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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
51c2fe10-4de3-4c32-b52f-9088febf94f3 | findings-of-the-shared-task-on-troll-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.16 | https://aclanthology.org/2021.dravidianlangtech-1.16.pdf | Findings of the Shared Task on Troll Meme Classification in Tamil | The internet has facilitated its user-base with a platform to communicate and express their views without any censorship. On the other hand, this freedom of expression or free speech can be abused by its user or a troll to demean an individual or a group. Demeaning people based on their gender, sexual orientation, reli... | ['Bharathi Raja Chakravarthi', 'Shardul Suryawanshi'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-1.95640102e-01 -1.88634768e-01 2.38491166e-02 -3.85923758e-02
-4.09429073e-01 -1.07810938e+00 9.66336966e-01 2.64912575e-01
-4.06370521e-01 8.17565143e-01 1.31547883e-01 -3.26325566e-01
3.89193386e-01 -7.06765831e-01 -3.05688381e-01 -7.06067979e-01
2.36560270e-01 8.25275257e-02 1.20046996e-01 -1.83078915... | [8.474454879760742, 10.637733459472656] |
eb773ea9-a688-4fbc-8be0-3f4936abf259 | semi-supervised-learning-of-galaxy-morphology | 2011.08714 | null | https://arxiv.org/abs/2011.08714v1 | https://arxiv.org/pdf/2011.08714v1.pdf | Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders | The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the ever growing set of unlabelled images, semi-supervised learning could be an effective way of reducing the required labelling and increasing cla... | ['Yarin Gal', 'Lewis Smith', 'Mizu Nishikawa-Toomey'] | 2020-11-17 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 2.66966581e-01 3.98089647e-01 4.80357438e-01 -4.53067631e-01
-5.60006261e-01 -8.58199358e-01 9.52951074e-01 -1.98422268e-01
-5.15370131e-01 4.18229431e-01 -1.70842066e-01 -4.14538562e-01
-2.09739525e-02 -8.74266386e-01 -5.17610013e-01 -9.85541582e-01
3.34356010e-01 1.05470300e+00 3.40877771e-01 1.07130222... | [8.045433044433594, 2.9098246097564697] |
c9e51d27-4b9b-481e-b745-0d4969179aa2 | greedy-transition-based-dependency-parsing | null | null | https://aclanthology.org/J17-2002 | https://aclanthology.org/J17-2002.pdf | Greedy Transition-Based Dependency Parsing with Stack LSTMs | We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks{---}the stack long short-term memory unit (LSTM). Like the conventional stack data... | ['Miguel Ballesteros', 'Noah A. Smith', 'Chris Dyer', 'Yoav Goldberg'] | 2017-06-01 | null | null | null | cl-2017-6 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.45358825e-01 3.71263236e-01 -2.65100092e-01 -3.21695805e-01
-8.73318434e-01 -7.98959196e-01 1.94612250e-01 4.70820963e-01
-6.27088130e-01 2.61804581e-01 3.86347145e-01 -1.05122876e+00
4.60613757e-01 -1.33000886e+00 -9.69377875e-01 -5.20821750e-01
-3.26603532e-01 5.26162922e-01 6.40388846e-01 -2.25711390... | [10.379966735839844, 9.53250503540039] |
b5d9e025-f4e2-4c00-a389-57ea56ce87d3 | cloudsegnet-a-deep-network-for-nychthemeron | 1904.07979 | null | http://arxiv.org/abs/1904.07979v1 | http://arxiv.org/pdf/1904.07979v1.pdf | CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation | We analyze clouds in the earth's atmosphere using ground-based sky cameras.
An accurate segmentation of clouds in the captured sky/cloud image is
difficult, owing to the fuzzy boundaries of clouds. Several techniques have
been proposed that use color as the discriminatory feature for cloud detection.
In the existing li... | ['Yee Hui Lee', 'Soumyabrata Dev', 'Stefan Winkler', 'Atul Nautiyal'] | 2019-04-16 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-3.31476569e-01 -1.07844222e+00 1.60103947e-01 -5.02461195e-01
-3.52323622e-01 -9.04571295e-01 4.27450359e-01 -3.26676428e-01
-4.12059754e-01 5.54519296e-01 -6.23174548e-01 -3.60532075e-01
2.63696015e-01 -7.62214899e-01 -4.09954458e-01 -1.10582924e+00
1.79516599e-01 3.34394693e-01 3.01876098e-01 3.93445455... | [9.749017715454102, -1.7143698930740356] |
394343e5-0d56-4fe4-b331-da9bffc59552 | to-be-or-not-to-be-a-translation-reception | 2307.02358 | null | https://arxiv.org/abs/2307.02358v1 | https://arxiv.org/pdf/2307.02358v1.pdf | To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translation | This article presents the results of a study involving the reception of a fictional story by Kurt Vonnegut translated from English into Catalan and Dutch in three conditions: machine-translated (MT), post-edited (PE) and translated from scratch (HT). 223 participants were recruited who rated the reading conditions usin... | ['Antonio Toral', 'Ana Guerberof Arenas'] | 2023-07-05 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [-1.91368591e-02 3.68149370e-01 -2.86584526e-01 2.29835227e-01
-7.83501744e-01 -7.95475841e-01 9.82414544e-01 2.77228892e-01
-5.64272523e-01 6.64477825e-01 1.26561427e+00 -1.39530182e-01
4.12860326e-02 -4.79176849e-01 -6.54432416e-01 -2.67414123e-01
6.46660149e-01 1.53777227e-01 -4.39857453e-01 -5.10115564... | [11.65351676940918, 8.797774314880371] |
4e44bb86-11f3-473c-a82b-b0eb5ce3cf19 | emoji-prediction-using-transformer-models | 2307.02054 | null | https://arxiv.org/abs/2307.02054v1 | https://arxiv.org/pdf/2307.02054v1.pdf | Emoji Prediction using Transformer Models | In recent years, the use of emojis in social media has increased dramatically, making them an important element in understanding online communication. However, predicting the meaning of emojis in a given text is a challenging task due to their ambiguous nature. In this study, we propose a transformer-based approach for... | ['Mehreen Alam', 'Zeeshan Habib', 'Muhammad Osama Nusrat'] | 2023-07-05 | null | null | null | null | ['marketing', 'sentiment-analysis'] | ['miscellaneous', 'natural-language-processing'] | [-0.27197492 -0.2366489 0.02260845 -0.6162265 -0.06712865 -0.31807554
0.21374594 0.24972008 -0.14340734 0.570944 0.20887032 -0.33056033
0.34799558 -0.8043518 0.03574866 -0.06850619 0.4669145 0.1669895
0.06661758 -0.6556566 0.25767383 0.16546452 -1.3835642 0.5770686
1.029278 1.332853 -0.03... | [11.406001091003418, 6.911930084228516] |
de65e4c5-6785-488e-bf19-382fe629313a | plug-and-play-split-gibbs-sampler-embedding | 2304.11134 | null | https://arxiv.org/abs/2304.11134v1 | https://arxiv.org/pdf/2304.11134v1.pdf | Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference | This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based on split Gibbs sampling (SGS) draws inspiration from the alternating direction method of multipliers (ADMM). It divides the challenging tas... | ['Pierre Chainais', 'Nicolas Dobigeon', 'Florentin Coeurdoux'] | 2023-04-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 3.30049127e-01 9.79543775e-02 1.12707149e-02 -1.51391670e-01
-1.16094089e+00 -1.27546966e-01 9.03246701e-01 -5.97891845e-02
-5.09134293e-01 1.05878735e+00 -1.95542544e-01 -1.55055448e-01
-1.88395858e-01 -8.41809571e-01 -6.60014570e-01 -1.30001366e+00
3.50278795e-01 6.60502255e-01 -4.60117124e-02 3.06810439... | [6.971826553344727, 3.8488807678222656] |
432d5027-a580-451c-ae9c-3a4ce02c32a8 | automating-dbscan-via-deep-reinforcement | 2208.04537 | null | https://arxiv.org/abs/2208.04537v1 | https://arxiv.org/pdf/2208.04537v1.pdf | Automating DBSCAN via Deep Reinforcement Learning | DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the clustering result depends heavily on practical experience. In this paper, we first propose a novel Deep Reinforcement Learning guided automatic... | ['Philip S. Yu', 'Jingyi Zhang', 'Qingyun Sun', 'Jia Wu', 'Yingtong Dou', 'Hao Peng', 'Ruitong Zhang'] | 2022-08-09 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [-5.85694194e-01 -5.08456230e-01 -1.88776851e-01 -3.03879797e-01
-8.53612721e-01 -7.72654712e-01 3.74375105e-01 -1.57493770e-01
-4.31254655e-01 6.56762779e-01 -1.13074802e-01 -1.19647853e-01
-5.49718916e-01 -7.30257094e-01 -5.68834364e-01 -1.31641483e+00
-1.12802155e-01 9.40023661e-01 3.54527980e-01 2.64391392... | [4.093035697937012, 2.245962619781494] |
b989a5ab-191b-430a-ba9a-710bb5f99d6d | triplet-knowledge-distillation | 2305.15975 | null | https://arxiv.org/abs/2305.15975v1 | https://arxiv.org/pdf/2305.15975v1.pdf | Triplet Knowledge Distillation | In Knowledge Distillation, the teacher is generally much larger than the student, making the solution of the teacher likely to be difficult for the student to learn. To ease the mimicking difficulty, we introduce a triplet knowledge distillation mechanism named TriKD. Besides teacher and student, TriKD employs a third ... | ['Shiguang Shan', 'Zhongqin Wu', 'Chunrui Han', 'Meina Kan', 'Dongyang Liu', 'Xijun Wang'] | 2023-05-25 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [-1.57888949e-01 3.61386806e-01 -2.48396412e-01 -1.67065293e-01
-4.80039209e-01 -5.33708513e-01 -3.64669263e-02 -5.56409881e-02
-1.18946642e-01 6.82802022e-01 -4.78228748e-01 -3.39518249e-01
-2.55805790e-01 -8.52245212e-01 -8.31879973e-01 -9.44820940e-01
3.54022980e-01 4.55731332e-01 2.02791747e-02 -1.32057607... | [9.530455589294434, 3.2976768016815186] |
33cc7f50-d343-4517-bd0b-94feacf48c54 | cnerv-content-adaptive-neural-representation | 2211.10421 | null | https://arxiv.org/abs/2211.10421v1 | https://arxiv.org/pdf/2211.10421v1.pdf | CNeRV: Content-adaptive Neural Representation for Visual Data | Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have used deep learning to improve or refine existing pipelines, while others have proposed end-to-end approaches, including autoencoders and impl... | ['Abhinav Shrivastava', 'Ser-Nam Lim', 'Bo He', 'Matt Gwilliam', 'Hao Chen'] | 2022-11-18 | null | null | null | null | ['data-compression'] | ['time-series'] | [-1.94544151e-01 1.36239260e-01 -2.17365883e-02 -3.12886268e-01
-2.64005333e-01 -1.51276171e-01 5.45525312e-01 -2.64327943e-01
-5.21905243e-01 2.70187020e-01 6.73830986e-01 -1.20280765e-01
2.20619634e-01 -6.61337376e-01 -9.90546584e-01 -4.72072363e-01
1.85412645e-01 1.49993554e-01 8.84955749e-02 -1.93233620... | [11.174076080322266, -1.4050374031066895] |
742ad067-1b49-4b25-8bb8-7035b711e346 | a-conditional-point-diffusion-refinement-1 | 2112.03530 | null | https://arxiv.org/abs/2112.03530v4 | https://arxiv.org/pdf/2112.03530v4.pdf | A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion | 3D point cloud is an important 3D representation for capturing real world 3D objects. However, real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. Th... | ['Dahua Lin', 'Liang Pan', 'Xudong Xu', 'Zhifeng Kong', 'Zhaoyang Lyu'] | 2021-12-07 | a-conditional-point-diffusion-refinement | https://openreview.net/forum?id=wqD6TfbYkrn | https://openreview.net/pdf?id=wqD6TfbYkrn | iclr-2022-4 | ['point-cloud-completion', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-4.02547084e-02 2.36184597e-02 1.70027003e-01 -9.22494754e-02
-1.00188696e+00 -4.15882021e-01 6.12301290e-01 3.15090492e-02
-2.10958105e-02 2.54786789e-01 -2.31529474e-01 -1.48298264e-01
-6.03079014e-02 -1.18493843e+00 -1.09229779e+00 -6.01071000e-01
1.47327393e-01 9.80136693e-01 3.43039304e-01 -1.61123332... | [8.406919479370117, -3.5717623233795166] |
dc12ad10-8575-4380-8b61-1d7400c89cdc | estimating-risk-and-uncertainty-in-deep | 1905.09638 | null | https://arxiv.org/abs/1905.09638v5 | https://arxiv.org/pdf/1905.09638v5.pdf | Estimating Risk and Uncertainty in Deep Reinforcement Learning | Reinforcement learning agents are faced with two types of uncertainty. Epistemic uncertainty stems from limited data and is useful for exploration, whereas aleatoric uncertainty arises from stochastic environments and must be accounted for in risk-sensitive applications. We highlight the challenges involved in simultan... | ['Sébastien Toth', 'Benoît-Marie Robaglia', 'Reda Bahi Slaoui', 'Bastien Van Delft', 'William R. Clements'] | 2019-05-23 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.40641832e-01 4.23132807e-01 -2.01619357e-01 -3.68162841e-01
-1.26347864e+00 -7.70888865e-01 6.11822963e-01 2.29843616e-01
-9.59707379e-01 1.67647541e+00 1.83303014e-03 -3.06419224e-01
-7.92602837e-01 -8.44701469e-01 -8.01370621e-01 -7.61142135e-01
-7.75594175e-01 7.45854378e-01 1.71340361e-01 8.35398287... | [4.252823352813721, 2.5113062858581543] |
50357ad8-6865-4388-829f-6fda23374bf2 | learning-the-effects-of-physical-actions-in-a | 2301.11845 | null | https://arxiv.org/abs/2301.11845v2 | https://arxiv.org/pdf/2301.11845v2.pdf | Learning the Effects of Physical Actions in a Multi-modal Environment | Large Language Models (LLMs) handle physical commonsense information inadequately. As a result of being trained in a disembodied setting, LLMs often fail to predict an action's outcome in a given environment. However, predicting the effects of an action before it is executed is crucial in planning, where coherent seque... | ['Alex Lascarides', 'Frank Keller', 'Gautier Dagan'] | 2023-01-27 | null | null | null | null | ['physical-commonsense-reasoning'] | ['reasoning'] | [ 6.75609112e-01 2.58199368e-02 -1.59992665e-01 -2.00455934e-01
-5.16402423e-01 -3.82198632e-01 1.02508533e+00 1.53090999e-01
-3.54435265e-01 7.21913099e-01 8.06947231e-01 -8.89290646e-02
-6.73812404e-02 -8.42368305e-01 -8.82843614e-01 -4.62358505e-01
1.94114730e-01 3.13119650e-01 -7.88509324e-02 -6.95457906... | [4.5295610427856445, 0.8791645765304565] |
9cbf85b8-f864-4d1c-bf7f-06dc89656dc0 | dc3dcd-unsupervised-learning-for-multiclass | 2305.05421 | null | https://arxiv.org/abs/2305.05421v1 | https://arxiv.org/pdf/2305.05421v1.pdf | DC3DCD: unsupervised learning for multiclass 3D point cloud change detection | In a constant evolving world, change detection is of prime importance to keep updated maps. To better sense areas with complex geometry (urban areas in particular), considering 3D data appears to be an interesting alternative to classical 2D images. In this context, 3D point clouds (PCs) obtained by LiDAR or photogramm... | ['Thomas Corpetti', 'Sébastien Lefèvre', 'Iris de Gélis'] | 2023-05-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 1.89488336e-01 -2.17923760e-01 7.38843307e-02 -3.81911337e-01
-4.81125325e-01 -6.31329656e-01 1.03576076e+00 6.37400150e-01
-5.89244306e-01 5.50161302e-01 -2.49049932e-01 -2.78661072e-01
-2.73245871e-01 -1.18069589e+00 -6.69719040e-01 -7.46488869e-01
-4.95127916e-01 8.27848315e-01 3.92914712e-01 -2.12717921... | [8.516683578491211, -2.4198503494262695] |
9cb750b0-6dae-467e-a61e-d9b86ca7ff94 | evolutionary-n-level-hypergraph-partitioning | 1803.09258 | null | http://arxiv.org/abs/1803.09258v3 | http://arxiv.org/pdf/1803.09258v3.pdf | Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening | Hypergraph partitioning is an NP-hard problem that occurs in many computer
science applications where it is necessary to reduce large problems into a
number of smaller, computationally tractable sub-problems. Current techniques
use a multilevel approach wherein an initial partitioning is performed after
compressing the... | ['Jim Smith', 'Richard J. Preen'] | 2018-03-25 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 4.29589093e-01 4.84238327e-01 -1.21921651e-01 1.30495399e-01
-4.23504859e-01 -4.74714786e-01 1.31508291e-01 4.10011172e-01
-1.79712281e-01 1.14993382e+00 -1.54902428e-01 -1.15542017e-01
-7.63230085e-01 -1.24340701e+00 -3.95902961e-01 -8.19542527e-01
-1.97883010e-01 1.13017011e+00 5.63392758e-01 -2.29431316... | [5.853832721710205, 3.90830135345459] |
c85e8351-3d56-4e25-93b3-c52b6a748e59 | wakeword-detection-under-distribution-shifts | 2207.06423 | null | https://arxiv.org/abs/2207.06423v1 | https://arxiv.org/pdf/2207.06423v1.pdf | Wakeword Detection under Distribution Shifts | We propose a novel approach for semi-supervised learning (SSL) designed to overcome distribution shifts between training and real-world data arising in the keyword spotting (KWS) task. Shifts from training data distribution are a key challenge for real-world KWS tasks: when a new model is deployed on device, the gating... | ['Joseph Wang', 'Christin Jose', 'Lu Zeng', 'Sree Hari Krishnan Parthasarathi'] | 2022-07-13 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.32210147e-01 3.75110596e-01 -2.25797936e-01 -5.82997262e-01
-1.33494723e+00 -5.65761983e-01 1.02614075e-01 3.65809500e-01
-4.96761441e-01 8.52678061e-01 -1.62241757e-01 -3.17650110e-01
-7.61270821e-02 -4.17585969e-01 -1.12871015e+00 -6.72516465e-01
-1.49833784e-02 4.50616896e-01 3.43734026e-01 3.94811541... | [14.290728569030762, 5.861223220825195] |
73a78b09-a21c-48a4-8023-73674d3b82a7 | a-bayesian-approach-to-multimodal-visual | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Irie_A_Bayesian_Approach_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Irie_A_Bayesian_Approach_2013_CVPR_paper.pdf | A Bayesian Approach to Multimodal Visual Dictionary Learning | nary learning methods rely on image descriptors alone or together with class labels. However, Web images are often associated with text data which may carry substantial information regarding image semantics, and may be exploited for visual dictionary learning. This paper explores this idea by leveraging relational info... | ['Shih-Fu Chang', 'Zhenguo Li', 'Go Irie', 'Dong Liu'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['image-categorization'] | ['computer-vision'] | [-1.24719724e-01 -5.73316455e-01 -7.25080013e-01 -4.45048153e-01
-6.29351735e-01 -6.31881654e-01 7.95856416e-01 6.15776658e-01
-4.91338462e-01 1.71626195e-01 2.35326603e-01 6.37048334e-02
-2.84744889e-01 -6.94595873e-01 -5.15478194e-01 -9.50572610e-01
3.16764712e-01 2.70028144e-01 2.92921271e-02 7.71355331... | [10.403409957885742, 1.1571249961853027] |
fe8679f2-4639-4e56-98aa-ebd41a88ba7b | non-parametric-probabilistic-time-series | 2306.03782 | null | https://arxiv.org/abs/2306.03782v1 | https://arxiv.org/pdf/2306.03782v1.pdf | Non-parametric Probabilistic Time Series Forecasting via Innovations Representation | Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical in risk-based decision-making and planning under uncertainties. Existing approaches are primarily based on parametric or semi-parametric time... | ['Lang Tong', 'Qing Zhao', 'Meijen Lee', 'Xinyi Wang'] | 2023-06-05 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.38697866e-02 -2.12228537e-01 3.51268947e-01 -3.58399779e-01
-7.21640170e-01 -6.91083133e-01 1.15573490e+00 -6.80742413e-02
1.06289513e-01 8.56385410e-01 3.46059889e-01 -7.34322786e-01
-4.37193334e-01 -1.07718277e+00 -8.28090549e-01 -8.83145332e-01
-3.47161800e-01 6.80895209e-01 -5.66974394e-02 1.77386831... | [6.863265514373779, 3.2465429306030273] |
7e1180a3-bf40-4622-b68d-64fc2c76fbec | object-wake-up-3-d-object-reconstruction | 2108.02708 | null | https://arxiv.org/abs/2108.02708v3 | https://arxiv.org/pdf/2108.02708v3.pdf | Object Wake-up: 3D Object Rigging from a Single Image | Given a single image of a general object such as a chair, could we also restore its articulated 3D shape similar to human modeling, so as to animate its plausible articulations and diverse motions? This is an interesting new question that may have numerous downstream augmented reality and virtual reality applications. ... | ['Minglun Gong', 'Xingyu Li', 'Zhenbo Yu', 'Xinxin Zuo', 'Ji Yang', 'Li Cheng', 'Bingbing Ni', 'Sen Wang'] | 2021-08-05 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.21419382e-01 5.40956199e-01 -7.41551444e-02 -9.01208445e-02
-7.21064508e-01 -4.60567802e-01 4.99743640e-01 -6.11515582e-01
5.86636662e-02 3.55514586e-01 2.76629746e-01 -4.97725047e-02
4.73626107e-02 -5.47560692e-01 -1.00225890e+00 -3.36747199e-01
1.62847817e-01 9.60726321e-01 4.75351125e-01 -1.51792958... | [7.02018404006958, -1.350393533706665] |
57b1ca4e-f450-466b-a869-48a333ae53ea | image-synthesis-with-disentangled-attributes | 2207.09389 | null | https://arxiv.org/abs/2207.09389v1 | https://arxiv.org/pdf/2207.09389v1.pdf | Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection | Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust... | ['Dinggang Shen', 'Qian Wang', 'Jie-Zhi Cheng', 'Bin Xiao', 'Xi Ouyang', 'Zhenrong Shen'] | 2022-07-19 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 4.16654140e-01 5.79516053e-01 -8.02193731e-02 -1.48630023e-01
-8.75626445e-01 -4.79573280e-01 4.42877293e-01 -2.47011438e-01
1.67338610e-01 4.59998935e-01 2.07954645e-02 -5.65562546e-01
5.50261065e-02 -1.07358289e+00 -6.57987177e-01 -7.64092624e-01
5.40656745e-01 6.64856553e-01 4.48947728e-01 6.74339458... | [15.336407661437988, -2.1384620666503906] |
638e18eb-4633-4b25-b327-1505d161b718 | static-hand-gesture-recognition-for-american | 2207.12559 | null | https://arxiv.org/abs/2207.12559v3 | https://arxiv.org/pdf/2207.12559v3.pdf | Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware | In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) mod... | ['Mohammadreza Mohammadi', 'Ramtin Zand', 'Sara Hendrix', 'James Seekings', 'Peyton Chandarana'] | 2022-07-25 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.15027973e-02 -4.19846922e-01 -6.45253900e-03 1.68015450e-01
1.81431994e-01 -3.92415762e-01 3.78813326e-01 -4.25590724e-01
-9.85720038e-01 5.20999491e-01 -3.37308973e-01 -2.14245170e-01
5.83031774e-02 -5.83323836e-01 -7.25044787e-01 -9.01504576e-01
1.84676722e-01 2.38077193e-01 5.54075003e-01 1.27894372... | [8.24474811553955, 2.4696450233459473] |
f5887485-d648-4ef6-a1a1-1b8069513969 | autoqgs-auto-prompt-for-low-resource | 2208.12461 | null | https://arxiv.org/abs/2208.12461v1 | https://arxiv.org/pdf/2208.12461v1.pdf | AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL | This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. Moreover, due to the costly annotation of large-scale SPARQL-questio... | ['Xiaodong He', 'Youzheng Wu', 'Wen Zhao', 'Junwei Bao', 'Guanming Xiong'] | 2022-08-26 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-3.57400537e-01 6.70867503e-01 -1.42303901e-02 -3.39508653e-01
-1.38754427e+00 -7.42991745e-01 6.07289732e-01 1.81769773e-01
-2.15745896e-01 1.18678951e+00 4.11683410e-01 -5.54828227e-01
-1.80157423e-01 -1.58051729e+00 -1.04303586e+00 2.84728408e-01
3.89673859e-01 8.59085083e-01 4.17086661e-01 -6.31459475... | [10.788351058959961, 7.928165435791016] |
ecf619a3-0bba-4e61-be95-f9511e81f7c2 | precision-anti-cancer-drug-selection-via | 2306.17771 | null | https://arxiv.org/abs/2306.17771v1 | https://arxiv.org/pdf/2306.17771v1.pdf | Precision Anti-Cancer Drug Selection via Neural Ranking | Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such ... | ['Xia Ning', 'Vishal Dey'] | 2023-06-30 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 4.21106309e-01 -5.75558782e-01 -7.53748894e-01 -2.70630836e-01
-1.39173388e+00 -5.00630558e-01 5.23576140e-01 5.34995198e-01
-1.07647441e-01 1.24591660e+00 4.34882075e-01 -1.49052203e-01
-6.91856861e-01 -7.59600103e-01 -5.50017595e-01 -1.01486373e+00
1.16494358e-01 6.71723902e-01 -1.86266646e-01 -4.45112698... | [5.662940979003906, 5.7301130294799805] |
a8cec014-e3df-4043-bcfe-5fb994f8b9f2 | solving-cosine-similarity-underestimation | 2305.10610 | null | https://arxiv.org/abs/2305.10610v1 | https://arxiv.org/pdf/2305.10610v1.pdf | Solving Cosine Similarity Underestimation between High Frequency Words by L2 Norm Discounting | Cosine similarity between two words, computed using their contextualised token embeddings obtained from masked language models (MLMs) such as BERT has shown to underestimate the actual similarity between those words (Zhou et al., 2022). This similarity underestimation problem is particularly severe for highly frequent ... | ['Danushka Bollegala', 'Yi Zhou', 'Saeth Wannasuphoprasit'] | 2023-05-17 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-7.76382862e-03 9.33247805e-02 -9.21374410e-02 -5.49219370e-01
-4.45421070e-01 -3.10975373e-01 6.95496023e-01 8.72429848e-01
-1.15215647e+00 3.46037328e-01 3.97196263e-01 -1.72169924e-01
1.10992201e-01 -6.49634302e-01 -2.59816438e-01 -8.26860547e-01
-1.23855390e-01 2.11784810e-01 1.61388993e-01 -1.88260168... | [10.484764099121094, 8.866049766540527] |
5d6666e3-75d1-42b7-8336-d68878dee00a | exploiting-large-neuroimaging-datasets-to | 2305.17300 | null | https://arxiv.org/abs/2305.17300v1 | https://arxiv.org/pdf/2305.17300v1.pdf | Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence | Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron ... | ['Joan A. Hoffmann', 'William R. Gray-Roncal', 'Brock A. Wester', 'I-Jeng Wang', 'Matthew J. Roos', 'Nathan Drenkow', 'Elizabeth P. Reilly', 'Patricia K. Rivlin', 'Lindsey Kitchell', 'Arun V. Reddy', 'Michael S. Robinette', 'Martha Cervantes', 'Marisel Villafañe-Delgado', 'Isaac Western', 'Raphael Norman-Tenazas', 'Jor... | 2023-05-26 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.80398726e-01 1.72175586e-01 4.00276572e-01 -2.59931177e-01
-3.04063827e-01 -8.44951212e-01 5.80604255e-01 -1.35112122e-01
-5.30405164e-01 5.43024361e-01 -1.58521030e-02 -1.81281745e-01
-4.39929247e-01 -3.58478040e-01 -9.42075908e-01 -6.08949780e-01
-5.20526886e-01 3.52041900e-01 3.32987681e-02 -2.19289511... | [9.558963775634766, 2.4881625175476074] |
4cf8cbd5-e55f-4b25-a031-aba096de3b21 | towards-real-time-single-channel-speech | 2303.07569 | null | https://arxiv.org/abs/2303.07569v2 | https://arxiv.org/pdf/2303.07569v2.pdf | Towards Real-Time Single-Channel Speech Separation in Noisy and Reverberant Environments | Real-time single-channel speech separation aims to unmix an audio stream captured from a single microphone that contains multiple people talking at once, environmental noise, and reverberation into multiple de-reverberated and noise-free speech tracks, each track containing only one talker. While large state-of-the-art... | ['Sebastian Braun', 'Julian Neri'] | 2023-03-14 | null | null | null | null | ['speech-separation'] | ['speech'] | [-1.77439023e-03 -3.78907889e-01 4.80911642e-01 4.10005152e-02
-1.21190703e+00 -6.25802219e-01 3.43840361e-01 -2.72353917e-01
-1.77178174e-01 5.07702768e-01 6.86439514e-01 -5.34056723e-01
5.96067868e-02 7.40959942e-02 -3.99029374e-01 -7.62304008e-01
-2.04559863e-01 2.39781007e-01 2.78210100e-02 3.16199102... | [15.004122734069824, 5.869960308074951] |
169ed8d4-9ae8-4b04-aa98-d18f3ff6e909 | language-identification-and-named-entity | null | null | https://aclanthology.org/P18-3008 | https://aclanthology.org/P18-3008.pdf | Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets | While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging. To meet this challenge, a family of tools for analyzing code-mixed data such as language identifiers, parts-of... | ['Ponnurangam Kumaraguru', 'Kushagra Singh', 'Indira Sen'] | 2018-07-01 | null | null | null | acl-2018-7 | ['abuse-detection'] | ['natural-language-processing'] | [-1.40304714e-01 1.48638710e-01 -3.69633943e-01 -1.52623475e-01
-7.95884788e-01 -8.29579115e-01 4.11383033e-01 7.48293519e-01
-4.62284833e-01 5.95798731e-01 6.15270674e-01 -4.34386492e-01
7.28652775e-02 -4.90900010e-01 -3.04404467e-01 4.49247509e-02
-7.52035305e-02 2.11983442e-01 5.14167547e-01 -4.51176912... | [9.691502571105957, 9.875917434692383] |
8bcc7e01-3c90-44da-9140-3c7e2842a84e | deep-learning-methods-for-small-molecule-drug | 2303.00313 | null | https://arxiv.org/abs/2303.00313v2 | https://arxiv.org/pdf/2303.00313v2.pdf | Deep Learning Methods for Small Molecule Drug Discovery: A Survey | With the development of computer-assisted techniques, research communities including biochemistry and deep learning have been devoted into the drug discovery field for over a decade. Various applications of deep learning have drawn great attention in drug discovery, such as molecule generation, molecular property predi... | ['Gaoang Wang', 'Hongwei Wang', 'Hangyue Chen', 'Wenhao Chai', 'Xuanyu Chen', 'Yingying Liu', 'Wenhao Hu'] | 2023-03-01 | null | null | null | null | ['drug-discovery', 'retrosynthesis', 'molecular-property-prediction'] | ['medical', 'medical', 'miscellaneous'] | [ 3.24197710e-01 -2.28521332e-01 -8.86551440e-01 -3.61069553e-02
-4.54642087e-01 -6.05024755e-01 4.72196311e-01 6.52282536e-01
-1.37493581e-01 1.17206192e+00 1.29833907e-01 -4.43071395e-01
-1.78434387e-01 -6.38920069e-01 -4.60050762e-01 -1.01242185e+00
-2.55739748e-01 2.33683243e-01 -1.69125527e-01 -1.78162992... | [5.06015682220459, 5.8448920249938965] |
af346a13-b199-432a-898f-b72b32826b97 | impacts-and-risk-of-generative-ai-technology | 2306.13033 | null | https://arxiv.org/abs/2306.13033v1 | https://arxiv.org/pdf/2306.13033v1.pdf | Impacts and Risk of Generative AI Technology on Cyber Defense | Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesi... | ['Shahram Rahimi', 'Sudip Mittal', 'Ivan A. Fernandez', 'Subash Neupane'] | 2023-06-22 | null | null | null | null | ['face-swapping', 'misinformation'] | ['computer-vision', 'miscellaneous'] | [ 5.86108863e-01 3.28469425e-01 5.44717163e-02 6.53565407e-01
-5.16848683e-01 -1.16551554e+00 1.27700746e+00 8.97690654e-03
2.23081727e-02 3.80494356e-01 4.75415617e-01 -6.10702634e-01
-3.28904688e-01 -8.95370901e-01 -5.90647340e-01 -5.06995440e-01
5.64983673e-02 -6.07991703e-02 -2.71092385e-01 -4.21852142... | [6.217748641967773, 7.777961254119873] |
589cc696-2046-4303-aeac-f7dd467b5510 | deep-recursive-hdri-inverse-tone-mapping | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Siyeong_Lee_Deep_Recursive_HDRI_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Siyeong_Lee_Deep_Recursive_HDRI_ECCV_2018_paper.pdf | Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks | High dynamic range images contain luminance information of the physical world and provide more realistic experience than conventional low dynamic range images. Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent. We propose a novel met... | ['Suk-Ju Kang', 'Siyeong Lee', 'Gwon Hwan An'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 4.94820774e-01 -4.46526408e-01 5.05169392e-01 -4.09489602e-01
-8.41739833e-01 -4.67709869e-01 2.99780786e-01 -7.11848855e-01
-4.24552739e-01 8.47003222e-01 1.12691633e-01 -3.62082459e-02
-1.02231830e-01 -1.16816163e+00 -1.24079812e+00 -9.58036423e-01
3.16180319e-01 -1.59700677e-01 3.02623600e-01 -3.08664501... | [10.899666786193848, -2.2002456188201904] |
c7086fb6-6e08-480b-b99e-62743cfc57c7 | hifi-hierarchical-feature-integration-for | null | null | http://kaizhao.net/hifi | https://arxiv.org/abs/1801.01849 | Hifi: Hierarchical feature integration for skeleton detection | In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts. Thus, robust skeleton detection requires powerful multi-scale feature integration ability. To address this issue, we present a new convolutional neural network (CNN) architecture by introducing a novel hi... | ['Ming-Ming Cheng', 'Dandan Li', 'ShangHua Gao', 'Wei Shen', 'Kai Zhao'] | 2018-07-01 | null | null | null | null | ['object-skeleton-detection'] | ['computer-vision'] | [ 1.08615063e-01 -1.67651847e-01 -1.99541718e-01 -2.88997352e-01
-5.63430309e-01 -7.65684769e-02 3.60308409e-01 9.78726000e-02
-2.87650168e-01 4.01140958e-01 2.62134790e-01 4.44623291e-01
-1.14562780e-01 -1.04145312e+00 -5.82154751e-01 -5.53133130e-01
-1.34972066e-01 5.56151830e-02 1.12708473e+00 -1.89497411... | [9.5739107131958, -0.6423471570014954] |
0e37c28a-407a-4445-8bdc-8dd9fe38a3b5 | a-novel-patent-similarity-measurement | 2303.16767 | null | https://arxiv.org/abs/2303.16767v1 | https://arxiv.org/pdf/2303.16767v1.pdf | A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance | Measuring similarity between patents is an essential step to ensure novelty of innovation. However, a large number of methods of measuring the similarity between patents still rely on manual classification of patents by experts. Another body of research has proposed automated methods; nevertheless, most of it solely fo... | ['Deaho Seo', 'Zachary Schimke', 'Junwon Lee', 'Sanguk Gim', 'Cheonkam Jeong', 'Yongmin Yoo'] | 2023-03-23 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.93047825e-02 -2.34602898e-01 -4.29855496e-01 -3.78491431e-02
-5.18712401e-01 -1.21209192e+00 8.16421747e-01 3.28636467e-01
-1.48133725e-01 4.54600453e-01 9.74516571e-02 -6.23580694e-01
-6.28422379e-01 -8.56705546e-01 -1.39898106e-01 -1.76173031e-01
6.03276312e-01 4.88346927e-02 1.42778590e-01 1.81834940... | [9.72999095916748, 8.370230674743652] |
fe0f46de-882d-4c3a-9a6c-4534ba5cbed7 | an-integrated-semantic-web-service-discovery | 1502.02840 | null | http://arxiv.org/abs/1502.02840v1 | http://arxiv.org/pdf/1502.02840v1.pdf | An Integrated Semantic Web Service Discovery and Composition Framework | In this paper we present a theoretical analysis of graph-based service
composition in terms of its dependency with service discovery. Driven by this
analysis we define a composition framework by means of integration with
fine-grained I/O service discovery that enables the generation of a graph-based
composition which c... | ['Pablo Rodriguez-Mier', 'Manuel Mucientes', 'Manuel Lama', 'Carlos Pedrinaci'] | 2015-02-10 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 6.69236630e-02 1.86420694e-01 1.40977979e-01 -4.07556891e-01
-1.35743082e-01 -6.75856769e-01 7.00834572e-01 2.89043576e-01
8.07134956e-02 1.64475635e-01 2.49673799e-01 -4.55954134e-01
-8.26606452e-01 -1.07726502e+00 -2.44067818e-01 -6.68714345e-01
-3.52108181e-01 7.20806658e-01 6.35391116e-01 -5.37720263... | [8.628315925598145, 6.960301876068115] |
9a55348c-11f7-4dd4-8d8d-15f9fa0714be | leveraging-explicit-lexico-logical-alignments | null | null | https://aclanthology.org/2022.acl-short.31 | https://aclanthology.org/2022.acl-short.31.pdf | Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing | Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases. Previous work has observed that leveraging lexico-logical alignments is very helpful to improve parsing performance. However, current attention-based approaches can only mod... | ['Kang Liu', 'Jun Zhao', 'Jinlong Li', 'Yaohan He', 'Chong Zhu', 'Shizhu He', 'Runxin Sun'] | null | null | null | null | acl-2022-5 | ['text-to-sql'] | ['computer-code'] | [-1.45244792e-01 5.48591204e-02 -4.84930426e-01 -6.96515381e-01
-1.18323648e+00 -6.22147560e-01 2.65344918e-01 5.92533410e-01
-3.60721648e-01 5.70857942e-01 2.84979433e-01 -1.01151288e+00
1.75875127e-01 -1.21932459e+00 -7.82188535e-01 1.41702339e-01
1.09801516e-01 7.53654480e-01 6.63182676e-01 -3.98420751... | [9.95201587677002, 7.780074596405029] |
da00adea-1b2c-4f62-bb85-ed57c73152e8 | towards-improved-room-impulse-response | 2211.04473 | null | https://arxiv.org/abs/2211.04473v2 | https://arxiv.org/pdf/2211.04473v2.pdf | Towards Improved Room Impulse Response Estimation for Speech Recognition | We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (ASR). We first draw the connection between improved RIR estimation and improved ASR performance, as a means of evaluating neural RIR estimator... | ['Paul Calamia', 'Dinesh Manocha', 'Pablo Hoffmann', 'Vamsi Krishna Ithapu', 'Ishwarya Ananthabhotla', 'Anton Ratnarajah'] | 2022-11-08 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 3.39620024e-01 -2.53621936e-01 6.47913992e-01 -2.45324045e-01
-1.95731282e+00 -4.83085185e-01 5.56609869e-01 -4.77847457e-01
-2.69336611e-01 4.65588301e-01 9.93343711e-01 -4.50505883e-01
1.87448263e-02 -3.13340187e-01 -7.45188475e-01 -8.70318711e-01
-1.93366840e-01 -2.39195704e-01 -3.93846393e-01 -2.86265492... | [15.093616485595703, 5.939564228057861] |
3e5e62ef-d224-4fa5-855f-3f05af6864cf | learning-warped-guidance-for-blind-face | 1804.04829 | null | http://arxiv.org/abs/1804.04829v2 | http://arxiv.org/pdf/1804.04829v2.pdf | Learning Warped Guidance for Blind Face Restoration | This paper studies the problem of blind face restoration from an
unconstrained blurry, noisy, low-resolution, or compressed image (i.e.,
degraded observation). For better recovery of fine facial details, we modify
the problem setting by taking both the degraded observation and a high-quality
guided image of the same id... | ['WangMeng Zuo', 'Yuting Ye', 'Ruigang Yang', 'Liang Lin', 'Xiaoming Li', 'Ming Liu'] | 2018-04-13 | learning-warped-guidance-for-blind-face-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaoming_Li_Learning_Warped_Guidance_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaoming_Li_Learning_Warped_Guidance_ECCV_2018_paper.pdf | eccv-2018-9 | ['blind-face-restoration'] | ['computer-vision'] | [ 3.53574872e-01 -9.05720964e-02 1.06015347e-01 -2.24762723e-01
-3.05740297e-01 -3.29636574e-01 4.58768189e-01 -9.19093430e-01
-9.37835574e-02 8.23132575e-01 4.69649673e-01 -1.66912064e-01
2.18948741e-02 -7.93348253e-01 -7.51671314e-01 -9.54787791e-01
3.42869371e-01 -1.90863132e-01 -2.89174557e-01 -1.95799708... | [12.793498039245605, -0.03583632782101631] |
5ef737b3-701f-4e00-9dce-196a299c49e6 | neural-factor-graph-models-for-cross-lingual | 1805.04570 | null | http://arxiv.org/abs/1805.04570v3 | http://arxiv.org/pdf/1805.04570v3.pdf | Neural Factor Graph Models for Cross-lingual Morphological Tagging | Morphological analysis involves predicting the syntactic traits of a word
(e.g. {POS: Noun, Case: Acc, Gender: Fem}). Previous work in morphological
tagging improves performance for low-resource languages (LRLs) through
cross-lingual training with a high-resource language (HRL) from the same
family, but is limited by t... | ['Matthew R. Gormley', 'Graham Neubig', 'Chaitanya Malaviya'] | 2018-05-11 | neural-factor-graph-models-for-cross-lingual-1 | https://aclanthology.org/P18-1247 | https://aclanthology.org/P18-1247.pdf | acl-2018-7 | ['morphological-tagging'] | ['natural-language-processing'] | [ 6.94710156e-03 2.38024756e-01 -3.35813522e-01 -6.05426490e-01
-8.07388783e-01 -9.53861356e-01 1.81606740e-01 3.82447809e-01
-6.24091268e-01 9.23569739e-01 3.77700001e-01 -6.09087348e-01
1.82800099e-01 -7.13807821e-01 -5.26353896e-01 -4.24585342e-01
-2.01082170e-01 5.46903908e-01 7.86008760e-02 -3.56932655... | [10.434612274169922, 10.02780818939209] |
1b2ef3a5-249d-4ff1-8cce-4523508b77cd | gan-q-learning | 1805.04874 | null | http://arxiv.org/abs/1805.04874v3 | http://arxiv.org/pdf/1805.04874v3.pdf | GAN Q-learning | Distributional reinforcement learning (distributional RL) has seen empirical
success in complex Markov Decision Processes (MDPs) in the setting of nonlinear
function approximation. However, there are many different ways in which one can
leverage the distributional approach to reinforcement learning. In this paper,
we p... | ['Thang Doan', 'Clare Lyle', 'Bogdan Mazoure'] | 2018-05-13 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.51177347e-01 2.63186604e-01 -2.27667972e-01 -1.64043397e-01
-1.11265600e+00 -8.06828260e-01 8.16426396e-01 -3.70226622e-01
-3.65146816e-01 1.25415349e+00 3.73914570e-01 -7.42275476e-01
2.32496276e-03 -9.94272053e-01 -5.63242018e-01 -8.36449265e-01
8.82568806e-02 7.49962926e-01 -2.90591300e-01 -2.58611530... | [4.0774617195129395, 2.564365863800049] |
57ee820c-5f72-4ab2-a431-3a95bee53acc | self-supervised-few-shot-learning-on-point | 2009.14168 | null | https://arxiv.org/abs/2009.14168v1 | https://arxiv.org/pdf/2009.14168v1.pdf | Self-Supervised Few-Shot Learning on Point Clouds | The increased availability of massive point clouds coupled with their utility in a wide variety of applications such as robotics, shape synthesis, and self-driving cars has attracted increased attention from both industry and academia. Recently, deep neural networks operating on labeled point clouds have shown promisin... | ['Charu Sharma', 'Manohar Kaul'] | 2020-09-29 | null | http://proceedings.neurips.cc/paper/2020/hash/50c1f44e426560f3f2cdcb3e19e39903-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/50c1f44e426560f3f2cdcb3e19e39903-Paper.pdf | neurips-2020-12 | ['few-shot-3d-point-cloud-classification'] | ['computer-vision'] | [ 1.07191376e-01 1.63353652e-01 -2.69274414e-01 -5.50138772e-01
-7.73911297e-01 -4.95736659e-01 5.97251296e-01 3.33636671e-01
-3.56931239e-01 2.95075893e-01 -2.65655190e-01 -2.52710611e-01
1.01186074e-01 -9.45982814e-01 -9.34335291e-01 -5.84108949e-01
-2.62322351e-02 8.67402315e-01 5.77405691e-01 -1.09992102... | [8.022809028625488, -3.234128713607788] |
b1e21df8-2c81-4120-9e2e-835cfcedf626 | an-empirical-evaluation-of-doc2vec-with | 1607.05368 | null | http://arxiv.org/abs/1607.05368v1 | http://arxiv.org/pdf/1607.05368v1.pdf | An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation | Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec
(Mikolov et al., 2013a) to learn document-level embeddings. Despite promising
results in the original paper, others have struggled to reproduce those
results. This paper presents a rigorous empirical evaluation of doc2vec over
two tasks. We co... | ['Timothy Baldwin', 'Jey Han Lau'] | 2016-07-19 | an-empirical-evaluation-of-doc2vec-with-1 | https://aclanthology.org/W16-1609 | https://aclanthology.org/W16-1609.pdf | ws-2016-8 | ['document-embedding'] | ['methodology'] | [-4.88084704e-01 -1.14165649e-01 -3.63653094e-01 -2.26399198e-01
-8.08858812e-01 -8.30698669e-01 1.13213825e+00 3.37647259e-01
-7.36906111e-01 3.57067943e-01 8.20734978e-01 -6.40114188e-01
6.93964809e-02 -6.32226586e-01 -3.43280844e-02 -2.73092598e-01
-1.97591946e-01 3.52241993e-01 6.85717957e-03 -2.73775637... | [10.490885734558105, 8.568870544433594] |
390c0165-6d5e-4172-941f-9e416a623b57 | a-vector-quantized-masked-autoencoder-for | 2304.11117 | null | https://arxiv.org/abs/2304.11117v1 | https://arxiv.org/pdf/2304.11117v1.pdf | A vector quantized masked autoencoder for speech emotion recognition | Recent years have seen remarkable progress in speech emotion recognition (SER), thanks to advances in deep learning techniques. However, the limited availability of labeled data remains a significant challenge in the field. Self-supervised learning has recently emerged as a promising solution to address this challenge.... | ['Renaud Séguier', 'Simon Leglaive', 'Samir Sadok'] | 2023-04-21 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.72916681e-01 3.60901356e-01 1.63509861e-01 -4.27919567e-01
-7.28474975e-01 -1.03520021e-01 4.20919329e-01 -7.30497167e-02
-1.65241361e-01 4.42976207e-01 4.21134561e-01 1.21913143e-01
3.46490055e-01 -4.55711901e-01 -5.95011830e-01 -7.59465456e-01
-3.16997059e-02 1.68940261e-01 -2.08195284e-01 -4.05680597... | [14.005913734436035, 5.66741418838501] |
d35683a2-4153-42d8-bf66-7b1e18de228e | automatic-feature-learning-for-robust-shadow | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Khan_Automatic_Feature_Learning_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Khan_Automatic_Feature_Learning_2014_CVPR_paper.pdf | Automatic Feature Learning for Robust Shadow Detection | We present a practical framework to automatically detect shadows in real world scenes from a single photograph. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervis... | ['Salman Hameed Khan', 'Mohammed Bennamoun', 'Ferdous Sohel', 'Roberto Togneri'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['shadow-detection'] | ['computer-vision'] | [ 7.71050274e-01 5.82405962e-02 1.68579161e-01 -8.46912086e-01
-4.70770478e-01 -1.59966275e-01 5.37584245e-01 -5.25399983e-01
-2.83906996e-01 8.52964044e-01 1.35953188e-01 -2.13696137e-01
2.23890066e-01 -8.86306226e-01 -7.17214823e-01 -8.18646729e-01
9.45941918e-03 2.28154585e-01 8.84166837e-01 1.52153587... | [10.851943016052246, -4.117950916290283] |
1fe1c481-c468-4e70-9c24-42655490ec59 | simultaneous-translation-and-paraphrase-for | null | null | https://aclanthology.org/2020.ngt-1.28 | https://aclanthology.org/2020.ngt-1.28.pdf | Simultaneous Translation and Paraphrase for Language Education | We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of a... | ['Burr Settles', 'Will Monroe', 'Bill McDowell', 'Klinton Bicknell', 'Stephen Mayhew', 'Chris Brust'] | 2020-07-01 | null | null | null | ws-2020-7 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 3.65774870e-01 -7.06544593e-02 -1.37118727e-01 -3.57303560e-01
-1.61019099e+00 -1.05791414e+00 6.10405564e-01 1.62864700e-01
-6.65179551e-01 9.93028283e-01 4.87407774e-01 -9.08245265e-01
2.52425224e-01 -3.87088537e-01 -9.53270137e-01 2.27433905e-01
7.51455307e-01 9.78683531e-01 8.95491149e-03 -7.82525361... | [11.618091583251953, 10.29741096496582] |
1e63d14f-8a32-4314-83c0-bac87f0cb174 | applications-of-deep-learning-for-top-view | 2304.08193 | null | https://arxiv.org/abs/2304.08193v1 | https://arxiv.org/pdf/2304.08193v1.pdf | Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey | A large field-of-view fisheye camera allows for capturing a large area with minimal numbers of cameras when they are mounted on a high position facing downwards. This top-view omnidirectional setup greatly reduces the work and cost for deployment compared to traditional solutions with multiple perspective cameras. In r... | ['Gangolf Hirtz', 'Ana Cecilia Perez Grassi', 'Jingrui Yu'] | 2023-04-17 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.15784431e-01 -5.98847985e-01 -4.88184243e-02 -2.62492567e-01
1.68441057e-01 -6.14880264e-01 6.83750451e-01 -7.10104406e-01
-7.80651271e-01 4.86882925e-01 2.02577665e-01 -2.98804464e-03
1.04863137e-01 -4.32536572e-01 -6.51628733e-01 -6.72302961e-01
2.03461587e-01 3.30973417e-01 8.30031559e-02 1.76714987... | [7.272392749786377, -1.0471357107162476] |
4cb5520f-cb82-4c12-9599-c42d168233ad | moglow-probabilistic-and-controllable-motion | 1905.06598 | null | https://arxiv.org/abs/1905.06598v3 | https://arxiv.org/pdf/1905.06598v3.pdf | MoGlow: Probabilistic and controllable motion synthesis using normalising flows | Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics. This paper introduces a new class of probabilistic, generative, and controllable motion-data models based on normalising flows. Models of this kind can describe highly complex d... | ['Gustav Eje Henter', 'Simon Alexanderson', 'Jonas Beskow'] | 2019-05-16 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 1.60744309e-01 1.69151947e-01 -1.67285204e-01 -6.75658435e-02
-3.23123217e-01 -5.92522025e-01 9.65566576e-01 -4.74262595e-01
-4.16128069e-01 9.25065279e-01 3.89577150e-01 9.69767496e-02
4.51330096e-02 -9.17645156e-01 -7.57141411e-01 -9.32057977e-01
-2.65841752e-01 6.69756353e-01 5.14732122e-01 -3.40600222... | [7.29831075668335, -0.16431424021720886] |
1866ea6c-fb5c-4815-9687-4f34d560bc4d | generalized-time-warping-invariant-dictionary | 2306.17690 | null | https://arxiv.org/abs/2306.17690v1 | https://arxiv.org/pdf/2306.17690v1.pdf | Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering | Dictionary learning is an effective tool for pattern recognition and classification of time series data. Among various dictionary learning techniques, the dynamic time warping (DTW) is commonly used for dealing with temporal delays, scaling, transformation, and many other kinds of temporal misalignments issues. However... | ['Jianguo Wu', 'Yongxiang Li', 'Chao Wang', 'Ruiyu Xu'] | 2023-06-30 | null | null | null | null | ['clustering', 'dictionary-learning', 'time-series-classification', 'dynamic-time-warping'] | ['methodology', 'methodology', 'time-series', 'time-series'] | [ 1.76604763e-01 -8.90419662e-01 -1.55959010e-01 -1.07771322e-01
-3.89813215e-01 -5.23603499e-01 4.93966073e-01 2.99099535e-01
-4.79633033e-01 4.49983031e-01 3.38528991e-01 -1.06674068e-01
-5.87189376e-01 -5.12118995e-01 -3.29734683e-02 -1.11684906e+00
-8.13819394e-02 2.27876112e-01 3.13535356e-03 -2.99811214... | [7.4920830726623535, 3.285782814025879] |
145e6cc9-f9ed-4e9b-a78c-46d9b4a6561c | confident-head-circumference-measurement-from | 1908.02582 | null | https://arxiv.org/abs/1908.02582v1 | https://arxiv.org/pdf/1908.02582v1.pdf | Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers | Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose... | ['Alberto Gomez', 'Matthew Sinclair', 'Emma Robinson', 'David Lloyd', 'Nicolas Toussaint', 'Jacqueline Matthew', 'Bernhard Kainz', 'Samuel Budd', 'Bishesh Khanal'] | 2019-08-07 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.05335638e-01 9.17926729e-01 2.88471967e-01 -7.68985331e-01
-9.76733088e-01 -6.47983253e-01 -1.51991069e-01 3.15012366e-01
-2.60181516e-01 3.54680717e-01 -3.24102044e-01 -4.29504871e-01
-3.06091666e-01 -6.91296220e-01 -1.08780968e+00 -6.91470861e-01
-2.75348455e-01 6.89102352e-01 7.71312267e-02 5.81649125... | [14.2352294921875, -2.4078328609466553] |
9864dd77-892a-48b4-be51-41e9bc7f82d0 | ensemble-diffusion-for-retrieval | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Bai_Ensemble_Diffusion_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Bai_Ensemble_Diffusion_for_ICCV_2017_paper.pdf | Ensemble Diffusion for Retrieval | As a postprocessing procedure, diffusion process has demonstrated its ability of substantially improving the performance of various visual retrieval systems. Whereas, great efforts are also devoted to similarity (or metric) fusion, seeing that only one individual type of similarity cannot fully reveal the intrinsic rel... | ['Zhichao Zhou', 'Qi Tian', 'Song Bai', 'Longin Jan Latecki', 'Jingdong Wang', 'Xiang Bai'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 9.99491438e-02 -7.74425924e-01 -1.91821344e-02 -2.63168633e-01
-7.12603748e-01 -4.32298511e-01 1.10020316e+00 2.90855825e-01
-3.95315468e-01 1.76255807e-01 2.46790081e-01 1.85861111e-01
-5.63794911e-01 -6.12865746e-01 -4.88902107e-02 -1.09608579e+00
7.17614070e-02 1.02002241e-01 2.63970912e-01 -3.82552594... | [10.994397163391113, 0.9314542412757874] |
d35d7bbb-90ff-486c-b7df-96ec0d5af7cc | sunet-scale-aware-unified-network-for | 2209.02877 | null | https://arxiv.org/abs/2209.02877v1 | https://arxiv.org/pdf/2209.02877v1.pdf | SUNet: Scale-aware Unified Network for Panoptic Segmentation | Panoptic segmentation combines the advantages of semantic and instance segmentation, which can provide both pixel-level and instance-level environmental perception information for intelligent vehicles. However, it is challenged with segmenting objects of various scales, especially on extremely large and small ones. In ... | ['Ming Yang', 'Chunxiang Wang', 'Yeqiang Qian', 'Weihao Yan'] | 2022-09-07 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.79493237e-02 -2.12774351e-01 -1.42393291e-01 -6.80701852e-01
-3.96558702e-01 -4.99684423e-01 2.90010393e-01 1.20452158e-02
-3.04345757e-01 4.16420192e-01 -5.68375960e-02 -2.48123016e-02
-9.13006365e-02 -1.36082637e+00 -3.25311929e-01 -6.58459663e-01
1.25959143e-01 3.30046445e-01 1.07803118e+00 -2.54532158... | [9.511253356933594, -0.35330837965011597] |
533e0ac6-29c9-4b08-bcc3-041cc07f09aa | parameter-efficient-fine-tuning-with-layer | 2305.08285 | null | https://arxiv.org/abs/2305.08285v3 | https://arxiv.org/pdf/2305.08285v3.pdf | Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling | The increasing size of language models raises great research interests in parameter-efficient fine-tuning such as LoRA that freezes the pre-trained model, and injects small-scale trainable parameters for multiple downstream tasks (e.g., summarization, question answering and translation). To further enhance the efficien... | ['Wensheng Zhang', 'Yuanyuan Wu', 'Xuebing Yang', 'Yunqi Zhu'] | 2023-05-15 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 4.56885844e-01 5.57607174e-01 -2.64761806e-01 -4.66262281e-01
-1.39352727e+00 -6.82269990e-01 2.82207251e-01 5.85058570e-01
-6.26488566e-01 9.48609948e-01 7.10875630e-01 -6.00947499e-01
1.98667213e-01 -6.12129629e-01 -7.48258173e-01 -2.42751911e-01
1.50044233e-01 6.12018168e-01 1.10970937e-01 -7.60608315... | [12.058844566345215, 9.231460571289062] |
b6d4ae24-6fad-4c22-b0a4-a86e93cf4117 | pricing-european-options-with-google-automl | 2307.00476 | null | https://arxiv.org/abs/2307.00476v1 | https://arxiv.org/pdf/2307.00476v1.pdf | Pricing European Options with Google AutoML, TensorFlow, and XGBoost | Researchers have been using Neural Networks and other related machine-learning techniques to price options since the early 1990s. After three decades of improvements in machine learning techniques, computational processing power, cloud computing, and data availability, this paper is able to provide a comparison of usin... | ['Juan Esteban Berger'] | 2023-07-02 | null | null | null | null | ['automl'] | ['methodology'] | [-7.59239376e-01 -7.10558593e-01 -4.55721110e-01 -4.96595651e-01
-1.25869408e-01 -4.48394507e-01 6.73459947e-01 -1.41698495e-01
-5.37900388e-01 8.65341246e-01 -3.09819996e-01 -1.43727624e+00
-4.01005387e-01 -1.28962672e+00 -1.10602453e-01 -1.78968415e-01
-4.30286288e-01 6.02027833e-01 -1.79963559e-01 -3.45452011... | [4.4819440841674805, 4.222935199737549] |
2e801adb-72ca-4731-b213-5d503b51de39 | understanding-humans-in-crowded-scenes-deep | 1804.03287 | null | http://arxiv.org/abs/1804.03287v3 | http://arxiv.org/pdf/1804.03287v3.pdf | Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing | Despite the noticeable progress in perceptual tasks like detection, instance
segmentation and human parsing, computers still perform unsatisfactorily on
visually understanding humans in crowded scenes, such as group behavior
analysis, person re-identification and autonomous driving, etc. To this end,
models need to com... | ['Shuicheng Yan', 'Jianshu Li', 'Li Zhou', 'Jian Zhao', 'Yu Cheng', 'Terence Sim', 'Jiashi Feng'] | 2018-04-10 | null | null | null | null | ['multi-human-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 2.67357409e-01 2.93759368e-02 4.74351257e-01 -7.09379673e-01
-7.10602283e-01 -2.66604185e-01 3.16583812e-01 -1.22438654e-01
-5.40615559e-01 5.52982211e-01 1.05131559e-01 2.34318182e-01
4.39520478e-01 -5.83031416e-01 -9.33784485e-01 -4.34906721e-01
1.12186030e-01 8.69261742e-01 6.77146494e-01 -2.74786949... | [8.41385555267334, -0.17422939836978912] |
ceeb4bc9-8e59-4959-99fb-f847bf065a6a | novel-and-fast-algorithm-for-extracting | 1407.6496 | null | http://arxiv.org/abs/1407.6496v2 | http://arxiv.org/pdf/1407.6496v2.pdf | Novel and Fast Algorithm for Extracting License Plate Location Based on Edge Analysis | Nowadays in developing or developed countries, the Intelligent Transportation
System (ITS) technology has attracted so much attention to itself. License
Plate Recognition (LPR) systems have many applications in ITSs, such as the
payment of parking fee, controlling the traffic volume, traffic data
collection, etc. This ... | ['Reza Azad', 'Mohammad Baghdadi'] | 2014-07-24 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-1.42405629e-01 -8.97858858e-01 4.62593976e-03 -8.89712665e-03
-4.29820448e-01 -5.65824568e-01 3.24023038e-01 -3.94325703e-01
-5.43107092e-01 6.09972060e-01 -2.13901520e-01 -6.16111994e-01
9.85959396e-02 -9.31587458e-01 -1.86465591e-01 -5.07358491e-01
5.63401341e-01 2.76451558e-01 7.76649296e-01 -1.27347335... | [9.798116683959961, -4.993718147277832] |
5b490c77-7685-4164-a960-50649b7971cf | computer-aided-interpretable-features-for | 2106.08077 | null | https://arxiv.org/abs/2106.08077v3 | https://arxiv.org/pdf/2106.08077v3.pdf | Computer-aided Interpretable Features for Leaf Image Classification | Plant species identification is time consuming, costly, and requires lots of efforts, and expertise knowledge. In recent, many researchers use deep learning methods to classify plants directly using plant images. While deep learning models have achieved a great success, the lack of interpretability limit their widespre... | ['Thiyanga S. Talagala', 'Jayani P. G. Lakshika'] | 2021-06-15 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 3.29090387e-01 -4.46006060e-01 -2.39397243e-01 -2.67051160e-01
-7.36070424e-02 -1.00915706e+00 3.34593594e-01 3.38684827e-01
-7.99950659e-02 4.60190862e-01 -3.83436978e-01 -5.13942301e-01
-5.74786842e-01 -1.04228687e+00 4.83802594e-02 -1.05586493e+00
-1.04393408e-01 9.29729640e-02 1.84758827e-01 7.77998641... | [9.167213439941406, -1.5549505949020386] |
79944a1d-de27-4f2b-92fa-0b11bc82fd3a | twenty-five-years-of-advances-in-beamforming | 2211.02165 | null | https://arxiv.org/abs/2211.02165v3 | https://arxiv.org/pdf/2211.02165v3.pdf | Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques | Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in mul... | ['Robert W. Heath Jr', 'Sergiy A. Vorobyov', 'Kumar Vijay Mishra', 'Ahmet M. Elbir'] | 2022-11-03 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 3.58844131e-01 -4.71494406e-01 3.35163385e-01 -3.57033074e-01
-7.09103167e-01 -4.87250984e-01 2.32439965e-01 -3.77425045e-01
-1.09186739e-01 6.49769545e-01 7.31090009e-01 -3.85486543e-01
-6.38313830e-01 -4.28260416e-01 -1.39535651e-01 -1.17778313e+00
-6.72816694e-01 -1.09139897e-01 -4.03454959e-01 -1.32379308... | [6.489408016204834, 1.340433955192566] |
9c555204-f079-46d4-9e8e-7b0b7197b8bb | ai-ml-nit-patna-trac-2-deep-learning-approach | null | null | https://aclanthology.org/2020.trac-1.18 | https://aclanthology.org/2020.trac-1.18.pdf | AI\_ML\_NIT\_Patna @ TRAC - 2: Deep Learning Approach for Multi-lingual Aggression Identification | This paper describes the details of developed models and results of team AI{\_}ML{\_}NIT{\_}Patna for the shared task of TRAC - 2. The main objective of the said task is to identify the level of aggression and whether the comment is gendered based or not. The aggression level of each comment can be marked as either Ove... | ['Kirti Kumari', 'Jyoti Prakash Singh'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['aggression-identification'] | ['natural-language-processing'] | [-5.96931040e-01 1.25677273e-01 2.42452845e-01 -5.34504950e-01
-4.53966022e-01 -1.61135003e-01 7.69954026e-01 2.39517227e-01
-1.01497030e+00 6.60892069e-01 3.86638612e-01 -1.57743514e-01
-3.52402478e-01 -5.80491304e-01 -8.79193619e-02 -6.15306318e-01
-1.10420994e-01 9.17140901e-01 1.42119627e-03 -6.35438979... | [8.810683250427246, 10.756573677062988] |
b263c0ba-0e2b-4dd6-ba9f-3ec438b4decf | antm-an-aligned-neural-topic-model-for | 2302.01501 | null | https://arxiv.org/abs/2302.01501v2 | https://arxiv.org/pdf/2302.01501v2.pdf | ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics | This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM maintains the temporal continuity of evolving topics by extracting time-aware features from documen... | ['Bernd Amann', 'Camelia Constantin', 'Hubert Naacke', 'Hamed Rahimi'] | 2023-02-03 | null | null | null | null | ['topic-models', 'dynamic-topic-modeling'] | ['natural-language-processing', 'natural-language-processing'] | [-2.55382061e-01 -4.63617817e-02 -5.26949704e-01 -3.82029802e-01
-5.61920524e-01 -6.38470888e-01 1.20069873e+00 6.96791768e-01
-1.80360302e-02 2.93795586e-01 3.80491734e-01 -1.66360825e-01
-4.96859312e-01 -1.01274407e+00 -1.50950521e-01 -6.06194794e-01
-8.86857986e-01 8.86949956e-01 7.36039340e-01 1.25643089... | [10.351737022399902, 7.041601181030273] |
8fa37918-3946-489b-b0d1-c1abfe4391cf | clean-text-and-full-body-transformer | 2210.13326 | null | https://arxiv.org/abs/2210.13326v1 | https://arxiv.org/pdf/2210.13326v1.pdf | Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation | This paper describes Microsoft's submission to the first shared task on sign language translation at WMT 2022, a public competition tackling sign language to spoken language translation for Swiss German sign language. The task is very challenging due to data scarcity and an unprecedented vocabulary size of more than 20... | ['Oscar Koller', 'Cyrine Chaabani', 'Abhilash Pal', 'Subhadeep Dey'] | 2022-10-24 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 2.48496026e-01 4.48630929e-01 -2.38761798e-01 -2.34355688e-01
-1.56518054e+00 -4.90934610e-01 8.62008035e-01 -6.49902046e-01
-7.31605709e-01 7.77353287e-01 7.32468367e-01 1.15381800e-01
3.73964727e-01 2.86007166e-01 -7.18165934e-01 -7.05083668e-01
1.77240655e-01 5.21013916e-01 3.14594299e-01 -1.15834810... | [9.2039213180542, -6.532049655914307] |
399ee552-fb85-49e8-a338-097e8c5a54a3 | minimalistic-unsupervised-learning-with-the | 2209.15261 | null | https://arxiv.org/abs/2209.15261v2 | https://arxiv.org/pdf/2209.15261v2.pdf | Minimalistic Unsupervised Learning with the Sparse Manifold Transform | We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that achieves performance close to the SOTA SSL methods. Our approach leverages the sparse manifold transform, which unifies sparse coding, manifold... | ['Yann Lecun', 'Bruno Olshausen', 'Yi Ma', 'Zeyu Yun', 'Yubei Chen'] | 2022-09-30 | null | null | null | null | ['sparse-representation-based-classification', 'unsupervised-image-classification', 'spectral-graph-clustering', 'unsupervised-mnist'] | ['computer-vision', 'computer-vision', 'graphs', 'methodology'] | [-1.04534790e-01 4.53745931e-01 -3.35708201e-01 -2.53882170e-01
-6.35916531e-01 -3.43831867e-01 7.12733388e-01 -9.47570354e-02
-2.53245860e-01 5.51138520e-01 3.43204290e-01 -3.63890886e-01
-3.38346422e-01 -6.50050640e-01 -6.05037332e-01 -8.48924220e-01
-2.16403261e-01 3.85657102e-01 -1.90954208e-01 -7.09868148... | [9.102083206176758, 3.0067641735076904] |
af65dc06-c6fc-464a-a65f-1610b88e8f50 | composing-structure-aware-batches-for | null | null | https://openreview.net/forum?id=DKVhbjXs9aG | https://openreview.net/pdf?id=DKVhbjXs9aG | Composing Structure-Aware Batches for Pairwise Sentence Classification | Identifying the relation between two sentences requires datasets with pairwise annotations. In many cases, these datasets contain instances that are annotated multiple times as part of different pairs. They constitute a structure that contains additional helpful information about the inter-relatedness of the text insta... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 3.26423734e-01 3.49932700e-01 -6.83325380e-02 -8.94271076e-01
-1.18205142e+00 -9.40177381e-01 7.27786839e-01 8.85462821e-01
-6.54971361e-01 8.28508019e-01 7.72288978e-01 -1.75825655e-01
5.99609576e-02 -3.02681893e-01 -7.14008272e-01 -6.83912396e-01
-1.32011101e-01 5.99632382e-01 1.93536937e-01 -3.87894034... | [9.613173484802246, 8.893842697143555] |
eb5443ac-0bdd-4e82-8b65-7dc64ac2fdb0 | liquid-state-machine-empowered-reflection | 2208.04400 | null | https://arxiv.org/abs/2208.04400v1 | https://arxiv.org/pdf/2208.04400v1.pdf | Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications | Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction ... | ['Ekram Hossain', 'Hina Tabassum', 'Mehdi Rasti', 'Mohamad Robat Mili', 'Narges Gholipoor', 'Hosein Zarini'] | 2022-08-08 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 6.31502032e-01 2.71478415e-01 2.55094916e-01 -7.73392096e-02
-7.90852427e-01 -1.94290981e-01 5.11651993e-01 2.19953246e-03
-2.22091079e-01 7.28926897e-01 1.56367287e-01 -5.08898377e-01
-5.59304476e-01 -1.03954065e+00 -6.71891928e-01 -1.25380003e+00
-1.38719277e-02 1.35170966e-01 1.10982224e-01 -3.28781068... | [6.094210147857666, 1.4617098569869995] |
0b637763-d13e-4b4c-9ee5-360ccd3349d5 | joint-face-detection-and-facial-motion | 1902.10744 | null | http://arxiv.org/abs/1902.10744v1 | http://arxiv.org/pdf/1902.10744v1.pdf | Joint Face Detection and Facial Motion Retargeting for Multiple Faces | Facial motion retargeting is an important problem in both computer graphics
and vision, which involves capturing the performance of a human face and
transferring it to another 3D character. Learning 3D morphable model (3DMM)
parameters from 2D face images using convolutional neural networks is common in
2D face alignme... | ['Baoyuan Wang', 'Noranart Vesdapunt', 'Bindita Chaudhuri'] | 2019-02-27 | joint-face-detection-and-facial-motion-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Chaudhuri_Joint_Face_Detection_and_Facial_Motion_Retargeting_for_Multiple_Faces_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chaudhuri_Joint_Face_Detection_and_Facial_Motion_Retargeting_for_Multiple_Faces_CVPR_2019_paper.pdf | cvpr-2019-6 | ['motion-retargeting'] | ['computer-vision'] | [ 1.18415214e-01 8.63738284e-02 -8.98744091e-02 -4.92494375e-01
-5.04594564e-01 -4.84580666e-01 4.32837605e-01 -8.67287934e-01
-3.07066500e-01 1.72429264e-01 -5.02393991e-02 -1.20321047e-02
5.09311318e-01 -1.69669494e-01 -8.22045326e-01 -5.78705251e-01
1.23526782e-01 6.24958336e-01 -3.27431154e-03 2.57303454... | [13.262887001037598, 0.18482699990272522] |
816d14a5-f7b5-489e-b829-5e52a48e2b16 | metaadapt-domain-adaptive-few-shot | 2305.12692 | null | https://arxiv.org/abs/2305.12692v1 | https://arxiv.org/pdf/2305.12692v1.pdf | MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning | With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e., source domain) and such target domains remains a non-trivial task for misinformation detection. This presents an elusive challenge for earl... | ['Dong Wang', 'Lanyu Shang', 'Yang Zhang', 'Huimin Zeng', 'Zhenrui Yue'] | 2023-05-22 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 1.54055834e-01 -1.53798729e-01 -5.06999075e-01 -2.50568420e-01
-1.02297151e+00 -4.44879800e-01 1.00744236e+00 1.81128681e-01
-5.22065222e-01 6.48552775e-01 4.93738383e-01 2.97759417e-02
1.06082276e-01 -6.13075197e-01 -5.16269922e-01 -4.72093284e-01
1.04986981e-01 6.44489408e-01 4.54171658e-01 -4.52800959... | [10.2212553024292, 3.655172824859619] |
45dab76e-b7ff-4283-b374-a74c2c503aa1 | fine-grained-propaganda-detection-with-fine | null | null | https://aclanthology.org/D19-5011 | https://aclanthology.org/D19-5011.pdf | Fine-Grained Propaganda Detection with Fine-Tuned BERT | This paper presents the winning solution of the Fragment Level Classification (FLC) task in the Fine Grained Propaganda Detection competition at the NLP4IF{'}19 workshop. The goal of the FLC task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles d... | ['Yin Yang', 'Shehel Yoosuf'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 3.01791430e-01 2.12105975e-01 -8.12348902e-01 -2.10849434e-01
-1.13470268e+00 -7.53265202e-01 1.23453724e+00 4.99824643e-01
-5.97331405e-01 4.37363058e-01 1.06386721e+00 -7.42700100e-01
-8.33244547e-02 -5.23926497e-01 -7.59370744e-01 -4.75104153e-01
2.01436765e-02 3.82949710e-01 6.17970265e-02 -3.52906168... | [8.475038528442383, 10.670292854309082] |
f45d9186-82d0-4ccf-84b0-6c1a3b0fc99a | skin-lesion-classification-using-class | 1703.01053 | null | http://arxiv.org/abs/1703.01053v1 | http://arxiv.org/pdf/1703.01053v1.pdf | Skin Lesion Classification using Class Activation Map | We proposed a two stage framework with only one network to analyze skin
lesion images, we firstly trained a convolutional network to classify these
images, and cropped the import regions which the network has the maximum
activation value. In the second stage, we retrained this CNN with the image
regions extracted from ... | ['Xi Jia', 'Linlin Shen'] | 2017-03-03 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.33651733e-01 4.08963710e-01 -5.35666049e-01 -2.17084035e-01
-7.12727249e-01 -4.57333058e-01 2.79897660e-01 1.40962765e-01
-8.93855214e-01 6.08049452e-01 -7.84220099e-02 -4.06504810e-01
-3.39575820e-02 -6.91799462e-01 -5.00115931e-01 -8.54566991e-01
-6.05847612e-02 -7.41068348e-02 5.57893813e-01 4.27388430... | [15.68127155303955, -2.9838926792144775] |
ae433c0d-3f57-4c46-9673-0f6d5493c53d | tog-targeted-adversarial-objectness-gradient | 2004.04320 | null | https://arxiv.org/abs/2004.04320v1 | https://arxiv.org/pdf/2004.04320v1.pdf | TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems | The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems. Real-time object recognition on the edge is one of the representative deep neural network (DNN) powered edge systems for real-world mission-critical applications, such as autonomous driving an... | ['Stacey Truex', 'Ka-Ho Chow', 'Mehmet Emre Gursoy', 'Ling Liu', 'Yanzhao Wu', 'Wenqi Wei'] | 2020-04-09 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [-5.11715673e-02 -7.66096590e-03 6.74325526e-02 -1.40726298e-01
-4.77605253e-01 -7.09962308e-01 5.45497000e-01 -3.65297586e-01
-7.04148769e-01 3.18300486e-01 -6.54169321e-01 -6.48801923e-01
2.54741549e-01 -7.40400136e-01 -1.03117764e+00 -5.26864290e-01
-3.62192720e-01 9.69751999e-02 6.69094920e-01 -3.20422262... | [5.439186096191406, 7.89467191696167] |
807fce78-b28b-48fc-ab47-6fd8d58125a8 | heterogeneous-graph-neural-networks-for-3 | 2109.04703 | null | https://arxiv.org/abs/2109.04703v1 | https://arxiv.org/pdf/2109.04703v1.pdf | Heterogeneous Graph Neural Networks for Keyphrase Generation | The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyp... | ['Qi Zhang', 'Tao Gui', 'Ruijian Cai', 'Jiacheng Ye'] | 2021-09-10 | null | https://aclanthology.org/2021.emnlp-main.213 | https://aclanthology.org/2021.emnlp-main.213.pdf | emnlp-2021-11 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.01788670e-01 5.18073253e-02 -4.66858625e-01 3.02620471e-01
-1.05289721e+00 -6.46591127e-01 9.34116900e-01 8.69062424e-01
-1.99157819e-01 8.51532996e-01 7.55001903e-01 -2.12140262e-01
2.98651066e-02 -8.97071064e-01 -1.03031218e+00 -3.84055912e-01
4.30496819e-02 3.47398221e-01 5.49512327e-01 -4.20909673... | [12.314680099487305, 8.888960838317871] |
fd8bc1b3-ebf5-4470-b3ba-7120512bf674 | efficient-document-image-classification-using | 2106.13802 | null | https://arxiv.org/abs/2106.13802v1 | https://arxiv.org/pdf/2106.13802v1.pdf | Efficient Document Image Classification Using Region-Based Graph Neural Network | Document image classification remains a popular research area because it can be commercialized in many enterprise applications across different industries. Recent advancements in large pre-trained computer vision and language models and graph neural networks has lent document image classification many tools. However us... | ['Glenn Fung', 'Qian You', 'Eric Bunch', 'Jaya Krishna Mandivarapu'] | 2021-06-25 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 1.67698503e-01 -4.03542846e-01 -6.48969859e-02 -3.51341397e-01
-3.27433944e-01 -6.79474950e-01 7.01977491e-01 3.40316087e-01
-2.59536058e-01 1.33828819e-01 -2.38349736e-01 -8.04242909e-01
-1.22002825e-01 -1.11288035e+00 -5.21821737e-01 -4.20285374e-01
1.70543179e-01 6.77048981e-01 -9.98376906e-02 1.07359238... | [11.458502769470215, 2.612680673599243] |
7b597170-c605-4b1a-8015-3f05e098950d | offline-rl-with-realistic-datasets | 2211.01052 | null | https://arxiv.org/abs/2211.01052v2 | https://arxiv.org/pdf/2211.01052v2.pdf | Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints | Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection. Practical applications of offline RL will inevitably require learning from datasets where the variability of demonstrated behaviors changes non-uniformly across the ... | ['Sergey Levine', 'Yevgen Chebotar', 'Quan Vuong', 'Aviral Kumar', 'Anikait Singh'] | 2022-11-02 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-3.37620169e-01 1.03294671e-01 -6.55339837e-01 -2.86134034e-01
-6.75522327e-01 -9.71403956e-01 3.07419151e-01 -1.51617909e-02
-8.46678972e-01 9.96519387e-01 1.33647630e-02 -6.27261400e-01
-4.33929026e-01 -7.27544963e-01 -9.67900157e-01 -6.71120346e-01
-2.66221583e-01 4.55647498e-01 2.49246284e-01 -3.07852477... | [4.138972759246826, 2.196963310241699] |
48d2f864-8d6a-4146-abb0-b25ecd64c206 | llms-as-factual-reasoners-insights-from | 2305.14540 | null | https://arxiv.org/abs/2305.14540v1 | https://arxiv.org/pdf/2305.14540v1.pdf | LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond | With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation and improve trust in model outputs. When testing on existing factual consistency benchmarks, we find that a few large language models (LLMs)... | ['Chien-Sheng Wu', 'Shafiq Joty', 'Caiming Xiong', 'Alexander R. Fabbri', 'Divyansh Agarwal', 'Wojciech Kryściński', 'Philippe Laban'] | 2023-05-23 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-2.37485781e-01 7.71294773e-01 -6.08002543e-01 -5.42555273e-01
-1.28781450e+00 -5.75247526e-01 9.28231180e-01 6.29866481e-01
-3.01298350e-01 1.07390904e+00 2.82698572e-01 -4.24442589e-01
-1.33545905e-01 -2.85693914e-01 -7.81462133e-01 5.88180497e-02
4.15956713e-02 6.12861276e-01 2.99928665e-01 -1.28192067... | [9.765311241149902, 8.327980995178223] |
0bf6b095-b4ab-4900-af32-7c69cc310e32 | synfeal-a-data-driven-simulator-for-end-to | 2305.18260 | null | https://arxiv.org/abs/2305.18260v1 | https://arxiv.org/pdf/2305.18260v1.pdf | Synfeal: A Data-Driven Simulator for End-to-End Camera Localization | Collecting real-world data is often considered the bottleneck of Artificial Intelligence, stalling the research progress in several fields, one of which is camera localization. End-to-end camera localization methods are still outperformed by traditional methods, and we argue that the inconsistencies associated with the... | ['Paulo Dias', 'Miguel Oliveira', 'Daniel Coelho'] | 2023-05-29 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.90713727e-01 -2.73044020e-01 2.74193566e-02 -3.05074453e-01
-8.97426486e-01 -9.77575779e-01 5.69427729e-01 -9.91969034e-02
-6.40448749e-01 4.75504816e-01 5.95728233e-02 -8.18140730e-02
2.17601471e-02 -6.65489972e-01 -1.10188723e+00 -5.47541797e-01
1.92130700e-01 6.79668665e-01 1.39985174e-01 6.16243705... | [7.58809757232666, -2.1733407974243164] |
e5b54665-8299-4a19-9776-8078b18bfaac | ecqed-emotion-cause-quadruple-extraction-in | 2306.03969 | null | https://arxiv.org/abs/2306.03969v2 | https://arxiv.org/pdf/2306.03969v2.pdf | ECQED: Emotion-Cause Quadruple Extraction in Dialogs | The existing emotion-cause pair extraction (ECPE) task, unfortunately, ignores extracting the emotion type and cause type, while these fine-grained meta-information can be practically useful in real-world applications, i.e., chat robots and empathic dialog generation. Also the current ECPE is limited to the scenario of... | ['Chong Teng', 'Bobo Li', 'Jingye Li', 'Shengqiong Wu', 'Hao Fei', 'Fei Li', 'Donghong Ji', 'Li Zheng'] | 2023-06-06 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-8.88087526e-02 3.22144657e-01 8.45860168e-02 -4.37955678e-01
-6.83003604e-01 -6.79900706e-01 7.96446621e-01 -1.74834561e-02
-5.81396222e-02 1.09713423e+00 6.33122623e-01 7.05392063e-02
-1.32024065e-01 -6.36939883e-01 -1.04009993e-01 -6.39416158e-01
1.45803258e-01 6.85302913e-01 -3.08240987e-02 -1.01110780... | [12.889557838439941, 6.580177307128906] |
6d9a2994-f410-4aa6-ac2a-b826e5858df9 | random-projection-tree-similarity-metric-for | 2302.13168 | null | https://arxiv.org/abs/2302.13168v1 | https://arxiv.org/pdf/2302.13168v1.pdf | Random projection tree similarity metric for SpectralNet | SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with $k$-nn graphs, which are usually constructed using a distance metric (e.g., Euclidean distance). $k$-nn graphs restrict the points to have a fixed number of neighbors regardless o... | ['Masahiro Takatsuka', 'Adel F. Ahmed', 'John Stavrakakis', 'Mashaan Alshammari'] | 2023-02-25 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering', 'graph-partitioning'] | ['graphs', 'graphs', 'graphs'] | [-2.70542920e-01 -4.79023084e-02 -8.98588225e-02 -2.78231561e-01
1.41887972e-02 -5.52989841e-01 9.16527361e-02 1.71155468e-01
-5.77796817e-01 2.19213337e-01 -5.53133897e-03 -3.08068782e-01
-7.68662512e-01 -1.20626509e+00 -1.94554999e-01 -7.32816219e-01
-5.04461884e-01 3.70841771e-01 2.78033167e-01 6.52125999... | [7.397940635681152, 4.857438087463379] |
ba863939-3d9f-4620-a0a5-1525b62849bb | mousai-text-to-music-generation-with-long | 2301.11757 | null | https://arxiv.org/abs/2301.11757v2 | https://arxiv.org/pdf/2301.11757v2.pdf | Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion | The recent surge in popularity of diffusion models for image generation has brought new attention to the potential of these models in other areas of media synthesis. One area that has yet to be fully explored is the application of diffusion models to music generation. Music generation requires to handle multiple aspect... | ['Bernhard Schölkopf', 'Zhijing Jin', 'Flavio Schneider'] | 2023-01-27 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [-7.49866590e-02 -1.92556635e-01 5.89621663e-02 8.28738138e-03
-1.22198558e+00 -6.63891256e-01 7.22758114e-01 -1.22611612e-01
-7.27941934e-03 4.65052545e-01 7.60944366e-01 -1.74545199e-01
4.51230183e-02 -7.34559774e-01 -4.97967511e-01 -4.99068677e-01
-2.33273014e-01 2.77037710e-01 1.28578991e-01 -7.94873685... | [15.622060775756836, 5.71360445022583] |
5fbee0cd-d00a-43cd-ae7a-b60bbc028b15 | s3i-pointhop-so-3-invariant-pointhop-for-3d | 2302.11506 | null | https://arxiv.org/abs/2302.11506v1 | https://arxiv.org/pdf/2302.11506v1.pdf | S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification | Many point cloud classification methods are developed under the assumption that all point clouds in the dataset are well aligned with the canonical axes so that the 3D Cartesian point coordinates can be employed to learn features. When input point clouds are not aligned, the classification performance drops significant... | ['C. -C. Jay Kuo', 'Shan Liu', 'Jintang Xue', 'Min Zhang', 'Hardik Prajapati', 'Pranav Kadam'] | 2023-02-22 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-2.83984333e-01 -2.66656756e-01 -1.51283056e-01 -2.66160190e-01
-5.13697147e-01 -7.24740684e-01 7.82479465e-01 3.79283041e-01
-1.53298885e-01 4.02842909e-01 -6.24843776e-01 -2.27964118e-01
-3.15422237e-01 -7.88394570e-01 -8.02848756e-01 -5.83051085e-01
-2.61433512e-01 7.39557743e-01 6.45291626e-01 -1.99029878... | [7.790439128875732, -2.959681749343872] |
08be0153-1293-4695-a9e3-5fd26cd1ef50 | mitigating-backdoor-attack-via-prerequisite | 2306.01983 | null | https://arxiv.org/abs/2306.01983v1 | https://arxiv.org/pdf/2306.01983v1.pdf | Mitigating Backdoor Attack Via Prerequisite Transformation | In recent years, with the successful application of DNN in fields such as NLP and CV, its security has also received widespread attention. (Author) proposed the method of backdoor attack in Badnet. Switch implanted backdoor into the model by poisoning the training samples. The model with backdoor did not exhibit any ab... | ['Han Gao'] | 2023-06-03 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 9.35973506e-03 2.30125323e-01 -1.23012532e-02 -3.20926914e-03
1.86537951e-02 -7.61915326e-01 6.25620067e-01 -5.47002964e-02
-4.98014182e-01 8.59368563e-01 -5.50187945e-01 -5.32567203e-01
1.03649579e-01 -1.16349840e+00 -8.96073997e-01 -7.68417299e-01
3.08844298e-01 9.19290558e-02 6.80508077e-01 -2.87664086... | [5.516923427581787, 7.882723808288574] |
be0a8f45-3280-497b-b8a3-8933f925a435 | unist-unified-end-to-end-model-for-streaming | 2109.07368 | null | https://arxiv.org/abs/2109.07368v4 | https://arxiv.org/pdf/2109.07368v4.pdf | Learning When to Translate for Streaming Speech | How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. In this paper, we propose MoSST, a simple yet... | ['Lei LI', 'Mingxuan Wang', 'Yaoming Zhu', 'Qianqian Dong'] | 2021-09-15 | null | https://aclanthology.org/2022.acl-long.50 | https://aclanthology.org/2022.acl-long.50.pdf | acl-2022-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 4.24493730e-01 -1.00072168e-01 -2.45310903e-01 -4.26458836e-01
-1.58695269e+00 -8.49149704e-01 1.63741648e-01 -1.88102588e-01
-2.66598195e-01 5.20694256e-01 2.97529817e-01 -8.97792041e-01
5.20763993e-01 -3.77725810e-01 -8.98176074e-01 -3.77349436e-01
2.34978139e-01 6.54259264e-01 4.24842834e-01 -1.04604848... | [14.524024963378906, 7.0771942138671875] |
4fa36b25-fddc-4fc6-95a8-eff4abb0816d | time-series-clustering-based-on-the | 1810.11624 | null | http://arxiv.org/abs/1810.11624v1 | http://arxiv.org/pdf/1810.11624v1.pdf | Time series clustering based on the characterisation of segment typologies | Time series clustering is the process of grouping time series with respect to
their similarity or characteristics. Previous approaches usually combine a
specific distance measure for time series and a standard clustering method.
However, these approaches do not take the similarity of the different
subsequences of each ... | ['César Hervás-Martínez', 'Pedro Antonio Gutiérrez', 'Antonio Manuel Durán-Rosal', 'David Guijo-Rubio', 'Alicia Troncoso'] | 2018-10-27 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 2.21568700e-02 -6.71724677e-01 5.89322597e-02 -1.88883856e-01
-3.95156860e-01 -6.41908407e-01 3.73801023e-01 7.78221667e-01
-4.93671268e-01 1.03934735e-01 -1.95938185e-01 -1.77207917e-01
-5.08518994e-01 -8.41657877e-01 -9.18362588e-02 -9.60396349e-01
-5.28240681e-01 5.30248225e-01 4.22942847e-01 5.92651218... | [7.238276481628418, 3.383274555206299] |
b31064f8-8956-489b-b59c-0683e140ff56 | autogpart-intermediate-supervision-search-for | 2203.06558 | null | https://arxiv.org/abs/2203.06558v4 | https://arxiv.org/pdf/2203.06558v4.pdf | AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation | Training a generalizable 3D part segmentation network is quite challenging but of great importance in real-world applications. To tackle this problem, some works design task-specific solutions by translating human understanding of the task to machine's learning process, which faces the risk of missing the optimal strat... | ['Li Yi', 'Chuang Gan', 'Anyi Rao', 'Xiaomeng Xu', 'Xueyi Liu'] | 2022-03-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_AutoGPart_Intermediate_Supervision_Search_for_Generalizable_3D_Part_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_AutoGPart_Intermediate_Supervision_Search_for_Generalizable_3D_Part_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-part-segmentation'] | ['computer-vision'] | [ 0.3424037 0.5234862 -0.3031513 -0.49691108 -0.34423572 -0.6637228
0.15103869 -0.4290018 -0.17214265 0.34977025 -0.34005108 -0.41969037
0.05740527 -0.66496414 -0.87286246 -0.5101998 0.25076768 1.051043
0.48473665 -0.15090641 0.18278025 0.5975062 -1.2855269 0.03213824
1.1444839 0.9314388 0.546... | [9.320815086364746, 0.4884880781173706] |
7628b838-f161-4dfa-bd6d-e7a5e65d9950 | learning-context-graph-for-person-search | 1904.01830 | null | http://arxiv.org/abs/1904.01830v1 | http://arxiv.org/pdf/1904.01830v1.pdf | Learning Context Graph for Person Search | Person re-identification has achieved great progress with deep convolutional
neural networks. However, most previous methods focus on learning individual
appearance feature embedding, and it is hard for the models to handle difficult
situations with different illumination, large pose variance and occlusion. In
this wor... | ['Bingbing Ni', 'Yichao Yan', 'Minghao Xu', 'Xiaokang Yang', 'Wendong Zhang', 'Qiang Zhang'] | 2019-04-03 | learning-context-graph-for-person-search-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yan_Learning_Context_Graph_for_Person_Search_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yan_Learning_Context_Graph_for_Person_Search_CVPR_2019_paper.pdf | cvpr-2019-6 | ['person-search'] | ['computer-vision'] | [ 1.94764454e-02 -5.21090329e-01 6.83692005e-03 -3.82739902e-01
-4.72464949e-01 -3.36529195e-01 6.79795861e-01 1.69689327e-01
-5.73998392e-01 3.82657856e-01 2.82932222e-01 2.15279400e-01
-1.41796917e-01 -6.29935682e-01 -2.59617418e-01 -4.59942192e-01
2.67371207e-01 3.08717966e-01 1.65075928e-01 4.60587367... | [14.728042602539062, 0.8811573386192322] |
692ae69b-786b-4b4a-9f03-ceafb44ba754 | deep-simplex-classifier-for-maximizing-the | 2212.11747 | null | https://arxiv.org/abs/2212.11747v1 | https://arxiv.org/pdf/2212.11747v1.pdf | Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces | The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classification for the methods maximizing the margin in Euclidean spaces whereas t... | ['Hasan Saribas', 'Hakan Cevikalp'] | 2022-12-22 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-8.92212540e-02 9.56428349e-02 -1.95302457e-01 -5.85594893e-01
-7.79000521e-02 -2.58250654e-01 3.51872057e-01 4.23777670e-01
-7.83984661e-01 8.48946571e-01 -6.55126870e-01 4.75374460e-02
-4.52625543e-01 -1.03427243e+00 -5.82828224e-01 -1.06375492e+00
4.40575331e-02 5.46866477e-01 1.41802743e-01 9.41113681... | [8.527623176574707, 3.8928706645965576] |
fdd987a4-5d9f-4829-9069-2623035af53c | temporal-aggregate-representations-for-long | 2006.00830 | null | https://arxiv.org/abs/2006.00830v2 | https://arxiv.org/pdf/2006.00830v2.pdf | Temporal Aggregate Representations for Long-Range Video Understanding | Future prediction, especially in long-range videos, requires reasoning from current and past observations. In this work, we address questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework. We show that it is possible to achieve state of the ... | ['Angela Yao', 'Fadime Sener', 'Dipika Singhania'] | 2020-06-01 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2515_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610154.pdf | eccv-2020-8 | ['action-anticipation'] | ['computer-vision'] | [ 1.79569393e-01 4.73267511e-02 -3.66097957e-01 -5.84792912e-01
-4.82761592e-01 -3.56915623e-01 5.84764063e-01 -8.39354172e-02
-2.67795473e-01 5.56152880e-01 6.14258587e-01 1.10733256e-01
-1.96954817e-01 -5.41520178e-01 -6.90387249e-01 -3.84055257e-01
-6.23936355e-01 5.39468192e-02 5.81196368e-01 -1.14318967... | [8.189238548278809, 0.5144723653793335] |
a1cbb5b2-fa63-493e-99b9-084da12fbbe5 | deep-relation-learning-for-regression-and-its | 2204.06598 | null | https://arxiv.org/abs/2204.06598v1 | https://arxiv.org/pdf/2204.06598v1.pdf | Deep Relation Learning for Regression and Its Application to Brain Age Estimation | Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relatio... | ['Yangming Ou', 'P. Ellen Grant', 'Yanfang Feng', 'Sheng He'] | 2022-04-13 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 5.02933599e-02 1.66848674e-01 -8.05898085e-02 -7.35313356e-01
-2.42551878e-01 1.93233177e-01 4.88377124e-01 4.30019975e-01
-8.42164040e-01 8.25030327e-01 -3.22159141e-01 -5.59877828e-02
-4.79857415e-01 -7.32251704e-01 -6.34494424e-01 -6.12474561e-01
-6.08942986e-01 1.91372871e-01 9.65096727e-02 -1.23734605... | [14.060895919799805, -1.3528903722763062] |
34361398-f7ca-4af4-8301-50259a6bf7a5 | image-to-voxel-model-translation-for-3d-scene | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/156_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520103.pdf | Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation | Objects class, depth, and shape are instantly reconstructed by a human looking at a 2D image. While modern deep models solve each of these challenging tasks separately, they struggle to perform simultaneous scene 3D reconstruction and segmentation. We propose a single shot image-to-semantic voxel model translation fram... | ['Vladimir V. Kniaz', 'Vladimir A. Knyaz', 'Artem Bordodymov', 'Petr Moshkantsev', 'Fabio Remondino'] | null | null | null | null | eccv-2020-8 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.33208197e-01 5.67960262e-01 7.06739053e-02 -5.68140984e-01
-1.03582847e+00 -8.43428910e-01 6.52868867e-01 -3.46813798e-01
-9.72759724e-02 3.40992808e-01 -1.43046856e-01 -1.13632642e-01
3.46400321e-01 -1.09655488e+00 -1.33842134e+00 -4.55328435e-01
2.67985731e-01 1.33938646e+00 5.11365056e-01 1.98790848... | [8.848272323608398, -3.3188180923461914] |
f6150243-e78c-4103-9424-3b6c0b101834 | aligning-language-models-to-user-opinions | 2305.14929 | null | https://arxiv.org/abs/2305.14929v1 | https://arxiv.org/pdf/2305.14929v1.pdf | Aligning Language Models to User Opinions | An important aspect of developing LLMs that interact with humans is to align models' behavior to their users. It is possible to prompt an LLM into behaving as a certain persona, especially a user group or ideological persona the model captured during its pertaining stage. But, how to best align an LLM with a specific u... | ['Niket Tandon', 'Bodhisattwa Prasad Majumder', 'EunJeong Hwang'] | 2023-05-24 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-2.45290294e-01 3.63682866e-01 -7.91788757e-01 -8.88223827e-01
-3.33772808e-01 -8.08456838e-01 8.56183648e-01 5.85699975e-01
-3.29192102e-01 2.13857457e-01 1.06183231e+00 -7.27054954e-01
3.07561308e-01 -6.12954140e-01 -8.89941379e-02 3.64721976e-02
4.50245917e-01 7.68445849e-01 -2.36427665e-01 -3.65182877... | [9.133151054382324, 10.094581604003906] |
7ffe3bba-b86c-4e67-bfd1-a5eae879026c | compiler-optimization-for-quantum-computing | 2212.04508 | null | https://arxiv.org/abs/2212.04508v2 | https://arxiv.org/pdf/2212.04508v2.pdf | Compiler Optimization for Quantum Computing Using Reinforcement Learning | Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the develop... | ['Robert Wille', 'Lukas Burgholzer', 'Nils Quetschlich'] | 2022-12-08 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 9.52541009e-02 -9.74518508e-02 -9.84272286e-02 -2.48838186e-01
-1.11863351e+00 -6.93678379e-01 6.35836661e-01 3.24520826e-01
-3.55768055e-01 8.62793386e-01 -1.60014525e-01 -8.23011577e-01
9.49365422e-02 -1.07172048e+00 -7.76444197e-01 -7.70911813e-01
-7.10549504e-02 6.15918696e-01 -2.21081495e-01 -6.16349876... | [5.606978893280029, 4.933511257171631] |
cfed94db-002c-4fe5-bf16-f1a8914fa4a4 | sinfusion-training-diffusion-models-on-a | 2211.11743 | null | https://arxiv.org/abs/2211.11743v3 | https://arxiv.org/pdf/2211.11743v3.pdf | SinFusion: Training Diffusion Models on a Single Image or Video | Diffusion models exhibited tremendous progress in image and video generation, exceeding GANs in quality and diversity. However, they are usually trained on very large datasets and are not naturally adapted to manipulate a given input image or video. In this paper we show how this can be resolved by training a diffusion... | ['Michal Irani', 'Niv Haim', 'Yaniv Nikankin'] | 2022-11-21 | null | null | null | null | ['video-generation', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 4.47629482e-01 1.21667668e-01 -1.71226546e-01 1.32270681e-04
-5.92319071e-01 -6.49104238e-01 7.45100379e-01 -8.17283034e-01
-1.66212440e-01 1.01655281e+00 1.20779857e-01 1.02404198e-02
2.86793858e-01 -7.79381573e-01 -1.02723908e+00 -9.81471121e-01
1.55630350e-01 4.20238972e-01 9.03798789e-02 1.00113340... | [10.861023902893066, -0.6587976813316345] |
73c04141-acf3-4ab9-bc7f-33f0b427ec8d | neural-headline-generation-on-abstract | null | null | https://aclanthology.org/D16-1112 | https://aclanthology.org/D16-1112.pdf | Neural Headline Generation on Abstract Meaning Representation | null | ['Tsutomu Hirao', 'Sho Takase', 'Masaaki Nagata', 'Jun Suzuki', 'Naoaki Okazaki'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['video-description', 'headline-generation'] | ['computer-vision', '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... | [-7.194465160369873, 3.620304584503174] |
5f1e87f7-db7b-4c85-80bf-91a650c0caaf | event-guided-person-re-identification-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cao_Event-Guided_Person_Re-Identification_via_Sparse-Dense_Complementary_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cao_Event-Guided_Person_Re-Identification_via_Sparse-Dense_Complementary_Learning_CVPR_2023_paper.pdf | Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning | Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications. Most existing methods utilize spatial and temporal correlations in frame sequences to obtain discriminative person features. However, inevitable degradations, e.g., motion blur... | ['Zheng-Jun Zha', 'Jiebo Luo', 'Kunyu Wang', 'Yukun Huang', 'Hongjian Liu', 'Xueyang Fu', 'Chengzhi Cao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification'] | ['computer-vision'] | [ 1.59250364e-01 -9.58565176e-01 8.83977413e-02 -3.72701317e-01
-2.22480431e-01 -3.84875327e-01 6.01568520e-01 -2.04536021e-02
-5.84003985e-01 7.50429749e-01 3.98989379e-01 7.25914359e-01
1.55427292e-01 -7.31314778e-01 -6.60012782e-01 -9.35085475e-01
2.25516260e-01 -2.93722332e-01 2.93766022e-01 7.39451721... | [14.625662803649902, 0.9359079003334045] |
5e7941c7-f41a-440e-a998-764357c4be68 | knowledge-of-knowledge-exploring-known | 2305.13712 | null | https://arxiv.org/abs/2305.13712v1 | https://arxiv.org/pdf/2305.13712v1.pdf | Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models | This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their own knowledge and measuring their uncertainty. We argue this is an important feature for mitigating hallucinations. Specifically, we focus on addressing \textit{known-unknown} questions, characterized by high ... | ['William Wang', 'Wenhu Chen', 'Liangming Pan', 'Alfonso Amayuelas'] | 2023-05-23 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [-3.31363887e-01 5.70005596e-01 4.10985053e-02 -4.05570090e-01
-1.23110592e+00 -8.32027912e-01 5.65901279e-01 5.19752860e-01
-2.70462036e-01 9.02246833e-01 8.42307210e-01 -2.70606041e-01
-7.27748394e-01 -6.52306914e-01 -3.37235540e-01 4.58939234e-03
5.52218020e-01 5.79959810e-01 -1.96897835e-02 -2.18001515... | [11.163863182067871, 8.013045310974121] |
1eca1bb0-3490-4b85-9cbe-a4bb5004d2c6 | sequential-best-arm-identification-with | 2305.11908 | null | https://arxiv.org/abs/2305.11908v1 | https://arxiv.org/pdf/2305.11908v1.pdf | Sequential Best-Arm Identification with Application to Brain-Computer Interface | A brain-computer interface (BCI) is a technology that enables direct communication between the brain and an external device or computer system. It allows individuals to interact with the device using only their thoughts, and holds immense potential for a wide range of applications in medicine, rehabilitation, and human... | ['Lexin Li', 'Tor Lattimore', 'Jian Kang', 'Botao Hao', 'Xin Zhou'] | 2023-05-17 | null | null | null | null | ['thompson-sampling', 'eeg', 'multi-armed-bandits', 'eeg'] | ['methodology', 'methodology', 'miscellaneous', 'time-series'] | [ 6.42341375e-01 -4.25854623e-01 -3.50446343e-01 4.15776111e-02
-9.12913620e-01 -5.34930468e-01 4.27697927e-01 -4.96722341e-01
-5.98464251e-01 1.16682434e+00 1.35293916e-01 -6.23919070e-01
-4.38009173e-01 -3.09536576e-01 -5.96032858e-01 -8.93074691e-01
8.67131278e-02 2.81664252e-01 -3.81781608e-01 1.62003636... | [13.150221824645996, 3.430433511734009] |
10e52217-9ccc-44c6-a499-0fa776116975 | spa-former-transformer-image-shadow-detection | 2206.10910 | null | https://arxiv.org/abs/2206.10910v3 | https://arxiv.org/pdf/2206.10910v3.pdf | SpA-Former: Transformer image shadow detection and removal via spatial attention | In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping... | ['Shan Ying Zhu', 'Chao Chen Gu', 'Xiao Feng Zhang'] | 2022-06-22 | null | null | null | null | ['shadow-removal', 'shadow-detection-and-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.33644164e-01 2.38230005e-01 5.71223259e-01 -5.71052790e-01
-5.41198969e-01 -2.00919107e-01 3.59137446e-01 -4.83416617e-01
-3.26851279e-01 5.96738219e-01 -3.12365964e-02 -5.57592869e-01
4.03508574e-01 -7.22594380e-01 -8.37126791e-01 -9.00678694e-01
2.33292565e-01 1.83536544e-01 9.50887382e-01 -2.10868195... | [10.846233367919922, -4.108004093170166] |
e35f441f-9777-4140-b51f-328eb2c7cbea | weakly-supervised-photo-realistic-texture | 2106.08148 | null | https://arxiv.org/abs/2106.08148v1 | https://arxiv.org/pdf/2106.08148v1.pdf | Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction | Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relatively little work has focused on generating high-fidelity face textures. Compared with the prosperity of photo-realistic 2D face image generat... | ['Liming Chen', 'Yunhong Wang', 'Zehua Fu', 'Di Huang', 'Xiangnan Yin'] | 2021-06-14 | null | null | null | null | ['texture-synthesis', '3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.84363782e-01 4.83018816e-01 4.29058298e-02 -4.33024317e-01
-6.63087904e-01 -2.14401141e-01 8.30546737e-01 -5.74749827e-01
3.55308294e-01 6.80091023e-01 -9.98974293e-02 -9.60829109e-03
3.72704685e-01 -1.20979464e+00 -9.80477154e-01 -7.80095994e-01
5.03922164e-01 7.84085512e-01 -1.10240772e-01 3.48822251... | [12.713021278381348, -0.3267768323421478] |
3640d994-c92b-4451-8551-6d1bd62eae75 | one-shot-object-detection-with-co-attention-1 | 1911.12529 | null | https://arxiv.org/abs/1911.12529v1 | https://arxiv.org/pdf/1911.12529v1.pdf | One-Shot Object Detection with Co-Attention and Co-Excitation | This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the same class in a target image. To this end, we develop a novel {\em co-attention and co-excitation} (CoA... | ['Tyng-Luh Liu', 'Hwann-Tzong Chen', 'Yi-Chen Lo', 'Ting-I Hsieh'] | 2019-11-28 | one-shot-object-detection-with-co-attention | http://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation | http://papers.nips.cc/paper/8540-one-shot-object-detection-with-co-attention-and-co-excitation.pdf | neurips-2019-12 | ['one-shot-object-detection'] | ['computer-vision'] | [ 3.71867537e-01 1.00007109e-01 -1.30376041e-01 -2.84071714e-01
-1.23286319e+00 -3.63820642e-01 6.71747923e-01 7.63579085e-02
-4.13043171e-01 2.64856845e-01 -2.14689210e-01 2.15659529e-01
4.63051125e-02 -4.76348072e-01 -8.42543423e-01 -8.95356774e-01
1.92874715e-01 2.57143795e-01 5.88349879e-01 7.84624591... | [9.474352836608887, 1.4505455493927002] |
85896b7e-3ee6-4e3a-92cf-b3448ad9738d | interpretability-in-linear-brain-decoding | 1606.05672 | null | http://arxiv.org/abs/1606.05672v1 | http://arxiv.org/pdf/1606.05672v1.pdf | Interpretability in Linear Brain Decoding | Improving the interpretability of brain decoding approaches is of primary
interest in many neuroimaging studies. Despite extensive studies of this type,
at present, there is no formal definition for interpretability of brain
decoding models. As a consequence, there is no quantitative measure for
evaluating the interpre... | ['Seyed Mostafa Kia', 'Andrea Passerini'] | 2016-06-17 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.37330073e-01 2.76091605e-01 1.21225663e-01 -7.83246696e-01
-5.24004698e-01 -4.66320693e-01 5.09104371e-01 1.34614602e-01
-5.64971268e-01 6.68397665e-01 9.43251476e-02 -4.32908267e-01
-5.44657171e-01 -1.95534062e-02 -4.07587171e-01 -7.15812147e-01
7.66029432e-02 6.31108224e-01 -2.48659924e-01 2.14871258... | [12.607677459716797, 3.4045729637145996] |
246f4662-fc5d-4912-ad07-496af18a928a | density-uncertainty-layers-for-reliable | 2306.12497 | null | https://arxiv.org/abs/2306.12497v1 | https://arxiv.org/pdf/2306.12497v1.pdf | Density Uncertainty Layers for Reliable Uncertainty Estimation | Assessing the predictive uncertainty of deep neural networks is crucial for safety-related applications of deep learning. Although Bayesian deep learning offers a principled framework for estimating model uncertainty, the approaches that are commonly used to approximate the posterior often fail to deliver reliable esti... | ['David M. Blei', 'Yookoon Park'] | 2023-06-21 | null | null | null | null | ['out-of-distribution-detection'] | ['computer-vision'] | [-3.09906781e-01 2.72993743e-01 -3.13690640e-02 -9.31628644e-01
-1.01324964e+00 -4.06750172e-01 6.34212554e-01 9.92816593e-03
-3.92646402e-01 1.01462841e+00 -1.12086898e-02 -5.77041388e-01
-5.04474461e-01 -8.55647385e-01 -1.12890840e+00 -6.41492128e-01
-2.09157720e-01 4.70873713e-01 2.39961028e-01 5.61010897... | [7.393165111541748, 3.813516616821289] |
0d9ed0e1-2419-4fa9-9f5c-9d9d587a97d7 | pose-estimation-of-specular-and-symmetrical | 2011.00372 | null | https://arxiv.org/abs/2011.00372v1 | https://arxiv.org/pdf/2011.00372v1.pdf | Pose Estimation of Specular and Symmetrical Objects | In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental... | ['Henrik Christensen', 'Aayush Naik', 'Priyam Parashar', 'Hongyi Ling', 'Jiaming Hu'] | 2020-10-31 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.50304954e-02 -4.13947463e-01 6.41295910e-02 -3.77242327e-01
-7.63087720e-02 -5.82465827e-01 2.09065810e-01 -5.02179563e-01
-1.68702845e-02 2.12424934e-01 -3.10197055e-01 3.18772823e-01
-3.51298638e-02 -4.85130191e-01 -8.47939968e-01 -7.69161165e-01
4.12954628e-01 6.37908220e-01 2.32155055e-01 -2.77238768... | [7.021805763244629, -2.0793802738189697] |
aa074d41-5fea-4d1f-8689-9935acb04550 | cerfgan-a-compact-effective-robust-and-fast | 1805.10871 | null | http://arxiv.org/abs/1805.10871v2 | http://arxiv.org/pdf/1805.10871v2.pdf | CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation | In this paper, we aim at solving the multi-domain image-to-image translation
problem with a unified model in an unsupervised manner. The most successful
work in this area refers to StarGAN, which works well in tasks like face
attribute modulation. However, StarGAN is unable to match multiple translation
mappings when e... | ['Xiao Liu', 'Hong Liu', 'Shengchuan Zhang', 'Xin Liu', 'Rongrong Ji', 'Cheng Deng'] | 2018-05-28 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 4.67373073e-01 -1.30483195e-01 -7.36045092e-02 -1.95798099e-01
-6.10814810e-01 -2.59432346e-01 6.38372719e-01 -6.59642041e-01
4.29931544e-02 8.36643219e-01 1.12158787e-02 2.40891217e-03
2.49278918e-01 -7.53842115e-01 -7.79220045e-01 -8.82369757e-01
6.88087821e-01 3.60656470e-01 -4.30319943e-02 -3.83864284... | [11.885010719299316, -0.37508898973464966] |
a40eb5ea-cadd-40b6-bff7-94a0ddabeb5c | mp-senet-a-speech-enhancement-model-with | 2305.13686 | null | https://arxiv.org/abs/2305.13686v1 | https://arxiv.org/pdf/2305.13686v1.pdf | MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra | This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by convolution-augmented transformers. The encoder aims to encode time-frequency representation... | ['Zhen-Hua Ling', 'Yang Ai', 'Ye-Xin Lu'] | 2023-05-23 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 4.05442148e-01 7.77686685e-02 2.70356387e-01 -2.39601687e-01
-1.18829572e+00 -2.87208736e-01 1.02997638e-01 -3.07466775e-01
-3.17048013e-01 4.44125354e-01 4.15250242e-01 -1.74960971e-01
1.18677929e-01 -3.28538477e-01 -6.59473062e-01 -8.45663548e-01
-2.20471710e-01 -4.46790874e-01 -1.31613851e-01 -4.59933817... | [15.020979881286621, 6.002279281616211] |
6fe6d603-5f00-441b-b273-7e5dde596adb | read-reciprocal-attention-discriminator-for | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2135_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590324.pdf | READ: Reciprocal Attention Discriminator for Image-to-Video Re-Identification | Person re-identification (re-ID) is the problem of visually identifying a person given a database of identities. In this work, we focus on image-to-video re-ID which compares a single query image to videos in the gallery. The main challenge is the asymmetry association of an image and a video, and overcoming the differ... | ['Hsuan-I Ho', 'Minho Shim', 'Dongyoon Wee', 'Jinhyung Kim'] | null | null | null | null | eccv-2020-8 | ['image-to-video-person-re-identification'] | ['computer-vision'] | [ 1.30844027e-01 -6.58883512e-01 2.22604685e-02 -2.37667456e-01
-8.03978264e-01 -6.35313213e-01 8.27526927e-01 -1.56120677e-02
-5.56519091e-01 4.43050027e-01 4.71908301e-01 4.29001749e-01
1.30001962e-01 -1.73384666e-01 -4.34713542e-01 -3.79925549e-01
1.09644800e-01 2.77423054e-01 -3.75693254e-02 2.01609120... | [14.61725902557373, 0.9784344434738159] |
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