://arxiv.org/abs/2305.05583v1 | https://arxiv.org/pdf/2305.05583v1.pdf | Group Activity Recognition via Dynamic Composition and Interaction | Previous group activity recognition approaches were limited to reasoning using human relations or finding important subgroups and tended to ignore indispensable group composition and human-object interactions. This absence makes a partial interpretation of the scene and increases the interference of irrelevant actions ... | ['Zheng Wang', 'Danni Xu', 'Wenxuan Liu', 'Zhuo Zhou', 'Youliang Zhang'] | 2023-05-09 | null | null | null | null | ['human-object-interaction-detection', 'group-activity-recognition'] | ['computer-vision', 'computer-vision'] | [-6.29810318e-02 -1.28235772e-01 1.92441046e-01 -5.01352191e-01
2.45227531e-01 -3.74303699e-01 1.04569066e+00 -3.02035771e-02
-6.23532176e-01 2.53271312e-01 8.48086536e-01 1.96087763e-01
-3.21549028e-01 -8.46355796e-01 -5.06444752e-01 -6.06663048e-01
-2.78829634e-01 5.64073384e-01 4.18961763e-01 -1.08891847... | [8.214689254760742, 0.5870494246482849] |
4beb1297-4b7d-474f-b18b-910aff577c16 | simplifying-model-based-rl-learning | 2209.08466 | null | https://arxiv.org/abs/2209.08466v3 | https://arxiv.org/pdf/2209.08466v3.pdf | Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective | While reinforcement learning (RL) methods that learn an internal model of the environment have the potential to be more sample efficient than their model-free counterparts, learning to model raw observations from high dimensional sensors can be challenging. Prior work has addressed this challenge by learning low-dimens... | ['Ruslan Salakhutdinov', 'Sergey Levine', 'Benjamin Eysenbach', 'Homanga Bharadhwaj', 'Raj Ghugare'] | 2022-09-18 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 1.50317222e-01 4.38982189e-01 -8.26806068e-01 -2.46670172e-01
-1.23237181e+00 -6.08277500e-01 6.77676916e-01 1.48623526e-01
-7.32856810e-01 9.98382688e-01 2.23144591e-01 -3.78887206e-01
-2.34769478e-01 -6.72124028e-01 -1.01681900e+00 -6.33801341e-01
-2.17428967e-01 7.83234358e-01 -1.97948471e-01 3.43886673... | [4.1476664543151855, 2.2184669971466064] |
b96d3bb6-fd67-4578-a5c9-547cb737868b | exploring-novel-prognostic-biomarkers-and | 2306.16184 | null | https://arxiv.org/abs/2306.16184v1 | https://arxiv.org/pdf/2306.16184v1.pdf | Exploring novel prognostic biomarkers and biologic processes involved in NASH, cirrhosis and HCC based on survival analysis using systems biology approach | There is an unmet need to develop medications or drug combinations which can stop advancement of NASH to liver cirrhosis and HCC. Therefore, identifying key biomarkers based on overall survival and the exploring biological processes involved in the pathogenesis and progression of NASH toward cirrhosis and HCC to improv... | ['Sedigheh Behrouzifar'] | 2023-06-28 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.52480519e-01 -5.72426856e-01 -1.11604847e-01 -2.94953566e-02
-1.03614651e-01 -4.80723441e-01 4.27528750e-03 2.96447605e-01
2.08320439e-01 7.85647929e-01 4.21707243e-01 -5.02992451e-01
-2.71499753e-01 -9.04510438e-01 -5.87069942e-03 -1.25526392e+00
-8.96072507e-01 2.29787767e-01 -3.11768919e-01 -4.59150523... | [5.9919915199279785, 5.675660133361816] |
09e80ae1-c978-4958-b85a-8a5dde0198d2 | a-continuous-relaxation-of-beam-search-for | 1708.00111 | null | http://arxiv.org/abs/1708.00111v2 | http://arxiv.org/pdf/1708.00111v2.pdf | A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models | Beam search is a desirable choice of test-time decoding algorithm for neural
sequence models because it potentially avoids search errors made by simpler
greedy methods. However, typical cross entropy training procedures for these
models do not directly consider the behaviour of the final decoding method. As
a result, f... | ['Taylor Berg-Kirkpatrick', 'Chris Dyer', 'Kartik Goyal', 'Graham Neubig'] | 2017-08-01 | null | null | null | null | ['ccg-supertagging'] | ['natural-language-processing'] | [ 3.83485734e-01 4.55112725e-01 -4.16447148e-02 -5.39633811e-01
-1.27833688e+00 -5.91809988e-01 6.23632550e-01 2.82115936e-01
-6.95014834e-01 9.98378217e-01 2.58023548e-03 -6.67211652e-01
1.40669465e-01 -5.74440718e-01 -8.48000705e-01 -7.44925916e-01
5.85213676e-02 6.85595453e-01 -3.70446295e-02 9.77666825... | [11.674718856811523, 9.569070816040039] |
c0ad57d5-8a2c-434b-91b0-1cfee5a5f427 | myriad-a-multi-array-room-acoustic-database | 2301.13057 | null | https://arxiv.org/abs/2301.13057v2 | https://arxiv.org/pdf/2301.13057v2.pdf | MYRiAD: A Multi-Array Room Acoustic Database | In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another co... | ['Toon van Waterschoot', 'Maja Taseska', 'Randall Ali', 'Thomas Dietzen'] | 2023-01-30 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 7.10359514e-02 -5.81895888e-01 1.20279217e+00 -3.45826484e-02
-1.19035149e+00 -7.22674012e-01 1.40619963e-01 -2.55289793e-01
-3.16068441e-01 2.06437930e-01 4.15956944e-01 -3.95368785e-01
-1.84706658e-01 -2.47626781e-01 -3.53434771e-01 -9.77405250e-01
-3.18764597e-01 -1.77534923e-01 1.80717781e-01 -8.84175859... | [15.112653732299805, 5.783239841461182] |
f63882c9-2ef0-42b1-8569-85e0803c0974 | deepscreen-high-performance-drug-target | null | null | https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc03414e#!divAbstract | https://pubs.rsc.org/en/content/articlepdf/2020/sc/c9sc03414e | DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations | The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug–target int... | ['Rengul Cetin-Atalay', 'Esra Nalbat', 'Volkan Atalay', 'Tunca Dogan', 'Ahmet Sureyya Rifaioglu', 'Maria Jesus Martin'] | 2020-01-08 | null | null | null | chemical-science-2020-1 | ['molecular-docking'] | ['medical'] | [ 1.14129290e-01 -3.90601724e-01 -4.81406122e-01 -1.53059676e-01
-6.88558042e-01 -6.88479006e-01 3.23501408e-01 6.49638414e-01
-2.87712157e-01 1.34677458e+00 -2.22521409e-01 -6.30626142e-01
-2.13764265e-01 -6.19189620e-01 -8.43382239e-01 -9.38681066e-01
-2.21911848e-01 6.89389765e-01 8.47058594e-02 -1.63135171... | [5.054746150970459, 5.662865161895752] |
818c0f0e-0256-4928-826b-5bbba7c70174 | kilm-knowledge-injection-into-encoder-decoder | 2302.09170 | null | https://arxiv.org/abs/2302.09170v1 | https://arxiv.org/pdf/2302.09170v1.pdf | KILM: Knowledge Injection into Encoder-Decoder Language Models | Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection into Language Models (KILM), a novel approach that injects entity-related knowledge into encoder-decoder PLMs, via a generative knowledge infi... | ['Dilek Hakkani-Tür', 'Yang Liu', 'Aishwarya Padmakumar', 'Devamanyu Hazarika', 'Mahdi Namazifar', 'Yan Xu'] | 2023-02-17 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [ 7.45166242e-02 8.00981343e-01 -4.88583863e-01 -4.91678342e-02
-9.99549448e-01 -5.25358737e-01 9.92137074e-01 1.98465288e-02
-5.93193769e-01 9.41888452e-01 6.03963733e-01 -1.71282113e-01
1.84647575e-01 -4.93482322e-01 -9.16703284e-01 -7.77261183e-02
-6.33997694e-02 6.91960931e-01 2.47810811e-01 -1.32757321... | [10.594982147216797, 8.26716423034668] |
0710e70c-8b5c-46ae-b3c4-e15f243764df | computing-expected-motif-counts-for | 2305.01089 | null | https://arxiv.org/abs/2305.01089v1 | https://arxiv.org/pdf/2305.01089v1.pdf | Computing Expected Motif Counts for Exchangeable Graph Generative Models | Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure applies for generative mixture models of the type used in neural and B... | ['Oliver Schulte'] | 2023-05-01 | null | null | null | null | ['type'] | ['speech'] | [ 3.24829996e-01 2.58464098e-01 -3.71444464e-01 -4.40681487e-01
-3.77305359e-01 -5.14828265e-01 4.84319866e-01 2.06362978e-01
-3.13074499e-01 8.26003671e-01 -1.18678704e-01 -5.54690778e-01
-1.45328194e-01 -9.24974740e-01 -7.12255120e-01 -7.28815258e-01
-5.56949079e-01 7.19046772e-01 2.27745190e-01 4.58399624... | [6.92313289642334, 4.487251281738281] |
3e7a7aed-515e-498b-8147-24a72bfa0e64 | responsible-task-automation-empowering-large | 2306.01242 | null | https://arxiv.org/abs/2306.01242v1 | https://arxiv.org/pdf/2306.01242v1.pdf | Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators | The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a promising prospect in automatically completing tasks upon user instructions, functioning as brain-like coordinators. The associated risks will be revealed as we delegate an increa... | ['Yan Lu', 'Wenxuan Xie', 'Xiaoyi Zhang', 'Zhizheng Zhang'] | 2023-06-02 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.39090443e-01 6.20903075e-01 1.06445588e-02 -4.12133306e-01
-4.69370008e-01 -7.49177575e-01 7.32467532e-01 -2.61696994e-01
-4.35286105e-01 4.68538642e-01 2.31664523e-01 -4.95076060e-01
-1.93106338e-01 -4.41364616e-01 -5.32113254e-01 -3.02171618e-01
1.00063078e-01 4.87256378e-01 -2.15627760e-01 -9.36334953... | [10.499284744262695, 7.737028121948242] |
d7a4b79e-650a-4e4c-baaf-d8ba48b255ce | threshnet-segmentation-refinement-inspired-by | 2211.06560 | null | https://arxiv.org/abs/2211.06560v2 | https://arxiv.org/pdf/2211.06560v2.pdf | ThreshNet: Segmentation Refinement Inspired by Region-Specific Thresholding | We present ThreshNet, a post-processing method to refine the output of neural networks designed for binary segmentation tasks. ThreshNet uses the confidence map produced by a base network along with global and local patch information to significantly improve the performance of even state-of-the-art methods. Binary segm... | ['Daniel Kifer', 'Chaopeng Shen', 'Savinay Nagendra'] | 2022-11-12 | null | null | null | null | ['saliency-detection'] | ['computer-vision'] | [ 6.60681427e-01 3.31872046e-01 -3.09332341e-01 -6.45955861e-01
-7.55506217e-01 -4.55795497e-01 2.76390791e-01 3.73508453e-01
-5.74596941e-01 4.94023234e-01 -1.83289781e-01 -2.95749575e-01
3.66911918e-01 -6.83795393e-01 -9.53049004e-01 -3.71890306e-01
8.01131129e-02 4.57909942e-01 1.34057939e+00 -2.18586281... | [9.540233612060547, 0.2643972933292389] |
edb3ea14-d99f-4a1c-8488-9f68543d9e68 | abnormal-event-detection-in-videos-using-1 | 1708.09644 | null | http://arxiv.org/abs/1708.09644v1 | http://arxiv.org/pdf/1708.09644v1.pdf | Abnormal Event Detection in Videos using Generative Adversarial Nets | In this paper we address the abnormality detection problem in crowded scenes.
We propose to use Generative Adversarial Nets (GANs), which are trained using
normal frames and corresponding optical-flow images in order to learn an
internal representation of the scene normality. Since our GANs are trained with
only normal... | ['Moin Nabi', 'Mahdyar Ravanbakhsh', 'Lucio Marcenaro', 'Enver Sangineto', 'Carlo Regazzoni', 'Nicu Sebe'] | 2017-08-31 | null | null | null | null | ['abnormal-event-detection-in-video', 'semi-supervised-anomaly-detection', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.79061395e-01 7.33351111e-02 5.66484511e-01 2.44769175e-02
-1.89812377e-01 -2.98818409e-01 5.56318879e-01 -1.48769364e-01
-2.80009389e-01 7.86280513e-01 5.10582179e-02 2.01302722e-01
6.42170787e-01 -8.85801196e-01 -7.26210296e-01 -7.33700156e-01
5.38846441e-02 2.24975333e-01 4.58723426e-01 -7.05182552... | [7.870909214019775, 1.5821926593780518] |
e255e841-ca3a-4047-8eb1-ea234277723f | robust-transformer-with-locality-inductive | 2301.11553 | null | https://arxiv.org/abs/2301.11553v1 | https://arxiv.org/pdf/2301.11553v1.pdf | Robust Transformer with Locality Inductive Bias and Feature Normalization | Vision transformers have been demonstrated to yield state-of-the-art results on a variety of computer vision tasks using attention-based networks. However, research works in transformers mostly do not investigate robustness/accuracy trade-off, and they still struggle to handle adversarial perturbations. In this paper, ... | ['Shahriar Baradaran Shokouhi', 'Hojat Asgarian Dehkordi', 'Hossein Kashiani', 'Omid Nejati Manzari'] | 2023-01-27 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 5.37329540e-02 -4.57942039e-01 6.13521188e-02 -9.21604857e-02
-5.71678817e-01 -5.89655161e-01 6.62363350e-01 -3.84425461e-01
-3.76845181e-01 4.74568516e-01 1.42116278e-01 -4.57672238e-01
-6.78777918e-02 -5.79485476e-01 -8.69644105e-01 -8.73688936e-01
1.32148966e-01 -4.07174945e-01 4.81221735e-01 -2.70409197... | [5.4156928062438965, 7.915650844573975] |
7f9bcac8-cdbe-4f85-809e-c0dc61f5085f | linguistic-information-in-neural-semantic | null | null | https://aclanthology.org/W19-0504 | https://aclanthology.org/W19-0504.pdf | Linguistic Information in Neural Semantic Parsing with Multiple Encoders | Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown tha... | ['Rik van Noord', 'Antonio Toral', 'Johan Bos'] | 2019-05-01 | null | null | null | ws-2019-5 | ['drs-parsing'] | ['natural-language-processing'] | [ 4.92980808e-01 3.54275674e-01 -2.58920550e-01 -5.41751862e-01
-1.07114899e+00 -5.81721187e-01 7.28104413e-01 2.12858200e-01
-5.20783305e-01 8.73407960e-01 7.21368074e-01 -6.25708818e-01
2.92753875e-01 -7.93465972e-01 -8.08808804e-01 -1.51728109e-01
2.93669283e-01 4.80526268e-01 2.56752312e-01 -4.52320337... | [10.631903648376465, 9.307860374450684] |
85acee1a-c489-48e7-9e8e-3524007f7618 | safe-exploration-in-markov-decision-processes-1 | 1205.4810 | null | https://arxiv.org/abs/1205.4810v3 | https://arxiv.org/pdf/1205.4810v3.pdf | Safe Exploration in Markov Decision Processes | In environments with uncertain dynamics exploration is necessary to learn how to perform well. Existing reinforcement learning algorithms provide strong exploration guarantees, but they tend to rely on an ergodicity assumption. The essence of ergodicity is that any state is eventually reachable from any other state by ... | ['Pieter Abbeel', 'Teodor Mihai Moldovan'] | 2012-05-22 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-5.80218248e-02 3.46838325e-01 -4.85531509e-01 2.11284369e-01
-4.58545625e-01 -7.09388733e-01 6.85277283e-01 1.09741390e-02
-5.48764765e-01 1.40600085e+00 -1.34811178e-01 -7.22598195e-01
-5.39259970e-01 -1.13320374e+00 -7.70173371e-01 -1.07462037e+00
-6.38813734e-01 6.28777146e-01 3.39327335e-01 -4.40129429... | [4.4851555824279785, 2.1935787200927734] |
8f13ee0b-d96c-4309-8586-f86d428dd2a6 | pslt-a-light-weight-vision-transformer-with | 2304.03481 | null | https://arxiv.org/abs/2304.03481v1 | https://arxiv.org/pdf/2304.03481v1.pdf | PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift | Vision Transformer (ViT) has shown great potential for various visual tasks due to its ability to model long-range dependency. However, ViT requires a large amount of computing resource to compute the global self-attention. In this work, we propose a ladder self-attention block with multiple branches and a progressive ... | ['Qi Tian', 'Yutong Lu', 'Wei-Shi Zheng', 'Gaojie Wu'] | 2023-04-07 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 8.37274715e-02 -2.76953608e-01 1.99085057e-01 -4.14924920e-02
-2.67419368e-01 3.89431678e-02 3.41345847e-01 -8.77148414e-04
-5.03390491e-01 4.73519683e-01 -1.40751138e-01 -1.18845649e-01
7.21485987e-02 -9.42882299e-01 -7.40685880e-01 -9.40378904e-01
3.12028021e-01 1.14604905e-01 6.52913451e-01 -2.15896651... | [10.853123664855957, -1.6956642866134644] |
3f9f0f89-ac6e-40ce-87e0-fd16ba25ba6c | deep-intra-image-contrastive-learning-for | 2302.04607 | null | https://arxiv.org/abs/2302.04607v1 | https://arxiv.org/pdf/2302.04607v1.pdf | Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search | Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only person bounding-box annotations. Recently, the idea of contrastive learning is initially applied to weakly supervised person search, where two common contrast strategies are memory-based contrast and intra... | ['Xuelong Li', 'Zhuang Shao', 'Hanqing Sun', 'Jiale Cao', 'Yanwei Pang', 'Jiabei Wang'] | 2023-02-09 | null | null | null | null | ['pedestrian-detection', 'person-search'] | ['computer-vision', 'computer-vision'] | [-1.53807998e-01 -4.84554917e-01 -2.65463412e-01 -3.84237051e-01
-6.63910568e-01 -2.86671251e-01 7.19984531e-01 -2.39491984e-01
-9.50592756e-01 7.17987716e-01 3.15939486e-01 2.54379958e-01
7.13979229e-02 -6.57718122e-01 -5.97486317e-01 -6.65721297e-01
5.57971671e-02 4.61151063e-01 5.45348108e-01 -2.67643183... | [14.851670265197754, 0.7884725332260132] |
f73cbf79-20ec-4bb6-ba01-97a4c1c45f28 | an-object-slam-framework-for-association | 2305.07299 | null | https://arxiv.org/abs/2305.07299v1 | https://arxiv.org/pdf/2305.07299v1.pdf | An Object SLAM Framework for Association, Mapping, and High-Level Tasks | Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensi... | ['Jian Zhang', 'Xin Chen', 'Wenkai Sun', 'Zhiqiang Deng', 'Delong Zhu', 'Yunzhou Zhang', 'Yanmin Wu'] | 2023-05-12 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [ 1.16598666e-01 -3.19128990e-01 -2.15241432e-01 -8.54074717e-01
-5.47905087e-01 -4.28077281e-01 5.16047597e-01 3.75896186e-01
-4.53473479e-01 5.66903412e-01 -1.70975521e-01 2.14089781e-01
-8.63652408e-01 -6.44004643e-01 -8.32163513e-01 -4.56309855e-01
-2.02421859e-01 1.04036164e+00 3.04325253e-01 3.03896740... | [7.230001449584961, -2.181178092956543] |
5906942b-1741-4d51-90d2-35318357ccdc | beyond-low-frequency-information-in-graph | 2101.00797 | null | https://arxiv.org/abs/2101.00797v1 | https://arxiv.org/pdf/2101.00797v1.pdf | Beyond Low-frequency Information in Graph Convolutional Networks | Graph neural networks (GNNs) have been proven to be effective in various network-related tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which gives rise to one fundamental question: is the low-frequency information all we need in the real world applications? In this paper, we firs... | ['HuaWei Shen', 'Chuan Shi', 'Xiao Wang', 'Deyu Bo'] | 2021-01-04 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-3.56303565e-02 1.37599781e-01 -3.25023770e-01 -1.16234370e-01
1.01945512e-01 -1.16103984e-01 4.33673143e-01 2.80658364e-01
-3.07538241e-01 4.98352319e-01 5.70904575e-02 -2.94120193e-01
-4.84795272e-01 -1.30835402e+00 -6.20309591e-01 -7.96331823e-01
-7.55913496e-01 -5.29121533e-02 6.32678628e-01 -7.43212998... | [7.075004577636719, 6.184177875518799] |
fe0e89c4-1912-48d8-94c1-6413738c60a7 | compress-self-supervised-learning-by | 2010.14713 | null | https://arxiv.org/abs/2010.14713v1 | https://arxiv.org/pdf/2010.14713v1.pdf | CompRess: Self-Supervised Learning by Compressing Representations | Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller models. As a result, the gap between supervised and self-supervised learning has been greatly reduced for larger models. In this work, inste... | ['Hamed Pirsiavash', 'Ajinkya Tejankar', 'Soroush Abbasi Koohpayegani'] | 2020-10-28 | null | http://proceedings.neurips.cc/paper/2020/hash/975a1c8b9aee1c48d32e13ec30be7905-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/975a1c8b9aee1c48d32e13ec30be7905-Paper.pdf | neurips-2020-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.08444266e-01 4.66505349e-01 -5.95775306e-01 -7.85783350e-01
-4.48511422e-01 -2.32098550e-01 4.15388465e-01 2.48416692e-01
-6.23836339e-01 6.40217245e-01 2.34561577e-01 -1.15340114e-01
1.91709161e-01 -7.11462259e-01 -8.10372651e-01 -4.66126323e-01
8.75843838e-02 6.41138554e-01 -1.64766274e-02 5.58949746... | [9.476737976074219, 2.702094078063965] |
e6f11218-0e25-4299-9840-74885360cd5d | convolutional-recurrent-neural-networks-for-4 | 1703.02317 | null | http://arxiv.org/abs/1703.02317v1 | http://arxiv.org/pdf/1703.02317v1.pdf | Convolutional Recurrent Neural Networks for Bird Audio Detection | Bird sounds possess distinctive spectral structure which may exhibit small
shifts in spectrum depending on the bird species and environmental conditions.
In this paper, we propose using convolutional recurrent neural networks on the
task of automated bird audio detection in real-life environments. In the
proposed metho... | ['EmreÇakır', 'Sharath Adavanne', 'Tuomas Virtanen', 'Konstantinos Drossos', 'Giambattista Parascandolo'] | 2017-03-07 | null | null | null | null | ['bird-audio-detection'] | ['audio'] | [ 7.12163448e-02 -8.36021781e-01 3.71180445e-01 -1.20346166e-01
-3.44338298e-01 -5.72909296e-01 -4.22183089e-02 1.66243896e-01
-6.23101711e-01 9.17093754e-02 4.22934026e-01 2.50197709e-01
-9.70287845e-02 -5.68332970e-01 -3.30007732e-01 -3.65757823e-01
-8.37305248e-01 -7.03310609e-01 3.20623189e-01 -3.08953494... | [15.220247268676758, 5.283101558685303] |
2df0b15b-c560-4e54-aa3d-9aafbd53cc35 | improving-weakly-supervised-sound-event-1 | 2303.05678 | null | https://arxiv.org/abs/2303.05678v1 | https://arxiv.org/pdf/2303.05678v1.pdf | Improving Weakly Supervised Sound Event Detection with Causal Intervention | Existing weakly supervised sound event detection (WSSED) work has not explored both types of co-occurrences simultaneously, i.e., some sound events often co-occur, and their occurrences are usually accompanied by specific background sounds, so they would be inevitably entangled, causing misclassification and biased loc... | ['Yuexian Zou', 'Yujun Wang', 'Fan Cui', 'Dongchao Yang', 'Yifei Xin'] | 2023-03-10 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 4.92999762e-01 -4.09882665e-02 -1.67021021e-01 -3.43149483e-01
-8.41795564e-01 -3.77418756e-01 4.86478657e-01 2.26321489e-01
-7.48556107e-02 6.64701879e-01 6.72702014e-01 -7.93824568e-02
-1.29793555e-01 -5.88205874e-01 -1.00708830e+00 -7.36508071e-01
-4.70322043e-01 -3.55492502e-01 6.57086074e-01 3.70008558... | [8.894486427307129, 0.8135494589805603] |
3ac7a656-215b-4450-ae4c-efe071d7ba22 | sportsmot-a-large-multi-object-tracking | 2304.05170 | null | https://arxiv.org/abs/2304.05170v2 | https://arxiv.org/pdf/2304.05170v2.pdf | SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes | Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking data... | ['LiMin Wang', 'Gangshan Wu', 'Yichun Yang', 'Xiaoyu Zhao', 'Chenkai Zeng', 'Yutao Cui'] | 2023-04-11 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-2.80582309e-01 -6.74164176e-01 -4.18532073e-01 9.98418406e-02
-6.98847353e-01 -7.36525416e-01 2.69186884e-01 -3.76034491e-02
-4.87470239e-01 5.50996900e-01 1.32464439e-01 7.87186399e-02
-1.32572636e-01 -4.18069243e-01 -7.56540656e-01 -6.18343174e-01
-2.71201521e-01 5.86275041e-01 1.00249493e+00 -3.51652652... | [6.36254358291626, -2.0535950660705566] |
47d481e6-5ea8-48ce-8ba5-098ce0b9b410 | a-multilingual-evaluation-of-ner-robustness | 2305.18933 | null | https://arxiv.org/abs/2305.18933v1 | https://arxiv.org/pdf/2305.18933v1.pdf | A Multilingual Evaluation of NER Robustness to Adversarial Inputs | Adversarial evaluations of language models typically focus on English alone. In this paper, we performed a multilingual evaluation of Named Entity Recognition (NER) in terms of its robustness to small perturbations in the input. Our results showed the NER models we explored across three languages (English, German and H... | ['Sowmya Vajjala', 'Akshay Srinivasan'] | 2023-05-30 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-2.41490349e-01 1.72556981e-01 4.33629602e-01 -2.73798585e-01
-1.01627743e+00 -1.22992146e+00 8.00911605e-01 1.21504746e-01
-1.03371716e+00 1.08771837e+00 5.08692443e-01 -3.87117624e-01
2.34100863e-01 -7.84694016e-01 -7.12285519e-01 -2.57034868e-01
5.96911311e-02 3.95105213e-01 2.86855549e-01 -6.58818543... | [9.841837882995605, 9.695231437683105] |
2d878105-a086-44fc-880e-0c4a6e50fefc | skin-cancer-reorganization-and-classification | 1703.00534 | null | http://arxiv.org/abs/1703.00534v1 | http://arxiv.org/pdf/1703.00534v1.pdf | Skin cancer reorganization and classification with deep neural network | As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
early detection and recarbonization strategy is critical for melanoma therapy.
However, well-trained dermatologists dominant the diagnostic accuracy. In order
to solve this problem, many effort focus on developing automatic image analysis
systems.... | ['Hao Chang'] | 2017-03-01 | null | null | null | null | ['melanoma-diagnosis', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 2.53049701e-01 -1.00969456e-01 -2.40100771e-01 4.99956980e-02
-2.94930756e-01 -1.00065820e-01 2.06803337e-01 -1.90681927e-02
-4.09546643e-01 5.45413852e-01 -2.04810172e-01 -5.34919858e-01
1.05844662e-01 -1.10367560e+00 -1.65650845e-01 -8.14395964e-01
2.82354116e-01 3.36695239e-02 4.53449935e-01 -8.45978260... | [15.663595199584961, -3.011268377304077] |
089072da-ec05-4529-bf25-cf7901ef1d9f | satellite-dynamics-toolbox-library-a-tool-to | 2303.15872 | null | https://arxiv.org/abs/2303.15872v1 | https://arxiv.org/pdf/2303.15872v1.pdf | Satellite Dynamics Toolbox Library: a tool to model multi-body space systems for robust control synthesis and analysis | The level of maturity reached by robust control theory techniques nowadays contributes to a considerable minimization of the development time of an end-to-end control design of a spacecraft system. The advantage offered by this framework is twofold: all system uncertainties can be included from the very beginning of th... | ['Franca Somers', 'Ervan Kassarian', 'Daniel Alazard', 'Francesco Sanfedino'] | 2023-03-28 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-1.38761923e-01 1.57306984e-01 2.27082044e-01 4.98696417e-02
-2.47215673e-01 -1.03370476e+00 8.59078765e-01 -3.22374366e-02
-3.72702517e-02 1.17344570e+00 -6.17525876e-01 -4.26828206e-01
-7.28333473e-01 -6.31994188e-01 -6.48137629e-01 -8.24828565e-01
-2.81090647e-01 6.58265710e-01 4.01769318e-02 -5.68456411... | [5.370342254638672, 2.3436851501464844] |
b615da15-b7a8-4d32-be55-52bb030f124c | perfect-match-a-simple-method-for-learning | 1810.00656 | null | https://arxiv.org/abs/1810.00656v5 | https://arxiv.org/pdf/1810.00656v5.pdf | Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks | Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment $t_... | ['Walter Karlen', 'Patrick Schwab', 'Lorenz Linhardt'] | 2018-10-01 | perfect-match-a-simple-method-for-learning-1 | https://openreview.net/forum?id=rkxt8oC9FQ | https://openreview.net/pdf?id=rkxt8oC9FQ | iclr-2019-5 | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.27015328e-01 4.13192421e-01 -8.35721493e-01 -5.56955218e-01
-6.89629614e-01 -2.64330775e-01 8.08098853e-01 1.94352135e-01
-6.27684116e-01 1.56233442e+00 8.51781785e-01 -1.08152962e+00
-6.06224775e-01 -8.78914475e-01 -1.00205958e+00 -6.61830664e-01
-3.75128627e-01 6.15030885e-01 -6.83496237e-01 -1.36618288... | [8.082780838012695, 5.413482189178467] |
59760a98-6bec-4abb-81c2-54af1c9d0c5e | deep-kernel-learning-via-random-fourier | 1910.02660 | null | https://arxiv.org/abs/1910.02660v1 | https://arxiv.org/pdf/1910.02660v1.pdf | Deep Kernel Learning via Random Fourier Features | Kernel learning methods are among the most effective learning methods and have been vigorously studied in the past decades. However, when tackling with complicated tasks, classical kernel methods are not flexible or "rich" enough to describe the data and hence could not yield satisfactory performance. In this paper, vi... | ['Jiaxuan Xie', 'Kaijie Wang', 'Fanghui Liu', 'Xiaolin Huang'] | 2019-10-07 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-5.30970275e-01 -4.62462872e-01 -2.25562334e-01 -4.78179961e-01
-3.79567057e-01 -4.82225984e-01 2.67759085e-01 4.99522984e-02
-4.84893680e-01 3.73588502e-01 -9.61125642e-02 -4.08023447e-01
-2.66308159e-01 -8.62425983e-01 -5.49637437e-01 -7.90871620e-01
-1.63769379e-01 -3.69833894e-02 6.01892710e-01 -3.12933028... | [8.970918655395508, 2.439159631729126] |
bd8eb5fe-618c-4220-aabc-f4670850b386 | primitive3d-3d-object-dataset-synthesis-from | 2205.12627 | null | https://arxiv.org/abs/2205.12627v1 | https://arxiv.org/pdf/2205.12627v1.pdf | Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives | Numerous advancements in deep learning can be attributed to the access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collection cost. To alleviate this issue, we propose a cost-effective method for automatically generating a l... | ['Yeow Meng Chee', 'Yuwei Wu', 'Zekun Tong', 'Henghui Ding', 'Xinke Li'] | 2022-05-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Primitive3D_3D_Object_Dataset_Synthesis_From_Randomly_Assembled_Primitives_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Primitive3D_3D_Object_Dataset_Synthesis_From_Randomly_Assembled_Primitives_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-classification'] | ['computer-vision'] | [ 2.53962934e-01 1.90666512e-01 -2.15622131e-02 -4.86019045e-01
-8.92346203e-01 -4.13204640e-01 4.74029124e-01 -1.71502009e-02
-3.14168960e-01 4.43959087e-01 -1.24839291e-01 -1.46276161e-01
1.73451409e-01 -8.27006638e-01 -8.63330901e-01 -7.37255275e-01
3.17960858e-01 8.46581697e-01 3.53450835e-01 2.32758522... | [8.08565902709961, -3.0518105030059814] |
41d3249a-a0b6-415e-a5c4-e8aca8d91b97 | dimensionality-reduction-for-general-kde-mode | 2305.18755 | null | https://arxiv.org/abs/2305.18755v3 | https://arxiv.org/pdf/2305.18755v3.pdf | Dimensionality Reduction for General KDE Mode Finding | Finding the mode of a high dimensional probability distribution $D$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $D$ is represented as a mixture model or kernel density estimate, although few algorithmic result... | ['Cas Widdershoven', 'Christopher Musco', 'Xinyu Luo'] | 2023-05-30 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-4.10953224e-01 -1.36404425e-01 -8.18282589e-02 -5.99056818e-02
-8.83727312e-01 -8.58557045e-01 -7.22089633e-02 2.54034609e-01
-4.69945997e-01 5.79735637e-01 -4.67483819e-01 -6.83440149e-01
-6.63562357e-01 -9.36426938e-01 -6.08881831e-01 -8.49772871e-01
-5.67611158e-01 5.46062946e-01 2.70241350e-01 1.49789602... | [7.349287986755371, 4.181121349334717] |
dd509644-f755-4208-b17c-4dc7986a698c | homological-neural-networks-a-sparse | 2306.15337 | null | https://arxiv.org/abs/2306.15337v1 | https://arxiv.org/pdf/2306.15337v1.pdf | Homological Neural Networks: A Sparse Architecture for Multivariate Complexity | The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability. In this study, we apply advanced network-based information filtering techniques to de... | ['Tomaso Aste', 'Antonio Briola', 'Yuanrong Wang'] | 2023-06-27 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 2.66365916e-01 3.66421528e-02 -2.85907179e-01 -1.93720460e-01
-1.28481448e-01 -5.15697896e-01 6.81711555e-01 4.04170066e-01
-2.99371123e-01 7.52672315e-01 -1.50816932e-01 -8.12357903e-01
-5.98034739e-01 -7.73878098e-01 -7.69964635e-01 -5.91526866e-01
-5.97509623e-01 4.35194433e-01 -6.35383949e-02 -2.94610143... | [6.601881980895996, 5.775589942932129] |
677246ba-c3eb-426f-a9ed-1d1a51189467 | bevstereo-accurate-depth-estimation-in-multi | 2304.04185 | null | https://arxiv.org/abs/2304.04185v1 | https://arxiv.org/pdf/2304.04185v1.pdf | BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo | Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal multi-view stereo (MVS) technology is the natural knowledge for tackling this ambiguity. However, traditional attempts of MVS has two limitati... | ['Li Xiao', 'Zheng Ge', 'Han Bao', 'Jianjian Sun', 'Jinrong Yang', 'Yinhao Li'] | 2023-04-09 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 1.87103704e-01 -1.82078317e-01 -2.01235134e-02 -7.17326859e-03
-4.14440483e-01 -5.91047704e-01 7.56807983e-01 -1.72233149e-01
-3.40796381e-01 3.02831203e-01 5.54822646e-02 -9.62713733e-02
2.45546624e-02 -7.97416449e-01 -5.68744302e-01 -5.83456039e-01
1.99993625e-01 2.25473985e-01 1.01569796e+00 -4.22989368... | [8.08616828918457, -2.229245185852051] |
13051aa7-e796-42ef-980a-47936e05f220 | confidence-aware-levenberg-marquardt | 1609.01524 | null | http://arxiv.org/abs/1609.01524v1 | http://arxiv.org/pdf/1609.01524v1.pdf | Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution | Motion estimation across low-resolution frames and the reconstruction of
high-resolution images are two coupled subproblems of multi-frame
super-resolution. This paper introduces a new joint optimization approach for
motion estimation and image reconstruction to address this interdependence. Our
method is formulated vi... | ['Thomas Köhler', 'Cosmin Bercea', 'Andreas Maier'] | 2016-09-06 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 1.63496569e-01 -4.00608063e-01 -1.22167729e-01 -1.51083902e-01
-1.29012275e+00 -1.03098422e-01 3.37044626e-01 -5.94273269e-01
-5.03525019e-01 8.88157308e-01 3.26935589e-01 4.27355260e-01
-1.29910991e-01 -2.70083934e-01 -5.12355685e-01 -9.05915618e-01
1.42111450e-01 -1.13730639e-01 4.56071615e-01 6.76160231... | [11.109798431396484, -2.175032377243042] |
52964099-8913-46b9-901b-a59f07399be9 | speaker-verification-using-attentive-multi | 2306.00426 | null | https://arxiv.org/abs/2306.00426v1 | https://arxiv.org/pdf/2306.00426v1.pdf | Speaker verification using attentive multi-scale convolutional recurrent network | In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial information and global sequential information from the input speech recordings. In the proposed method, logarithm Mel spectrum is extracted from ... | ['Qisheng Huang', 'Wenchang Cao', 'Zhongjie Jiang', 'Yanxiong Li'] | 2023-06-01 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 1.01313576e-01 -3.52129221e-01 -4.72691432e-02 -4.86625254e-01
-1.04324305e+00 -3.22805315e-01 3.08137119e-01 -1.41281083e-01
-4.94291484e-01 3.06306452e-01 3.51384491e-01 -3.10826987e-01
1.11992501e-01 -3.02256376e-01 -3.69369835e-01 -6.96799576e-01
-4.83034812e-02 -8.98651928e-02 1.95536733e-01 -1.82981506... | [14.343430519104004, 6.067890644073486] |
138ecf85-4a40-4c96-b99b-aeab28db503b | hicem-a-high-coverage-emotion-model-for | 2206.07593 | null | https://arxiv.org/abs/2206.07593v1 | https://arxiv.org/pdf/2206.07593v1.pdf | HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence | As social robots and other intelligent machines enter the home, artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. To accomplish such efficacious interaction, the next-generation AEI need comprehensive human emotion models for t... | ['James Z. Wang', 'Benjamin Wortman'] | 2022-06-15 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-8.04248750e-02 4.36652690e-01 -1.73048928e-01 -6.18441701e-01
4.15184572e-02 -2.87876815e-01 2.59923697e-01 4.43890035e-01
-5.73384047e-01 4.67158735e-01 3.90661180e-01 1.40393123e-01
-3.34063247e-02 -4.87996548e-01 1.61085173e-01 -9.60251689e-02
-1.07125789e-01 2.96926647e-01 -6.38286889e-01 -6.38787806... | [13.018081665039062, 5.744825839996338] |
825314e7-ea7b-4d8f-ae20-1c89ba2bce1d | computing-and-exploiting-document-structure | 2211.03229 | null | https://arxiv.org/abs/2211.03229v1 | https://arxiv.org/pdf/2211.03229v1.pdf | Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions | Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case summarization dataset demonstrates that sentences se... | ['Diane Litman', 'Yang Zhong'] | 2022-11-06 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.74612057e-01 6.13486767e-01 -8.35546613e-01 -6.32784367e-01
-8.31592798e-01 -8.72368515e-01 9.63888645e-01 9.26763713e-01
-2.51791447e-01 7.44216740e-01 1.51873732e+00 -7.75420606e-01
-6.78879023e-01 -7.59031057e-01 -1.42270312e-01 -9.37232599e-02
1.94814935e-01 4.96779889e-01 3.04014981e-01 -5.67981958... | [12.11978530883789, 9.598570823669434] |
5883ad4a-4b8d-4e07-a3b6-4269d0898846 | computer-aided-recognition-and-assessment-of | 2201.11987 | null | https://arxiv.org/abs/2201.11987v2 | https://arxiv.org/pdf/2201.11987v2.pdf | Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications | Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images... | ['Jiao Yu', 'Aliona Dreglea', 'Jia Sun', 'Yanying Zhu', 'Kaixuan Guo', 'Dun Wang'] | 2022-01-28 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.35383242e-01 -1.97317719e-01 -3.83700728e-02 3.21257085e-01
-2.80634344e-01 -2.92890131e-01 -1.28898248e-01 5.36489785e-01
-2.18844011e-01 3.80670488e-01 6.39767498e-02 -1.37201026e-01
-3.43019813e-01 -7.74776220e-01 -3.06423992e-01 -1.20113051e+00
-3.36946517e-01 3.75529468e-01 6.62807167e-01 1.58202481... | [13.674081802368164, -2.7929961681365967] |
e13d9191-d8c9-4142-baf0-f6dd8df1976f | smiles-transformer-pre-trained-molecular | 1911.04738 | null | https://arxiv.org/abs/1911.04738v1 | https://arxiv.org/pdf/1911.04738v1.pdf | SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery | In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) are calculated through rule-based algorithms that map molecules to a sparse discrete space. However,... | ['Shoi Shi', 'Shion Honda', 'Hiroki R. Ueda'] | 2019-11-12 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 7.82507300e-01 -2.15213090e-01 -7.32119441e-01 -3.74683172e-01
-6.60643756e-01 -6.86985254e-01 7.52088547e-01 8.79484892e-01
-1.79362386e-01 9.01882708e-01 2.96716560e-02 -6.17492616e-01
-3.27179670e-01 -1.12890995e+00 -8.49855125e-01 -4.81142104e-01
-2.90226489e-01 4.67873335e-01 -2.33138911e-02 9.62665230... | [5.134660720825195, 5.779274940490723] |
36364e1b-075c-43e9-98e5-6aec9bd8a050 | srn-side-output-residual-network-for-object-1 | 1703.02243 | null | http://arxiv.org/abs/1703.02243v2 | http://arxiv.org/pdf/1703.02243v2.pdf | SRN: Side-output Residual Network for Object Symmetry Detection in the Wild | In this paper, we establish a baseline for object symmetry detection in
complex backgrounds by presenting a new benchmark and an end-to-end deep
learning approach, opening up a promising direction for symmetry detection in
the wild. The new benchmark, named Sym-PASCAL, spans challenges including
object diversity, multi... | ['Jie Chen', 'Jianbin Jiao', 'Qixiang Ye', 'Wei Ke', 'Guoying Zhao'] | 2017-03-07 | srn-side-output-residual-network-for-object-2 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ke_SRN_Side-output_Residual_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ke_SRN_Side-output_Residual_CVPR_2017_paper.pdf | cvpr-2017-7 | ['symmetry-detection'] | ['computer-vision'] | [ 2.92424858e-01 -2.62093339e-02 1.81996480e-01 -5.12481153e-01
-7.03049362e-01 -5.30653477e-01 3.19314718e-01 -5.22383690e-01
1.64148256e-01 9.14949998e-02 1.76295027e-01 1.21333294e-01
-1.09495498e-01 -4.58071381e-01 -1.06651914e+00 -5.64769685e-01
2.74795502e-01 3.16940308e-01 7.28087485e-01 -3.57854962... | [8.556533813476562, -1.7436587810516357] |
300547c9-fe33-4a1c-84e8-82f11e7c9e8b | texture-generation-using-graph-generative | 2206.08547 | null | https://arxiv.org/abs/2206.08547v2 | https://arxiv.org/pdf/2206.08547v2.pdf | Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering | Novel photo-realistic texture synthesis is an important task for generating novel scenes, including asset generation for 3D simulations. However, to date, these methods predominantly generate textured objects in 2D space. If we rely on 2D object generation, then we need to make a computationally expensive forward pass ... | ['Bradley Walls', 'Clayton T. Morrison', 'Dharma KC'] | 2022-06-17 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.21312118e-01 2.33582139e-01 4.98142898e-01 6.72265813e-02
-6.50272310e-01 -7.04217315e-01 7.89266229e-01 -3.35809827e-01
3.63184176e-02 7.91591644e-01 -3.79377246e-01 -2.69899547e-01
2.55471528e-01 -1.36111271e+00 -1.18806350e+00 -8.63799453e-01
2.71490335e-01 8.37624252e-01 3.68367821e-01 -2.35361293... | [9.148998260498047, -3.461620807647705] |
dd6be1d3-ec4c-4ba9-adc9-2a1afe9a8d8d | minimum-norm-method-for-linear-and-planar | 2106.03666 | null | https://arxiv.org/abs/2106.03666v1 | https://arxiv.org/pdf/2106.03666v1.pdf | Minimum Norm Method for Linear and Planar Sparse Arrays | Coprime and nested arrays are sparse arrays with enhanced degrees of freedom, which can be exploited in direction of arrival estimation using algorithms such as product processing, min processing, and MUSIC. This paper applies the minimum norm method for direction of arrival estimation. Comparison of the root mean squa... | ['Kaushallya Adhikari', 'Tyler M. Trosclair'] | 2021-06-07 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 4.51594472e-01 -4.65934277e-01 2.90430546e-01 1.25447229e-01
-8.60783815e-01 -8.09194803e-01 7.94130266e-02 3.02990172e-02
-3.15347314e-02 7.07698822e-01 4.75251079e-01 -3.92460674e-02
-9.94071960e-01 -4.74288523e-01 -3.52445513e-01 -1.02590942e+00
-5.54141521e-01 -1.47862479e-01 -2.35046998e-01 -1.40952803... | [6.496457099914551, 1.3351184129714966] |
55e243e9-151b-455f-bfa1-157b36bace30 | near-realtime-facial-animation-by-deep-3d | 2305.03216 | null | https://arxiv.org/abs/2305.03216v1 | https://arxiv.org/pdf/2305.03216v1.pdf | Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution | We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics-based simulation to a level of detail that closely approximates that of a reference-quality off-line simulator with much higher resolution ... | ['Eftychios Sifakis', 'Ken Museth', 'Jonathan Swartz', 'Byungsoo Kim', 'Doyub Kim', 'Matthew Cong', 'Sangeetha Grama Srinivasan', 'Hyojoon Park'] | 2023-05-05 | null | null | null | null | ['semantic-correspondence'] | ['computer-vision'] | [ 2.32778475e-01 3.99228454e-01 2.71043956e-01 -4.35586065e-01
-5.82407415e-01 -3.01775515e-01 7.99282491e-01 -2.51289070e-01
-2.05061182e-01 6.92770481e-01 2.89927218e-02 1.23801433e-01
-5.49191758e-02 -8.82404685e-01 -9.48669434e-01 -3.54527116e-01
-3.23309392e-01 7.28818476e-01 2.30242193e-01 -8.26931894... | [12.607062339782715, -0.4288526773452759] |
66aa5e0c-cdb0-4134-81a3-f2e9940e398e | a-fully-automated-pipeline-for-detection-and | 1703.06418 | null | http://arxiv.org/abs/1703.06418v1 | http://arxiv.org/pdf/1703.06418v1.pdf | A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes | We propose a fully-automated method for accurate and robust detection and
segmentation of potentially cancerous lesions found in the liver and in lymph
nodes. The process is performed in three steps, including organ detection,
lesion detection and lesion segmentation. Our method applies machine learning
techniques such... | ['Daniel L. Rubin', 'Yefeng Zheng', 'John W. Lambert', 'Dorin Comaniciu', 'Assaf Hoogi'] | 2017-03-19 | null | null | null | null | ['organ-detection'] | ['medical'] | [-1.61283731e-01 1.16899282e-01 -2.36379296e-01 -1.33840129e-01
-8.67367983e-01 -6.70422792e-01 3.76458913e-01 5.61136782e-01
-5.13536274e-01 5.22872627e-01 9.70744416e-02 -5.84770083e-01
8.80689770e-02 -6.74011528e-01 -3.26678455e-02 -9.41597700e-01
-7.19431639e-01 7.24142492e-01 3.07961583e-01 4.69161421... | [14.4744234085083, -2.6865508556365967] |
0b89b5e8-91f6-4603-894f-3d04a3fff943 | heterogeneous-information-network-based-1 | 2204.11849 | null | https://arxiv.org/abs/2204.11849v2 | https://arxiv.org/pdf/2204.11849v2.pdf | Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users | Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications. With the expansion of inclusive finance, recent attentions are paid to micro and small-sized enterprises (MSEs). Compared with large companie... | ['Guangwen Yang', 'Xi Zhang', 'Jiachen Shen', 'Yingsheng Ji', 'Zheng Zhang'] | 2022-04-24 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [-4.67758924e-01 1.30507261e-01 -2.74163276e-01 -2.72186130e-01
1.61479954e-02 -2.06044152e-01 4.32717085e-01 3.57543796e-01
-2.16209680e-01 4.00727302e-01 2.93636292e-01 -4.80763972e-01
-5.36117792e-01 -1.32229435e+00 -2.25676924e-01 -4.65889037e-01
-4.38216686e-01 4.19545531e-01 2.46648446e-01 -4.04554367... | [7.229859352111816, 6.072728157043457] |
13a5eeb4-5273-45dc-954a-770acab0ded9 | deeply-supervised-density-regression-for | 2011.03683 | null | https://arxiv.org/abs/2011.03683v2 | https://arxiv.org/pdf/2011.03683v2.pdf | Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images | Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance... | ['Hua Li', 'Mark A. Anastasio', 'Lilianna Solnica-Krezel', 'Kyaw Thu Minn', 'Shenghua He'] | 2020-11-07 | null | null | null | null | ['automatic-cell-counting'] | ['miscellaneous'] | [ 3.51379216e-01 -4.29449886e-01 4.14600194e-01 -3.31137031e-01
-6.02553487e-01 -1.53503090e-01 4.38933223e-01 2.85054743e-01
-9.55320954e-01 1.00384736e+00 -4.23312604e-01 -8.60764310e-02
2.70303607e-01 -6.64090395e-01 -4.34288383e-01 -1.07378864e+00
7.59750828e-02 6.83077753e-01 3.83895814e-01 4.93288904... | [14.749423027038574, -3.1843934059143066] |
541276ea-e497-4266-ab14-391591b17136 | dna-steganalysis-using-deep-recurrent-neural | 1704.08443 | null | http://arxiv.org/abs/1704.08443v3 | http://arxiv.org/pdf/1704.08443v3.pdf | DNA Steganalysis Using Deep Recurrent Neural Networks | Recent advances in next-generation sequencing technologies have facilitated
the use of deoxyribonucleic acid (DNA) as a novel covert channels in
steganography. There are various methods that exist in other domains to detect
hidden messages in conventional covert channels. However, they have not been
applied to DNA steg... | ['Byunghan Lee', 'Sunyoung Kwon', 'Sungroh Yoon', 'Ho Bae'] | 2017-04-27 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 1.18914104e+00 -5.69415689e-02 -1.35749310e-01 1.79170564e-01
-4.89158273e-01 -6.12202525e-01 5.26566327e-01 -1.31020352e-01
-2.53348529e-01 7.79525936e-01 1.00602970e-01 -7.19029844e-01
4.94963735e-01 -9.62518573e-01 -6.33359790e-01 -1.20058429e+00
-8.75834003e-02 1.83285087e-01 4.65405375e-01 -2.46037573... | [4.305810928344727, 8.04938793182373] |
4e88dac4-e285-4095-808b-3efe127fa741 | groupnet-multiscale-hypergraph-neural | 2204.08770 | null | https://arxiv.org/abs/2204.08770v2 | https://arxiv.org/pdf/2204.08770v2.pdf | GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning | Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only consider pair-wise interactions with limited relational reasoning. To promote more comprehensive interaction modeling for relational reasoning,... | ['Siheng Chen', 'Ya zhang', 'Zhenyang Ni', 'Maosen Li', 'Chenxin Xu'] | 2022-04-19 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_GroupNet_Multiscale_Hypergraph_Neural_Networks_for_Trajectory_Prediction_With_Relational_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_GroupNet_Multiscale_Hypergraph_Neural_Networks_for_Trajectory_Prediction_With_Relational_CVPR_2022_paper.pdf | cvpr-2022-1 | ['relational-reasoning'] | ['natural-language-processing'] | [-4.36917245e-01 1.67674571e-01 -2.68199295e-01 -2.44105414e-01
2.78264266e-02 -3.27091932e-01 8.39285672e-01 1.19954780e-01
2.10911945e-01 6.91519141e-01 5.28262734e-01 -4.54868019e-01
-6.50563598e-01 -1.22722208e+00 -1.03417516e+00 -3.71585935e-01
-7.58557022e-01 9.99170899e-01 3.10184091e-01 -6.17754579... | [5.8844122886657715, 0.8531733751296997] |
ab048ab7-44d5-4e60-8e11-1a6c1b1541ce | an-mil-derived-transformer-for-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_An_MIL-Derived_Transformer_for_Weakly_Supervised_Point_Cloud_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_An_MIL-Derived_Transformer_for_Weakly_Supervised_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation | We address weakly supervised point cloud segmentation by proposing a new model, MIL-derived transformer, to mine additional supervisory signals. First, the transformer model is derived based on multiple instance learning (MIL) to explore pair-wise cloud-level supervision, where two clouds of the same category yield... | ['Yen-Yu Lin', 'Yung-Yu Chuang', 'Kai-Syun Chen', 'Ji-Jia Wu', 'Cheng-Kun Yang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.76758641e-01 1.15969047e-01 -3.63885403e-01 -6.72833443e-01
-1.26173711e+00 -5.00248671e-01 4.82205212e-01 2.36140322e-02
-2.16563687e-01 2.94095874e-01 -2.08308682e-01 8.32673013e-02
-2.69238241e-02 -7.08515823e-01 -1.28218651e+00 -8.09925318e-01
-1.48057081e-02 6.40597880e-01 8.48532796e-01 3.23155560... | [8.027639389038086, -3.323402166366577] |
98b4dfd9-b80f-4ec1-9266-cd018f9b8fda | creating-training-corpora-for-nlg-micro | null | null | https://aclanthology.org/P17-1017 | https://aclanthology.org/P17-1017.pdf | Creating Training Corpora for NLG Micro-Planners | In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases. Because our method pairs data of varying size and shape with texts ranging from simple clauses to short texts, a dataset created using this framework ... | ['Laura Perez-Beltrachini', 'Claire Gardent', 'Shashi Narayan', 'Anastasia Shimorina'] | 2017-07-01 | null | null | null | acl-2017-7 | ['referring-expression-generation'] | ['computer-vision'] | [ 3.77866507e-01 7.95170724e-01 1.26747102e-01 -4.53660160e-01
-1.12504387e+00 -8.83631766e-01 1.01315463e+00 3.73508930e-01
-5.92282832e-01 1.15338850e+00 5.51167071e-01 -2.83566028e-01
-1.68461934e-01 -1.02359939e+00 -8.92128289e-01 -1.65061191e-01
1.88150823e-01 1.37068832e+00 3.37963164e-01 -6.70753121... | [11.196165084838867, 9.100493431091309] |
e36f032b-f48c-46f5-a36e-5b171dc267e0 | playing-lottery-tickets-with-vision-and | 2104.11832 | null | https://arxiv.org/abs/2104.11832v2 | https://arxiv.org/pdf/2104.11832v2.pdf | Playing Lottery Tickets with Vision and Language | Large-scale pre-training has recently revolutionized vision-and-language (VL) research. Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide range of VL tasks. However, the large number of parameters in such models hinders their application in practice. In parallel, work on the lo... | ['Zicheng Liu', 'Lijuan Wang', 'Jingjing Liu', 'Shuohang Wang', 'Yu Cheng', 'Tianlong Chen', 'Linjie Li', 'Yen-Chun Chen', 'Zhe Gan'] | 2021-04-23 | null | null | null | null | ['visual-commonsense-reasoning', 'visual-entailment'] | ['reasoning', 'reasoning'] | [ 1.92224473e-01 3.24172318e-01 -1.49984390e-01 -1.77805081e-01
-5.59398532e-01 -5.56476831e-01 5.71451068e-01 -1.13128521e-01
-5.10985315e-01 5.65698922e-01 8.17092657e-02 -6.28246129e-01
-2.95030866e-02 -6.52794719e-01 -9.48670805e-01 -3.99199814e-01
5.44516779e-02 2.54599780e-01 1.93158351e-02 -2.72558153... | [10.564112663269043, 1.8268144130706787] |
fd430378-bdaf-4f0c-ae7f-37ab79f1aed6 | da-gan-instance-level-image-translation-by-1 | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Ma_DA-GAN_Instance-Level_Image_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_DA-GAN_Instance-Level_Image_CVPR_2018_paper.pdf | DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks | Unsupervised image translation, which aims in translating two independent sets of images, is challenging in discovering the correct correspondences without paired data. Existing works build upon Generative Adversarial Networks (GANs) such that the distribution of the translated images are indistinguishable from the dis... | ['Jianlong Fu', 'Chang Wen Chen', 'Shuang Ma', 'Tao Mei'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['deep-attention', 'image-animation', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 5.96877456e-01 3.57273012e-01 -7.70022720e-02 -2.85709918e-01
-1.00298882e+00 -7.40886152e-01 7.79106915e-01 -6.30951762e-01
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4.79262434e-02 -9.84143496e-01 -1.25724971e+00 -9.31284606e-01
5.69988132e-01 6.87696397e-01 -2.29938492e-01 -1.38440937... | [11.66914176940918, -0.37902504205703735] |
3d79bf4c-ab1d-48fe-b2f4-87623bf49181 | de-risking-geological-carbon-storage-from | 2211.03527 | null | https://arxiv.org/abs/2211.03527v1 | https://arxiv.org/pdf/2211.03527v1.pdf | De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection | Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the inje... | ['Felix J. Herrmann', 'Mathias Louboutin', 'Abhinav Prakash Gahlot', 'Huseyin Tuna Erdinc', 'Ziyi Yin'] | 2022-10-07 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 3.78206968e-01 -2.02982113e-01 4.78903234e-01 2.60678399e-02
-9.58082736e-01 -7.03734815e-01 8.16435218e-01 1.60023451e-01
-5.14803469e-01 8.63258541e-01 -1.42229080e-01 -4.77396727e-01
-2.86907494e-01 -1.05142760e+00 -6.09236658e-01 -1.05062103e+00
-5.14683962e-01 1.06886238e-01 4.83417839e-01 8.63344688... | [6.843639373779297, 2.646791696548462] |
415e4e8e-77f5-4a93-bfb7-e2a2a23ed8a5 | faster-unsupervised-semantic-inpainting-a-gan | 1908.04968 | null | https://arxiv.org/abs/1908.04968v1 | https://arxiv.org/pdf/1908.04968v1.pdf | Faster Unsupervised Semantic Inpainting: A GAN Based Approach | In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting. This is made possible with better initialization of the core iterative optimization involved in the framework. To our best knowledge, this... | ['Arnav Kumar Jain', 'Avisek Lahiri', 'Prabir Kumar Biswas', 'Divyasri Nadendla'] | 2019-08-14 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.54850501e-01 2.79246449e-01 3.71969678e-02 -1.29649997e-01
-8.90508711e-01 -3.28659713e-01 5.78225851e-01 -5.97047806e-01
-3.45990479e-01 1.05253458e+00 1.36608273e-01 -1.19084731e-01
1.95259154e-01 -7.69828737e-01 -1.15197754e+00 -5.23976088e-01
4.62312102e-02 2.61124104e-01 -4.42478321e-02 -1.86102837... | [11.29134750366211, -0.6649293303489685] |
67d70ada-74b3-424f-a334-19eb928b425a | crysgnn-distilling-pre-trained-knowledge-to | 2301.05852 | null | https://arxiv.org/abs/2301.05852v1 | https://arxiv.org/pdf/2301.05852v1.pdf | CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials | In recent years, graph neural network (GNN) based approaches have emerged as a powerful technique to encode complex topological structure of crystal materials in an enriched representation space. These models are often supervised in nature and using the property-specific training data, learn relationship between crysta... | ['Niloy Ganguly', 'Satadeep Bhattacharjee', 'Seung-Cheol Lee', 'Pawan Goyal', 'Bidisha Samanta', 'Kishalay Das'] | 2023-01-14 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 2.44392291e-01 2.88470984e-01 -4.41386998e-01 -2.38326624e-01
-4.72597957e-01 -2.94937879e-01 2.97479123e-01 2.65752077e-01
-8.58408585e-03 1.08102322e+00 1.51425511e-01 -2.24030092e-01
-2.19534591e-01 -1.30056274e+00 -9.91554081e-01 -9.89463985e-01
-2.81911969e-01 6.31522477e-01 2.13336214e-01 -4.98588920... | [5.22313117980957, 5.533278465270996] |
6dc43e81-201a-4b86-aee5-6608edf25deb | progressive-one-shot-human-parsing | 2012.11810 | null | https://arxiv.org/abs/2012.11810v3 | https://arxiv.org/pdf/2012.11810v3.pdf | Progressive One-shot Human Parsing | Prior human parsing models are limited to parsing humans into classes pre-defined in the training data, which is not flexible to generalize to unseen classes, e.g., new clothing in fashion analysis. In this paper, we propose a new problem named one-shot human parsing (OSHP) that requires to parse human into an open set... | ['DaCheng Tao', 'Bhavani Thuraisingham', 'Jing Zhang', 'Haoyu He'] | 2020-12-22 | null | null | null | null | ['one-shot-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 2.90077925e-01 3.83617848e-01 -2.13763133e-01 -6.43265605e-01
-9.22134638e-01 -4.72722054e-01 -6.87911827e-03 2.21276488e-02
-4.46374208e-01 3.74250501e-01 -8.24697316e-02 1.36397839e-01
1.69343278e-01 -1.04681230e+00 -7.20705450e-01 -5.38161159e-01
3.33322376e-01 6.55233681e-01 6.68150604e-01 -1.76888496... | [9.023524284362793, 0.39980393648147583] |
8e00996b-d9cd-4b13-9728-26c66370e988 | glasses-detection-using-convolutional-neural | null | null | https://doi.org/10.1007/978-3-319-46654-5_78 | https://link.springer.com/chapter/10.1007/978-3-319-46654-5_78#chapter-info | Glasses Detection Using Convolutional Neural Networks | Glasses detection plays an important role in face recognition and soft biometrices for person identification. However, automatic glasses detection is still a challenging problem under real application scenarios, because face variations, light conditions, and self-occlusion, have significant influence on its performance... | ['Qijun Zhao', 'Ronghang Zhu', 'Li Shao'] | 2016-09-21 | null | null | null | conference-paper-2016-9 | ['person-identification', 'face-identification'] | ['computer-vision', 'computer-vision'] | [ 1.86332747e-01 -2.14078650e-01 1.48634404e-01 -5.70394993e-01
-1.73504110e-02 -2.89043456e-01 3.34542841e-01 -7.99612820e-01
-8.28667358e-02 4.95256841e-01 4.78455611e-02 2.78695077e-02
2.29848668e-01 -7.28387892e-01 -6.04132771e-01 -9.15078223e-01
7.75997192e-02 2.03455463e-02 1.02277905e-01 -5.11202291... | [13.295698165893555, 0.6522663831710815] |
c44becba-bf4b-4d37-9289-4a4a393ed954 | lightweight-convolution-transformer-for-cross | 2305.04325 | null | https://arxiv.org/abs/2305.04325v1 | https://arxiv.org/pdf/2305.04325v1.pdf | Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals | Background: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizure frequency and severity in order to assess the efficacy of pharmacological therapy for epilepsy. The drug quant... | ['Anil K. Tiwari', 'Salim Rukhsar'] | 2023-05-07 | null | null | null | null | ['seizure-detection', 'eeg', 'eeg'] | ['medical', 'methodology', 'time-series'] | [ 1.61410883e-01 -4.85213697e-01 2.88078517e-01 -2.50096321e-01
-1.05065107e+00 -3.36256683e-01 1.82436630e-01 1.88880399e-01
-6.46170080e-01 9.01791632e-01 7.93886259e-02 -2.65229642e-02
-3.29493791e-01 -3.76130491e-01 -4.35484350e-01 -8.17087829e-01
-5.50670505e-01 -2.56626666e-01 -1.11037344e-01 1.62957251... | [13.223191261291504, 3.5023458003997803] |
fe1e4e2c-3676-470f-933d-ae7ed7d45087 | feature-robustness-in-non-stationary-health | 1908.00690 | null | https://arxiv.org/abs/1908.00690v1 | https://arxiv.org/pdf/1908.00690v1.pdf | Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks | When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve. Due to de-identification requirements, however, current experimental pra... | ['Willie Boag', 'Michael C. Hughes', 'Tristan Naumann', 'Matthew B. A. McDermott', 'Bret Nestor', 'Marzyeh Ghassemi', 'Gabriela Berner', 'Anna Goldenberg'] | 2019-08-02 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [-5.90685159e-02 4.53344360e-02 -2.78625757e-01 -5.54736972e-01
-9.15275216e-01 -6.73639536e-01 2.33378589e-01 9.26439881e-01
-4.03326005e-01 7.65486598e-01 4.77915913e-01 -8.02661955e-01
-5.25433838e-01 -8.27627301e-01 -6.55086279e-01 -3.19975555e-01
-4.05841857e-01 5.09311914e-01 -1.59186006e-01 1.71707347... | [7.8495869636535645, 6.145815849304199] |
7e11f526-1f81-4aa5-a8e3-2a24cb99fe14 | meta-mimicking-embedding-via-others | 2112.08684 | null | https://arxiv.org/abs/2112.08684v3 | https://arxiv.org/pdf/2112.08684v3.pdf | Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification | Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time, which is a realistic but challenging problem. In contrast to methods assuming an identical model for different domains, Mixture of Experts (MoE) exploits multiple domai... | ['Zhenan Sun', 'Lingxiao He', 'Jian Liang', 'Boqiang Xu'] | 2021-12-16 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [-1.93869293e-01 -1.37115926e-01 -2.31663793e-01 -4.24101263e-01
-6.47308528e-01 -6.54360473e-01 7.52197802e-01 -1.69486195e-01
-5.71481764e-01 7.59207368e-01 1.29277885e-01 2.53984511e-01
-1.09489709e-01 -6.68071628e-01 -5.23756564e-01 -5.59653878e-01
1.55442610e-01 6.84262931e-01 9.82285738e-02 -2.47126177... | [14.727566719055176, 1.1051923036575317] |
24060922-a809-4e73-94a3-93becfd02c19 | cross-modality-attention-with-semantic-graph | 1912.07872 | null | https://arxiv.org/abs/1912.07872v2 | https://arxiv.org/pdf/1912.07872v2.pdf | Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification | Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cros... | ['Shilei Wen', 'Yingze Bao', 'Zhiyao Guo', 'Xiang Long', 'Lei Cui', 'Renchun You'] | 2019-12-17 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.69918680e-01 -4.39545125e-01 -5.40445268e-01 -4.43788797e-01
-7.40335584e-01 -6.13384724e-01 4.29702669e-01 2.04915658e-01
-3.20650846e-01 2.61982203e-01 9.32566896e-02 8.50869864e-02
-3.14793587e-01 -2.64535725e-01 -5.11382997e-01 -6.08135998e-01
8.64401087e-02 1.19721934e-01 1.56199917e-01 2.55111098... | [9.737200736999512, 3.963042974472046] |
fe4b795b-bece-4175-8df5-27df8b1ac15b | vehicle-trajectory-prediction-works-but-not | 2112.03909 | null | https://arxiv.org/abs/2112.03909v2 | https://arxiv.org/pdf/2112.03909v2.pdf | Vehicle trajectory prediction works, but not everywhere | Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by providing public benchmarks. While state-of-the-art methods are impressive, i.e., they have no off-road prediction, their generalization to citi... | ['Amir-Hossein Shahidzadeh', 'Alexandre Alahi', 'Seyed-Mohsen Moosavi-Dezfooli', 'Mohammad Shaverdikondori', 'Ahmad Rahimi', 'Saeed Saadatnejad', 'Mohammadhossein Bahari'] | 2021-12-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-generation'] | ['computer-vision'] | [ 2.06948504e-01 2.89836287e-01 -1.23300612e-01 -2.47797564e-01
-7.89732993e-01 -5.88491261e-01 9.12920415e-01 -7.69398570e-01
5.03765307e-02 6.71696067e-01 6.34667575e-02 -7.43470550e-01
3.63181055e-01 -1.17155027e+00 -1.06880665e+00 -5.24908364e-01
1.75534785e-01 2.94018835e-01 6.77349925e-01 -7.04185367... | [6.358780860900879, 0.5161541700363159] |
c6fe0493-e6bb-465a-bda9-b4c90fbdebfe | computational-protein-design-with-deep | 1801.07130 | null | http://arxiv.org/abs/1801.07130v2 | http://arxiv.org/pdf/1801.07130v2.pdf | Computational Protein Design with Deep Learning Neural Networks | Computational protein design has a wide variety of applications. Despite its
remarkable success, designing a protein for a given structure and function is
still a challenging task. On the other hand, the number of solved protein
structures is rapidly increasing while the number of unique protein folds has
reached a ste... | [] | 2018-02-24 | null | null | null | null | ['protein-design'] | ['medical'] | [ 1.07027486e-01 -3.43137011e-02 -3.28535065e-02 -5.58048725e-01
-2.71281183e-01 -3.07056487e-01 -8.40420350e-02 9.36130583e-02
-5.22233129e-01 1.23738563e+00 8.99201911e-03 -4.91187423e-01
1.57093868e-01 -7.44357586e-01 -8.92197847e-01 -1.06627715e+00
1.09388568e-02 4.16382849e-01 9.69989449e-02 -3.47359329... | [4.714326858520508, 5.665234565734863] |
b2b9a45f-585e-43cd-b208-bf662afbf0f6 | efficient-multi-task-rgb-d-scene-analysis-for | 2207.04526 | null | https://arxiv.org/abs/2207.04526v1 | https://arxiv.org/pdf/2207.04526v1.pdf | Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments | Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separ... | ['Horst-Michael Groß', 'Mona Köhler', 'Söhnke Benedikt Fischedick', 'Daniel Seichter'] | 2022-07-10 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 4.10763770e-01 -2.87196726e-01 3.67833138e-01 -6.12286210e-01
-6.12422824e-01 -7.71465063e-01 4.26196814e-01 1.67929575e-01
-6.35666251e-01 5.12651205e-01 -6.22866273e-01 -4.53769058e-01
-1.81490421e-01 -8.75652969e-01 -8.37467730e-01 -6.53436184e-01
1.44645989e-01 7.02413261e-01 6.03606999e-01 -2.64690638... | [8.396195411682129, -2.354335069656372] |
fb356699-c860-4ecd-9ca7-7456f7c99cc7 | learning-pixel-level-distinctions-for-video | 2204.04615 | null | https://arxiv.org/abs/2204.04615v1 | https://arxiv.org/pdf/2204.04615v1.pdf | Learning Pixel-Level Distinctions for Video Highlight Detection | The goal of video highlight detection is to select the most attractive segments from a long video to depict the most interesting parts of the video. Existing methods typically focus on modeling relationship between different video segments in order to learning a model that can assign highlight scores to these segments;... | ['Lixin Duan', 'Wen Li', 'Yuning Jiang', 'Tiezheng Ge', 'Biao Wang', 'Fanyue Wei'] | 2022-04-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wei_Learning_Pixel-Level_Distinctions_for_Video_Highlight_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wei_Learning_Pixel-Level_Distinctions_for_Video_Highlight_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['highlight-detection'] | ['computer-vision'] | [ 7.57866725e-02 -3.13543051e-01 -5.14594913e-01 -2.95286626e-01
-1.97683126e-01 -2.66977876e-01 4.28198904e-01 3.92881900e-01
-2.28948966e-01 3.47890764e-01 5.75584888e-01 1.08057491e-01
1.62952155e-01 -7.30698287e-01 -8.47714484e-01 -5.73799908e-01
-3.93611014e-01 -5.76294482e-01 5.85881293e-01 -1.90671712... | [10.042572021484375, 0.3743288516998291] |
39461cb6-fb18-4997-a607-cb7715a70889 | image-animation-with-keypoint-mask | 2112.10457 | null | https://arxiv.org/abs/2112.10457v1 | https://arxiv.org/pdf/2112.10457v1.pdf | Image Animation with Keypoint Mask | Motion transfer is the task of synthesizing future video frames of a single source image according to the motion from a given driving video. This task is challenging due to the complexity of motion representation and the unknown relations between the driving video and the source image. Despite this difficulty, this pro... | ['Dov Gertz', 'Yanir Marmor', 'Or Toledano'] | 2021-12-20 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 4.73451346e-01 1.24444909e-01 -5.97694470e-03 -3.07429638e-02
-2.99668521e-01 -6.26048684e-01 9.24591243e-01 -3.46788913e-01
-3.76770109e-01 5.72931111e-01 -4.62924509e-04 8.04812834e-02
5.48739098e-02 -5.73139548e-01 -9.07637954e-01 -1.03678739e+00
3.66750389e-01 2.06300601e-01 2.51572520e-01 -2.25558072... | [10.819844245910645, -0.8381256461143494] |
7e5ae592-2d29-4e3a-b81f-d3ff34dfe6d6 | a-class-wise-non-salient-region-generalized | 2212.14154 | null | https://arxiv.org/abs/2212.14154v1 | https://arxiv.org/pdf/2212.14154v1.pdf | A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation | Video semantic segmentation (VSS) is beneficial for dealing with dynamic scenes due to the continuous property of the real-world environment. On the one hand, some methods alleviate the predicted inconsistent problem between continuous frames. On the other hand, other methods employ the previous frame as the prior info... | ['Chen Xu', 'Wenbin Zou', 'Zhengyu Zhang', 'Muxin Liao', 'Shishun Tian', 'Yuhang Zhang'] | 2022-12-29 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 3.81740272e-01 -2.09455028e-01 -2.24372432e-01 -5.64512610e-01
-5.94871044e-01 -2.54269540e-01 1.73159316e-01 -1.62780598e-01
-3.00525665e-01 4.84233052e-01 -2.25694049e-02 1.40576273e-01
-1.08374611e-01 -5.96639812e-01 -7.80602038e-01 -7.79028118e-01
2.37088948e-01 1.24630801e-01 9.16175604e-01 -3.18136960... | [9.363456726074219, -0.12327134609222412] |
fdafd2a3-453d-49f4-a1e8-77cc1f77ccca | iconqa-a-new-benchmark-for-abstract-diagram | 2110.13214 | null | https://arxiv.org/abs/2110.13214v4 | https://arxiv.org/pdf/2110.13214v4.pdf | IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning | Current visual question answering (VQA) tasks mainly consider answering human-annotated questions for natural images. However, aside from natural images, abstract diagrams with semantic richness are still understudied in visual understanding and reasoning research. In this work, we introduce a new challenge of Icon Que... | ['Song-Chun Zhu', 'Xiaodan Liang', 'Zhou Yu', 'Wei zhang', 'Yizhou Zhao', 'Tony Xia', 'Jiaqi Chen', 'Liang Qiu', 'Pan Lu'] | 2021-10-25 | null | null | null | null | ['math-word-problem-solving', 'mathematical-question-answering', 'mathematical-reasoning', 'arithmetic-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'reasoning', 'reasoning', 'time-series'] | [ 1.03029490e-01 6.22882368e-03 1.37559131e-01 -2.99039185e-01
-6.96856797e-01 -9.19317305e-01 5.31783938e-01 4.73243743e-02
-9.82001796e-02 3.11896831e-01 3.59395176e-01 -7.09746063e-01
-6.35041073e-02 -9.27308500e-01 -9.98486876e-01 -5.35766743e-02
6.83254004e-01 5.48727036e-01 2.37090915e-01 -4.57430869... | [10.777029991149902, 1.797850489616394] |
a5b0b7d2-659b-4f9d-9907-b6610a4c6534 | reducing-quantum-annealing-biases-for-solving | 2103.04963 | null | https://arxiv.org/abs/2103.04963v1 | https://arxiv.org/pdf/2103.04963v1.pdf | Reducing quantum annealing biases for solving the graph partitioning problem | Quantum annealers offer an efficient way to compute high quality solutions of NP-hard problems when expressed in a QUBO (quadratic unconstrained binary optimization) or an Ising form. This is done by mapping a problem onto the physical qubits and couplers of the quantum chip, from which a solution is read after a proce... | ['Hristo N. Djidjev', 'Georg Hahn', 'Elijah Pelofske'] | 2021-03-08 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 4.91142601e-01 3.54368597e-01 2.09856719e-01 -1.34314150e-01
-5.43253362e-01 -7.51845419e-01 1.22934006e-01 3.67345661e-01
-4.23644662e-01 7.46668041e-01 -3.65626782e-01 -3.19500834e-01
-3.96351814e-01 -1.38307977e+00 -9.45715249e-01 -9.48118746e-01
3.40923965e-01 7.78403223e-01 8.82493854e-02 -3.17828655... | [5.631623268127441, 4.904772758483887] |
7f4d64e8-0497-4a78-8b25-a4e842a507f6 | deformation-aware-3d-model-embedding-and | 2004.01228 | null | https://arxiv.org/abs/2004.01228v3 | https://arxiv.org/pdf/2004.01228v3.pdf | Deformation-Aware 3D Model Embedding and Retrieval | We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task. 3D model retrieval is a fundamental operation for recovering a clean and complete 3D model from a noisy and partial 3D scan. However, given a f... | ['Jingwei Huang', 'Tolga Birdal', 'Minhyuk Sung', 'Mikaela Angelina Uy', 'Leonidas Guibas'] | 2020-04-02 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/264_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520392.pdf | eccv-2020-8 | ['3d-object-reconstruction'] | ['computer-vision'] | [-2.84259498e-01 -6.23987839e-02 -4.34004664e-02 -4.37466413e-01
-1.06650496e+00 -8.12070370e-01 4.93605614e-01 1.72330253e-02
-9.20760334e-02 8.59572440e-02 3.12305868e-01 1.96246594e-01
-4.04426903e-01 -8.22497010e-01 -9.22259033e-01 -5.76403797e-01
2.03110158e-01 9.12681997e-01 3.03239167e-01 -1.75285071... | [8.270700454711914, -3.411470890045166] |
83eff9f6-4714-4d36-a7fc-39d41287fbba | self-supervised-text-independent-speaker | 2012.07178 | null | https://arxiv.org/abs/2012.07178v2 | https://arxiv.org/pdf/2012.07178v2.pdf | Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning | In this study, we investigate self-supervised representation learning for speaker verification (SV). First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examp... | ['Dong Yu', 'Meng Yu', 'Chao Weng', 'Chunlei Zhang', 'Wei Xia'] | 2020-12-13 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 8.03013891e-02 2.72943497e-01 -4.16816771e-01 -7.94504106e-01
-1.00436580e+00 -3.49544495e-01 6.87484801e-01 1.20092565e-02
-2.97238261e-01 4.29354757e-01 4.31250334e-01 -2.30035990e-01
1.22832991e-01 -1.89447820e-01 -5.42053640e-01 -9.18633699e-01
-4.10350300e-02 5.16323566e-01 -1.26254216e-01 -2.59820849... | [14.341720581054688, 6.178313732147217] |
d93a6ed3-b4a8-4153-9025-8f56cfc1d424 | emailsum-abstractive-email-thread | 2107.14691 | null | https://arxiv.org/abs/2107.14691v1 | https://arxiv.org/pdf/2107.14691v1.pdf | EmailSum: Abstractive Email Thread Summarization | Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency. To spur research in thread summar... | ['Mohit Bansal', 'Jianfeng Gao', 'Asli Celikyilmaz', 'Shiyue Zhang'] | 2021-07-30 | null | https://aclanthology.org/2021.acl-long.537 | https://aclanthology.org/2021.acl-long.537.pdf | acl-2021-5 | ['email-thread-summarization'] | ['natural-language-processing'] | [ 4.62667108e-01 2.92658538e-01 -3.03234190e-01 -1.49519667e-01
-1.23027420e+00 -6.97436869e-01 9.54662740e-01 6.49410069e-01
-2.48634607e-01 1.05072522e+00 1.04656589e+00 -3.09698462e-01
2.11714476e-01 -2.23629773e-01 -1.50120929e-01 -2.68169552e-01
1.17537491e-01 4.86047804e-01 4.09073122e-02 -2.28401572... | [12.462868690490723, 9.344890594482422] |
af276a6f-ca04-4028-bcc2-265970df0545 | intelligent-sampling-for-surrogate-modeling | 2306.04066 | null | https://arxiv.org/abs/2306.04066v1 | https://arxiv.org/pdf/2306.04066v1.pdf | Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis | Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from... | ['Chandrika Kamath'] | 2023-06-06 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 4.37102526e-01 -6.65247366e-02 -3.38188499e-01 -2.10779920e-01
-4.30584490e-01 -4.74381834e-01 4.03012156e-01 2.89267898e-01
-5.59848070e-01 9.99146163e-01 -5.34757785e-02 -2.49507666e-01
-4.17028576e-01 -1.02625072e+00 -3.42928469e-01 -6.08521223e-01
2.31427744e-01 7.51548946e-01 3.55313152e-01 7.48054907... | [7.128853797912598, 4.366183757781982] |
c29c690d-9c05-4268-a11f-9f1348fbea35 | large-discourse-treebanks-from-scalable | 2212.06038 | null | https://arxiv.org/abs/2212.06038v1 | https://arxiv.org/pdf/2212.06038v1.pdf | Large Discourse Treebanks from Scalable Distant Supervision | Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most recent discourse parsers (and consequently downstream tasks) still rely on small-scale human-annotated discourse treebanks, trying to infer g... | ['Giuseppe Carenini', 'Patrick Huber'] | 2022-10-18 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.34267789e-01 1.00370860e+00 -4.34013188e-01 -7.01705992e-01
-9.50062871e-01 -7.74266660e-01 9.91510749e-01 5.11973560e-01
-4.90117759e-01 1.27655447e+00 8.51998389e-01 -7.64742851e-01
3.64997745e-01 -7.67924607e-01 -3.37728679e-01 -2.99885690e-01
2.41884097e-01 5.63737810e-01 6.07283652e-01 -7.28339136... | [10.823064804077148, 9.378169059753418] |
995d736f-f743-4a4c-9903-02494885f782 | eigensubspace-of-temporal-difference-dynamics | 2306.16750 | null | https://arxiv.org/abs/2306.16750v1 | https://arxiv.org/pdf/2306.16750v1.pdf | Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning | We propose a novel value approximation method, namely Eigensubspace Regularized Critic (ERC) for deep reinforcement learning (RL). ERC is motivated by an analysis of the dynamics of Q-value approximation error in the Temporal-Difference (TD) method, which follows a path defined by the 1-eigensubspace of the transition ... | ['Setareh Maghsudi', 'Meng Fang', 'Tianyi Zhou', 'Qiang He'] | 2023-06-29 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-5.53845108e-01 1.32838011e-01 -3.09126198e-01 2.19924375e-01
-9.33264017e-01 -3.44677150e-01 3.33389670e-01 -1.45992398e-01
-5.63277960e-01 1.03926158e+00 3.31863075e-01 -3.74644399e-01
-3.39775175e-01 -4.81521726e-01 -8.47708702e-01 -1.02935290e+00
-1.16287939e-01 1.86966404e-01 -1.07724272e-01 -3.66495848... | [4.091449737548828, 2.3552799224853516] |
3cf8be71-1ed8-4f7a-8a27-c4d801595ad6 | sigmorphon-2019-task-2-system-description | null | null | https://aclanthology.org/W19-4211 | https://aclanthology.org/W19-4211.pdf | Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few | This paper presents the UNT HiLT+Ling system for the Sigmorphon 2019 shared Task 2: Morphological Analysis and Lemmatization in Context. Our core approach focuses on the morphological tagging task; part-of-speech tagging and lemmatization are treated as secondary tasks. Given the highly multilingual nature of the task,... | ['Suleyman Olcay Polat', 'Brad Aiken', 'Rodney Nielsen', 'Taraka Rama', 'Jared Kelly', 'Alexis Palmer'] | 2019-08-01 | null | null | null | ws-2019-8 | ['morphological-tagging'] | ['natural-language-processing'] | [ 6.94941282e-02 -1.04999794e-02 -8.76086131e-02 -4.59795862e-01
-1.08198714e+00 -1.17837489e+00 7.78634250e-01 4.87212032e-01
-9.65308309e-01 5.13792217e-01 5.99270642e-01 -6.66369617e-01
2.63122112e-01 -4.93821651e-01 -2.96135217e-01 -4.61154014e-01
1.33000940e-01 7.08164811e-01 -9.44606885e-02 -3.38944405... | [10.561917304992676, 10.027105331420898] |
407e0afd-3911-4e02-8d3d-9797298e50ee | code-recommendation-for-open-source-software | 2210.08332 | null | https://arxiv.org/abs/2210.08332v3 | https://arxiv.org/pdf/2210.08332v3.pdf | Code Recommendation for Open Source Software Developers | Open Source Software (OSS) is forming the spines of technology infrastructures, attracting millions of talents to contribute. Notably, it is challenging and critical to consider both the developers' interests and the semantic features of the project code to recommend appropriate development tasks to OSS developers. In ... | ['Wei Wang', 'Yizhou Sun', 'Yanqiao Zhu', 'Yunsheng Bai', 'Yiqiao Jin'] | 2022-10-15 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-6.86015785e-01 -3.22167993e-01 -1.80864438e-01 -1.53677151e-01
-1.55672491e-01 -5.57120264e-01 7.05338567e-02 9.16529745e-02
4.48291302e-01 -5.52506968e-02 2.97490478e-01 -3.56460065e-01
-4.62354630e-01 -6.97140276e-01 -3.62831265e-01 -1.15748204e-01
-6.16395921e-02 -2.18699515e-01 5.40089250e-01 -2.19490960... | [7.556730270385742, 7.983922958374023] |
59e2f5bc-000e-4486-a626-6d4171102d4c | mfe-ner-multi-feature-fusion-embedding-for | 2109.07877 | null | https://arxiv.org/abs/2109.07877v1 | https://arxiv.org/pdf/2109.07877v1.pdf | MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition | Pre-trained language models lead Named Entity Recognition (NER) into a new era, while some more knowledge is needed to improve their performance in specific problems. In Chinese NER, character substitution is a complicated linguistic phenomenon. Some Chinese characters are quite similar for sharing the same components ... | ['Kui Meng', 'Jiatong Li'] | 2021-09-16 | mfe-ner-multi-feature-fusion-embedding-for-1 | https://openreview.net/forum?id=5N4bCRdqHAw | https://openreview.net/pdf?id=5N4bCRdqHAw | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-3.26228827e-01 -6.12841427e-01 3.57417837e-02 -3.83565962e-01
-3.92394662e-01 -6.11612022e-01 3.90483648e-01 -4.32535028e-03
-7.67067432e-01 6.59667969e-01 6.97680056e-01 -2.58899420e-01
3.69793802e-01 -9.48770761e-01 -1.76539317e-01 -5.46479762e-01
5.63132405e-01 -4.35407087e-02 1.84493378e-01 -1.56219378... | [9.841055870056152, 9.805038452148438] |
21f97291-83a1-4596-bb08-adf456d62efa | learning-semantic-relationship-among | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_Learning_Semantic_Relationship_Among_Instances_for_Image-Text_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_Learning_Semantic_Relationship_Among_Instances_for_Image-Text_Matching_CVPR_2023_paper.pdf | Learning Semantic Relationship Among Instances for Image-Text Matching | Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities. However, previous studies only focus on capturing fragment-level relation within a sample from a particu... | ['Yongdong Zhang', 'Yan Song', 'Zhendong Mao', 'Zheren Fu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['network-embedding', 'cross-modal-retrieval', 'text-matching', 'multimodal-deep-learning'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.07612617e-01 -3.30457389e-01 -3.95306408e-01 -5.34940839e-01
-9.24420714e-01 -2.80400127e-01 7.78007746e-01 3.54361176e-01
-3.18666041e-01 2.49961495e-01 4.33574021e-01 2.36645877e-01
-1.86835006e-01 -6.54460490e-01 -7.11974382e-01 -6.99351609e-01
2.88786590e-01 -9.84041858e-03 1.23077080e-01 5.22867106... | [10.820562362670898, 1.3179210424423218] |
7d5749ec-ca69-4100-883e-3e062efc3b87 | uncovering-the-potential-of-chatgpt-for | 2305.08391 | null | https://arxiv.org/abs/2305.08391v1 | https://arxiv.org/pdf/2305.08391v1.pdf | Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study | Large Language Models (LLMs) like ChatGPT have proven a great shallow understanding of many traditional NLP tasks, such as translation, summarization, etc. However, its performance on high-level understanding, such as dialogue discourse analysis task that requires a higher level of understanding and reasoning, remains ... | ['Feng Jiang', 'Yaxin Fan'] | 2023-05-15 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.66529197e-01 9.80110943e-01 -1.34953156e-01 -2.71823049e-01
-1.05873108e+00 -7.28584170e-01 1.10177672e+00 4.36760366e-01
-3.66595536e-02 6.33873940e-01 7.46738851e-01 -8.47916603e-01
8.41794834e-02 -5.70376277e-01 -5.47644980e-02 -2.53047377e-01
1.21273026e-01 8.34463358e-01 3.84211302e-01 -4.90540713... | [10.96152400970459, 9.36782455444336] |
d6a4a6bc-511a-4969-961b-1dc106765bd0 | botied-multi-objective-bayesian-optimization | 2306.00344 | null | https://arxiv.org/abs/2306.00344v1 | https://arxiv.org/pdf/2306.00344v1.pdf | BOtied: Multi-objective Bayesian optimization with tied multivariate ranks | Many scientific and industrial applications require joint optimization of multiple, potentially competing objectives. Multi-objective Bayesian optimization (MOBO) is a sample-efficient framework for identifying Pareto-optimal solutions. We show a natural connection between non-dominated solutions and the highest multiv... | ['Kyunghyun Cho', 'Stephen Ra', 'Michael Maser', 'Nataša Tagasovska', 'Ji Won Park'] | 2023-06-01 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.71312258e-01 -3.40389252e-01 -3.35544907e-02 -1.12381339e-01
-1.06829691e+00 -9.12436485e-01 2.83015162e-01 4.46867496e-01
-4.55240309e-01 9.83292818e-01 3.71099636e-02 -4.48149405e-02
-9.89604235e-01 -7.49062538e-01 -5.83392620e-01 -9.64600146e-01
-3.30009639e-01 7.82478392e-01 1.59964934e-01 8.26070681... | [6.05806303024292, 3.6391899585723877] |
5cda383a-d10a-4a2b-a92a-8f7c465f1b33 | can-we-use-diffusion-probabilistic-models-for | 2302.14503 | null | https://arxiv.org/abs/2302.14503v1 | https://arxiv.org/pdf/2302.14503v1.pdf | Can We Use Diffusion Probabilistic Models for 3D Motion Prediction? | After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our objective is to evaluate the potential of diffusion probabilistic models for 3D human motion-related tasks. To this end, this paper present... | ['Dongheui Lee', 'Esteve Valls Mascaro', 'Hyemin Ahn'] | 2023-02-28 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-1.38036877e-01 1.11895382e-01 -3.97371858e-01 -5.21943755e-02
-4.88413483e-01 -4.20813918e-01 1.12040043e+00 -2.98666269e-01
-3.46325547e-01 3.96467179e-01 6.31139457e-01 -2.77964592e-01
-7.09158108e-02 -6.65569723e-01 -3.62672418e-01 -6.02212608e-01
-1.41171172e-01 4.91565138e-01 5.22163808e-01 -5.66599816... | [7.3195600509643555, -0.10635749995708466] |
4945bb43-7f8e-45fe-a53a-c8a1bf63bfef | label-efficient-learning-in-agriculture-a | 2305.14691 | null | https://arxiv.org/abs/2305.14691v1 | https://arxiv.org/pdf/2305.14691v1.pdf | Label-Efficient Learning in Agriculture: A Comprehensive Review | The past decade has witnessed many great successes of machine learning (ML) and deep learning (DL) applications in agricultural systems, including weed control, plant disease diagnosis, agricultural robotics, and precision livestock management. Despite tremendous progresses, one downside of such ML/DL models is that th... | ['Xiaobo Tan', 'Daniel Morris', 'Yanbo Huang', 'Zhaojian Li', 'Xinda Qi', 'Dong Chen', 'Jiajia Li'] | 2023-05-24 | null | null | null | null | ['plant-phenotyping', 'active-learning', 'active-learning'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 3.89117450e-01 -7.52391368e-02 -9.99765515e-01 -3.17866325e-01
-1.52961269e-01 -8.03896725e-01 -3.39930877e-02 7.60394573e-01
-1.47951189e-02 6.64223611e-01 -5.96747696e-01 -5.51276743e-01
-1.19868226e-01 -1.12631345e+00 -6.64324224e-01 -9.27109838e-01
-1.84171692e-01 3.32986355e-01 -1.69568822e-01 -8.88419449... | [9.157144546508789, -1.535788655281067] |
647d0671-bc8f-448c-b752-8414151eab5e | cross-domain-few-shot-classification-via-2 | 2208.08015 | null | https://arxiv.org/abs/2208.08015v1 | https://arxiv.org/pdf/2208.08015v1.pdf | Cross-Domain Few-Shot Classification via Inter-Source Stylization | Cross-Domain Few Shot Classification (CDFSC) leverages prior knowledge learned from a supervised auxiliary dataset to solve a target task with limited supervised information available, where the auxiliary and target datasets come from the different domains. It is challenging due to the domain shift between these datase... | ['Li Liu', 'Huali Xu'] | 2022-08-17 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.97945261e-01 -1.23026073e-01 -4.74758595e-01 -2.12561652e-01
-5.59662104e-01 -6.09038293e-01 6.22955620e-01 -2.10822120e-01
-4.09664392e-01 1.06732953e+00 2.95245945e-01 2.33858556e-01
1.49616286e-01 -6.94896698e-01 -7.26555586e-01 -6.49829805e-01
6.54280186e-01 3.36142510e-01 7.42718756e-01 -3.53346497... | [10.260570526123047, 2.9254136085510254] |
431d14c8-74a9-4200-8aa5-8cc84b69d07d | impact-of-face-image-quality-estimation-on | 2209.15489 | null | https://arxiv.org/abs/2209.15489v1 | https://arxiv.org/pdf/2209.15489v1.pdf | Impact of Face Image Quality Estimation on Presentation Attack Detection | Non-referential face image quality assessment methods have gained popularity as a pre-filtering step on face recognition systems. In most of them, the quality score is usually designed with face matching in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Atta... | ['Christoph Busch', 'Juan E. Tapia', 'Diego Pasmino', 'Carlos Aravena'] | 2022-09-30 | null | null | null | null | ['image-quality-estimation', 'face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.16135398e-01 -1.07262462e-01 1.19655170e-01 -4.39095646e-01
-5.10830998e-01 -5.46534359e-01 6.05610073e-01 2.06846356e-01
-3.88627112e-01 2.65037328e-01 -2.62584500e-02 -3.62648726e-01
-1.69277042e-01 -9.15484607e-01 -4.73339587e-01 -5.12446702e-01
-6.65495470e-02 1.23560317e-02 3.72301489e-01 -9.47652757... | [13.042490005493164, 0.9620637893676758] |
867fc792-687f-4b3c-8b40-6332d7c03a55 | trap-attention-monocular-depth-estimation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper.pdf | Trap Attention: Monocular Depth Estimation With Manual Traps | Predicting a high quality depth map from a single image is a challenging task, because it exists infinite possibility to project a 2D scene to the corresponding 3D scene. Recently, some studies introduced multi-head attention (MHA) modules to perform long-range interaction, which have shown significant progress in ... | ['Hongping Gan', 'Chao Ning'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['monocular-depth-estimation'] | ['computer-vision'] | [ 7.69247711e-02 -1.44513026e-01 1.79515071e-02 -4.71428305e-01
-5.49160898e-01 -6.21308722e-02 4.99697179e-01 -6.12204611e-01
-4.93885875e-01 4.00279999e-01 2.80787379e-01 -1.56989619e-02
1.74134746e-01 -9.90165532e-01 -9.64095473e-01 -8.49666893e-01
3.50753367e-01 7.71724880e-02 4.26413000e-01 1.24021508... | [9.074688911437988, -2.4780542850494385] |
0f678ce7-61c8-4315-8d59-e20b301e7d60 | mets-cov-a-dataset-of-medical-entity-and | 2209.13773 | null | https://arxiv.org/abs/2209.13773v1 | https://arxiv.org/pdf/2209.13773v1.pdf | METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets | The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media. However, models train... | ['Jie Yang', 'Jiageng Wu', 'Zhiyang Teng', 'Zichang Su', 'Yining Hua', 'Zhijiang Guo', 'Dading Chong', 'Zeqiang Wang', 'Peilin Zhou'] | 2022-09-28 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.48689920e-01 6.21410040e-03 -4.44238365e-01 -2.31777608e-01
-4.31271344e-01 -5.22627711e-01 5.79251111e-01 1.10615575e+00
-8.35954309e-01 8.63731205e-01 6.29549682e-01 -3.34498465e-01
2.91926503e-01 -1.07939816e+00 -4.67708498e-01 -4.26602244e-01
-2.69549061e-02 7.98620462e-01 -1.26224682e-01 -5.42917728... | [8.455174446105957, 9.264328956604004] |
9be45d5d-80ce-44c5-83e7-d8ed87b08015 | a-motion-assessment-method-for-reference | 2306.17434 | null | https://arxiv.org/abs/2306.17434v1 | https://arxiv.org/pdf/2306.17434v1.pdf | A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank Approximation | Purpose: Slice-to-volume registration and super-resolution reconstruction (SVR-SRR) is commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion as a reference for registrati... | ['Dan Wu', 'Guangbin Wang', 'Sun Yi', 'Cong Sun', 'Tianshu Zheng', 'Jiwei Sun', 'Wen Shi', 'Haoan Xu'] | 2023-06-30 | null | null | null | null | ['super-resolution', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.75399303e-02 -3.42611521e-01 1.83679745e-01 -2.26763964e-01
-8.77394319e-01 -4.66106325e-01 1.73207045e-01 1.07261799e-01
-3.52499902e-01 2.75792927e-01 4.24462885e-01 -1.35202389e-02
-3.08123827e-01 -5.32900691e-01 -3.77134562e-01 -8.51255298e-01
-5.01423478e-01 4.13706899e-01 4.18126583e-01 -5.05564660... | [13.779532432556152, -2.463430643081665] |
d61d33bc-b9b3-4cfa-93e7-8cb4605fe171 | deep-multi-patch-aggregation-network-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Lu_Deep_Multi-Patch_Aggregation_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Lu_Deep_Multi-Patch_Aggregation_ICCV_2015_paper.pdf | Deep Multi-Patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation | This paper investigates problems of image style, aesthetics, and quality estimation, which require fine-grained details from high-resolution images, utilizing deep neural network training approach. Existing deep convolutional neural networks mostly extracted one patch such as a down-sized crop from each image as a trai... | ['Radomir Mech', 'Xin Lu', 'Xiaohui Shen', 'Zhe Lin', 'James Z. Wang'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['image-quality-estimation', 'aesthetics-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.92189980e-01 -4.30414677e-01 6.53561279e-02 -5.53246915e-01
-6.26172662e-01 -3.54131758e-01 2.46008299e-02 6.68699369e-02
-1.09540410e-01 4.11924452e-01 -4.20297356e-03 1.09255180e-01
-1.67589635e-01 -1.07644570e+00 -9.62602079e-01 -5.74660540e-01
3.30154479e-01 -8.10128748e-02 -1.66825294e-01 -5.79618849... | [11.518881797790527, -1.0880171060562134] |
51baa05c-6f31-4fca-a155-9dc24c648774 | unsupervised-learning-of-object-landmarks | 1806.07823 | null | http://arxiv.org/abs/1806.07823v2 | http://arxiv.org/pdf/1806.07823v2.pdf | Unsupervised Learning of Object Landmarks through Conditional Image Generation | We propose a method for learning landmark detectors for visual objects (such
as the eyes and the nose in a face) without any manual supervision. We cast
this as the problem of generating images that combine the appearance of the
object as seen in a first example image with the geometry of the object as seen
in a second... | ['Tomas Jakab', 'Hakan Bilen', 'Andrea Vedaldi', 'Ankush Gupta'] | 2018-06-20 | unsupervised-learning-of-object-landmarks-1 | http://papers.nips.cc/paper/7657-unsupervised-learning-of-object-landmarks-through-conditional-image-generation | http://papers.nips.cc/paper/7657-unsupervised-learning-of-object-landmarks-through-conditional-image-generation.pdf | neurips-2018-12 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 4.09402937e-01 5.61543286e-01 4.10893112e-01 -2.96193868e-01
-7.02304125e-01 -8.04653883e-01 8.25297952e-01 -9.20488983e-02
-3.32297176e-01 3.72441113e-01 -2.06502527e-01 1.97529513e-02
3.39341730e-01 -6.09990060e-01 -1.04268026e+00 -6.64584458e-01
1.60112098e-01 6.18538916e-01 1.90447822e-01 -5.70814200... | [11.69188117980957, -0.6408027410507202] |
ad1917c3-d4d2-41d1-95e2-402695784132 | learning-a-3d-morphable-face-reflectance | 2303.11686 | null | https://arxiv.org/abs/2303.11686v1 | https://arxiv.org/pdf/2303.11686v1.pdf | Learning a 3D Morphable Face Reflectance Model from Low-cost Data | Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Sta... | ['Feng Xu', 'Zhibo Wang', 'Yuxuan Han'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Han_Learning_a_3D_Morphable_Face_Reflectance_Model_From_Low-Cost_Data_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Han_Learning_a_3D_Morphable_Face_Reflectance_Model_From_Low-Cost_Data_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 2.14746550e-01 -3.52806449e-02 1.78202152e-01 -6.47632957e-01
-3.27055871e-01 -3.59991878e-01 2.81132251e-01 -1.10905051e+00
3.45676094e-01 6.27833664e-01 4.94023226e-03 -8.22546110e-02
1.42782331e-01 -8.98034513e-01 -5.54681957e-01 -8.74092817e-01
5.26414692e-01 1.76249281e-01 -1.48177221e-01 -2.17478067... | [12.693463325500488, -0.3595455586910248] |
1d2fddeb-686e-42d0-9823-35c5363da6b1 | exploring-font-independent-features-for-scene | 2009.07447 | null | https://arxiv.org/abs/2009.07447v1 | https://arxiv.org/pdf/2009.07447v1.pdf | Exploring Font-independent Features for Scene Text Recognition | Scene text recognition (STR) has been extensively studied in last few years. Many recently-proposed methods are specially designed to accommodate the arbitrary shape, layout and orientation of scene texts, but ignoring that various font (or writing) styles also pose severe challenges to STR. These methods, where font f... | ['Zhouhui Lian', 'Yizhi Wang'] | 2020-09-16 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.87999940e-01 -6.61051512e-01 1.25112861e-01 -3.93420517e-01
-1.15292631e-01 -6.93623006e-01 8.31858814e-01 -2.30292261e-01
-1.73017412e-01 3.09528232e-01 4.58025485e-01 -2.37731323e-01
3.19918036e-01 -7.00806558e-01 -7.77913332e-01 -6.29794955e-01
6.65824890e-01 3.01356643e-01 2.11923808e-01 -4.66518581... | [11.908699035644531, 1.8202598094940186] |
515db804-a856-4fe5-a35c-840d68cd405d | resolving-head-on-conflicts-for-multi-agent | 2007.03575 | null | https://arxiv.org/abs/2007.03575v1 | https://arxiv.org/pdf/2007.03575v1.pdf | Resolving Head-On Conflicts for Multi-Agent Path Finding with Conflict-Based Search | Conflict-Based Search (CBS) is a popular framework for solving the Multi-Agent Path Finding problem. Some of the conflicts incur a foreseeable conflict in one or both of the children nodes when splitting on them. This paper introduces a new technique, namely the head-on technique that finds out such conflicts, so they ... | ['Lun Yang'] | 2020-07-07 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 4.93873656e-02 4.11045939e-01 -4.14858490e-01 -1.08359613e-01
-2.33108416e-01 -4.73937422e-01 3.33842069e-01 3.99081767e-01
-3.40517282e-01 1.60001421e+00 -1.98825106e-01 -3.36426616e-01
-6.79822803e-01 -1.12018573e+00 -1.86083987e-01 -6.75068200e-01
-5.31490684e-01 1.16905451e+00 1.12464654e+00 -5.86461604... | [4.984013080596924, 1.8396251201629639] |
1b6ba259-43ca-472e-bf7f-bd7bdbe817df | real-world-video-for-zoom-enhancement-based | 2306.13875 | null | https://arxiv.org/abs/2306.13875v1 | https://arxiv.org/pdf/2306.13875v1.pdf | Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling | In recent years, single-frame image super-resolution (SR) has become more realistic by considering the zooming effect and using real-world short- and long-focus image pairs. In this paper, we further investigate the feasibility of applying realistic multi-frame clips to enhance zoom quality via spatio-temporal informat... | ['Jinyue Yan', 'Ryosuke Shibasaki', 'Zekun Cai', 'Xiaodan Shi', 'Haoran Zhang', 'Yinqiang Zheng', 'Zhiling Guo'] | 2023-06-24 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.99265951e-01 -7.33946443e-01 1.15180865e-01 -2.26136237e-01
-3.35356802e-01 -6.95876554e-02 3.47556561e-01 -3.52711827e-01
-2.92245150e-01 7.07454443e-01 2.90770441e-01 2.19088420e-01
-3.50102007e-01 -6.04731560e-01 -6.23633087e-01 -5.85488737e-01
-2.33621433e-01 -3.81663859e-01 8.13622534e-01 -3.88422370... | [11.000901222229004, -2.0100250244140625] |
13bbec89-6d19-4245-ac63-e9bab9f635ca | supervised-word-sense-disambiguation-on | null | null | https://aclanthology.org/2021.paclic-1.12 | https://aclanthology.org/2021.paclic-1.12.pdf | Supervised Word Sense Disambiguation on Taiwan Hakka Polysemy with Neural Network Models: A Case Study of BUN, TUNG and LAU | null | ['Yanhong Chen', 'Chia-Hung Lin', 'Jyi-Shane Liu', 'Hsiao-Ling Hsu', 'Huei-ling Lai'] | null | null | https://aclanthology.org/2021.paclic-1.78 | https://aclanthology.org/2021.paclic-1.78.pdf | paclic-2021-11 | ['word-sense-disambiguation'] | ['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... | [-7.346590518951416, 3.6740894317626953] |
632c85f6-221e-44bd-be12-b1ba59d5f7e6 | monotone-comparative-statics-for-submodular | 2304.12171 | null | https://arxiv.org/abs/2304.12171v1 | https://arxiv.org/pdf/2304.12171v1.pdf | Monotone comparative statics for submodular functions, with an application to aggregated deferred acceptance | We propose monotone comparative statics results for maximizers of submodular functions, as opposed to maximizers of supermodular functions as in the classical theory put forth by Veinott, Topkis, Milgrom, and Shannon among others. We introduce matrons, a natural structure that is dual to sublattices that generalizes ex... | ['Maxime Sylvestre', 'Yu-Wei Hsieh', 'Alfred Galichon'] | 2023-04-24 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 2.74127215e-01 5.66180527e-01 -5.19722044e-01 -4.28622633e-01
-9.31797922e-02 -1.04409683e+00 2.60405332e-01 8.69497210e-02
-2.96334982e-01 8.59175920e-01 3.06900054e-01 -2.22236186e-01
-9.08318877e-01 -1.18688095e+00 -9.43592310e-01 -5.16885817e-01
-5.22385418e-01 8.11368406e-01 -2.62598693e-01 -5.70296228... | [6.600698947906494, 4.836030960083008] |
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