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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d46c5ac7-7c41-4d1e-b7bc-c1a2ac3394a4 | unsupervised-latent-tree-induction-with-deep-1 | null | null | https://aclanthology.org/N19-1116 | https://aclanthology.org/N19-1116.pdf | Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders | We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use... | ['Andrew McCallum', 'Patrick Verga', 'Mohit Yadav', 'Andrew Drozdov', 'Mohit Iyyer'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 3.91142040e-01 7.88369596e-01 -2.38082156e-01 -8.31737339e-01
-5.49804449e-01 -7.46914983e-01 9.92798731e-02 3.71126294e-01
-4.58384037e-01 7.20191300e-01 5.88672519e-01 -7.90671408e-01
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-2.23957896e-01 6.89368963e-01 4.77918833e-02 -1.13277197... | [10.395528793334961, 9.591530799865723] |
3a64bc58-e309-4ef0-98d0-4f5ae6f7fe74 | exploiting-reducibility-in-unsupervised | null | null | https://aclanthology.org/D12-1028 | https://aclanthology.org/D12-1028.pdf | Exploiting Reducibility in Unsupervised Dependency Parsing | null | ["Zden{\\v{e}}k {\\v{Z}}abokrtsk{\\'y}", 'David Mare{\\v{c}}ek'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['unsupervised-dependency-parsing'] | ['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
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.178629398345947, 3.8342108726501465] |
01ee91da-b0bd-4051-846e-6873554b4087 | curvature-filtrations-for-graph-generative | 2301.12906 | null | https://arxiv.org/abs/2301.12906v2 | https://arxiv.org/pdf/2301.12906v2.pdf | Curvature Filtrations for Graph Generative Model Evaluation | Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property of graphs, and has recently started to prove useful in characterising graph... | ['Bastian Rieck', 'Michael Bronstein', 'Jeremy Wayland', 'Joshua Southern'] | 2023-01-30 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 1.23973042e-01 2.27755919e-01 -1.51496142e-01 -1.66478708e-01
-3.39675814e-01 -9.46546197e-01 1.02961576e+00 6.56582475e-01
4.42057848e-02 2.98896343e-01 3.59989524e-01 -3.33481878e-01
-5.33767104e-01 -1.02688813e+00 -1.20078050e-01 -6.30683899e-01
-6.86136842e-01 3.59296530e-01 8.19675177e-02 -2.78371602... | [7.01699161529541, 5.880551815032959] |
0fc9b517-511a-451e-82ed-fbd8372e5a69 | end-to-end-dereverberation-beamforming-and | 2102.11525 | null | https://arxiv.org/abs/2102.11525v1 | https://arxiv.org/pdf/2102.11525v1.pdf | End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend | Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions. However, severe performance degradation is still observed in the reverberant and noisy scenarios, and there is still a large performance gap between anec... | ['Yanmin Qian', 'Reinhold Haeb-Umbach', 'Naoyuki Kamo', 'Tsubasa Ochiai', 'Keisuke Kinoshita', 'Marc Delcroix', 'Tomohiro Nakatani', 'Shinji Watanabe', 'Christoph Boeddeker', 'Wangyou Zhang'] | 2021-02-23 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.84108235e-03 -5.06191432e-01 6.73000813e-01 3.76940034e-02
-1.34172428e+00 -6.34875476e-01 6.88122287e-02 -4.03997719e-01
-3.04215461e-01 6.08814597e-01 5.17956495e-01 -5.52451551e-01
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3.16066816e-02 -2.50772178e-01 -2.08521351e-01 -2.27026016... | [14.962642669677734, 5.874068260192871] |
3a1dc508-0bdd-45fa-8ff2-8ce4352c8351 | findings-of-the-nlp4if-2019-shared-task-on | 1910.09982 | null | https://arxiv.org/abs/1910.09982v1 | https://arxiv.org/pdf/1910.09982v1.pdf | Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection | We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific pr... | ['Alberto Barrón-Cedeño', 'Giovanni Da San Martino', 'Preslav Nakov'] | 2019-10-20 | findings-of-the-nlp4if-2019-shared-task-on-1 | https://aclanthology.org/D19-5024 | https://aclanthology.org/D19-5024.pdf | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.53244311e-01 9.39965546e-02 -4.37480539e-01 -1.97710425e-01
-1.15993690e+00 -7.70985961e-01 1.31236887e+00 5.12894809e-01
-4.55886096e-01 7.65845656e-01 8.86227846e-01 -6.02412045e-01
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-4.07888703e-02 4.47874993e-01 1.20150208e-01 -2.76941568... | [8.470013618469238, 10.656709671020508] |
59348bec-2539-4c71-b4f6-16f93681c4cf | intersectionality-and-testimonial-injustice | 2306.13675 | null | https://arxiv.org/abs/2306.13675v1 | https://arxiv.org/pdf/2306.13675v1.pdf | Intersectionality and Testimonial Injustice in Medical Records | Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient's experi... | ['Lu Cheng', 'Bhuvani Shah', 'Kenya S. Andrews'] | 2023-06-20 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 1.85192198e-01 3.24868441e-01 -5.75590730e-01 -3.33379358e-01
-4.49946463e-01 -5.94938874e-01 4.65763837e-01 1.01587570e+00
-5.05110443e-01 6.01101637e-01 1.18319428e+00 -9.49154496e-01
-6.41267955e-01 -6.24333858e-01 -4.17766184e-01 -4.79066283e-01
3.34766686e-01 -4.41166200e-02 -7.47267962e-01 5.20572551... | [8.832578659057617, 5.604638576507568] |
30d9c954-e07b-4601-a7f9-796c47414d34 | improving-visual-representation-learning | 2212.14504 | null | https://arxiv.org/abs/2212.14504v2 | https://arxiv.org/pdf/2212.14504v2.pdf | Improving Visual Representation Learning through Perceptual Understanding | We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction of a perceptual similarity term between generated and real images (ii) incorporating several technique... | ['Ken Chatfield', 'Frederick Hoffman', 'Samyakh Tukra'] | 2022-12-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tukra_Improving_Visual_Representation_Learning_Through_Perceptual_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tukra_Improving_Visual_Representation_Learning_Through_Perceptual_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.84840178e-01 3.54208320e-01 3.26168239e-01 -2.93337971e-01
-8.75766993e-01 -5.42138040e-01 9.89141464e-01 -2.28334695e-01
-5.11445224e-01 7.84306705e-01 5.11343241e-01 -2.68308848e-01
2.81989634e-01 -7.66633213e-01 -1.10555434e+00 -6.01925373e-01
-1.45116284e-01 -3.38091031e-02 3.97546113e-01 -4.76810992... | [11.44682502746582, -0.43848925828933716] |
b6a4c0d2-2398-4b5d-a5d8-ca085444273b | evolvement-constrained-adversarial-learning | 1811.02476 | null | http://arxiv.org/abs/1811.02476v1 | http://arxiv.org/pdf/1811.02476v1.pdf | Evolvement Constrained Adversarial Learning for Video Style Transfer | Video style transfer is a useful component for applications such as augmented
reality, non-photorealistic rendering, and interactive games. Many existing
methods use optical flow to preserve the temporal smoothness of the synthesized
video. However, the estimation of optical flow is sensitive to occlusions and
rapid mo... | ['Xiao Bian', 'Longyin Wen', 'Siwei Lyu', 'Wenbo Li'] | 2018-11-06 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 1.85119554e-01 -3.10365707e-01 9.91925374e-02 -1.04760058e-01
-3.23497266e-01 -6.47144496e-01 5.60630620e-01 -7.33605087e-01
-1.31765723e-01 9.67553079e-01 1.51847929e-01 -1.98725283e-01
4.22930837e-01 -8.53296399e-01 -8.01275790e-01 -3.90481055e-01
3.12771231e-01 -1.56890765e-01 3.28334421e-01 -2.82813340... | [11.230539321899414, -0.950326144695282] |
3c22d30c-deb5-4479-84b5-c3f967bbc420 | scalable-computation-of-optimized-queries-for | 1612.04791 | null | http://arxiv.org/abs/1612.04791v3 | http://arxiv.org/pdf/1612.04791v3.pdf | Scalable Computation of Optimized Queries for Sequential Diagnosis | In many model-based diagnosis applications it is impossible to provide such a
set of observations and/or measurements that allow to identify the real cause
of a fault. Therefore, diagnosis systems often return many possible candidates,
leaving the burden of selecting the correct diagnosis to a user. Sequential
diagnosi... | ['Wolfgang Schmid', 'Kostyantyn Shchekotykhin', 'Patrick Rodler'] | 2016-12-14 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 2.92872667e-01 3.00720274e-01 -1.72469854e-01 -2.94157594e-01
-9.28654373e-01 -4.83198583e-01 3.22118074e-01 7.40957320e-01
-8.11558589e-03 7.60245383e-01 -5.53978741e-01 -5.80031216e-01
-7.03658104e-01 -1.05072749e+00 -4.68688548e-01 -5.98464429e-01
1.51324823e-01 1.23052895e+00 5.81988215e-01 2.04853952... | [5.463347911834717, 2.77248215675354] |
e17c523f-c18b-4a05-bec8-aee3416748b7 | fast-and-accurate-least-mean-squares-solvers | 1906.04705 | null | https://arxiv.org/abs/1906.04705v2 | https://arxiv.org/pdf/1906.04705v2.pdf | Fast and Accurate Least-Mean-Squares Solvers | Least-mean squares (LMS) solvers such as Linear / Ridge / Lasso-Regression, SVD and Elastic-Net not only solve fundamental machine learning problems, but are also the building blocks in a variety of other methods, such as decision trees and matrix factorizations. We suggest an algorithm that gets a finite set of $n$ $d... | ['Dan Feldman', 'Ibrahim Jubran', 'Alaa Maalouf'] | 2019-06-11 | fast-and-accurate-least-mean-squares-solvers-1 | http://papers.nips.cc/paper/9040-fast-and-accurate-least-mean-squares-solvers | http://papers.nips.cc/paper/9040-fast-and-accurate-least-mean-squares-solvers.pdf | neurips-2019-12 | ['data-summarization'] | ['miscellaneous'] | [ 8.25528502e-02 4.98272553e-02 -2.80256599e-01 -2.62807548e-01
-9.89653826e-01 -5.87827742e-01 -1.16150275e-01 2.01729029e-01
-6.29446626e-01 7.33123243e-01 1.07307822e-01 -5.09379506e-01
-4.09710139e-01 -8.42014968e-01 -9.69007432e-01 -9.73480582e-01
-6.53483033e-01 6.28618360e-01 -1.10690676e-01 -4.23313975... | [6.739251136779785, 4.638258934020996] |
5b13a2b9-37de-4042-b244-4aec0a253dda | partial-3d-object-retrieval-using-local | 2107.03368 | null | https://arxiv.org/abs/2107.03368v1 | https://arxiv.org/pdf/2107.03368v1.pdf | Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing | A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure ca... | ['Theoharis Theoharis', 'Bart Iver van Blokland'] | 2021-07-07 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [ 1.91553310e-01 -7.54856229e-01 -4.11688417e-01 -2.98696339e-01
-1.08152127e+00 -6.75608218e-01 7.79318631e-01 5.70210218e-01
-3.77372503e-01 2.15253368e-01 -4.40413654e-02 -6.55357018e-02
-6.25753462e-01 -8.39144528e-01 -1.84450358e-01 -4.99875039e-01
-4.40543264e-01 5.60049951e-01 8.93400669e-01 -1.76858544... | [10.615935325622559, 0.2708977162837982] |
a8b263b6-5056-4d00-8be6-b37a58f7aa93 | dopa-a-fast-and-comprehensive-cnn-defense | 1905.08790 | null | https://arxiv.org/abs/1905.08790v4 | https://arxiv.org/pdf/1905.08790v4.pdf | DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks | Recently, Convolutional Neural Networks (CNNs) demonstrate a considerable vulnerability to adversarial attacks, which can be easily misled by adversarial perturbations. With more aggressive methods proposed, adversarial attacks can be also applied to the physical world, causing practical issues to various CNN powered a... | ['Fuxun Yu', 'Zirui Xu', 'Xiang Chen'] | 2019-05-21 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 3.17231834e-01 9.39810649e-02 1.30837396e-01 -1.00826100e-01
-6.36066437e-01 -1.12565339e+00 6.17479086e-01 -1.44739464e-01
-1.18398860e-01 3.69761139e-01 -9.46479365e-02 -4.63368744e-01
1.87328503e-01 -9.25895631e-01 -8.43915999e-01 -6.48047984e-01
-1.56084420e-02 -2.49240041e-01 4.44145709e-01 -3.74472797... | [5.59773063659668, 7.898801803588867] |
b2a4bf14-fe86-428a-8234-1b1730e5f3d0 | black-box-testing-of-deep-neural-networks | 2112.12591 | null | https://arxiv.org/abs/2112.12591v5 | https://arxiv.org/pdf/2112.12591v5.pdf | Black-Box Testing of Deep Neural Networks Through Test Case Diversity | Deep Neural Networks (DNNs) have been extensively used in many areas including image processing, medical diagnostics, and autonomous driving. However, DNNs can exhibit erroneous behaviours that may lead to critical errors, especially when used in safety-critical systems. Inspired by testing techniques for traditional s... | ['Mojtaba Bagherzadeh', 'Ramesh S', 'Lionel Briand', 'Manel Abdellatif', 'Zohreh Aghababaeyan'] | 2021-12-20 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 3.46518815e-01 2.33185459e-02 1.22346513e-01 -4.06465530e-01
-1.51906639e-01 -4.13120180e-01 4.59198266e-01 2.60525227e-01
-5.25727332e-01 8.67931247e-01 -4.40985262e-01 -7.44091153e-01
-5.66357374e-01 -9.54426050e-01 -7.74518132e-01 -4.73676831e-01
-1.47956824e-02 3.49147797e-01 5.53962529e-01 -6.65737540... | [6.562127113342285, 7.635762691497803] |
17fa26e4-c1dd-41b3-9d9f-8bc917b752a1 | hierarchical-pronunciation-assessment-with | 2211.08102 | null | https://arxiv.org/abs/2211.08102v2 | https://arxiv.org/pdf/2211.08102v2.pdf | Hierarchical Pronunciation Assessment with Multi-Aspect Attention | Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However... | ['Gary Geunbae Lee', 'Yunsu Kim', 'Heejin Do'] | 2022-11-15 | null | null | null | null | ['phone-level-pronunciation-scoring', 'word-level-pronunciation-scoring', 'utterance-level-pronounciation-scoring'] | ['speech', 'speech', 'speech'] | [-1.86862215e-01 -1.77531630e-01 -3.53920639e-01 -5.72288871e-01
-1.17163575e+00 -5.81770420e-01 4.28414851e-01 4.53803688e-01
-2.35909954e-01 5.65996170e-01 6.97267592e-01 -3.22532684e-01
-1.91297919e-01 -7.50220597e-01 -3.22507024e-01 -3.13828737e-01
5.73569596e-01 6.68893874e-01 -8.93028975e-02 -2.07337037... | [14.31932258605957, 6.821648597717285] |
63a243b4-2771-452c-8fcb-2122639056cb | learning-an-animatable-detailed-3d-face-model | 2012.04012 | null | https://arxiv.org/abs/2012.04012v2 | https://arxiv.org/pdf/2012.04012v2.pdf | Learning an Animatable Detailed 3D Face Model from In-The-Wild Images | While current monocular 3D face reconstruction methods can recover fine geometric details, they suffer several limitations. Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression. Other methods are trained on high-quality face scans and do not genera... | ['Timo Bolkart', 'Michael J. Black', 'Haiwen Feng', 'Yao Feng'] | 2020-12-07 | null | null | null | null | ['3d-face-animation', '3d-face-modeling', 'face-alignment', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.78335354e-02 -4.88325320e-02 1.02926977e-01 -7.92182624e-01
-5.78035235e-01 -7.33120084e-01 7.83139944e-01 -7.73512006e-01
1.31419823e-01 5.30281186e-01 2.96065688e-01 2.52132595e-01
3.68129402e-01 -6.65616751e-01 -7.61881530e-01 -8.06147695e-01
4.84618433e-02 5.33557475e-01 -5.67695498e-01 -1.59908831... | [12.755997657775879, -0.3493427336215973] |
5438dc8a-f314-4409-a708-07245d97653a | safe-exploration-for-identifying-linear | 1711.11165 | null | http://arxiv.org/abs/1711.11165v1 | http://arxiv.org/pdf/1711.11165v1.pdf | Safe Exploration for Identifying Linear Systems via Robust Optimization | Safely exploring an unknown dynamical system is critical to the deployment of
reinforcement learning (RL) in physical systems where failures may have
catastrophic consequences. In scenarios where one knows little about the
dynamics, diverse transition data covering relevant regions of state-action
space is needed to ap... | ['Tyler Lu', 'Craig Boutilier', 'Binz Roy', 'Dale Schuurmans', 'Martin Zinkevich'] | 2017-11-30 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.46869081e-02 2.77190000e-01 -2.48558074e-01 3.02990645e-01
-8.81889820e-01 -7.02254713e-01 3.61134201e-01 3.50691378e-01
-3.92016858e-01 9.53516066e-01 -4.23328727e-01 -6.64770722e-01
-5.50145268e-01 -5.44662476e-01 -9.42608356e-01 -9.03091311e-01
-3.62757921e-01 5.94836771e-01 2.01221153e-01 -2.17310172... | [4.660819053649902, 2.264647960662842] |
548787cb-136a-4346-90c0-a24acc0e8890 | weakly-supervised-contrastive-learning-for | 2109.12242 | null | https://arxiv.org/abs/2109.12242v1 | https://arxiv.org/pdf/2109.12242v1.pdf | Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation | Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to gener... | ['Chun-Nan Hsu', 'Julian McAuley', 'Amilcare Gentili', 'Eric Chang', 'Jiang Du', 'Xing Lu', 'Zexue He', 'An Yan'] | 2021-09-25 | null | https://aclanthology.org/2021.findings-emnlp.336 | https://aclanthology.org/2021.findings-emnlp.336.pdf | findings-emnlp-2021-11 | ['medical-report-generation'] | ['medical'] | [ 7.96318889e-01 1.00270164e+00 -1.71873033e-01 -6.44923031e-01
-1.85383344e+00 -1.50599301e-01 5.81304371e-01 5.30983210e-01
-3.01350176e-01 1.13135791e+00 9.78268862e-01 -3.23097587e-01
2.04375699e-01 -6.20353103e-01 -7.72399664e-01 -3.81377727e-01
-5.43891005e-02 6.46534503e-01 -1.87027499e-01 1.26455739... | [15.03172492980957, -1.4003006219863892] |
275a9994-0336-4a2b-a110-391587dee18a | graph-conditioned-sparse-attention-for | 2112.00663 | null | https://arxiv.org/abs/2112.00663v2 | https://arxiv.org/pdf/2112.00663v2.pdf | Graph Conditioned Sparse-Attention for Improved Source Code Understanding | Transformer architectures have been successfully used in learning source code representations. The fusion between a graph representation like Abstract Syntax Tree (AST) and a source code sequence makes the use of current approaches computationally intractable for large input sequence lengths. Source code can have long-... | ['Barry Boehm', 'Iordanis Fostiropoulos', 'Junyan Cheng'] | 2021-12-01 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 3.10119420e-01 2.14509368e-01 -1.89130083e-01 -1.46005765e-01
-7.71100640e-01 -6.05984867e-01 4.45073247e-01 9.34469163e-01
-2.72416621e-01 2.87521631e-01 4.62024957e-01 -7.74942875e-01
8.45155343e-02 -5.85510850e-01 -9.46020246e-01 -1.30059332e-01
-3.02409559e-01 2.08727926e-01 2.84781456e-01 -1.36452943... | [7.55947732925415, 7.9532623291015625] |
b30cf50a-6bfd-4758-902d-a7b903cc8239 | dscribe-library-of-descriptors-for-machine | 1904.08875 | null | http://arxiv.org/abs/1904.08875v1 | http://arxiv.org/pdf/1904.08875v1.pdf | DScribe: Library of Descriptors for Machine Learning in Materials Science | DScribe is a software package for machine learning that provides popular
feature transformations ("descriptors") for atomistic materials simulations.
DScribe accelerates the application of machine learning for atomistic property
prediction by providing user-friendly, off-the-shelf descriptor
implementations. The packag... | ['Adam S. Foster', 'Patrick Rinke', 'Filippo Federici Canova', 'Marc O. J. Jäger', 'David Z. Gao', 'Yashasvi S. Ranawat', 'Eiaki V. Morooka', 'Lauri Himanen'] | 2019-04-18 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [-2.02148050e-01 -4.50399101e-01 -1.32809877e-01 -4.03710991e-01
-4.49505806e-01 -3.13572377e-01 5.93530297e-01 7.02695072e-01
-3.36394787e-01 1.02975607e+00 3.56786884e-02 -5.05999744e-01
1.18537468e-03 -9.18978035e-01 -6.10348105e-01 -1.19655073e+00
-3.57441276e-01 6.81737900e-01 2.44684249e-01 -4.93975282... | [5.120254993438721, 5.380608081817627] |
2db6145d-2251-4257-827d-e3dce423530b | i-2r-net-intra-and-inter-human-relation | 2206.10892 | null | https://arxiv.org/abs/2206.10892v2 | https://arxiv.org/pdf/2206.10892v2.pdf | I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation | In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relati... | ['Ming Zeng', 'Dong Chen', 'Jianmin Bao', 'Xuan Cheng', 'Meihong Wang', 'PengFei Liu', 'Yinglin Zheng', 'Wenjin Deng', 'Yiwei Ding'] | 2022-06-22 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-3.30113381e-01 9.10478830e-02 1.78397447e-01 -3.02695185e-01
-3.39204878e-01 -2.68809915e-01 5.29443562e-01 -9.50979143e-02
-4.85320956e-01 7.03947544e-01 2.89235294e-01 3.75497252e-01
-2.29748964e-01 -4.93788093e-01 -5.00996709e-01 -3.40838909e-01
-3.83923471e-01 7.38921881e-01 5.00443161e-01 -5.11338174... | [7.190167427062988, -0.757649302482605] |
61da4ca4-9a28-43a8-8526-8fc27c1d4f45 | momentum-contrastive-voxel-wise | 2105.07059 | null | https://arxiv.org/abs/2105.07059v4 | https://arxiv.org/pdf/2105.07059v4.pdf | Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation | Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contrast a single positive vector (i.e., an augmentation of the same image) against a set of negatives within the entire remainder of the batch by ... | ['James S. Duncan', 'Lawrence Staib', 'Ruihan Zhao', 'Chenyu You'] | 2021-05-14 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 4.35750395e-01 8.09733495e-02 -2.99985617e-01 -4.50514138e-01
-1.11275434e+00 -5.69030225e-01 3.47357780e-01 2.01472774e-01
-3.76186937e-01 6.07894957e-01 1.73349097e-01 -1.97438329e-01
-4.93767083e-01 -4.92691755e-01 -7.47705877e-01 -9.72353280e-01
-2.51891434e-01 2.25559875e-01 1.51356339e-01 -3.00865304... | [14.723199844360352, -2.134382963180542] |
49e8cda7-c125-4660-8841-30fb346a57d4 | leveraging-instance-image-and-dataset-level | null | null | https://ieeexplore.ieee.org/abstract/document/9193980 | http://mftp.mmcheng.net/Papers/21PAMI_InsImgDatasetWSIS.pdf | Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation | Weakly supervised semantic instance segmentation with only image-level supervision, instead of relying on expensive pixel wise masks or bounding box annotations, is an important problem to alleviate the data-hungry nature of deep learning. In this paper, we tackle this challenging problem by aggregating the image-level... | ['Ming-Ming Cheng', 'Yu Qiu', 'Yu-Jun Shi', 'Pei-Song Wen', 'Yu-Huan Wu', 'Yun Liu'] | 2020-09-10 | null | null | null | null | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.59197390e-01 6.15674615e-01 -2.59329826e-01 -6.19983077e-01
-1.20981276e+00 -4.65152562e-01 3.88457686e-01 3.15683693e-01
-5.11869788e-01 4.95314062e-01 -3.72633189e-01 8.39924961e-02
-6.49713427e-02 -8.81625295e-01 -1.04066789e+00 -8.90886486e-01
3.46567661e-01 7.65265584e-01 5.09653211e-01 2.55570233... | [9.503747940063477, 0.5350584983825684] |
ed1d1dc9-5e8a-474d-88d3-652bdf2e5dc0 | self-training-for-domain-adaptive-scene-text | 2005.11487 | null | https://arxiv.org/abs/2005.11487v1 | https://arxiv.org/pdf/2005.11487v1.pdf | Self-Training for Domain Adaptive Scene Text Detection | Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive... | ['Dongbao Yang', 'Yudi Chen', 'Yu Zhou', 'Fei Yang', 'Wei Wang', 'Weiping Wang'] | 2020-05-23 | null | null | null | null | ['scene-text-detection', 'image-to-video'] | ['computer-vision', 'computer-vision'] | [ 2.03395605e-01 -3.29904974e-01 -9.25492197e-02 -4.07455087e-01
-6.31019890e-01 -1.00816861e-01 6.18413508e-01 -1.56429306e-01
-5.55582285e-01 3.74545336e-01 -2.22707078e-01 1.34101659e-02
5.14324307e-01 -7.41745472e-01 -6.72452688e-01 -5.90956211e-01
4.55029517e-01 5.04463613e-01 5.56361139e-01 1.51551530... | [11.760824203491211, 2.138737678527832] |
798553ce-74c4-4eb7-baf2-db79729f1ee5 | dual-swin-transformer-based-mutual | 2206.03105 | null | https://arxiv.org/abs/2206.03105v1 | https://arxiv.org/pdf/2206.03105v1.pdf | Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection | Salient Object Detection is the task of predicting the human attended region in a given scene. Fusing depth information has been proven effective in this task. The main challenge of this problem is how to aggregate the complementary information from RGB modality and depth modality. However, conventional deep models hea... | ['Sam Kwong', 'Chao Zeng'] | 2022-06-07 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.37528855e-01 -4.99991104e-02 -1.54573128e-01 -6.11847281e-01
-4.08338726e-01 -3.47467251e-02 4.68093306e-01 2.79960898e-03
-5.03991127e-01 4.94331568e-01 3.70582163e-01 -9.00298506e-02
7.06357881e-02 -8.05937409e-01 -6.83973134e-01 -8.33710730e-01
4.16395217e-01 -3.62912238e-01 7.44238973e-01 -2.46472940... | [9.747942924499512, -0.7042974829673767] |
dff35287-ca63-4c1e-8410-4c9590ed87ea | co-design-hardware-and-algorithm-for-vector | 2306.11182 | null | https://arxiv.org/abs/2306.11182v3 | https://arxiv.org/pdf/2306.11182v3.pdf | Co-design Hardware and Algorithm for Vector Search | Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents.... | ['Gustavo Alonso', 'Torsten Hoefler', 'Theodoros Rekatsinas', 'Shuai Zhang', 'Cedric Renggli', 'Runbin Shi', 'Zhenhao He', 'Johannes De Fine Licht', 'Yu Zhu', 'Shigang Li', 'Wenqi Jiang'] | 2023-06-19 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 1.56140745e-01 -9.10359919e-01 -4.74285901e-01 -3.39381635e-01
-7.71544039e-01 -7.22157776e-01 6.05329990e-01 5.01653254e-01
-6.40577555e-01 2.16915086e-01 -1.58384189e-01 -1.04478538e+00
-3.62005889e-01 -9.54175174e-01 -2.51630127e-01 -3.27872574e-01
-1.39484033e-01 4.68932062e-01 3.42745423e-01 -3.07392895... | [8.547296524047852, 3.2680563926696777] |
84748616-27f5-4bd3-a097-e68314a8d5d6 | a-data-and-analysis-resourcea-fora-ana | null | null | https://aclanthology.org/L12-1629 | https://aclanthology.org/L12-1629.pdf | A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic. | The analysis of a corpus of micro-blogs on the topic of the 2011 UK referendum about the Alternative Vote has been undertaken as a joint activity by text miners and social scientists. To facilitate the collaboration, the corpus and its analysis is managed in a Web-accessible framework that allows users to upload their ... | ['Steven Gray', 'Sophia Ananiadou', 'William Black', 'Rob Procter'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['text-annotation'] | ['natural-language-processing'] | [ 2.37909764e-01 7.78519094e-01 -5.54578662e-01 -3.65138024e-01
-9.40365016e-01 -8.84873271e-01 9.22624826e-01 1.05699551e+00
-6.11921966e-01 8.15602958e-01 1.02531660e+00 -8.54340017e-01
1.09770358e-01 -3.95469069e-01 -2.17270091e-01 -3.48986804e-01
4.54419851e-01 5.60045898e-01 1.56143352e-01 -1.20413877... | [9.101018905639648, 9.798480033874512] |
92bb9a5b-5189-49e5-983f-bc7d37a3801b | unsupervised-gaze-prediction-in-egocentric | 2001.11580 | null | https://arxiv.org/abs/2001.11580v2 | https://arxiv.org/pdf/2001.11580v2.pdf | Unsupervised Gaze Prediction in Egocentric Videos by Energy-based Surprise Modeling | Egocentric perception has grown rapidly with the advent of immersive computing devices. Human gaze prediction is an important problem in analyzing egocentric videos and has primarily been tackled through either saliency-based modeling or highly supervised learning. We quantitatively analyze the generalization capabilit... | ['Arunkumar Bagavathi', 'Sathyanarayanan N. Aakur'] | 2020-01-30 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 2.02785566e-01 2.22694069e-01 -2.27298081e-01 -5.00133276e-01
-1.51454851e-01 -1.91322371e-01 3.61443371e-01 -1.51451141e-01
-4.38835561e-01 3.72799903e-01 2.41436616e-01 4.66168206e-03
-1.48045510e-01 -1.74791202e-01 -8.65842819e-01 -6.17214322e-01
-6.85335919e-02 7.04953671e-02 4.15011644e-01 -1.87889159... | [13.928386688232422, 0.05947059392929077] |
a7ab6e05-7c22-48fa-b235-760b250343ad | a-comprehensive-review-of-image-line-segment | 2305.00264 | null | https://arxiv.org/abs/2305.00264v1 | https://arxiv.org/pdf/2305.00264v1.pdf | A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges | Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills the gap by comprehensively reviewing related studies on detecting and describing two-... | ['Ce Zhu', 'Yipeng Liu', 'Yingjie Zhou', 'Xinyu Lin'] | 2023-04-29 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 2.65275538e-01 -5.32717228e-01 -6.42162740e-01 -2.50668317e-01
-3.35042626e-01 -6.93443954e-01 8.78077149e-02 1.39540043e-02
-4.75365994e-03 4.20139790e-01 -2.83009589e-01 -3.06070447e-01
1.14193805e-01 -4.98778731e-01 -3.30265552e-01 -6.90064251e-01
-1.61539808e-01 -5.51148541e-02 5.30983210e-01 1.51409544... | [8.357903480529785, -1.588034987449646] |
713e1b40-fe66-4d2d-8b92-1c236e4080d9 | multimodal-adaptive-distillation-for | 2204.10496 | null | https://arxiv.org/abs/2204.10496v2 | https://arxiv.org/pdf/2204.10496v2.pdf | Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks | Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples, the labor cost is prohibitive to scale further. Conversely, unimodal encoders are pretrained with simpler annotations that are less cost-pr... | ['Lu Yuan', 'Shih-Fu Chang', 'Kai-Wei Chang', 'Haoxuan You', 'Jianwei Yang', 'Bin Xiao', 'Xiyang Dai', 'Luowei Zhou', 'Yen-Chun Chen', 'Noel Codella', 'Zhecan Wang'] | 2022-04-22 | null | null | null | null | ['visual-commonsense-reasoning', 'visual-entailment'] | ['reasoning', 'reasoning'] | [ 5.25574759e-02 -7.99935609e-02 -2.50699699e-01 -3.51126939e-01
-8.83038044e-01 -6.72697484e-01 8.24108720e-01 -2.62336105e-01
-5.39874136e-01 5.94202638e-01 2.31107578e-01 -6.05182469e-01
3.30541700e-01 -5.64923882e-01 -9.71172929e-01 -2.92052805e-01
4.47713256e-01 3.92961144e-01 3.19210067e-02 -2.15477586... | [10.795576095581055, 1.6715483665466309] |
bfe9efc8-1659-498c-94e2-c6219c443f01 | comparing-in-context-improving-cosine | 2203.14996 | null | https://arxiv.org/abs/2203.14996v1 | https://arxiv.org/pdf/2203.14996v1.pdf | Comparing in context: Improving cosine similarity measures with a metric tensor | Cosine similarity is a widely used measure of the relatedness of pre-trained word embeddings, trained on a language modeling goal. Datasets such as WordSim-353 and SimLex-999 rate how similar words are according to human annotators, and as such are often used to evaluate the performance of language models. Thus, any im... | ['Adriana D. Correia', 'Mara D. Fennema', 'Ghislaine L. van den Boogerd', 'Isa M. Apallius de Vos'] | 2022-03-28 | null | https://aclanthology.org/2021.icon-main.17 | https://aclanthology.org/2021.icon-main.17.pdf | icon-2021-12 | ['word-similarity'] | ['natural-language-processing'] | [ 1.08912528e-01 -2.05158621e-01 -2.22289085e-01 -5.21381378e-01
-7.32364118e-01 -7.35839903e-01 1.09391677e+00 8.88657451e-01
-1.19652379e+00 4.50278521e-01 6.77602470e-01 -3.11399490e-01
-1.83953613e-01 -6.32326484e-01 -1.48437008e-01 -4.07826930e-01
-2.31071133e-02 5.12192190e-01 2.89652139e-01 -4.96718347... | [10.472134590148926, 8.863777160644531] |
47c5f576-2931-48d5-9df1-f99310d5661d | self-supervised-representation-learning-on | 2004.10605 | null | https://arxiv.org/abs/2004.10605v2 | https://arxiv.org/pdf/2004.10605v2.pdf | Self-Supervised Representation Learning on Document Images | This work analyses the impact of self-supervised pre-training on document images in the context of document image classification. While previous approaches explore the effect of self-supervision on natural images, we show that patch-based pre-training performs poorly on document images because of their different struct... | ['Michael Panaitescu-Liess', 'Mihai Ghidoveanu', 'Marius Popescu', 'Adrian Cosma'] | 2020-04-18 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 6.97845340e-01 2.40756404e-02 -4.95903432e-01 -6.03031933e-01
-6.64516211e-01 -6.29686892e-01 1.20241666e+00 4.05723870e-01
-6.77179337e-01 4.96579260e-01 3.30765367e-01 -2.00425997e-01
4.98139448e-02 -5.65883458e-01 -8.67004812e-01 -5.76828897e-01
2.56157875e-01 4.67030466e-01 3.07723463e-01 -2.04982162... | [9.622245788574219, 2.5111751556396484] |
1f9db6a5-b596-4d0d-8b67-1aba1d238650 | statistics-and-samples-in-distributional | 1902.08102 | null | http://arxiv.org/abs/1902.08102v1 | http://arxiv.org/pdf/1902.08102v1.pdf | Statistics and Samples in Distributional Reinforcement Learning | We present a unifying framework for designing and analysing distributional
reinforcement learning (DRL) algorithms in terms of recursively estimating
statistics of the return distribution. Our key insight is that DRL algorithms
can be decomposed as the combination of some statistical estimator and a method
for imputing... | ['Rémi Munos', 'Saurabh Kumar', 'Robert Dadashi', 'Mark Rowland', 'Will Dabney', 'Marc G. Bellemare'] | 2019-02-21 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.19427580e-01 -4.08713222e-02 -2.05284834e-01 -2.80357957e-01
-1.19956470e+00 -8.54981184e-01 7.77161062e-01 1.44066766e-01
-6.97834134e-01 1.15366268e+00 1.32691428e-01 -8.45101833e-01
-6.26539946e-01 -9.20833051e-01 -7.77703762e-01 -7.85684526e-01
-4.72924501e-01 7.71266997e-01 9.82879698e-02 -3.71133476... | [4.020107746124268, 2.646852970123291] |
1ab67cac-9d62-421b-a0c9-30a3a1bed33a | dependency-parsing-past-present-and-future | null | null | https://aclanthology.org/C14-3006 | https://aclanthology.org/C14-3006.pdf | Dependency Parsing: Past, Present, and Future | null | ['Zhenghua Li', 'Wenliang Chen', 'Min Zhang'] | 2014-08-01 | dependency-parsing-past-present-and-future-1 | https://aclanthology.org/C14-3006 | https://aclanthology.org/C14-3006.pdf | coling-2014-8 | ['lexical-analysis'] | ['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... | [-7.174190521240234, 3.425464153289795] |
c9ec9f42-33ed-46ab-967d-d039f1ad6594 | rotation-equivariant-cnns-for-digital | 1806.03962 | null | http://arxiv.org/abs/1806.03962v1 | http://arxiv.org/pdf/1806.03962v1.pdf | Rotation Equivariant CNNs for Digital Pathology | We propose a new model for digital pathology segmentation, based on the
observation that histopathology images are inherently symmetric under rotation
and reflection. Utilizing recent findings on rotation equivariant CNNs, the
proposed model leverages these symmetries in a principled manner. We present a
visual analysi... | ['Taco Cohen', 'Bastiaan S. Veeling', 'Max Welling', 'Jasper Linmans', 'Jim Winkens'] | 2018-06-08 | null | null | null | null | ['breast-tumour-classification'] | ['medical'] | [ 4.50964391e-01 6.50376081e-01 -5.81355572e-01 -1.14835225e-01
-7.72606075e-01 -6.85860217e-01 6.02853358e-01 -1.30173657e-02
-3.10772717e-01 2.33267158e-01 3.17221642e-01 -6.67196929e-01
-1.78226814e-01 -2.87964702e-01 -5.30546904e-01 -9.30555880e-01
-1.44357942e-02 2.05985606e-01 -9.75317359e-02 -2.61865139... | [15.061229705810547, -2.9261105060577393] |
18ab874d-550a-449c-8ab7-c341ef34e882 | bert-rankers-are-brittle-a-study-using | 2206.11724 | null | https://arxiv.org/abs/2206.11724v1 | https://arxiv.org/pdf/2206.11724v1.pdf | BERT Rankers are Brittle: a Study using Adversarial Document Perturbations | Contextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under adversarial inputs is under-explored. In this paper, we argue that BERT-rankers are not immune to adversarial attacks targeting retrieved do... | ['Avishek Anand', 'Lijun Lyu', 'Yumeng Wang'] | 2022-06-23 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 3.01633179e-01 -6.39630258e-02 -2.10998915e-02 -1.81416199e-01
-1.51922154e+00 -1.42708778e+00 1.27806556e+00 5.47699571e-01
-4.59665149e-01 7.70009279e-01 6.15428030e-01 -3.33225489e-01
-4.86559391e-01 -9.19995546e-01 -1.04006529e+00 -6.77383602e-01
-3.31690460e-01 7.93296933e-01 5.42002976e-01 -7.89372027... | [6.043377876281738, 8.131400108337402] |
268f92d2-0d3f-4409-a8a8-494a608aadc2 | an-iterative-multi-path-fully-convolutional | null | null | https://doi.org/10.1002/mp.13859 | https://doi.org/10.1002/mp.13859 | An Iterative Multi‐path Fully Convolutional Neural Network for Automatic Cardiac Segmentation in Cine MR Images | Purpose: Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information may be highly beneficial for segmentation performance improvement. To ... | ['Jiliu Zhou', 'Yan Wang 1', 'Kunlin Cao 3', 'Youbing Yin 3', 'Qi Song 3', 'Xin Wang 3', 'Xi Wu 2', 'Zongqing Ma 1'] | 2019-11-01 | null | null | null | med-phy-2019-11 | ['cardiac-segmentation'] | ['medical'] | [ 2.57256150e-01 -1.27954140e-01 2.27284193e-01 -4.96820867e-01
-7.80781567e-01 -3.68752301e-01 1.61203057e-01 2.54670233e-01
-4.96396244e-01 6.30352795e-01 4.61827666e-02 -1.24110542e-01
-2.24076092e-01 -3.66434574e-01 -2.90639877e-01 -9.56912637e-01
-4.87985492e-01 3.97318959e-01 2.83191383e-01 1.96919143... | [14.287158966064453, -2.4278767108917236] |
9f50088f-88d8-4842-b5ab-be085aa92bf8 | afd-net-aggregated-feature-difference | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Quan_AFD-Net_Aggregated_Feature_Difference_Learning_for_Cross-Spectral_Image_Patch_Matching_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Quan_AFD-Net_Aggregated_Feature_Difference_Learning_for_Cross-Spectral_Image_Patch_Matching_ICCV_2019_paper.pdf | AFD-Net: Aggregated Feature Difference Learning for Cross-Spectral Image Patch Matching | Image patch matching across different spectral domains is more challenging than in a single spectral domain. We consider the reason is twofold: 1. the weaker discriminative feature learned by conventional methods; 2. the significant appearance difference between two images domains. To tackle these problems, we propose ... | [' Licheng Jiao', ' Ning Huyan', ' Yanfeng Li', ' Shaowei Wei', ' Shuang Wang', ' Xuefeng Liang', 'Dou Quan'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['patch-matching'] | ['computer-vision'] | [ 5.44482589e-01 -4.46729034e-01 -3.33557308e-01 -5.54264307e-01
-8.75942707e-01 -5.13042510e-01 4.89089072e-01 -1.36094481e-01
-2.57714272e-01 4.57068205e-01 4.51013260e-02 2.36770824e-01
-3.64479899e-01 -7.37861633e-01 -5.25529027e-01 -8.19914877e-01
8.57376829e-02 -2.42625639e-01 3.74253124e-01 -8.99620950... | [9.78939437866211, 2.0874929428100586] |
ebeacdb8-2eed-4c58-87a1-1e21d5534ad9 | improving-question-answering-performance-1 | 2212.08897 | null | https://arxiv.org/abs/2212.08897v1 | https://arxiv.org/pdf/2212.08897v1.pdf | Improving Question Answering Performance through Manual Annotation: Costs, Benefits and Strategies | Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is known to be time-consuming and therefore expensive to acquire. As a result, the appropriate datasets are available only for a handful of langu... | ['Maciej Ogrodniczuk', 'Piotr Przybyła', 'Piotr Rybak'] | 2022-12-17 | null | null | null | null | ['passage-retrieval', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [-3.79178017e-01 1.00999914e-01 1.59721673e-01 -1.55083537e-01
-1.65458369e+00 -1.03025997e+00 4.75577414e-01 6.55463517e-01
-7.02630937e-01 1.17366898e+00 1.60929292e-01 -3.53115201e-01
-2.56785691e-01 -8.40231478e-01 -6.03190839e-01 -3.04530084e-01
4.07496452e-01 1.08780551e+00 7.41939127e-01 -4.53576297... | [11.317534446716309, 8.00059700012207] |
e3e461ca-0a51-484e-a74e-6f534eb4b6a1 | a-gaussian-process-regression-based-dynamical | 2211.14162 | null | https://arxiv.org/abs/2211.14162v1 | https://arxiv.org/pdf/2211.14162v1.pdf | A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking | Maneuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions. This paper proposes a novel data-driven method for learning the dynamical motion model of a target. Non-parametric Gaussian process regression (GPR) is used to learn a target's naturally shif... | ['James R. Hopgood', 'Ian K. Proudler', 'Mike E. Davies', 'Mengwei Sun'] | 2022-11-25 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.43529791e-01 -2.76062727e-01 2.14274839e-01 -9.70470235e-02
-6.19562566e-01 -5.23204744e-01 7.40836680e-01 1.63492739e-01
-3.13001275e-01 5.79607308e-01 -2.12617934e-01 -2.93362170e-01
-4.51057434e-01 -5.77423990e-01 -6.02451861e-01 -1.25204206e+00
-3.95469248e-01 5.64100802e-01 7.80937910e-01 -8.32056180... | [6.553560256958008, -1.995778203010559] |
81d15ea3-fdb4-4398-a79c-6dc701e80b5d | improving-the-naturalness-and-diversity-of | null | null | https://aclanthology.org/2020.inlg-1.7 | https://aclanthology.org/2020.inlg-1.7.pdf | Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training | In this paper we consider the problem of optimizing neural Referring Expression Generation (REG) models with sequence level objectives. Recently reinforcement learning (RL) techniques have been adopted to train deep end-to-end systems to directly optimize sequence-level objectives. However, there are two issues associa... | ['Dimitra Gkatzia', 'Emma Hart', 'Nikolaos Panagiaris'] | null | null | null | null | inlg-acl-2020-12 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.98411340e-01 2.42857546e-01 -1.37428259e-02 -2.94174641e-01
-1.17690289e+00 -5.82204878e-01 5.17701924e-01 -1.85744986e-02
-5.55918097e-01 1.17535925e+00 4.75410849e-01 -1.03685118e-01
1.72188075e-03 -8.01907241e-01 -7.77056932e-01 -4.33208913e-01
8.12107977e-03 2.21776694e-01 -3.63048911e-01 -4.50599164... | [11.885564804077148, 9.175776481628418] |
0c5ec7fd-97d6-4724-9e07-c68916335205 | towards-automatic-neural-architecture-search | 2305.18030 | null | https://arxiv.org/abs/2305.18030v1 | https://arxiv.org/pdf/2305.18030v1.pdf | Towards Automatic Neural Architecture Search within General Super-Networks | Existing neural architecture search (NAS) methods typically rely on pre-specified super deep neural networks (super-networks) with handcrafted search spaces beforehand. Such requirements make it challenging to extend them onto general scenarios without significant human expertise and manual intervention. To overcome th... | ['Ilya Zharkov', 'Tianyu Ding', 'Luming Liang', 'Tianyi Chen'] | 2023-05-25 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.51655778e-01 -4.27800678e-02 -2.12471291e-01 -3.80604118e-01
-5.22487879e-01 -3.95929337e-01 1.48560688e-01 -6.18088901e-01
-4.87796396e-01 7.25937724e-01 1.75039414e-02 -4.29538608e-01
-4.84967113e-01 -5.09046674e-01 -8.46844852e-01 -6.99726880e-01
-1.25581622e-01 5.37579894e-01 2.53143664e-02 -4.68270361... | [8.615863800048828, 3.2816951274871826] |
300f6188-9fc0-464f-b8fc-4ec5d7188644 | multi-slice-dense-sparse-learning-for | 2108.06761 | null | https://arxiv.org/abs/2108.06761v1 | https://arxiv.org/pdf/2108.06761v1.pdf | Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation | Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNN... | ['Pierce KH Chow', 'Zeng Zeng', 'Yanjie Liu', 'Zeyu Ma', 'Ziyuan Zhao'] | 2021-08-15 | null | null | null | null | ['automatic-liver-and-tumor-segmentation', 'sparse-learning'] | ['medical', 'methodology'] | [-1.00885155e-02 1.07889883e-02 -5.17494977e-01 -4.96656299e-01
-2.06483826e-01 -7.81062320e-02 2.63258427e-01 8.26643407e-02
-3.59685570e-01 4.59547102e-01 4.44338888e-01 -4.70871776e-01
-6.45123422e-02 -8.66683543e-01 -2.66516477e-01 -8.10079396e-01
7.06300559e-03 1.74664930e-02 2.34341159e-01 2.97333032... | [14.632064819335938, -2.56558895111084] |
5c43aab5-2bad-4d54-9b00-683669a3d2fd | cross-node-federated-graph-neural-network-for-1 | 2106.05223 | null | https://arxiv.org/abs/2106.05223v1 | https://arxiv.org/pdf/2106.05223v1.pdf | Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues. While federated lea... | ['Yan Liu', 'Sirisha Rambhatla', 'Chuizheng Meng'] | 2021-06-09 | cross-node-federated-graph-neural-network-for | https://openreview.net/forum?id=HWX5j6Bv_ih | https://openreview.net/pdf?id=HWX5j6Bv_ih | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-2.08762720e-01 7.20428899e-02 -6.93976998e-01 -1.73946604e-01
-1.12364709e-01 -4.68939126e-01 4.16912615e-01 -2.41366066e-02
4.23727632e-02 7.15162218e-01 7.14150891e-02 -8.51400435e-01
-6.07436419e-01 -1.08678257e+00 -8.11986387e-01 -6.77975953e-01
-6.85365856e-01 4.88856435e-01 -5.81325665e-02 -1.62846327... | [6.711524963378906, 2.736300230026245] |
b924ec88-3e23-453a-9668-d31b487b3272 | a-hungarian-sentiment-corpus-manually | null | null | https://aclanthology.org/L16-1459 | https://aclanthology.org/L16-1459.pdf | A Hungarian Sentiment Corpus Manually Annotated at Aspect Level | In this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim of creating the corpus was to produce an appropriate database providing possibilities for developing text mining software tools... | ["Katalin Ilona Simk{\\'o}", "Martina Katalin Szab{\\'o}", 'Viktor Hangya', 'Veronika Vincze', 'Viktor Varga'] | 2016-05-01 | a-hungarian-sentiment-corpus-manually-1 | https://aclanthology.org/L16-1459 | https://aclanthology.org/L16-1459.pdf | lrec-2016-5 | ['text-annotation'] | ['natural-language-processing'] | [-4.69185412e-03 6.76190555e-01 -6.73286989e-02 -6.96161330e-01
-3.13954443e-01 -6.78607702e-01 5.58765829e-01 7.83778727e-01
-4.97992426e-01 6.83442891e-01 3.19186896e-01 -1.78963006e-01
-6.92891181e-02 -9.35870171e-01 -1.28874362e-01 -5.53345323e-01
5.24346173e-01 8.07379544e-01 2.27982864e-01 -6.72739208... | [11.04214859008789, 6.985107421875] |
38c7dc59-37aa-4fc0-8f5d-5d969fe8f621 | federated-semi-supervised-classification-of | 2205.00550 | null | https://arxiv.org/abs/2205.00550v1 | https://arxiv.org/pdf/2205.00550v1.pdf | Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks | Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, netwo... | ['Alberto Gotta', 'Pietro Cassarà', 'Lorenzo Valerio', 'Achilles Machumilane', 'Saira Bano'] | 2022-05-01 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-5.42191230e-02 -4.50776905e-01 -6.69095159e-01 -5.46448529e-01
-4.38764542e-02 -8.15404654e-01 3.75083014e-02 1.15033768e-01
-1.04698636e-01 6.50101960e-01 -4.37056929e-01 -1.04474974e+00
-3.96910280e-01 -1.04495156e+00 2.98850775e-01 -7.21009612e-01
-3.95915955e-01 3.64446342e-01 5.38313150e-01 1.98841821... | [5.070310115814209, 7.231068134307861] |
87667442-83c1-4c51-a626-af48b682d07b | zju-reler-submission-for-epic-kitchen | 2307.02010 | null | https://arxiv.org/abs/2307.02010v2 | https://arxiv.org/pdf/2307.02010v2.pdf | ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation | The Associating Objects with Transformers (AOT) framework has exhibited exceptional performance in a wide range of complex scenarios for video object segmentation. In this study, we introduce MSDeAOT, a variant of the AOT series that incorporates transformers at multiple feature scales. Leveraging the hierarchical Gate... | ['Yueting Zhuang', 'Yi Yang', 'Zongxin Yang', 'Yuanyou Xu', 'Jiahao Li'] | 2023-07-05 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.77737755e-01 -2.97469735e-01 -1.15515016e-01 -1.64521426e-01
-6.53651893e-01 -4.70808804e-01 5.05836785e-01 -5.48326150e-02
-5.37278056e-01 1.55539230e-01 -1.94083109e-01 -1.32612020e-01
-1.30441055e-01 -5.20977795e-01 -6.88700855e-01 -5.17686605e-01
-2.20378324e-01 3.82095486e-01 9.57012057e-01 -2.51163244... | [9.03046989440918, -0.16447462141513824] |
5287aced-dc66-40b2-a018-f4bac35ab143 | touch-and-go-learning-from-human-collected | 2211.12498 | null | https://arxiv.org/abs/2211.12498v2 | https://arxiv.org/pdf/2211.12498v2.pdf | Touch and Go: Learning from Human-Collected Vision and Touch | The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world. We propose a dataset with paired visual and tactile data called Touch and Go, in which human data collectors probe objects in natural environments using tactile sensors, while simultaneously r... | ['Andrew Owens', 'Wenzhen Yuan', 'Jing Zhu', 'Jiacheng Zhang', 'Chenyang Ma', 'Fengyu Yang'] | 2022-11-22 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 4.80145633e-01 -4.05480415e-01 1.26298696e-01 -2.71414459e-01
-2.07094803e-01 -8.20838630e-01 5.77262580e-01 -3.29325736e-01
-2.76221901e-01 4.57301468e-01 1.04639336e-01 3.20458077e-02
5.41463643e-02 -4.72769886e-01 -9.84829724e-01 -3.20679605e-01
6.31199628e-02 1.43983096e-01 1.54132813e-01 2.94750363... | [5.947760105133057, -0.8843041062355042] |
0de596b1-5dd7-4eb8-87c6-f41cba1ddedd | iterative-document-representation-learning | 1809.10324 | null | https://arxiv.org/abs/1809.10324v2 | https://arxiv.org/pdf/1809.10324v2.pdf | Iterative Document Representation Learning Towards Summarization with Polishing | In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization appro... | ['Yan Song', 'Shen Gao', 'Xiuying Chen', 'Chongyang Tao', 'Rui Yan', 'Dongyan Zhao'] | 2018-09-27 | iterative-document-representation-learning-1 | https://aclanthology.org/D18-1442 | https://aclanthology.org/D18-1442.pdf | emnlp-2018-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.32973552e-01 6.03021026e-01 -1.99219316e-01 -2.73056269e-01
-8.54725003e-01 -6.78712726e-01 8.21684718e-01 7.87776291e-01
-5.86353600e-01 6.93781078e-01 1.10431266e+00 -1.30606383e-01
1.62571207e-01 -5.73008955e-01 -6.81545556e-01 -1.51169986e-01
3.77694964e-01 6.52444541e-01 1.66370228e-01 -2.83893824... | [12.512659072875977, 9.49335765838623] |
99247d94-0094-445c-8702-e8fc27db83a7 | boosting-video-object-segmentation-via-space | 2304.06211 | null | https://arxiv.org/abs/2304.06211v1 | https://arxiv.org/pdf/2304.06211v1.pdf | Boosting Video Object Segmentation via Space-time Correspondence Learning | Current top-leading solutions for video object segmentation (VOS) typically follow a matching-based regime: for each query frame, the segmentation mask is inferred according to its correspondence to previously processed and the first annotated frames. They simply exploit the supervisory signals from the groundtruth mas... | ['Wenjun Zhang', 'Li Song', 'Rong Xie', 'Wenguan Wang', 'Liulei Li', 'Yurong Zhang'] | 2023-04-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Boosting_Video_Object_Segmentation_via_Space-Time_Correspondence_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Boosting_Video_Object_Segmentation_via_Space-Time_Correspondence_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.68620622e-01 2.00908110e-01 -6.22761607e-01 -2.44499624e-01
-6.87224329e-01 -4.66319114e-01 3.87969315e-01 -1.38933018e-01
-2.64327377e-01 3.95828396e-01 -1.08711012e-01 -2.82739282e-01
-5.18652573e-02 -4.90591109e-01 -9.99854386e-01 -5.33089995e-01
2.47794151e-01 3.88512820e-01 7.64326930e-01 9.48557481... | [9.137992858886719, -0.09593158960342407] |
87247fe2-a043-49cc-996a-061fed83564d | combining-evaluation-metrics-via-the | 1401.4590 | null | http://arxiv.org/abs/1401.4590v1 | http://arxiv.org/pdf/1401.4590v1.pdf | Combining Evaluation Metrics via the Unanimous Improvement Ratio and its Application to Clustering Tasks | Many Artificial Intelligence tasks cannot be evaluated with a single quality
criterion and some sort of weighted combination is needed to provide system
rankings. A problem of weighted combination measures is that slight changes in
the relative weights may produce substantial changes in the system rankings.
This paper ... | ['Enrique Amigó', 'Julio Gonzalo', 'Javier Artiles', 'Felisa Verdejo'] | 2014-01-18 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [ 2.17620879e-01 -2.00415492e-01 -9.82370451e-02 -5.66066027e-01
-6.61305428e-01 -7.05708921e-01 7.38159478e-01 6.45835817e-01
-7.40694642e-01 4.60896075e-01 3.46342951e-01 -3.03519785e-01
-7.49507666e-01 -5.47525406e-01 -1.66890025e-01 -5.10780990e-01
1.85429037e-01 3.38462800e-01 2.51810908e-01 -3.63722920... | [9.017226219177246, 4.7619309425354] |
621db338-31b0-4f05-a2d6-be39f01ec77d | srpol-dialogue-systems-at-semeval-2021-task-5 | null | null | https://aclanthology.org/2021.semeval-1.133 | https://aclanthology.org/2021.semeval-1.133.pdf | SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection | This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified and generated by two language models. For the toxic word classification, the prediction threshold valu... | ['Piotr Andruszkiewicz', 'Pawe{\\l} Bujnowski', 'Christian Goltz', 'Zuzanna Bordzicka', 'Katarzyna Beksa', 'Klaudia Firl{\\k{a}}g', 'Joanna Kolis', 'Jaros{\\l}aw Piersa', "Katarzyna Zam{\\l}y{\\'n}ska", 'Micha{\\l} Sat{\\l}awa'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.57113036e-02 1.40573338e-01 -5.48332810e-01 -1.51559457e-01
-9.02709842e-01 -4.85679865e-01 6.92646742e-01 7.34351039e-01
-7.48925805e-01 9.96742249e-01 5.07701576e-01 -3.57935011e-01
1.16600409e-01 -6.45332694e-01 1.96014307e-02 -4.31995392e-01
-3.91722843e-02 1.46986395e-01 1.74620003e-01 -1.44801453... | [8.970885276794434, 10.644497871398926] |
dd5e6407-dedf-46fd-9e5f-f15592bf714d | transformer-based-multilingual-document | 2008.08567 | null | https://arxiv.org/abs/2008.08567v2 | https://arxiv.org/pdf/2008.08567v2.pdf | Transformer based Multilingual document Embedding model | One of the current state-of-the-art multilingual document embedding model LASER is based on the bidirectional LSTM neural machine translation model. This paper presents a transformer-based sentence/document embedding model, T-LASER, which makes three significant improvements. Firstly, the BiLSTM layers is replaced by t... | ['Wei Li', 'Brian Mak'] | 2020-08-19 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 2.70978548e-03 1.27456546e-01 -3.38963270e-01 -1.96845055e-01
-8.94286931e-01 -2.90359795e-01 8.60189974e-01 -2.19710190e-02
-5.83013237e-01 7.17054844e-01 4.92578715e-01 -8.68390203e-01
4.44897830e-01 -7.43058681e-01 -9.95688379e-01 -5.03319919e-01
2.83906192e-01 4.44397807e-01 3.28577422e-02 -3.04486454... | [11.551376342773438, 10.037117004394531] |
7bc2b8a6-834f-4d16-bbab-904ae9e0ddc9 | an-enactivist-account-of-mind-reading-in | 2111.06179 | null | https://arxiv.org/abs/2111.06179v5 | https://arxiv.org/pdf/2111.06179v5.pdf | An Enactivist account of Mind Reading in Natural Language Understanding | In this paper we apply our understanding of the radical enactivist agenda to the classic AI-hard problem of Natural Language Understanding. When Turing devised his famous test the assumption was that a computer could use language and the challenge would be to mimic human intelligence. It turned out playing chess and fo... | ['Peter Wallis'] | 2021-11-11 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 2.39941522e-01 9.60739791e-01 2.94343770e-01 -3.35637420e-01
-2.45759003e-02 -8.39046061e-01 1.17333126e+00 -2.73855682e-02
-2.23314986e-01 6.12441421e-01 2.11596116e-01 -9.08296764e-01
1.89232796e-01 -1.02642179e+00 -5.16207933e-01 -2.09112421e-01
3.76678407e-01 7.77727067e-01 3.74742776e-01 -9.91410673... | [9.36721134185791, 7.146583080291748] |
dcc5b68a-38ee-4bce-b051-39827cd6cb0c | weakly-supervised-learning-for-tool | 1806.05573 | null | http://arxiv.org/abs/1806.05573v2 | http://arxiv.org/pdf/1806.05573v2.pdf | Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos | Surgical tool localization is an essential task for the automatic analysis of
endoscopic videos. In the literature, existing methods for tool localization,
tracking and segmentation require training data that is fully annotated,
thereby limiting the size of the datasets that can be used and the
generalization of the ap... | ['Didier Mutter', 'Armine Vardazaryan', 'Nicolas Padoy', 'Jacques Marescaux'] | 2018-06-14 | null | null | null | null | ['surgical-tool-detection'] | ['computer-vision'] | [ 2.10769966e-01 2.13788286e-01 -4.70256478e-01 -8.87108296e-02
-7.18174517e-01 -8.45127106e-01 2.47142002e-01 1.88483834e-01
-7.56290674e-01 1.09254830e-01 -1.69721823e-02 -3.37162584e-01
1.46593347e-01 -3.56419355e-01 -7.95742512e-01 -2.86379993e-01
-7.92178139e-03 6.35079294e-03 4.75663334e-01 1.87965959... | [14.099921226501465, -3.300269365310669] |
da4bb28a-9ee2-4c29-94cb-cb7d1d08a43a | frustratingly-simple-but-effective-zero-shot | 2302.07319 | null | https://arxiv.org/abs/2302.07319v1 | https://arxiv.org/pdf/2302.07319v1.pdf | Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline | Methods for object detection and segmentation often require abundant instance-level annotations for training, which are time-consuming and expensive to collect. To address this, the task of zero-shot object detection (or segmentation) aims at learning effective methods for identifying and localizing object instances fo... | ['Leonid Sigal', 'Jayan Eledath', 'Behjat Siddiquie', 'Anirudth Nambirajan', 'Siddhesh Khandelwal'] | 2023-02-14 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 6.14913166e-01 4.89033349e-02 -3.69674385e-01 -5.08738756e-01
-9.79535282e-01 -5.15751123e-01 6.16422236e-01 2.04036444e-01
-3.74872357e-01 2.38306284e-01 -1.27929002e-01 -1.11831628e-01
8.59963894e-02 -6.38889909e-01 -6.38455808e-01 -6.71778083e-01
4.70055155e-02 5.00268936e-01 6.31970525e-01 1.35151863... | [9.570816993713379, 1.5918128490447998] |
8aee189e-fee7-4474-a6f5-3f89b1ce7f79 | generating-multiple-hypotheses-for-3d-human | 1904.05547 | null | http://arxiv.org/abs/1904.05547v1 | http://arxiv.org/pdf/1904.05547v1.pdf | Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network | 3D human pose estimation from a monocular image or 2D joints is an ill-posed
problem because of depth ambiguity and occluded joints. We argue that 3D human
pose estimation from a monocular input is an inverse problem where multiple
feasible solutions can exist. In this paper, we propose a novel approach to
generate mul... | ['Chen Li', 'Gim Hee Lee'] | 2019-04-11 | generating-multiple-hypotheses-for-3d-human-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Generating_Multiple_Hypotheses_for_3D_Human_Pose_Estimation_With_Mixture_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Generating_Multiple_Hypotheses_for_3D_Human_Pose_Estimation_With_Mixture_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-hypotheses-3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.85866112e-01 2.82579809e-01 6.96729347e-02 -3.05292130e-01
-8.39906633e-01 -5.16041815e-01 4.56783682e-01 -8.40611517e-01
-3.87554944e-01 8.85913372e-01 2.90557444e-01 1.52057841e-01
-1.49714410e-01 -3.38123024e-01 -1.00905120e+00 -4.15592909e-01
1.41827539e-01 1.28750980e+00 -2.29959353e-03 -1.21979550... | [7.028101921081543, -1.018384575843811] |
6f166c88-cb79-4a15-bd3e-7fb940594ec3 | peakconv-learning-peak-receptive-field-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_PeakConv_Learning_Peak_Receptive_Field_for_Radar_Semantic_Segmentation_CVPR_2023_paper.pdf | PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation | The modern machine learning-based technologies have shown considerable potential in automatic radar scene understanding. Among these efforts, radar semantic segmentation (RSS) can provide more refined and detailed information including the moving objects and background clutters within the effective receptive field ... | ['Zhe Ma', 'Xuhui Huang', 'Yuanpei Chen', 'Yufei Guo', 'Youcheng Zhang', 'Xinyan Zhang', 'Liwen Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['scene-understanding'] | ['computer-vision'] | [ 5.15010893e-01 -5.41574538e-01 2.80927420e-01 -7.32954443e-01
-2.25526899e-01 -4.93045866e-01 4.76676375e-01 -4.51816797e-01
-3.98144424e-01 3.32306772e-01 -4.87479232e-02 -3.83890659e-01
-2.99113333e-01 -8.28881323e-01 -5.39883494e-01 -8.04846585e-01
-8.18443149e-02 7.16028512e-02 3.31712186e-01 -1.16324022... | [8.294219970703125, -1.203277349472046] |
071cdca0-694e-4d1b-9545-dad8712fb959 | ousiometrics-and-telegnomics-the-essence-of | 2110.06847 | null | https://arxiv.org/abs/2110.06847v2 | https://arxiv.org/pdf/2110.06847v2.pdf | Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety bias | We define `ousiometrics' to be the study of essential meaning in whatever context that meaningful signals are communicated, and `telegnomics' as the study of remotely sensed knowledge. From work emerging through the middle of the 20th century, the essence of meaning has become generally accepted as being well captured ... | ['C. M. Danforth', 'A. J. Reagan', 'M. V. Arnold', 'J. R. Minot', 'S. Beaulieu', 'J. Lovato', 'J. W. Zimmerman', 'M. I. Fudolig', 'T. Alshaabi', 'P. S. Dodds'] | 2021-10-13 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.21944684e-01 7.08769634e-03 -3.53185773e-01 -2.77885914e-01
2.15816759e-02 -7.47202456e-01 9.20524776e-01 5.38692296e-01
-6.00579083e-01 5.52740395e-01 7.67733097e-01 -3.64795506e-01
-4.42300916e-01 -7.76414394e-01 3.71931121e-02 -6.42350614e-01
-1.74087465e-01 1.04239449e-01 -3.52736801e-01 -6.74577892... | [9.882827758789062, 8.699287414550781] |
c69cb337-9ad5-41f0-b15e-7e272b42f921 | learning-to-remove-clutter-in-real-world-gpr | 2205.08135 | null | https://arxiv.org/abs/2205.08135v1 | https://arxiv.org/pdf/2205.08135v1.pdf | Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data | The clutter in the ground-penetrating radar (GPR) radargram disguises or distorts subsurface target responses, which severely affects the accuracy of target detection and identification. Existing clutter removal methods either leave residual clutter or deform target responses when facing complex and irregular clutter i... | ['Zheng Fan', 'Weixia Cheng', 'Hai-Han Sun'] | 2022-05-17 | null | null | null | null | ['gpr', 'ms-ssim', 'gpr'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.01943386e-01 -4.64904875e-01 7.19710350e-01 -3.66273195e-01
-1.04661834e+00 -2.93442488e-01 7.76794776e-02 -3.22493941e-01
-1.66755900e-01 4.88886058e-01 1.44668579e-01 -4.66893584e-01
-2.87062764e-01 -9.38158751e-01 -4.27170753e-01 -1.05137479e+00
-3.96149904e-01 1.10522851e-01 1.39214635e-01 -4.10915077... | [6.923391342163086, 1.1690303087234497] |
a7213808-0dd1-4553-b43e-86e6f4d57ff0 | a-deep-pyramid-deformable-part-model-for-face | 1508.04389 | null | http://arxiv.org/abs/1508.04389v1 | http://arxiv.org/pdf/1508.04389v1.pdf | A Deep Pyramid Deformable Part Model for Face Detection | We present a face detection algorithm based on Deformable Part Models and
deep pyramidal features. The proposed method called DP2MFD is able to detect
faces of various sizes and poses in unconstrained conditions. It reduces the
gap in training and testing of DPM on deep features by adding a normalization
layer to the d... | ['Rajeev Ranjan', 'Vishal M. Patel', 'Rama Chellappa'] | 2015-08-18 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.94239428e-02 1.45720184e-01 6.07866123e-02 -4.64590073e-01
-4.54844050e-02 -5.26404500e-01 5.16552031e-01 -7.16507018e-01
-1.66721418e-01 1.01412974e-01 -2.93085605e-01 3.28182839e-02
3.59779149e-01 -9.04240072e-01 -7.29988992e-01 -5.90879798e-01
-3.00702065e-01 3.51704657e-01 4.76035863e-01 1.69816807... | [13.342473983764648, 0.6957021355628967] |
ec26e01c-3cec-4105-bb49-16aeea65934e | saving-dense-retriever-from-shortcut | 2202.07280 | null | https://arxiv.org/abs/2202.07280v2 | https://arxiv.org/pdf/2202.07280v2.pdf | Saving Dense Retriever from Shortcut Dependency in Conversational Search | Conversational search (CS) needs a holistic understanding of conversational inputs to retrieve relevant passages. In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question. With in-depth an... | ['Gangwoo Kim', 'Sungdong Kim'] | 2022-02-15 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.74202934e-01 3.48670632e-02 -4.35289979e-01 -1.94397420e-01
-1.26866436e+00 -8.67704928e-01 6.08728230e-01 1.58934399e-01
-5.99727929e-01 6.84644699e-01 5.18483400e-01 -7.42217839e-01
-1.98746845e-01 -7.87156343e-01 -5.92395246e-01 -2.97226012e-01
1.10759892e-01 5.25852799e-01 6.32578254e-01 -7.56242812... | [11.783440589904785, 7.802545547485352] |
37c6401b-1f44-45a5-8588-ee4c8bb75d96 | hybridqa-a-dataset-of-multi-hop-question | 2004.07347 | null | https://arxiv.org/abs/2004.07347v3 | https://arxiv.org/pdf/2004.07347v3.pdf | HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data | Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present Hyb... | ['Hong Wang', 'Wenhan Xiong', 'Zhiyu Chen', 'Wenhu Chen', 'Hanwen Zha', 'William Wang'] | 2020-04-15 | null | https://aclanthology.org/2020.findings-emnlp.91 | https://aclanthology.org/2020.findings-emnlp.91.pdf | findings-of-the-association-for-computational | ['multi-hop-question-answering'] | ['knowledge-base'] | [-2.82846153e-01 5.00380039e-01 -1.18811086e-01 -1.10489376e-01
-1.55298638e+00 -8.31709146e-01 3.04476887e-01 4.21407253e-01
-4.52653915e-01 1.03594685e+00 3.36352617e-01 -4.69490111e-01
-7.01421797e-02 -1.00934553e+00 -7.03468382e-01 -2.06193864e-01
7.92523623e-01 9.48804259e-01 8.48800480e-01 -7.81391144... | [10.752554893493652, 7.945376396179199] |
70b89555-9ad6-41e8-b94a-4f17c92e506b | coil-sketching-for-computationally-efficient | 2305.06482 | null | https://arxiv.org/abs/2305.06482v2 | https://arxiv.org/pdf/2305.06482v2.pdf | Coil Sketching for computationally-efficient MR iterative reconstruction | Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for 3D non-Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in co... | ['Shreyas S. Vasanawala', 'Mert Pilanci', 'Daniel B. Ennis', 'Batu Ozturkler', 'Christopher M. Sandino', 'Zhitao Li', 'Siddharth S. Iyer', 'Frank Ong', 'Julio A. Oscanoa'] | 2023-05-10 | null | null | null | null | ['image-reconstruction', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.87299347e-01 5.81588633e-02 8.08152258e-02 -1.68759212e-01
-9.43434477e-01 -6.06427550e-01 1.74820960e-01 -1.26797929e-01
-4.27832395e-01 4.59452331e-01 5.16158640e-01 -4.55587447e-01
-4.94305402e-01 -2.49548212e-01 -6.37741983e-01 -9.42112505e-01
-6.75217092e-01 3.55945557e-01 -1.16917253e-01 1.56500578... | [13.466877937316895, -2.425082206726074] |
71837f7e-1907-4785-8a40-c4abc804bf45 | known-unknowns-uncertainty-quality-in | 1612.01251 | null | http://arxiv.org/abs/1612.01251v2 | http://arxiv.org/pdf/1612.01251v2.pdf | Known Unknowns: Uncertainty Quality in Bayesian Neural Networks | We evaluate the uncertainty quality in neural networks using anomaly
detection. We extract uncertainty measures (e.g. entropy) from the predictions
of candidate models, use those measures as features for an anomaly detector,
and gauge how well the detector differentiates known from unknown classes. We
assign higher unc... | ['Pedro Tabacof', 'Ramon Oliveira', 'Eduardo Valle'] | 2016-12-05 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [-9.15763974e-02 4.57607567e-01 -8.23977962e-03 -8.58523965e-01
-8.04647744e-01 -2.98339009e-01 5.01900136e-01 -1.26485387e-02
-6.89244568e-01 8.50098014e-01 -2.38459921e-04 -2.44319424e-01
-3.41160595e-01 -5.69175780e-01 -9.84461963e-01 -6.51125669e-01
-1.90931223e-02 8.55898261e-01 6.84116662e-01 4.89118010... | [7.4073638916015625, 3.7843267917633057] |
338bdd44-41e6-42f1-8de4-703f56b9fafb | combining-graph-and-sequence-information-to | null | null | https://openreview.net/forum?id=Skx73lBFDS | https://openreview.net/pdf?id=Skx73lBFDS | Combining graph and sequence information to learn protein representations | Computational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is ... | ['Ali Abdalla', 'Pelkins Ajanoh', 'Mohamed Coulibali', 'Hassan Kané'] | 2019-09-25 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 6.78020239e-01 2.06235111e-01 -4.33497161e-01 -2.80223668e-01
-5.37726462e-01 -4.02034223e-01 7.14854121e-01 6.78146720e-01
-1.93204060e-01 1.18533516e+00 3.91701072e-01 -3.99467707e-01
-1.07665300e-01 -7.13557005e-01 -9.40832853e-01 -8.96390319e-01
-2.62569398e-01 4.96843606e-01 7.54684210e-02 -1.31853774... | [4.973657608032227, 5.686740875244141] |
d55e39d4-7941-4805-8f78-892259baa28e | adaptively-accumulated-knowledge-transfer-for | 2008.11873 | null | https://arxiv.org/abs/2008.11873v1 | https://arxiv.org/pdf/2008.11873v1.pdf | Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation | Partial domain adaptation (PDA) attracts appealing attention as it deals with a realistic and challenging problem when the source domain label space substitutes the target domain. Most conventional domain adaptation (DA) efforts concentrate on learning domain-invariant features to mitigate the distribution disparity ac... | ['Haifeng Xia', 'Zhengming Ding', 'Taotao Jing'] | 2020-08-27 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 2.19145745e-01 -1.07675165e-01 -4.80116218e-01 -6.30011320e-01
-6.38922036e-01 -4.24636275e-01 3.94066125e-01 1.84827924e-01
-3.97113353e-01 7.48951614e-01 6.92505911e-02 1.49986714e-01
-4.89182830e-01 -6.78314567e-01 -5.34176648e-01 -8.82034779e-01
3.49668682e-01 4.58578467e-01 4.54692125e-01 -1.58891827... | [10.348881721496582, 3.0766618251800537] |
07122283-9fea-4b7b-be35-9985ef8aa3f7 | combination-of-single-and-multi-frame-image | 2303.03212 | null | https://arxiv.org/abs/2303.03212v1 | https://arxiv.org/pdf/2303.03212v1.pdf | Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective | Super-resolution is the process of obtaining a high-resolution image from one or more low-resolution images. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) methods have been evolved almost independently for years. A neglected study in this field is the theoretical analysis of finding the o... | ['Aliazam Abbasfar', 'Reshad Hosseini', 'Mohammad Mahdi Afrasiabi'] | 2023-03-06 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 6.83610439e-01 -2.09321350e-01 -2.92047765e-02 -2.54878908e-01
-9.36654270e-01 -7.26084262e-02 2.97963738e-01 -5.10779560e-01
-1.74261987e-01 9.67755079e-01 3.21843535e-01 3.19715559e-01
-1.44983456e-01 -3.99337947e-01 -2.13202059e-01 -7.04099953e-01
4.52999808e-02 -1.53378975e-02 7.05571651e-01 -2.74935126... | [11.020029067993164, -2.2026050090789795] |
ad32b296-9bbc-4dd7-9b62-fbaf14da7adf | implicit-3d-human-mesh-recovery-using-1 | 2306.17651 | null | https://arxiv.org/abs/2306.17651v2 | https://arxiv.org/pdf/2306.17651v2.pdf | Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view | From an image of a person, we can easily infer the natural 3D pose and shape of the person even if ambiguity exists. This is because we have a mental model that allows us to imagine a person's appearance at different viewing directions from a given image and utilize the consistency between them for inference. However, ... | ['Junmo Kim', 'Jaesung Ahn', 'Yooshin Cho', 'Hanbyel Cho'] | 2023-06-30 | implicit-3d-human-mesh-recovery-using | http://openaccess.thecvf.com//content/CVPR2023/html/Cho_Implicit_3D_Human_Mesh_Recovery_Using_Consistency_With_Pose_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cho_Implicit_3D_Human_Mesh_Recovery_Using_Consistency_With_Pose_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-supervised-learning', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 1.00124605e-01 3.43950510e-01 -1.96165852e-02 -5.20271897e-01
-2.70330638e-01 -3.19997281e-01 4.49727654e-01 -3.26243371e-01
-1.55221477e-01 5.13934851e-01 2.13949651e-01 3.50396752e-01
2.19895363e-01 -8.57492030e-01 -8.51186752e-01 -4.71789896e-01
5.52675545e-01 7.72579074e-01 -1.93204403e-01 -1.58720203... | [7.191318511962891, -1.3257355690002441] |
fa22dc2f-02b0-4e42-a783-768996471e0c | n-gram-opcode-analysis-for-android-malware | 1612.01445 | null | http://arxiv.org/abs/1612.01445v1 | http://arxiv.org/pdf/1612.01445v1.pdf | N-gram Opcode Analysis for Android Malware Detection | Android malware has been on the rise in recent years due to the increasing
popularity of Android and the proliferation of third party application markets.
Emerging Android malware families are increasingly adopting sophisticated
detection avoidance techniques and this calls for more effective approaches for
Android mal... | ['Kieran McLaughlin', 'Suleiman Y. Yerima', 'Sakir Sezer', 'BooJoong Kang'] | 2016-12-05 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.68854320e-01 -1.53831407e-01 -6.40786469e-01 -1.30869329e-01
-5.90408564e-01 -7.02633560e-01 6.96891427e-01 3.02166581e-01
-4.24196690e-01 3.36079359e-01 -5.26678003e-02 -8.43366385e-01
-1.68959141e-01 -4.33014691e-01 -2.73406863e-01 -4.08377200e-01
-3.56942832e-01 5.81265613e-02 4.47781533e-01 1.61194131... | [14.423629760742188, 9.68017578125] |
ea27ab7f-3709-457e-94ea-5276d56778f2 | advancing-pico-element-detection-in-medical | 1810.12780 | null | https://arxiv.org/abs/1810.12780v4 | https://arxiv.org/pdf/1810.12780v4.pdf | Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks | In evidence-based medicine (EBM), defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called ... | ['Peter Szolovits', 'Di Jin'] | 2018-10-30 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 5.46413183e-01 2.08912604e-02 -2.88193703e-01 -2.72403389e-01
-1.27340627e+00 -3.30709994e-01 5.47039509e-01 9.15273786e-01
-6.49989665e-01 9.71738815e-01 3.77364457e-01 -5.65651953e-01
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1.49910077e-01 3.82502317e-01 7.11605623e-02 1.30055755... | [8.525064468383789, 8.707735061645508] |
593aaf33-ca49-4985-b1b7-94003223c2bc | semantic-answer-type-prediction-task-smart-at | 2012.00555 | null | https://arxiv.org/abs/2012.00555v1 | https://arxiv.org/pdf/2012.00555v1.pdf | SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge | Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain. The SeMantic AnsweR Type prediction task (SMART) was part of ISWC 2020 challenges. Question type and answer type prediction... | ['Ricardo Usbeck', 'Axel-Cyrille Ngonga Ngomo', 'Jens Lehmann', 'Alfio Gliozzo', 'Mohnish Dubey', 'Nandana Mihindukulasooriya'] | 2020-12-01 | null | null | null | null | ['type-prediction', 'knowledge-base-question-answering'] | ['computer-code', 'natural-language-processing'] | [-5.34227863e-02 7.16864109e-01 -7.12143257e-02 -4.29566085e-01
-8.06122065e-01 -7.10233808e-01 6.66842341e-01 5.40800512e-01
-4.18994933e-01 8.66830945e-01 5.12433112e-01 -2.78636754e-01
-4.77425218e-01 -1.26032400e+00 -4.91241366e-01 5.06252885e-01
5.43657601e-01 9.78047311e-01 1.08009028e+00 -9.79066789... | [10.523064613342285, 7.895973205566406] |
da079252-90ff-4174-9498-df938ba93401 | a-comprehensive-evaluation-of-the-copy | 2304.07772 | null | https://arxiv.org/abs/2304.07772v2 | https://arxiv.org/pdf/2304.07772v2.pdf | A Comprehensive Evaluation of the Copy Mechanism for Natural Language to SPARQL Query Generation | In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed a significant growth. Recently, the incorporation of the copy mechanism with traditional encoder-decoder architectures and the use of pre-trained encoder-decoders have set new performance benchmarks. This paper pres... | ['Papa Abdou Karim Karou Diallo', 'Amal Zouaq', 'Samuel Reyd'] | 2023-04-16 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.19868882e-01 5.54174364e-01 -1.39672354e-01 -5.86322069e-01
-1.32106376e+00 -5.08091807e-01 8.64761055e-01 1.76101655e-01
-5.97590268e-01 8.85205865e-01 4.85194623e-01 -4.50714648e-01
4.03809249e-01 -1.04377329e+00 -1.23839593e+00 4.19727862e-01
2.45786846e-01 1.07921267e+00 2.76333451e-01 -6.24925375... | [11.227594375610352, 8.408230781555176] |
0664970b-ba06-4187-8d8c-ce6acedc765f | learning-to-complete-object-shapes-for-object | 2208.05067 | null | https://arxiv.org/abs/2208.05067v1 | https://arxiv.org/pdf/2208.05067v1.pdf | Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes | In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete their full geometries by conditioning on reconstructions from depth inputs and a category-level shape prior with the aim that completed obj... | ['Stefan Leutenegger', 'Andrew J. Davison', 'Binbin Xu'] | 2022-08-09 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 4.29702759e-01 3.75344515e-01 1.31022528e-01 -2.82749712e-01
-7.19464719e-01 -4.99281138e-01 6.48659706e-01 3.32556516e-01
-3.10882688e-01 3.36744547e-01 -1.90107808e-01 2.68157244e-01
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1.60558283e-01 1.27966034e+00 9.81431663e-01 3.09817344... | [7.43649959564209, -2.4860997200012207] |
b047a3cc-91c0-4ed4-96f8-820aa11428ad | a-survey-on-explainability-of-graph-neural | 2306.01958 | null | https://arxiv.org/abs/2306.01958v1 | https://arxiv.org/pdf/2306.01958v1.pdf | A Survey on Explainability of Graph Neural Networks | Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems. However, combining feature information and combinatorial graph ... | ['Sourav Medya', 'Charu Aggarwal', 'Kartik Sharma', 'Jaspal Jannu', 'Jaykumar Kakkad'] | 2023-06-02 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 2.17372879e-01 6.88521147e-01 -7.18549192e-01 -3.08748454e-01
3.52612913e-01 -4.09474403e-01 4.12205964e-01 5.54329455e-01
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-5.91736019e-01 -8.58965099e-01 -6.55477166e-01 -1.96040913e-01
-2.62594759e-01 3.48220050e-01 -3.27118248e-01 -3.31144929... | [7.585259914398193, 6.306099891662598] |
ad51125d-7b37-4ba0-850f-95ba94c48609 | hollywood-3d-recognizing-actions-in-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Hadfield_Hollywood_3D_Recognizing_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Hadfield_Hollywood_3D_Recognizing_2013_CVPR_paper.pdf | Hollywood 3D: Recognizing Actions in 3D Natural Scenes | Action recognition in unconstrained situations is a difficult task, suffering from massive intra-class variations. It is made even more challenging when complex 3D actions are projected down to the image plane, losing a great deal of information. The recent emergence of 3D data, both in broadcast content, and commercia... | ['Simon Hadfield', 'Richard Bowden'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['interest-point-detection'] | ['computer-vision'] | [ 3.89647424e-01 -3.40854287e-01 -2.14971870e-01 -2.48870373e-01
-5.50082803e-01 -5.66078842e-01 6.50724292e-01 -1.04619153e-01
-5.47359824e-01 5.28116465e-01 4.34359103e-01 4.45374787e-01
-1.21132098e-01 -5.87245584e-01 -2.56527543e-01 -7.70456910e-01
-3.98668438e-01 3.66149545e-01 8.66342306e-01 -5.26651889... | [7.872889995574951, 0.24741613864898682] |
c117105f-f367-4856-ba53-9a5017118395 | a-question-answering-approach-for-emotion | null | null | https://aclanthology.org/D17-1167 | https://aclanthology.org/D17-1167.pdf | A Question Answering Approach for Emotion Cause Extraction | Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identifica... | ['Qin Lu', 'Jiachen Du', 'Jiannan Hu', 'Ruifeng Xu', 'Lin Gui', 'Yulan He'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 4.56004292e-01 9.26737711e-02 9.43467766e-02 -5.10724187e-01
-9.43344235e-01 -4.29569632e-01 5.34428716e-01 6.67846322e-01
-6.73845947e-01 7.64679253e-01 5.81890345e-01 -1.11003570e-01
5.89865111e-02 -7.10465550e-01 -6.26275480e-01 -2.68617839e-01
1.13755219e-01 1.30705044e-01 -1.34528503e-01 -4.20615107... | [12.667574882507324, 6.21804666519165] |
e37c0742-4ab3-4497-a0ab-f8f1b45e259e | active-cost-aware-labeling-of-streaming-data | 2304.06808 | null | https://arxiv.org/abs/2304.06808v2 | https://arxiv.org/pdf/2304.06808v2.pdf | Active Cost-aware Labeling of Streaming Data | We study actively labeling streaming data, where an active learner is faced with a stream of data points and must carefully choose which of these points to label via an expensive experiment. Such problems frequently arise in applications such as healthcare and astronomy. We first study a setting when the data's inputs ... | ['Kirthevasan Kandasamy', 'Ting Cai'] | 2023-04-13 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 3.27600569e-01 3.18680882e-01 -1.62944973e-01 -3.84778112e-01
-1.35050678e+00 -6.72015309e-01 -6.19928464e-02 5.29307067e-01
-1.00371420e+00 8.09855759e-01 -3.65163326e-01 -4.29510385e-01
-5.50485373e-01 -7.83452153e-01 -1.07584977e+00 -1.03348517e+00
-9.22641635e-01 5.26708543e-01 2.59898096e-01 8.14717785... | [6.346229076385498, 4.474951267242432] |
c3fd19c3-b65f-41e1-9338-d9c7a6d093ef | learning-equivariant-representations | 2012.02771 | null | https://arxiv.org/abs/2012.02771v1 | https://arxiv.org/pdf/2012.02771v1.pdf | Learning Equivariant Representations | State-of-the-art deep learning systems often require large amounts of data and computation. For this reason, leveraging known or unknown structure of the data is paramount. Convolutional neural networks (CNNs) are successful examples of this principle, their defining characteristic being the shift-equivariance. By slid... | ['Carlos Esteves'] | 2020-12-04 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 2.67624319e-01 -8.61767158e-02 -1.58174291e-01 -3.59302521e-01
-2.36036897e-01 -1.06182754e+00 8.12676013e-01 -3.56454015e-01
-4.62338805e-01 1.95971876e-01 3.85616094e-01 -1.30622491e-01
-2.65352041e-01 -7.79610515e-01 -9.65578496e-01 -7.95726061e-01
2.75782403e-02 5.62972724e-01 3.70450988e-02 -5.12100279... | [8.890793800354004, 2.3777015209198] |
42cde11b-d6aa-4489-b8a3-e61d04b7a136 | learning-accurate-dense-correspondences-and | 2101.01710 | null | https://arxiv.org/abs/2101.01710v2 | https://arxiv.org/pdf/2101.01710v2.pdf | Learning Accurate Dense Correspondences and When to Trust Them | Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacements or homogeneous regions. For most applications and down-stream tasks, such as pose estimation, image manipulation, or 3D reconstruction, i... | ['Radu Timofte', 'Luc van Gool', 'Martin Danelljan', 'Prune Truong'] | 2021-01-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.pdf | cvpr-2021-1 | ['geometric-matching', 'dense-pixel-correspondence-estimation'] | ['computer-vision', 'computer-vision'] | [-8.85868669e-02 -1.34642437e-01 -3.31117995e-02 -3.64472657e-01
-7.22382367e-01 -5.30560374e-01 4.71993208e-01 2.84652054e-01
-2.50842333e-01 6.20572507e-01 9.48349759e-02 1.96854576e-01
-2.26049185e-01 -4.59601134e-01 -7.77189732e-01 -4.14652407e-01
-2.63790321e-02 6.40457213e-01 3.46118897e-01 3.61531466... | [8.539700508117676, -2.0436747074127197] |
816220ba-fd7a-4936-b999-465428bc4921 | toward-characteristic-preserving-image-based | 1807.07688 | null | http://arxiv.org/abs/1807.07688v3 | http://arxiv.org/pdf/1807.07688v3.pdf | Toward Characteristic-Preserving Image-based Virtual Try-On Network | Image-based virtual try-on systems for fitting new in-shop clothes into a
person image have attracted increasing research attention, yet is still
challenging. A desirable pipeline should not only transform the target clothes
into the most fitting shape seamlessly but also preserve well the clothes
identity in the gener... | ['Yimin Chen', 'Liang Lin', 'Bochao Wang', 'Xiaodan Liang', 'Meng Yang', 'Huabin Zheng'] | 2018-07-20 | toward-characteristic-preserving-image-based-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Bochao_Wang_Toward_Characteristic-Preserving_Image-based_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Bochao_Wang_Toward_Characteristic-Preserving_Image-based_ECCV_2018_paper.pdf | eccv-2018-9 | ['geometric-matching'] | ['computer-vision'] | [ 1.60481915e-01 6.60370439e-02 3.21143091e-01 -2.78308183e-01
-5.34557700e-01 -4.89549845e-01 2.47871920e-01 -5.49533367e-01
1.82119310e-01 2.54811764e-01 -3.70925628e-02 3.20772320e-01
1.32200196e-01 -7.41228104e-01 -1.00010908e+00 -5.20442963e-01
3.56886208e-01 3.83707941e-01 2.22985998e-01 -4.38892275... | [11.92065715789795, -0.8834880590438843] |
3c650f63-687f-417a-881a-20392df2a8eb | better-sign-language-translation-with | 2304.10844 | null | https://arxiv.org/abs/2304.10844v1 | https://arxiv.org/pdf/2304.10844v1.pdf | Better Sign Language Translation with Monolingual Data | Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relies on the availability of large-scale parallel G2T pairs. However, the manual annotation of pivot gloss, which is a sequence of transcribed w... | ['Junbo Zhao', 'Yawen Zeng', 'Ru Peng'] | 2023-04-21 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 4.27223712e-01 -2.47567862e-01 -2.52034903e-01 -3.85633230e-01
-1.27387810e+00 -8.44547153e-01 7.08474100e-01 -6.67019546e-01
-2.78021604e-01 6.53092861e-01 4.23312873e-01 -4.82856572e-01
3.28343302e-01 -2.05014274e-01 -7.26565540e-01 -6.31967068e-01
5.75582266e-01 7.46015787e-01 1.07703745e-01 -2.20416859... | [9.213510513305664, -6.535788536071777] |
139b26a8-8c9b-4886-b754-f642d6e8876a | exploring-the-effect-of-primitives-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Exploring_the_Effect_of_Primitives_for_Compositional_Generalization_in_Vision-and-Language_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Exploring_the_Effect_of_Primitives_for_Compositional_Generalization_in_Vision-and-Language_CVPR_2023_paper.pdf | Exploring the Effect of Primitives for Compositional Generalization in Vision-and-Language | Compositionality is one of the fundamental properties of human cognition (Fodor & Pylyshyn, 1988). Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community. It is essential to understand the effect of the ... | ['Yuwei Wu', 'Yunde Jia', 'Chenchen Jing', 'Zhen Li', 'Chuanhao Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-grounding'] | ['computer-vision'] | [ 2.65393764e-01 -1.62010029e-01 -1.38698146e-01 -3.93560022e-01
-2.03795191e-02 -5.87545216e-01 8.74618351e-01 7.90469497e-02
-1.98785350e-01 1.08417235e-01 3.79007876e-01 -2.36346886e-01
7.80066997e-02 -6.48299515e-01 -7.49169827e-01 -3.27585369e-01
-7.27173081e-03 -9.85945091e-02 5.41707873e-01 -2.75410622... | [10.549153327941895, 1.5905457735061646] |
76c2869c-2803-4005-946c-943c96fcfa77 | da-lstm-a-dynamic-drift-adaptive-learning | 2305.08767 | null | https://arxiv.org/abs/2305.08767v1 | https://arxiv.org/pdf/2305.08767v1.pdf | DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks | Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL... | ['Jonas Forsman', 'Andreas Theocharis', 'Andreas Kassler', 'Bestoun S. Ahmed', 'Phil Aupke', 'Firas Bayram'] | 2023-05-15 | null | null | null | null | ['change-detection', 'load-forecasting', 'energy-management'] | ['computer-vision', 'miscellaneous', 'time-series'] | [-2.38463908e-01 -7.76033819e-01 -1.36325732e-01 -4.33454067e-01
-1.47969902e-01 -4.74965304e-01 5.32361329e-01 1.24207869e-01
-3.50679368e-01 8.29418898e-01 -1.61161557e-01 -1.53627560e-01
-2.99628615e-01 -9.63189662e-01 -2.09335864e-01 -1.12329173e+00
-2.48894185e-01 5.11482060e-01 7.93223456e-02 -1.76007807... | [6.18073034286499, 2.7780234813690186] |
c518076f-0113-48ef-8177-7f70f4420e0a | enhancing-aspect-term-extraction-with-soft | null | null | https://aclanthology.org/2020.emnlp-main.164 | https://aclanthology.org/2020.emnlp-main.164.pdf | Enhancing Aspect Term Extraction with Soft Prototypes | Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on. Existing studies mostly focus on designing neural sequence taggers to extract linguistic features from the token level. However, since the aspect terms and context words usually exhibit long-tail dist... | ['Tieyun Qian', 'Zhuang Chen'] | null | null | null | null | emnlp-2020-11 | ['extract-aspect'] | ['natural-language-processing'] | [ 9.63646472e-02 1.03992531e-02 -6.68917954e-01 -3.96707863e-01
-1.00815773e+00 -6.95754826e-01 7.91988552e-01 3.36465478e-01
-4.23041344e-01 6.65441394e-01 2.60496080e-01 -2.63881266e-01
2.44513139e-01 -7.48994410e-01 -4.68948275e-01 -6.16603732e-01
2.50179589e-01 4.54468966e-01 9.48416889e-02 -2.52290636... | [11.35962963104248, 6.749284744262695] |
be4f35ed-86e4-40bb-86ff-f00a27efc7a0 | dpf-learning-dense-prediction-fields-with | 2303.16890 | null | https://arxiv.org/abs/2303.16890v1 | https://arxiv.org/pdf/2303.16890v1.pdf | DPF: Learning Dense Prediction Fields with Weak Supervision | Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impossible (e.g., for intrinsic image decomposition), motivating us to leverage cheap point-level weak supervision. However, existing pointly-su... | ['Ya-Qin Zhang', 'Guyue Zhou', 'Hao Zhao', 'Qiang Zhou', 'Yupeng Zheng', 'Yuhang Zheng', 'Xiaoxue Chen'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_DPF_Learning_Dense_Prediction_Fields_With_Weak_Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_DPF_Learning_Dense_Prediction_Fields_With_Weak_Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-parsing', 'intrinsic-image-decomposition', 'semantic-parsing'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.26443666e-01 3.80242288e-01 -2.91258574e-01 -6.31684601e-01
-8.43714535e-01 -6.93975747e-01 7.31345177e-01 1.26417428e-01
-2.34937608e-01 4.65054750e-01 1.00939527e-01 -2.20924631e-01
1.83949359e-02 -1.07347369e+00 -1.24342775e+00 -6.82869613e-01
2.29107112e-01 2.21641511e-01 3.45723897e-01 -2.80974984... | [8.459720611572266, -2.873986005783081] |
ac6fe3db-f765-4db0-88ac-856ae92c15f0 | optimising-game-tactics-for-football | 2003.10294 | null | https://arxiv.org/abs/2003.10294v1 | https://arxiv.org/pdf/2003.10294v1.pdf | Optimising Game Tactics for Football | In this paper we present a novel approach to optimise tactical and strategic decision making in football (soccer). We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and a stochastic game to model the in-match state transitions and decisions. Using... | ['Timothy J. Norman', 'Ryan Beal', 'Georgios Chalkiadakis', 'Sarvapali D. Ramchurn'] | 2020-03-23 | null | null | null | null | ['game-of-football'] | ['playing-games'] | [-2.60102190e-02 1.76383391e-01 1.39984593e-01 -2.84299850e-01
-2.95977980e-01 -4.74705935e-01 5.28585911e-01 -2.02121790e-02
-7.87207842e-01 9.23389971e-01 1.93161964e-01 -3.19485366e-01
-8.54361176e-01 -1.05232298e+00 -2.93038011e-01 -3.85423034e-01
-3.30199301e-01 1.19886911e+00 6.61761224e-01 -8.58912408... | [3.4814980030059814, 1.4902387857437134] |
8f16e554-4a5a-458d-8569-727e8f3eece9 | sample-level-cnn-architectures-for-music-auto | 1710.10451 | null | http://arxiv.org/abs/1710.10451v2 | http://arxiv.org/pdf/1710.10451v2.pdf | Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms | Recent work has shown that the end-to-end approach using convolutional neural
network (CNN) is effective in various types of machine learning tasks. For
audio signals, the approach takes raw waveforms as input using an 1-D
convolution layer. In this paper, we improve the 1-D CNN architecture for music
auto-tagging by a... | ['Jongpil Lee', 'Taejun Kim', 'Juhan Nam'] | 2017-10-28 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [-1.60458982e-01 -4.32575792e-02 3.35305184e-02 -2.10222766e-01
-3.53019655e-01 -7.20814824e-01 3.31652522e-01 -2.20095202e-01
-3.98991764e-01 1.80530638e-01 3.86634976e-01 1.82789881e-02
-5.47474623e-02 -8.44486952e-01 -8.39417517e-01 -1.96069390e-01
-4.51748878e-01 1.42182693e-01 2.31421530e-01 -3.81403983... | [15.711594581604004, 5.220409393310547] |
3355c682-d9c0-4218-83f7-ad9bbbb0e1cd | an-iterative-step-function-estimator-for | 1412.2129 | null | http://arxiv.org/abs/1412.2129v2 | http://arxiv.org/pdf/1412.2129v2.pdf | An iterative step-function estimator for graphons | Exchangeable graphs arise via a sampling procedure from measurable functions
known as graphons. A natural estimation problem is how well we can recover a
graphon given a single graph sampled from it. One general framework for
estimating a graphon uses step-functions obtained by partitioning the nodes of
the graph accor... | ['Nathanael Ackerman', 'Diana Cai', 'Cameron Freer'] | 2014-12-05 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 1.04000665e-01 5.79454958e-01 -3.58961552e-01 -1.70582741e-01
-6.44059122e-01 -7.05715597e-01 6.33183062e-01 2.51680374e-01
8.65057763e-03 1.03715956e+00 -7.19465241e-02 4.17894498e-02
-4.14805919e-01 -1.21751571e+00 -8.64666581e-01 -6.14374936e-01
-4.03700829e-01 1.13737667e+00 3.46755922e-01 2.83929616... | [6.927732944488525, 5.350561618804932] |
81dcf421-d7cc-4cdf-af39-7229bfc292da | bpgc-at-semeval-2020-task-11-propaganda | 2006.00593 | null | https://arxiv.org/abs/2006.00593v2 | https://arxiv.org/pdf/2006.00593v2.pdf | BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning | Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Task-11 consists of two sub-tasks, namely, Span Identification - given any news article, the system tag... | ['Rajaswa Patil', 'Swati Agarwal', 'Somesh Singh'] | 2020-05-31 | null | https://aclanthology.org/2020.semeval-1.226 | https://aclanthology.org/2020.semeval-1.226.pdf | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [-8.31565186e-02 -3.39586735e-02 -7.22050250e-01 -2.76580572e-01
-7.71689653e-01 -4.83211845e-01 1.15284979e+00 7.70720780e-01
-3.40949625e-01 5.54221332e-01 1.34246194e+00 -3.70567709e-01
1.05474330e-01 -1.00733137e+00 -2.92093635e-01 -4.22310680e-01
1.39565602e-01 3.19635540e-01 -2.51058668e-01 -3.29197675... | [8.524947166442871, 10.530829429626465] |
668dea0b-e83f-4825-a04f-9ecbab544051 | local-global-fusion-network-for-video-super | null | null | https://ieeexplore.ieee.org/document/9203860/authors#authors | https://ieeexplore.ieee.org/document/9203860/authors#authors | Local-Global Fusion Network for Video Super-Resolution | The goal of video super-resolution technique is to address the problem of effectively restoring high-resolution (HR) videos from low-resolution (LR) ones. Previous methods commonly used optical flow to perform frame alignment and designed a framework from the perspective of space and time. However, inaccurate optical f... | ['Xinyi Peng', 'Xianfang Sun', 'Longcun Jin', 'Hua Wang', 'Dewei Su'] | 2020-09-22 | null | null | null | ieee-access-2020-9 | ['video-super-resolution'] | ['computer-vision'] | [ 1.16753690e-01 -5.78717411e-01 -3.24122980e-02 -1.56036854e-01
-4.07830447e-01 -1.68694407e-01 3.00507575e-01 -4.30670857e-01
-2.96677917e-01 9.45530057e-01 2.59700745e-01 2.25466508e-02
-1.28553569e-01 -7.24407077e-01 -5.63378274e-01 -8.31917226e-01
1.54233068e-01 -4.87316966e-01 4.46529150e-01 -2.78617024... | [11.042531967163086, -1.840067982673645] |
c312a07c-c1ea-4676-8172-121302b423f0 | highly-parallel-autoregressive-entity-linking | 2109.03792 | null | https://arxiv.org/abs/2109.03792v1 | https://arxiv.org/pdf/2109.03792v1.pdf | Highly Parallel Autoregressive Entity Linking with Discriminative Correction | Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-paralle... | ['Ivan Titov', 'Wilker Aziz', 'Nicola De Cao'] | 2021-09-08 | null | https://aclanthology.org/2021.emnlp-main.604 | https://aclanthology.org/2021.emnlp-main.604.pdf | emnlp-2021-11 | ['entity-disambiguation'] | ['natural-language-processing'] | [-2.01166272e-01 3.47623855e-01 6.12392873e-02 -2.28319690e-01
-1.48561323e+00 -6.80420816e-01 6.62674785e-01 2.43670732e-01
-5.91134250e-01 8.48324955e-01 2.39102080e-01 -3.09634924e-01
3.61210227e-01 -6.64866567e-01 -8.31762016e-01 -5.59818864e-01
7.41980746e-02 1.03823507e+00 7.12817386e-02 -2.65044183... | [9.529861450195312, 8.946061134338379] |
e346d4dd-a9e1-439f-b6d4-41c4fad2caa1 | cyber-attack-detection-in-socio-technical | 2103.11422 | null | https://arxiv.org/abs/2103.11422v1 | https://arxiv.org/pdf/2103.11422v1.pdf | Cyber-Attack Detection in Socio-Technical Transportation Systems Exploiting Redundancies Between Physical and Social Data | Cyber-physical-social connectivity is a key element in Intelligent Transportation Systems (ITSs) due to the ever-increasing interaction between human users and technological systems. Such connectivity translates the ITSs into dynamical systems of socio-technical nature. Exploiting this socio-technical feature to our ad... | ['Satadru Dey', 'Sara Sattarzadeh', 'Tanushree Roy'] | 2021-03-21 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 5.03110047e-03 3.49601865e-01 1.53136045e-01 3.54242176e-01
-2.44049802e-01 -6.17011309e-01 6.73080385e-01 -2.81429626e-02
-3.39383066e-01 8.86222541e-01 -7.48535916e-02 -7.62762487e-01
-1.86391622e-01 -9.40751255e-01 -6.49826407e-01 -7.54290998e-01
-7.83963799e-02 -3.22599530e-01 7.87442029e-01 -4.74208891... | [5.551424980163574, 1.6717194318771362] |
bbca93b5-56ef-46a1-8c31-f1f5b750721f | a-new-target-specific-object-proposal | 1803.10098 | null | http://arxiv.org/abs/1803.10098v1 | http://arxiv.org/pdf/1803.10098v1.pdf | A New Target-specific Object Proposal Generation Method for Visual Tracking | Object proposal generation methods have been widely applied to many computer
vision tasks. However, existing object proposal generation methods often suffer
from the problems of motion blur, low contrast, deformation, etc., when they
are applied to video related tasks. In this paper, we propose an effective and
highly ... | ['Hong-Yuan Mark Liao', 'Yan Yan', 'Hanzi Wang', 'Guanjun Guo', 'Bo Li'] | 2018-03-27 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [-7.09012896e-03 -4.88700390e-01 -2.60392457e-01 -1.33775726e-01
-6.05270863e-01 -2.87965477e-01 6.16274953e-01 -3.55311893e-02
-4.52831686e-01 5.93250096e-01 -4.26105484e-02 1.10383511e-01
8.48094076e-02 -5.02421856e-01 -5.58688939e-01 -8.19328785e-01
2.44119659e-01 3.20973426e-01 1.02919745e+00 -5.00791445... | [6.327364921569824, -2.128077507019043] |
26e46a11-a6b2-4f3f-bb7b-c43342b6b9f6 | diffuscene-scene-graph-denoising-diffusion | 2303.14207 | null | https://arxiv.org/abs/2303.14207v1 | https://arxiv.org/pdf/2303.14207v1.pdf | DiffuScene: Scene Graph Denoising Diffusion Probabilistic Model for Generative Indoor Scene Synthesis | We present DiffuScene for indoor 3D scene synthesis based on a novel scene graph denoising diffusion probabilistic model, which generates 3D instance properties stored in a fully-connected scene graph and then retrieves the most similar object geometry for each graph node i.e. object instance which is characterized as ... | ['Matthias Nießner', 'Justus Thies', 'Angela Dai', 'Lev Markhasin', 'Yinyu Nie', 'Jiapeng Tang'] | 2023-03-24 | null | null | null | null | ['indoor-scene-synthesis'] | ['computer-vision'] | [ 2.43240401e-01 -3.53271291e-02 4.21899587e-01 -2.60214508e-01
-3.73845577e-01 -7.98873007e-01 6.41011715e-01 2.70050883e-01
2.83450693e-01 2.91393012e-01 4.26118553e-01 -2.48286560e-01
-3.06797594e-01 -1.14756823e+00 -8.70196879e-01 -4.80137289e-01
1.46035552e-01 7.00527132e-01 3.59212279e-01 8.48715827... | [9.226799964904785, -3.137993097305298] |
fc35441a-6e2b-4dde-b154-6aa1af4bd74d | content-determination-for-chess-as-a-source | null | null | https://aclanthology.org/W18-6605 | https://aclanthology.org/W18-6605.pdf | Content Determination for Chess as a Source for Suspenseful Narratives | null | ["Pablo Gerv{\\'a}s", 'Richard Doust'] | 2018-11-01 | null | null | null | ws-2018-11 | ['game-of-chess'] | ['playing-games'] | [-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... | [-7.269283771514893, 3.769989252090454] |
2a3582f9-130f-45cd-ba5e-092f047ddfd8 | contrastive-lift-3d-object-instance | 2306.04633 | null | https://arxiv.org/abs/2306.04633v1 | https://arxiv.org/pdf/2306.04633v1.pdf | Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion | Instance segmentation in 3D is a challenging task due to the lack of large-scale annotated datasets. In this paper, we show that this task can be addressed effectively by leveraging instead 2D pre-trained models for instance segmentation. We propose a novel approach to lift 2D segments to 3D and fuse them by means of a... | ['Andrea Vedaldi', 'Andrew Zisserman', 'João F. Henriques', 'Iro Laina', 'Yash Bhalgat'] | 2023-06-07 | null | null | null | null | ['object-tracking'] | ['computer-vision'] | [ 6.90689981e-02 -7.42483214e-02 2.30904251e-01 -5.06046772e-01
-8.40660870e-01 -6.70771003e-01 6.06184542e-01 6.69250041e-02
-4.11136240e-01 2.49629721e-01 2.02134307e-02 -1.34381698e-02
-1.33536592e-01 -4.42534655e-01 -9.20915008e-01 -5.51776946e-01
-1.86719999e-01 8.43392134e-01 7.31281936e-01 6.07441105... | [8.282027244567871, -2.6273043155670166] |
996a3500-638a-4227-afe3-e7913c21f09b | putting-3d-spatially-sparse-networks-on-a | 2112.01316 | null | https://arxiv.org/abs/2112.01316v2 | https://arxiv.org/pdf/2112.01316v2.pdf | Putting 3D Spatially Sparse Networks on a Diet | 3D neural networks have become prevalent for many 3D vision tasks including object detection, segmentation, registration, and various perception tasks for 3D inputs. However, due to the sparsity and irregularity of 3D data, custom 3D operators or network designs have been the primary focus of research, while the size o... | ['Jaesik Park', 'Christopher Choy', 'Junha Lee'] | 2021-12-02 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.54366982e-01 1.30502298e-01 -6.63824603e-02 -5.16230822e-01
3.67712140e-01 -1.99945018e-01 1.05691738e-01 -1.37652025e-01
-5.73112130e-01 2.95411259e-01 -1.70429990e-01 -5.52357793e-01
-4.90630805e-01 -7.33111739e-01 -7.97852993e-01 -4.74236995e-01
-3.95812392e-01 3.74516957e-02 5.16937792e-01 1.06441244... | [8.003463745117188, -3.565056800842285] |
a1ffb3e6-32ae-48c4-9317-dfaf08437a8c | a-multi-task-deep-learning-framework-for | 2104.09375 | null | https://arxiv.org/abs/2104.09375v1 | https://arxiv.org/pdf/2104.09375v1.pdf | A Multi-Task Deep Learning Framework for Building Footprint Segmentation | The task of building footprint segmentation has been well-studied in the context of remote sensing (RS) as it provides valuable information in many aspects, however, difficulties brought by the nature of RS images such as variations in the spatial arrangements and in-consistent constructional patterns require studying ... | ['Elif Sertel', 'Burak Ekim'] | 2021-04-19 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.97193539e-01 -4.11573537e-02 2.42239445e-01 -6.07866645e-01
-9.65670943e-01 -3.06359440e-01 4.65046883e-01 1.44046038e-01
-5.27800500e-01 6.05563879e-01 2.08831653e-02 -3.32004339e-01
-4.72573668e-01 -8.47235620e-01 -8.69987905e-01 -7.88504720e-01
-1.17966942e-01 2.99312890e-01 4.29663025e-02 1.58015452... | [9.637982368469238, -1.3832008838653564] |
fa4a0a23-13b7-4b9f-b36d-ebf3795ea7e3 | 190600041 | 1906.00041 | null | https://arxiv.org/abs/1906.00041v1 | https://arxiv.org/pdf/1906.00041v1.pdf | Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval | Tables contain valuable knowledge in a structured form. We employ neural language modeling approaches to embed tabular data into vector spaces. Specifically, we consider different table elements, such caption, column headings, and cells, for training word and entity embeddings. These embeddings are then utilized in thr... | ['Li Deng', 'Shuo Zhang', 'Krisztian Balog'] | 2019-05-31 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [-2.70584494e-01 6.00423254e-02 -8.22262466e-01 -1.77429572e-01
-1.19192171e+00 -6.72843039e-01 6.97913527e-01 1.05452204e+00
-3.72041196e-01 8.18255186e-01 9.63568330e-01 -2.61294067e-01
2.99151745e-02 -1.07511950e+00 -7.29189098e-01 -1.59584731e-01
1.73885718e-01 6.20209157e-01 -1.90423980e-01 -5.86015880... | [9.749168395996094, 7.888470649719238] |
e56c6bce-2e96-4670-8b06-752e3350387c | dynamic-steerable-blocks-in-deep-residual | 1706.00598 | null | http://arxiv.org/abs/1706.00598v2 | http://arxiv.org/pdf/1706.00598v2.pdf | Dynamic Steerable Blocks in Deep Residual Networks | Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and De... | ['Bert de Brabandere', 'Jörn-Henrik Jacobsen', 'Arnold W. M. Smeulders'] | 2017-06-02 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.18812345e-01 1.61159530e-01 -1.11809904e-02 -5.25873542e-01
-6.73494563e-02 -7.68664718e-01 9.18351173e-01 -1.57698050e-01
-8.57531309e-01 5.46250880e-01 1.98184893e-01 -2.40206835e-03
-8.73666555e-02 -9.43875492e-01 -1.33826017e+00 -7.31564701e-01
1.49177462e-01 1.96640328e-01 5.50651312e-01 -3.52013946... | [9.095501899719238, 2.229712963104248] |
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