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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
896ef490-f89c-4c10-a0e7-b0ec6aab90f8 | diving-deep-into-sentiment-understanding-fine | 1508.05056 | null | http://arxiv.org/abs/1508.05056v2 | http://arxiv.org/pdf/1508.05056v2.pdf | Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction | Visual media are powerful means of expressing emotions and sentiments. The
constant generation of new content in social networks highlights the need of
automated visual sentiment analysis tools. While Convolutional Neural Networks
(CNNs) have established a new state-of-the-art in several vision problems,
their applicat... | ['Xavier Giró-i-Nieto', 'Victor Campos', 'Brendan Jou', 'Amaia Salvador'] | 2015-08-20 | null | null | null | null | ['visual-sentiment-prediction'] | ['computer-vision'] | [-3.90175618e-02 -8.17839336e-03 -5.30678481e-02 -6.24547899e-01
1.72307983e-01 -5.60069263e-01 6.20179415e-01 2.26066485e-01
-3.71168196e-01 2.95867860e-01 3.32658619e-01 -5.30786753e-01
4.20911580e-01 -6.60526752e-01 -5.98107755e-01 -4.39369678e-01
-6.98391497e-02 -1.30655076e-02 9.71809253e-02 -7.18512237... | [11.023090362548828, 2.679241180419922] |
bea18260-4ceb-4a65-9a44-5511400fef85 | actions-speak-louder-than-goals-valuing | 1802.07127 | null | https://arxiv.org/abs/1802.07127v2 | https://arxiv.org/pdf/1802.07127v2.pdf | Actions Speak Louder Than Goals: Valuing Player Actions in Soccer | Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in ... | ['Tom Decroos', 'Lotte Bransen', 'Jesse Davis', 'Jan Van Haaren'] | 2018-02-18 | null | null | null | null | ['football-action-valuation'] | ['playing-games'] | [ 1.19738758e-01 -1.73474804e-01 -8.31465274e-02 1.87054984e-02
-6.29102349e-01 -8.38941932e-01 5.43405175e-01 5.25609493e-01
-8.85950506e-01 6.47266626e-01 6.45148993e-01 -8.98197889e-02
-6.77114367e-01 -7.66957402e-01 -1.96249187e-01 -5.32244682e-01
2.25234941e-01 4.96345162e-01 4.37105507e-01 -8.05512071... | [6.512857437133789, 0.3959474265575409] |
9ed67807-24c7-40ae-86fd-5cc0abf7e489 | towards-open-world-eeg-decoding-via-deep | 2112.06654 | null | https://arxiv.org/abs/2112.06654v2 | https://arxiv.org/pdf/2112.06654v2.pdf | Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey | Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when applied to data acquired in static, well-controlled lab environments. However, a... | ['Z. Jane Wang', 'Ruobing Qian', 'Martin J. McKeown', 'Aiping Liu', 'Chang Li', 'Xun Chen'] | 2021-12-08 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.82611901e-01 -3.69494617e-01 2.00425327e-01 -3.52830261e-01
-7.24060357e-01 -4.85705614e-01 3.78217131e-01 2.58882791e-01
-5.61968923e-01 1.02119434e+00 5.28509878e-02 -2.01948136e-02
-1.97856754e-01 -4.90206122e-01 -5.28614819e-01 -7.77739882e-01
-4.12775308e-01 1.18222803e-01 -6.56896457e-02 1.72266942... | [13.151094436645508, 3.431887626647949] |
22445b16-9418-4b2a-bce2-9436edae2db0 | weak-label-supervision-for-monaural-source | 1810.13104 | null | https://arxiv.org/abs/1810.13104v3 | https://arxiv.org/pdf/1810.13104v3.pdf | Audio Source Separation Using Variational Autoencoders and Weak Class Supervision | In this paper, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source con... | ['Serap Kırbız', 'Ertuğ Karamatlı', 'Ali Taylan Cemgil'] | 2018-10-31 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.56957072e-01 2.01425731e-01 -1.79639667e-01 -2.26756185e-01
-9.38400805e-01 -6.31749868e-01 5.70793688e-01 -1.07962780e-01
-2.12871544e-02 5.44442534e-01 1.39418110e-01 -1.43287078e-01
8.62743333e-02 -5.32455504e-01 -6.91233754e-01 -1.04555786e+00
7.02161156e-03 5.53018987e-01 1.01206906e-01 1.57816991... | [15.309370994567871, 5.626613616943359] |
47f1f9e4-374d-4c0d-8394-36cd263d9a2d | pay-attention-when-required | 2009.04534 | null | https://arxiv.org/abs/2009.04534v3 | https://arxiv.org/pdf/2009.04534v3.pdf | Pay Attention when Required | Transformer-based models consist of interleaved feed-forward blocks - that capture content meaning, and relatively more expensive self-attention blocks - that capture context meaning. In this paper, we explored trade-offs and ordering of the blocks to improve upon the current Transformer architecture and proposed PAR T... | ['Szymon Migacz', 'Alex Fit Florea', 'Swetha Mandava'] | 2020-09-09 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.58285254e-02 3.12615931e-01 6.35020360e-02 -4.85095412e-01
-5.70194125e-01 -2.75798500e-01 6.93095982e-01 -8.77368003e-02
-6.91945195e-01 7.24619806e-01 8.74355912e-01 -7.99336851e-01
4.46987040e-02 -7.71001160e-01 -5.52995741e-01 -4.13218170e-01
-6.65689930e-02 6.45962179e-01 2.24658042e-01 -3.84561688... | [10.931312561035156, 7.370661735534668] |
666d9f31-5335-466e-9c9a-2549d785c100 | a-peek-at-peak-emotion-recognition | 2205.09791 | null | https://arxiv.org/abs/2205.09791v1 | https://arxiv.org/pdf/2205.09791v1.pdf | A Peek at Peak Emotion Recognition | Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak emotions. In this work we analyze how deep learning fares on this challenge. We find th... | ['Shmuel Peleg', 'Hillel Aviezer', 'Tzvi Michelson'] | 2022-05-19 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-6.27142340e-02 7.76436031e-02 -3.33730340e-01 -7.74185836e-01
-5.15295506e-01 -5.04183888e-01 6.33822143e-01 -5.50083108e-02
-6.39758706e-01 5.07048190e-01 3.95363271e-02 1.63842157e-01
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-3.11397642e-01 2.31358156e-01 -4.07737017e-01 -4.32212114... | [13.547638893127441, 1.8747297525405884] |
18804102-8de7-4bbd-b409-041c3b7c123c | learnable-frontends-that-do-not-learn | 2302.10014 | null | https://arxiv.org/abs/2302.10014v1 | https://arxiv.org/pdf/2302.10014v1.pdf | Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation | While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic frontends learnt jointly with a backend. In this study, we focus specifically on learnable filterbanks. Prior studies have reported that in fronte... | ['Naomi Harte', 'Tomi Kinnunen', 'Mark Anderson'] | 2023-02-20 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.98236215e-01 1.38971061e-01 3.43892068e-01 -4.21735317e-01
-8.19321990e-01 -7.91373670e-01 4.35549110e-01 6.47950247e-02
-8.39213967e-01 5.09648740e-01 4.57979053e-01 -1.10661715e-01
-3.10296118e-01 -4.35577840e-01 -5.48600793e-01 -6.35118663e-01
-6.23052716e-01 -1.05955705e-01 3.00425619e-01 -2.74380416... | [15.211323738098145, 5.5144572257995605] |
89579b3e-59ef-458f-b2e0-17caa5ee9f8e | discovering-the-representation-bottleneck-of | 2205.07266 | null | https://arxiv.org/abs/2205.07266v4 | https://arxiv.org/pdf/2205.07266v4.pdf | Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions | Graph neural networks (GNNs) mainly rely on the message-passing paradigm to propagate node features and build interactions, and different graph learning tasks require different ranges of node interactions. In this work, we explore the capacity of GNNs to capture interactions between nodes under contexts with different ... | ['Stan Z. Li', 'Dragomir Radev', 'Lirong Wu', 'Siyuan Li', 'Fang Wu'] | 2022-05-15 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.22301304e-01 4.07578021e-01 -2.65644222e-01 -1.25219852e-01
3.74775261e-01 -5.10535657e-01 6.42887831e-01 2.85655588e-01
-2.69302398e-01 4.60629225e-01 7.34220594e-02 -4.42324221e-01
-2.44830072e-01 -1.22828758e+00 -8.99827778e-01 -6.31435394e-01
-3.86186779e-01 2.60868907e-01 4.86603409e-01 -5.30737996... | [7.018253326416016, 6.188572883605957] |
05b36adf-9a54-4d2f-a1d4-d7c3528f2ada | shapes-of-emotions-multimodal-emotion | 2112.01938 | null | https://arxiv.org/abs/2112.01938v2 | https://arxiv.org/pdf/2112.01938v2.pdf | Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts | Emotion Recognition in Conversations (ERC) is an important and active research area. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some stimuli evokes a change. There i... | ['Ashutosh Modi', 'Abhinav Joshi', 'Keshav Bansal', 'Harsh Agarwal'] | 2021-12-03 | null | https://aclanthology.org/2022.mmmpie-1.6 | https://aclanthology.org/2022.mmmpie-1.6.pdf | mmmpie-coling-2022-10 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-1.09637482e-02 -1.21925287e-01 -1.56766232e-02 -5.36541641e-01
-3.24062407e-01 -5.07840335e-01 6.87828600e-01 4.40618657e-02
-3.08133394e-01 5.16116142e-01 6.09469652e-01 1.64988056e-01
2.67988443e-01 -3.04458439e-01 -1.80416152e-01 -5.38177192e-01
1.10581994e-01 -2.26889357e-01 3.60960476e-02 -6.24658763... | [13.160859107971191, 5.58691930770874] |
d226a141-156d-4261-9918-adf2044c2b89 | robust-learning-through-cross-task | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.pdf | Robust Learning Through Cross-Task Consistency | Visual perception entails solving a wide set of tasks (e.g., object detection, depth estimation, etc). The predictions made for different tasks out of one image are not independent, and therefore, are expected to be 'consistent'. We propose a flexible and fully computational framework for learning while enforcing Cross... | [' Leonidas J. Guibas', ' Jitendra Malik', ' Zhangjie Cao', ' Rohan Suri', ' Nikhil Cheerla', ' Alexander Sax', 'Amir R. Zamir'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['single-view-3d-reconstruction', 'surface-normals-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.83830157e-01 -1.12172738e-01 1.46860301e-01 -5.14694571e-01
-6.40642583e-01 -4.78843451e-01 6.61057234e-01 1.64115816e-01
-3.50380689e-01 7.87569523e-01 -1.43949106e-01 2.32606083e-01
-4.63133723e-01 -3.56792629e-01 -7.25093186e-01 -8.14964414e-01
-2.47022882e-02 -6.16330542e-02 5.89051545e-01 2.56656170... | [9.673563957214355, 2.0581531524658203] |
794b2da7-a75c-4c25-8534-396159df8004 | cardigraphormer-unveiling-the-power-of-self | 2307.00859 | null | https://arxiv.org/abs/2307.00859v2 | https://arxiv.org/pdf/2307.00859v2.pdf | CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery | In the expansive realm of drug discovery, with approximately 15,000 known drugs and only around 4,200 approved, the combinatorial nature of the chemical space presents a formidable challenge. While Artificial Intelligence (AI) has emerged as a powerful ally, traditional AI frameworks face significant hurdles. This manu... | ['Arnab Mukherjee', 'Abhijit Gupta'] | 2023-07-03 | null | null | null | null | ['self-supervised-learning', 'drug-discovery'] | ['computer-vision', 'medical'] | [ 6.04503214e-01 8.20918307e-02 -8.40060830e-01 1.55236244e-01
-2.46691599e-01 -7.20908403e-01 2.56403387e-01 7.25169003e-01
2.89815422e-02 1.02747226e+00 -9.78399441e-02 -8.48511934e-01
-5.22697747e-01 -8.88349056e-01 -4.22191620e-01 -8.23585212e-01
-4.87689108e-01 4.59201992e-01 -3.97894345e-02 -2.86511451... | [5.16267204284668, 5.831884860992432] |
d42b0a30-d1f7-41e3-b591-6fc2a33cc4cf | unsupervised-geometry-aware-representation | 1804.01110 | null | http://arxiv.org/abs/1804.01110v1 | http://arxiv.org/pdf/1804.01110v1.pdf | Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation | Modern 3D human pose estimation techniques rely on deep networks, which
require large amounts of training data. While weakly-supervised methods require
less supervision, by utilizing 2D poses or multi-view imagery without
annotations, they still need a sufficiently large set of samples with 3D
annotations for learning ... | ['Pascal Fua', 'Mathieu Salzmann', 'Helge Rhodin'] | 2018-04-03 | unsupervised-geometry-aware-representation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.pdf | eccv-2018-9 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.08478732e-01 4.91843015e-01 -5.25926590e-01 -6.60334468e-01
-7.64212608e-01 -6.10194981e-01 4.11681056e-01 -6.21425211e-02
-4.80914056e-01 5.61325133e-01 3.09152395e-01 1.76774144e-01
5.42978108e-01 -6.81183100e-01 -1.23956800e+00 -1.97451085e-01
1.24490336e-01 9.43892360e-01 1.22644743e-02 -7.04724118... | [6.988636493682861, -1.0220003128051758] |
813c2e9a-ede3-4736-9502-499714770797 | how-to-sift-out-a-clean-data-subset-in-the | 2210.06516 | null | https://arxiv.org/abs/2210.06516v2 | https://arxiv.org/pdf/2210.06516v2.pdf | How to Sift Out a Clean Data Subset in the Presence of Data Poisoning? | Given the volume of data needed to train modern machine learning models, external suppliers are increasingly used. However, incorporating external data poses data poisoning risks, wherein attackers manipulate their data to degrade model utility or integrity. Most poisoning defenses presume access to a set of clean data... | ['Ruoxi Jia', 'Lingjuan Lyu', 'Ming Jin', 'Himanshu Jahagirdar', 'Minzhou Pan', 'Yi Zeng'] | 2022-10-12 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.24922238e-01 -4.19257879e-01 -2.66929597e-01 1.17028534e-01
-1.09486949e+00 -1.29141498e+00 5.18254042e-01 3.89825493e-01
-4.35068041e-01 4.72881436e-01 -6.03688210e-02 -6.27242088e-01
-9.99745578e-02 -7.96682715e-01 -8.22289228e-01 -8.33934247e-01
-2.01833189e-01 2.66960591e-01 1.53885394e-01 -1.35011300... | [5.8097686767578125, 7.561938285827637] |
2bfca8ea-0a54-4973-bca8-eba8840b7a39 | recbaselines2023-a-new-dataset-for-choosing | 2306.14292 | null | https://arxiv.org/abs/2306.14292v1 | https://arxiv.org/pdf/2306.14292v1.pdf | RecBaselines2023: a new dataset for choosing baselines for recommender models | The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number of recommender models, it is difficult to estimate which algorithms to choose in the article. To solve this problem, we have collected and ... | ['Sergey Kolesnikov', 'Marina Ananyeva', 'Oleg Lashinin', 'Veronika Ivanova'] | 2023-06-25 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.94408581e-01 -1.16619997e-01 -2.30605721e-01 -3.97833169e-01
-2.42216378e-01 -7.35470831e-01 6.54518247e-01 -1.01817325e-01
-8.01501349e-02 5.04535735e-01 6.14836454e-01 -2.03316703e-01
-8.44712913e-01 -7.70855725e-01 -3.93250465e-01 -3.86200219e-01
3.44902538e-02 6.88241720e-01 1.77620098e-01 -5.04419744... | [10.049314498901367, 5.774102687835693] |
c4785505-c270-4176-8844-9d605685acbf | regression-based-model-error-compensation-for | 2306.09080 | null | https://arxiv.org/abs/2306.09080v1 | https://arxiv.org/pdf/2306.09080v1.pdf | Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System | One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An ... | ['Tobias Rodemann', 'Jens Engel', 'Thomas Schmitt'] | 2023-06-15 | null | null | null | null | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-7.23261088e-02 3.92185263e-02 9.71474871e-02 3.77502851e-02
-2.05758780e-01 -5.72752357e-01 5.23516297e-01 4.49050963e-01
6.00025833e-01 9.03047442e-01 -1.85886651e-01 -1.18213117e-01
-5.19246459e-01 -1.01367831e+00 -3.19306523e-01 -1.00616753e+00
3.23329240e-01 3.37218046e-01 2.66801625e-01 -2.23385930... | [5.705544471740723, 2.473729372024536] |
08bc404d-f516-4f90-ae25-36d318e7ab60 | pixel-aware-deep-function-mixture-network-for | 1903.10501 | null | http://arxiv.org/abs/1903.10501v1 | http://arxiv.org/pdf/1903.10501v1.pdf | Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution | Spectral super-resolution (SSR) aims at generating a hyperspectral image
(HSI) from a given RGB image. Recently, a promising direction for SSR is to
learn a complicated mapping function from the RGB image to the HSI counterpart
using a deep convolutional neural network. This essentially involves mapping
the RGB context... | ['Yanning Zhang', 'Shengcai Liao', 'Peng Wang', 'Zhiqiang Lang', 'Wei Wei', 'Lei Zhang', 'Ling Shao'] | 2019-03-24 | null | null | null | null | ['spectral-super-resolution'] | ['computer-vision'] | [ 7.99895942e-01 -3.67812753e-01 1.61908850e-01 -3.96103144e-01
-5.19422352e-01 -4.66260344e-01 3.47640157e-01 -4.12203461e-01
-2.31508866e-01 5.90254962e-01 -7.17603043e-02 -1.47662923e-01
-7.50349462e-02 -1.28962076e+00 -7.61643827e-01 -1.27664959e+00
4.02873099e-01 -3.96569014e-01 1.92868814e-01 -2.31102854... | [10.128504753112793, -1.8206275701522827] |
ace1c359-5914-481c-8aec-d5a7c2b048ef | deep-learning-hyperspectral-image | 1807.10574 | null | http://arxiv.org/abs/1807.10574v1 | http://arxiv.org/pdf/1807.10574v1.pdf | Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations | Herein, we present a system for hyperspectral image segmentation that
utilizes multiple class--based denoising autoencoders which are efficiently
trained. Moreover, we present a novel hyperspectral data augmentation method
for labelled HSI data using linear mixtures of pixels from each class, which
helps the system wit... | ['Wei Pan', 'Ball John E.'] | 2018-07-11 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 7.22264469e-01 -1.85671300e-01 2.63055980e-01 -3.24444145e-01
-4.44448292e-01 -4.84884709e-01 1.67741656e-01 -2.74572760e-01
-4.56995934e-01 7.18776166e-01 -2.53042430e-01 -3.90220731e-01
-1.25827193e-01 -1.18231106e+00 -6.54548049e-01 -1.14125001e+00
1.65859938e-01 2.15587262e-02 -1.94728836e-01 -2.24622875... | [9.98850154876709, -1.841872215270996] |
0dc0642e-01cb-4d30-835d-37d0a629fb89 | talktomodel-understanding-machine-learning | 2207.04154 | null | https://arxiv.org/abs/2207.04154v4 | https://arxiv.org/pdf/2207.04154v4.pdf | TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations | Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To this end, researchers have proposed several techniques to explain model predictions. However, practitioners struggle to use these explainabili... | ['Sameer Singh', 'Himabindu Lakkaraju', 'Satyapriya Krishna', 'Dylan Slack'] | 2022-07-08 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 2.49126311e-02 7.86411643e-01 -3.10488045e-01 -8.39562595e-01
-7.01556206e-01 -5.41024685e-01 3.02518189e-01 3.93081307e-01
1.87642455e-01 7.09697783e-01 5.42162538e-01 -8.51791382e-01
-7.89673850e-02 -4.47050571e-01 -4.30198848e-01 9.73664001e-02
2.87274659e-01 9.18779433e-01 -3.57856601e-01 -1.03454523... | [9.318374633789062, 6.783052921295166] |
b5245095-6f90-454a-a879-9e886abc4373 | head-detection-with-depth-images-in-the-wild | 1707.06786 | null | http://arxiv.org/abs/1707.06786v2 | http://arxiv.org/pdf/1707.06786v2.pdf | Head Detection with Depth Images in the Wild | Head detection and localization is a demanding task and a key element for
many computer vision applications, like video surveillance, Human Computer
Interaction and face analysis. The stunning amount of work done for detecting
faces on RGB images, together with the availability of huge face datasets,
allowed to setup v... | ['Guido Borghi', 'Roberto Vezzani', 'Rita Cucchiara', 'Diego Ballotta'] | 2017-07-21 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 7.10902587e-02 -4.54087295e-02 2.21366867e-01 -5.83479643e-01
-1.84415162e-01 -2.81022727e-01 3.37241292e-01 -2.37945229e-01
-7.13862240e-01 5.60337663e-01 -1.16888098e-01 1.65189564e-01
1.66039079e-01 -6.33269072e-01 -4.94351596e-01 -9.23809528e-01
3.56437229e-02 2.17020050e-01 3.18718255e-01 1.12533703... | [13.60761547088623, 0.36205101013183594] |
2019ceac-f771-49c4-95b3-cd65a7f0b1e9 | on-the-importance-of-video-action-recognition | 1903.09616 | null | https://arxiv.org/abs/1903.09616v2 | https://arxiv.org/pdf/1903.09616v2.pdf | On the Importance of Video Action Recognition for Visual Lipreading | We focus on the word-level visual lipreading, which requires to decode the word from the speaker's video. Recently, many state-of-the-art visual lipreading methods explore the end-to-end trainable deep models, involving the use of 2D convolutional networks (e.g., ResNet) as the front-end visual feature extractor and th... | ['Xinshuo Weng'] | 2019-03-22 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.28959656e-01 -2.62792315e-02 -5.47277212e-01 -1.42726555e-01
-6.58734262e-01 -6.03043176e-02 5.70127010e-01 -7.16571510e-01
-4.19928402e-01 3.49170625e-01 6.22102976e-01 -5.55121958e-01
6.54394150e-01 -9.40708257e-03 -8.37038040e-01 -5.87805152e-01
2.28866488e-01 -4.10885096e-01 2.41280243e-01 8.27682465... | [14.336807250976562, 5.006595134735107] |
62d722d6-3c73-4997-b129-c8d146f40471 | improving-pre-trained-vision-and-language | null | null | https://aclanthology.org/2021.emnlp-main.513 | https://aclanthology.org/2021.emnlp-main.513.pdf | Improving Pre-trained Vision-and-Language Embeddings for Phrase Grounding | Phrase grounding aims to map textual phrases to their associated image regions, which can be a prerequisite for multimodal reasoning and can benefit tasks requiring identifying objects based on language. With pre-trained vision-and-language models achieving impressive performance across tasks, it remains unclear if we ... | ['Nanyun Peng', 'Zi-Yi Dou'] | null | null | null | null | emnlp-2021-11 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.39252204e-01 2.05000594e-01 -5.18756270e-01 -4.59111333e-01
-1.24395585e+00 -7.09006310e-01 8.35564792e-01 2.39068508e-01
-6.24482632e-01 4.58789766e-01 6.14889562e-01 -2.53576785e-01
1.52543515e-01 -5.30062318e-01 -9.48976874e-01 -3.01487088e-01
3.11317682e-01 4.16404516e-01 -2.70232521e-02 -7.37605942... | [10.589125633239746, 1.5363458395004272] |
5fcc8865-f5fe-46ec-9141-43d333042b23 | pedhunter-occlusion-robust-pedestrian | 1909.06826 | null | https://arxiv.org/abs/1909.06826v1 | https://arxiv.org/pdf/1909.06826v1.pdf | PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes | Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling abili... | ['Xudong Zou', 'Shifeng Zhang', 'Junliang Xing', 'Zhen Lei', 'Stan Z. Li', 'Cheng Chi'] | 2019-09-15 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-2.32309699e-01 -4.06723201e-01 6.97218105e-02 -4.73437607e-01
-2.29699314e-01 -3.14347774e-01 4.32981908e-01 -2.03026995e-01
-7.63220429e-01 7.52764583e-01 6.74236789e-02 -1.87075451e-01
5.89957952e-01 -8.85275483e-01 -6.63555920e-01 -7.88128018e-01
7.64053373e-04 -2.06761975e-02 9.29503262e-01 -1.09973893... | [8.065807342529297, -0.5993462204933167] |
9babea93-9b3b-40cf-95a6-f9eb532c1760 | multilingual-language-model-adaptive-fine | 2204.06487 | null | https://arxiv.org/abs/2204.06487v3 | https://arxiv.org/pdf/2204.06487v3.pdf | Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning | Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective app... | ['Dietrich Klakow', 'Marius Mosbach', 'David Ifeoluwa Adelani', 'Jesujoba O. Alabi'] | 2022-04-13 | null | https://aclanthology.org/2022.coling-1.382 | https://aclanthology.org/2022.coling-1.382.pdf | coling-2022-10 | ['xlm-r'] | ['natural-language-processing'] | [-3.20012301e-01 -1.64946601e-01 -3.59492779e-01 -4.51072335e-01
-1.26492071e+00 -8.32221806e-01 5.96521795e-01 -5.86504452e-02
-1.10987663e+00 9.89230394e-01 3.13851774e-01 -6.64603174e-01
3.11021000e-01 -5.42462826e-01 -9.35810387e-01 -2.93048650e-01
1.16086155e-01 7.42459953e-01 5.25492877e-02 -2.23267302... | [10.97188949584961, 9.956121444702148] |
aca36137-49ba-468c-84b3-02690f77cf54 | learning-credit-assignment-for-cooperative | 2210.05367 | null | https://arxiv.org/abs/2210.05367v2 | https://arxiv.org/pdf/2210.05367v2.pdf | Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient | Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment plays an important role in MAPG and can induce cooperation among multiple agents. However, most MAPG algorithms cannot achieve good credit a... | ['Yang Gao', 'Shangdong Yang', 'Xiao Liu', 'Wenbin Li', 'Wubing Chen'] | 2022-10-10 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-5.15181124e-01 3.12059879e-01 -2.41551921e-01 2.13435024e-01
-4.74243611e-01 -5.45653582e-01 4.10892785e-01 1.43749177e-01
-7.68256545e-01 1.22466791e+00 -2.46250153e-01 -3.84902060e-01
-6.20252669e-01 -7.45906472e-01 -6.70032263e-01 -9.16046619e-01
-5.40969074e-01 9.17153358e-01 2.60756254e-01 -6.66741431... | [4.121977806091309, 2.5387468338012695] |
50056bf2-e726-4352-8595-5db3416b9fb6 | cross-cbam-a-lightweight-network-for-scene | 2306.02306 | null | https://arxiv.org/abs/2306.02306v1 | https://arxiv.org/pdf/2306.02306v1.pdf | Cross-CBAM: A Lightweight network for Scene Segmentation | Scene parsing is a great challenge for real-time semantic segmentation. Although traditional semantic segmentation networks have made remarkable leap-forwards in semantic accuracy, the performance of inference speed is unsatisfactory. Meanwhile, this progress is achieved with fairly large networks and powerful computat... | ['Juan Xiong', 'Xingsheng Gu', 'Zhenhao Xu', 'Zhengbin Zhang'] | 2023-06-04 | null | null | null | null | ['scene-parsing', 'scene-segmentation', 'real-time-semantic-segmentation', 'edge-computing'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [ 1.19935587e-01 -1.25073090e-01 -1.18350536e-01 -5.49907267e-01
-5.89502752e-01 -1.51731730e-01 1.77060172e-01 -2.15623543e-01
-6.44017696e-01 3.24005127e-01 -2.80293912e-01 -3.68669361e-01
1.13629080e-01 -1.13262999e+00 -7.84941196e-01 -5.92417777e-01
2.64799923e-01 9.63419378e-02 7.51500547e-01 -7.94164613... | [9.358586311340332, -0.4793314039707184] |
21bdf747-c09d-4ca1-8b8e-9b6740090a2c | accented-text-to-speech-synthesis-with | 2305.04816 | null | https://arxiv.org/abs/2305.04816v1 | https://arxiv.org/pdf/2305.04816v1.pdf | Accented Text-to-Speech Synthesis with Limited Data | This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference) and prosodic (pitch pattern and phoneme duration) variations. The proposed accented TTS framework consists of two models: an accented front... | ['Haizhou Li', 'Zhizheng Wu', 'Yi Zhou', 'Mingyang Zhang', 'Xuehao Zhou'] | 2023-05-08 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [-1.48790047e-01 1.20577596e-01 -7.05762655e-02 -4.98735785e-01
-8.01714659e-01 -6.62082255e-01 3.53792943e-02 -1.06021941e-01
-3.58206570e-01 6.65733814e-01 4.98133987e-01 -4.07334685e-01
3.56667608e-01 -5.31734109e-01 -3.22805077e-01 -6.39939964e-01
1.90796569e-01 3.63125950e-01 1.53685108e-01 -4.51192290... | [14.730019569396973, 6.693434715270996] |
80b2e86e-d7cf-4101-aa75-26804fe5a134 | are-pre-trained-cnns-good-feature-extractors | 1811.08495 | null | http://arxiv.org/abs/1811.08495v1 | http://arxiv.org/pdf/1811.08495v1.pdf | Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos? | Recently, several techniques have been explored to detect unusual behaviour
in surveillance videos. Nevertheless, few studies leverage features from
pre-trained CNNs and none of then present a comparison of features generate by
different models. Motivated by this gap, we compare features extracted by four
state-of-the-... | ['Tiago S. Nazare', 'Rodrigo F. de Mello', 'Moacir A. Ponti'] | 2018-11-20 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 9.15945172e-02 -1.45088926e-01 1.38569340e-01 -2.45200470e-01
8.88189822e-02 -3.78645658e-01 9.59658623e-01 4.10920560e-01
-7.43517876e-01 2.97887862e-01 1.20789930e-01 -1.07581437e-01
-1.49617210e-01 -7.43770003e-01 -5.14088631e-01 -6.39934003e-01
-3.83860439e-01 -1.92719668e-01 7.35419571e-01 -3.17630887... | [7.926241397857666, 1.453847885131836] |
bd89bb3c-6c54-42e2-b5ae-024546ae292f | small-language-models-improve-giants-by | 2305.13514 | null | https://arxiv.org/abs/2305.13514v1 | https://arxiv.org/pdf/2305.13514v1.pdf | Small Language Models Improve Giants by Rewriting Their Outputs | Large language models (LLMs) have demonstrated impressive few-shot learning capabilities, but they often underperform compared to fine-tuned models on challenging tasks. Furthermore, their large size and restricted access only through APIs make task-specific fine-tuning impractical. Moreover, LLMs are sensitive to diff... | ['Eric Malmi', 'Aliaksei Severyn', 'Jonathan Mallinson', 'Jakub Adamek', 'Arthur Bražinskas', 'Giorgos Vernikos'] | 2023-05-22 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.08157322e-01 -5.55682071e-02 -6.03263751e-02 -2.90877968e-01
-1.09534669e+00 -7.45659471e-01 8.66357207e-01 -1.17760068e-02
-5.11762917e-01 4.31444168e-01 1.59570068e-01 -5.27803957e-01
1.51447833e-01 -3.36738080e-01 -7.79493809e-01 -1.05454832e-01
4.18313116e-01 3.66948664e-01 5.00747740e-01 -1.76096916... | [10.963512420654297, 8.368489265441895] |
65e23f67-73fb-473f-935d-6feec673899b | syntactically-look-ahead-attention-network | 2002.01145 | null | https://arxiv.org/abs/2002.01145v2 | https://arxiv.org/pdf/2002.01145v2.pdf | Syntactically Look-Ahead Attention Network for Sentence Compression | Sentence compression is the task of compressing a long sentence into a short one by deleting redundant words. In sequence-to-sequence (Seq2Seq) based models, the decoder unidirectionally decides to retain or delete words. Thus, it cannot usually explicitly capture the relationships between decoded words and unseen word... | ['Hidetaka Kamigaito', 'Manabu Okumura'] | 2020-02-04 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 4.79252189e-01 2.14969397e-01 -1.93603076e-02 -4.61337030e-01
-8.92842174e-01 -3.32220286e-01 1.95078403e-01 3.86871845e-01
-5.26149511e-01 1.13375843e+00 1.07306266e+00 -1.75158054e-01
1.44856006e-01 -6.52017534e-01 -7.14736819e-01 -4.05846447e-01
2.04494104e-01 2.62793750e-01 -1.57319345e-02 -3.93925816... | [12.24384593963623, 9.370707511901855] |
013a232f-ac91-4967-9b48-d7d1dd76bc71 | tensor-program-optimization-with | 2205.13603 | null | https://arxiv.org/abs/2205.13603v2 | https://arxiv.org/pdf/2205.13603v2.pdf | Tensor Program Optimization with Probabilistic Programs | Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space which lacks the ability to efficiently enable domain experts to grow the... | ['Tianqi Chen', 'Cody Hao Yu', 'Masahiro Masuda', 'Wuwei Lin', 'Hongyi Jin', 'Ruihang Lai', 'Bohan Hou', 'Siyuan Feng', 'Xiyou Zhou', 'Junru Shao'] | 2022-05-26 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-6.23898983e-01 -5.79817593e-01 -6.54489398e-01 -5.06370664e-01
-6.78770244e-01 -5.89982808e-01 -1.75191425e-02 1.33770853e-01
-4.85017926e-01 5.35892099e-02 -4.62765284e-02 -5.30084729e-01
9.39626098e-02 -9.39141095e-01 -7.19944894e-01 -4.24143374e-01
-1.95905939e-01 3.99629653e-01 5.10926366e-01 -1.73717380... | [8.472899436950684, 3.4681551456451416] |
a704f006-1464-4bd8-80e5-f773e8c89cfc | forming-a-sparse-representation-for-visual | 2109.14916 | null | https://arxiv.org/abs/2109.14916v1 | https://arxiv.org/pdf/2109.14916v1.pdf | Forming a sparse representation for visual place recognition using a neurorobotic approach | This paper introduces a novel unsupervised neural network model for visual information encoding which aims to address the problem of large-scale visual localization. Inspired by the structure of the visual cortex, the model (namely HSD) alternates layers of topologic sparse coding and pooling to build a more compact co... | ['Olivier Romain', 'Guillaume Bresson', 'Nicolas Cuperlier', 'Sylvain Colomer'] | 2021-09-30 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.67233878e-01 -1.09152362e-01 -2.27006465e-01 -2.44730964e-01
-4.82776970e-01 -3.17618519e-01 7.90529251e-01 3.84370536e-01
-6.26489401e-01 5.04641593e-01 2.55300641e-01 7.77138025e-02
1.51799828e-01 -6.29164219e-01 -9.23634470e-01 -6.17923975e-01
-3.76186132e-01 4.05022688e-02 6.25605345e-01 8.96522552... | [7.730695724487305, -1.7970095872879028] |
ebdf2c1d-05d5-407d-83a9-3b2ebc58a3d6 | snipper-a-spatiotemporal-transformer-for | 2207.04320 | null | https://arxiv.org/abs/2207.04320v2 | https://arxiv.org/pdf/2207.04320v2.pdf | Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet | Multi-person pose understanding from RGB videos includes three complex tasks: pose estimation, tracking and motion forecasting. Among these three tasks, pose estimation and tracking are correlated, and tracking is crucial to motion forecasting. Most existing works either focus on a single task or employ cascaded method... | ['Minh Vo', 'Li Cheng', 'Lingni Ma', 'Chao Li', 'Yuanlu Xu', 'Shihao Zou'] | 2022-07-09 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.90516904e-01 -4.59614307e-01 -1.98532298e-01 -4.62345511e-01
-9.54220414e-01 -6.45804942e-01 4.57638174e-01 -5.14243841e-01
-4.09150571e-01 6.24629378e-01 6.05008960e-01 4.23402697e-01
1.80866659e-01 -2.74449676e-01 -8.45784843e-01 -5.11449516e-01
2.47506738e-01 5.77805340e-01 1.65216878e-01 7.37446249... | [7.037853240966797, -0.8500020503997803] |
ca630eb9-29ad-467e-bb10-9aa8f89fa1c8 | dive-into-the-resolution-augmentations-and | 2302.05621 | null | https://arxiv.org/abs/2302.05621v1 | https://arxiv.org/pdf/2302.05621v1.pdf | Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline | Although deep learning has significantly improved Face Recognition (FR), dramatic performance deterioration may occur when processing Low Resolution (LR) faces. To alleviate this, approaches based on unified feature space are proposed with the sacrifice under High Resolution (HR) circumstances. To deal with the huge do... | ['Dongchao Wen', 'Hongzhi Shi', 'Xingchen Cui', 'Yingjie Zhang', 'Weihong Deng', 'Wenqi Xu', 'Yichen Lu', 'Xu Ling'] | 2023-02-11 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-8.28758776e-02 -6.88399002e-02 -1.30170763e-01 -4.69520062e-01
-7.94336081e-01 -2.37953141e-01 2.84078926e-01 -4.43704545e-01
-1.47672176e-01 6.33136630e-01 2.27535993e-01 3.10981840e-01
-3.61095876e-01 -9.34963584e-01 -5.92199624e-01 -7.80401170e-01
1.27672791e-01 -3.13610844e-02 2.15151444e-01 -4.12232548... | [13.070780754089355, 0.4186554551124573] |
22eae513-22e1-4b82-878e-dac1bfd79ada | babel-bodies-action-and-behavior-with-english | 2106.09696 | null | https://arxiv.org/abs/2106.09696v2 | https://arxiv.org/pdf/2106.09696v2.pdf | BABEL: Bodies, Action and Behavior with English Labels | Understanding the semantics of human movement -- the what, how and why of the movement -- is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human mot... | ['Michael J. Black', 'Alejandra Quiros-Ramirez', 'Nikos Athanasiou', 'Arjun Chandrasekaran', 'Abhinanda R. Punnakkal'] | 2021-06-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.77580357e-01 -3.68398875e-01 -6.47268176e-01 -2.28277504e-01
-6.72152519e-01 -5.80972254e-01 6.63437903e-01 -4.25644189e-01
-3.19158494e-01 5.68311989e-01 8.16002786e-01 1.36070877e-01
3.22670341e-01 -3.07032883e-01 -6.71608329e-01 -7.49753356e-01
-1.94802716e-01 3.93145263e-01 3.90796214e-01 -1.03472009... | [8.141790390014648, 0.4831882119178772] |
b45cd340-1075-4cc3-9476-bef5559caa9f | few-shot-class-incremental-learning-for-3d | 2205.15225 | null | https://arxiv.org/abs/2205.15225v2 | https://arxiv.org/pdf/2205.15225v2.pdf | Few-shot Class-incremental Learning for 3D Point Cloud Objects | Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D poi... | ['Shafin Rahman', 'Morteza Saberi', 'Sahar Ahmadi', 'Sameera Ramasinghe', 'Ali Cheraghian', 'Townim Chowdhury'] | 2022-05-30 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 3.50082606e-01 -9.55033582e-03 -6.30500354e-03 -4.21954334e-01
-5.52658498e-01 -5.02737343e-01 6.72158897e-01 -6.60530850e-02
-2.18285322e-01 6.10101998e-01 -4.10732120e-01 -5.05117550e-02
-2.66095817e-01 -8.48117769e-01 -1.08087921e+00 -4.38105583e-01
-1.66482672e-01 8.21492076e-01 8.05319607e-01 -1.92183346... | [7.961592674255371, -3.211730718612671] |
132a996b-4283-4c4b-bd79-f6bcdaa79af2 | electronic-excited-states-in-deep-variational | 2203.09472 | null | https://arxiv.org/abs/2203.09472v3 | https://arxiv.org/pdf/2203.09472v3.pdf | Electronic excited states in deep variational Monte Carlo | Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schr\"odinger equation that scales favorably for large systems is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach uses a... | ['Frank Noé', 'Jan Hermann', 'Paolo A. Erdman', 'Zeno Schätzle', 'Mike Entwistle'] | 2022-03-17 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 6.74443925e-03 -2.77273208e-01 -1.85383260e-02 -9.71179381e-02
-1.08126950e+00 -3.93459409e-01 4.96693134e-01 2.81922162e-01
-5.79357743e-01 1.43275321e+00 -7.45723322e-02 -4.97985840e-01
1.25116706e-01 -1.01011312e+00 -5.39511919e-01 -1.08421290e+00
-8.09872597e-02 7.10596800e-01 -1.76516756e-01 -5.07705212... | [5.356338024139404, 5.165130138397217] |
03119499-f861-40f9-b6c3-e5d151e26ba3 | model-based-learning-for-accelerated-limited | 1708.09832 | null | http://arxiv.org/abs/1708.09832v3 | http://arxiv.org/pdf/1708.09832v3.pdf | Model based learning for accelerated, limited-view 3D photoacoustic tomography | Recent advances in deep learning for tomographic reconstructions have shown
great potential to create accurate and high quality images with a considerable
speed-up. In this work we present a deep neural network that is specifically
designed to provide high resolution 3D images from restricted photoacoustic
measurements... | ['Marta Betcke', 'Sebastien Ourselin', 'Jonas Adler', 'Simon Arridge', 'Felix Lucka', 'Ben Cox', 'Andreas Hauptmann', 'Paul Beard', 'Nam Huynh'] | 2017-08-31 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.90859866e-01 3.60406786e-02 3.07410657e-01 -3.26118708e-01
-7.34858394e-01 5.95532507e-02 2.56150275e-01 -1.26521096e-01
-6.27472639e-01 4.70099032e-01 1.29127875e-01 -3.45096797e-01
-1.50617629e-01 -8.07316899e-01 -5.95440984e-01 -8.74961734e-01
-3.46474499e-02 6.32965744e-01 4.28503960e-01 2.60109961... | [13.180741310119629, -2.6288888454437256] |
2199f63e-3a8c-4e15-ba1b-ef539e3d4093 | locally-transferred-fisher-vectors-for | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Song_Locally-Transferred_Fisher_Vectors_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Song_Locally-Transferred_Fisher_Vectors_ICCV_2017_paper.pdf | Locally-Transferred Fisher Vectors for Texture Classification | Texture classification has been extensively studied in computer vision. Recent research shows that the combination of Fisher vector (FV) encoding and convolutional neural network (CNN) provides significant improvement in texture classification over the previous feature representation methods. However, by truncating the... | ["Lauren J. O'Donnell", 'Heng Huang', 'Qing Li', 'Weidong Cai', 'Yang Song', 'Fan Zhang'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['texture-classification'] | ['computer-vision'] | [ 2.55734831e-01 -3.14431399e-01 -3.59273672e-01 -7.20885336e-01
-5.29133439e-01 -1.63543895e-01 4.02752548e-01 -2.37579063e-01
-1.95458218e-01 3.43814790e-01 5.87222204e-02 -8.41350853e-02
-2.10876048e-01 -8.91722202e-01 -6.11185670e-01 -8.03393304e-01
-9.02943462e-02 -2.67768413e-01 1.67043239e-01 -3.58030759... | [10.21349811553955, -0.1315307319164276] |
c7c11b1c-ae5f-4af9-bc9e-c98746121a8b | hyperspectral-image-classification-of | null | null | http://doi.org/10.1088/1742-6596/1549/5/052011 | https://iopscience.iop.org/article/10.1088/1742-6596/1549/5/052011/pdf | Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples | Aiming at the problem that the manual labeling of samples in the hyperspectral image classification is expensive and laborious, a large number of unlabeled samples are not effectively utilized and the classification results are not ideal. A method which can provide valuable samples and employ convolutional neural netwo... | ['Yufan Wei', 'Xiaobo Luo', 'Lixin Hu'] | 2020-06-01 | null | null | null | journal-of-physics-conference-series-2020-6 | ['few-shot-image-classification'] | ['computer-vision'] | [ 5.34949124e-01 -3.41814488e-01 -4.35936272e-01 -4.56605583e-01
-4.51537460e-01 -6.34388030e-01 1.42535135e-01 6.98407441e-02
-4.22222018e-01 8.80265176e-01 -2.84580737e-01 -3.91642869e-01
-5.73674202e-01 -1.12016237e+00 -7.15912879e-02 -1.09064662e+00
-1.35896042e-01 3.25047046e-01 4.63711657e-03 1.36507347... | [9.863030433654785, -1.5641884803771973] |
ec9c3256-2f3b-4fbf-bbc5-7e44ea58a154 | diminishing-return-of-value-expansion-methods | 2303.03955 | null | https://arxiv.org/abs/2303.03955v1 | https://arxiv.org/pdf/2303.03955v1.pdf | Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning | Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key limitations. A natural question to ask is: How much more sample efficiency can be gained by improving ... | ['Jan Peters', 'Joao Carvalho', 'Michael Lutter', 'Daniel Palenicek'] | 2023-03-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.29840299e-01 2.98922360e-01 -7.13191807e-01 2.96262741e-01
-8.93282175e-01 -6.71577871e-01 4.18322980e-01 8.06134194e-02
-6.07204378e-01 1.30972862e+00 -1.13947853e-01 -5.08001566e-01
-5.33879876e-01 -7.03390062e-01 -6.16404951e-01 -6.16588473e-01
-3.49762172e-01 4.01253104e-01 1.36849657e-01 -2.34416753... | [4.210270881652832, 2.329653263092041] |
4c0e673b-cf32-41f0-9c3d-4400eb6127fe | optimal-feature-transport-for-cross-view | 1907.05021 | null | https://arxiv.org/abs/1907.05021v3 | https://arxiv.org/pdf/1907.05021v3.pdf | Optimal Feature Transport for Cross-View Image Geo-Localization | This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep ... | ['Xin Yu', 'Yujiao Shi', 'Tong Zhang', 'Liu Liu', 'Hongdong Li'] | 2019-07-11 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-1.50096700e-01 -5.15961051e-01 -3.79531085e-02 -6.03393853e-01
-1.11567283e+00 -8.49772751e-01 6.78576767e-01 6.10615574e-02
-3.92611057e-01 3.53238165e-01 1.94738302e-02 2.94240471e-02
-2.80027956e-01 -8.99808228e-01 -9.29269671e-01 -5.89582622e-01
-8.95290971e-02 2.07257330e-01 8.43395293e-02 -1.86056256... | [7.733282089233398, -1.9070545434951782] |
e23cc124-f1a0-4305-a561-71e25b2982ef | dense-resolution-network-for-point-cloud | 2005.06734 | null | https://arxiv.org/abs/2005.06734v2 | https://arxiv.org/pdf/2005.06734v2.pdf | Dense-Resolution Network for Point Cloud Classification and Segmentation | Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. However, it is always challenging due to irregularities, unorderedness, and sparsity. In this article, we propose a novel network named Dense-... | ['Shi Qiu', 'Nick Barnes', 'Saeed Anwar'] | 2020-05-14 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [-2.92761326e-01 -4.61014032e-01 -1.09121576e-01 -2.65793860e-01
-2.23468289e-01 -2.94591159e-01 3.49335343e-01 3.09885144e-01
-1.59832552e-01 3.91551346e-01 -3.62335443e-01 -1.40905797e-01
-5.31508148e-01 -1.15153396e+00 -7.84958482e-01 -2.71160513e-01
-3.54817688e-01 7.54449010e-01 4.39153612e-01 -2.99429506... | [7.851707935333252, -3.225336790084839] |
649e5ab4-4e8e-4eed-81b2-e6e69e417a0d | single-and-multi-task-architectures-for-1 | 1610.08844 | null | http://arxiv.org/abs/1610.08844v2 | http://arxiv.org/pdf/1610.08844v2.pdf | Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016 | The surgical workflow challenge at M2CAI 2016 consists of identifying 8
surgical phases in cholecystectomy procedures. Here, we propose to use deep
architectures that are based on our previous work where we presented several
architectures to perform multiple recognition tasks on laparoscopic videos. In
this technical r... | ['Michel de Mathelin', 'Didier Mutter', 'Andru P. Twinanda', 'Nicolas Padoy', 'Jacques Marescaux'] | 2016-10-27 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 4.04340893e-01 2.59188712e-01 1.84831023e-02 -1.59846365e-01
-7.27331519e-01 -4.46357608e-01 9.29754794e-01 2.02854440e-01
-6.60847068e-01 -6.13813885e-02 3.11866462e-01 -5.98165691e-01
-3.40415776e-01 -2.00934708e-01 -3.88718188e-01 -5.62465429e-01
-3.39498758e-01 6.30194426e-01 3.13725442e-01 1.27768889... | [14.075703620910645, -3.3587512969970703] |
b86cae94-2f7b-427f-87e6-d25647259f70 | ct-dqn-control-tutored-deep-reinforcement | 2212.01343 | null | https://arxiv.org/abs/2212.01343v1 | https://arxiv.org/pdf/2212.01343v1.pdf | CT-DQN: Control-Tutored Deep Reinforcement Learning | One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algorithm, a Deep Reinforcement Learning algorithm that leverages a control tutor, i.e., an exogenous co... | ['Mario di Bernardo', 'Mirco Musolesi', 'Giovanni Russo', 'Marco Coraggio', 'Francesco De Lellis'] | 2022-12-02 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.56286025e-01 6.49811983e-01 -2.40843967e-01 1.85609356e-01
-4.27414268e-01 -6.70410097e-01 4.94519293e-01 7.51639083e-02
-4.55427319e-01 1.13755810e+00 -2.23799914e-01 -6.50730133e-01
-2.88507372e-01 -7.79837668e-01 -1.11993992e+00 -5.81208050e-01
-8.80265385e-02 5.26784062e-01 4.80974242e-02 -7.24826217... | [4.295992851257324, 1.8983036279678345] |
274bbdf6-61d8-4317-ac1a-4d08dcb1a852 | transfer-learning-from-speaker-verification | 1806.04558 | null | http://arxiv.org/abs/1806.04558v4 | http://arxiv.org/pdf/1806.04558v4.pdf | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | Clone a voice in 5 seconds to generate arbitrary speech in real-time | ['Patrick Nguyen', 'Ron J. Weiss', 'Ye Jia', 'Fei Ren', 'Yonghui Wu', 'Ruoming Pang', 'Jonathan Shen', 'Ignacio Lopez Moreno', 'Zhifeng Chen', 'Yu Zhang', 'Quan Wang'] | 2018-06-12 | transfer-learning-from-speaker-verification-1 | http://papers.nips.cc/paper/7700-transfer-learning-from-speaker-verification-to-multispeaker-text-to-speech-synthesis | http://papers.nips.cc/paper/7700-transfer-learning-from-speaker-verification-to-multispeaker-text-to-speech-synthesis.pdf | neurips-2018-12 | ['voice-cloning'] | ['speech'] | [ 7.17807189e-02 3.57013881e-01 5.89586735e-01 -1.20749678e-02
-9.45443213e-01 -1.30160999e+00 3.23132932e-01 -1.53527999e+00
3.19853783e-01 1.30055022e+00 4.75652516e-01 -8.94604325e-01
4.76105750e-01 -5.88925540e-01 -3.17172617e-01 -5.73979378e-01
-5.80303855e-02 4.94717568e-01 1.80852354e-01 -2.83555180... | [15.105846405029297, 6.437905311584473] |
e8f969fc-4544-4578-8335-7e861cc96c0f | bi-directional-relationship-inferring-network | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.pdf | Bi-Directional Relationship Inferring Network for Referring Image Segmentation | Most existing methods do not explicitly formulate the mutual guidance between vision and language. In this work, we propose a bi-directional relationship inferring network (BRINet) to model the dependencies of cross-modal information. In detail, the vision-guided linguistic attention is used to learn the adaptive lingu... | [' Huchuan Lu', ' Lihe Zhang', ' Jiayu Sun', ' Guang Feng', 'Zhiwei Hu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['referring-expression-segmentation'] | ['computer-vision'] | [-2.31074318e-01 -3.20715189e-01 -3.65450561e-01 -5.74585378e-01
-5.81859350e-01 -1.49523929e-01 8.18353534e-01 1.05577953e-01
-4.28292006e-01 4.70829934e-01 3.79389703e-01 -8.00390169e-02
-1.24459537e-02 -7.34398723e-01 -5.45523524e-01 -5.07807851e-01
3.41934204e-01 -1.29536510e-01 3.78088534e-01 -2.02492669... | [10.340043067932129, 1.197568416595459] |
96443da8-8c3a-4f13-a1e3-8f244dcbb97f | towards-precision-in-appearance-based-gaze | 2302.02353 | null | https://arxiv.org/abs/2302.02353v2 | https://arxiv.org/pdf/2302.02353v2.pdf | Towards Precision in Appearance-based Gaze Estimation in the Wild | Appearance-based gaze estimation systems have shown great progress recently, yet the performance of these techniques depend on the datasets used for training. Most of the existing gaze estimation datasets setup in interactive settings were recorded in laboratory conditions and those recorded in the wild conditions disp... | ['Pradipta Biswas', 'Ketan Anand', 'Shambhavi Aggarwal', 'Abhishek Mukhopadhyay', 'Murthy L. R. D.'] | 2023-02-05 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-9.06696171e-02 -4.43935469e-02 -1.95081398e-01 -7.47376800e-01
-3.60432148e-01 -4.23760027e-01 3.34781170e-01 -3.60338032e-01
-2.52346903e-01 6.22071624e-01 -8.00883211e-03 9.97645035e-02
2.30876744e-01 3.62417489e-01 -5.86550355e-01 -5.91586173e-01
-3.91448997e-02 -4.08304594e-02 1.36092320e-01 -3.68127488... | [14.111180305480957, 0.09774181246757507] |
92930ebc-0e8e-4278-b0c0-5ca6f4d0a1ae | feature-decoupling-in-self-supervised | 2209.14385 | null | https://arxiv.org/abs/2209.14385v1 | https://arxiv.org/pdf/2209.14385v1.pdf | Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition | Assuming unknown classes could be present during classification, the open set recognition (OSR) task aims to classify an instance into a known class or reject it as unknown. In this paper, we use a two-stage training strategy for the OSR problems. In the first stage, we introduce a self-supervised feature decoupling me... | ['Philip K. Chan', 'Jingyun Jia'] | 2022-09-28 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.89120007e-01 -3.07177663e-01 -3.81981641e-01 -3.62444699e-01
-6.07415617e-01 -7.65731573e-01 7.52653182e-01 1.69018984e-01
-1.71847299e-01 6.16383135e-01 -2.03641787e-01 -8.97392631e-02
-1.80427477e-01 -7.86649406e-01 -4.22339112e-01 -7.80246735e-01
9.75230485e-02 4.10798490e-01 1.21047750e-01 -1.05215214... | [9.704136848449707, 3.0193428993225098] |
3efb4a7f-7fbd-4911-98fe-3496b1ceb976 | improving-native-language-identification-with | null | null | https://aclanthology.org/W13-1728 | https://aclanthology.org/W13-1728.pdf | Improving Native Language Identification with TF-IDF Weighting | null | ['Peter Wittenburg', 'Binyam Gebrekidan Gebre', 'Tom Heskes', 'Marcos Zampieri'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['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... | [-7.164094924926758, 3.59242582321167] |
501b8c8c-bf47-493d-8895-f6c2a3785625 | multiview-regenerative-morphing-with-dual | 2208.01287 | null | https://arxiv.org/abs/2208.01287v1 | https://arxiv.org/pdf/2208.01287v1.pdf | Multiview Regenerative Morphing with Dual Flows | This paper aims to address a new task of image morphing under a multiview setting, which takes two sets of multiview images as the input and generates intermediate renderings that not only exhibit smooth transitions between the two input sets but also ensure visual consistency across different views at any transition s... | ['Hwann-Tzong Chen', 'Cheng Sun', 'Chih-Jung Tsai'] | 2022-08-02 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 1.52021676e-01 2.16749206e-01 1.22281164e-01 -4.52685386e-01
-7.44575024e-01 -6.06532931e-01 7.44648695e-01 1.20776474e-01
5.22081666e-02 4.14155573e-01 -2.67455041e-01 3.64256091e-02
1.12586796e-01 -1.00462079e+00 -9.79000866e-01 -3.95637810e-01
3.37223947e-01 4.76453900e-01 3.62611800e-01 -1.43578127... | [9.153169631958008, -3.1434760093688965] |
c5882e5b-fc22-43f6-8e96-da79d5240915 | pure-transformers-are-powerful-graph-learners | 2207.02505 | null | https://arxiv.org/abs/2207.02505v2 | https://arxiv.org/pdf/2207.02505v2.pdf | Pure Transformers are Powerful Graph Learners | We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent tokens, augment them with token embeddings, and feed them to a Transformer. With an appropriate choice of... | ['Seunghoon Hong', 'Honglak Lee', 'Moontae Lee', 'Sungjun Cho', 'Seonwoo Min', 'Tien Dat Nguyen', 'Jinwoo Kim'] | 2022-07-06 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 1.10949032e-01 6.58796251e-01 -4.59789813e-01 7.91725144e-02
-5.48182487e-01 -6.75234795e-01 9.89665329e-01 2.26042211e-01
-3.22029859e-01 4.35581535e-01 3.84125710e-01 -8.08407009e-01
2.42417336e-01 -1.30080664e+00 -1.09157407e+00 -5.62107027e-01
-5.73669016e-01 5.10831296e-01 1.03543699e-01 -1.24013789... | [6.931245803833008, 6.2589430809021] |
06cf9d20-a79c-4dae-82bd-7bbc755fb734 | simplifying-deep-learning-based-model-for | 2005.14373 | null | https://arxiv.org/abs/2005.14373v2 | https://arxiv.org/pdf/2005.14373v2.pdf | CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words | To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval based models for code search, but they fail to connect the semantic gap between query and code. An early successful... | ['Chao Liu', 'Ahmed E. Hassan', 'Xin Xia', 'Zhiwei Liu', 'David Lo', 'Shanping Li'] | 2020-05-29 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-6.39644563e-01 -6.70089006e-01 -6.78016782e-01 1.94762219e-02
-9.84956920e-01 -7.35959947e-01 9.47610289e-02 4.88063246e-01
-3.35770279e-01 1.80121046e-02 1.83304116e-01 -2.70367265e-01
-5.10783851e-01 -6.62088215e-01 -6.39645100e-01 -8.22468773e-02
-1.04419284e-01 2.76743978e-01 5.51286101e-01 -1.89035371... | [7.504638195037842, 8.079793930053711] |
676115fe-972f-4c96-937b-3b4cbd0a523f | frequency-domain-blind-quality-assessment-of | 2303.02753 | null | https://arxiv.org/abs/2303.02753v1 | https://arxiv.org/pdf/2303.02753v1.pdf | Frequency-domain Blind Quality Assessment of Blurred and Blocking-artefact Images using Gaussian Process Regression model | Most of the standard image and video codecs are block-based and depending upon the compression ratio the compressed images/videos suffer from different distortions. At low ratios, blurriness is observed and as compression increases blocking artifacts occur. Generally, in order to reduce blockiness, images are low-pass ... | ['M. Ghanbari', 'Ekram Khan', 'Athar A. Moinuddin', 'Maryam Viqar'] | 2023-03-05 | null | null | null | null | ['gpr', 'gpr', 'blocking'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 3.21848392e-01 -7.87371695e-01 2.77084764e-02 -5.99177089e-03
-4.09848839e-01 -3.72020245e-01 5.27268052e-01 1.53650150e-01
-1.73813641e-01 7.05095470e-01 3.84781063e-01 6.93593174e-02
-3.46900791e-01 -6.24439061e-01 -4.20639277e-01 -9.57994342e-01
-2.76335716e-01 -2.59889215e-01 4.99928482e-02 2.74949968... | [11.69080924987793, -2.1015031337738037] |
c37e9622-fce8-4b01-9343-02ba0e24b14f | query-based-video-summarization-with-pseudo | 2307.01945 | null | https://arxiv.org/abs/2307.01945v1 | https://arxiv.org/pdf/2307.01945v1.pdf | Query-based Video Summarization with Pseudo Label Supervision | Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels to pr... | ['Marcel Worring', 'Marta Mrak', 'Luka Murn', 'Jia-Hong Huang'] | 2023-07-04 | null | null | null | null | ['video-summarization', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 7.41711438e-01 7.90139362e-02 -6.33784473e-01 -6.43327534e-01
-1.01742494e+00 -2.61966974e-01 6.19371951e-01 2.07501039e-01
-3.88722777e-01 5.48669755e-01 8.74630213e-01 2.48743474e-01
4.52649534e-01 -3.61850858e-01 -9.51625228e-01 -4.63814557e-01
1.79104358e-01 2.51729608e-01 9.35874283e-02 -9.57039930... | [10.441788673400879, 0.5152862668037415] |
ace358a9-9966-4658-945d-33ac46e87ba2 | eider-evidence-enhanced-document-level | 2106.08657 | null | https://arxiv.org/abs/2106.08657v2 | https://arxiv.org/pdf/2106.08657v2.pdf | Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion | Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair... | ['Jiawei Han', 'Yuning Mao', 'Sha Li', 'Jiaming Shen', 'Yiqing Xie'] | 2021-06-16 | null | https://aclanthology.org/2022.findings-acl.23 | https://aclanthology.org/2022.findings-acl.23.pdf | findings-acl-2022-5 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 4.57064770e-02 5.66494524e-01 -4.83597875e-01 -2.09580734e-01
-7.94666767e-01 -5.58334351e-01 6.49959207e-01 4.72026527e-01
-5.65589845e-01 8.19325268e-01 2.50919998e-01 -3.28164279e-01
-1.98756605e-01 -9.49355483e-01 -6.72339141e-01 -2.39740640e-01
1.30751371e-01 6.63114548e-01 5.13107777e-01 -2.24267002... | [9.34903335571289, 8.623761177062988] |
3684815c-fbb2-4624-96dc-1b9ae47ac706 | predicting-issue-types-with-sebert | 2205.01335 | null | https://arxiv.org/abs/2205.01335v1 | https://arxiv.org/pdf/2205.01335v1.pdf | Predicting Issue Types with seBERT | Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the NLBSE challenge for the task of issue type predicti... | ['Steffen Herbold', 'Alexander Trautsch'] | 2022-05-03 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 7.17732981e-02 5.42709410e-01 -4.18213814e-01 -1.60415947e-01
-1.32405877e+00 -4.92052495e-01 7.70947456e-01 3.74801755e-01
-3.48057657e-01 3.52598250e-01 2.30753496e-01 -8.06096792e-01
2.61247694e-01 -5.95287263e-01 -8.28802228e-01 4.81300473e-01
2.71681070e-01 4.17274147e-01 7.36778378e-01 -6.21306598... | [10.723470687866211, 8.892197608947754] |
b4c41d5a-3243-46ce-97d2-8b3e37031c2c | rnn-based-early-cyber-attack-detection-for | 1709.02232 | null | http://arxiv.org/abs/1709.02232v1 | http://arxiv.org/pdf/1709.02232v1.pdf | RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process | An RNN-based forecasting approach is used to early detect anomalies in
industrial multivariate time series data from a simulated Tennessee Eastman
Process (TEP) with many cyber-attacks. This work continues a previously
proposed LSTM-based approach to the fault detection in simpler data. It is
considered necessary to ad... | ['Andrey Lavrentyev', 'Pavel Filonov', 'Fedor Kitashov'] | 2017-09-07 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 1.90927237e-01 -2.76004940e-01 6.21662199e-01 -2.31059968e-01
-2.88910806e-01 -1.92701906e-01 4.94011432e-01 1.32639930e-01
1.88770536e-02 6.56008780e-01 -1.21100597e-01 -7.84093797e-01
-4.67336625e-01 -5.83515406e-01 -1.74988613e-01 -7.93382049e-01
-7.81412184e-01 3.36658716e-01 9.33719650e-02 -2.81635135... | [6.957039833068848, 2.5726842880249023] |
22676740-414d-4354-90f5-af0733ef52eb | perturbation-of-deep-autoencoder-weights-for | 2205.08358 | null | https://arxiv.org/abs/2205.08358v1 | https://arxiv.org/pdf/2205.08358v1.pdf | Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data | Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, the performance of DNN is often challenged by traditional machine learning models in tabular data classification. In this paper, we propose periodical perturbations (prune and re... | ['Sakib Abrar', 'Manar Samad'] | 2022-05-17 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.85366049e-02 2.45790616e-01 -2.26271793e-01 -2.67380327e-01
-2.69023359e-01 -2.53549218e-01 7.54374340e-02 1.71201732e-02
-5.04173338e-01 8.59914243e-01 -5.40937111e-02 -3.37911189e-01
-2.46840373e-01 -9.40793037e-01 -9.90523815e-01 -9.14971530e-01
3.45579498e-02 6.62971437e-01 -1.49738997e-01 -8.93104821... | [8.736376762390137, 3.068572759628296] |
6eb70c24-cca8-4faf-8a79-276aae5c56cf | taming-detection-transformers-for-medical | 2306.15472 | null | https://arxiv.org/abs/2306.15472v1 | https://arxiv.org/pdf/2306.15472v1.pdf | Taming Detection Transformers for Medical Object Detection | The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support clinicians during this difficult task, several automated solutions were proposed relying on complex methods with many hyperparameters. In thi... | ['Klaus H. Maier-Hein', 'Tassilo Wald', 'Saikat Roy', 'Michael Baumgartner', 'Marc K. Ickler'] | 2023-06-27 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 2.41328076e-01 3.48218679e-01 -1.10434704e-01 -1.73707351e-01
-6.80750668e-01 -3.02364707e-01 6.15542710e-01 4.14678037e-01
-4.62332010e-01 6.43777311e-01 -2.01558694e-01 -5.26592791e-01
-1.03909753e-01 -5.18944502e-01 -2.27935195e-01 -5.85524976e-01
-1.72587544e-01 7.40137458e-01 1.08763301e+00 2.41364643... | [15.050665855407715, -2.3602144718170166] |
eaaa7ee7-14f9-4b70-be63-d8804da754ce | the-use-of-second-life-for-deception | null | null | https://aclanthology.org/W16-0805 | https://aclanthology.org/W16-0805.pdf | The Use of Second Life for Deception Detection Research | null | ['Kevin McCabe', 'Stephen Kunath'] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.401474952697754, 3.6845099925994873] |
33544e91-57fb-4aad-a702-a2e8a75f367e | you-are-catching-my-attention-are-vision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.pdf | You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks? | Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention ... | ['Yu Cheng', 'Kai Zou', 'Pan Zhou', 'Zenghui Yuan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['backdoor-attack'] | ['adversarial'] | [-4.25818227e-02 -1.34023294e-01 -1.23709723e-01 2.74910808e-01
-5.38137555e-02 -8.45767081e-01 5.52709103e-01 -2.44013876e-01
-2.70348966e-01 9.04391259e-02 -2.03980550e-01 -5.51214397e-01
1.77103415e-01 -9.31398988e-01 -8.57334912e-01 -1.05625057e+00
1.18859872e-01 -6.40697837e-01 7.05883384e-01 -4.73112911... | [5.647037982940674, 7.768864154815674] |
2804ab8e-8dae-42a3-8dae-e625b81e2517 | a-note-on-the-regularity-of-images-generated | 2204.10588 | null | https://arxiv.org/abs/2204.10588v2 | https://arxiv.org/pdf/2204.10588v2.pdf | A Note on the Regularity of Images Generated by Convolutional Neural Networks | The regularity of images generated by convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed. In a resolution-independent, infinite dimensional setting, it is shown that such images, represented as functions, are always continuous and, in some circumstances, even con... | ['Martin Holler', 'Andreas Habring'] | 2022-04-22 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 3.17203373e-01 6.88781142e-01 1.22559384e-01 -6.64167106e-03
-1.94380045e-01 -4.36075121e-01 6.63577020e-01 -3.93836856e-01
-2.88256198e-01 8.71335745e-01 1.65495187e-01 -1.70670763e-01
-3.81533474e-01 -8.48349571e-01 -8.65352750e-01 -8.57340932e-01
2.48530079e-02 -7.36172348e-02 -1.71602353e-01 1.91927273... | [11.829960823059082, -2.4065184593200684] |
f7546f87-6c64-49bb-8f16-a9e579cc311c | extending-label-smoothing-regularization-with | 2009.05226 | null | https://arxiv.org/abs/2009.05226v1 | https://arxiv.org/pdf/2009.05226v1.pdf | Extending Label Smoothing Regularization with Self-Knowledge Distillation | Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the LSR method to the KD regime and applying a softer temperature. Then we improve the LsrKD by a Teacher Correction(TC) method, which manually... | ['Wen-feng Pang', 'Ji-Yue Wang', 'Pei Zhang', 'Jie Li'] | 2020-09-11 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-1.29319534e-01 2.68893331e-01 -4.97942597e-01 -1.32892504e-01
-4.01398748e-01 -4.38530296e-01 6.26366854e-01 -8.98925401e-03
-7.03202546e-01 1.01773024e+00 -1.24423809e-01 -4.56214905e-01
-2.22578451e-01 -6.58578634e-01 -9.76704299e-01 -1.22556543e+00
2.68595874e-01 3.72173429e-01 7.04069376e-01 -1.34513617... | [9.445252418518066, 3.420530319213867] |
4b0afed6-3a95-407e-88ef-866df39fb1ac | deep-matching-prior-test-time-optimization | 2106.03090 | null | https://arxiv.org/abs/2106.03090v3 | https://arxiv.org/pdf/2106.03090v3.pdf | Deep Matching Prior: Test-Time Optimization for Dense Correspondence | Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model. To overcome this, recent learning-based methods have attempted to learn a good matching prior within a model itself on large trainin... | ['Seungryong Kim', 'Sunghwan Hong'] | 2021-06-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Hong_Deep_Matching_Prior_Test-Time_Optimization_for_Dense_Correspondence_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Hong_Deep_Matching_Prior_Test-Time_Optimization_for_Dense_Correspondence_ICCV_2021_paper.pdf | iccv-2021-1 | ['geometric-matching', 'dense-pixel-correspondence-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.41140956e-01 4.66337986e-02 -9.63210091e-02 -4.50931728e-01
-1.01675832e+00 -3.01768005e-01 5.48183560e-01 8.32672417e-02
-4.24089968e-01 2.74439037e-01 -2.31473714e-01 -1.22062184e-01
-1.28603339e-01 -7.56724656e-01 -1.19589961e+00 -4.47162002e-01
1.87689722e-01 6.62664175e-01 4.55749810e-01 -4.80039828... | [8.533226013183594, -2.1891167163848877] |
b4fba6da-279a-423e-a819-76fdd9b169e1 | a-deep-neural-network-model-for-the-task-of | null | null | https://www.researchgate.net/publication/330556058_A_Deep_Neural_Network_Model_for_the_task_of_Named_Entity_Recognition | http://www.ijmlc.org/vol9/758-ML0025.pdf | A Deep Neural Network Model for the Task of Named Entity Recognition | One of the most important factors which directly and significantly affects the quality of the neural sequence labeling is the selection and encoding the input features to generate rich semantic and grammatical representation vectors. In this paper, we propose a deep neural network model to address a particular task of ... | ['Anh Le. Mikhail S. Burtsev'] | 2018-02-01 | null | null | null | international-journal-of-machine-learning-and-1 | ['named-entity-recognition-in-vietnamese'] | ['natural-language-processing'] | [ 7.65921324e-02 -2.40480185e-01 -2.95228790e-03 -6.45365655e-01
-4.86773133e-01 -7.27530718e-01 3.81991535e-01 1.70927197e-01
-1.03166556e+00 1.04466546e+00 1.25183791e-01 -3.81300986e-01
3.85433853e-01 -8.97634506e-01 -6.00827157e-01 -2.56880343e-01
7.40483254e-02 2.89288104e-01 -2.97841895e-02 -2.62168854... | [9.856161117553711, 9.635419845581055] |
456f7b72-41b2-4337-9e7a-e7af4115cea0 | proppy-a-system-to-unmask-propaganda-in | 1912.06810 | null | https://arxiv.org/abs/1912.06810v1 | https://arxiv.org/pdf/1912.06810v1.pdf | Proppy: A System to Unmask Propaganda in Online News | We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the ... | ['Alberto Barrón-Cedeño', 'Israa Jaradat', 'Giovanni Da San Martino', 'Preslav Nakov'] | 2019-12-14 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [-3.31296861e-01 -2.06447795e-01 -9.05465901e-01 1.39767766e-01
-7.75504887e-01 -8.34364653e-01 1.48690081e+00 8.16204190e-01
-3.15079868e-01 4.51340973e-01 1.05001163e+00 -5.19847989e-01
1.38831660e-01 -9.93035376e-01 -3.52873623e-01 -4.03268665e-01
-1.90467328e-01 5.02228141e-01 3.39201689e-01 -3.76148731... | [8.468461990356445, 10.63969612121582] |
e5b7beef-91ef-4866-9b58-3cbeb5385e0f | sample-complexity-of-variance-reduced | 2305.18420 | null | https://arxiv.org/abs/2305.18420v1 | https://arxiv.org/pdf/2305.18420v1.pdf | Sample Complexity of Variance-reduced Distributionally Robust Q-learning | Dynamic decision making under distributional shifts is of fundamental interest in theory and applications of reinforcement learning: The distribution of the environment on which the data is collected can differ from that of the environment on which the model is deployed. This paper presents two novel model-free algorit... | ['Zhengyuan Zhou', 'Jose Blanchet', 'Nian Si', 'Shengbo Wang'] | 2023-05-28 | null | null | null | null | ['q-learning'] | ['methodology'] | [-8.44629258e-02 6.22824989e-02 -2.39321291e-01 -1.40297160e-01
-1.16824293e+00 -5.91603696e-01 2.73892973e-02 4.83661056e-01
-1.03001666e+00 1.04413974e+00 -4.34534162e-01 -6.95731401e-01
-9.17530477e-01 -7.33390093e-01 -6.58866048e-01 -1.02054119e+00
-6.50048912e-01 4.79110271e-01 -1.81849301e-02 -4.78238985... | [4.357432842254639, 2.7879159450531006] |
80f2775d-8ee9-4fe7-ae97-1215e2f423c2 | efficient-splitting-based-method-for-global | 1604.07681 | null | http://arxiv.org/abs/1604.07681v1 | http://arxiv.org/pdf/1604.07681v1.pdf | Efficient Splitting-based Method for Global Image Smoothing | Edge-preserving smoothing (EPS) can be formulated as minimizing an objective
function that consists of data and prior terms. This global EPS approach shows
better smoothing performance than a local one that typically has a form of
weighted averaging, at the price of high computational cost. In this paper, we
introduce ... | ['Bumsub Ham', 'Youngjung Kim', 'Kwanghoon Sohn', 'Dongbo Min'] | 2016-04-26 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 1.09070092e-01 2.93619316e-02 3.88197780e-01 -4.45788354e-01
-1.10911024e+00 -1.50286928e-01 1.95859358e-01 2.03835174e-01
-6.02534115e-01 6.54201865e-01 -1.06212027e-01 9.95118544e-02
-6.23920858e-02 -4.60599184e-01 -6.78987861e-01 -8.06749701e-01
-6.93432167e-02 1.64331540e-01 6.39628232e-01 -1.06710918... | [11.53538990020752, -2.5561835765838623] |
c13a425b-dc5f-4c3a-a29b-5c85f5ea17c7 | gaussian-processes-meet-neuralodes-a-bayesian | 2103.03385 | null | https://arxiv.org/abs/2103.03385v1 | https://arxiv.org/pdf/2103.03385v1.pdf | Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data | This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differ... | ['Paris Perdikaris', 'Mohamed Aziz Bhouri'] | 2021-03-04 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-1.44210488e-01 -5.87316006e-02 -1.72799826e-02 1.62094593e-01
-5.23564577e-01 -4.07908887e-01 7.15432703e-01 -1.77968591e-02
-2.96918042e-02 1.14740002e+00 -1.93775788e-01 -3.60776484e-01
-6.56376541e-01 -4.35514838e-01 -5.25451422e-01 -1.02133965e+00
-6.00408196e-01 6.28648698e-01 -2.52196193e-01 4.19565663... | [6.602405071258545, 3.603386163711548] |
2328a386-e74a-49bf-9873-18b949af5aea | elf-opengo-an-analysis-and-open | 1902.04522 | null | https://arxiv.org/abs/1902.04522v5 | https://arxiv.org/pdf/1902.04522v5.pdf | ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these prom... | ['James Pinkerton', 'Yuandong Tian', 'Qucheng Gong', 'Jerry Ma', 'C. Lawrence Zitnick', 'Zhuoyuan Chen', 'Shubho Sengupta'] | 2019-02-12 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-3.78363281e-01 4.40611303e-01 -9.00219232e-02 -9.88703445e-02
-3.87315691e-01 -3.67618650e-01 4.02480334e-01 -3.00541550e-01
-4.91104394e-01 9.33119178e-01 1.19317904e-01 -3.53610307e-01
-3.75212133e-01 -5.76796412e-01 -5.57001412e-01 -3.94034594e-01
-5.01210749e-01 6.68140352e-01 -9.65038016e-02 -7.65685081... | [3.613926649093628, 1.4338847398757935] |
2aa693ab-9944-4dd5-87a4-1beac4345809 | multi-scenario-ranking-with-adaptive-feature | 2306.16732 | null | https://arxiv.org/abs/2306.16732v1 | https://arxiv.org/pdf/2306.16732v1.pdf | Multi-Scenario Ranking with Adaptive Feature Learning | Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network struct... | ['Chenliang Li', 'Qian Wang', 'Bo Zheng', 'Jian Xu', 'Hongbo Deng', 'Xubin Li', 'Si Chen', 'Bofang Li', 'Yu Tian'] | 2023-06-29 | null | null | null | null | ['retrieval', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [-4.27926108e-02 -3.33325326e-01 -8.39767814e-01 -5.23488164e-01
-2.52912611e-01 -5.76668501e-01 3.37902725e-01 -1.56100616e-01
1.33620948e-02 4.70907569e-01 1.76862851e-01 -3.58532906e-01
-1.27214646e+00 -7.62436986e-01 -3.93842012e-01 -5.75349569e-01
-2.71602571e-01 4.53287035e-01 2.00747773e-02 -7.35792220... | [10.073874473571777, 5.482692241668701] |
7b5eb436-25a1-4958-9983-efa45dcae66b | analysis-and-forecasting-of-financial-time | 2011.08011 | null | https://arxiv.org/abs/2011.08011v2 | https://arxiv.org/pdf/2011.08011v2.pdf | Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models | Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive sy... | ['Subhasis Dasgupta', 'Jaydip Sen', 'Sidra Mehtab'] | 2020-11-07 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-7.62320399e-01 -6.65354013e-01 -2.23697990e-01 -3.67444664e-01
-2.40987822e-01 -4.84589159e-01 6.76255703e-01 -1.20251648e-01
-2.70021141e-01 8.84983659e-01 9.00088325e-02 -8.42408776e-01
-2.10342959e-01 -1.32638156e+00 -5.71255684e-01 -4.38972890e-01
-5.21874428e-01 1.88678220e-01 3.28078941e-02 -5.60971260... | [4.4630913734436035, 4.233850955963135] |
c4306c0a-7a56-42fd-b0c6-fb57bb3eabbe | contrastmask-contrastive-learning-to-segment | 2203.09775 | null | https://arxiv.org/abs/2203.09775v2 | https://arxiv.org/pdf/2203.09775v2.pdf | ContrastMask: Contrastive Learning to Segment Every Thing | Partially-supervised instance segmentation is a task which requests segmenting objects from novel unseen categories via learning on limited seen categories with annotated masks thus eliminating demands of heavy annotation burden. The key to addressing this task is to build an effective class-agnostic mask segmentation ... | ['Wei Shen', 'Yan Wang', 'Shouhong Ding', 'Ruixin Zhang', 'Kai Zhao', 'Xuehui Wang'] | 2022-03-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 8.43705475e-01 2.92297751e-01 -3.03311825e-01 -6.09080732e-01
-5.04364610e-01 -7.55065203e-01 5.88219404e-01 -2.43280679e-01
-3.86319250e-01 5.80108523e-01 -2.03065321e-01 -1.23290047e-02
3.85331482e-01 -5.62765658e-01 -7.79092729e-01 -9.00720477e-01
2.45628625e-01 5.48380435e-01 7.51372278e-01 1.74896166... | [9.59369945526123, 0.6701580882072449] |
7ff0473a-cb88-45d7-9d51-03317fb8174e | analysis-of-semi-supervised-methods-for | 2208.00544 | null | https://arxiv.org/abs/2208.00544v1 | https://arxiv.org/pdf/2208.00544v1.pdf | Analysis of Semi-Supervised Methods for Facial Expression Recognition | Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in... | ['Ali Etemad', 'Shuvendu Roy'] | 2022-07-31 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 8.41646865e-02 1.01167440e-01 -3.26270044e-01 -1.05400276e+00
-6.72364652e-01 -6.78052977e-02 3.79198372e-01 -3.26365560e-01
-4.98543918e-01 9.26110923e-01 -2.19340906e-01 4.50179391e-02
2.13651076e-01 -3.55698317e-01 -6.24877691e-01 -5.95910013e-01
1.79294646e-02 4.63921994e-01 -1.99076697e-01 -1.40643209... | [13.57992172241211, 1.669561743736267] |
3417430a-a008-483b-adf3-7b19f461f16a | material-identification-from-radiographs | 2303.06005 | null | https://arxiv.org/abs/2303.06005v1 | https://arxiv.org/pdf/2303.06005v1.pdf | Material Identification From Radiographs Without Energy Resolution | We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use... | ['Marc L. Klasky', 'Jennifer L. Schei', 'Lauren A. Misurek', 'Samuel M. Gonzales', 'Elena Guardincerri', 'Michael T. McCann'] | 2023-03-10 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 5.32175720e-01 -1.65133551e-01 9.38340127e-02 -3.03049266e-01
-1.03024852e+00 -3.04891914e-01 1.02605127e-01 4.89952326e-01
-6.31942272e-01 5.99577904e-01 -3.81468356e-01 -3.09476376e-01
-4.57809418e-01 -6.22247398e-01 -7.59080052e-01 -8.43822718e-01
1.06331855e-01 1.19355333e+00 4.63945836e-01 1.92217395... | [13.0095796585083, -2.710577964782715] |
1b9cfe20-673f-48f0-a63f-18f84513b063 | localization-using-multi-focal-spatial | 2305.01905 | null | https://arxiv.org/abs/2305.01905v1 | https://arxiv.org/pdf/2305.01905v1.pdf | Localization using Multi-Focal Spatial Attention for Masked Face Recognition | Since the beginning of world-wide COVID-19 pandemic, facial masks have been recommended to limit the spread of the disease. However, these masks hide certain facial attributes. Hence, it has become difficult for existing face recognition systems to perform identity verification on masked faces. In this context, it is n... | ['Junmo Kim', 'JungWoo Chang', 'Dongmin Cho', 'Jaesung Ahn', 'Hyeong Gwon Hong', 'Hanbyel Cho', 'Yooshin Cho'] | 2023-05-03 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 4.31269586e-01 -1.53775796e-01 5.14539331e-02 -4.18682963e-01
-5.30596614e-01 -4.75544989e-01 4.42099661e-01 -5.95225096e-01
-3.29312235e-01 6.71993971e-01 1.01035960e-01 -2.51624554e-01
-2.93350909e-02 -3.40466976e-01 -4.13434505e-01 -8.26740205e-01
5.09583019e-02 -1.46730185e-01 -3.75163615e-01 -2.04460502... | [13.124866485595703, 0.6656903624534607] |
162c53ca-450d-4500-8286-71d0229012ec | contravening-esotery-cryptanalysis-of | 1606.06047 | null | http://arxiv.org/abs/1606.06047v1 | http://arxiv.org/pdf/1606.06047v1.pdf | Contravening Esotery: Cryptanalysis of Knapsack Cipher using Genetic Algorithms | Cryptanalysis of knapsack cipher is a fascinating problem which has eluded
the computing fraternity for decades. However, in most of the cases either the
time complexity of the proposed algorithm is colossal or an insufficient number
of samples have been taken for verification. The present work proposes a
Genetic Algor... | ['Harmeet Singh'] | 2016-06-20 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 3.66322786e-01 -3.02911788e-01 7.73189887e-02 1.11140171e-02
3.38530429e-02 -7.16596603e-01 2.61470258e-01 3.90774637e-01
-5.16262293e-01 9.94205117e-01 -3.63320887e-01 -7.40338743e-01
-5.94163239e-01 -8.72934580e-01 -2.52678990e-01 -9.81879532e-01
-1.89888969e-01 1.73995793e-01 1.22739092e-01 -4.20174837... | [5.749305248260498, 4.515336513519287] |
24cdd531-8367-4ce1-b906-0f2cc4e26917 | how-to-train-your-agent-to-read-and-write | 2101.00916 | null | https://arxiv.org/abs/2101.00916v1 | https://arxiv.org/pdf/2101.00916v1.pdf | How to Train Your Agent to Read and Write | Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (\eg{students}) to fully {grasp} this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and ... | ['Qi Wu', 'Mingkui Tan', 'Guanghui Xu', 'Mengge He', 'Li Liu'] | 2021-01-04 | null | null | null | null | ['kg-to-text'] | ['natural-language-processing'] | [ 2.33073160e-01 6.93458736e-01 -2.03582793e-01 -1.93604857e-01
-4.89591092e-01 -9.16360140e-01 6.87791467e-01 1.89247116e-01
-5.24824783e-02 1.00079703e+00 1.25990823e-01 -6.42751515e-01
-3.40105832e-01 -1.08824515e+00 -9.57340419e-01 -3.37438941e-01
4.88214195e-01 6.37418926e-01 6.70935139e-02 -1.21100739... | [11.922272682189941, 8.961125373840332] |
3cc9d7eb-697a-4610-ada7-6aa2c0fd1a11 | itcm-a-real-time-internet-traffic-classifier | 1501.01321 | null | http://arxiv.org/abs/1501.01321v1 | http://arxiv.org/pdf/1501.01321v1.pdf | ITCM: A Real Time Internet Traffic Classifier Monitor | The continual growth of high speed networks is a challenge for real-time
network analysis systems. The real time traffic classification is an issue for
corporations and ISPs (Internet Service Providers). This work presents the
design and implementation of a real time flow-based network traffic
classification system. Th... | ['José Everardo Bessa Maia', 'Silas Santiago Lopes Pereira', 'Jorge Luiz de Castro e Silva'] | 2015-01-06 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.11715639e-01 -5.11192739e-01 -1.64315507e-01 -5.91429770e-01
2.35961318e-01 -6.02430582e-01 3.95631433e-01 5.87520540e-01
-4.48273659e-01 6.64006412e-01 -6.53548658e-01 -8.95878136e-01
-4.70232725e-01 -1.09225547e+00 1.69673949e-01 -3.56956482e-01
-1.78232163e-01 1.07505858e+00 1.06474352e+00 7.68691972... | [5.110086441040039, 7.183934688568115] |
dc50ac39-9fe5-4ba2-ba18-cd94a204e4db | fusing-multiple-features-for-depth-based | null | null | https://doi.org/10.1145/2629483 | http://xperzy.github.io/paper/zhu_rgbdaction_tist.pdf | Fusing multiple features for depth-based action recognition | Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the three-dimensional (3D) depth data captured by the emerging RGB-D sensors. Several features and/or algorithms have been proposed for depth-b... | ['Wenbin Chen', 'Guodong Guo', 'Yu Zhu'] | 2015-05-01 | null | null | null | acm-transactions-on-intelligent-systems-and | ['multimodal-activity-recognition', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.26988626e-01 -4.87215757e-01 -4.16539580e-01 -3.07119131e-01
-5.69230676e-01 4.56348509e-02 5.50831854e-01 -2.07833007e-01
-2.42551059e-01 4.68171507e-01 4.33993161e-01 3.04132640e-01
-2.89814651e-01 -6.67211711e-01 4.14669402e-02 -1.16532123e+00
1.91154987e-01 -2.13572457e-02 6.08176112e-01 -1.53366596... | [7.901707649230957, 0.3697359263896942] |
22fffb0b-b60f-417b-9384-5e66656353f7 | use-cases-of-quantum-optimization-for-finance | 2010.01312 | null | https://arxiv.org/abs/2010.01312v1 | https://arxiv.org/pdf/2010.01312v1.pdf | Use Cases of Quantum Optimization for Finance | In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optim... | ['Roman Orus', 'Enrique Lizaso', 'Samuel Mugel'] | 2020-10-03 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-4.26131725e-01 2.52650641e-02 1.60738170e-01 -2.72899240e-01
-2.98456311e-01 -4.10400301e-01 4.09722120e-01 -1.52637109e-01
-4.83008534e-01 9.30917144e-01 2.74566486e-02 -5.46044648e-01
-4.74345267e-01 -1.43822432e+00 -3.42809886e-01 -8.05550098e-01
-4.65285867e-01 9.65064168e-01 -1.35531556e-02 -9.44061756... | [5.565478324890137, 4.930283546447754] |
96c3c952-8a22-4d47-8038-2af0935ac5fc | robust-website-fingerprinting-through-the | 1811.07153 | null | http://arxiv.org/abs/1811.07153v3 | http://arxiv.org/pdf/1811.07153v3.pdf | Robust Website Fingerprinting Through the Cache Occupancy Channel | Website fingerprinting attacks, which use statistical analysis on network
traffic to compromise user privacy, have been shown to be effective even if the
traffic is sent over anonymity-preserving networks such as Tor. The classical
attack model used to evaluate website fingerprinting attacks assumes an on-path
adversar... | ['Anatoly Shusterman', 'Yuval Yarom', 'Yossi Oren', 'Yarden Haskal', 'Yosef Meltser', 'Prateek Mittal', 'Lachlan Kang'] | 2018-11-17 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [ 1.81656964e-02 -3.38507712e-01 -6.09126687e-01 1.39587089e-01
-5.82728684e-01 -1.33152258e+00 5.82415164e-01 -3.29444408e-02
-3.83782268e-01 2.58919686e-01 -2.22396910e-01 -1.12986553e+00
2.44312927e-01 -1.31868291e+00 -7.07248569e-01 -4.06801969e-01
-3.70001167e-01 4.07619417e-01 9.59403157e-01 -1.56828031... | [5.552857875823975, 7.360994815826416] |
b2086a03-53c1-4a04-9108-0fb272ffda92 | multi-modality-multi-scale-cardiovascular | 2304.09322 | null | https://arxiv.org/abs/2304.09322v1 | https://arxiv.org/pdf/2304.09322v1.pdf | Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History | Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved ou... | ['Xianling Cong', 'Jianhui Zhuang', 'Xiankai Li', 'Lele Cong', 'Hongren Zhou', 'Chengyou Jia', 'Hechang Chen', 'Bo Yu'] | 2023-04-18 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.08794385e-01 -4.62675244e-01 -2.25704968e-01 -2.76017010e-01
-8.32145452e-01 -1.93990678e-01 3.66202474e-01 2.00357810e-01
-7.23751411e-02 6.81261182e-01 3.27499181e-01 -1.17655627e-01
-3.60201269e-01 -9.45692539e-01 -1.65932313e-01 -1.11455631e+00
3.78386863e-02 1.19246401e-01 6.03133924e-02 -1.00794874... | [14.251232147216797, 3.095715045928955] |
ac39e483-8674-49eb-8cb5-fd016b97e1e4 | anonet-weakly-supervised-anomaly-detection-in | 1911.10608 | null | https://arxiv.org/abs/1911.10608v1 | https://arxiv.org/pdf/1911.10608v1.pdf | AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces | Humans can easily detect a defect (anomaly) because it is different or salient when compared to the surface it resides on. Today, manual human visual inspection is still the norm because it is difficult to automate anomaly detection. Neural networks are a useful tool that can teach a machine to find defects. However, t... | ['John Zelek', 'Manpreet Singh Minhas'] | 2019-11-24 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.41116142e-01 2.58159876e-01 3.18008453e-01 -2.16447979e-01
-3.05801570e-01 -1.59439161e-01 1.98749989e-01 4.45526093e-01
-5.44244885e-01 1.72938004e-01 -4.74765569e-01 -4.00585175e-01
8.56863856e-02 -7.36457109e-01 -8.89147937e-01 -5.81209302e-01
-2.33479410e-01 2.82722086e-01 7.17229724e-01 -1.43265784... | [7.744766712188721, 2.120084285736084] |
254706d9-276f-4cef-a673-3191c30c4b59 | masked-modeling-duo-learning-representations | 2210.14648 | null | https://arxiv.org/abs/2210.14648v3 | https://arxiv.org/pdf/2210.14648v3.pdf | Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input | Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it learns representations indirectly by reconstructing masked input patches. Several methods learn representations directly by predicting representations of masked patches; however, we think using all patches to encode training signa... | ['Kunio Kashino', 'Noboru Harada', 'Yasunori Ohishi', 'Daiki Takeuchi', 'Daisuke Niizumi'] | 2022-10-26 | null | null | null | null | ['audio-tagging', 'keyword-spotting', 'speaker-identification'] | ['audio', 'speech', 'speech'] | [ 9.80698168e-02 6.46381915e-01 -3.75512034e-01 -1.90442681e-01
-9.04200315e-01 -1.53080672e-01 3.89007300e-01 -5.26090860e-01
1.60424829e-01 6.97398007e-01 6.67500496e-01 7.24153146e-02
4.02410239e-01 -7.05311656e-01 -1.32694399e+00 -6.38225675e-01
-2.89419174e-01 1.76983833e-01 2.89860815e-01 -1.97707236... | [9.414860725402832, 1.4334845542907715] |
418e4e4c-621e-4030-b6f4-03992360e92e | scene-restoring-for-narrative-machine-reading | null | null | https://aclanthology.org/2020.emnlp-main.247 | https://aclanthology.org/2020.emnlp-main.247.pdf | Scene Restoring for Narrative Machine Reading Comprehension | This paper focuses on machine reading comprehension for narrative passages. Narrative passages usually describe a chain of events. When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively. Inspired by this... | ['Zhicheng Sheng', 'Yantao Jia', 'Jun Zhao', 'Kang Liu', 'Yuanzhe Zhang', 'Zhixing Tian'] | null | null | null | null | emnlp-2020-11 | ['cloze-test'] | ['natural-language-processing'] | [ 1.89560756e-01 1.84832960e-01 -8.93845037e-02 -2.55789995e-01
-4.30024087e-01 -6.19397104e-01 6.25678360e-01 4.32000607e-01
-1.56983227e-01 5.08988917e-01 9.87578630e-01 -3.64071727e-01
-4.08624709e-02 -1.20435834e+00 -8.37132752e-01 -4.10777256e-02
2.75101513e-01 4.22753483e-01 2.28906170e-01 -4.53304559... | [11.2647123336792, 8.813675880432129] |
65d20078-ae14-4e1b-ac13-860d91577e48 | desnet-decomposed-scale-consistent-network | 2211.10994 | null | https://arxiv.org/abs/2211.10994v1 | https://arxiv.org/pdf/2211.10994v1.pdf | DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion | Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts... | ['Jian Yang', 'Jun Li', 'Zhenyu Zhang', 'Xiang Li', 'Kun Wang', 'Zhiqiang Yan'] | 2022-11-20 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 3.07158440e-01 1.24775171e-01 -2.72354603e-01 -5.68586230e-01
-1.08091426e+00 -1.65371448e-01 3.12100947e-01 -9.55062807e-02
-3.00178081e-01 7.34583318e-01 4.70701903e-01 3.06285322e-01
-2.27931872e-01 -9.98030722e-01 -6.40671074e-01 -7.87140250e-01
2.19723314e-01 4.26514119e-01 2.02452749e-01 -1.67352576... | [8.844820976257324, -2.64323091506958] |
6de82205-2639-4161-83d9-81cb4a629e41 | a-fairness-aware-hybrid-recommender-system | 1809.09030 | null | http://arxiv.org/abs/1809.09030v1 | http://arxiv.org/pdf/1809.09030v1.pdf | A Fairness-aware Hybrid Recommender System | Recommender systems are used in variety of domains affecting people's lives.
This has raised concerns about possible biases and discrimination that such
systems might exacerbate. There are two primary kinds of biases inherent in
recommender systems: observation bias and bias stemming from imbalanced data.
Observation b... | ['Getoor Lise', 'Srinivasan Sriram', 'Thompson Spencer K.', 'Kouki Pigi', 'Farnadi Golnoosh'] | 2018-09-13 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-6.03057183e-02 3.23408514e-01 -6.70514524e-01 -1.09318614e+00
-4.57624495e-02 -3.57760221e-01 7.13527083e-01 3.55707914e-01
-4.39659208e-01 9.55514729e-01 7.51402915e-01 -1.31661505e-01
-4.01044041e-01 -1.01999569e+00 -4.52408135e-01 -1.45676211e-01
2.97850877e-01 5.38537979e-01 8.39978755e-02 -6.03306711... | [9.657923698425293, 5.686207294464111] |
24247d14-e779-45c4-942c-920419c3284e | automated-annotation-with-generative-ai | 2306.00176 | null | https://arxiv.org/abs/2306.00176v1 | https://arxiv.org/pdf/2306.00176v1.pdf | Automated Annotation with Generative AI Requires Validation | Generative large language models (LLMs) can be a powerful tool for augmenting text annotation procedures, but their performance varies across annotation tasks due to prompt quality, text data idiosyncrasies, and conceptual difficulty. Because these challenges will persist even as LLM technology improves, we argue that ... | ['Neil Fasching', 'Samuel Wolken', 'Nicholas Pangakis'] | 2023-05-31 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 3.35999370e-01 4.85811412e-01 -1.24140009e-01 -3.35859269e-01
-1.06713998e+00 -1.03899646e+00 8.66795003e-01 6.09650433e-01
-5.32046497e-01 4.68188673e-01 4.27496284e-01 -5.77106118e-01
-6.02636039e-02 -2.18684077e-01 -4.81491476e-01 -4.60385755e-02
5.16393900e-01 8.58364582e-01 9.55958888e-02 1.38994912... | [9.64116096496582, 8.528844833374023] |
adea4965-f0f9-42f8-a6c4-82703d1bdbb6 | assessing-four-neural-networks-on-handwritten | 1811.08278 | null | https://arxiv.org/abs/1811.08278v2 | https://arxiv.org/pdf/1811.08278v2.pdf | Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST) | Although the image recognition has been a research topic for many years, many researchers still have a keen interest in it[1]. In some papers[2][3][4], however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accor... | ['Nan Chen', 'Hanyang Mao', 'Feiyang Chen', 'Hanlin Hu'] | 2018-11-16 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 2.23276809e-01 -6.22960255e-02 -2.14205310e-01 -4.90533918e-01
-7.07014427e-02 -3.10868919e-01 4.50516850e-01 -5.48265100e-01
-4.04508829e-01 6.07313871e-01 1.67790070e-01 -2.85399407e-01
-2.55772769e-01 -8.00614297e-01 -8.03373575e-01 -5.45262218e-01
-1.64284166e-02 -2.45187450e-02 3.78056616e-01 -3.46504152... | [9.342048645019531, 2.183336019515991] |
7622642d-ec31-42b9-a5c8-55c5dd9008dc | self-supervised-video-representation-learning-7 | 2106.10137 | null | https://arxiv.org/abs/2106.10137v3 | https://arxiv.org/pdf/2106.10137v3.pdf | Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting | Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation learning. They are not suitable for exploiting the rich dynamical structure of video however, as operations are done on many augmented instance... | ['Vincent Tao Hu', 'Maarten Stol', 'Ioannis Gatopoulos', 'Martine Toering'] | 2021-06-18 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 2.14647308e-01 -2.29683518e-01 -6.94866598e-01 -2.36333072e-01
-5.27091742e-01 -5.10279059e-01 7.82374620e-01 1.39150880e-02
-5.75502396e-01 4.28390235e-01 4.89118099e-01 1.42378032e-01
-6.93668723e-02 -4.14860368e-01 -8.68001819e-01 -7.23589838e-01
-3.76388907e-01 5.35908163e-01 2.41847321e-01 -7.14754760... | [8.686342239379883, 0.7110565304756165] |
eb28c8e2-f46f-4216-bc07-1b79d50722e4 | propnet-propagating-2d-annotation-to-3d | 2305.17871 | null | https://arxiv.org/abs/2305.17871v1 | https://arxiv.org/pdf/2305.17871v1.pdf | propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans | **Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors, and the inefficiency of existing methods. **Purpose:** We conducted a study to introduce a model, utilizing human-guided knowledge and uniq... | ['Li Zhang', 'Lei Tang', 'Bin Dong', 'Hongfeng Li', 'Yiting Liu', 'Jie Zhao', 'Jiazheng Li', 'ZiFan Chen'] | 2023-05-29 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.81921439e-02 4.46013510e-01 -4.98504102e-01 -5.83636649e-02
-1.24108171e+00 -5.06053030e-01 -6.69677258e-02 5.38856030e-01
-4.86389637e-01 5.15549958e-01 2.70591259e-01 -6.73403800e-01
-2.07738802e-02 -6.73477054e-01 -2.22149208e-01 -1.00251687e+00
-1.74488902e-01 7.25225985e-01 5.27058661e-01 1.86378717... | [14.618683815002441, -2.701077461242676] |
49dfbef3-e6f8-4571-bc05-691fb8abab58 | diffskill-skill-abstraction-from-1 | 2203.17275 | null | https://arxiv.org/abs/2203.17275v1 | https://arxiv.org/pdf/2203.17275v1.pdf | DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools | We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorit... | ['Chuang Gan', 'David Held', 'Joshua B. Tenenbaum', 'Yunzhu Li', 'Zhiao Huang', 'Xingyu Lin'] | 2022-03-31 | diffskill-skill-abstraction-from | https://openreview.net/forum?id=Kef8cKdHWpP | https://openreview.net/pdf?id=Kef8cKdHWpP | iclr-2022-4 | ['deformable-object-manipulation'] | ['robots'] | [-1.08824529e-01 1.11556441e-01 7.83842057e-02 -5.04368171e-02
-6.51837885e-01 -9.58224118e-01 3.21099281e-01 -9.32805911e-02
-5.77497721e-01 8.68936181e-01 -3.45951468e-01 -6.01875829e-03
-5.24434209e-01 -6.56892657e-01 -1.29480243e+00 -6.12783611e-01
-2.92151630e-01 8.21587205e-01 4.55462635e-01 -5.16946495... | [4.776680946350098, 0.5727523565292358] |
f2d0845c-ba14-4ab1-abde-964a27fa4867 | smile-semantically-guided-multi-attribute | 2010.02315 | null | https://arxiv.org/abs/2010.02315v1 | https://arxiv.org/pdf/2010.02315v1.pdf | SMILE: Semantically-guided Multi-attribute Image and Layout Editing | Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within a transformation is a very challenging task due to the multiple and mutually-inclusive nature of the facial images, where different labels (eyegla... | ['Radu Timofte', 'Luc van Gool', 'Andrés Romero'] | 2020-10-05 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 5.42886078e-01 1.90013479e-02 -2.06808075e-02 -3.72117817e-01
-4.69437301e-01 -9.66138780e-01 8.75625134e-01 -5.20785272e-01
-2.10375041e-01 8.20143878e-01 1.77634552e-01 2.04291567e-01
-2.57507771e-01 -7.16347516e-01 -6.07712090e-01 -1.12124097e+00
4.07323658e-01 5.60514212e-01 -2.03445256e-01 -3.29765737... | [12.711227416992188, 0.017525680363178253] |
60e94794-7da3-48b1-a018-863a49cb862b | seeds-emulation-of-weather-forecast-ensembles | 2306.14066 | null | https://arxiv.org/abs/2306.14066v1 | https://arxiv.org/pdf/2306.14066v1.pdf | SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models | Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose t... | ['John Anderson', 'Fei Sha', 'Ignacio Lopez-Gomez', 'Rob Carver', 'Lizao Li'] | 2023-06-24 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty', 'decision-making'] | ['medical', 'reasoning', 'reasoning'] | [-2.50409454e-01 1.36900917e-01 5.90826571e-01 -5.60432911e-01
-7.96638250e-01 -8.13549757e-01 1.07478416e+00 -2.02612415e-01
1.34725019e-01 1.21465993e+00 4.66742903e-01 -6.31009519e-01
-4.96882461e-02 -1.36094964e+00 -4.46446925e-01 -9.84548509e-01
-1.31276920e-01 8.44225824e-01 -3.51744115e-01 -5.41455507... | [6.548797130584717, 3.0263218879699707] |
c248d4cc-1b12-489c-b931-42bc68f04d18 | simon-a-simple-framework-for-online-temporal | 2211.04905 | null | https://arxiv.org/abs/2211.04905v1 | https://arxiv.org/pdf/2211.04905v1.pdf | SimOn: A Simple Framework for Online Temporal Action Localization | Online Temporal Action Localization (On-TAL) aims to immediately provide action instances from untrimmed streaming videos. The model is not allowed to utilize future frames and any processing techniques to modify past predictions, making On-TAL much more challenging. In this paper, we propose a simple yet effective fra... | ['Kwanghoon Sohn', 'Kwonyoung Kim', 'Jungin Park', 'Tuan N. Tang'] | 2022-11-08 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 7.37317353e-02 -1.53044194e-01 -5.66196263e-01 -3.71402353e-01
-6.99035883e-01 -5.27478576e-01 6.34248257e-01 -2.49645129e-01
-5.24214447e-01 3.57063442e-01 5.68044126e-01 1.71560403e-02
2.28999764e-01 -3.54342610e-01 -7.52883136e-01 -4.91468489e-01
-4.13212627e-01 -9.91670638e-02 6.97322905e-01 1.11967335... | [8.339548110961914, 0.48956796526908875] |
ee70da49-8ce0-4622-89ff-891e255cbcc0 | imagen-editor-and-editbench-advancing-and | 2212.06909 | null | https://arxiv.org/abs/2212.06909v2 | https://arxiv.org/pdf/2212.06909v2.pdf | Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting | Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to input text prompts, while consistent with input images. We present Imagen Editor, a cascaded diffusion model built, by fine-tuning Imagen on text-guided image inpaint... | ['William Chan', 'Peter Anderson', 'Mohammad Norouzi', 'Jason Baldridge', 'Radu Soricut', 'David J. Fleet', 'Sarah Laszlo', 'Yasumasa Onoe', 'Stefano Pellegrini', 'Shai Noy', 'Jordi Pont-Tuset', 'Ceslee Montgomery', 'Chitwan Saharia', 'Su Wang'] | 2022-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-guided-image-editing', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 6.82676673e-01 -1.37447044e-02 3.70180346e-02 -4.01061416e-01
-7.14394093e-01 -6.11851275e-01 7.46030331e-01 -1.50256678e-01
-2.90532321e-01 4.23602164e-01 3.05486709e-01 3.32244523e-02
1.38467997e-01 -5.02319574e-01 -1.09497654e+00 -1.73884571e-01
5.72572470e-01 6.44375384e-01 1.95272982e-01 -2.74150968... | [11.39923095703125, -0.3002430498600006] |
55e9fdcd-f12a-4385-8a80-5599e57927dc | temporal-sequence-object-based-cnn-ts-ocnn | null | null | https://doi.org/10.1016/j.cj.2022.07.005 | https://www.sciencedirect.com/science/article/pii/S2214514122001751?via%3Dihub | Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series | Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution (FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attemp... | ['Huapeng Li'] | 2022-07-05 | null | null | null | the-crop-journal-2022-7 | ['crop-yield-prediction', 'crop-classification', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 5.03998458e-01 -6.91568077e-01 -3.07769954e-01 -1.19976819e-01
-5.81047058e-01 -7.21416891e-01 2.68013656e-01 3.04344416e-01
-3.44557762e-01 9.03075695e-01 -6.36143804e-01 -4.39799666e-01
-4.22490507e-01 -1.40793431e+00 -6.85955465e-01 -1.04423738e+00
-5.17505944e-01 -2.38982081e-01 -1.63078561e-01 -4.58549857... | [9.382157325744629, -1.579405665397644] |
266903c8-fc08-4bfb-955c-0012141d59a9 | prosfda-prompt-learning-based-source-free | 2211.11514 | null | https://arxiv.org/abs/2211.11514v1 | https://arxiv.org/pdf/2211.11514v1.pdf | ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation | The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the pre-trained model due to the huge data size and privacy concern, sourc... | ['Yong Xia', 'Zehui Liao', 'Shishuai Hu'] | 2022-11-21 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.02975726e-01 3.53420645e-01 -5.19802630e-01 -6.86566412e-01
-1.21453881e+00 -5.32990873e-01 2.30327144e-01 2.27842450e-01
-5.11047542e-01 7.80736148e-01 7.99530745e-02 -3.23801965e-01
-1.55493617e-01 -4.04102653e-01 -6.44838691e-01 -8.47212255e-01
3.90138745e-01 8.69722426e-01 2.01187640e-01 2.34049425... | [14.60484790802002, -1.9961415529251099] |
101fd148-63f0-40b9-9747-8b8f7d749014 | unsupervised-light-field-depth-estimation-via | 2301.08433 | null | https://arxiv.org/abs/2301.08433v1 | https://arxiv.org/pdf/2301.08433v1.pdf | Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction | Depth estimation from light field (LF) images is a fundamental step for some applications. Recently, learning-based methods have achieved higher accuracy and efficiency than the traditional methods. However, it is costly to obtain sufficient depth labels for supervised training. In this paper, we propose an unsupervise... | ['Edmund Y. Lam', 'Nan Meng', 'Shansi Zhang'] | 2023-01-20 | null | null | null | null | ['disparity-estimation', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [ 1.74924716e-01 -4.92562532e-01 -7.70496130e-02 -8.72244596e-01
-3.53392899e-01 1.43245384e-01 9.75148156e-02 -4.29143876e-01
-2.80092120e-01 7.13200510e-01 2.79154360e-01 1.91828847e-01
5.26355430e-02 -1.07277668e+00 -4.60402966e-01 -8.34512472e-01
7.14774847e-01 -1.99164152e-02 4.77977157e-01 2.08207548... | [9.178482055664062, -2.4517691135406494] |
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