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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6489f455-d269-4069-bc79-413c4da0cc95 | turbo-training-with-token-dropout | 2210.04889 | null | https://arxiv.org/abs/2210.04889v1 | https://arxiv.org/pdf/2210.04889v1.pdf | Turbo Training with Token Dropout | The objective of this paper is an efficient training method for video tasks. We make three contributions: (1) We propose Turbo training, a simple and versatile training paradigm for Transformers on multiple video tasks. (2) We illustrate the advantages of Turbo training on action classification, video-language represen... | ['Andrew Zisserman', 'Weidi Xie', 'Tengda Han'] | 2022-10-10 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 2.44122550e-01 -3.81143242e-01 -6.73971236e-01 -9.66749042e-02
-9.55960274e-01 -2.34767750e-01 3.19889992e-01 -6.30347848e-01
-5.23698509e-01 6.58195853e-01 2.94858456e-01 -4.91240948e-01
1.52312204e-01 -5.64646395e-03 -8.63332152e-01 -5.11023521e-01
-6.89881027e-01 -9.64052081e-02 3.06378633e-01 2.41135836... | [9.124082565307617, 0.756371259689331] |
709440d6-03a3-4226-81d8-c73bff27f615 | incentivizing-exploration-with-linear | 2306.01990 | null | https://arxiv.org/abs/2306.01990v1 | https://arxiv.org/pdf/2306.01990v1.pdf | Incentivizing Exploration with Linear Contexts and Combinatorial Actions | We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work has shown under certain independence assumptions that after collecting enough initial samples, the popular Thompson sampling algorithm becomes inc... | ['Mark Sellke'] | 2023-06-03 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.90034419e-01 5.55823326e-01 -1.24035907e+00 -4.51287985e-01
-7.28899837e-01 -7.75629997e-01 2.88819790e-01 -1.06257319e-01
-6.40988827e-01 1.11861515e+00 5.17933965e-01 -9.19279575e-01
-8.81310225e-01 -8.14324737e-01 -9.62932467e-01 -6.34389997e-01
1.76405348e-03 7.07545400e-01 -5.33306241e-01 1.96385920... | [4.490444660186768, 3.2543203830718994] |
ba940022-d4d5-435f-ab6e-20903e6a38ce | non-linear-pairwise-language-mappings-for-low | 2207.03391 | null | https://arxiv.org/abs/2207.03391v1 | https://arxiv.org/pdf/2207.03391v1.pdf | Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion | Multilingual speech recognition has drawn significant attention as an effective way to compensate data scarcity for low-resource languages. End-to-end (e2e) modelling is preferred over conventional hybrid systems, mainly because of no lexicon requirement. However, hybrid DNN-HMMs still outperform e2e models in limited ... | ['Thomas Hain', 'Darshan Adiga Haniya Narayana', 'Muhammad Umar Farooq'] | 2022-07-07 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-1.87285244e-01 1.67064667e-02 1.21826552e-01 -3.90524834e-01
-1.70456958e+00 -4.96170998e-01 6.36264205e-01 -1.07385851e-01
-7.64216900e-01 8.02883029e-01 2.60450393e-01 -5.35168231e-01
4.20749009e-01 -2.32646301e-01 -7.74571955e-01 -6.44885182e-01
5.45244336e-01 6.96952283e-01 6.09265231e-02 -1.99918285... | [14.397988319396973, 6.8668999671936035] |
fb7fdb81-30f7-47eb-8537-ec33ee89e706 | linear-mode-connectivity-and-the-lottery | 1912.05671 | null | https://arxiv.org/abs/1912.05671v4 | https://arxiv.org/pdf/1912.05671v4.pdf | Linear Mode Connectivity and the Lottery Ticket Hypothesis | We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random data order and augmentation). We find that standard vision models become stable to SGD noise in this way early in training. From then on, the outcome of optimization is determined to a ... | ['Daniel M. Roy', 'Michael Carbin', 'Gintare Karolina Dziugaite', 'Jonathan Frankle'] | 2019-12-11 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5787-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5787-Paper.pdf | icml-2020-1 | ['linear-mode-connectivity'] | ['knowledge-base'] | [ 2.96438992e-01 3.49633515e-01 2.32284248e-01 -3.02500010e-01
-1.95094079e-01 -6.12498820e-01 6.07122421e-01 -1.98362514e-01
-7.91323960e-01 7.64302313e-01 -8.26775804e-02 -3.62492830e-01
-2.41227791e-01 -7.24322140e-01 -9.52325881e-01 -6.07950568e-01
-1.41179875e-01 5.03341973e-01 4.49915767e-01 -1.53505104... | [8.5222806930542, 3.326758861541748] |
9919dfe6-c689-4e16-a0b7-4ea8bf82313a | unsupervised-concept-to-text-generation-with | null | null | https://aclanthology.org/N12-1093 | https://aclanthology.org/N12-1093.pdf | Unsupervised Concept-to-text Generation with Hypergraphs | null | ['Ioannis Konstas', 'Mirella Lapata'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.280180931091309, 3.7802298069000244] |
830d1f62-e509-494d-9b50-7bc50d79af16 | fully-connected-neural-network-with-advance | null | null | https://aclanthology.org/W18-4404 | https://aclanthology.org/W18-4404.pdf | Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter | Paper presents the different methodologies developed {\&} tested and discusses their results, with the goal of identifying the best possible method for the aggression identification problem in social media. | ['Teresa Gon{\\c{c}}alves', 'Vitor Beires Nogueira', 'Paulo Quaresma', 'Kashyap Raiyani'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-3.80740047e-01 -4.08089757e-02 2.97317266e-01 -2.02551156e-01
1.39672965e-01 5.55699319e-02 3.04801345e-01 4.00977939e-01
-7.56942749e-01 6.14088476e-01 -4.87583205e-02 2.96353221e-01
-8.31297755e-01 -5.35080016e-01 5.46590447e-01 -5.15668213e-01
-4.99450266e-01 7.60798872e-01 3.00834119e-01 -5.25279522... | [8.76432991027832, 10.742283821105957] |
dac71238-a74b-4a35-a190-fa5eeda0018b | an-anchor-free-detector-for-continuous-speech | 2208.04622 | null | https://arxiv.org/abs/2208.04622v1 | https://arxiv.org/pdf/2208.04622v1.pdf | An Anchor-Free Detector for Continuous Speech Keyword Spotting | Continuous Speech Keyword Spotting (CSKWS) is a task to detect predefined keywords in a continuous speech. In this paper, we regard CSKWS as a one-dimensional object detection task and propose a novel anchor-free detector, named AF-KWS, to solve the problem. AF-KWS directly regresses the center locations and lengths of... | ['Chong Luo', 'Chengdong Yao', 'Chuanxin Tang', 'Zhiyuan Zhao'] | 2022-08-09 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.28029823e-01 1.98394924e-01 -1.48288190e-01 -4.64644164e-01
-1.46287239e+00 -4.46722418e-01 5.58416665e-01 -1.20482333e-01
-5.39985240e-01 2.31177866e-01 3.60024154e-01 -5.71806729e-01
1.97674528e-01 9.12091285e-02 -7.39627600e-01 -6.76779389e-01
-2.12396495e-02 2.68150210e-01 5.07240534e-01 2.42990572... | [14.287323951721191, 6.40095853805542] |
237593a5-e28c-4904-8637-0abdafc8ee96 | predicting-breast-tumor-proliferation-from | 1807.08284 | null | http://arxiv.org/abs/1807.08284v2 | http://arxiv.org/pdf/1807.08284v2.pdf | Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge | Tumor proliferation is an important biomarker indicative of the prognosis of
breast cancer patients. Assessment of tumor proliferation in a clinical setting
is highly subjective and labor-intensive task. Previous efforts to automate
tumor proliferation assessment by image analysis only focused on mitosis
detection in p... | ['Josien P. W. Pluim', 'Yan Xu', 'Eric I-Chao Chang', 'Kyunghyun Paeng', 'Erik Sjöblom', 'Hady Ahmady Phoulady', 'Francesco Ciompi', 'Thomas Wollmann', 'Babak Ehteshami Bejnordi', 'Simon Graham', 'Paul J. van Diest', 'Mitko Veta', 'Mikael Rousson', 'Erwan Zerhouni', 'Dayong Wang', 'Andrew H. Beck', 'Yujing J. Heng', 'M... | 2018-07-22 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.75537807e-01 3.06644142e-01 -3.97062123e-01 -7.40879849e-02
-1.29927790e+00 -4.55263585e-01 4.54027146e-01 7.35918939e-01
-5.97983718e-01 8.26043844e-01 -3.80379260e-02 -5.19633472e-01
8.25577751e-02 -8.06799829e-01 -3.57682765e-01 -1.29312897e+00
2.19308957e-01 6.65635645e-01 3.43959421e-01 2.70444155... | [15.063216209411621, -3.1614439487457275] |
902e2713-d4a7-4e4b-9cdf-d80f40548b7d | vihos-hate-speech-spans-detection-for | 2301.10186 | null | https://arxiv.org/abs/2301.10186v2 | https://arxiv.org/pdf/2301.10186v2.pdf | ViHOS: Hate Speech Spans Detection for Vietnamese | The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vie... | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Khanh Quoc Tran', 'Canh Duc Luu', 'Phu Gia Hoang'] | 2023-01-24 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-3.81426841e-01 1.24331854e-01 -1.43074855e-01 -6.73097447e-02
-7.37861454e-01 -1.07528961e+00 4.76898015e-01 4.86372769e-01
-4.12674010e-01 8.01227331e-01 4.24680084e-01 -2.78612286e-01
1.74510404e-01 -2.39000693e-01 -3.55824642e-02 -3.25622827e-01
-9.31026507e-03 -1.73892960e-01 9.12343711e-02 -5.27863026... | [8.793437004089355, 10.566301345825195] |
8bcc0cfb-d9e2-4e1c-9d51-b8faecf9c73c | fast-and-accurate-tensor-completion-with | 1804.06128 | null | http://arxiv.org/abs/1804.06128v3 | http://arxiv.org/pdf/1804.06128v3.pdf | Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains | We propose a new tensor completion method based on tensor trains. The
to-be-completed tensor is modeled as a low-rank tensor train, where we use the
known tensor entries and their coordinates to update the tensor train. A novel
tensor train initialization procedure is proposed specifically for image and
video completio... | ['Ching-Yun Ko', 'Kim Batselier', 'Ngai Wong', 'Wenjian Yu'] | 2018-04-17 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-1.35616705e-01 -3.46631229e-01 -4.48891446e-02 -3.93820591e-02
-8.64802241e-01 -5.98984718e-01 4.52467293e-01 -2.18674317e-01
-5.03426135e-01 3.73211652e-01 3.59532982e-01 -1.55545130e-01
-3.13937098e-01 2.86281258e-02 -8.22855830e-01 -7.41455317e-01
-4.07641411e-01 3.57868284e-01 -1.64445892e-01 -1.79928139... | [7.3702898025512695, 4.525243282318115] |
648fb307-3a04-43df-a01c-f5f253453a3f | bore-bayesian-optimization-by-density-ratio | 2102.09009 | null | https://arxiv.org/abs/2102.09009v1 | https://arxiv.org/pdf/2102.09009v1.pdf | BORE: Bayesian Optimization by Density-Ratio Estimation | Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among... | ['Fabio Ramos', 'Cedric Archambeau', 'Edwin V. Bonilla', 'Matthias Seeger', 'Aaron Klein', 'Louis C. Tiao'] | 2021-02-17 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.24638298e-01 6.15392588e-02 -4.21720058e-01 -2.77089447e-01
-7.84509182e-01 -7.01044559e-01 7.28782475e-01 1.59164101e-01
-4.78069663e-01 1.01619303e+00 -1.41606972e-01 -4.26945984e-01
-8.30743313e-01 -7.83561647e-01 -4.77991968e-01 -8.08795571e-01
2.38625258e-02 3.74087125e-01 -1.99731752e-01 1.43487751... | [6.401566982269287, 3.770442008972168] |
8e921f82-c418-4c7c-8315-43c6f829bf5e | deep-material-recognition-in-light-fields-via | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4750_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690647.pdf | Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information | Light-field cameras capture sub-views from multiple perspectives simultaneously, with possibly reflectance variations that can be used to augment material recognition in remote sensing, autonomous driving, etc. Existing approaches for light-field based material recognition suffer from the entanglement between angular a... | ['Bichuan Guo', 'Yuxing Han', 'Jiangtao Wen'] | null | null | null | null | eccv-2020-8 | ['material-recognition'] | ['computer-vision'] | [ 7.52597809e-01 -2.29643732e-01 -1.08078003e-01 -2.94048965e-01
-5.14410853e-01 -6.49201572e-01 6.20724738e-01 -1.98056191e-01
-3.85612786e-01 7.80717611e-01 5.47366925e-02 7.57572204e-02
-3.88385475e-01 -8.64395916e-01 -7.91848421e-01 -1.00539112e+00
3.50255758e-01 4.00360161e-03 3.08712780e-01 5.08365501... | [9.814162254333496, -2.7188754081726074] |
ec6b59e1-0d64-4d04-8ea5-a27e34233309 | an-exact-hypergraph-matching-algorithm-for | 2104.10003 | null | https://arxiv.org/abs/2104.10003v3 | https://arxiv.org/pdf/2104.10003v3.pdf | An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans | Finding an optimal correspondence between point sets is a common task in computer vision. Existing techniques assume relatively simple relationships among points and do not guarantee an optimal match. We introduce an algorithm capable of exactly solving point set matching by modeling the task as hypergraph matching. Th... | ['Radu Balan', 'Hari Shroff', 'Ryan Christensen', 'Andrew Lauziere'] | 2021-04-20 | null | null | null | null | ['set-matching', 'hypergraph-matching'] | ['computer-vision', 'graphs'] | [ 2.02074662e-01 1.14763431e-01 -2.66673505e-01 -1.32356778e-01
-4.25941765e-01 -8.33181858e-01 3.60881090e-01 6.69372141e-01
-5.25009692e-01 4.24901634e-01 -6.14262700e-01 -5.95333613e-02
-4.03704077e-01 -8.20008278e-01 -8.04377377e-01 -4.96414959e-01
-2.75206983e-01 9.05730605e-01 4.78342414e-01 -1.30110711... | [7.955733776092529, -2.2897374629974365] |
25aa3840-6501-4f81-9e19-95e0a4db6ee0 | beyond-top-grasps-through-scene-completion | 1909.12908 | null | https://arxiv.org/abs/1909.12908v2 | https://arxiv.org/pdf/1909.12908v2.pdf | Beyond Top-Grasps Through Scene Completion | Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top-grasp planning methods to generate full six-degree-of-fre... | ['Ville Kyrki', 'Jens Lundell', 'Francesco Verdoja'] | 2019-09-15 | null | null | null | null | ['grasp-generation'] | ['computer-vision'] | [-2.29109764e-01 -7.59167224e-02 3.00646335e-01 -2.68784642e-01
-4.19567257e-01 -1.03563690e+00 8.44617411e-02 -1.03722900e-01
-1.44688085e-01 1.47575215e-01 -1.73727080e-01 -1.44136008e-02
-3.35673124e-01 -7.63715029e-01 -1.05295420e+00 -5.29989004e-01
-4.79267240e-01 1.04110920e+00 -5.31904697e-02 -3.98550212... | [5.736574649810791, -0.8199937343597412] |
cd24d02a-606b-4b8a-9815-e85ab74e7a9e | shared-space-transfer-learning-for-analyzing | 2010.15594 | null | https://arxiv.org/abs/2010.15594v1 | https://arxiv.org/pdf/2010.15594v1.pdf | Shared Space Transfer Learning for analyzing multi-site fMRI data | Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data, for distinguishing when subjects are performing different cognitive tasks -- e.g., watching movies or making decisions. MVPA works best with a well-designed feature set and an adequate sample ... | ['Russell Greiner', 'Andrew J. Greenshaw', 'Daoqiang Zhang', 'Alessandro Selvitella', 'Muhammad Yousefnezhad'] | 2020-10-24 | null | http://proceedings.neurips.cc/paper/2020/hash/b837305e43f7e535a1506fc263eee3ed-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/b837305e43f7e535a1506fc263eee3ed-Paper.pdf | neurips-2020-12 | ['art-analysis'] | ['computer-vision'] | [ 3.45098317e-01 -6.45351708e-01 -8.00597444e-02 -6.56829715e-01
-9.71013188e-01 -1.67730078e-01 2.35326603e-01 -1.78165346e-01
-5.40606499e-01 7.53201723e-01 9.46943760e-02 8.22168868e-03
-6.02094412e-01 -5.24536371e-01 -6.18067980e-01 -7.32217431e-01
-3.49214226e-01 4.50155616e-01 3.85556340e-01 2.90906757... | [12.637799263000488, 3.330491542816162] |
797695d3-f96a-4993-99ef-4ab570c0203b | revisiting-machine-translation-for-cross | 2305.14240 | null | https://arxiv.org/abs/2305.14240v1 | https://arxiv.org/pdf/2305.14240v1.pdf | Revisiting Machine Translation for Cross-lingual Classification | Machine Translation (MT) has been widely used for cross-lingual classification, either by translating the test set into English and running inference with a monolingual model (translate-test), or translating the training set into the target languages and finetuning a multilingual model (translate-train). However, most ... | ['Luke Zettlemoyer', 'Angela Fan', 'Shruti Bhosale', 'Vedanuj Goswami', 'Mikel Artetxe'] | 2023-05-23 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 1.25478789e-01 -2.73932293e-02 -6.05643332e-01 -5.26387751e-01
-1.58071804e+00 -1.15190530e+00 9.10803318e-01 -9.44166258e-02
-5.94634652e-01 1.07891679e+00 1.31677657e-01 -1.19427943e+00
4.07669634e-01 -3.84716034e-01 -9.26068902e-01 -3.86525601e-01
5.83110631e-01 9.06605899e-01 -3.35305021e-03 -2.55850792... | [11.231080055236816, 10.242451667785645] |
bdc52381-9ed8-451f-b94b-87268465202c | phasebook-and-friends-leveraging-discrete | 1810.01395 | null | http://arxiv.org/abs/1810.01395v2 | http://arxiv.org/pdf/1810.01395v2.pdf | Phasebook and Friends: Leveraging Discrete Representations for Source Separation | Deep learning based speech enhancement and source separation systems have
recently reached unprecedented levels of quality, to the point that performance
is reaching a new ceiling. Most systems rely on estimating the magnitude of a
target source by estimating a real-valued mask to be applied to a
time-frequency represe... | ['Shinji Watanabe', 'John R. Hershey', 'Jonathan Le Roux', 'Gordon Wichern', 'Andy Sarroff'] | 2018-10-02 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 2.52080977e-01 -1.20672747e-01 7.44813541e-03 -3.59795511e-01
-1.41686928e+00 -5.18188953e-01 6.21667504e-01 1.43789396e-01
-6.54877782e-01 5.40263951e-01 2.56520271e-01 -1.94813639e-01
-2.22174644e-01 -1.13631681e-01 -6.47003591e-01 -9.83594000e-01
-3.58877540e-01 1.50073588e-01 -1.07686815e-03 -1.96181372... | [15.12677001953125, 5.782646179199219] |
a1a017ec-cc62-4474-80bc-275023d491df | dual-local-global-contextual-pathways-for | 1605.05462 | null | http://arxiv.org/abs/1605.05462v1 | http://arxiv.org/pdf/1605.05462v1.pdf | Dual Local-Global Contextual Pathways for Recognition in Aerial Imagery | Visual context is important in object recognition and it is still an open
problem in computer vision. Along with the advent of deep convolutional neural
networks (CNN), using contextual information with such systems starts to
receive attention in the literature. At the same time, aerial imagery is
gaining momentum. Whi... | ['Alina Marcu', 'Marius Leordeanu'] | 2016-05-18 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.29099703e-01 -2.77699113e-01 -2.09500524e-03 -5.25256753e-01
-2.77661085e-01 -8.60951781e-01 7.50653625e-01 4.75202680e-01
-3.79921615e-01 4.73203868e-01 1.08490214e-01 -4.45308745e-01
-2.01778010e-01 -1.08308852e+00 -7.82284617e-01 -7.44588673e-01
5.23789531e-05 1.75319329e-01 2.67834097e-01 -3.81628066... | [9.515789031982422, -0.40352290868759155] |
13a165d7-42ba-486a-bf06-43b87bc3dc25 | exploiting-long-term-dependencies-for | 2112.09828 | null | https://arxiv.org/abs/2112.09828v2 | https://arxiv.org/pdf/2112.09828v2.pdf | Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs | Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions. We hypothesize that capturing long-term temporal dependencies is the key to effective generation of dynamic scene graphs. We propose to learn the long-term dependen... | ['Somdeb Majumdar', 'Marcel Nassar', 'Hesham Mostafa', 'Subarna Tripathi', 'Shengyu Feng'] | 2021-12-18 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 1.64156109e-01 -1.77335575e-01 -2.21627772e-01 -2.01721862e-01
-3.50076586e-01 -4.44281965e-01 5.55746257e-01 -2.02424049e-01
1.51824504e-01 3.86039525e-01 4.02851641e-01 4.66167256e-02
-4.00984287e-01 -5.72489560e-01 -1.09965348e+00 -5.06620347e-01
-4.16462570e-01 2.96709090e-01 7.42811143e-01 -3.31659839... | [9.20423412322998, 0.2541642487049103] |
e3d17559-9b61-4b3a-9140-860a242e6b20 | iterated-support-vector-machines-for-distance | 1502.00363 | null | http://arxiv.org/abs/1502.00363v1 | http://arxiv.org/pdf/1502.00363v1.pdf | Iterated Support Vector Machines for Distance Metric Learning | Distance metric learning aims to learn from the given training data a valid
distance metric, with which the similarity between data samples can be more
effectively evaluated for classification. Metric learning is often formulated
as a convex or nonconvex optimization problem, while many existing metric
learning algorit... | ['Liang Lin', 'Yuchi Huang', 'David Zhang', 'Faqiang Wang', 'Wangmeng Zuo', 'Lei Zhang', 'Deyu Meng'] | 2015-02-02 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.49490680e-02 -3.76918823e-01 -4.32333201e-01 -7.36134231e-01
-9.00513113e-01 -3.76918226e-01 1.96305722e-01 -1.76594719e-01
-3.63749236e-01 9.32766080e-01 -1.36733949e-01 -2.34050229e-01
-7.72047639e-01 -3.58969778e-01 -3.33504915e-01 -7.00864613e-01
-2.07512796e-01 6.00239336e-01 -2.56493211e-01 3.65890563... | [9.212203025817871, 3.2988345623016357] |
d590cec3-0ad6-40cf-a937-590dbb4671ab | understanding-generalized-label-smoothing-1 | null | null | https://openreview.net/forum?id=UQQgMRq58O | https://openreview.net/pdf?id=UQQgMRq58O | Understanding Generalized Label Smoothing when Learning with Noisy Labels | Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model. Later it was repo... | ['Yang Liu', 'Gang Niu', 'Tongliang Liu', 'Hangyu Liu', 'Jiaheng Wei'] | 2021-09-29 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 6.10465229e-01 5.64356267e-01 -2.31646523e-01 -7.15889037e-01
-1.02152789e+00 -6.85696661e-01 3.83495063e-01 2.33953685e-01
-4.37753320e-01 8.13532650e-01 -1.09782152e-01 -2.92990476e-01
-3.40991199e-01 -3.81699085e-01 -6.51298761e-01 -1.17062390e+00
1.08572975e-01 9.00468901e-02 1.43320441e-01 1.03673324... | [9.35429859161377, 4.033620834350586] |
a6b45a37-7768-4440-9c36-555f337dbf7a | logicsolver-towards-interpretable-math-word | 2205.08232 | null | https://arxiv.org/abs/2205.08232v3 | https://arxiv.org/pdf/2205.08232v3.pdf | LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning | Recently, deep learning models have made great progress in MWP solving on answer accuracy. However, they are uninterpretable since they mainly rely on shallow heuristics to achieve high performance without understanding and reasoning the grounded math logic. To address this issue and make a step towards interpretable M... | ['Xiaodan Liang', 'Liang Lin', 'Jiaqi Chen', 'Jinghui Qin', 'Zhicheng Yang'] | 2022-05-17 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-9.55867618e-02 7.70626664e-01 -5.32230377e-01 -5.19273937e-01
-8.88892531e-01 -8.22064102e-01 5.39936088e-02 1.43726915e-01
3.09152991e-01 8.75297666e-01 2.83463657e-01 -6.94186270e-01
-4.24461871e-01 -1.50019813e+00 -1.11031592e+00 3.71323526e-02
2.88959503e-01 1.06989968e+00 -7.20674098e-02 -3.61967713... | [9.472515106201172, 7.508758068084717] |
07020888-ad77-48d2-b5c3-feaf0851ce2a | a-conditional-adversarial-network-for-scene | 1904.11163 | null | http://arxiv.org/abs/1904.11163v1 | http://arxiv.org/pdf/1904.11163v1.pdf | A Conditional Adversarial Network for Scene Flow Estimation | The problem of Scene flow estimation in depth videos has been attracting
attention of researchers of robot vision, due to its potential application in
various areas of robotics. The conventional scene flow methods are difficult to
use in reallife applications due to their long computational overhead. We
propose a condi... | ['Snehasis Mukherjee', 'Ravi Kumar Thakur'] | 2019-04-25 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 3.33672851e-01 -1.20872492e-02 3.39816302e-01 -2.32212126e-01
-1.18806884e-01 -4.06605244e-01 6.21885955e-01 -4.84417886e-01
-5.73722124e-01 8.62910271e-01 -6.24527000e-02 -1.18324004e-01
3.15796971e-01 -8.25606048e-01 -6.97870433e-01 -6.67095184e-01
1.65208578e-01 1.19284891e-01 3.96145105e-01 -3.80814970... | [8.684539794921875, -2.071789026260376] |
ed2fff21-1378-466b-8bdb-73bd7f5fcb75 | retrieve-anyone-a-general-purpose-person-re | 2306.07520 | null | https://arxiv.org/abs/2306.07520v3 | https://arxiv.org/pdf/2306.07520v3.pdf | Retrieve Anyone: A General-purpose Person Re-identification Task with Instructions | Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identification (ReID) tasks in different scenarios separately, which limits the applications in the real world. This paper strives to resolve this prob... | ['Yunfeng Yan', 'Donglian Qi', 'Wanli Ouyang', 'Rui Zhao', 'Feng Zhu', 'Lei Bai', 'Yizhou Wang', 'Qingsong Xie', 'Qihao Chen', 'Yiheng Deng', 'Shixiang Tang', 'Weizhen He'] | 2023-06-13 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-3.50259423e-01 -4.31799799e-01 1.19228020e-01 -4.18013483e-01
-5.48450351e-01 -5.84816992e-01 6.68288112e-01 -3.28144163e-01
-9.45173085e-01 6.74524188e-01 -3.08439974e-02 7.05540925e-02
1.49385661e-01 -5.24539292e-01 -7.28752673e-01 -6.31054878e-01
1.82978436e-01 5.80717683e-01 -1.26637697e-01 -2.68041700... | [14.643767356872559, 0.923568069934845] |
4cf4ae84-8820-4ced-8ab3-8aab98e5108c | pipe-overflow-smashing-voice-authentication | 2202.02751 | null | https://arxiv.org/abs/2202.02751v2 | https://arxiv.org/pdf/2202.02751v2.pdf | Tubes Among Us: Analog Attack on Automatic Speaker Identification | Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial examples. A large number of modern systems protect themselves against such attacks by targeting artificiality, i.e., they deploy mechanisms... | ['Kassem Fawaz', 'Nicolas Papernot', 'Ilia Shumailov', 'Mohammad Yaghini', 'Ali Shahin Shamsabadi', 'Yash Wani', 'Shimaa Ahmed'] | 2022-02-06 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.91019666e-01 3.78902465e-01 2.07354963e-01 -2.54096121e-01
-9.16681290e-01 -1.19466662e+00 5.47554910e-01 -1.90857425e-01
-2.70847648e-01 5.21852672e-01 -2.84094602e-01 -7.57684767e-01
3.88987243e-01 -5.27309597e-01 -8.18121970e-01 -6.97377801e-01
6.24808446e-02 1.60147548e-01 -1.71950400e-01 -1.11299232... | [13.896913528442383, 5.80698823928833] |
23a55474-b9d4-4b1a-a8ad-47c0bf5a98d0 | cmw-net-learning-a-class-aware-sample | 2202.05613 | null | https://arxiv.org/abs/2202.05613v3 | https://arxiv.org/pdf/2202.05613v3.pdf | CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning | Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require to manually pre-specify the weighting schemes as well as their additional hyper-... | ['Zongben Xu', 'Deyu Meng', 'Xiang Yuan', 'Jun Shu'] | 2022-02-11 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.42020059e-01 4.68495488e-03 -3.79331797e-01 -1.00991499e+00
-7.13296950e-01 -3.61450166e-01 3.88406128e-01 2.54931487e-02
-7.88911581e-01 8.18643630e-01 -1.42810956e-01 -1.04953155e-01
-3.89764577e-01 -6.55955255e-01 -5.16120493e-01 -9.15236294e-01
1.54212952e-01 6.13901675e-01 1.12185568e-01 -2.01814592... | [9.230220794677734, 3.920546770095825] |
12f1e9d4-77b8-470b-87ac-31e7b5ea63c4 | unified-conversational-models-with-system | 2307.01664 | null | https://arxiv.org/abs/2307.01664v1 | https://arxiv.org/pdf/2307.01664v1.pdf | Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues | Spoken dialogue systems (SDSs) have been separately developed under two different categories, task-oriented and chit-chat. The former focuses on achieving functional goals and the latter aims at creating engaging social conversations without special goals. Creating a unified conversational model that can engage in both... | ['Wolfgang Maier', 'Wolfgang Minker', 'Stefan Ultes', 'Ye Liu'] | 2023-07-04 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [ 4.50524181e-01 7.08878338e-01 2.28356183e-01 -5.87051630e-01
-6.76507592e-01 -5.98292351e-01 1.28547227e+00 1.03425607e-01
-2.21944690e-01 9.32791352e-01 6.51686192e-01 -3.39896530e-01
1.83827475e-01 -5.50015926e-01 -2.93794647e-02 -6.21491075e-01
1.82591200e-01 9.70013976e-01 4.27821368e-01 -8.25332224... | [12.878854751586914, 7.988559246063232] |
072c9333-2caa-49f6-9642-e2b0ec7a2bf9 | federated-sufficient-dimension-reduction | 2301.09500 | null | https://arxiv.org/abs/2301.09500v1 | https://arxiv.org/pdf/2301.09500v1.pdf | Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression | Federated learning has become a popular tool in the big data era nowadays. It trains a centralized model based on data from different clients while keeping data decentralized. In this paper, we propose a federated sparse sliced inverse regression algorithm for the first time. Our method can simultaneously estimate the ... | ['Haoyang Cheng', 'Jianjun Xu', 'Yue Zhao', 'Wenquan Cui'] | 2023-01-23 | null | null | null | null | ['variable-selection'] | ['methodology'] | [-3.64091754e-01 -2.13674963e-01 -4.80964422e-01 -3.82624149e-01
-1.31350720e+00 -4.24370259e-01 2.61243999e-01 -6.59987926e-01
-9.50550586e-02 8.11516225e-01 3.59252602e-01 -3.45670372e-01
-4.20605093e-01 -4.03930753e-01 -8.50195050e-01 -1.24003553e+00
-1.95082113e-01 6.11657619e-01 -5.26777387e-01 3.11590374... | [5.94724178314209, 6.063390254974365] |
72b67489-044d-4a84-a116-85ee44bbb557 | handling-inter-annotator-agreement-for | 1906.02415 | null | https://arxiv.org/abs/1906.02415v1 | https://arxiv.org/pdf/1906.02415v1.pdf | Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation | In this work, we explore the issue of the inter-annotator agreement for training and evaluating automated segmentation of skin lesions. We explore what different degrees of agreement represent, and how they affect different use cases for segmentation. We also evaluate how conditioning the ground truths using different ... | ['Sandra Avila', 'Eduardo Valle', 'Vinicius Ribeiro'] | 2019-06-06 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.01249439e-01 1.33483618e-01 -6.50351495e-02 -3.83979857e-01
-9.23279226e-01 -1.00627220e+00 2.78210014e-01 7.44461238e-01
-5.81898034e-01 5.17959058e-01 -6.94738925e-02 -2.71286488e-01
3.15702483e-02 -4.51719105e-01 -1.93377063e-01 -7.39806592e-01
2.38123953e-01 5.84076285e-01 6.09724879e-01 1.46653458... | [15.555456161499023, -2.9780287742614746] |
a87472e0-40cd-413f-a868-b9612414314c | travelbert-pre-training-language-model | 2109.01048 | null | https://arxiv.org/abs/2109.01048v2 | https://arxiv.org/pdf/2109.01048v2.pdf | TravelBERT: Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation | Existing technologies expand BERT from different perspectives, e.g. designing different pre-training tasks, different semantic granularities and different model architectures. Few models consider expanding BERT from different text formats. In this paper, we propose a heterogeneous knowledge language model (HKLM), a uni... | ['Zhiheng Lyu', 'Jinghui Xiao', 'Juanzi Li', 'Lei Hou', 'Hao Peng', 'Hongyin Zhu'] | 2021-09-02 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-1.95747301e-01 3.48236531e-01 -5.33377409e-01 -1.99872717e-01
-4.28900927e-01 -6.94453239e-01 6.74654126e-01 6.36453182e-02
-8.13190162e-01 1.04972947e+00 5.83005011e-01 -3.90979350e-01
-4.58201289e-01 -1.12461746e+00 -5.35828412e-01 -1.06235221e-01
-2.44923178e-02 8.71269107e-01 6.60487413e-01 -6.44845843... | [9.374539375305176, 8.417804718017578] |
2ea8c5f9-d448-46ba-aa30-b3aa8fcb5b07 | from-parse-execute-to-parse-execute-refine | 2305.03356 | null | https://arxiv.org/abs/2305.03356v1 | https://arxiv.org/pdf/2305.03356v1.pdf | From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base | Parsing questions into executable logical forms has showed impressive results for knowledge-base question answering (KBQA). However, complex KBQA is a more challenging task that requires to perform complex multi-step reasoning. Recently, a new semantic parser called KoPL has been proposed to explicitly model the reason... | ['Jian Yin', 'Hanjiang Lai', 'Linyin Luo', 'Wangzhen Guo'] | 2023-05-05 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.14029229e-01 5.93043983e-01 2.29483396e-01 -5.86304963e-01
-1.23326433e+00 -7.66556025e-01 3.82558793e-01 -8.63127131e-03
-2.37571657e-01 5.90950012e-01 3.34025890e-01 -7.49710143e-01
3.68423760e-02 -1.20146608e+00 -7.76644170e-01 -8.28838050e-02
5.52889645e-01 8.98372114e-01 9.86359060e-01 -5.87456167... | [10.456854820251465, 7.887436389923096] |
838321c3-9437-40fe-b949-1f651ac14146 | exploiting-cross-lingual-subword-similarities | 1812.09617 | null | https://arxiv.org/abs/1812.09617v4 | https://arxiv.org/pdf/1812.09617v4.pdf | Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification | Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classifi... | ['Jordan Boyd-Graber', 'Mozhi Zhang', 'Yoshinari Fujinuma'] | 2018-12-22 | null | https://openreview.net/forum?id=S1e_H3AqYQ | https://openreview.net/pdf?id=S1e_H3AqYQ | null | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 9.14343446e-02 -4.23752218e-01 -7.21359193e-01 -5.99660337e-01
-9.24848437e-01 -7.11483240e-01 5.33160925e-01 3.63280207e-01
-8.97564232e-01 7.55261242e-01 2.91227788e-01 -4.39875901e-01
4.26846087e-01 -8.95983160e-01 -5.87640047e-01 -2.44852796e-01
3.34879845e-01 4.16109800e-01 -8.75856280e-02 -3.66818994... | [10.949043273925781, 9.936883926391602] |
31d69d60-6560-484e-8ef9-d74ce2a26091 | feature-engineering-for-predictive-modeling | 1709.07150 | null | http://arxiv.org/abs/1709.07150v1 | http://arxiv.org/pdf/1709.07150v1.pdf | Feature Engineering for Predictive Modeling using Reinforcement Learning | Feature engineering is a crucial step in the process of predictive modeling.
It involves the transformation of given feature space, typically using
mathematical functions, with the objective of reducing the modeling error for a
given target. However, there is no well-defined basis for performing effective
feature engin... | ['Horst Samulowitz', 'Deepak Turaga', 'Udayan Khurana'] | 2017-09-21 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 3.86476755e-01 6.06251806e-02 -1.53634414e-01 -2.45696291e-01
-5.35864770e-01 -5.38583100e-01 7.71333456e-01 1.83778375e-01
-1.83542833e-01 8.85473669e-01 -2.66057640e-01 -5.43146849e-01
-6.25521719e-01 -8.85393023e-01 -5.94562709e-01 -3.01948339e-01
1.91363364e-01 5.12075305e-01 -8.81836563e-02 -2.24090427... | [6.410566329956055, 3.7960588932037354] |
f0ae6e63-bb30-4d42-8b8d-91b8531e1df7 | libcity-an-open-library-for-traffic | null | null | https://dl.acm.org/doi/10.1145/3474717.3483923 | https://dl.acm.org/doi/10.1145/3474717.3483923 | LibCity: An Open Library for Traffic Prediction | With the increase of traffic prediction models, there has become an urgent need to develop a standardized framework to implement and evaluate these methods. This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool ... | ['Wayne Xin Zhao', 'Chao Li', 'Wenjun Jiang', 'Jiawei Jiang', 'Jingyuan Wang'] | 2021-11-04 | null | null | null | international-conference-on-advances-in-2 | ['time-series-prediction', 'spatio-temporal-forecasting'] | ['time-series', 'time-series'] | [-7.39538193e-01 -9.24089730e-01 -4.70704198e-01 -5.28545439e-01
-2.75691628e-01 -1.06585786e-01 2.28526935e-01 -4.83865172e-01
-3.09337657e-02 6.88791931e-01 -3.81692462e-02 -6.96270645e-01
3.05718966e-02 -1.22009492e+00 -2.49422178e-01 -5.58771968e-01
2.73332715e-01 5.26124239e-01 8.05935502e-01 -4.10980135... | [6.385086536407471, 1.9210418462753296] |
3ed580c1-b99f-4269-97dd-f208438e154e | semi-supervised-multi-task-learning-for-lung | 1802.06181 | null | http://arxiv.org/abs/1802.06181v2 | http://arxiv.org/pdf/1802.06181v2.pdf | Semi-supervised multi-task learning for lung cancer diagnosis | Early detection of lung nodules is of great importance in lung cancer
screening. Existing research recognizes the critical role played by CAD systems
in early detection and diagnosis of lung nodules. However, many CAD systems,
which are used as cancer detection tools, produce a lot of false positives (FP)
and require a... | ['Naji Khosravan', 'Ulas Bagci'] | 2018-02-17 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 1.57964323e-02 1.80701673e-01 -3.86760652e-01 -2.19537169e-01
-9.83280241e-01 -5.63038886e-01 4.67437804e-01 1.26797304e-01
-4.56243724e-01 2.79304028e-01 -6.47362247e-02 -6.82731807e-01
1.58186723e-02 -8.15317512e-01 -4.88394469e-01 -6.18310809e-01
1.52146488e-01 8.27781022e-01 8.34689498e-01 1.73107952... | [15.357747077941895, -2.1584713459014893] |
9c635475-7451-4998-9397-8574bf558b4d | is-independent-learning-all-you-need-in-the | 2011.09533 | null | https://arxiv.org/abs/2011.09533v1 | https://arxiv.org/pdf/2011.09533v1.pdf | Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? | Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function. In this paper, we demonstrate that, despite its various theoretical shortcomings, Independent PPO (IPPO), a... | ['Shimon Whiteson', 'Mingfei Sun', 'Philip H. S. Torr', 'Viktor Makoviychuk', 'Denys Makoviichuk', 'Tarun Gupta', 'Christian Schroeder de Witt'] | 2020-11-18 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.15534544e-01 -1.07975163e-01 -6.07398391e-01 -7.65453354e-02
-1.06717873e+00 -4.51164275e-01 7.24178016e-01 1.92530230e-01
-7.28104532e-01 1.54281306e+00 1.11259408e-01 -1.30696326e-01
-3.98240358e-01 -2.89746046e-01 -8.17516983e-01 -1.11210108e+00
-8.27861190e-01 9.19024765e-01 1.43361673e-01 -3.52611482... | [3.794020891189575, 2.0233681201934814] |
73d305df-94ec-4b49-8918-1b621d63189c | nonparametric-quantile-regression-non | 2210.10161 | null | https://arxiv.org/abs/2210.10161v1 | https://arxiv.org/pdf/2210.10161v1.pdf | Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction | We propose a nonparametric quantile regression method using deep neural networks with a rectified linear unit penalty function to avoid quantile crossing. This penalty function is computationally feasible for enforcing non-crossing constraints in multi-dimensional nonparametric quantile regression. We establish non-asy... | ['Jian Huang', 'Yuanyuan Lin', 'Guohao Shen', 'Wenlu Tang'] | 2022-10-18 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-1.54184341e-01 2.40516201e-01 -4.27402496e-01 -8.39253783e-01
-9.97421741e-01 -4.42359924e-01 -5.92096383e-03 2.86389053e-01
-3.19426209e-01 1.04718709e+00 2.20178179e-02 -4.73353028e-01
-7.25714922e-01 -8.94224226e-01 -9.11971450e-01 -8.23132992e-01
-1.60002172e-01 3.43717694e-01 -2.52682388e-01 1.28207773... | [7.291483402252197, 3.9114229679107666] |
05530041-cc60-4645-870d-e2b14a59c0d5 | deep-bidirectional-language-knowledge-graph | 2210.09338 | null | https://arxiv.org/abs/2210.09338v2 | https://arxiv.org/pdf/2210.09338v2.pdf | Deep Bidirectional Language-Knowledge Graph Pretraining | Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent works show that a knowledge graph (KG) can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning. However, these works are not pretrained to learn a deep fusion of... | ['Jure Leskovec', 'Percy Liang', 'Christopher D Manning', 'Xikun Zhang', 'Hongyu Ren', 'Antoine Bosselut', 'Michihiro Yasunaga'] | 2022-10-17 | null | null | null | null | ['riddle-sense', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [-2.93371174e-02 8.66069138e-01 -6.44488037e-01 -5.26588798e-01
-1.18410182e+00 -6.04928374e-01 5.17130196e-01 5.00576019e-01
-4.12402809e-01 8.00439358e-01 6.07467592e-01 -6.58420622e-01
-1.87242150e-01 -9.06038940e-01 -1.13566411e+00 -1.15166031e-01
2.93980271e-01 9.33135569e-01 2.71110564e-01 -2.17341974... | [10.421198844909668, 7.986384868621826] |
e93b6b9a-1dc9-4a10-ad88-93b666aaeb70 | robust-face-recognition-with-deeply | 1805.00406 | null | http://arxiv.org/abs/1805.00406v1 | http://arxiv.org/pdf/1805.00406v1.pdf | Robust Face Recognition with Deeply Normalized Depth Images | Depth information has been proven useful for face recognition. However,
existing depth-image-based face recognition methods still suffer from noisy
depth values and varying poses and expressions. In this paper, we propose a
novel method for normalizing facial depth images to frontal pose and neutral
expression and extr... | ['Qijun Zhao', 'Ziqing Feng'] | 2018-05-01 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.63763809e-01 9.75337997e-02 4.15401213e-04 -1.06769931e+00
-4.53753442e-01 -2.94604361e-01 3.47058475e-01 -7.37735927e-01
-3.06641787e-01 4.83816922e-01 -7.33721107e-02 1.29529431e-01
1.67411845e-02 -1.04737496e+00 -4.61840898e-01 -8.41026485e-01
-4.99807149e-02 4.92455401e-02 -5.81273139e-01 -1.21790379... | [13.438067436218262, 0.9264023900032043] |
ce4dce16-0db2-4701-9aaf-0bd126ec4c5e | dg-labeler-and-dgl-mots-dataset-boost-the | 2110.07790 | null | https://arxiv.org/abs/2110.07790v1 | https://arxiv.org/pdf/2110.07790v1.pdf | DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception | Multi-object tracking and segmentation (MOTS) is a critical task for autonomous driving applications. The existing MOTS studies face two critical challenges: 1) the published datasets inadequately capture the real-world complexity for network training to address various driving settings; 2) the working pipeline annotat... | ['Dongfang Liu', 'Lin Li', 'Yingjie Chen', 'Feng Tao', 'Xingyu Jiang', 'Yixin Xie', 'Zhiwen Cao', 'Yiming Cui'] | 2021-10-15 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 2.09410533e-01 -6.88619763e-02 -3.57875079e-01 -5.46724200e-01
-6.74448967e-01 -5.59713244e-01 5.72063625e-01 -6.48747385e-02
-4.13445264e-01 7.04129159e-01 -2.76198655e-01 -3.64223242e-01
-2.29274660e-01 -5.97684026e-01 -7.58755565e-01 -5.11616349e-01
-4.85222088e-03 7.14439392e-01 1.15750790e+00 -4.41804260... | [7.864168167114258, -1.5466086864471436] |
7422889b-1478-4b71-a7e2-ebce8fb98c8f | automatic-liver-and-tumor-segmentation-of-ct | 1702.05970 | null | http://arxiv.org/abs/1702.05970v2 | http://arxiv.org/pdf/1702.05970v2.pdf | Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks | Automatic segmentation of the liver and hepatic lesions is an important step
towards deriving quantitative biomarkers for accurate clinical diagnosis and
computer-aided decision support systems. This paper presents a method to
automatically segment liver and lesions in CT and MRI abdomen images using
cascaded fully con... | ['Seyed-Ahmad Ahmadi', 'Freba Ahmaddy', 'Sebastian Schlecht', 'Jana Lipkova', 'Felix Grün', 'Wieland Sommer', 'Rickmer Braren', 'Mohamed Ezzeldin A. Elshaera', 'Melvin D Anastasi', 'Markus Rempfler', 'Marc Bickel', 'Bjoern Menze', 'Volker Heinemann', 'Julian Holch', 'Sunil Tatavarty', 'Patrick Ferdinand Christ', 'Patri... | 2017-02-20 | null | null | null | null | ['automatic-liver-and-tumor-segmentation'] | ['medical'] | [ 4.36137021e-02 1.91921443e-01 -8.67283642e-02 -6.04829729e-01
-6.62060559e-01 -4.87988800e-01 3.90466988e-01 2.35573247e-01
-3.38011891e-01 4.01987731e-01 2.65294373e-01 -5.55667162e-01
7.31232166e-02 -6.56141043e-01 -2.42261454e-01 -7.75346577e-01
-6.36498868e-01 8.72350574e-01 1.41951263e-01 5.84929228... | [14.503683090209961, -2.6657230854034424] |
b33551f8-e81f-4f30-9aa4-81b1ca3b2c03 | sign-pose-based-transformer-for-word-level | null | null | https://openaccess.thecvf.com/content/WACV2022W/HADCV/html/Bohacek_Sign_Pose-Based_Transformer_for_Word-Level_Sign_Language_Recognition_WACVW_2022_paper.html | https://openaccess.thecvf.com/content/WACV2022W/HADCV/papers/Bohacek_Sign_Pose-Based_Transformer_for_Word-Level_Sign_Language_Recognition_WACVW_2022_paper.pdf | Sign Pose-Based Transformer for Word-Level Sign Language Recognition | In this paper we present a system for word-level sign language recognition based on the Transformer model. We aim at a solution with low computational cost, since we see great potential in the usage of such recognition system on handheld devices. We base the recognition on the estimation of the pose of the human body i... | ['Marek Hrúz', 'Matyáš Boháček'] | 2022-01-04 | null | null | null | wacv-2022-1 | ['sign-language-recognition'] | ['computer-vision'] | [ 3.17272097e-01 -1.00729279e-01 1.41019039e-02 -2.91076154e-01
-1.17699087e+00 -4.65079159e-01 6.62742555e-01 -6.18955493e-01
-8.55224252e-01 2.49923587e-01 2.51287222e-01 -9.97453742e-03
2.64926068e-02 -2.13003352e-01 -5.52216470e-01 -8.63011777e-01
2.78004110e-01 4.87453520e-01 2.46911153e-01 -2.88111418... | [9.143054008483887, -6.460640907287598] |
faea83aa-a950-4d1d-9c44-5f8ce06bb7cd | cross-modal-global-interaction-and-local | 2305.09212 | null | https://arxiv.org/abs/2305.09212v1 | https://arxiv.org/pdf/2305.09212v1.pdf | Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition | Audio-visual speech recognition (AVSR) research has gained a great success recently by improving the noise-robustness of audio-only automatic speech recognition (ASR) with noise-invariant visual information. However, most existing AVSR approaches simply fuse the audio and visual features by concatenation, without expli... | ['Eng Siong Chng', 'Qiushi Zhu', 'Heqing Zou', 'Chen Chen', 'Ruizhe Li', 'Yuchen Hu'] | 2023-05-16 | null | null | null | null | ['visual-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [-1.49286002e-01 -4.50732559e-01 -1.97422475e-01 -3.38745117e-01
-1.29673362e+00 -4.27509248e-01 9.61644709e-01 5.21936044e-02
-2.33343407e-03 1.92868449e-02 7.26051211e-01 -1.93260953e-01
-4.21942733e-02 -2.70565689e-01 -4.81019795e-01 -9.72115159e-01
1.76617593e-01 -1.65280059e-01 9.42783505e-02 -3.13857615... | [14.217751502990723, 5.126428127288818] |
c3d964fd-6f70-4022-ab5d-39d0a1a0e294 | what-can-i-cook-with-these-ingredients | 2112.04788 | null | https://arxiv.org/abs/2112.04788v2 | https://arxiv.org/pdf/2112.04788v2.pdf | "What can I cook with these ingredients?" -- Understanding cooking-related information needs in conversational search | As conversational search becomes more pervasive, it becomes increasingly important to understand the user's underlying information needs when they converse with such systems in diverse domains. We conduct an in-situ study to understand information needs arising in a home cooking context as well as how they are verbally... | ['Bernd Ludwig', 'David Elsweiler', 'Alexander Frummet'] | 2021-12-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.85770530e-01 2.18287647e-01 1.24242812e-04 -1.02401447e+00
-7.59683967e-01 -8.69688988e-01 4.02203739e-01 4.91651624e-01
-2.66371727e-01 5.93205571e-01 9.17704105e-01 -5.00728846e-01
-1.80816814e-01 -1.43227518e-01 4.71587898e-03 -2.62315989e-01
3.79191458e-01 6.92808807e-01 -1.55933574e-01 -5.89398861... | [12.54422664642334, 7.851447582244873] |
2e177aba-a050-4181-93a1-2783f4fcb9ab | a-blackbox-approach-to-best-of-both-worlds-in | 2302.09739 | null | https://arxiv.org/abs/2302.09739v1 | https://arxiv.org/pdf/2302.09739v1.pdf | A Blackbox Approach to Best of Both Worlds in Bandits and Beyond | Best-of-both-worlds algorithms for online learning which achieve near-optimal regret in both the adversarial and the stochastic regimes have received growing attention recently. Existing techniques often require careful adaptation to every new problem setup, including specialised potentials and careful tuning of algori... | ['Julian Zimmert', 'Chen-Yu Wei', 'Christoph Dann'] | 2023-02-20 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 0.27764204 0.5518552 -0.3580169 -0.47301787 -1.3344393 -1.0476863
0.17842028 0.31583345 -0.47964102 1.1115861 -0.24282932 -0.74908704
-0.8397168 -0.921372 -1.218093 -0.7928211 -0.4472296 1.0497701
0.2628674 -0.25427088 -0.0578295 0.29509252 -0.9403496 0.02373636
0.8776661 1.230896 -0.29... | [4.609299182891846, 3.400695562362671] |
4f2660de-2467-43a4-9a2d-48c5337301b6 | tkdp-threefold-knowledge-enriched-deep-prompt | 2306.03974 | null | https://arxiv.org/abs/2306.03974v2 | https://arxiv.org/pdf/2306.03974v2.pdf | TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition | Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions. Effectively transferring the internal or external resources thus becomes the key to few-shot NER. While the existing prompt tuning methods have shown remarkable few-shot performances, they still fail to make full us... | ['Donghong Ji', 'Chong Teng', 'Liang Zhao', 'Bobo Li', 'Jingye Li', 'Fei Li', 'Hao Fei', 'Jiang Liu'] | 2023-06-06 | null | null | null | null | ['few-shot-ner', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing'] | [-1.83865309e-01 2.95389384e-01 -3.47065419e-01 -3.69458705e-01
-1.12204301e+00 -5.92805564e-01 6.78273737e-01 1.07486390e-01
-1.00129795e+00 8.89074922e-01 7.50176668e-01 -9.25076380e-02
2.41510332e-01 -8.84584129e-01 -5.65600276e-01 -4.20628458e-01
4.03146327e-01 3.74118000e-01 4.47370768e-01 -3.58054399... | [9.699664115905762, 9.342737197875977] |
cfe0e21c-d7da-4dc6-81fb-d00aa230dcc9 | motion-detection-using-csi-from-raspberry-pi | 2111.09091 | null | https://arxiv.org/abs/2111.09091v1 | https://arxiv.org/pdf/2111.09091v1.pdf | Motion Detection using CSI from Raspberry Pi 4 | Monitoring behaviour in smart homes using sensors can offer insights into changes in the independent ability and long-term health of residents. Passive Infrared motion sensors (PIRs) are standard, however may not accurately track the full duration of movement. They also require line-of-sight to detect motion which can ... | ['Christopher Clare', 'Susan Craw', 'Stewart Massie', 'Glenn Forbes'] | 2021-11-17 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.89761209e-01 -3.18674564e-01 -8.54106620e-02 -1.28003150e-01
-7.60632694e-01 -2.92316556e-01 7.48161077e-02 -1.00266241e-01
-7.24608243e-01 9.82425928e-01 4.81758505e-01 -2.52872795e-01
-1.05923191e-01 -8.73731554e-01 -2.81659335e-01 -8.09140384e-01
-4.80694741e-01 -2.80530006e-01 5.48529863e-01 -1.50348172... | [6.897494792938232, 0.6319673657417297] |
dac838f2-8657-4acc-9ea1-81cb9d2cc757 | the-effectiveness-of-simple-hybrid-systems | null | null | https://aclanthology.org/P19-1327 | https://aclanthology.org/P19-1327.pdf | The Effectiveness of Simple Hybrid Systems for Hypernym Discovery | Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods. However, recent works utilizing a mix of these strategies have yielded state-of-the-art results. This paper evaluates the contribution of both paradigms to hybrid success by evaluating the benefit... | ['Nizar Habash', 'William Held'] | 2019-07-01 | null | null | null | acl-2019-7 | ['hypernym-discovery'] | ['natural-language-processing'] | [-6.11061603e-02 1.92390695e-01 -5.91899633e-01 -1.82150587e-01
-3.59673232e-01 -5.24561584e-01 1.20569909e+00 4.65993077e-01
-9.42458630e-01 6.91712797e-01 2.12587819e-01 -3.66735399e-01
-6.38479769e-01 -8.90495479e-01 -8.28354619e-03 -4.60273802e-01
2.02037588e-01 1.16926658e+00 3.29413742e-01 -6.61398172... | [9.868945121765137, 8.720837593078613] |
627633a5-c184-42d8-a97a-70b1d54f7a5d | well-calibrated-probabilistic-predictive | 2306.06642 | null | https://arxiv.org/abs/2306.06642v1 | https://arxiv.org/pdf/2306.06642v1.pdf | Well-Calibrated Probabilistic Predictive Maintenance using Venn-Abers | When using machine learning for fault detection, a common problem is the fact that most data sets are very unbalanced, with the minority class (a fault) being the interesting one. In this paper, we investigate the usage of Venn-Abers predictors, looking specifically at the effect on the minority class predictions. A ke... | ['Cecilia Sönströd', 'Tuwe Löfström', 'Ulf Johansson'] | 2023-06-11 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.11947790e-01 4.90423024e-01 -4.86623645e-01 -7.26535201e-01
-2.35327661e-01 -1.82928354e-01 3.59233260e-01 5.21765709e-01
9.64668319e-02 1.23366451e+00 -6.88662902e-02 -6.05604947e-01
-7.86893368e-01 -9.73654747e-01 -5.99682748e-01 -6.24285460e-01
-5.05537316e-02 9.21377182e-01 3.82232100e-01 2.07454666... | [8.43179988861084, 4.5119099617004395] |
e3944391-467b-497a-8428-88c840035be8 | weakly-supervised-person-search-with-region | 2109.06109 | null | https://arxiv.org/abs/2109.06109v1 | https://arxiv.org/pdf/2109.06109v1.pdf | Weakly Supervised Person Search with Region Siamese Networks | Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to collect, especially for person identities. A natural question is whether a good person search model can be trained without the need of identity s... | ['Changhu Wang', 'Yi Yang', 'Nong Sang', 'Changxin Gao', 'Zehuan Yuan', 'Dongdong Yu', 'Kai Su', 'Chuchu Han'] | 2021-09-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Han_Weakly_Supervised_Person_Search_With_Region_Siamese_Networks_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Han_Weakly_Supervised_Person_Search_With_Region_Siamese_Networks_ICCV_2021_paper.pdf | iccv-2021-1 | ['person-search'] | ['computer-vision'] | [-1.07101858e-01 -4.08612676e-02 -3.79632086e-01 -7.40638256e-01
-8.13275158e-01 -6.29173100e-01 7.80973911e-01 3.18643972e-02
-6.29495025e-01 7.80328631e-01 6.54806048e-02 3.17385852e-01
-5.67509085e-02 -5.30369043e-01 -7.52272487e-01 -7.89223611e-01
-1.32908002e-01 7.00429738e-01 3.74049395e-02 -3.60396393... | [14.76862621307373, 0.9778971076011658] |
c0463176-fdc5-4002-bff3-678ee46ab522 | can-very-large-pretrained-language-models | 2301.09790 | null | https://arxiv.org/abs/2301.09790v2 | https://arxiv.org/pdf/2301.09790v2.pdf | Can Very Large Pretrained Language Models Learn Storytelling With A Few Examples? | While pre-trained language models can generate individually fluent sentences for automatic story generation, they struggle to generate stories that are coherent, sensible and interesting. Current state-of-the-art (SOTA) story generation models explore using higher-level features such as plots or commonsense knowledge t... | ['Jey Han Lau', 'Trevor Cohn', 'Zhuohan Xie'] | 2023-01-24 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 3.55144203e-01 5.94293475e-01 8.14460497e-03 1.18411072e-01
-1.03724504e+00 -6.98151588e-01 1.45021963e+00 1.23483703e-01
2.41662621e-01 1.26474059e+00 1.17067957e+00 -9.43629295e-02
1.30065605e-01 -1.15079582e+00 -8.02182555e-01 -5.58126122e-02
3.22289944e-01 6.32815063e-01 -4.19654474e-02 -7.37054825... | [11.775208473205566, 8.88914680480957] |
9923c1ad-cde7-4a74-bad6-063ab33841a4 | a-personalized-zero-shot-ecg-arrhythmia | 2207.07089 | null | https://arxiv.org/abs/2207.07089v1 | https://arxiv.org/pdf/2207.07089v1.pdf | A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance | This paper proposes a low-cost and highly accurate ECG-monitoring system intended for personalized early arrhythmia detection for wearable mobile sensors. Earlier supervised approaches for personalized ECG monitoring require both abnormal and normal heartbeats for the training of the dedicated classifier. However, in a... | ['Moncef Gabbouj', 'Serkan Kiranyaz', 'İlke Adalıoğlu', 'Mert Duman', 'Mehmet Yamaç'] | 2022-07-14 | null | null | null | null | ['sparse-representation-based-classification', 'arrhythmia-detection', 'ecg-classification'] | ['computer-vision', 'medical', 'medical'] | [ 7.52192974e-01 6.46392033e-02 -7.51636326e-02 -1.17074057e-01
-6.60507977e-01 -1.47208139e-01 -1.05165079e-01 2.32790709e-01
-1.05558909e-01 7.90790439e-01 -2.26256181e-03 -1.00165829e-01
-2.18559995e-01 -6.78350449e-01 -3.97635460e-01 -8.39633346e-01
-1.38011247e-01 -4.74030189e-02 -1.92144990e-01 1.12143962... | [14.248369216918945, 3.251767873764038] |
913cdabd-1d33-46a1-aaaf-1e4d1afb8221 | tracking-and-reconstructing-hand-object | 2209.12009 | null | https://arxiv.org/abs/2209.12009v1 | https://arxiv.org/pdf/2209.12009v1.pdf | Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild | In this work, we tackle the challenging task of jointly tracking hand object pose and reconstructing their shapes from depth point cloud sequences in the wild, given the initial poses at frame 0. We for the first time propose a point cloud based hand joint tracking network, HandTrackNet, to estimate the inter-frame han... | ['He Wang', 'Shuran Song', 'Li Yi', 'Yijia Weng', 'Xiaolong Li', 'Yinzhen Xu', 'Jiazhao Zhang', 'Mi Yan', 'Jiayi Chen'] | 2022-09-24 | null | null | null | null | ['hand-object-pose'] | ['computer-vision'] | [-3.31515849e-01 -1.63950503e-01 2.57630926e-02 1.73456222e-01
-5.98223388e-01 -7.08723009e-01 3.93154502e-01 -4.10267353e-01
-4.79920506e-01 5.00556231e-01 -3.03764462e-01 1.83522366e-02
2.98335347e-02 -3.74088407e-01 -6.46987736e-01 -6.02698982e-01
2.52975523e-01 1.26682365e+00 6.58071697e-01 3.35081406... | [6.583222389221191, -0.9390112161636353] |
b34daa5c-5ddd-4c99-949b-2bf7b13f2f68 | rethinking-pose-in-3d-multi-stage-refinement | 1808.01525 | null | http://arxiv.org/abs/1808.01525v1 | http://arxiv.org/pdf/1808.01525v1.pdf | Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture | We propose a CNN-based approach for multi-camera markerless motion capture of
the human body. Unlike existing methods that first perform pose estimation on
individual cameras and generate 3D models as post-processing, our approach
makes use of 3D reasoning throughout a multi-stage approach. This novelty
allows us to us... | ['Denis Tome', 'Chris Russell', 'Lourdes Agapito', 'Matteo Toso'] | 2018-08-04 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [-3.06602065e-02 2.78497100e-01 1.81948289e-01 -1.21071286e-01
-7.39349902e-01 -6.36291265e-01 4.22306508e-01 -2.34285280e-01
-6.21517420e-01 4.15854871e-01 3.43251050e-01 2.97689270e-02
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1.84689865e-01 6.74320817e-01 5.83003044e-01 -3.06132048... | [7.052670955657959, -0.9975826144218445] |
07130297-a684-46a7-a15e-fd38026d6cff | guiding-the-guidance-a-comparative-analysis | 2303.06942 | null | https://arxiv.org/abs/2303.06942v1 | https://arxiv.org/pdf/2303.06942v1.pdf | Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images | Interactive segmentation reduces the annotation time of medical images and allows annotators to iteratively refine labels with corrective interactions, such as clicks. While existing interactive models transform clicks into user guidance signals, which are combined with images to form (image, guidance) pairs, the quest... | ['Jens Kleesiek', 'Rainer Stiefelhagen', 'Zdravko Marinov'] | 2023-03-13 | null | null | null | null | ['interactive-segmentation', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 2.91040570e-01 2.98978925e-01 8.48098174e-02 -7.11429477e-01
-7.50167370e-01 -8.91723692e-01 1.83687210e-01 4.35509831e-01
-5.20179033e-01 1.03821054e-01 2.06560344e-01 -7.34851718e-01
-1.47687584e-01 -2.90685266e-01 -2.69420266e-01 -4.35232580e-01
-2.74147004e-01 5.82649887e-01 6.57534301e-01 1.24627903... | [14.709507942199707, -2.305349826812744] |
233c4236-88db-4691-9b3c-0cf33545a754 | is-end-to-end-learning-enough-for-fitness | 2305.08191 | null | https://arxiv.org/abs/2305.08191v1 | https://arxiv.org/pdf/2305.08191v1.pdf | Is end-to-end learning enough for fitness activity recognition? | End-to-end learning has taken hold of many computer vision tasks, in particular, related to still images, with task-specific optimization yielding very strong performance. Nevertheless, human-centric action recognition is still largely dominated by hand-crafted pipelines, and only individual components are replaced by ... | ['Roland Memisevic', 'Ingo Bax', 'Nahua Kang', 'Cornelius Boehm', 'Florian Letsch', 'Sunny Panchal', 'Guillaume Berger', 'Antoine Mercier'] | 2023-05-14 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 3.37723643e-01 -1.08389460e-01 -9.66517702e-02 -2.92438865e-01
-7.75743306e-01 -4.15560395e-01 7.15075314e-01 -1.72666341e-01
-8.13533604e-01 4.97166365e-01 2.85131782e-01 1.79855451e-01
1.14349529e-01 -2.69360900e-01 -8.92876267e-01 -5.69268107e-01
-4.24557418e-01 7.31987298e-01 8.96166444e-01 -2.51445860... | [8.046395301818848, 0.3584420084953308] |
e75bbd80-d7e6-49bc-a36a-83f71f4d71b1 | multi-task-image-based-dietary-assessment-for | 2004.13188 | null | https://arxiv.org/abs/2004.13188v1 | https://arxiv.org/pdf/2004.13188v1.pdf | Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation | Deep learning based methods have achieved impressive results in many applications for image-based diet assessment such as food classification and food portion size estimation. However, existing methods only focus on one task at a time, making it difficult to apply in real life when multiple tasks need to be processed t... | ['Fengqing Zhu', 'Carol Boushey', 'Zeman Shao', 'Janine Wright', 'Jiangpeng He', 'Deborah Kerr'] | 2020-04-27 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 2.18715653e-01 -4.83564168e-01 -5.57360053e-01 -7.65369773e-01
-9.13788855e-01 -4.14980322e-01 -6.07362054e-02 1.03176141e+00
-4.96603936e-01 9.30296108e-02 3.32186073e-01 1.94102883e-01
2.13373244e-01 -8.36131454e-01 -9.51654255e-01 -6.88841879e-01
-8.63517523e-02 2.01011777e-01 -1.63059637e-01 3.57942767... | [11.560769081115723, 4.3974504470825195] |
42d8d5cd-e9bc-466c-979b-a737a56b7d5c | graph-embedded-multi-layer-kernel-extreme | 1904.06491 | null | http://arxiv.org/abs/1904.06491v1 | http://arxiv.org/pdf/1904.06491v1.pdf | Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification) | A brain can detect outlier just by using only normal samples. Similarly,
one-class classification (OCC) also uses only normal samples to train the model
and trained model can be used for outlier detection. In this paper, a
multi-layer architecture for OCC is proposed by stacking various Graph-Embedded
Kernel Ridge Regr... | ['Chandan Gautam', 'M. Tanveer', 'Aruna Tiwari'] | 2019-04-13 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-2.08960980e-01 9.77933854e-02 -4.40741070e-02 -4.05499607e-01
-2.67676383e-01 1.21173643e-01 4.43004131e-01 4.12815392e-01
-3.71523887e-01 6.62473321e-01 -1.30181655e-01 -1.95269361e-01
-3.27123523e-01 -7.86227882e-01 -7.59543478e-01 -6.44571006e-01
-7.04324067e-01 4.44136858e-02 2.15932786e-01 2.55262218... | [8.210896492004395, 3.732754707336426] |
976e6e4b-865f-44e1-8828-1996c73e96b1 | enhancement-encoding-a-new-imbalanced | 2208.11056 | null | https://arxiv.org/abs/2208.11056v2 | https://arxiv.org/pdf/2208.11056v2.pdf | Enhancement Encoding: A Novel Imbalanced Classification Approach via Encoding the Training Labels | Class imbalance, which is also called long-tailed distribution, is a common problem in classification tasks based on machine learning. If it happens, the minority data will be overwhelmed by the majority, which presents quite a challenge for data science. To address the class imbalance problem, researchers have propose... | ['Jia-Chen Zhao'] | 2022-08-23 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-2.99002789e-02 -3.55389833e-01 -2.25675553e-01 -6.74353123e-01
-1.01822741e-01 -8.82022679e-02 6.50083944e-02 2.81616479e-01
-5.05412221e-01 8.69154513e-01 -3.95277292e-02 3.98540491e-04
-2.37809986e-01 -1.07492197e+00 -2.37174243e-01 -9.27562714e-01
3.85750085e-01 3.10479701e-01 1.86318353e-01 -4.52510953... | [9.048050880432129, 3.976097345352173] |
2f08f94e-8f9e-44e1-ad2f-13a6cff15490 | class-wise-and-reduced-calibration-methods | 2210.03702 | null | https://arxiv.org/abs/2210.03702v1 | https://arxiv.org/pdf/2210.03702v1.pdf | Class-wise and reduced calibration methods | For many applications of probabilistic classifiers it is important that the predicted confidence vectors reflect true probabilities (one says that the classifier is calibrated). It has been shown that common models fail to satisfy this property, making reliable methods for measuring and improving calibration important ... | ['Miguel de Benito Delgado', 'Anes Benmerzoug', 'Michael Panchenko'] | 2022-10-07 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 1.87425479e-01 2.17333883e-01 -2.24581689e-01 -6.19236231e-01
-9.54690158e-01 -8.19563687e-01 3.45680058e-01 4.07038361e-01
-2.48009399e-01 7.99647331e-01 -2.12332696e-01 -4.08988237e-01
-3.28822076e-01 -7.14145243e-01 -8.28962266e-01 -1.01342905e+00
2.30557650e-01 8.31451952e-01 2.77867794e-01 -1.46263475... | [8.410452842712402, 4.2953901290893555] |
40d7130f-1d33-41ad-867c-44f012e1d95b | learning-key-value-store-design | 1907.05443 | null | https://arxiv.org/abs/1907.05443v1 | https://arxiv.org/pdf/1907.05443v1.pdf | Learning Key-Value Store Design | We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different d... | ['James Lennon', 'Andrew Ross', 'Sophie Hilgard', 'Mali Akmanalp', 'Harshita Gupta', 'David Li', 'Wilson Qin', 'Stratos Idreos', 'Niv Dayan', 'Zichen Zhu', 'Varun Jain'] | 2019-07-11 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-3.77886564e-01 -1.16404511e-01 -4.12821740e-01 -3.09195459e-01
-1.46037027e-01 -7.86886334e-01 3.15102518e-01 4.83698368e-01
5.10154888e-02 3.72123301e-01 4.88703072e-01 -8.79125237e-01
-4.14025635e-01 -9.87760007e-01 -6.10522449e-01 -3.18400532e-01
-2.57354200e-01 4.30295706e-01 5.20843029e-01 -4.35779840... | [8.050891876220703, 4.707298755645752] |
f9d22de0-8caa-4f6c-89e9-b6b861c12f05 | distributional-inclusion-vector-embedding-for | 1710.00880 | null | http://arxiv.org/abs/1710.00880v3 | http://arxiv.org/pdf/1710.00880v3.pdf | Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection | Modeling hypernymy, such as poodle is-a dog, is an important generalization
aid to many NLP tasks, such as entailment, coreference, relation extraction,
and question answering. Supervised learning from labeled hypernym sources, such
as WordNet, limits the coverage of these models, which can be addressed by
learning hyp... | ['Haw-Shiuan Chang', 'Andrew McCallum', 'ZiYun Wang', 'Luke Vilnis'] | 2017-10-02 | distributional-inclusion-vector-embedding-for-1 | https://aclanthology.org/N18-1045 | https://aclanthology.org/N18-1045.pdf | naacl-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [-2.74126953e-03 4.46353227e-01 -8.46260011e-01 -4.48226452e-01
-4.09985900e-01 -5.69472134e-01 6.22339010e-01 7.08513856e-01
-8.91407311e-01 7.61109531e-01 7.13763714e-01 -4.90539342e-01
-5.17979622e-01 -7.92847455e-01 5.87990917e-02 -4.07343209e-01
-2.47187130e-02 1.12718892e+00 2.16048270e-01 -5.42514145... | [9.897062301635742, 8.744596481323242] |
c713a904-f6c1-4ebc-8eda-40fd10999061 | bringing-generalization-to-deep-multi-view | 2109.12227 | null | https://arxiv.org/abs/2109.12227v4 | https://arxiv.org/pdf/2109.12227v4.pdf | Bringing Generalization to Deep Multi-View Pedestrian Detection | Multi-view Detection (MVD) is highly effective for occlusion reasoning in a crowded environment. While recent works using deep learning have made significant advances in the field, they have overlooked the generalization aspect, which makes them impractical for real-world deployment. The key novelty of our work is to f... | ['Vineet Gandhi', 'Shyamgopal Karthik', 'Kanishk Jain', 'Swetanjal Dutta', 'Jeet Vora'] | 2021-09-24 | null | null | null | null | ['multiview-detection'] | ['computer-vision'] | [-3.86975199e-01 -6.27907634e-01 2.84981310e-01 -4.08844233e-01
-3.05964708e-01 -7.14334249e-01 6.41470730e-01 -1.66663215e-01
-1.77389666e-01 4.73088443e-01 -7.28256330e-02 -3.07923257e-01
-8.34811032e-02 -5.32619298e-01 -7.38413215e-01 -5.90998352e-01
3.23202610e-02 2.44205400e-01 6.43849254e-01 -2.81584054... | [8.046614646911621, -2.2086572647094727] |
35fa6153-0dda-47f6-9028-1ac771eeba11 | cross-modal-fusion-techniques-for-utterance | 2302.02447 | null | https://arxiv.org/abs/2302.02447v1 | https://arxiv.org/pdf/2302.02447v1.pdf | cross-modal fusion techniques for utterance-level emotion recognition from text and speech | Multimodal emotion recognition (MER) is a fundamental complex research problem due to the uncertainty of human emotional expression and the heterogeneity gap between different modalities. Audio and text modalities are particularly important for a human participant in understanding emotions. Although many successful att... | ['Joshua Reiss', 'Huy Phan', 'Jiachen Luo'] | 2023-02-05 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.01138549e-02 -5.11064470e-01 2.61394799e-01 -5.71108758e-01
-1.11682487e+00 -3.80188555e-01 6.41658306e-01 -5.79398237e-02
-3.15912843e-01 3.45723510e-01 6.39582872e-01 4.28232998e-01
-4.93873507e-02 -1.21094962e-03 -2.13396281e-01 -6.99606061e-01
-2.56569624e-01 4.20256779e-02 -3.16762209e-01 -4.37930554... | [13.232353210449219, 5.43088960647583] |
f9466f93-0db4-4649-bd66-e8b0e24354e1 | zero-shot-learning-by-convex-combination-of | 1312.5650 | null | http://arxiv.org/abs/1312.5650v3 | http://arxiv.org/pdf/1312.5650v3.pdf | Zero-Shot Learning by Convex Combination of Semantic Embeddings | Several recent publications have proposed methods for mapping images into
continuous semantic embedding spaces. In some cases the embedding space is
trained jointly with the image transformation. In other cases the semantic
embedding space is established by an independent natural language processing
task, and then the ... | ['Samy Bengio', 'Mohammad Norouzi', 'Jonathon Shlens', 'Greg S. Corrado', 'Yoram Singer', 'Andrea Frome', 'Tomas Mikolov', 'Jeffrey Dean'] | 2013-12-19 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 5.18065393e-01 4.13967282e-01 -3.14561635e-01 -5.88585973e-01
-5.10041952e-01 -3.35826427e-01 8.53569746e-01 1.81974828e-01
-7.28874862e-01 3.37264121e-01 4.58105505e-02 -4.43728939e-02
-8.51229131e-02 -1.00357044e+00 -6.17891431e-01 -7.49905050e-01
2.86004782e-01 3.97386312e-01 7.93789849e-02 -1.47521988... | [10.053356170654297, 2.3336997032165527] |
5909ec70-d2c3-49c0-a061-b5b381f3561b | uamd-net-a-unified-adaptive-multimodal-neural | 2204.07791 | null | https://arxiv.org/abs/2204.07791v1 | https://arxiv.org/pdf/2204.07791v1.pdf | UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion | Depth prediction is a critical problem in robotics applications especially autonomous driving. Generally, depth prediction based on binocular stereo matching and fusion of monocular image and laser point cloud are two mainstream methods. However, the former usually suffers from overfitting while building cost volume, a... | ['Huabiao Qin', 'Junli Lin', 'Guancheng Chen'] | 2022-04-16 | null | null | null | null | ['depth-completion', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 7.75109157e-02 -2.40002304e-01 -2.18253270e-01 -5.65003753e-01
-4.88654435e-01 6.32976964e-02 3.62754107e-01 -5.15364051e-01
-3.59008402e-01 5.40035129e-01 1.10094054e-02 -4.72817086e-02
-1.57754108e-01 -9.22932327e-01 -9.36198950e-01 -8.87273192e-01
6.64907932e-01 3.55596334e-01 3.21723014e-01 -1.41180918... | [8.899906158447266, -2.570754289627075] |
a9f88254-2412-4c22-93df-0b6f01f0ad83 | multi-instance-partial-label-learning-towards | 2212.08997 | null | https://arxiv.org/abs/2212.08997v1 | https://arxiv.org/pdf/2212.08997v1.pdf | Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision | Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one ground-truth ... | ['Min-Ling Zhang', 'Weijia Zhang', 'Wei Tang'] | 2022-12-18 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 5.60769141e-01 2.91859776e-01 -5.25048196e-01 -4.49966311e-01
-1.39572108e+00 -7.37129986e-01 4.32141751e-01 3.47062290e-01
-1.75579071e-01 1.03475440e+00 -6.94946945e-01 -1.64920747e-01
-4.05768394e-01 -9.26732183e-01 -8.52801740e-01 -1.08394706e+00
4.59227152e-02 1.23091519e+00 1.37574136e-01 6.69233978... | [9.461615562438965, 4.050572395324707] |
db6fe230-b20f-4ee6-a190-63bdc33c6523 | long-form-video-language-pre-training-with | 2210.06031 | null | https://arxiv.org/abs/2210.06031v2 | https://arxiv.org/pdf/2210.06031v2.pdf | Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning | Large-scale video-language pre-training has shown significant improvement in video-language understanding tasks. Previous studies of video-language pretraining mainly focus on short-form videos (i.e., within 30 seconds) and sentences, leaving long-form video-language pre-training rarely explored. Directly learning repr... | ['Jianlong Fu', 'Huan Yang', 'Bei Liu', 'Ruihua Song', 'Hongwei Xue', 'Yuchong Sun'] | 2022-10-12 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 8.92177001e-02 -5.00693798e-01 -5.58105052e-01 -5.27772844e-01
-1.26748383e+00 -6.05897069e-01 5.54596484e-01 -2.78623939e-01
-6.06664419e-01 3.93176675e-01 5.58831632e-01 -4.13635343e-01
2.41415367e-01 -2.65400767e-01 -1.20187366e+00 -2.77775764e-01
4.54740822e-02 3.21469605e-01 2.71782628e-03 6.36971816... | [10.245963096618652, 0.8877231478691101] |
20651a5d-a8c1-4c15-85f9-403447c9048a | self-supervision-with-superpixels-training | 2007.09886 | null | https://arxiv.org/abs/2007.09886v2 | https://arxiv.org/pdf/2007.09886v2.pdf | Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation | Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. However, these methods may not be applicable for medical images due to the lack of annotations. To address this problem we make several... | ['Turkay Kart', 'Cheng Ouyang', 'Daniel Rueckert', 'Chen Chen', 'Carlo Biffi', 'Huaqi Qiu'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6977_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740749.pdf | eccv-2020-8 | ['cardiac-segmentation'] | ['medical'] | [ 6.29447520e-01 4.11464661e-01 -1.92729324e-01 -6.12019002e-01
-7.16559470e-01 -2.44680464e-01 3.16029608e-01 4.19475824e-01
-5.23594856e-01 6.04015648e-01 -2.37707943e-01 -1.93399534e-01
3.51915602e-03 -5.25316238e-01 -5.84028840e-01 -7.62840331e-01
1.12003513e-01 4.08487618e-01 8.15315485e-01 4.73303795... | [14.687374114990234, -2.1445515155792236] |
ad58b656-2cc4-4d5f-a98e-82cf9fa50c71 | many-hands-make-light-work-using-essay-traits | 2102.00781 | null | https://arxiv.org/abs/2102.00781v1 | https://arxiv.org/pdf/2102.00781v1.pdf | Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays | Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been some work done on scoring individual essay traits. In this paper, we describe a way to score essays holistically using a multi-task learning (MTL) approach, where scoring the essay holis... | ['Pushpak Bhattacharyya', 'Sriparna Saha', 'Sandeep Mathias', 'Rahul Kumar'] | 2021-02-01 | null | https://aclanthology.org/2022.naacl-main.106 | https://aclanthology.org/2022.naacl-main.106.pdf | naacl-2022-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-9.93793458e-02 -1.84536576e-01 7.36246035e-02 -5.35932660e-01
-1.20181060e+00 -6.15175724e-01 4.36543345e-01 1.27612323e-01
-4.81136531e-01 5.71441114e-01 3.04563582e-01 -3.47756475e-01
-5.67289479e-02 -4.99276787e-01 -5.23444377e-02 -6.27742589e-01
5.90876520e-01 4.57283378e-01 2.78073490e-01 -3.43968600... | [11.29997444152832, 9.366113662719727] |
0a230356-2f28-449d-812e-f8a64ae3450b | 2d-medical-image-synthesis-using-transformer | null | null | https://iopscience.iop.org/article/10.1088/1361-6560/acca5c/meta | https://iopscience.iop.org/article/10.1088/1361-6560/acca5c/pdf | 2D medical image synthesis using transformer-based denoising diffusion probabilistic model | Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the ... | ['Justin Roper', 'Sagar A Patel', 'Joseph Shelton', 'Ashish B Patel', 'Junbo Peng', 'Chih-Wei Chang', 'Marian Axente', 'Richard L J Qiu', 'Tonghe Wang', 'Shaoyan Pan'] | 2023-05-05 | null | null | null | physics-in-medicine-biology-2023-5 | ['image-generation', 'specificity'] | ['computer-vision', 'natural-language-processing'] | [ 4.62462366e-01 2.86375850e-01 3.04238617e-01 -2.49318793e-01
-9.36165273e-01 -4.51652825e-01 5.22565186e-01 -7.25480765e-02
-5.18805027e-01 4.66020495e-01 -6.31013364e-02 -2.65079767e-01
-2.68396378e-01 -5.77381611e-01 -5.25272071e-01 -9.35137570e-01
-1.68489709e-01 6.20602012e-01 -1.14257134e-01 -1.73329860... | [14.2398099899292, -1.933763861656189] |
df951591-f105-449c-9e15-e41399da974c | generation-of-anonymous-chest-radiographs | 2211.01323 | null | https://arxiv.org/abs/2211.01323v2 | https://arxiv.org/pdf/2211.01323v2.pdf | Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems | The availability of large-scale chest X-ray datasets is a requirement for developing well-performing deep learning-based algorithms in thoracic abnormality detection and classification. However, biometric identifiers in chest radiographs hinder the public sharing of such data for research purposes due to the risk of pa... | ['Andreas Maier', 'Florian Thamm', 'Lukas Folle', 'Kai Packhäuser'] | 2022-11-02 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 5.54771364e-01 4.14497852e-01 -1.02502517e-01 -6.25383735e-01
-1.11801314e+00 -3.35484385e-01 2.92237669e-01 3.21857989e-01
-6.40866339e-01 8.49166453e-01 -4.16830704e-02 -4.04957801e-01
-4.17709686e-02 -8.98782790e-01 -7.24320531e-01 -8.29850852e-01
3.20780665e-01 1.79108784e-01 -3.87726724e-01 4.99164343... | [6.160682201385498, 6.856271743774414] |
f0dfd360-ccb4-4f41-971f-3e162d589804 | recurrent-calibration-network-for-irregular | 1812.07145 | null | http://arxiv.org/abs/1812.07145v1 | http://arxiv.org/pdf/1812.07145v1.pdf | Recurrent Calibration Network for Irregular Text Recognition | Scene text recognition has received increased attention in the research
community. Text in the wild often possesses irregular arrangements, typically
including perspective text, curved text, oriented text. Most existing methods
are hard to work well for irregular text, especially for severely distorted
text. In this pa... | ['Hanqing Lu', 'Xiao-Yu Zhang', 'Yunze Gao', 'Yingying Chen', 'Zhen Lei', 'Jinqiao Wang'] | 2018-12-18 | null | null | null | null | ['irregular-text-recognition'] | ['computer-vision'] | [ 3.25181842e-01 -4.78086650e-01 -8.82158056e-02 -2.34272599e-01
-4.52431887e-01 -5.49526572e-01 5.62658131e-01 -1.00608610e-01
-1.79676428e-01 2.71277696e-01 1.73893228e-01 2.88812593e-02
8.96059647e-02 -5.42170584e-01 -6.74248338e-01 -9.84664857e-01
9.55225170e-01 4.42772895e-01 1.49937049e-01 9.31273848... | [12.030805587768555, 2.2143514156341553] |
3e340ada-35a6-4455-80a5-6dde2b5279a9 | sinsy-a-deep-neural-network-based-singing | 2108.02776 | null | https://arxiv.org/abs/2108.02776v1 | https://arxiv.org/pdf/2108.02776v1.pdf | Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System | This paper presents Sinsy, a deep neural network (DNN)-based singing voice synthesis (SVS) system. In recent years, DNNs have been utilized in statistical parametric SVS systems, and DNN-based SVS systems have demonstrated better performance than conventional hidden Markov model-based ones. SVS systems are required to ... | ['Keiichi Tokuda', 'Yoshihiko Nankaku', 'Keiichiro Oura', 'Kei Hashimoto', 'Yukiya Hono'] | 2021-08-05 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-4.60831255e-01 -4.11529273e-01 1.51367448e-02 7.00012371e-02
-5.05437672e-01 -4.57282066e-01 -1.12738997e-01 -7.13053882e-01
1.33016542e-01 4.45539594e-01 2.09992245e-01 9.78602655e-03
1.92979813e-01 -6.51990294e-01 -3.42412710e-01 -7.35381544e-01
2.56137967e-01 4.87503037e-02 2.45652780e-01 -3.16788018... | [15.509612083435059, 6.225228786468506] |
a40d6181-0e4b-495d-b863-1268fb53a3c3 | modeling-multimodal-dynamic-spatiotemporal | 1810.05993 | null | https://arxiv.org/abs/1810.05993v3 | https://arxiv.org/pdf/1810.05993v3.pdf | The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs | Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron,... | ['Marco Pavone', 'Boris Ivanovic'] | 2018-10-14 | the-trajectron-probabilistic-multi-agent | http://openaccess.thecvf.com/content_ICCV_2019/html/Ivanovic_The_Trajectron_Probabilistic_Multi-Agent_Trajectory_Modeling_With_Dynamic_Spatiotemporal_Graphs_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ivanovic_The_Trajectron_Probabilistic_Multi-Agent_Trajectory_Modeling_With_Dynamic_Spatiotemporal_Graphs_ICCV_2019_paper.pdf | iccv-2019-10 | ['trajectory-modeling'] | ['time-series'] | [-2.96518475e-01 1.41943857e-01 1.68368012e-01 -1.00626044e-01
-4.20116305e-01 -7.81433105e-01 1.49149334e+00 6.01547072e-03
-1.84690595e-01 6.33103967e-01 6.93068385e-01 -1.74560308e-01
-5.92162535e-02 -8.35678577e-01 -5.39074659e-01 -6.60069048e-01
-4.48312819e-01 1.22039247e+00 3.07472199e-01 -4.36358631... | [5.866784572601318, 0.7998443841934204] |
2c879843-373f-40c5-a8dc-5fc15c7275f5 | generative-table-pre-training-empowers-models | 2305.09696 | null | https://arxiv.org/abs/2305.09696v1 | https://arxiv.org/pdf/2305.09696v1.pdf | Generative Table Pre-training Empowers Models for Tabular Prediction | Recently, the topic of table pre-training has attracted considerable research interest. However, how to employ table pre-training to boost the performance of tabular prediction remains an open challenge. In this paper, we propose TapTap, the first attempt that leverages table pre-training to empower models for tabular ... | ['Qian Liu', 'Jian Li', 'Shuicheng Yan', 'Shaowen Wang', 'Tianping Zhang'] | 2023-05-16 | null | null | null | null | ['imputation', 'imbalanced-classification', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 1.85733791e-02 4.60736305e-01 -7.03360140e-01 -4.53855038e-01
-1.16873634e+00 -6.01194620e-01 3.49120528e-01 4.40783679e-01
1.53512537e-01 1.31320369e+00 2.00049862e-01 -5.44038534e-01
2.75265843e-01 -9.75357890e-01 -1.19110811e+00 -4.12891269e-01
2.72536725e-01 9.83119130e-01 -3.01373899e-01 -2.51485020... | [9.684686660766602, 7.891162872314453] |
8d3c79b3-32c8-4cd2-b543-d05d4e7f6b76 | lys-acoruna-at-semeval-2022-task-10 | 2204.12820 | null | https://arxiv.org/abs/2204.12820v1 | https://arxiv.org/pdf/2204.12820v1.pdf | LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing | This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models. For the monolingual setup, we considered: (i) training on a single treebank, and (ii) relaxing the setup by training on treebanks ... | ['Carlos Gómez-Rodríguez', 'David Vilares', 'Iago Alonso-Alonso'] | 2022-04-27 | null | https://aclanthology.org/2022.semeval-1.193 | https://aclanthology.org/2022.semeval-1.193.pdf | semeval-naacl-2022-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-2.00011730e-01 3.14387679e-01 -8.97838101e-02 -5.44491887e-01
-1.53844619e+00 -9.45687056e-01 2.64618874e-01 4.02189553e-01
-8.63439858e-01 9.89324272e-01 1.99003458e-01 -7.24700212e-01
4.20563728e-01 -5.80708802e-01 -7.91336000e-01 -3.19708198e-01
1.00840069e-01 7.38270104e-01 2.19972953e-01 -5.04967332... | [10.437235832214355, 9.78281021118164] |
c6aeb2ba-ea69-46c9-b36d-2e436c36d834 | nade-a-benchmark-for-robust-adverse-drug | 2109.10080 | null | https://arxiv.org/abs/2109.10080v2 | https://arxiv.org/pdf/2109.10080v2.pdf | NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations | Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pe... | ['Giuseppe Serra', 'Enrico Santus', 'Emmanuele Chersoni', 'Beatrice Portelli', 'Simone Scaboro'] | 2021-09-21 | null | https://aclanthology.org/2021.wnut-1.26 | https://aclanthology.org/2021.wnut-1.26.pdf | wnut-acl-2021-11 | ['negation-detection'] | ['natural-language-processing'] | [ 3.50693643e-01 4.44722474e-01 -3.07737947e-01 -2.61750191e-01
-8.67798209e-01 -8.47964942e-01 8.23850751e-01 9.28905845e-01
-4.66793656e-01 9.79172885e-01 3.14415634e-01 -6.82663023e-01
-1.02850787e-01 -7.56131470e-01 -7.05322146e-01 -2.05812067e-01
-9.31699574e-02 5.39889932e-01 3.45002949e-01 -1.95577055... | [8.468567848205566, 8.840911865234375] |
1e9197ce-c779-4329-9e70-19e7751deedf | benchmarking-robustness-of-deep-reinforcement | 2306.10950 | null | https://arxiv.org/abs/2306.10950v1 | https://arxiv.org/pdf/2306.10950v1.pdf | Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management | Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implies a need for extensive efforts in market representation, behavior objectives, and training processes, which have often been lacking in previ... | ['Fabrice Daniel', 'Fabrice Popineau', 'Arpad Rimmel', 'Bich-Liên Doan', 'Marc Velay'] | 2023-06-19 | null | null | null | null | ['management', 'benchmarking', 'benchmarking'] | ['miscellaneous', 'miscellaneous', 'robots'] | [-3.64318371e-01 -3.41963381e-01 -1.63468286e-01 -1.75158054e-01
-5.97046673e-01 -8.23600709e-01 6.00157917e-01 8.95781666e-02
-5.16143799e-01 1.09881997e+00 1.22948652e-02 -5.69780171e-01
-6.02415502e-01 -8.23658943e-01 -5.22464514e-01 -4.07001853e-01
-3.65684897e-01 6.89870298e-01 -1.16829574e-01 -6.49225771... | [4.370500087738037, 3.797865867614746] |
e622a9bc-7d74-4a92-aa04-8aa2661ac547 | a-novel-multi-scale-dilated-3d-cnn-for | 2105.02823 | null | https://arxiv.org/abs/2105.02823v1 | https://arxiv.org/pdf/2105.02823v1.pdf | A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction | Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN) is proposed to analyze time, frequency, and channel information of electroencephalography (EEG) signals. The model uses three-dimensional ... | ['Mohamad Sawan', 'Jie Yang', 'Ziyu Wang'] | 2021-05-05 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-4.39420640e-01 -5.04804075e-01 4.36714500e-01 -1.84660986e-01
-1.45057350e-01 6.06657229e-02 1.12188756e-01 -1.89467028e-01
-6.19843900e-01 8.19637001e-01 9.44752768e-02 -1.94057673e-01
-4.53785002e-01 -4.19130474e-01 -2.63364255e-01 -6.17555737e-01
-9.49621737e-01 -5.11049628e-01 2.11366508e-02 7.04653412... | [13.189807891845703, 3.4912312030792236] |
254d9393-f686-4183-b65e-9e6dd4d62dc3 | solving-visual-madlibs-with-multiple-cues | 1608.03410 | null | http://arxiv.org/abs/1608.03410v1 | http://arxiv.org/pdf/1608.03410v1.pdf | Solving Visual Madlibs with Multiple Cues | This paper focuses on answering fill-in-the-blank style multiple choice
questions from the Visual Madlibs dataset. Previous approaches to Visual
Question Answering (VQA) have mainly used generic image features from networks
trained on the ImageNet dataset, despite the wide scope of questions. In
contrast, our approach ... | ['Tatiana Tommasi', 'Bryan Plummer', 'Tamara L. Berg', 'Svetlana Lazebnik', 'Arun Mallya', 'Alexander C. Berg'] | 2016-08-11 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.96184540e-01 -3.36938798e-02 2.41488274e-02 -7.81894267e-01
-9.36505616e-01 -6.77965403e-01 9.41544414e-01 4.08561289e-01
-6.70598388e-01 5.30746400e-01 5.27926624e-01 -1.35458618e-01
-1.49191186e-01 -6.14497662e-01 -5.43560207e-01 -3.82545650e-01
4.03556824e-01 5.40232480e-01 3.28268856e-01 -6.07979409... | [10.858418464660645, 1.7601937055587769] |
dae54178-fd7c-4c85-aef8-6c8410756fb1 | visual-speech-enhancement-without-a-real | 2012.10852 | null | https://arxiv.org/abs/2012.10852v1 | https://arxiv.org/pdf/2012.10852v1.pdf | Visual Speech Enhancement Without A Real Visual Stream | In this work, we re-think the task of speech enhancement in unconstrained real-world environments. Current state-of-the-art methods use only the audio stream and are limited in their performance in a wide range of real-world noises. Recent works using lip movements as additional cues improve the quality of generated sp... | ['C. V. Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'K R Prajwal', 'Sindhu B Hegde'] | 2020-12-20 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 1.25323132e-01 5.14757484e-02 1.31690567e-02 -1.60832092e-01
-1.08251715e+00 -3.47055078e-01 4.09847856e-01 -3.01331997e-01
-3.65737140e-01 6.77695811e-01 5.00621021e-01 -2.96534866e-01
2.03832611e-01 -3.37002754e-01 -7.71247089e-01 -7.94360757e-01
2.36320227e-01 -2.28452921e-01 3.26693714e-01 -3.83298516... | [14.576987266540527, 5.377188682556152] |
05c4cf9b-e85d-49b3-b5eb-569a37883789 | in-memory-hyperdimensional-computing | 1906.01548 | null | https://arxiv.org/abs/1906.01548v2 | https://arxiv.org/pdf/1906.01548v2.pdf | In-memory hyperdimensional computing | Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness. When employed for machine learning tasks such as learning and classification, HDC involve... | ['Giovanni Cherubini', 'Abbas Rahimi', 'Geethan Karunaratne', 'Manuel Le Gallo', 'Luca Benini', 'Abu Sebastian'] | 2019-06-04 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 5.07793605e-01 -2.54967034e-01 4.33060974e-02 7.92072564e-02
-2.44017139e-01 -4.80170846e-01 6.77015662e-01 1.02653824e-01
-8.53763282e-01 6.91785276e-01 -1.00903645e-01 -8.68161246e-02
-4.78007227e-01 -7.97896266e-01 -8.11856151e-01 -1.01834667e+00
-1.26475781e-01 4.26174760e-01 3.76548976e-01 -7.61448313... | [8.219341278076172, 2.478900909423828] |
7df031ac-d05d-4871-97da-afb1a13dea45 | combining-chest-x-rays-and-ehr-data-using | 2108.12530 | null | https://arxiv.org/abs/2108.12530v2 | https://arxiv.org/pdf/2108.12530v2.pdf | Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure | Objective: When patients develop acute respiratory failure, accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding... | ['Michael W Sjoding', 'Jenna Wiens', 'Ella Kazerooni', 'David Fouhey', 'Sarah Jabbour'] | 2021-08-27 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [-1.46326929e-01 -9.19543356e-02 -4.13235992e-01 -1.91449508e-01
-9.57601905e-01 -7.55760550e-01 -6.96047544e-02 6.52289629e-01
-2.98636854e-01 4.75386024e-01 3.36774170e-01 -8.23534012e-01
-5.91033816e-01 -5.52911639e-01 -2.29082063e-01 -3.79698545e-01
-8.22175667e-02 9.76760983e-01 -1.02228217e-01 8.26729715... | [15.454805374145508, -1.9620283842086792] |
d5f84da8-1505-4aab-a9d3-92aeed3a6d69 | nocola-the-norwegian-corpus-of-linguistic | 2306.07790 | null | https://arxiv.org/abs/2306.07790v1 | https://arxiv.org/pdf/2306.07790v1.pdf | NoCoLA: The Norwegian Corpus of Linguistic Acceptability | While there has been a surge of large language models for Norwegian in recent years, we lack any tool to evaluate their understanding of grammaticality. We present two new Norwegian datasets for this task. NoCoLA_class is a supervised binary classification task where the goal is to discriminate between acceptable and n... | ['David Samuel', 'Matias Jentoft'] | 2023-06-13 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [-2.89826870e-01 3.81193846e-01 7.81885535e-02 -8.16857457e-01
-7.20463037e-01 -6.42751753e-01 4.39001560e-01 4.05410111e-01
-5.06405652e-01 7.08765507e-01 6.29908890e-02 -7.87478387e-01
8.40309262e-02 -6.30665541e-01 -1.01735435e-01 -4.25876111e-01
2.02416897e-01 6.16973579e-01 -8.96210521e-02 -5.34771621... | [10.726436614990234, 9.726648330688477] |
1afd1347-7826-4193-9437-b508f47b006e | selftalk-a-self-supervised-commutative | 2306.10799 | null | https://arxiv.org/abs/2306.10799v1 | https://arxiv.org/pdf/2306.10799v1.pdf | SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces | Speech-driven 3D face animation technique, extending its applications to various multimedia fields. Previous research has generated promising realistic lip movements and facial expressions from audio signals. However, traditional regression models solely driven by data face several essential problems, such as difficult... | ['Zhaoxin Fan', 'Jun He', 'Hongyan Liu', 'Xiangyu Zhu', 'Hao Xu', 'Yue Shi', 'Yihao Luo', 'Ziqiao Peng'] | 2023-06-19 | null | null | null | null | ['3d-face-animation'] | ['computer-vision'] | [ 1.55787870e-01 3.11719060e-01 -4.33848262e-01 -5.50456464e-01
-8.68623257e-01 -2.23414466e-01 5.13801634e-01 -6.46942973e-01
1.30188406e-01 4.54806268e-01 4.18141097e-01 -2.79661100e-02
3.28987509e-01 -3.29839855e-01 -6.53238356e-01 -4.35460746e-01
1.19430460e-01 1.90079868e-01 -5.89005686e-02 -1.15270838... | [13.235984802246094, -0.4164407253265381] |
08a60798-78ec-49b1-a557-e2c82b9cfd49 | enhancing-entity-boundary-detection-for | null | null | https://aclanthology.org/2021.acl-short.4 | https://aclanthology.org/2021.acl-short.4.pdf | Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition | In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspective... | ['Fang Kong', 'Chun Chen'] | 2021-08-01 | null | null | null | acl-2021-5 | ['boundary-detection', 'chinese-named-entity-recognition'] | ['computer-vision', 'natural-language-processing'] | [-3.01303715e-01 6.88305199e-02 -7.96100721e-02 -5.42918444e-01
-6.28932357e-01 -6.03239238e-01 1.59088925e-01 1.55788243e-01
-7.53146768e-01 8.11064005e-01 5.50202549e-01 -3.55653018e-01
2.61463284e-01 -7.59786904e-01 -4.70557958e-01 -4.64536369e-01
2.79878616e-01 1.90655977e-01 2.82564729e-01 -1.51832297... | [9.75252628326416, 9.741922378540039] |
3c29e238-a1e4-4dee-b2f0-47397ee57c6e | end-to-end-3d-point-cloud-instance | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_End-to-End_3D_Point_Cloud_Instance_Segmentation_Without_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_End-to-End_3D_Point_Cloud_Instance_Segmentation_Without_Detection_CVPR_2020_paper.pdf | End-to-End 3D Point Cloud Instance Segmentation Without Detection | 3D instance segmentation plays a predominant role in environment perception of robotics and augmented reality. Many deep learning based methods have been presented recently for this task. These methods rely on either a detection branch to propose objects or a grouping step to assemble same-instance points. However, det... | [' Jun Xiao', ' Jianmin Zheng', ' Jianfei Cai', ' Feilong Yan', 'Haiyong Jiang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 3.68882596e-01 9.02170315e-02 -2.56458133e-01 -6.86576605e-01
-6.25841916e-01 -2.34501511e-01 4.68878448e-01 2.89988697e-01
-3.95579845e-01 4.21762019e-01 -3.10883701e-01 -5.27531356e-02
-1.97743490e-01 -8.88442814e-01 -7.26061702e-01 -5.73505104e-01
5.89474589e-02 6.34761393e-01 6.74194157e-01 7.77675435... | [8.089330673217773, -2.9907619953155518] |
777a1cd3-9926-4d42-8a8a-3131ff92687f | crop-rotation-modeling-for-deep-learning | 2110.08187 | null | https://arxiv.org/abs/2110.08187v2 | https://arxiv.org/pdf/2110.08187v2.pdf | Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series | While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter- and intra-... | ['Loic Landrieu', 'Félix Quinton'] | 2021-10-15 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [-1.66729480e-01 -2.55795032e-01 -5.40553391e-01 -3.30218643e-01
-3.23203117e-01 -9.19307172e-01 4.59823489e-01 7.11210251e-01
-2.54882455e-01 8.61707270e-01 -6.45155385e-02 -5.56566119e-01
7.54036009e-02 -1.30715132e+00 -9.91660297e-01 -6.51414156e-01
-5.80649197e-01 1.70442536e-01 6.46157861e-02 -8.01900864... | [9.403570175170898, -1.5687239170074463] |
6d758273-4ddd-4c98-afaf-ee53d3f322c3 | towards-language-free-training-for-text-to | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_Towards_Language-Free_Training_for_Text-to-Image_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_Towards_Language-Free_Training_for_Text-to-Image_Generation_CVPR_2022_paper.pdf | Towards Language-Free Training for Text-to-Image Generation | One of the major challenges in training text-to-image generation models is the need of a large number of high-quality text-image pairs. While image samples are often easily accessible, the associated text description typically requires careful human captioning, which is particularly time- and cost-consuming. In thi... | ['Tong Sun', 'Jinhui Xu', 'Jiuxiang Gu', 'Tong Yu', 'Chris Tensmeyer', 'Chunyuan Li', 'Changyou Chen', 'Ruiyi Zhang', 'Yufan Zhou'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 7.62479126e-01 3.19416076e-02 -8.45572874e-02 -2.58818865e-01
-1.41491771e+00 -2.52171338e-01 9.38878477e-01 -2.91443288e-01
-4.09391016e-01 6.53602123e-01 3.72084975e-02 -1.07319467e-01
3.75653625e-01 -7.31613934e-01 -8.88569355e-01 -6.67550385e-01
6.49545848e-01 5.43347776e-01 -7.96560869e-02 -2.57363617... | [11.215081214904785, 0.5328801870346069] |
cb67536d-2158-4fb5-9ec6-f6e7ab655839 | mrcn-a-novel-modality-restitution-and | 2303.14626 | null | https://arxiv.org/abs/2303.14626v1 | https://arxiv.org/pdf/2303.14626v1.pdf | MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification | Visible-infrared person re-identification (VI-ReID), which aims to search identities across different spectra, is a challenging task due to large cross-modality discrepancy between visible and infrared images. The key to reduce the discrepancy is to filter out identity-irrelevant interference and effectively learn moda... | ['Hanzi Wang', 'Jie Li', 'Yan Yan', 'Yukang Zhang'] | 2023-03-26 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 3.22573692e-01 -4.08902735e-01 2.01834813e-02 -5.32790720e-01
-5.50901294e-01 -4.21970069e-01 6.68335319e-01 -2.52407968e-01
-6.44702733e-01 5.21196187e-01 3.85025114e-01 8.74670222e-02
-3.34034175e-01 -5.88919222e-01 -5.61206043e-01 -8.50777447e-01
4.05379742e-01 -1.42994538e-01 -3.14282894e-01 -3.42946678... | [14.7119722366333, 0.9526773691177368] |
4e8abb5c-dfe9-4065-af60-7d00e4bf57e5 | demodulation-of-sparse-ppm-signals-with-low | 1309.5854 | null | http://arxiv.org/abs/1309.5854v1 | http://arxiv.org/pdf/1309.5854v1.pdf | Demodulation of Sparse PPM Signals with Low Samples Using Trained RIP Matrix | Compressed sensing (CS) theory considers the restricted isometry property
(RIP) as a sufficient condition for measurement matrix which guarantees the
recovery of any sparse signal from its compressed measurements. The RIP
condition also preserves enough information for classification of sparse
symbols, even with fewer ... | ['Mehdi Chehel Amirani', 'Seyed Hossein Hosseini', 'Mahrokh G. Shayesteh'] | 2013-09-01 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [ 9.06222224e-01 -3.55754122e-02 -1.97491512e-01 -3.81564289e-01
-6.78704500e-01 -2.38195583e-01 2.91281372e-01 -3.03376038e-02
-1.49820611e-01 8.55290651e-01 -1.19531266e-02 -4.38260943e-01
-3.85415226e-01 -4.86606538e-01 -6.49636269e-01 -1.02994990e+00
-5.82645297e-01 1.22463591e-02 -3.46826255e-01 2.97807306... | [6.484221935272217, 1.3733774423599243] |
c17cb597-3e41-4fa9-bfe1-c3005d305caa | concurrent-ischemic-lesion-age-estimation-and | 2306.12242 | null | https://arxiv.org/abs/2306.12242v1 | https://arxiv.org/pdf/2306.12242v1.pdf | Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network | The cornerstone of stroke care is expedient management that varies depending on the time since stroke onset. Consequently, clinical decision making is centered on accurate knowledge of timing and often requires a radiologist to interpret Computed Tomography (CT) of the brain to confirm the occurrence and age of an even... | ['Daniel Rueckert', 'Paul Bentley', 'Adam Marcus'] | 2023-06-21 | null | null | null | null | ['age-estimation', 'computed-tomography-ct', 'age-estimation'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-0.05933563 -0.34482217 -0.20581767 -0.45170715 -1.2312866 -0.43060175
0.34701705 0.41176784 -0.69575125 0.76922315 0.34726864 -0.5318789
-0.22337495 -0.5041366 -0.5776902 -0.6142305 -0.3718525 0.8078494
0.3669524 0.4579186 0.08517648 0.6181612 -0.9720506 0.24040438
1.219663 1.0538172 0.29... | [14.367881774902344, -2.0885422229766846] |
79ba0ddb-8608-4954-ac41-cadc5b16a29c | target-aware-tracking-with-long-term-context | 2302.13840 | null | https://arxiv.org/abs/2302.13840v1 | https://arxiv.org/pdf/2302.13840v1.pdf | Target-Aware Tracking with Long-term Context Attention | Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above pr... | ['Zhiwen Wang', 'Zhixin Li', 'Sheng Xie', 'Canlong Zhang', 'Kaijie He'] | 2023-02-27 | null | null | null | null | ['visual-tracking', 'video-object-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.43948209e-01 -5.76269627e-01 -2.56312937e-01 -2.56982058e-01
-5.05417407e-01 -5.04041433e-01 5.01215994e-01 -1.08921677e-01
-4.62503701e-01 4.40070570e-01 2.67328080e-02 2.40848646e-01
2.55952418e-01 -4.22029227e-01 -6.49207771e-01 -8.76986623e-01
-5.37500717e-02 7.00176433e-02 7.59296775e-01 1.02606609... | [6.270144939422607, -2.1452155113220215] |
b270e3df-49f3-4827-917b-31b9f1a9c8a4 | hrtf-upsampling-with-a-generative-adversarial | 2306.05812 | null | https://arxiv.org/abs/2306.05812v1 | https://arxiv.org/pdf/2306.05812v1.pdf | HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection | An individualised head-related transfer function (HRTF) is essential for creating realistic virtual reality (VR) and augmented reality (AR) environments. However, acoustically measuring high-quality HRTFs requires expensive equipment and an acoustic lab setting. To overcome these limitations and to make this measuremen... | ['Lorenzo Picinali', 'Samuel J. Cooper', 'Isaac Squires', 'He Liu', 'Mads Jenkins', 'Aidan O. T. Hogg'] | 2023-06-09 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 4.63378519e-01 2.30589569e-01 4.73817945e-01 -3.19295555e-01
-1.57824266e+00 -3.67614836e-01 4.76750016e-01 -6.45584881e-01
-2.11290747e-01 8.78154516e-01 6.15892828e-01 8.40139315e-02
8.74230340e-02 -5.65588057e-01 -7.94229388e-01 -8.77817273e-01
1.37494057e-01 -1.83118917e-02 -5.12391888e-02 -2.85201937... | [15.34433364868164, 6.036479949951172] |
b2df7c36-6c7f-4658-8874-253b22f0d77c | openhls-high-level-synthesis-for-low-latency | 2302.06751 | null | https://arxiv.org/abs/2302.06751v4 | https://arxiv.org/pdf/2302.06751v4.pdf | OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science | In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity... | ['Kazutomo Yoshii', 'Ryan Chard', 'Ian Foster', 'Kyle Chard', 'Arham Khan', 'Maksim Levental'] | 2023-02-13 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 2.76776105e-01 3.49637344e-02 1.79238409e-01 -5.56851804e-01
-3.65384281e-01 1.29525606e-02 2.05607206e-01 7.01714873e-01
-9.46983337e-01 5.73018610e-01 -2.28320032e-01 -5.69551587e-01
2.95624137e-02 -1.13260627e+00 -8.76723707e-01 -6.83735907e-01
-1.67870864e-01 7.06701696e-01 5.06575286e-01 7.78463297... | [8.334893226623535, 2.9283699989318848] |
9c87021d-64e8-493c-b2c3-e25027d381e4 | a-factor-based-framework-for-decision-making | 2203.11981 | null | https://arxiv.org/abs/2203.11981v1 | https://arxiv.org/pdf/2203.11981v1.pdf | A Factor-Based Framework for Decision-Making Competency Self-Assessment | We summarize our efforts to date in developing a framework for generating succinct human-understandable competency self-assessments in terms of machine self confidence, i.e. a robot's self-trust in its functional abilities to accomplish assigned tasks. Whereas early work explored machine self-confidence in ad hoc ways ... | ['Nisar Ahmed', 'Brett W. Israelsen'] | 2022-03-22 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 6.15918748e-02 1.21327531e+00 -3.31245661e-01 -7.64029086e-01
-7.28538454e-01 -4.34294462e-01 6.62974358e-01 5.64261913e-01
-3.63250703e-01 9.20418859e-01 3.58107150e-01 -3.63235146e-01
-6.78079963e-01 -6.29465580e-01 -7.56290615e-01 1.44461319e-02
9.33833420e-02 9.53752279e-01 1.67094484e-01 -9.04617179... | [4.191713809967041, 1.1556198596954346] |
5f535c89-768c-4fc5-bcd5-d0a53ff17fa2 | pegg-net-background-agnostic-pixel-wise | 2203.16301 | null | https://arxiv.org/abs/2203.16301v2 | https://arxiv.org/pdf/2203.16301v2.pdf | PEGG-Net: Background Agnostic Pixel-Wise Efficient Grasp Generation Under Closed-Loop Conditions | Performing closed-loop grasping at close proximity to an object requires a large field of view. However, such images will inevitably bring large amounts of unnecessary background information, especially when the camera is far away from the target object at the initial stage, resulting in performance degradation of the ... | ['Marcelo H Ang Jr', 'Huan Yin', 'Lei Zhou', 'Haozhe Wang', 'Zhiyang Liu'] | 2022-03-30 | null | null | null | null | ['grasp-generation'] | ['computer-vision'] | [ 2.16757491e-01 -2.21561864e-01 4.18157279e-01 -1.76794454e-01
-3.73226315e-01 -5.40159583e-01 -6.06277101e-02 -2.56368846e-01
-3.93673331e-01 3.12840551e-01 -4.57308859e-01 -8.70197453e-03
-6.50248751e-02 -8.52658153e-01 -1.30557084e+00 -7.76417375e-01
-5.27163804e-01 2.20538020e-01 3.60942602e-01 -8.76094960... | [5.79473876953125, -0.899118959903717] |
6e358af9-a460-42d9-80a1-3fadb1b83143 | dexvip-learning-dexterous-grasping-with-human | 2202.00164 | null | https://arxiv.org/abs/2202.00164v1 | https://arxiv.org/pdf/2202.00164v1.pdf | DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video | Dexterous multi-fingered robotic hands have a formidable action space, yet their morphological similarity to the human hand holds immense potential to accelerate robot learning. We propose DexVIP, an approach to learn dexterous robotic grasping from human-object interactions present in in-the-wild YouTube videos. We do... | ['Kristen Grauman', 'Priyanka Mandikal'] | 2022-02-01 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-4.80371743e-01 -1.42012402e-01 -3.20574045e-02 -5.68118915e-02
-4.57961679e-01 -9.58487689e-01 1.86932027e-01 -6.85955524e-01
-5.03307521e-01 6.50604904e-01 -1.66634068e-01 -3.97349983e-01
-1.51669979e-01 -9.01098773e-02 -1.13476026e+00 -4.39527333e-01
-3.37104023e-01 8.07054758e-01 1.61729530e-01 -1.80493325... | [4.756779670715332, 0.5417726635932922] |
4e40568f-1f12-4f3c-b10c-94783f5b2781 | personalized-federated-learning-on-long | 2303.15168 | null | https://arxiv.org/abs/2303.15168v1 | https://arxiv.org/pdf/2303.15168v1.pdf | Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation | Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner. Existing PFL methods generally assume that the underlying global data across all clients are uniformly distributed without considering the long-tail distribut... | ['Hanzi Wang', 'Gang Huang', 'Pinxin Qian', 'Yang Lu'] | 2023-03-27 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.97412002e-01 -1.50880799e-01 -5.17215312e-01 -7.00622380e-01
-1.10361385e+00 -6.90534234e-01 3.41618925e-01 -2.19351962e-01
-1.63403079e-02 7.87403941e-01 3.92241180e-01 -1.56655684e-02
-8.15676246e-03 -9.41494763e-01 -9.04966414e-01 -1.09944379e+00
2.45514400e-02 3.52371126e-01 -1.60544828e-01 9.77154300... | [5.837131977081299, 6.360169410705566] |
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