Split (2)

train · 44.6k rows

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05ebacde-4da0-4c32-be66-a8526c2e8ca8	gan-supervised-dense-visual-alignment	2112.05143	null	https://arxiv.org/abs/2112.05143v2	https://arxiv.org/pdf/2112.05143v2.pdf	GAN-Supervised Dense Visual Alignment	We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. We apply our framework to the dense visual alignment problem. Inspired by the classic Congealing method, our GANgealing algorithm trains a Spatial Transformer to map random samples from a GAN trained on unaligned data to a common, jointly-learned target mode. We show results on eight datasets, all of which demonstrate our method successfully aligns complex data and discovers dense correspondences. GANgealing significantly outperforms past self-supervised correspondence algorithms and performs on-par with (and sometimes exceeds) state-of-the-art supervised correspondence algorithms on several datasets -- without making use of any correspondence supervision or data augmentation and despite being trained exclusively on GAN-generated data. For precise correspondence, we improve upon state-of-the-art supervised methods by as much as $3\times$. We show applications of our method for augmented reality, image editing and automated pre-processing of image datasets for downstream GAN training.	['Alexei A. Efros', 'Eli Shechtman', 'Antonio Torralba', 'Richard Zhang', 'Jun-Yan Zhu', 'William Peebles']	2021-12-09	null	http://openaccess.thecvf.com//content/CVPR2022/html/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.pdf	cvpr-2022-1	['dense-pixel-correspondence-estimation']	['computer-vision']	[ 6.54285491e-01 5.57049453e-01 3.12506072e-02 -6.47574723e-01 -1.34492707e+00 -6.54781759e-01 9.28991437e-01 -4.87284333e-01 -8.36092457e-02 7.58998811e-01 4.54697073e-01 -2.33957116e-02 2.02097774e-01 -6.13153994e-01 -1.14871085e+00 -5.62367678e-01 3.42321187e-01 1.07043386e+00 -2.37579986e-01 -2.21259072e-01 7.97807127e-02 3.72298360e-01 -1.32754290e+00 1.57505661e-01 6.18188262e-01 6.58338904e-01 2.50747710e-01 7.04162300e-01 -1.14082489e-02 7.28210151e-01 -1.20348603e-01 -4.43007529e-01 5.59054434e-01 -1.06748366e+00 -7.40920186e-01 3.20725560e-01 1.13096857e+00 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8241716a-ba1b-415c-a56e-a234525f5c7f	generating-multiple-diverse-responses-for	1811.05696	null	http://arxiv.org/abs/1811.05696v3	http://arxiv.org/pdf/1811.05696v3.pdf	Generating Multiple Diverse Responses for Short-Text Conversation	Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-of-the-art generative models.	['Xiaojiang Liu', 'Jun Gao', 'Shuming Shi', 'Junhui Li', 'Wei Bi']	2018-11-14	null	null	null	null	['short-text-conversation']	['natural-language-processing']	[ 3.06097895e-01 6.21646494e-02 9.32704657e-03 -8.14643562e-01 -1.12532687e+00 -3.39340001e-01 7.88130879e-01 -1.82984725e-01 -8.86139870e-02 1.01981235e+00 6.85421109e-01 1.87349662e-01 2.01614290e-01 -8.29958737e-01 -3.73874158e-01 -5.99339664e-01 7.14730382e-01 9.95125473e-01 1.39232688e-02 -6.07132912e-01 3.46678346e-01 -4.06846255e-01 -1.30679989e+00 7.05323100e-01 8.63473177e-01 5.01846552e-01 5.78664243e-01 1.00060284e+00 -3.29128087e-01 9.25881803e-01 -1.18344533e+00 -6.03994191e-01 -2.49777228e-01 -1.17462862e+00 -9.00609434e-01 -1.01490863e-01 -1.50803670e-01 -4.96966958e-01 -3.74809593e-01 7.04271197e-01 8.30815375e-01 4.50503528e-01 5.79545975e-01 -1.24890876e+00 -9.00409997e-01 1.20941496e+00 -1.31706506e-01 1.28155947e-01 8.12495708e-01 5.41399606e-02 9.74947870e-01 -7.53108680e-01 4.13586110e-01 1.39025962e+00 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24d2a096-9f92-44d3-8646-b1a491914cbe	developing-a-multi-variate-prediction-model	2209.03727	null	https://arxiv.org/abs/2209.03727v1	https://arxiv.org/pdf/2209.03727v1.pdf	Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data	COVID-19 has affected more than 223 countries worldwide. There is a pressing need for non invasive, low costs and highly scalable solutions to detect COVID-19, especially in low-resource countries where PCR testing is not ubiquitously available. Our aim is to develop a deep learning model identifying COVID-19 using voice data recordings spontaneously provided by the general population (voice recordings and a short questionnaire) via their personal devices. The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings. Methods: We used the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app. Voice features were extracted using a Mel-spectrogram analysis. Based on the voice data, we developed deep learning classification models to detect positive COVID-19 cases. These models included Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). We compared their predictive power to baseline classification models, namely Logistic Regression and Support Vector Machine. Results: LSTM based on a Mel-frequency cepstral coefficients (MFCC) features achieved the highest accuracy (89%,) with a sensitivity and specificity of respectively 89% and 89%, The results achieved with the proposed model suggest a significant improvement in the prediction accuracy of COVID-19 diagnosis compared to the results obtained in the state of the art. Conclusion: Deep learning can detect subtle changes in the voice of COVID-19 patients with promising results. As an addition to the current testing techniques this model may aid health professionals in fast diagnosis and tracing of COVID-19 cases using simple voice analysis	['Visara Urovi', 'Sami O. Simmons', 'Wafaa Aljbawi']	2022-09-08	null	null	null	null	['covid-19-detection']	['medical']	[-2.08845418e-02 -5.35786569e-01 1.94079146e-01 9.38498750e-02 -8.48528385e-01 -5.52619636e-01 1.49401918e-01 3.53993297e-01 -6.76074266e-01 8.60389769e-01 1.27383143e-01 -3.88665706e-01 -3.75617057e-01 -5.07187307e-01 -2.06591696e-01 -5.31065643e-01 -2.10130617e-01 7.46653438e-01 -2.22004071e-01 7.45805204e-02 -2.70160288e-01 7.22267210e-01 -1.27303088e+00 4.66822207e-01 6.31518006e-01 8.00986052e-01 2.86669612e-01 1.22281778e+00 3.04861814e-01 3.27656686e-01 -9.61557388e-01 -1.64649203e-01 -2.71956950e-01 -2.49443591e-01 -6.08346045e-01 -4.56748217e-01 -1.83951277e-02 -4.59687352e-01 2.05737516e-01 4.58936512e-01 1.09310269e+00 -2.06678495e-01 6.86165631e-01 -7.95968473e-01 -2.26624608e-01 3.01333636e-01 -4.55825999e-02 6.14335001e-01 3.57038110e-01 3.95085216e-01 6.58849418e-01 -9.54922974e-01 6.39921784e-01 9.56883848e-01 1.51783705e+00 5.54677784e-01 -1.08142805e+00 -7.36398876e-01 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-1.67164341e-01 9.24160182e-01 -4.83509809e-01 -3.24228704e-01 -2.19017252e-01]	[14.45466136932373, 3.9248528480529785]
150ea2f6-62f3-4edf-99bd-fa0ca23db32d	a-simple-but-powerful-graph-encoder-for	null	null	https://openreview.net/forum?id=TBimhW-8Fk6	https://openreview.net/pdf?id=TBimhW-8Fk6	A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion	While knowledge graphs contain rich semantic knowledge about various entities and the relational information among them, temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent TKGC methods aim to integrate advanced deep learning techniques, e.g., Transformers, to boost model performance. However, we find that instead of adopting various kinds of complex modules, it is more beneficial to capture more extensive temporal information. In this paper, we propose a simple but powerful graph encoder for TKGC, namely, TARGCN. TARGCN is parameter-efficient, and it extensively utilizes the information from the whole temporal context. We perform experiments on three benchmark datasets. Our model can achieve a more than 46% relative improvement on the GDELT dataset compared with state-of-the-art models. Meanwhile, it outperforms the strongest baseline on the ICEWS05-15 dataset with around 18% fewer parameters.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['temporal-knowledge-graph-completion']	['knowledge-base']	[-3.30581158e-01 1.20428741e-01 -6.99650109e-01 -1.36074916e-01 -3.60417217e-01 -4.80302453e-01 7.19324529e-01 2.05367789e-01 -4.72156584e-01 5.17186046e-01 3.69832873e-01 -1.81444988e-01 -2.45334417e-01 -9.64403272e-01 -8.30569744e-01 -4.74676400e-01 -3.31301779e-01 3.22798103e-01 5.34202993e-01 -3.06170374e-01 -2.45598972e-01 1.60882726e-01 -1.04867256e+00 1.41694099e-01 7.66140640e-01 1.05861878e+00 8.10324997e-02 1.03486776e-01 -1.95355549e-01 1.37976861e+00 -1.77990302e-01 -6.62346244e-01 -1.70872837e-01 -8.74765664e-02 -1.07589710e+00 -1.34300977e-01 -8.28233585e-02 -2.00121254e-01 -1.09040141e+00 7.58489907e-01 2.25845605e-01 3.28154296e-01 2.67571300e-01 -1.18109560e+00 -6.94011331e-01 9.22518313e-01 -4.22862709e-01 3.15076947e-01 2.40412369e-01 7.79082859e-03 1.36097980e+00 -6.77059829e-01 7.83850133e-01 1.15017426e+00 5.57200313e-01 9.12602767e-02 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3fe3df91-2a33-4452-b41f-9a7d7911c128	winning-solution-for-the-cvpr2023-visual	2306.09067	null	https://arxiv.org/abs/2306.09067v1	https://arxiv.org/pdf/2306.09067v1.pdf	Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection	This technical report introduces the winning solution of the team \textit{Segment Any Anomaly} for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, \textit{e.g.}, language prompt, we present a novel framework, \textit{i.e.}, Segment Any Anomaly + (SAA$+$), for zero-shot anomaly segmentation with multi-modal prompts for the regularization of cascaded modern foundation models. Inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly (SAA) to leverage diverse multi-modal prior knowledge for anomaly localization. Subsequently, we further introduce multimodal prompts (SAA$+$) derived from domain expert knowledge and target image context to enable the non-parameter adaptation of foundation models to anomaly segmentation. The proposed SAA$+$ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA and MVTec-AD, in the zero-shot setting. We will release the code of our winning solution for the CVPR2023 VAND challenge at \href{Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly} \footnote{The extended-version paper with more details is available at ~\cite{cao2023segment}.}	['Weiming Shen', 'Liang Gao', 'Yuqi Cheng', 'Chen Sun', 'Xiaohao Xu', 'Yunkang Cao']	2023-06-15	null	null	null	null	['anomaly-detection']	['methodology']	[ 1.50410146e-01 3.67854722e-02 2.54236817e-01 -3.14584583e-01 -1.20634389e+00 -5.66379368e-01 3.94691020e-01 2.68475682e-01 -1.73535854e-01 -1.29041210e-01 -2.57469058e-01 -3.50888044e-01 -2.22786590e-02 -2.89979577e-01 -8.59267950e-01 -4.90626633e-01 6.68985322e-02 2.91075170e-01 3.79034251e-01 -3.27593058e-01 1.69539213e-01 2.66880125e-01 -1.65029848e+00 3.95671695e-01 9.39082265e-01 1.34224498e+00 -2.11665407e-01 8.89764130e-01 -4.24831808e-01 5.22029698e-01 -3.08293939e-01 -6.26666427e-01 4.98081714e-01 -3.96540344e-01 -7.60007620e-01 4.35926802e-02 1.01707423e+00 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2c7a11fd-50ae-4cef-80eb-a510be099bc2	convolutional-graph-auto-encoder-a-deep	1809.03538	null	http://arxiv.org/abs/1809.03538v1	http://arxiv.org/pdf/1809.03538v1.pdf	Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting	Machine Learning on graph-structured data is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph. In contrast to all learning formulations in the area of discriminative pattern recognition, we propose a scalable generative optimization/algorithm theoretically proved to capture distributions at the nodes of a graph. Our model is able to generate samples from the probability densities learned at each node. This probabilistic data generation model, i.e. convolutional graph auto-encoder (CGAE), is devised based on the localized first-order approximation of spectral graph convolutions, deep learning, and the variational Bayesian inference. We apply our CGAE to a new problem, the spatio-temporal probabilistic solar irradiance prediction. Multiple solar radiation measurement sites in a wide area in northern states of the US are modeled as an undirected graph. Using our proposed model, the distribution of future irradiance given historical radiation observations is estimated for every site/node. Numerical results on the National Solar Radiation Database show state-of-the-art performance for probabilistic radiation prediction on geographically distributed irradiance data in terms of reliability, sharpness, and continuous ranked probability score.	['Jianhui Wang', 'Saeed Mohammadi', 'Mohammad Khodayar', 'Mahdi Khodayar', 'Guangyi Liu']	2018-09-10	null	null	null	null	['solar-irradiance-forecasting']	['time-series']	[ 6.07054494e-02 2.45795641e-02 2.22572237e-01 -1.75914913e-01 -3.54752034e-01 -4.02463645e-01 7.34995425e-01 1.17243998e-01 3.90774906e-01 7.43889153e-01 2.03039691e-01 -3.78472537e-01 -6.41157985e-01 -1.36945736e+00 -8.96484137e-01 -1.14859629e+00 -1.02135003e-01 3.22161347e-01 -3.53683889e-01 3.79046276e-02 -8.29699636e-02 5.97297430e-01 -1.42689121e+00 -5.03374040e-01 1.00328875e+00 9.27522063e-01 1.22849919e-01 1.04755545e+00 1.10045768e-01 5.54795265e-01 -5.65516829e-01 -4.09076303e-01 1.21086344e-01 -4.87763464e-01 -2.44825304e-01 3.30383301e-01 4.72475797e-01 -4.33534309e-02 -8.66757274e-01 1.13305068e+00 4.73381639e-01 1.73203513e-01 1.11614776e+00 -1.38221574e+00 -8.04382920e-01 4.63999540e-01 -5.42478383e-01 2.87099510e-01 1.20934166e-01 -9.12722200e-02 1.23358238e+00 -3.44618618e-01 -7.20297024e-02 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0e1cce4e-3cee-4a6d-84cd-980e880fa433	self-supervised-learning-of-motion-capture	1712.01337	null	http://arxiv.org/abs/1712.01337v1	http://arxiv.org/pdf/1712.01337v1.pdf	Self-supervised Learning of Motion Capture	Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection matches measurements in the video (e.g. person segmentation, optical flow, keypoint detections etc.). Optimization models are susceptible to local minima. This has been the bottleneck that forced using clean green-screen like backgrounds at capture time, manual initialization, or switching to multiple cameras as input resource. In this work, we propose a learning based motion capture model for single camera input. Instead of optimizing mesh and skeleton parameters directly, our model optimizes neural network weights that predict 3D shape and skeleton configurations given a monocular RGB video. Our model is trained using a combination of strong supervision from synthetic data, and self-supervision from differentiable rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c) human-background segmentation, in an end-to-end framework. Empirically we show our model combines the best of both worlds of supervised learning and test-time optimization: supervised learning initializes the model parameters in the right regime, ensuring good pose and surface initialization at test time, without manual effort. Self-supervision by back-propagating through differentiable rendering allows (unsupervised) adaptation of the model to the test data, and offers much tighter fit than a pretrained fixed model. We show that the proposed model improves with experience and converges to low-error solutions where previous optimization methods fail.	['Hsiao-Wei Tung', 'Hsiao-Yu Fish Tung', 'Ersin Yumer', 'Katerina Fragkiadaki']	2017-12-04	self-supervised-learning-of-motion-capture-1	http://papers.nips.cc/paper/7108-self-supervised-learning-of-motion-capture	http://papers.nips.cc/paper/7108-self-supervised-learning-of-motion-capture.pdf	neurips-2017-12	['3d-human-reconstruction', 'weakly-supervised-3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[ 2.41049185e-01 1.13849584e-02 7.52022117e-02 -1.99656636e-01 -6.30167663e-01 -5.66653848e-01 5.05879581e-01 -3.69706452e-01 -6.17926061e-01 5.14353096e-01 1.04125015e-01 1.37298256e-01 3.11275482e-01 -5.05532205e-01 -9.50685978e-01 -4.13758039e-01 1.00446105e-01 9.56053913e-01 5.31772316e-01 -6.05637915e-02 7.21399114e-02 5.83013892e-01 -1.46088171e+00 -1.26405522e-01 5.07126510e-01 6.60513699e-01 -4.53434251e-02 1.32693040e+00 1.79618731e-01 4.14112508e-01 -3.29185486e-01 -4.09825981e-01 6.82586849e-01 -3.42623979e-01 -7.85645008e-01 6.36117816e-01 1.01666892e+00 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d1320e11-24e5-4249-b2b2-157adc16786c	adaptive-policy-learning-to-additional-tasks	2305.15193	null	https://arxiv.org/abs/2305.15193v1	https://arxiv.org/pdf/2305.15193v1.pdf	Adaptive Policy Learning to Additional Tasks	This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle of optimality with the policy gradient approach to improve the convergence rate. This paper provides theoretical analysis which guarantees the convergence rate and sample complexity of $\mathcal{O}(1/T)$ and $\mathcal{O}(1/\epsilon)$, respectively, where $T$ denotes the number of iterations and $\epsilon$ denotes the accuracy of the resulting stationary policy. Furthermore, several challenging numerical simulations, including cartpole, lunar lander, and robot arm, are provided to show that APG obtains similar performance compared to existing deterministic policy gradient methods while utilizing much less data and converging at a faster rate.	['Shaoshuai Mou', 'Tianyu Zhou', 'Zihao Liang', 'Zehui Lu', 'Wenjian Hao']	2023-05-24	null	null	null	null	['policy-gradient-methods']	['methodology']	[-3.17199171e-01 7.51170143e-03 -3.31737876e-01 -1.74699739e-01 -4.83875602e-01 -4.27130163e-01 2.79641122e-01 -5.49051240e-02 -9.99814510e-01 1.47590673e+00 -3.70681763e-01 -8.72642159e-01 -4.41067994e-01 -3.47766489e-01 -7.76772141e-01 -8.02289188e-01 -1.81615725e-01 2.79865295e-01 2.73264702e-02 -2.42848456e-01 6.37305617e-01 2.16224208e-01 -1.20342135e+00 -5.35537660e-01 1.29650664e+00 1.24081802e+00 5.37055612e-01 5.04651546e-01 9.89499837e-02 5.94212294e-01 -4.42204922e-01 1.35191187e-01 4.93437648e-01 -2.67036915e-01 -6.10547543e-01 -2.00753108e-01 -2.98171621e-02 -2.80646831e-01 -2.28677154e-01 1.26403916e+00 5.45922816e-01 7.09984958e-01 5.38537741e-01 -8.83757055e-01 -2.98115969e-01 4.78294134e-01 -5.71299911e-01 5.27002811e-01 -1.30059272e-01 7.46596381e-02 5.29213309e-01 -4.34150517e-01 2.00189799e-01 1.31185496e+00 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-1.37377036e+00 -1.12341976e+00 -5.22054672e-01 1.77788734e-03 8.40767995e-02 -1.07976937e+00 -2.08712623e-01 -2.66411275e-01]	[4.204388618469238, 2.488438844680786]
89d95b00-fa0d-42bc-bba3-1d3744b59ec8	hidden-gems-4d-radar-scene-flow-learning	2303.00462	null	https://arxiv.org/abs/2303.00462v3	https://arxiv.org/pdf/2303.00462v3.pdf	Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision	This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning. Our approach is motivated by the co-located sensing redundancy in modern autonomous vehicles. Such redundancy implicitly provides various forms of supervision cues to the radar scene flow estimation. Specifically, we introduce a multi-task model architecture for the identified cross-modal learning problem and propose loss functions to opportunistically engage scene flow estimation using multiple cross-modal constraints for effective model training. Extensive experiments show the state-of-the-art performance of our method and demonstrate the effectiveness of cross-modal supervised learning to infer more accurate 4D radar scene flow. We also show its usefulness to two subtasks - motion segmentation and ego-motion estimation. Our source code will be available on https://github.com/Toytiny/CMFlow.	['Chris Xiaoxuan Lu', 'Dariu M. Gavrila', 'Andras Palffy', 'Fangqiang Ding']	2023-03-01	null	http://openaccess.thecvf.com//content/CVPR2023/html/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.pdf	cvpr-2023-1	['motion-segmentation', 'scene-flow-estimation']	['computer-vision', 'computer-vision']	[ 2.03302845e-01 -1.24382950e-01 -6.42103434e-01 -6.24085963e-01 -1.17763853e+00 -4.46710467e-01 8.09156299e-01 -6.21721506e-01 -2.17254996e-01 6.09006822e-01 1.90491483e-01 -5.48695803e-01 -1.99893326e-01 -6.67894125e-01 -6.19881272e-01 -4.95245069e-01 -2.92827308e-01 3.71649235e-01 1.07250832e-01 8.98253173e-02 3.59052211e-01 8.36153090e-01 -1.27720952e+00 1.11009605e-01 8.72531116e-01 7.66252935e-01 2.89152980e-01 1.11560392e+00 -8.57438222e-02 1.10335147e+00 -1.05784990e-01 -1.35986879e-01 7.00959384e-01 -1.49903193e-01 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7c88b678-b2e5-4842-be12-53e7ed4076b6	mfnet-multi-feature-fusion-network-for-real	null	null	https://ieeexplore.ieee.org/document/9839297	https://ieeexplore.ieee.org/document/9839297	MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes	Although high-accuracy networks have been applied to semantic segmentation at present, their inference speeds remain slow. A trade-off between accuracy and speed is demanded for real-time applications. To approach this problem, we propose Multi-Feature Fusion Network (MFNet) with real-time efficient prediction capacity. MFNet adopts three branches (attention, semantic and spatial information) to capture low-level and high-level features. Additionally, MFNet exerts asymmetric factorized (AF) blocks to extract local and long-range features. As a result, without any pre-training or post-processing, MFNet using only 1.34 M parameters, achieves 72.1% mean intersection over union (mIoU) on the Cityscapes test set at a speed of 116 frames per second (FPS), with 512×1024 high resolution on a single Titan Xp graphics card. Our network’s performance stands out from other state-of-the-art networks on four datasets (Cityscapes, CamVid, KITTI, and Gatech).	['Mengxu Lu; Zhenxue Chen; Chengyun Liu; Sile Ma; Lei Cai; Hao Qin']	2022-11-01	null	null	null	ieee-transactions-on-intelligent-13	['real-time-semantic-segmentation']	['computer-vision']	[ 1.42238244e-01 -1.77910104e-01 -1.75965333e-03 -5.56557894e-01 -7.18587339e-01 -2.86433846e-01 5.12447238e-01 -7.77963847e-02 -8.29577923e-01 5.38109779e-01 -4.63926375e-01 -2.98541248e-01 -1.84976101e-01 -9.75764096e-01 -5.44821024e-01 -7.19622731e-01 -1.14812367e-01 3.50509793e-01 7.70580053e-01 -1.86037019e-01 -1.62925757e-02 6.17980599e-01 -1.93331397e+00 2.82949686e-01 1.05200720e+00 1.39732444e+00 2.94690788e-01 8.28195274e-01 4.12002504e-02 4.94746864e-01 -4.07961100e-01 -4.97364491e-01 3.79679471e-01 1.12312391e-01 -6.94186091e-01 -1.41469523e-01 6.85499609e-01 -4.36093211e-01 -4.11050051e-01 9.57019448e-01 5.34631133e-01 1.38957709e-01 5.24245918e-01 -1.21504617e+00 -9.96981412e-02 3.10301811e-01 -8.10271084e-01 4.71583664e-01 -1.07895486e-01 2.34855548e-01 8.42267990e-01 -7.91116774e-01 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a7d30e65-4f8e-4f14-8104-a6cae43d2ec4	technical-report-for-valence-arousal	2105.01502	null	https://arxiv.org/abs/2105.01502v2	https://arxiv.org/pdf/2105.01502v2.pdf	Technical Report for Valence-Arousal Estimation on Affwild2 Dataset	In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net \cite{deng2020mimamo} model to achieve information about micro-motion and macro-motion for improving video emotion recognition and achieve Concordance Correlation Coefficient (CCC) of 0.415 and 0.511 for valence and arousal on the reselected validation set.	['I-Hsuan Li']	2021-05-04	null	null	null	null	['video-emotion-recognition']	['computer-vision']	[-0.6072364 -0.1677435 -0.18302754 -0.96179765 -0.49712566 -0.44753522 0.11065575 -0.23720627 -0.4126133 0.7216593 0.46513698 0.71784055 0.3604531 -0.21056013 -0.1263927 -0.4562712 -0.5852224 -0.2565553 -0.44721776 -0.46545994 -0.44200408 -0.1777801 -1.3998502 0.9349158 0.25982165 1.6196884 -0.62587047 0.98455507 0.52438086 0.91955197 -0.5619825 -0.707454 0.20914525 -0.53930247 -0.8051192 -0.3531975 0.2627842 -0.06750946 -0.42433667 0.9635764 0.9228984 0.5551202 0.4628459 -1.6944501 -0.22911982 0.35384008 -0.7510108 0.578442 0.42062798 0.3179656 1.0243659 -0.89986974 0.64864016 1.0215691 0.7438959 1.2346098 -0.73151845 -0.6734033 0.24645743 0.6970253 -1.4125065 -0.45551845 0.8763033 -0.10609323 1.0277559 0.8136902 1.2415174 1.7594367 0.2916135 0.99578685 1.1986208 0.25416258 0.34187144 -0.01954052 0.37782288 0.3385681 -0.64612347 -0.05303297 -1.126669 0.22687593 0.13306142 -0.6430938 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13a8220f-4e97-4673-b37d-ef204451cf5b	common-sense-bias-in-semantic-role-labeling	null	null	https://aclanthology.org/2021.wnut-1.14	https://aclanthology.org/2021.wnut-1.14.pdf	Common Sense Bias in Semantic Role Labeling	Large-scale language models such as ELMo and BERT have pushed the horizon of what is possible in semantic role labeling (SRL), solving the out-of-vocabulary problem and enabling end-to-end systems, but they have also introduced significant biases. We evaluate three SRL parsers on very simple transitive sentences with verbs usually associated with animate subjects and objects, such as, “Mary babysat Tom”: a state-of-the-art parser based on BERT, an older parser based on GloVe, and an even older parser from before the days of word embeddings. When arguments are word forms predominantly used as person names, aligning with common sense expectations of animacy, the BERT-based parser is unsurprisingly superior; yet, with abstract or random nouns, the opposite picture emerges. We refer to this as “common sense bias” and present a challenge dataset for evaluating the extent to which parsers are sensitive to such a bias. Our code and challenge dataset are available here: github.com/coastalcph/comte	['Anders Søgaard', 'Heather Lent']	null	null	null	null	wnut-acl-2021-11	['semantic-role-labeling']	['natural-language-processing']	[ 1.77442044e-01 5.21096110e-01 -2.25064576e-01 -5.93653738e-01 -5.24783492e-01 -7.55436063e-01 8.79605770e-01 5.01001596e-01 -9.96695638e-01 4.60889369e-01 9.46642101e-01 -2.80225724e-01 -7.92289246e-03 -6.76913738e-01 -3.15647960e-01 -4.24061865e-01 1.95391208e-01 7.62497663e-01 4.22064692e-01 -6.58840716e-01 1.12079866e-01 6.48980811e-02 -1.28952205e+00 3.18407238e-01 2.39437178e-01 3.45620722e-01 2.31310800e-01 3.89935017e-01 -2.28738755e-01 1.07570076e+00 -4.36301976e-01 -6.63650393e-01 8.26413557e-02 -5.62918559e-02 -1.13953495e+00 -5.61073184e-01 3.45439643e-01 4.23939973e-02 -2.58506566e-01 8.82770896e-01 6.25392735e-01 8.37936401e-02 3.26000452e-01 -1.04103339e+00 -1.03343642e+00 1.33950710e+00 -1.83910012e-01 5.54240465e-01 5.30475795e-01 1.23760059e-01 1.88070583e+00 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e417c859-2628-4e42-b011-47f32242c391	zs-slr-zero-shot-sign-language-recognition	2108.10059	null	https://arxiv.org/abs/2108.10059v1	https://arxiv.org/pdf/2108.10059v1.pdf	ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos	Sign Language Recognition (SLR) is a challenging research area in computer vision. To tackle the annotation bottleneck in SLR, we formulate the problem of Zero-Shot Sign Language Recognition (ZS-SLR) and propose a two-stream model from two input modalities: RGB and Depth videos. To benefit from the vision Transformer capabilities, we use two vision Transformer models, for human detection and visual features representation. We configure a transformer encoder-decoder architecture, as a fast and accurate human detection model, to overcome the challenges of the current human detection models. Considering the human keypoints, the detected human body is segmented into nine parts. A spatio-temporal representation from human body is obtained using a vision Transformer and a LSTM network. A semantic space maps the visual features to the lingual embedding of the class labels via a Bidirectional Encoder Representations from Transformers (BERT) model. We evaluated the proposed model on four datasets, Montalbano II, MSR Daily Activity 3D, CAD-60, and NTU-60, obtaining state-of-the-art results compared to state-of-the-art ZS-SLR models.	['Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo']	2021-08-23	null	null	null	null	['sign-language-recognition']	['computer-vision']	[ 3.00187945e-01 -6.77685589e-02 -2.06684604e-01 -1.72568470e-01 -6.39790118e-01 -1.25200778e-01 7.39316106e-01 -8.38563144e-01 -6.58823192e-01 1.77740470e-01 4.16530162e-01 -7.73486774e-03 2.83735812e-01 -5.06172419e-01 -5.53857625e-01 -4.63500708e-01 3.12288076e-01 3.83088082e-01 6.92633748e-01 -2.58928686e-01 -2.41282396e-02 2.74881601e-01 -2.10878825e+00 5.61163962e-01 4.51820135e-01 9.68185782e-01 3.09489459e-01 8.82562816e-01 1.63616370e-02 1.35134733e+00 -1.59163743e-01 -2.97732294e-01 3.25515091e-01 -4.13737655e-01 -5.82648039e-01 2.95731902e-01 6.91751301e-01 -6.68908238e-01 -1.02448249e+00 9.46072400e-01 6.30964279e-01 -6.46296469e-03 6.99444890e-01 -1.35438454e+00 -5.61420977e-01 2.43667588e-02 -4.74460334e-01 -1.34237021e-01 5.34990370e-01 6.58621669e-01 8.50711584e-01 -1.08549261e+00 1.03802514e+00 1.35932338e+00 4.98448014e-01 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e8ea4c6e-8930-4947-960d-eaa6df1592e0	persistence-based-structural-recognition	null	null	http://openaccess.thecvf.com/content_cvpr_2014/html/Li_Persistence-based_Structural_Recognition_2014_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2014/papers/Li_Persistence-based_Structural_Recognition_2014_CVPR_paper.pdf	Persistence-based Structural Recognition	This paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, which captures the distribution of values of a given function, persistence diagrams can be used to characterize its structural properties, reflecting spatial information in an invariant way. In practice, the choice of function is simple: each dimension of the feature vector can be viewed as a function. The proposed method is general: it can work on various multimedia data, including 2D shapes, textures and triangle meshes. Extensive experiments on 3D shape retrieval, hand gesture recognition and texture classification demonstrate the performance of the proposed method in comparison with state-of-the-art methods. Additionally, our approach yields higher recognition accuracy when used in conjunction with the bag-of-features.	['Maks Ovsjanikov', 'Frederic Chazal', 'Chunyuan Li']	2014-06-01	null	null	null	cvpr-2014-6	['3d-shape-retrieval']	['computer-vision']	[ 5.33480888e-05 -4.75461513e-01 -3.46626103e-01 -2.96388417e-01 -4.22252417e-01 -7.70748734e-01 9.47310686e-01 4.48327810e-01 3.91852995e-03 3.89303863e-01 -2.54079923e-02 -6.71067238e-02 -5.45483708e-01 -1.18220484e+00 -4.27931309e-01 -1.05214381e+00 -3.55378360e-01 6.02256179e-01 4.88112509e-01 -1.06276527e-01 5.50805569e-01 1.25578666e+00 -1.96024561e+00 1.51223287e-01 3.93397063e-01 1.39910388e+00 -5.64566627e-02 4.68369514e-01 -2.61483043e-01 2.13043541e-01 -3.46643716e-01 -6.45003840e-02 4.76893038e-03 1.55848742e-01 -5.80544233e-01 2.36947507e-01 6.14981174e-01 -3.01122312e-02 -4.13011760e-01 7.76586890e-01 2.41575375e-01 1.94222525e-01 1.30224276e+00 -9.00154233e-01 -6.54791355e-01 -1.35284588e-01 -1.60472587e-01 -6.86407685e-02 6.39594734e-01 -3.60826403e-01 1.11637759e+00 -1.31597328e+00 6.18061006e-01 1.44584417e+00 5.01337886e-01 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69d13904-13e6-4d85-9ca5-e89b19b6006a	two-heads-are-better-than-one-geometric	2111.00231	null	https://arxiv.org/abs/2111.00231v1	https://arxiv.org/pdf/2111.00231v1.pdf	Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation	We present an innovative two-headed attention layer that combines geometric and latent features to segment a 3D scene into semantically meaningful subsets. Each head combines local and global information, using either the geometric or latent features, of a neighborhood of points and uses this information to learn better local relationships. This Geometric-Latent attention layer (Ge-Latto) is combined with a sub-sampling strategy to capture global features. Our method is invariant to permutation thanks to the use of shared-MLP layers, and it can also be used with point clouds with varying densities because the local attention layer does not depend on the neighbor order. Our proposal is simple yet robust, which allows it to achieve competitive results in the ShapeNetPart and ModelNet40 datasets, and the state-of-the-art when segmenting the complex dataset S3DIS, with 69.2% IoU on Area 5, and 89.7% overall accuracy using K-fold cross-validation on the 6 areas.	['Robert B. Fisher', 'Antonio Javier Gallego', 'Hanz Cuevas-Velasquez']	2021-10-30	null	null	null	null	['point-cloud-classification']	['computer-vision']	[-1.60296500e-01 2.32303962e-01 -1.56723678e-01 -3.20139974e-01 -8.04768145e-01 -3.85015786e-01 6.50005698e-01 2.65918285e-01 -4.17191356e-01 3.07294250e-01 -1.93948206e-02 -2.88032293e-02 -2.90280938e-01 -1.11687005e+00 -1.00890934e+00 -5.71246564e-01 -3.64231080e-01 1.00704265e+00 6.32989526e-01 -1.11343907e-02 2.18016207e-01 1.25842011e+00 -1.80254817e+00 2.08477587e-01 7.72518635e-01 1.36078489e+00 2.81201541e-01 4.77672964e-01 -1.49923399e-01 3.24913710e-01 -3.51333052e-01 -4.56863636e-05 4.49482232e-01 2.34304547e-01 -8.82600725e-01 -3.10277566e-02 8.99878323e-01 -2.67579943e-01 -1.34619698e-01 5.80170393e-01 5.04922390e-01 1.48722440e-01 9.05886829e-01 -9.25936878e-01 -6.65221930e-01 2.33796984e-01 -5.99431276e-01 8.76459479e-02 7.34627992e-02 3.15567292e-02 1.16107106e+00 -1.06926382e+00 4.83749509e-01 1.59312546e+00 7.95671821e-01 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438c69ed-d0a5-4b33-8cd3-cda2193663c2	artificial-benchmark-for-community-detection-1	2301.05749	null	https://arxiv.org/abs/2301.05749v2	https://arxiv.org/pdf/2301.05749v2.pdf	Artificial Benchmark for Community Detection with Outliers (ABCD+o)	The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter $\xi$ can be tuned to mimic its counterpart in the LFR model, the mixing parameter $\mu$. In this paper, we extend the ABCD model to include potential outliers. We perform some exploratory experiments on both the new ABCD+o model as well as a real-world network to show that outliers possess some desired, distinguishable properties.	['François Théberge', 'Paweł Prałat', 'Bogumił Kamiński']	2023-01-13	null	null	null	null	['community-detection']	['graphs']	[-3.62490864e-05 1.86420470e-01 2.43556444e-02 1.64918095e-01 1.81664433e-02 -5.68058312e-01 5.98518968e-01 3.80396754e-01 9.94348675e-02 8.11571479e-01 -1.79481804e-01 -5.32797098e-01 -3.22957128e-01 -9.87400711e-01 -3.68814379e-01 -5.07887006e-01 -1.05346334e+00 6.60280287e-01 7.04360843e-01 -3.01663756e-01 2.08126917e-01 5.18048108e-01 -8.91449451e-01 -1.96345285e-01 9.79258001e-01 1.74381956e-01 -2.88775206e-01 6.31603539e-01 2.41130307e-01 7.07595468e-01 -8.52060735e-01 -5.32835841e-01 6.00247324e-01 -4.15948629e-01 -4.68227863e-01 2.97410935e-01 -5.42418733e-02 2.96917826e-01 -8.26449335e-01 1.20481765e+00 3.38656783e-01 -3.05972695e-01 9.49069798e-01 -1.88627613e+00 -6.33662045e-01 8.72035265e-01 -1.15172434e+00 3.41764480e-01 7.20086515e-01 2.20375419e-01 9.14793134e-01 -4.46082622e-01 1.00258017e+00 1.38464344e+00 9.35195565e-01 1.66793302e-01 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6edbd1ca-760f-48d4-bb22-9bd56443de0e	investigating-the-dynamics-of-hand-and-lips	2306.08290	null	https://arxiv.org/abs/2306.08290v1	https://arxiv.org/pdf/2306.08290v1.pdf	Investigating the dynamics of hand and lips in French Cued Speech using attention mechanisms and CTC-based decoding	Hard of hearing or profoundly deaf people make use of cued speech (CS) as a communication tool to understand spoken language. By delivering cues that are relevant to the phonetic information, CS offers a way to enhance lipreading. In literature, there have been several studies on the dynamics between the hand and the lips in the context of human production. This article proposes a way to investigate how a neural network learns this relation for a single speaker while performing a recognition task using attention mechanisms. Further, an analysis of the learnt dynamics is utilized to establish the relationship between the two modalities and extract automatic segments. For the purpose of this study, a new dataset has been recorded for French CS. Along with the release of this dataset, a benchmark will be reported for word-level recognition, a novelty in the automatic recognition of French CS.	['Thomas Hueber', 'Olivier Perrotin', 'Frédéric Elisei', 'Denis Beautemps', 'Sanjana Sankar']	2023-06-14	null	null	null	null	['lipreading']	['computer-vision']	[ 4.21762168e-01 2.53448337e-01 -3.03754300e-01 -3.46468925e-01 -8.31246078e-01 -3.25038552e-01 7.83941090e-01 -9.69421417e-02 -4.68678772e-01 3.91947210e-01 6.01004720e-01 -6.40426949e-02 -4.22173999e-02 -2.19303489e-01 -7.09656060e-01 -9.21451330e-01 1.33294895e-01 -2.16218196e-02 2.74724956e-03 -2.77325153e-01 4.98250186e-01 5.85690022e-01 -2.18559361e+00 5.98298550e-01 5.03764093e-01 9.33317661e-01 6.66061580e-01 6.82364762e-01 -3.55465680e-01 2.93206543e-01 -4.87006247e-01 -1.28689334e-01 3.58005404e-03 -4.05561417e-01 -8.13030183e-01 -1.71448275e-01 6.99231029e-01 -3.26965675e-02 -2.11215392e-01 9.84137654e-01 8.57253194e-01 1.10556051e-01 7.77577102e-01 -8.24794471e-01 -3.59549314e-01 9.75571036e-01 1.01169273e-01 2.60511965e-01 7.44901955e-01 1.89398617e-01 8.27994645e-01 -9.55158353e-01 4.96907324e-01 1.26600623e+00 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8a85dbbd-a442-4be5-92fe-42c59e4180cd	deep-attention-guided-fusion-network-for	1807.08471	null	http://arxiv.org/abs/1807.08471v2	http://arxiv.org/pdf/1807.08471v2.pdf	Deep attention-guided fusion network for lesion segmentation	We participated the Task 1: Lesion Segmentation. The paper describes our algorithm and the final result of validation set for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection.	['Yangyang Hao', 'Ruixing Li', 'Hua Wang', 'Lizhuang Ma', 'Hengliang Zhu']	2018-07-23	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[ 1.00338101e+00 1.06042787e-01 -5.72891951e-01 1.25581669e-02 -1.21993876e+00 -4.99917060e-01 4.82029378e-01 8.42274055e-02 -8.76897156e-01 4.49910253e-01 -1.16994202e-01 -5.21770298e-01 3.77566874e-01 -9.11487266e-02 -2.34584674e-01 -9.13972795e-01 -8.18908289e-02 -1.64137751e-01 5.80380142e-01 5.44062480e-02 2.46729717e-01 5.17864823e-02 -6.38137162e-01 1.04607415e+00 8.54083061e-01 8.72468293e-01 -2.80715317e-01 1.58594012e+00 3.36036503e-01 2.59424418e-01 -3.42890263e-01 -6.60375774e-01 1.06434926e-01 -3.95182937e-01 -1.32631767e+00 4.56128806e-01 1.06824362e+00 8.87941942e-02 2.95664854e-02 1.25681412e+00 6.50190592e-01 -6.46357536e-01 1.08294261e+00 -7.19015360e-01 1.07090466e-01 3.33413363e-01 -8.87309492e-01 6.38376474e-01 1.25463665e-01 3.50039184e-01 2.28170365e-01 -6.09660506e-01 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d66429b0-9ae8-48f4-9abc-44e584718c98	mean-variance-hybrid-portfolio-optimization	2303.15830	null	https://arxiv.org/abs/2303.15830v2	https://arxiv.org/pdf/2303.15830v2.pdf	Mean-variance hybrid portfolio optimization with quantile-based risk measure	This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfolio policy when all market parameters are deterministic. Our hybrid model outperforms the classical continuous-time mean-variance portfolio policy by allocating a higher position of the risky asset in favorable market states and a less risky asset in unfavorable market states. This desirable property leads to promising numerical experiment results, including improved Sortino ratio and reduced downside risk compared to the benchmark models.	['Ke Zhou', 'WeiPing Wu', 'Yu Lin', 'Jianjun Gao']	2023-03-28	null	null	null	null	['portfolio-optimization']	['time-series']	[-3.44519645e-01 -5.51195219e-02 -1.91901118e-01 -8.88744276e-03 -5.51808417e-01 -7.64962614e-01 3.70725393e-01 -1.22125849e-01 -2.29830876e-01 1.03795123e+00 -7.60676637e-02 -5.43267429e-01 -9.68173981e-01 -1.21905565e+00 -1.21860608e-01 -9.44574714e-01 -2.11657211e-02 4.45804894e-01 -5.05452370e-03 -1.53090656e-01 6.50150537e-01 4.24213201e-01 -1.11115611e+00 -3.98791552e-01 9.12445188e-01 1.50613475e+00 -2.84337670e-01 3.28689724e-01 -1.20861962e-01 4.61758375e-01 -5.17766595e-01 -7.68305540e-01 1.02338505e+00 -4.09221023e-01 6.27272651e-02 -2.51933903e-01 -1.40120670e-01 -3.04574341e-01 3.09385240e-01 1.17985845e+00 5.93367994e-01 3.76468539e-01 8.50437105e-01 -1.10896111e+00 -4.94328529e-01 5.69415629e-01 -8.05001736e-01 5.09459198e-01 -4.43455428e-02 -1.01864263e-01 1.20028067e+00 -4.84619528e-01 -7.31630176e-02 1.08271897e+00 4.37162876e-01 3.19604099e-01 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-1.00866579e-01 1.04893588e-01 -8.62370193e-01 -6.39802098e-01 -5.69242418e-01]	[4.956301689147949, 3.926947832107544]
42157991-3426-4717-b601-2c8af0cbf999	transductive-universal-transport-for-zero	null	null	https://openreview.net/forum?id=Yp4sR6rmgFt	https://openreview.net/pdf?id=Yp4sR6rmgFt	Transductive Universal Transport for Zero-Shot Action Recognition	This work addresses the problem of recognizing action categories in videos for which no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a shared semantic space, either trained on large-scale seen actions or on objects. While effective, universal action and object models are biased to their seen categories. Such biases are further amplified due to biases between seen and unseen categories in the semantic space. The amplified biases result in many unseen action categories simply never being selected during inference, hampering zero-shot progress. We seeks to address this limitation and introduce transductive universal transport for zero-shot action recognition. Our proposal is to re-position unseen action embeddings through transduction, \ie by using the distribution of the unlabelled test set. For universal action models, we first find an optimal mapping from unseen actions to the mapped test videos in the shared hyperspherical space. We then define target embeddings as weighted Fr\'echet means, with the weights given by the transport couplings. Finally, we re-position unseen action embeddings along the geodesic between the original and target, as a form of semantic regularization. For universal object models, we outline a weighted transport variant from unseen action embeddings to object embeddings directly. Empirically, we show that our approach directly boosts universal action and object models, resulting in state-of-the-art performance for zero-shot classification and spatio-temporal localization.	['Pascal Mettes']	2021-09-29	null	null	null	null	['zero-shot-action-recognition']	['computer-vision']	[ 4.72766370e-01 1.02273032e-01 -4.11300391e-01 -2.35179350e-01 -6.19691730e-01 -3.35189193e-01 9.05958116e-01 -6.53520882e-01 -3.96388292e-01 6.49299860e-01 3.98118645e-01 3.81439269e-01 -1.70920238e-01 -7.82017887e-01 -8.74543250e-01 -1.11138952e+00 5.59349246e-02 5.14400482e-01 3.91763508e-01 2.64512878e-02 2.44288519e-01 3.36706728e-01 -1.82186818e+00 5.25845766e-01 3.98236006e-01 1.02483153e+00 5.52966483e-02 9.42200422e-01 -1.19552702e-01 9.16548610e-01 -2.45883629e-01 -1.12967834e-01 2.30905026e-01 -8.77197683e-01 -1.01570642e+00 3.53749990e-01 5.06461501e-01 -2.29240373e-01 -5.85144579e-01 1.08278573e+00 7.68540427e-02 6.39169633e-01 1.10872376e+00 -1.43502283e+00 -1.33234692e+00 8.58045146e-02 -1.03135094e-01 2.11222813e-01 2.49699771e-01 2.93185115e-01 1.12902951e+00 -1.02015615e+00 9.65604484e-01 1.28899050e+00 3.93310130e-01 1.07544291e+00 -1.32790422e+00 -1.38318449e-01 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207687db-2919-4982-992c-fbc80407b18a	relpose-predicting-probabilistic-relative	2208.05963	null	https://arxiv.org/abs/2208.05963v2	https://arxiv.org/pdf/2208.05963v2.pdf	RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild	We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object. This task is a core component of classic geometric pipelines such as SfM and SLAM, and also serves as a vital pre-processing requirement for contemporary neural approaches (e.g. NeRF) to object reconstruction and view synthesis. In contrast to existing correspondence-driven methods that do not perform well given sparse views, we propose a top-down prediction based approach for estimating camera viewpoints. Our key technical insight is the use of an energy-based formulation for representing distributions over relative camera rotations, thus allowing us to explicitly represent multiple camera modes arising from object symmetries or views. Leveraging these relative predictions, we jointly estimate a consistent set of camera rotations from multiple images. We show that our approach outperforms state-of-the-art SfM and SLAM methods given sparse images on both seen and unseen categories. Further, our probabilistic approach significantly outperforms directly regressing relative poses, suggesting that modeling multimodality is important for coherent joint reconstruction. We demonstrate that our system can be a stepping stone toward in-the-wild reconstruction from multi-view datasets. The project page with code and videos can be found at https://jasonyzhang.com/relpose.	['Shubham Tulsiani', 'Deva Ramanan', 'Jason Y. Zhang']	2022-08-11	null	null	null	null	['object-reconstruction']	['computer-vision']	[ 8.15461203e-02 -1.14593342e-01 -1.77141666e-01 -6.76800549e-01 -9.41908240e-01 -7.29400277e-01 8.97006094e-01 -3.99233520e-01 -3.31448615e-02 1.40650153e-01 5.02507925e-01 9.00438279e-02 -4.82768640e-02 -3.97912741e-01 -1.11233044e+00 -4.71736372e-01 4.20507938e-01 8.74367476e-01 5.89037165e-02 -2.89452840e-02 4.28291500e-01 6.99394524e-01 -1.41766000e+00 2.79062182e-01 1.24430381e-01 6.98027313e-01 5.44433713e-01 7.21558452e-01 1.73209339e-01 8.21252108e-01 1.34752870e-01 -2.78204918e-01 3.06616962e-01 -2.51156628e-01 -6.62140489e-01 4.18258339e-01 1.06969774e+00 -7.53221810e-01 -7.23463118e-01 9.48817134e-01 1.17729314e-01 2.50074118e-01 6.16413653e-01 -1.15352833e+00 -5.18820882e-01 2.31166199e-01 -7.07911432e-01 -1.18165739e-01 5.35355330e-01 -4.42074575e-02 1.30452275e+00 -1.25337553e+00 8.81084085e-01 1.32188118e+00 5.92082977e-01 3.27056199e-01 -1.49019635e+00 -3.23111832e-01 4.12032008e-01 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e8f1658d-4ff6-4fe5-bef4-3fa321c1d9e7	non-uniform-motion-deblurring-with-blurry	2101.06021	null	https://arxiv.org/abs/2101.06021v1	https://arxiv.org/pdf/2101.06021v1.pdf	Non-uniform Motion Deblurring with Blurry Component Divided Guidance	Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists major challenge with various non-uniform motion blur. Previous methods simply take all the image features as the input to the decoder, which handles different degrees (e.g. large blur, small blur) simultaneously, leading to challenges for sharp image generation. To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree. Specifically, two component attentive blocks are employed to learn attention maps to exploit useful deblurring feature representations on both large and small blurry regions. Then, the blur-aware features are fed into two-branch reconstruction decoders respectively. In addition, a new feature fusion mechanism, orientation-based feature fusion, is proposed to merge sharp features of the two branches. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art approaches.	['Yanning Zhang', 'Jinqiu Sun', 'Yu Zhu', 'Rui Li', 'Axi Niu', 'Qingsen Yan', 'Wei Sun', 'Pei Wang']	2021-01-15	null	null	null	null	['blind-image-deblurring']	['computer-vision']	[ 6.12713955e-02 -6.57746732e-01 1.82670176e-01 -2.26134539e-01 -6.04528546e-01 -3.13321739e-01 4.65042710e-01 -6.73311591e-01 -5.00017516e-02 7.42983997e-01 7.33448982e-01 6.94357902e-02 -1.85850233e-01 -1.66570708e-01 -5.78688562e-01 -9.67325330e-01 3.69160473e-01 -4.49750006e-01 1.62155554e-01 -1.18643986e-02 5.93415201e-01 1.49717569e-01 -1.30590117e+00 3.26979369e-01 1.24168944e+00 1.07970023e+00 5.98457813e-01 7.14308918e-01 2.42277816e-01 1.06127250e+00 -5.01971126e-01 1.17395669e-01 9.36987847e-02 -5.67867756e-01 -6.79229915e-01 2.30915859e-01 4.63832468e-01 -9.90520298e-01 -9.31279063e-01 1.53293395e+00 6.03888750e-01 1.02900051e-01 6.44791305e-01 -7.14003205e-01 -1.41172254e+00 3.50210905e-01 -8.92293334e-01 6.34035408e-01 2.71922380e-01 4.38802123e-01 4.26393569e-01 -1.04671764e+00 4.43779975e-02 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-1.21881628e+00 -8.01398933e-01 -1.39049721e+00 -5.49425900e-01 4.11207259e-01 1.22204590e+00 -5.75709641e-01 -7.05109358e-01 2.64312297e-01]	[11.502859115600586, -2.6508946418762207]
9ae43e28-ca38-41c7-9ea2-b2ed5a7c8c35	towards-multi-label-unknown-intent-detection	null	null	https://aclanthology.org/2022.coling-1.52	https://aclanthology.org/2022.coling-1.52.pdf	Towards Multi-label Unknown Intent Detection	Multi-class unknown intent detection has made remarkable progress recently. However, it has a strong assumption that each utterance has only one intent, which does not conform to reality because utterances often have multiple intents. In this paper, we propose a more desirable task, multi-label unknown intent detection, to detect whether the utterance contains the unknown intent, in which each utterance may contain multiple intents. In this task, the unique utterances simultaneously containing known and unknown intents make existing multi-class methods easy to fail. To address this issue, we propose an intuitive and effective method to recognize whether All Intents contained in the utterance are Known (AIK). Our high-level idea is to predict the utterance’s intent number, then check whether the utterance contains the same number of known intents. If the number of known intents is less than the number of intents, it implies that the utterance also contains unknown intents. We benchmark AIK over existing methods, and empirical results suggest that our method obtains state-of-the-art performances. For example, on the MultiWOZ 2.3 dataset, AIK significantly reduces the FPR95 by 12.25% compared to the best baseline.	['Jiajun Chen', 'ShuJian Huang', 'Xinyu Dai', 'Zhen Wu', 'Yawen Ouyang']	null	null	null	null	coling-2022-10	['intent-detection']	['natural-language-processing']	[ 3.24425578e-01 -1.96727868e-02 -1.66760236e-01 -6.70213997e-01 -9.77733910e-01 -7.39921153e-01 2.86242843e-01 1.03991434e-01 -8.01300704e-02 6.00251615e-01 2.29930162e-01 -2.74200708e-01 4.62301195e-01 -4.47042286e-01 -5.23697317e-01 -7.33230770e-01 2.48484969e-01 4.31312472e-01 1.71093836e-01 -2.65077174e-01 2.68375635e-01 -1.37392983e-01 -1.57643604e+00 5.46534240e-01 5.96970797e-01 9.58179414e-01 3.59484851e-01 5.88259935e-01 -2.15015456e-01 8.53586555e-01 -9.20721829e-01 -2.38556564e-01 -5.72726354e-02 -3.99968803e-01 -1.03489733e+00 1.17410921e-01 3.26010138e-01 -5.29856801e-01 4.65392433e-02 1.14416087e+00 1.21002324e-01 7.57089555e-02 7.14426398e-01 -1.46984959e+00 -4.27018821e-01 5.04980862e-01 -5.11381388e-01 5.64294495e-03 6.32978261e-01 -2.42179245e-01 1.30969608e+00 -8.00614297e-01 1.62486330e-01 1.20773578e+00 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9f17f0ef-cea5-473f-8b69-9237cff29ea0	self-supervised-multi-view-synchronization	2010.06218	null	https://arxiv.org/abs/2010.06218v1	https://arxiv.org/pdf/2010.06218v1.pdf	Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation	Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton poses. In contrast, we propose an approach that can exploit small annotated data sets by fine-tuning networks pre-trained via self-supervised learning on (large) unlabeled data sets. To drive such networks towards supporting 3D pose estimation during the pre-training step, we introduce a novel self-supervised feature learning task designed to focus on the 3D structure in an image. We exploit images extracted from videos captured with a multi-view camera system. The task is to classify whether two images depict two views of the same scene up to a rigid transformation. In a multi-view data set, where objects deform in a non-rigid manner, a rigid transformation occurs only between two views taken at the exact same time, i.e., when they are synchronized. We demonstrate the effectiveness of the synchronization task on the Human3.6M data set and achieve state-of-the-art results in 3D human pose estimation.	['Paolo Favaro', 'Simon Jenni']	2020-10-13	null	null	null	null	['monocular-3d-human-pose-estimation']	['computer-vision']	[ 1.82621568e-01 9.21818912e-02 -1.73550978e-01 -4.96515095e-01 -3.77892584e-01 -5.12735248e-01 5.53078532e-01 -3.87352169e-01 -6.85807228e-01 4.28896546e-01 1.83889404e-01 4.74534601e-01 2.58480251e-01 -3.77177000e-01 -1.04065883e+00 -3.59442025e-01 -3.64676751e-02 1.13731015e+00 1.50220916e-01 -1.88375995e-01 -1.70249805e-01 6.68488860e-01 -1.40162385e+00 1.74968347e-01 9.29017365e-03 7.03808606e-01 1.22674100e-01 7.47725904e-01 4.69565958e-01 5.13023973e-01 -4.38206762e-01 -1.98693812e-01 6.02243066e-01 -1.97655737e-01 -8.88934731e-01 7.22660244e-01 9.01996315e-01 -4.96314675e-01 -5.79306602e-01 7.20960200e-01 5.03843367e-01 7.48261064e-02 4.62695122e-01 -1.14636803e+00 -5.00556007e-02 5.44788167e-02 -5.21209419e-01 6.11859709e-02 7.02002704e-01 1.09330148e-01 6.53919518e-01 -8.93551826e-01 1.06246138e+00 1.09084034e+00 7.11243451e-01 8.11631620e-01 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99931752-a435-4e65-9401-e88a59014954	few-shot-nested-named-entity-recognition	2212.00953	null	https://arxiv.org/abs/2212.00953v1	https://arxiv.org/pdf/2212.00953v1.pdf	Few-Shot Nested Named Entity Recognition	While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories in the few-shot setting. Although prior work has briefly discussed nested structures in the context of few-shot learning, to our best knowledge, this paper is the first one specifically dedicated to studying the few-shot nested NER task. Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework. We first design a Biaffine span representation module for learning the contextual span dependency representation for each entity span rather than only learning its semantic representation. We then merge these two representations by the residual connection to distinguish nested entities. Finally, we build a contrastive learning framework to adjust the representation distribution for larger margin boundaries and more generalized domain transfer learning ability. We conducted experimental studies on three English, German, and Russian nested NER datasets. The results show that the BCL outperformed three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.	['Ning An', 'Yan Pan', 'Lili Jiang', 'Jiaoyun Yang', 'Hong Ming']	2022-12-02	null	null	null	null	['nested-named-entity-recognition']	['natural-language-processing']	[-1.50432691e-01 1.98939115e-01 -1.58643901e-01 -5.74799418e-01 -6.84642494e-01 -4.84733135e-01 5.32637060e-01 4.84985977e-01 -9.06961203e-01 6.74178839e-01 3.26742470e-01 -8.51276219e-02 -3.54358577e-03 -1.07569242e+00 -5.92442870e-01 -3.20953995e-01 -1.71402842e-01 3.12461197e-01 5.10594726e-01 -1.71933621e-01 2.54856776e-02 3.38339150e-01 -1.26838529e+00 1.39862254e-01 1.29628348e+00 8.66393328e-01 8.22308213e-02 2.83020198e-01 -5.08938491e-01 9.77238953e-01 -5.20493865e-01 -7.02654243e-01 6.68762550e-02 -1.94694340e-01 -1.09699905e+00 -3.40071768e-01 2.11389259e-01 -1.02764979e-01 -2.47099787e-01 8.69737387e-01 6.70508087e-01 7.78887510e-01 7.77692378e-01 -1.05912030e+00 -1.08736479e+00 8.54174972e-01 -3.25142294e-01 5.09913027e-01 2.60263264e-01 -1.78729787e-01 1.29371142e+00 -1.27703023e+00 6.02689445e-01 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60c5f399-730a-4240-bf5d-d204b728cd6d	pi2-text-vec-policy-representations-with	2306.09800	null	https://arxiv.org/abs/2306.09800v1	https://arxiv.org/pdf/2306.09800v1.pdf	$\pi2\text{vec}$: Policy Representations with Successor Features	This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of foundation model features change in response to the policy behavior in a task agnostic way, and can be trained from offline data, allowing them to be used in offline policy selection. This work provides a key piece of a recipe for fusing together three modern lines of research: Offline policy evaluation as a counterpart to offline RL, foundation models as generic and powerful state representations, and efficient policy selection in resource constrained environments.	['Misha Denil', 'Yutian Chen', 'Tom Le Paine', 'Claudio Fantacci', 'Ksenia Konyushkova', 'Gianluca Scarpellini']	2023-06-16	null	null	null	null	['offline-rl']	['playing-games']	[-2.40675300e-01 -3.26128900e-01 -9.43563104e-01 -4.97180194e-01 -2.67497897e-01 -9.41383004e-01 8.39626193e-01 1.18803136e-01 -5.40478647e-01 8.87963235e-01 3.29991281e-01 -7.35807538e-01 -8.10586568e-03 -4.25681561e-01 -5.15044332e-01 -5.37050843e-01 -4.17761266e-01 4.28860158e-01 1.34678513e-01 -4.97529060e-01 2.39321053e-01 8.25999320e-01 -1.54154706e+00 1.62645712e-01 2.10133493e-01 1.06842721e+00 -3.78273651e-02 6.93820238e-01 -1.23779602e-01 1.09358108e+00 -8.02707314e-01 1.55374482e-01 2.98352242e-01 -2.64040142e-01 -4.21836555e-01 -2.58699238e-01 2.56379251e-03 -5.28514802e-01 -8.14405262e-01 9.18151021e-01 3.12364638e-01 7.08833396e-01 5.75966537e-01 -1.06512940e+00 -2.39263982e-01 5.81350327e-01 1.39066428e-01 5.67025602e-01 3.17831904e-01 6.02087379e-01 7.60865569e-01 -1.86562523e-01 4.20186937e-01 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2a5b1c97-47d3-4ad2-a78e-423488df69b7	fgn-fusion-glyph-network-for-chinese-named	2001.05272	null	https://arxiv.org/abs/2001.05272v6	https://arxiv.org/pdf/2001.05272v6.pdf	FGN: Fusion Glyph Network for Chinese Named Entity Recognition	Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN, Fusion Glyph Network for Chinese NER. Except for adding glyph information, this method may also add extra interactive information with the fusion mechanism. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters. (2) we provide a method with sliding window and Slice-Attention to fuse the BERT representation and glyph representation for a character, which may capture potential interactive knowledge between context and glyph. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.	['Zhenyu Xuan', 'Rui Bao', 'Shengyi Jiang']	2020-01-15	null	null	null	null	['chinese-named-entity-recognition']	['natural-language-processing']	[ 6.44854456e-02 -1.05733141e-01 1.15269996e-01 -4.25505340e-01 -7.86565721e-01 -6.11682117e-01 5.27312100e-01 9.67401937e-02 -5.62102318e-01 5.55630505e-01 9.44215536e-01 -1.33546919e-01 6.00284219e-01 -9.58341777e-01 -5.90518832e-01 -5.68691790e-01 2.04668462e-01 -1.25956669e-01 1.60112962e-01 -3.51136506e-01 3.45531672e-01 4.75657254e-01 -7.61370540e-01 4.42495465e-01 1.26183701e+00 5.18957913e-01 8.17384124e-01 5.76772749e-01 -6.88647985e-01 7.15784609e-01 -8.07561815e-01 -3.95095170e-01 5.49615622e-02 -3.28383386e-01 -8.01682293e-01 -2.92211086e-01 4.08059657e-01 -3.14648330e-01 -3.16010118e-01 1.15737903e+00 6.40101850e-01 2.38935307e-01 1.98910683e-01 -4.12371755e-01 -1.15911925e+00 1.38627410e+00 -4.84994382e-01 -7.33613074e-02 2.96691924e-01 -2.79042363e-01 8.34700465e-01 -8.24077606e-01 4.02943432e-01 1.35985017e+00 8.14993203e-01 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cdc700d4-9c9a-4b82-9894-2ee7eb5033e9	inference-of-the-dynamic-aging-related	1807.05637	null	https://arxiv.org/abs/1807.05637v3	https://arxiv.org/pdf/1807.05637v3.pdf	Inference of the Dynamic Aging-related Biological Subnetwork via Network Propagation	Gene expression (GE) data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.) However, GE analyses ignore physical interactions between gene products, i.e., proteins. Since proteins function by interacting with each other, and since biological networks (BNs) capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP) to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related) subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP.	['Tijana Milenkovic', 'Khalique Newaz']	2018-07-15	null	null	null	null	['human-aging']	['miscellaneous']	[ 3.44900668e-01 -1.31052895e-03 -4.07319933e-01 -1.72617301e-01 3.02569985e-01 -5.04889309e-01 8.30338001e-02 3.16772074e-01 -7.99131095e-02 1.36312568e+00 1.73900396e-01 -4.23294127e-01 -4.98547196e-01 -1.07635546e+00 -6.49546444e-01 -8.12066078e-01 -5.91660440e-01 5.56494117e-01 3.35739285e-01 -1.27488285e-01 -9.24860463e-02 2.98450172e-01 -1.27248609e+00 -1.06903538e-01 9.09408271e-01 4.03711617e-01 -1.67849377e-01 6.59257889e-01 1.16701588e-01 -1.21684171e-01 -4.48622584e-01 -4.34985161e-01 -1.67019710e-01 -6.63457274e-01 -6.96362376e-01 -5.32133162e-01 -1.20035559e-01 3.55072498e-01 -2.95219690e-01 1.11527610e+00 5.01591623e-01 -2.62476891e-01 5.71789861e-01 -1.51001740e+00 -3.75725895e-01 7.60254264e-01 -4.93065417e-01 1.91077977e-01 3.79819661e-01 1.65822670e-01 1.29410923e+00 -2.33146608e-01 1.17871833e+00 1.33700025e+00 9.08431947e-01 7.35561907e-01 -1.61484027e+00 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257d2724-fd0f-4eb5-8f1b-40bc5f2d0720	a-new-perspective-to-boost-vision-transformer	2301.00989	null	https://arxiv.org/abs/2301.00989v1	https://arxiv.org/pdf/2301.00989v1.pdf	A New Perspective to Boost Vision Transformer for Medical Image Classification	Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.	['Yefeng Zheng', 'Kai Ma', 'Nanjun He', 'Yawen Huang', 'Yuexiang Li']	2023-01-03	null	null	null	null	['skin-lesion-classification', 'diabetic-retinopathy-grading']	['medical', 'medical']	[ 5.51669300e-01 2.82386154e-01 -3.91510159e-01 -2.36855179e-01 -5.18213749e-01 5.86017109e-02 2.59462386e-01 2.52362132e-01 -3.73744220e-01 2.19180226e-01 1.38232827e-01 -4.50119972e-02 -2.69729793e-01 -6.34779751e-01 -4.23765272e-01 -8.38780522e-01 1.17991030e-01 2.87661701e-01 2.80032717e-02 -1.99769706e-01 -1.51395136e-02 1.21231623e-01 -1.39355183e+00 3.99605781e-01 1.12546420e+00 1.39764893e+00 2.82154500e-01 1.23542517e-01 1.06378237e-03 9.57009792e-01 -3.19500983e-01 -2.14664280e-01 1.68358222e-01 -1.94705680e-01 -7.41197288e-01 3.99755716e-01 4.20888960e-01 -2.29408696e-01 -4.69808817e-01 1.04139531e+00 5.85559428e-01 -2.17803240e-01 7.02949643e-01 -1.31218302e+00 -7.56747007e-01 5.84617376e-01 -4.78453815e-01 3.81359547e-01 -9.39060003e-02 2.03921154e-01 9.38161850e-01 -8.80750597e-01 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91d3cdfb-5658-4f52-b6d2-ea3408b24a32	appliance-detection-using-very-low-frequency	2305.10352	null	https://arxiv.org/abs/2305.10352v2	https://arxiv.org/pdf/2305.10352v2.pdf	Appliance Detection Using Very Low-Frequency Smart Meter Time Series	In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption data at a very low frequency (every 30min), enabling utilities to bill customers more accurately. To provide more personalized recommendations, the next step is to detect the appliances owned by customers, which is a challenging problem, due to the very-low meter reading frequency. Even though the appliance detection problem can be cast as a time series classification problem, with many such classifiers having been proposed in the literature, no study has applied and compared them on this specific problem. This paper presents an in-depth evaluation and comparison of state-of-the-art time series classifiers applied to detecting the presence/absence of diverse appliances in very low-frequency smart meter data. We report results with five real datasets. We first study the impact of the detection quality of 13 different appliances using 30min sampled data, and we subsequently propose an analysis of the possible detection performance gain by using a higher meter reading frequency. The results indicate that the performance of current time series classifiers varies significantly. Some of them, namely deep learning-based classifiers, provide promising results in terms of accuracy (especially for certain appliances), even using 30min sampled data, and are scalable to the large smart meter time series collections of energy consumption data currently available to electricity suppliers. Nevertheless, our study shows that more work is needed in this area to further improve the accuracy of the proposed solutions. This paper appeared in ACM e-Energy 2023.	['Themis Palpanas', 'Paul Boniol', 'Philippe Charpentier', 'Adrien Petralia']	2023-05-10	null	null	null	null	['time-series-classification']	['time-series']	[-6.66186661e-02 -5.03633559e-01 -1.25472825e-02 -4.67697978e-01 -4.62074459e-01 -5.34995973e-01 6.71810091e-01 2.88924932e-01 -1.34046048e-01 4.99469787e-01 2.93030799e-03 -1.17127039e-01 -4.10468191e-01 -1.06672454e+00 -9.09714550e-02 -1.15734732e+00 -4.34527904e-01 5.39533675e-01 -1.86430305e-01 -1.42591238e-01 6.42378721e-03 5.28172433e-01 -1.75677001e+00 5.58766015e-02 9.10478950e-01 1.22152269e+00 2.34384075e-01 4.03144777e-01 1.12776592e-01 4.79166061e-01 -1.32602715e+00 2.80100405e-01 3.89698118e-01 -2.72359133e-01 -5.59590280e-01 3.36545147e-02 4.40391004e-02 -5.91205060e-01 1.55582711e-01 1.11032581e+00 7.42107928e-01 3.75752114e-02 3.10930967e-01 -1.64522898e+00 -3.71133089e-01 1.06825018e+00 -2.80905753e-01 5.86150765e-01 3.66323501e-01 9.44825336e-02 9.97930050e-01 -6.14242256e-02 -2.63713986e-01 5.85609376e-01 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d5802824-a962-44c8-a14b-5d4646f9d8a4	convex-space-learning-improves-deep	2206.09812	null	https://arxiv.org/abs/2206.09812v2	https://arxiv.org/pdf/2206.09812v2.pdf	ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets	Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classifier performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample generation, widely used for synthetic image generation. However, their scope on synthetic tabular data generation in the context of imbalanced classification is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation based approaches for imbalanced classification problems on smaller tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns the coefficients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classification on such small datasets, as compared to existing deep generative models, while being at-par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance and thus, improves the overall applicability of convex space learning.	['Olaf Wolkenhauer', 'Prashant Srivastava', 'Markus Wolfien', 'Waldemar Hahn', 'Saptarshi Bej', 'Kristian Schultz']	2022-06-20	null	null	null	null	['imbalanced-classification']	['miscellaneous']	[-6.82564229e-02 2.12762743e-01 -3.39181751e-01 -2.91573435e-01 -9.81486201e-01 -2.32290506e-01 3.91031802e-01 -7.01880082e-02 2.74104506e-01 1.19631481e+00 5.66148832e-02 -1.74503744e-01 1.19606890e-01 -1.23349702e+00 -1.08361018e+00 -8.26264083e-01 3.56265008e-01 8.38046610e-01 -4.68437105e-01 -3.85288179e-01 8.45840946e-02 2.62637973e-01 -1.79207957e+00 8.21471274e-01 1.35619175e+00 9.77519929e-01 -5.53428948e-01 2.51028240e-01 -4.56241578e-01 9.31188226e-01 -1.11338377e+00 -6.30412757e-01 4.13265854e-01 -5.24594367e-01 -5.35229027e-01 9.35151726e-02 4.94670779e-01 -2.74387449e-01 -2.33442470e-01 6.72170877e-01 6.15194857e-01 -2.92399287e-01 8.55572581e-01 -1.88693213e+00 -7.14324057e-01 5.01831234e-01 -6.58965111e-01 -2.93851405e-01 5.23878187e-02 1.79121435e-01 6.45470977e-01 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7c638760-0d55-4c08-aac8-24f630517b85	improving-trustworthiness-of-ai-disease	2207.02238	null	https://arxiv.org/abs/2207.02238v1	https://arxiv.org/pdf/2207.02238v1.pdf	Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets	The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic ways. A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent developments in distribution-free uncertainty quantification present practical solutions for these issues by providing reliability guarantees for black-box models on arbitrary data distributions as formally valid finite-sample prediction intervals. Our work applies these new uncertainty quantification methods -- specifically conformal prediction -- to a deep-learning model for grading the severity of spinal stenosis in lumbar spine MRI. We demonstrate a technique for forming ordinal prediction sets that are guaranteed to contain the correct stenosis severity within a user-defined probability (confidence interval). On a dataset of 409 MRI exams processed by the deep-learning model, the conformal method provides tight coverage with small prediction set sizes. Furthermore, we explore the potential clinical applicability of flagging cases with high uncertainty predictions (large prediction sets) by quantifying an increase in the prevalence of significant imaging abnormalities (e.g. motion artifacts, metallic artifacts, and tumors) that could degrade confidence in predictive performance when compared to a random sample of cases.	['Stuart Pomerantz', 'Anastasios N. Angelopoulos', 'Charles Lu']	2022-07-05	null	null	null	null	['prediction-intervals']	['miscellaneous']	[ 3.18644047e-01 7.24499166e-01 -2.72733241e-01 -6.99231207e-01 -1.29867327e+00 -4.07065034e-01 2.62487918e-01 5.16269028e-01 -3.68107587e-01 1.04850459e+00 2.88144767e-01 -6.87234581e-01 -8.29551280e-01 -5.50069273e-01 -9.05050039e-01 -5.81202924e-01 -4.65871185e-01 9.93494391e-01 -1.98153500e-02 5.13743341e-01 8.72540474e-02 2.32219651e-01 -9.88606870e-01 3.52355957e-01 1.12268543e+00 1.27248836e+00 -1.83654860e-01 3.35551172e-01 3.69585335e-01 1.02981234e+00 -6.88995540e-01 -3.65703017e-01 1.12174176e-01 -2.20392048e-01 -4.88905489e-01 -2.26333708e-01 3.64516139e-01 -6.09332740e-01 1.53550580e-01 9.22475994e-01 4.49752510e-01 -3.08662951e-01 1.10168505e+00 -1.03137493e+00 -5.17015576e-01 9.85715091e-01 -3.21548909e-01 8.94680768e-02 -1.04330719e-01 1.09894440e-01 5.99426508e-01 -3.12187999e-01 3.04038793e-01 7.86094308e-01 1.06506312e+00 5.00063658e-01 -1.20198679e+00 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1.46316782e-01 3.86929601e-01 -8.17954302e-01 -4.51209605e-01 -4.41155583e-01]	[14.350351333618164, -2.0513322353363037]
f68920fc-adcd-4454-be76-ac1691c12bb0	polynomial-implicit-neural-representations	2303.11424	null	https://arxiv.org/abs/2303.11424v1	https://arxiv.org/pdf/2303.11424v1.pdf	Polynomial Implicit Neural Representations For Large Diverse Datasets	Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Most INR architectures rely on sinusoidal positional encoding, which accounts for high-frequency information in data. However, the finite encoding size restricts the model's representational power. Higher representational power is needed to go from representing a single given image to representing large and diverse datasets. Our approach addresses this gap by representing an image with a polynomial function and eliminates the need for positional encodings. Therefore, to achieve a progressively higher degree of polynomial representation, we use element-wise multiplications between features and affine-transformed coordinate locations after every ReLU layer. The proposed method is evaluated qualitatively and quantitatively on large datasets like ImageNet. The proposed Poly-INR model performs comparably to state-of-the-art generative models without any convolution, normalization, or self-attention layers, and with far fewer trainable parameters. With much fewer training parameters and higher representative power, our approach paves the way for broader adoption of INR models for generative modeling tasks in complex domains. The code is available at \url{https://github.com/Rajhans0/Poly_INR}	['Pavan Turaga', 'Ankita Shukla', 'Rajhans Singh']	2023-03-20	null	http://openaccess.thecvf.com//content/CVPR2023/html/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.pdf	cvpr-2023-1	['conditional-image-generation']	['computer-vision']	[ 4.46418338e-02 6.96954504e-02 -2.57100053e-02 -4.46965039e-01 -8.38151157e-01 -2.87120908e-01 8.39710355e-01 -1.39997870e-01 -1.86767116e-01 4.51062679e-01 3.00283104e-01 4.25426289e-02 -3.42691690e-02 -8.20032120e-01 -7.00318456e-01 -7.90916979e-01 -2.46313661e-02 2.19851241e-01 9.95125845e-02 -1.92471161e-01 -3.89786065e-02 6.03939116e-01 -1.36290359e+00 2.58455753e-01 5.55670917e-01 1.11085737e+00 3.66304696e-01 4.19491291e-01 6.85643107e-02 6.21258438e-01 -4.74910855e-01 -2.61677086e-01 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6aa44b8e-82bf-477b-9e2b-ea9f343e1c5c	dialogue-term-extraction-using-transfer	2208.10448	null	https://arxiv.org/abs/2208.10448v1	https://arxiv.org/pdf/2208.10448v1.pdf	Dialogue Term Extraction using Transfer Learning and Topological Data Analysis	Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots, and values. As we move towards adaptable dialogue systems where knowledge about domains, slots, and values may change, there is an increasing need to automatically extract these terms from raw dialogues or related non-dialogue data on a large scale. In this paper, we take an important step in this direction by exploring different features that can enable systems to discover realizations of domains, slots, and values in dialogues in a purely data-driven fashion. The features that we examine stem from word embeddings, language modelling features, as well as topological features of the word embedding space. To examine the utility of each feature set, we train a seed model based on the widely used MultiWOZ data-set. Then, we apply this model to a different corpus, the Schema-Guided Dialogue data-set. Our method outperforms the previously proposed approach that relies solely on word embeddings. We also demonstrate that each of the features is responsible for discovering different kinds of content. We believe our results warrant further research towards ontology induction, and continued harnessing of topological data analysis for dialogue and natural language processing research.	['Milica Gašić', 'Marcus Zibrowius', 'Carel van Niekerk', 'Benjamin Matthias Ruppik', 'Michael Heck', 'Renato Vukovic']	2022-08-22	null	https://aclanthology.org/2022.sigdial-1.53	https://aclanthology.org/2022.sigdial-1.53.pdf	sigdial-acl-2022-9	['term-extraction', 'goal-oriented-dialogue-systems']	['natural-language-processing', 'natural-language-processing']	[ 9.95574743e-02 7.17601359e-01 -4.83308844e-02 -4.64281142e-01 -5.17010212e-01 -8.66899490e-01 1.12838912e+00 6.51742995e-01 -5.19487202e-01 7.85135627e-01 9.57448900e-01 -4.80797172e-01 -3.75974000e-01 -1.09398091e+00 -9.53582674e-02 -2.88661182e-01 -2.47037828e-01 8.76312494e-01 4.57767606e-01 -1.07409024e+00 5.48680425e-01 1.81476086e-01 -1.19966674e+00 1.65873677e-01 6.26687467e-01 6.30045593e-01 -1.37816146e-01 6.08945727e-01 -7.84027576e-01 5.16753078e-01 -5.15348732e-01 -3.96716982e-01 2.38358900e-02 -4.12754714e-01 -1.46420324e+00 4.40347865e-02 -6.68000802e-03 -1.62469685e-01 -9.26639065e-02 6.59348071e-01 3.86998266e-01 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19e46c0e-7abf-4ce5-bcf6-791fb0a6004d	learning-trajectory-dependencies-for-human	1908.05436	null	https://arxiv.org/abs/1908.05436v3	https://arxiv.org/pdf/1908.05436v3.pdf	Learning Trajectory Dependencies for Human Motion Prediction	Human motion prediction, i.e., forecasting future body poses given observed pose sequence, has typically been tackled with recurrent neural networks (RNNs). However, as evidenced by prior work, the resulted RNN models suffer from prediction errors accumulation, leading to undesired discontinuities in motion prediction. In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints. In this context, we then propose to encode temporal information by working in trajectory space, instead of the traditionally-used pose space. This alleviates us from manually defining the range of temporal dependencies (or temporal convolutional filter size, as done in previous work). Moreover, spatial dependency of human pose is encoded by treating a human pose as a generic graph (rather than a human skeletal kinematic tree) formed by links between every pair of body joints. Instead of using a pre-defined graph structure, we design a new graph convolutional network to learn graph connectivity automatically. This allows the network to capture long range dependencies beyond that of human kinematic tree. We evaluate our approach on several standard benchmark datasets for motion prediction, including Human3.6M, the CMU motion capture dataset and 3DPW. Our experiments clearly demonstrate that the proposed approach achieves state of the art performance, and is applicable to both angle-based and position-based pose representations. The code is available at https://github.com/wei-mao-2019/LearnTrajDep	['Miaomiao Liu', 'Mathieu Salzmann', 'Wei Mao', 'Hongdong Li']	2019-08-15	learning-trajectory-dependencies-for-human-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Mao_Learning_Trajectory_Dependencies_for_Human_Motion_Prediction_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Mao_Learning_Trajectory_Dependencies_for_Human_Motion_Prediction_ICCV_2019_paper.pdf	iccv-2019-10	['human-pose-forecasting']	['computer-vision']	[-1.28382305e-03 1.16815634e-01 -3.05723011e-01 -4.73366454e-02 -1.93345368e-01 -2.72692204e-01 5.38673401e-01 -1.91076890e-01 -4.95408684e-01 5.04499257e-01 4.45889562e-01 -1.59897506e-01 -5.48773594e-02 -7.06604421e-01 -8.62805009e-01 -5.53745985e-01 -4.01723951e-01 3.13529998e-01 2.28086188e-01 -2.68123001e-01 -1.88434035e-01 5.22878528e-01 -1.30794275e+00 -1.39492229e-01 5.16778171e-01 6.78340912e-01 1.11129306e-01 7.21237481e-01 3.45247746e-01 9.23682153e-01 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080ee9f6-531a-4aa0-922f-c3763dd1d072	crown-conversational-passage-ranking-by	1911.02850	null	https://arxiv.org/abs/1911.02850v3	https://arxiv.org/pdf/1911.02850v3.pdf	CROWN: Conversational Passage Ranking by Reasoning over Word Networks	Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers. In this paper, we present our submission to the TREC Conversational Assistance Track 2019, in which such a conversational setting is explored. We propose a simple unsupervised method for conversational passage ranking by formulating the passage score for a query as a combination of similarity and coherence. To be specific, passages are preferred that contain words semantically similar to the words used in the question, and where such words appear close by. We built a word-proximity network (WPN) from a large corpus, where words are nodes and there is an edge between two nodes if they co-occur in the same passages in a statistically significant way, within a context window. Our approach, named CROWN, improved nDCG scores over a provided Indri baseline on the CAsT training data. On the evaluation data for CAsT, our best run submission achieved above-average performance with respect to AP@5 and nDCG@1000.	['Magdalena Kaiser', 'Rishiraj Saha Roy', 'Gerhard Weikum']	2019-11-07	null	null	null	null	['passage-ranking']	['natural-language-processing']	[ 3.63794491e-02 9.38606262e-02 -7.17873275e-02 -5.28590739e-01 -1.06226826e+00 -7.71770656e-01 8.48165572e-01 6.86602831e-01 -6.60197258e-01 7.35162437e-01 1.13439393e+00 -3.18546951e-01 -4.24819052e-01 -5.96016109e-01 -3.58249575e-01 -3.69799793e-01 -1.76380828e-01 7.34506845e-01 5.30379713e-01 -6.58629417e-01 7.04396784e-01 -1.22450143e-01 -1.36368668e+00 8.81847203e-01 9.67283249e-01 5.87594628e-01 3.33974570e-01 8.34685504e-01 -4.65728819e-01 7.05144882e-01 -7.14289904e-01 -5.15070438e-01 -2.95876890e-01 -7.31152654e-01 -1.61166191e+00 -3.46617311e-01 2.29490697e-01 7.11469958e-03 -2.15930447e-01 5.45460999e-01 3.90659720e-01 6.10610127e-01 6.01128817e-01 -8.46902192e-01 -2.14030549e-01 7.28707314e-01 6.75320849e-02 5.07917762e-01 1.07382131e+00 -3.38171571e-01 1.55067337e+00 -7.58557498e-01 7.33465433e-01 1.14580023e+00 3.34212869e-01 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0f5807ab-873c-475c-83cd-fe34f9898e4e	evaluation-of-gpt-and-bert-based-models-on	2303.17728	null	https://arxiv.org/abs/2303.17728v1	https://arxiv.org/pdf/2303.17728v1.pdf	Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text	Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedical literature, there is a growing need for automated and accurate extraction of PPIs to facilitate scientific knowledge discovery. Pre-trained language models, such as generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT), have shown promising results in natural language processing (NLP) tasks. We evaluated the PPI identification performance of various GPT and BERT models using a manually curated benchmark corpus of 164 PPIs in 77 sentences from learning language in logic (LLL). BERT-based models achieved the best overall performance, with PubMedBERT achieving the highest precision (85.17%) and F1-score (86.47%) and BioM-ALBERT achieving the highest recall (93.83%). Despite not being explicitly trained for biomedical texts, GPT-4 achieved comparable performance to the best BERT models with 83.34% precision, 76.57% recall, and 79.18% F1-score. These findings suggest that GPT models can effectively detect PPIs from text data and have the potential for use in biomedical literature mining tasks.	['Junguk Hur', 'Arzucan Özgür', 'Yongqun He', 'Mert Basmaci', 'Nur Bengisu Çam', 'Hasin Rehana']	2023-03-30	null	null	null	null	['literature-mining']	['natural-language-processing']	[ 1.97312757e-01 3.67088497e-01 -3.73575926e-01 -2.42256939e-01 -1.07450533e+00 -4.66139197e-01 4.48897451e-01 7.31998682e-01 -3.23995262e-01 1.13455725e+00 1.75823420e-01 -5.34458339e-01 -2.39428222e-01 -7.05329657e-01 -9.64651048e-01 -6.62095428e-01 -1.54360548e-01 7.42864192e-01 -1.12183183e-01 6.42842725e-02 -2.12031603e-02 2.82474726e-01 -7.69703746e-01 4.54654545e-01 1.02237594e+00 4.26219076e-01 1.59591436e-01 5.30961156e-01 -6.73329681e-02 6.55401766e-01 -5.92450082e-01 -7.56583512e-01 -3.87542188e-01 -2.67363787e-01 -7.85858035e-01 -7.63842285e-01 -1.91088662e-01 3.21862727e-01 -1.83471426e-01 7.62067139e-01 5.78994930e-01 -5.33048272e-01 7.44930625e-01 -8.84552717e-01 -7.15022445e-01 7.70604432e-01 -5.97111642e-01 1.98315442e-01 5.66919327e-01 1.46069840e-01 1.39369035e+00 -7.44563937e-01 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bcf8a224-dce2-4068-b38f-b2a998352e84	explore-image-deblurring-via-blur-kernel	2104.00317	null	https://arxiv.org/abs/2104.00317v2	https://arxiv.org/pdf/2104.00317v2.pdf	Explore Image Deblurring via Blur Kernel Space	This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen blur operator by a kernel in the encoded space and searches for the corresponding sharp image. Unlike recent deep-learning-based methods, our system can handle unseen blur kernel, while avoiding using complicated handcrafted priors on the blur operator often found in classical methods. Due to the method's design, the encoded kernel space is fully differentiable, thus can be easily adopted in deep neural network models. Moreover, our method can be used for blur synthesis by transferring existing blur operators from a given dataset into a new domain. Finally, we provide experimental results to confirm the effectiveness of the proposed method.	['Minh Hoai', 'Quynh Phung', 'Anh Tran', 'Phong Tran']	2021-04-01	null	null	null	null	['blind-image-deblurring']	['computer-vision']	[ 7.39692226e-02 -2.86075622e-01 3.71195972e-01 -2.99697608e-01 -2.99966305e-01 -5.41982830e-01 2.98093081e-01 -7.02444971e-01 -2.78403997e-01 7.86980569e-01 3.92666847e-01 -1.17447570e-01 -2.60021597e-01 -3.27342808e-01 -7.92081594e-01 -8.22061598e-01 3.30785573e-01 -1.98799506e-01 -5.80122285e-02 2.15430647e-01 2.90859759e-01 3.27136427e-01 -9.71491098e-01 -2.37119235e-02 1.26999032e+00 9.47882056e-01 6.87421143e-01 6.78631127e-01 2.06827864e-01 1.12151659e+00 -4.15794253e-01 -3.90094697e-01 3.56528312e-01 -6.12675548e-01 -9.53377128e-01 3.43834400e-01 5.57733774e-01 -7.92983532e-01 -9.69915509e-01 1.51332033e+00 2.40098178e-01 2.25760937e-01 5.44713318e-01 -7.41788447e-01 -1.43472540e+00 4.47816491e-01 -4.76703405e-01 2.45117560e-01 3.23535427e-02 1.90194800e-01 5.69780409e-01 -1.05333507e+00 2.59945452e-01 1.08917451e+00 7.22306073e-01 3.56361508e-01 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d2de63f1-581d-44cc-bb8c-c9054cd7048e	diffusion-dataset-generation-towards-closing	2305.09401	null	https://arxiv.org/abs/2305.09401v1	https://arxiv.org/pdf/2305.09401v1.pdf	Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection	We propose a method that augments a simulated dataset using diffusion models to improve the performance of pedestrian detection in real-world data. The high cost of collecting and annotating data in the real-world has motivated the use of simulation platforms to create training datasets. While simulated data is inexpensive to collect and annotate, it unfortunately does not always closely match the distribution of real-world data, which is known as the sim2real gap. In this paper we propose a novel method of synthetic data creation meant to close the sim2real gap for the challenging pedestrian detection task. Our method uses a diffusion-based architecture to learn a real-world distribution which, once trained, is used to generate datasets. We mix this generated data with simulated data as a form of augmentation and show that training on a combination of generated and simulated data increases average precision by as much as 27.3% for pedestrian detection models in real-world data, compared against training on purely simulated data.	['Michael Greenspan', 'Mohsen Zand', 'Andrew Farley']	2023-05-16	null	null	null	null	['pedestrian-detection']	['computer-vision']	[-2.36499887e-02 -9.60672572e-02 3.39751393e-01 -3.94553721e-01 -6.23846531e-01 -5.16425133e-01 7.78576374e-01 3.03052545e-01 -9.32800472e-01 8.36230278e-01 2.16660071e-02 -4.02995646e-01 7.09797263e-01 -1.12349534e+00 -8.08601975e-01 -3.62064570e-01 -1.46844581e-01 8.56199026e-01 7.01278329e-01 -2.13963136e-01 -3.78581792e-01 5.05417824e-01 -1.70315814e+00 3.74461383e-01 7.73681343e-01 3.77830148e-01 1.49533615e-01 1.04751313e+00 -1.48267467e-02 3.58274281e-01 -1.11732864e+00 -4.91339594e-01 6.96968973e-01 -3.99640590e-01 -1.03416584e-01 1.01537779e-01 7.55877137e-01 -4.97663379e-01 -4.75218862e-01 8.18312764e-01 9.84174848e-01 -2.31023300e-02 6.29518092e-01 -1.54496062e+00 -2.44319916e-01 1.88123077e-01 -6.84129894e-01 3.30517381e-01 4.71685588e-01 8.45718443e-01 1.43182829e-01 -3.77258927e-01 6.82877958e-01 1.24338484e+00 9.75374222e-01 7.19450653e-01 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f289396d-e1e2-4215-891f-d8eb2df1d50e	fan-trans-online-knowledge-distillation-for	2211.06143	null	https://arxiv.org/abs/2211.06143v1	https://arxiv.org/pdf/2211.06143v1.pdf	FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection	Due to its importance in facial behaviour analysis, facial action unit (AU) detection has attracted increasing attention from the research community. Leveraging the online knowledge distillation framework, we propose the ``FANTrans" method for AU detection. Our model consists of a hybrid network of convolution and transformer blocks to learn per-AU features and to model AU co-occurrences. The model uses a pre-trained face alignment network as the feature extractor. After further transformation by a small learnable add-on convolutional subnet, the per-AU features are fed into transformer blocks to enhance their representation. As multiple AUs often appear together, we propose a learnable attention drop mechanism in the transformer block to learn the correlation between the features for different AUs. We also design a classifier that predicts AU presence by considering all AUs' features, to explicitly capture label dependencies. Finally, we make the attempt of adapting online knowledge distillation in the training stage for this task, further improving the model's performance. Experiments on the BP4D and DISFA datasets demonstrating the effectiveness of proposed method.	['Maja Pantic', 'Yordan Hristov', 'Yiming Lin', 'Jie Shen', 'Jing Yang']	2022-11-11	null	null	null	null	['face-alignment', 'action-unit-detection', 'facial-action-unit-detection']	['computer-vision', 'computer-vision', 'computer-vision']	[ 3.21744263e-01 2.79819220e-01 4.75290455e-02 -6.32980764e-01 -4.23387021e-01 -1.14467032e-01 5.60027659e-01 -2.51940489e-01 -4.38234180e-01 2.98109293e-01 2.23959059e-01 2.30463669e-01 3.14008236e-01 -7.49755442e-01 -7.18856514e-01 -7.83784091e-01 -9.59522650e-02 -1.44055143e-01 6.27696142e-02 -1.27470046e-01 -2.32494220e-01 4.98976380e-01 -1.62645566e+00 5.33194184e-01 5.27243793e-01 1.42838955e+00 -4.73527946e-02 4.92676675e-01 1.59205765e-01 1.10286868e+00 -2.85644710e-01 -6.44897461e-01 4.15664703e-01 -5.45128763e-01 -8.55030179e-01 1.84258774e-01 3.38407040e-01 -6.85717106e-01 -5.82240045e-01 7.94208825e-01 5.83231866e-01 1.01135910e-01 5.59037089e-01 -1.21095133e+00 -6.74797595e-01 4.51347709e-01 -7.40180254e-01 1.34547517e-01 2.61843652e-01 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ecc8f510-69ee-4e54-9277-595ad9a8f0b9	maximum-mean-discrepancy-is-aware-of	2010.11415	null	https://arxiv.org/abs/2010.11415v3	https://arxiv.org/pdf/2010.11415v3.pdf	Maximum Mean Discrepancy Test is Aware of Adversarial Attacks	The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of adversarial attacks -- the MMD test failed to detect the discrepancy between natural and adversarial data. Given this phenomenon, we raise a question: are natural and adversarial data really from different distributions? The answer is affirmative -- the previous use of the MMD test on the purpose missed three key factors, and accordingly, we propose three components. Firstly, the Gaussian kernel has limited representation power, and we replace it with an effective deep kernel. Secondly, the test power of the MMD test was neglected, and we maximize it following asymptotic statistics. Finally, adversarial data may be non-independent, and we overcome this issue with the wild bootstrap. By taking care of the three factors, we verify that the MMD test is aware of adversarial attacks, which lights up a novel road for adversarial data detection based on two-sample tests.	['Masashi Sugiyama', 'Gang Niu', 'Tongliang Liu', 'Bo Han', 'Jingfeng Zhang', 'Feng Liu', 'Ruize Gao']	2020-10-22	null	null	null	null	['adversarial-attack-detection', 'adversarial-attack-detection']	['computer-vision', 'knowledge-base']	[-6.26862049e-02 2.38898069e-01 2.87439495e-01 6.58470998e-03 -8.09730887e-01 -1.09720767e+00 6.08293653e-01 6.25098869e-03 -2.30751380e-01 1.01241755e+00 -1.10643968e-01 -6.76414847e-01 -1.20616734e-01 -9.82173204e-01 -8.29440296e-01 -8.32062066e-01 -1.80321574e-01 2.59626746e-01 4.77623194e-01 -2.25165993e-01 1.61539465e-01 3.67329150e-01 -1.27554870e+00 -8.51183012e-02 1.16265810e+00 8.47776592e-01 -5.48888922e-01 8.29945028e-01 1.35022223e-01 6.13659501e-01 -8.83690536e-01 -7.93367922e-01 6.36393547e-01 -6.49136186e-01 -6.56202435e-01 -4.52588141e-01 3.47488523e-01 -4.89282161e-01 -2.33451769e-01 1.52355051e+00 7.44000852e-01 -2.52570331e-01 8.46268713e-01 -1.65597785e+00 -7.29411542e-01 5.03045559e-01 -3.04817647e-01 1.14052065e-01 3.20646584e-01 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3b52b264-96d4-45ac-b350-29c5b8cfb6ce	impact-of-uavs-equipped-with-ads-b-on-the	2307.01534	null	https://arxiv.org/abs/2307.01534v1	https://arxiv.org/pdf/2307.01534v1.pdf	Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System	In recent years, there is an increasing demand for unmanned aerial vehicles (UAVs) to complete multiple applications. However, as unmanned equipments, UAVs lead to some security risks to general civil aviations. In order to strengthen the flight management of UAVs and guarantee the safety, UAVs can be equipped with automatic dependent surveillance-broadcast (ADS-B) devices. In addition, as an automatic system, ADS-B can periodically broadcast flight information to the nearby aircrafts or the ground stations, and the technology is already used in civil aviation systems. However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation. Further, the operation of civil aviation are seriously interfered. Hence, this paper firstly examines the packets loss of civil planes at different distance, then analyzes the impact of UAVs equipped with ADS-B on the packets updating of civil planes. The result indicates that the 1090MHz band blocking is affected by the density of UAVs. Besides, the frequency capacity is affected by the requirement of updating interval of civil planes. The position updating probability within 3s is 92.3% if there are 200 planes within 50km and 20 UAVs within 5km. The position updating probability within 3s is 86.9% if there are 200 planes within 50km and 40 UAVs within 5km.	['Bin Wang', 'Huiling Hu', 'Qihui Wu', 'Yifan Zhang', 'Chao Dong', 'Ziye Jia', 'Lei Zhang', 'Yiyang Liao']	2023-07-04	null	null	null	null	['blocking']	['natural-language-processing']	[-1.06575437e-01 -1.61018327e-01 9.68011394e-02 2.36357585e-01 3.54721785e-01 -8.92843425e-01 3.15021984e-02 -1.88845903e-01 -3.32207590e-01 9.85398352e-01 -4.92166877e-01 -8.64445210e-01 -6.46781087e-01 -1.13944733e+00 -5.47064960e-01 -8.91797125e-01 -5.14396846e-01 -5.04060090e-01 7.48516619e-01 -6.13022506e-01 -3.49767804e-01 8.42544436e-01 -1.31601787e+00 -2.25930452e-01 5.70596039e-01 1.37807858e+00 1.51886195e-02 4.14180338e-01 4.93725181e-01 -5.79019375e-02 -1.26721096e+00 -3.22202034e-02 6.81949675e-01 -1.74474567e-01 -3.01823318e-01 1.81936443e-01 -1.39341995e-01 -8.54466856e-01 -5.17206848e-01 1.02151370e+00 5.83318435e-02 -3.73593085e-02 4.14182872e-01 -1.54950452e+00 8.64367187e-02 9.67936590e-02 -7.63767421e-01 4.52047199e-01 1.31445453e-01 -3.79235521e-02 2.63984770e-01 -3.01612308e-03 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2a02039c-ed7b-41a5-9ab0-d8ac6d4d2c6f	micro-objective-learning-accelerating-deep	1703.03933	null	http://arxiv.org/abs/1703.03933v1	http://arxiv.org/pdf/1703.03933v1.pdf	Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals	Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems in sparse reward settings. It is difficult to explore, but also difficult to exploit, a small number of successes when learning policy. To solve this issue, the subgoal and option framework have been proposed. However, discovering subgoals online is too expensive to be used to learn options in large state spaces. We propose Micro-objective learning (MOL) to solve this problem. The main idea is to estimate how important a state is while training and to give an additional reward proportional to its importance. We evaluated our algorithm in two Atari games: Montezuma's Revenge and Seaquest. With three experiments to each game, MOL significantly improved the baseline scores. Especially in Montezuma's Revenge, MOL achieved two times better results than the previous state-of-the-art model.	['Byoung-Tak Zhang', 'Dong-Hyun Kwak', 'Sang-Woo Lee', 'Jinyoung Choi', 'Sungtae Lee']	2017-03-11	null	null	null	null	['game-of-go', 'montezumas-revenge']	['playing-games', 'playing-games']	[-4.32781845e-01 -1.30916107e-02 -4.31959659e-01 9.87084061e-02 -7.94111431e-01 -6.30727470e-01 3.33915770e-01 -1.48597971e-01 -7.97611475e-01 1.26447189e+00 -1.92607120e-02 -4.07021314e-01 -3.40512335e-01 -6.90482557e-01 -6.21644378e-01 -5.15175879e-01 -6.85815930e-01 7.10860908e-01 3.49749029e-01 -8.33520114e-01 4.41715568e-01 2.18908619e-02 -1.49254191e+00 -4.01118472e-02 1.03190684e+00 8.79659534e-01 4.28276211e-01 5.83764076e-01 2.01676771e-01 1.07945859e+00 -5.30480683e-01 1.39591128e-01 5.24579763e-01 -4.09280032e-01 -9.15245950e-01 -1.47979662e-01 -2.17090901e-02 -6.26737595e-01 -3.40789199e-01 1.06471801e+00 5.38387477e-01 5.31289399e-01 2.72899598e-01 -1.20720387e+00 4.51837964e-02 8.83953750e-01 -7.28562832e-01 4.60808277e-01 3.05185527e-01 5.31189024e-01 1.19507945e+00 -4.00340468e-01 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3102a062-cc74-470c-8327-6c421dc6ccc6	predicting-human-card-selection-in-magic-the	2105.11864	null	https://arxiv.org/abs/2105.11864v2	https://arxiv.org/pdf/2105.11864v2.pdf	Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking	Drafting, i.e., the selection of a subset of items from a larger candidate set, is a key element of many games and related problems. It encompasses team formation in sports or e-sports, as well as deck selection in many modern card games. The key difficulty of drafting is that it is typically not sufficient to simply evaluate each item in a vacuum and to select the best items. The evaluation of an item depends on the context of the set of items that were already selected earlier, as the value of a set is not just the sum of the values of its members - it must include a notion of how well items go together. In this paper, we study drafting in the context of the card game Magic: The Gathering. We propose the use of a contextual preference network, which learns to compare two possible extensions of a given deck of cards. We demonstrate that the resulting network is better able to evaluate card decks in this game than previous attempts.	['Martin Müller', 'Johannes Fürnkranz', 'Timo Bertram']	2021-05-25	null	null	null	null	['card-games']	['playing-games']	[ 2.51242071e-01 -1.77031025e-01 -2.43290424e-01 -1.75578743e-01 -3.83937925e-01 -7.40015805e-01 -5.63947000e-02 4.54610467e-01 -8.01146686e-01 8.47730517e-01 1.97012633e-01 -1.56741589e-01 -8.79910767e-01 -1.02076340e+00 -4.71175879e-01 -4.11855578e-01 -2.51391977e-01 1.18167543e+00 5.25411189e-01 -8.45390975e-01 4.44667608e-01 1.09134696e-01 -1.68815911e+00 3.34970415e-01 4.88853127e-01 9.47395444e-01 2.00147867e-01 6.88387454e-01 2.06772223e-01 3.54100078e-01 -6.83410883e-01 -6.14732921e-01 4.24155623e-01 -5.47385216e-01 -1.13766491e+00 1.32916734e-01 2.22429454e-01 -1.91508338e-01 3.77475023e-01 1.04458010e+00 2.78497845e-01 5.22031546e-01 2.60178775e-01 -1.13040388e+00 4.48377728e-02 1.18078315e+00 -1.12233989e-01 1.02836788e-01 4.40699935e-01 -4.23923016e-01 1.55024338e+00 -5.00196636e-01 6.95218503e-01 6.10269308e-01 5.24666011e-01 3.58550310e-01 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7f6b3479-3ac8-4430-b2ec-58194590866c	explicitly-controllable-3d-aware-portrait	2209.05434	null	https://arxiv.org/abs/2209.05434v3	https://arxiv.org/pdf/2209.05434v3.pdf	3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation	In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs. While plenty of works extend unconditional generative models and achieve some levels of controllability, it is still challenging to ensure multi-view consistency, especially in large poses. In this work, we propose a network that generates 3D-aware portraits while being controllable according to semantic parameters regarding pose, identity, expression and illumination. Our network uses neural scene representation to model 3D-aware portraits, whose generation is guided by a parametric face model that supports explicit control. While the latent disentanglement can be further enhanced by contrasting images with partially different attributes, there still exists noticeable inconsistency in non-face areas, e.g., hair and background, when animating expressions. Wesolve this by proposing a volume blending strategy in which we form a composite output by blending dynamic and static areas, with two parts segmented from the jointly learned semantic field. Our method outperforms prior arts in extensive experiments, producing realistic portraits with vivid expression in natural lighting when viewed from free viewpoints. It also demonstrates generalization ability to real images as well as out-of-domain data, showing great promise in real applications.	['Fang Wen', 'Lizhuang Ma', 'Dong Chen', 'Ting Zhang', 'Binxin Yang', 'Bo Zhang', 'Junshu Tang']	2022-09-12	null	null	null	null	['3d-face-animation', 'face-reenactment', 'face-model']	['computer-vision', 'computer-vision', 'computer-vision']	[ 2.18036175e-01 1.40951812e-01 1.21343911e-01 -4.72662777e-01 -4.30236578e-01 -8.09961259e-01 7.79842317e-01 -7.11518943e-01 3.61883283e-01 7.11019099e-01 2.35553965e-01 4.60877389e-01 2.82865800e-02 -9.45140839e-01 -7.55666256e-01 -8.90899718e-01 3.69582981e-01 6.36314273e-01 -2.18515366e-01 -4.51401234e-01 -2.20622092e-01 6.73815668e-01 -1.57522941e+00 1.09998748e-01 8.13541949e-01 9.46611106e-01 -1.13158397e-01 5.40029645e-01 1.28021598e-01 6.09200776e-01 -6.81928396e-01 -5.90353072e-01 4.79342371e-01 -6.14826858e-01 -2.56848902e-01 7.71855891e-01 9.66590941e-01 -5.26337504e-01 -2.36675829e-01 9.93509591e-01 4.39311653e-01 4.90980595e-02 5.96734464e-01 -1.56992936e+00 -1.03623760e+00 2.33286157e-01 -7.42168248e-01 -5.09561718e-01 3.92284960e-01 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328152d8-da1c-4c64-ad1c-073a97f4526a	differentiable-tracking-based-training-of	2111.00030	null	https://arxiv.org/abs/2111.00030v1	https://arxiv.org/pdf/2111.00030v1.pdf	Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers	Data-based and learning-based sound source localization (SSL) has shown promising results in challenging conditions, and is commonly set as a classification or a regression problem. Regression-based approaches have certain advantages over classification-based, such as continuous direction-of-arrival estimation of static and moving sources. However, multi-source scenarios require multiple regressors without a clear training strategy up-to-date, that does not rely on auxiliary information such as simultaneous sound classification. We investigate end-to-end training of such methods with a technique recently proposed for video object detectors, adapted to the SSL setting. A differentiable network is constructed that can be plugged to the output of the localizer to solve the optimal assignment between predictions and references, optimizing directly the popular CLEAR-MOT tracking metrics. Results indicate large improvements over directly optimizing mean squared errors, in terms of localization error, detection metrics, and tracking capabilities.	['Tuomas Virtanen', 'Archontis Politis', 'Sharath Adavanne']	2021-10-29	null	null	null	null	['direction-of-arrival-estimation', 'sound-classification']	['audio', 'audio']	[ 2.53396556e-02 -5.80408871e-01 1.06093600e-01 -4.17265207e-01 -1.64636743e+00 -6.35475576e-01 3.63312095e-01 1.63723990e-01 -4.98286664e-01 4.16557342e-01 5.41266054e-03 -2.49876991e-01 -2.38471776e-01 -5.76581098e-02 -8.10365915e-01 -8.41156721e-01 -3.72282654e-01 9.32008997e-02 5.02423346e-01 1.56829938e-01 2.41104007e-01 4.50100631e-01 -1.47401178e+00 -9.00986865e-02 3.61339211e-01 1.20125985e+00 4.71258461e-01 1.15699041e+00 1.20891467e-01 7.80839980e-01 -7.81726956e-01 2.58524209e-01 1.45903930e-01 -4.98505503e-01 -3.54407355e-02 -4.83913183e-01 9.78846014e-01 9.88399759e-02 -1.94646165e-01 7.83746541e-01 1.04545259e+00 3.62960577e-01 3.91379654e-01 -1.45368123e+00 -1.83674619e-01 4.09874797e-01 -4.96487737e-01 5.58244109e-01 4.88881916e-01 2.95974575e-02 9.11849618e-01 -8.93355608e-01 -5.08930534e-03 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3722904b-2808-4d0b-bb3b-0ff33217a19b	simulslt-end-to-end-simultaneous-sign	2112.04228	null	https://arxiv.org/abs/2112.04228v1	https://arxiv.org/pdf/2112.04228v1.pdf	SimulSLT: End-to-End Simultaneous Sign Language Translation	Sign language translation as a kind of technology with profound social significance has attracted growing researchers' interest in recent years. However, the existing sign language translation methods need to read all the videos before starting the translation, which leads to a high inference latency and also limits their application in real-life scenarios. To solve this problem, we propose SimulSLT, the first end-to-end simultaneous sign language translation model, which can translate sign language videos into target text concurrently. SimulSLT is composed of a text decoder, a boundary predictor, and a masked encoder. We 1) use the wait-k strategy for simultaneous translation. 2) design a novel boundary predictor based on the integrate-and-fire module to output the gloss boundary, which is used to model the correspondence between the sign language video and the gloss. 3) propose an innovative re-encode method to help the model obtain more abundant contextual information, which allows the existing video features to interact fully. The experimental results conducted on the RWTH-PHOENIX-Weather 2014T dataset show that SimulSLT achieves BLEU scores that exceed the latest end-to-end non-simultaneous sign language translation model while maintaining low latency, which proves the effectiveness of our method.	['Xiaofei He', 'Xingshan Zeng', 'Meng Zhang', 'Weike Jin', 'Jinglin Liu', 'Zhou Zhao', 'Aoxiong Yin']	2021-12-08	null	null	null	null	['sign-language-translation']	['computer-vision']	[ 3.09971094e-01 -3.91899467e-01 -2.30431005e-01 -2.54158050e-01 -5.38022816e-01 -4.06704128e-01 4.37284678e-01 -8.59500051e-01 -4.94653076e-01 4.13552046e-01 5.90404749e-01 -2.23471910e-01 3.51020753e-01 -5.20325422e-01 -6.61223888e-01 -6.23486578e-01 4.16932821e-01 1.09968707e-01 4.70232636e-01 4.06874344e-02 1.06077470e-01 -7.04466999e-02 -1.46581078e+00 5.21957994e-01 1.05544651e+00 7.41021216e-01 5.77483058e-01 6.82722628e-01 -2.86410511e-01 8.96052122e-01 -3.26428384e-01 -4.09108669e-01 2.70592272e-01 -9.38029289e-01 -3.67531866e-01 -4.74703051e-02 3.90213072e-01 -9.65761364e-01 -7.05244243e-01 8.48279536e-01 9.43234563e-01 -2.73006707e-01 1.90228328e-01 -1.28274977e+00 -5.40106118e-01 5.69053113e-01 -4.50892955e-01 -2.81546324e-01 5.24056017e-01 4.95545477e-01 8.29482615e-01 -8.96542728e-01 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c0ca3ab8-5653-4dff-a7d5-eef9655e943e	a-3d-face-modelling-approach-for-pose	1606.00474	null	http://arxiv.org/abs/1606.00474v1	http://arxiv.org/pdf/1606.00474v1.pdf	A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment	Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis.	['Matthias Rätsch', 'Philipp Kopp', 'Michael Grupp', 'Patrik Huber']	2016-06-01	null	null	null	null	['robust-face-recognition', '3d-face-modeling']	['computer-vision', 'computer-vision']	[ 3.56714576e-02 3.27205688e-01 3.89823079e-01 -6.20554209e-01 -4.13036980e-02 -1.70122415e-01 4.43539679e-01 -4.80037719e-01 -5.11059821e-01 2.73438275e-01 -3.36505532e-01 1.67457536e-01 -8.67637023e-02 -3.58157277e-01 -5.93984962e-01 -5.15954137e-01 -1.85487047e-01 1.03879511e+00 -5.00592068e-02 -1.82651699e-01 9.02247354e-02 1.21075284e+00 -2.07549238e+00 -1.93445787e-01 2.72637278e-01 9.42548275e-01 1.28239259e-01 3.34711164e-01 2.60099471e-01 1.13895789e-01 -3.86631548e-01 -3.54457259e-01 4.74418521e-01 -1.15483813e-02 -5.26166677e-01 5.63149214e-01 5.93537033e-01 -2.47272879e-01 -9.24602523e-03 8.17715049e-01 6.98098302e-01 -1.64501742e-02 6.66220427e-01 -1.35989094e+00 1.87162742e-01 -1.28582641e-01 -4.57841009e-01 -4.56346124e-01 7.91798294e-01 -2.65071839e-01 2.80076195e-03 -1.12105846e+00 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1a1a301e-f9d1-4d99-b41a-ec35ac8ff70e	recursive-gaussian-process-over-graphs-for	2209.01703	null	https://arxiv.org/abs/2209.01703v1	https://arxiv.org/pdf/2209.01703v1.pdf	Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems	The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi time-scale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. The proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.	['Balasubramaniam Natarajan', 'Shweta Dahale']	2022-09-04	null	null	null	null	['matrix-completion']	['methodology']	[ 1.22904498e-02 -3.64588022e-01 5.41471720e-01 -2.74104416e-01 -1.07559121e+00 -4.32818532e-01 4.93308246e-01 6.89069331e-01 3.68351042e-02 1.04739618e+00 2.93504819e-02 -9.57469866e-02 -6.84464872e-01 -9.76836383e-01 -3.16985011e-01 -1.23098862e+00 -7.13387251e-01 8.37725103e-01 -1.91168860e-01 8.37723613e-02 -9.14903581e-02 3.01295251e-01 -9.12237525e-01 -2.90742636e-01 1.21256578e+00 1.20322609e+00 4.78100926e-01 7.35818744e-01 3.25901955e-01 5.89100718e-01 -9.08757269e-01 3.86989146e-01 5.93869835e-02 -1.42043633e-02 -6.31262688e-03 4.51979816e-01 -6.99430943e-01 -2.60956138e-01 5.11744432e-02 1.49106383e+00 6.63026273e-01 1.44874796e-01 3.39302272e-01 -1.78430617e+00 -1.57839745e-01 6.11849070e-01 -8.30933869e-01 2.25657865e-01 3.38694632e-01 4.79434198e-03 4.54050303e-01 -5.92978895e-01 -2.10335106e-01 1.05749249e+00 5.31691432e-01 -5.49891710e-01 -1.28758645e+00 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-1.29666078e+00 1.10698998e-01 2.81809151e-01 -3.51605296e-01 -3.58078837e-01 -1.31213844e-01]	[5.799465179443359, 2.6110517978668213]
0b39aa90-b049-4cd6-87a4-a54bd80e68c0	surfit-learning-to-fit-surfaces-improves-few	2112.13942	null	https://arxiv.org/abs/2112.13942v2	https://arxiv.org/pdf/2112.13942v2.pdf	PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation	We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks. PriFit combines geometric primitive fitting with point-based representation learning. Its key idea is to learn point representations whose clustering reveals shape regions that can be approximated well by basic geometric primitives, such as cuboids and ellipsoids. The learned point representations can then be re-used in existing network architectures for 3D point cloud segmentation, and improves their performance in the few-shot setting. According to our experiments on the widely used ShapeNet and PartNet benchmarks, PriFit outperforms several state-of-the-art methods in this setting, suggesting that decomposability into primitives is a useful prior for learning representations predictive of semantic parts. We present a number of ablative experiments varying the choice of geometric primitives and downstream tasks to demonstrate the effectiveness of the method.	['Subhransu Maji', 'Rui Wang', 'Matheus Gadelha', 'Aruni RoyChowdhury', 'Erik Learned-Miller', 'Liangliang Cao', 'Evangelos Kalogerakis', 'Marios Loizou', 'Bidya Dash', 'Gopal Sharma']	2021-12-27	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[ 5.44251651e-02 3.81185532e-01 -2.25637570e-01 -3.59133422e-01 -7.94800520e-01 -7.70312905e-01 8.22026789e-01 3.16307962e-01 -4.90346402e-02 -9.29921344e-02 -1.84523597e-01 -2.19702333e-01 -4.76699956e-02 -7.25142479e-01 -1.03604090e+00 -3.59970927e-01 -1.71789050e-01 1.25101161e+00 6.67720735e-01 -1.46883339e-01 3.03260714e-01 1.30928421e+00 -1.53568447e+00 1.58603683e-01 6.59643352e-01 9.79263127e-01 2.73307133e-02 2.42649063e-01 -6.49645209e-01 3.48320663e-01 -3.92811954e-01 -1.70971170e-01 5.41235209e-01 3.80063891e-01 -1.05024028e+00 2.36112878e-01 5.89577258e-01 -2.46321425e-01 -8.35638121e-02 8.18558097e-01 1.10306509e-01 4.37152863e-01 1.23640943e+00 -1.27220571e+00 -4.05947864e-01 5.70922554e-01 -6.14229977e-01 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f4f1ae41-b6ed-43d3-b364-2dc4476c3fbe	supervised-dimensionality-reduction-and	2002.11934	null	https://arxiv.org/abs/2002.11934v2	https://arxiv.org/pdf/2002.11934v2.pdf	Supervised Dimensionality Reduction and Visualization using Centroid-encoder	Visualizing high-dimensional data is an essential task in Data Science and Machine Learning. The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label information to keep objects of a class close together in the reduced visualization space. CE exploits nonlinearity and labels to encode high variance in low dimensions while capturing the global structure of the data. We present a detailed analysis of the method using a wide variety of data sets and compare it with other supervised dimension reduction techniques, including NCA, nonlinear NCA, t-distributed NCA, t-distributed MCML, supervised UMAP, supervised PCA, Colored Maximum Variance Unfolding, supervised Isomap, Parametric Embedding, supervised Neighbor Retrieval Visualizer, and Multiple Relational Embedding. We empirically show that centroid-encoder outperforms most of these techniques. We also show that when the data variance is spread across multiple modalities, centroid-encoder extracts a significant amount of information from the data in low dimensional space. This key feature establishes its value to use it as a tool for data visualization.	['Tomojit Ghosh', 'Michael Kirby']	2020-02-27	null	null	null	null	['supervised-dimensionality-reduction']	['computer-vision']	[-2.68814206e-01 -6.57891762e-03 -9.77320448e-02 -2.91751623e-01 -6.92195669e-02 -6.81163132e-01 7.54472077e-01 2.62831271e-01 -4.77554947e-02 3.06738049e-01 7.24511504e-01 -1.33172274e-01 -8.01360846e-01 -5.04288435e-01 -1.57025456e-01 -8.87719870e-01 -7.06437409e-01 4.92144972e-01 -3.48731726e-01 8.45997185e-02 4.52522337e-01 9.12603855e-01 -1.67404163e+00 3.06605309e-01 2.70992815e-01 7.24061608e-01 -2.22409561e-01 7.29539275e-01 -3.25061768e-01 6.42969549e-01 -6.68750286e-01 1.14600673e-01 1.57556355e-01 -1.58548146e-01 -5.38803458e-01 -3.37186873e-01 3.60090166e-01 1.67701077e-02 -1.75004289e-01 9.20103073e-01 6.16944134e-01 2.42332876e-01 1.19622493e+00 -1.63182557e+00 -1.08942842e+00 1.57985762e-01 -6.58155441e-01 4.10020351e-02 2.39960790e-01 -4.45089161e-01 7.51816928e-01 -1.09058726e+00 9.36301410e-01 1.63634133e+00 7.39389539e-01 2.90160567e-01 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222a64d7-d86a-4d38-be63-63e49c8a44a6	generalizing-hierarchical-bayesian-bandits	2205.15124	null	https://arxiv.org/abs/2205.15124v3	https://arxiv.org/pdf/2205.15124v3.pdf	Mixed-Effect Thompson Sampling	A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a mixed-effect model where actions are related through multiple shared effect parameters. To explore efficiently using this structure, we propose Mixed-Effect Thompson Sampling (meTS) and bound its Bayes regret. The regret bound has two terms, one for learning the action parameters and the other for learning the shared effect parameters. The terms reflect the structure of our model and the quality of priors. Our theoretical findings are validated empirically using both synthetic and real-world problems. We also propose numerous extensions of practical interest. While they do not come with guarantees, they perform well empirically and show the generality of the proposed framework.	['Sumeet Katariya', 'Branislav Kveton', 'Imad Aouali']	2022-05-30	null	null	null	null	['thompson-sampling']	['methodology']	[ 7.97154307e-02 2.94871092e-01 -7.87070215e-01 -4.49377388e-01 -1.06997681e+00 -4.87098694e-01 5.59907377e-01 -1.27183005e-01 -3.74537468e-01 1.27257359e+00 3.67506832e-01 -2.82511890e-01 -5.98559797e-01 -5.37458956e-01 -1.07030141e+00 -8.62886548e-01 -4.03403819e-01 4.46970701e-01 2.49146111e-02 8.98088366e-02 3.11651170e-01 -4.21644375e-02 -1.12465477e+00 8.55407566e-02 1.01051545e+00 1.16851592e+00 1.45852089e-01 6.15970314e-01 1.96197778e-01 9.90271807e-01 -4.13320482e-01 -6.26825452e-01 6.86098278e-01 -4.01391447e-01 -7.16018200e-01 2.83820570e-01 -2.01054007e-01 -8.07266414e-01 -3.41160148e-01 1.07817793e+00 3.27719450e-01 3.48370373e-01 6.42551541e-01 -1.12643492e+00 -2.65604287e-01 1.08527720e+00 -7.19393134e-01 1.04246594e-01 1.35515898e-01 1.19657904e-01 1.46378708e+00 -1.14192329e-01 2.79359818e-01 1.51376212e+00 4.15719628e-01 2.77883619e-01 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1b1504c9-7367-4888-bef1-8568ef44b6a5	meccano-a-multimodal-egocentric-dataset-for	2209.08691	null	https://arxiv.org/abs/2209.08691v1	https://arxiv.org/pdf/2209.08691v1.pdf	MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain	Wearable cameras allow to acquire images and videos from the user's perspective. These data can be processed to understand humans behavior. Despite human behavior analysis has been thoroughly investigated in third person vision, it is still understudied in egocentric settings and in particular in industrial scenarios. To encourage research in this field, we present MECCANO, a multimodal dataset of egocentric videos to study humans behavior understanding in industrial-like settings. The multimodality is characterized by the presence of gaze signals, depth maps and RGB videos acquired simultaneously with a custom headset. The dataset has been explicitly labeled for fundamental tasks in the context of human behavior understanding from a first person view, such as recognizing and anticipating human-object interactions. With the MECCANO dataset, we explored five different tasks including 1) Action Recognition, 2) Active Objects Detection and Recognition, 3) Egocentric Human-Objects Interaction Detection, 4) Action Anticipation and 5) Next-Active Objects Detection. We propose a benchmark aimed to study human behavior in the considered industrial-like scenario which demonstrates that the investigated tasks and the considered scenario are challenging for state-of-the-art algorithms. To support research in this field, we publicy release the dataset at https://iplab.dmi.unict.it/MECCANO/.	['Giovanni Maria Farinella', 'Antonino Furnari', 'Francesco Ragusa']	2022-09-19	null	null	null	null	['human-object-interaction-detection', 'action-anticipation']	['computer-vision', 'computer-vision']	[ 3.41068834e-01 7.64258322e-04 5.11146225e-02 -3.38922501e-01 6.01238310e-02 -5.66788256e-01 6.43079638e-01 1.07933357e-01 -4.81029153e-01 3.69685262e-01 8.97787418e-03 2.80021161e-01 -1.84231445e-01 -1.42399952e-01 -7.90486872e-01 -8.35798442e-01 -1.10520367e-02 4.07049596e-01 -3.08548729e-03 -8.94310176e-02 2.58437663e-01 6.23309433e-01 -2.37271500e+00 5.43639064e-01 2.02983439e-01 9.55467403e-01 2.92011619e-01 9.42882657e-01 7.77699530e-01 8.39690804e-01 -3.20260942e-01 -3.35998349e-02 2.11422130e-01 -1.84714675e-01 -5.58282733e-01 4.82798368e-01 6.27111733e-01 -3.64322007e-01 -1.27715319e-01 9.06253159e-01 4.54228550e-01 3.66268635e-01 3.92343760e-01 -1.77729046e+00 1.37773558e-01 1.38916224e-01 -2.76887596e-01 3.18851590e-01 1.08689821e+00 8.45451176e-01 3.92104864e-01 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d7067ee5-5ab6-4233-941d-fb48809066a9	analysis-of-skin-lesion-images-with-deep	2101.03814	null	https://arxiv.org/abs/2101.03814v1	https://arxiv.org/pdf/2101.03814v1.pdf	Analysis of skin lesion images with deep learning	Skin cancer is the most common cancer worldwide, with melanoma being the deadliest form. Dermoscopy is a skin imaging modality that has shown an improvement in the diagnosis of skin cancer compared to visual examination without support. We evaluate the current state of the art in the classification of dermoscopic images based on the ISIC-2019 Challenge for the classification of skin lesions and current literature. Various deep neural network architectures pre-trained on the ImageNet data set are adapted to a combined training data set comprised of publicly available dermoscopic and clinical images of skin lesions using transfer learning and model fine-tuning. The performance and applicability of these models for the detection of eight classes of skin lesions are examined. Real-time data augmentation, which uses random rotation, translation, shear, and zoom within specified bounds is used to increase the number of available training samples. Model predictions are multiplied by inverse class frequencies and normalized to better approximate actual probability distributions. Overall prediction accuracy is further increased by using the arithmetic mean of the predictions of several independently trained models. The best single model has been published as a web service.	['Sten Hanke', 'Josef Steppan']	2021-01-11	null	null	null	null	['skin-lesion-classification']	['medical']	[ 6.84634507e-01 7.82641321e-02 -3.79131347e-01 -2.92182863e-01 -7.05861449e-01 -4.09363210e-01 6.10186577e-01 4.17205751e-01 -6.73036575e-01 5.89911461e-01 -1.37717053e-01 -4.57661778e-01 -1.12069689e-01 -6.39310241e-01 -3.33357036e-01 -7.76484609e-01 1.26832217e-01 2.77264386e-01 2.19990209e-01 3.33805420e-02 5.05550019e-02 5.35141110e-01 -1.19942951e+00 7.46235013e-01 9.71829653e-01 1.07090008e+00 -1.12252057e-01 1.17535067e+00 1.42865442e-02 4.78751093e-01 -5.29912293e-01 -4.55822855e-01 8.58139843e-02 -2.97373593e-01 -8.03801775e-01 1.96731910e-01 9.65083241e-01 -3.92084748e-01 -2.57033795e-01 9.12756562e-01 6.30473793e-01 -3.57893169e-01 9.59628880e-01 -6.38080299e-01 -6.36129856e-01 -8.76145810e-02 -5.62304139e-01 2.60437757e-01 1.45083740e-01 3.31725240e-01 3.06652337e-01 -2.56871343e-01 7.29840398e-01 6.46990001e-01 9.64495957e-01 9.56470072e-01 -1.03829026e+00 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9d362cc8-214e-4ad7-94b0-24aa309abdc9	transmatting-enhancing-transparent-objects	2208.03007	null	https://arxiv.org/abs/2208.03007v3	https://arxiv.org/pdf/2208.03007v3.pdf	TransMatting: Enhancing Transparent Objects Matting with Transformers	Image matting refers to predicting the alpha values of unknown foreground areas from natural images. Prior methods have focused on propagating alpha values from known to unknown regions. However, not all natural images have a specifically known foreground. Images of transparent objects, like glass, smoke, web, etc., have less or no known foreground. In this paper, we propose a Transformer-based network, TransMatting, to model transparent objects with a big receptive field. Specifically, we redesign the trimap as three learnable tri-tokens for introducing advanced semantic features into the self-attention mechanism. A small convolutional network is proposed to utilize the global feature and non-background mask to guide the multi-scale feature propagation from encoder to decoder for maintaining the contexture of transparent objects. In addition, we create a high-resolution matting dataset of transparent objects with small known foreground areas. Experiments on several matting benchmarks demonstrate the superiority of our proposed method over the current state-of-the-art methods.	['Lili Guo', 'Lele Xu', 'Fanglei Xue', 'Huanqia Cai']	2022-08-05	null	null	null	null	['transparent-objects', 'image-matting']	['computer-vision', 'computer-vision']	[ 4.95501816e-01 1.81473419e-02 7.23233968e-02 -6.34639561e-01 -4.11872894e-01 -2.06911922e-01 3.91018808e-01 -6.27387822e-01 1.11272261e-01 6.84471548e-01 1.35433316e-01 1.48395956e-01 4.29825664e-01 -9.08335447e-01 -1.31038427e+00 -9.79754686e-01 4.29463416e-01 3.09332758e-01 7.36184120e-01 1.89909831e-01 1.46865532e-01 1.06377400e-01 -1.27012503e+00 1.03943348e+00 9.31990921e-01 1.26615536e+00 6.12213612e-01 3.91902983e-01 -5.47874272e-01 1.16074574e+00 -5.79997003e-01 -6.26442134e-01 2.91774422e-01 -3.67890835e-01 -5.69438517e-01 4.88726676e-01 9.37647641e-01 -6.50576591e-01 -3.90864551e-01 1.05644441e+00 -7.26709813e-02 -2.49617159e-01 4.40911293e-01 -1.14557922e+00 -1.14550054e+00 9.79345143e-01 -8.12314093e-01 3.06831717e-01 -2.10785732e-01 2.42895186e-01 5.89957356e-01 -9.11116183e-01 4.76012319e-01 1.49542689e+00 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a0485bbb-e219-4f18-9574-19aaa77ecfe9	personalized-image-enhancement-featuring	2306.09334	null	https://arxiv.org/abs/2306.09334v1	https://arxiv.org/pdf/2306.09334v1.pdf	Personalized Image Enhancement Featuring Masked Style Modeling	We address personalized image enhancement in this study, where we enhance input images for each user based on the user's preferred images. Previous methods apply the same preferred style to all input images (i.e., only one style for each user); in contrast to these methods, we aim to achieve content-aware personalization by applying different styles to each image considering the contents. For content-aware personalization, we make two contributions. First, we propose a method named masked style modeling, which can predict a style for an input image considering the contents by using the framework of masked language modeling. Second, to allow this model to consider the contents of images, we propose a novel training scheme where we download images from Flickr and create pseudo input and retouched image pairs using a degrading model. We conduct quantitative evaluations and a user study, and our method trained using our training scheme successfully achieves content-aware personalization; moreover, our method outperforms other previous methods in this field. Our source code is available at https://github.com/satoshi-kosugi/masked-style-modeling.	['Toshihiko Yamasaki', 'Satoshi Kosugi']	2023-06-15	null	null	null	null	['image-enhancement']	['computer-vision']	[ 4.74082887e-01 -2.60727495e-01 -8.95945802e-02 -4.72681403e-01 -4.06613767e-01 -6.75963819e-01 3.01079601e-01 -2.77308941e-01 -6.28122747e-01 2.55612999e-01 3.76488924e-01 -6.02946384e-03 2.60199100e-01 -7.29498029e-01 -7.23213851e-01 -4.63829339e-01 4.82304752e-01 -2.24962667e-01 8.95277411e-02 -2.11956590e-01 2.11680442e-01 1.36299968e-01 -1.48864031e+00 5.71475685e-01 1.14038002e+00 6.96174860e-01 7.65006185e-01 8.00774336e-01 1.03639528e-01 3.24303597e-01 -4.06070679e-01 -5.03502309e-01 3.32987517e-01 -5.26025593e-01 -7.73424685e-01 5.31171441e-01 5.30770957e-01 -6.43378377e-01 -4.56347704e-01 1.10563242e+00 5.28059542e-01 2.72798419e-01 4.08372015e-01 -9.39396799e-01 -1.29300964e+00 6.44555926e-01 -5.54866374e-01 -8.67690220e-02 1.79129288e-01 5.95787354e-02 7.03003943e-01 -7.62114942e-01 5.32782495e-01 1.00311637e+00 3.28413457e-01 8.56040776e-01 -1.32329762e+00 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1dad70b2-c40c-4727-92a2-58de8d51246a	adaptive-adversarial-network-for-source-free	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Xia_Adaptive_Adversarial_Network_for_Source-Free_Domain_Adaptation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Xia_Adaptive_Adversarial_Network_for_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf	Adaptive Adversarial Network for Source-Free Domain Adaptation	Unsupervised Domain Adaptation solves knowledge transfer along with the coexistence of well-annotated source domain and unlabeled target instances. However, the source domain in many practical applications is not always accessible due to data privacy or the insufficient memory storage for small devices. This scenario defined as Source-free Domain Adaptation only allows accessing the well-trained source model for target learning. To address the challenge of source data unavailability, we develop an Adaptive Adversarial Network (A2Net) including three components. Specifically, the first one named Adaptive Adversarial Inference seeks a target-specific classifier to advance the recognition of samples which the provided source-specific classifier difficultly identifies. Then, the Contrastive Category-wise Matching module exploits the positive relation of every two target images to enforce the compactness of subspace for each category. Thirdly, Self-Supervised Rotation facilitates the model to learn additional semantics from target images by themselves. Extensive experiments on the popular cross-domain benchmarks verify the effectiveness of our proposed model on solving adaptation task without any source data.	['Zhengming Ding', 'Handong Zhao', 'Haifeng Xia']	2021-01-01	null	null	null	iccv-2021-1	['source-free-domain-adaptation']	['computer-vision']	[ 0.44871536 0.20746702 -0.5322131 -0.37525 -0.7065857 -0.764929 0.41248557 -0.21866024 -0.36492363 0.9266798 -0.12495644 -0.05717435 0.07227294 -0.7456856 -0.8803345 -0.8565614 0.4209853 0.4590098 0.17458017 -0.03321573 -0.06387831 0.40511385 -1.20612 0.16582826 1.093026 1.288898 0.08384477 -0.09496231 -0.3508519 0.48759848 -0.5814286 -0.350899 0.60768783 -0.40820497 -0.5983161 0.11793798 0.39672062 -0.31550938 -0.23309721 1.4114999 0.30089828 0.14079422 0.6775968 -1.6567374 -1.0906811 0.3579368 -0.54227436 0.13539897 0.16225569 0.25627488 0.41685376 -0.92750317 0.7299428 0.973078 0.29717538 0.80469966 -0.9811844 -1.0897378 0.17921038 0.252518 -1.5588554 -0.49746326 1.1544124 -0.22664188 0.27511168 -0.02561124 0.14654079 1.439081 -0.33062088 0.5980212 1.1895561 -0.32242438 0.3701944 0.8280089 0.07992604 0.3320537 0.2052952 0.17960411 -0.46889588 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728c8472-7310-45bb-97e0-66dfb39e5e46	storygraphs-visualizing-character	null	null	http://openaccess.thecvf.com/content_cvpr_2014/html/Tapaswi_StoryGraphs_Visualizing_Character_2014_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2014/papers/Tapaswi_StoryGraphs_Visualizing_Character_2014_CVPR_paper.pdf	StoryGraphs: Visualizing Character Interactions as a Timeline	We present a novel way to automatically summarize and represent the storyline of a TV episode by visualizing character interactions as a chart. We also propose a scene detection method that lends itself well to generate over-segmented scenes which is used to partition the video. The positioning of character lines in the chart is formulated as an optimization problem which trades between the aesthetics and functionality of the chart. Using automatic person identification, we present StoryGraphs for 3 diverse TV series encompassing a total of 22 episodes. We define quantitative criteria to evaluate StoryGraphs and also compare them against episode summaries to evaluate their ability to provide an overview of the episode.	['Rainer Stiefelhagen', 'Martin Bauml', 'Makarand Tapaswi']	2014-06-01	null	null	null	cvpr-2014-6	['person-identification']	['computer-vision']	[ 3.31931680e-01 6.06921390e-02 1.10035375e-01 -3.33936572e-01 -7.65698075e-01 -1.06778109e+00 7.16802657e-01 5.00240088e-01 2.28869915e-01 5.76244712e-01 7.11420000e-01 -5.22706993e-02 -7.72426724e-02 -4.53426242e-01 -4.53614563e-01 -1.76234782e-01 -1.66414112e-01 1.90820232e-01 1.16345003e-01 -9.42853093e-02 4.64031905e-01 5.22648096e-01 -1.33769214e+00 6.38976932e-01 8.15297008e-01 6.58895910e-01 7.31974840e-03 1.08133292e+00 -9.09726173e-02 9.56476569e-01 -1.38599229e+00 -4.97652173e-01 -1.47909805e-01 -9.96207118e-01 -5.76452971e-01 9.85255718e-01 6.00773215e-01 -1.37120754e-01 -2.44411394e-01 7.93109298e-01 1.72528028e-01 2.13504702e-01 8.50777209e-01 -1.09479952e+00 -2.60067612e-01 6.79626346e-01 -6.68503523e-01 1.83834523e-01 1.17545605e+00 -2.20891356e-01 9.93864417e-01 -3.50701988e-01 1.04657185e+00 1.07127333e+00 4.87844527e-01 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a64d94a6-c07c-444a-97c3-e13dffdcbb41	a-data-centric-framework-for-improving-domain	2304.00483	null	https://arxiv.org/abs/2304.00483v2	https://arxiv.org/pdf/2304.00483v2.pdf	A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets	Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation. Thus, it is vital to ensure high-quality domain-specific training data. In this paper, we propose a framework for enhancing the data quality of original datasets. We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.	['Josip Car', 'Shafiq Joty', 'Mathieu Ravaut', 'Duy Phung', 'Qi Chwen Ong', 'Sreeja Tar', 'Verena Suharman', 'Josef Halim', 'Iva Bojic']	2023-04-02	null	null	null	null	['reading-comprehension', 'machine-reading-comprehension']	['natural-language-processing', 'natural-language-processing']	[ 2.97443748e-01 4.00184155e-01 -4.46323723e-01 -5.95314562e-01 -1.56628597e+00 -5.13292551e-01 4.10507709e-01 6.17024422e-01 -7.03153253e-01 1.19146788e+00 4.50213313e-01 -3.12388480e-01 -3.75589639e-01 -7.92138755e-01 -1.07294953e+00 -3.32372606e-01 7.17264712e-01 5.32697439e-01 -1.40053168e-01 1.48990685e-02 3.78760010e-01 2.58855104e-01 -9.10674214e-01 6.81254983e-01 1.20853782e+00 3.60300124e-01 4.09428060e-01 3.99627596e-01 -1.84923038e-01 6.44471705e-01 -5.99282801e-01 -7.52457023e-01 1.71501860e-01 -1.06202170e-01 -7.26742446e-01 -5.88644028e-01 1.17169745e-01 -8.13685507e-02 2.32343584e-01 9.27521765e-01 7.73878098e-01 -4.42683041e-01 5.92687905e-01 -1.24393904e+00 -8.23284507e-01 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61258548-e9cf-43e4-9188-2d9dbabcf15a	ranking-differential-privacy	2301.00841	null	https://arxiv.org/abs/2301.00841v1	https://arxiv.org/pdf/2301.00841v1.pdf	Ranking Differential Privacy	Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.	['Guang Cheng', 'Will Wei Sun', 'SHIRONG XU']	2023-01-02	null	null	null	null	['inference-attack']	['adversarial']	[ 1.34112462e-01 -9.79188457e-02 2.75144950e-02 -7.16063738e-01 -9.87346828e-01 -1.12208796e+00 2.06677675e-01 -8.44559968e-02 -4.44523901e-01 7.14184344e-01 1.65387079e-01 -2.07889065e-01 -7.41821408e-01 -8.25301230e-01 -5.73149800e-01 -6.41969979e-01 -4.56062913e-01 -4.38616350e-02 -2.94459816e-02 -2.50290960e-01 5.41641474e-01 1.72856778e-01 -1.44657421e+00 5.46242893e-01 9.73768771e-01 1.12478745e+00 -3.32892090e-01 4.94526833e-01 6.18105412e-01 3.24621826e-01 -6.33562863e-01 -1.16277492e+00 9.25972283e-01 -6.42475560e-02 -4.27253366e-01 -4.87102360e-01 3.74860406e-01 -7.13534176e-01 -3.81697923e-01 1.60334182e+00 5.34882188e-01 4.47024852e-02 2.13249043e-01 -1.42746568e+00 -5.36004424e-01 8.43779922e-01 -3.29402119e-01 -2.13798240e-01 3.82651418e-01 -3.51507992e-01 1.37194443e+00 -4.70923334e-01 3.99997979e-01 1.00731230e+00 4.61182922e-01 4.81650472e-01 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6.36813223e-01 3.16438600e-02 -3.39711547e-01 -7.74142683e-01 -5.61542809e-01]	[5.976934909820557, 6.697381019592285]
116b76be-72ed-4571-8cbd-ce45eab15e75	good-better-best-textual-distractors	1910.09134	null	https://arxiv.org/abs/1910.09134v3	https://arxiv.org/pdf/1910.09134v3.pdf	Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning	Multiple-choice VQA has drawn increasing attention from researchers and end-users recently. As the demand for automatically constructing large-scale multiple-choice VQA data grows, we introduce a novel task called textual Distractors Generation for VQA (DG-VQA) focusing on generating challenging yet meaningful distractors given the context image, question, and correct answer. The DG-VQA task aims at generating distractors without ground-truth training samples since such resources are rarely available. To tackle the DG-VQA unsupervisedly, we propose Gobbet, a reinforcement learning(RL) based framework that utilizes pre-trained VQA models as an alternative knowledge base to guide the distractor generation process. In Gobbet, a pre-trained VQA model serves as the environment in RL setting to provide feedback for the input multi-modal query, while a neural distractor generator serves as the agent to take actions accordingly. We propose to use existing VQA models' performance degradation as indicators of the quality of generated distractors. On the other hand, we show the utility of generated distractors through data augmentation experiments, since robustness is more and more important when AI models apply to unpredictable open-domain scenarios or security-sensitive applications. We further conduct a manual case study on the factors why distractors generated by Gobbet can fool existing models.	['Yi Ren', 'Xin Ye', 'Jiaying Lu', 'Yezhou Yang']	2019-10-21	null	null	null	null	['distractor-generation']	['natural-language-processing']	[-8.60011801e-02 1.34272501e-01 7.09175318e-02 -1.84943825e-01 -1.27617705e+00 -7.45391965e-01 7.51790464e-01 1.26766175e-01 -4.96006638e-01 7.16903687e-01 2.08883598e-01 -4.20270652e-01 2.73268551e-01 -5.84117055e-01 -6.81884408e-01 -4.58193839e-01 4.24747676e-01 7.83462405e-01 1.98737606e-01 -7.30240047e-01 2.12221891e-01 3.06945115e-01 -1.41928363e+00 3.92000288e-01 1.29724991e+00 9.03155684e-01 3.22535664e-01 7.01345325e-01 -1.79013625e-01 9.90807950e-01 -1.03506327e+00 -5.86312890e-01 2.76923865e-01 -5.69666564e-01 -9.04367626e-01 -8.41544867e-02 2.68358439e-01 -4.56442744e-01 -2.22340986e-01 8.00776362e-01 6.43483400e-01 3.25315386e-01 8.29904497e-01 -1.60120344e+00 -9.97359872e-01 5.95920563e-01 -2.07958505e-01 4.51608241e-01 6.42518342e-01 9.55911815e-01 1.29600000e+00 -8.38007212e-01 7.39718139e-01 1.50029838e+00 5.04727289e-02 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64a73033-f8fa-4b6b-b8db-48592598542e	camerapose-weakly-supervised-monocular-3d	2301.02979	null	https://arxiv.org/abs/2301.02979v1	https://arxiv.org/pdf/2301.02979v1.pdf	CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations	To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data augmentation techniques are limited to the "seen" pose combinations and hard to infer poses with rare "unseen" joint positions. To address this problem, we present CameraPose, a weakly-supervised framework for 3D human pose estimation from a single image, which can not only be applied on 2D-3D pose pairs but also on 2D alone annotations. By adding a camera parameter branch, any in-the-wild 2D annotations can be fed into our pipeline to boost the training diversity and the 3D poses can be implicitly learned by reprojecting back to 2D. Moreover, CameraPose introduces a refinement network module with confidence-guided loss to further improve the quality of noisy 2D keypoints extracted by 2D pose estimators. Experimental results demonstrate that the CameraPose brings in clear improvements on cross-scenario datasets. Notably, it outperforms the baseline method by 3mm on the most challenging dataset 3DPW. In addition, by combining our proposed refinement network module with existing 3D pose estimators, their performance can be improved in cross-scenario evaluation.	['Jenq-Neng Hwang', 'Zhongyu Jiang', 'Ke Zhang', 'Nan Qiao', 'Yuyin Sun', 'Lu Xia', 'Jiajia Luo', 'Cheng-Yen Yang']	2023-01-08	null	null	null	null	['3d-human-pose-estimation', 'monocular-3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[-1.43473312e-01 3.38278770e-01 -1.32197618e-01 -4.23672408e-01 -1.00279617e+00 -4.57316816e-01 4.14334863e-01 -1.89638078e-01 -6.99082017e-01 4.84576762e-01 3.46264482e-01 4.52520907e-01 3.04498434e-01 -2.87827730e-01 -1.02583539e+00 -3.39058340e-01 1.39716879e-01 9.51109350e-01 3.83803129e-01 -4.37000960e-01 -3.05846602e-01 4.68399912e-01 -1.28814280e+00 -4.20563761e-03 5.15003800e-01 9.58551824e-01 1.08282045e-01 4.72592205e-01 3.60630065e-01 2.27433398e-01 -5.59888065e-01 -4.74203467e-01 5.73345661e-01 9.95809678e-03 -4.25155014e-01 2.29398400e-01 8.53674114e-01 -8.03860664e-01 -4.69806999e-01 9.16812778e-01 7.69658923e-01 4.82134009e-03 3.57125968e-01 -1.21610487e+00 -1.82862487e-02 1.43955514e-01 -7.98843801e-01 -3.26625615e-01 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323243c4-d970-4da7-899b-29791e95e54d	counterfactual-graph-learning-for-link	2106.02172	null	https://arxiv.org/abs/2106.02172v2	https://arxiv.org/pdf/2106.02172v2.pdf	Learning from Counterfactual Links for Link Prediction	Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the association between observed graph structure and existence of link between a pair of nodes. However, the causal relationship between the two variables was largely ignored for learning to predict links on a graph. In this work, we visit this factor by asking a counterfactual question: "would the link still exist if the graph structure became different from observation?" Its answer, counterfactual links, will be able to augment the graph data for representation learning. To create these links, we employ causal models that consider the information (i.e., learned representations) of node pairs as context, global graph structural properties as treatment, and link existence as outcome. We propose a novel data augmentation-based link prediction method that creates counterfactual links and learns representations from both the observed and counterfactual links. Experiments on benchmark data show that our graph learning method achieves state-of-the-art performance on the task of link prediction.	['Meng Jiang', 'Wenhao Yu', 'Daheng Wang', 'Gang Liu', 'Tong Zhao']	2021-06-03	counterfactual-graph-learning-for-link-1	https://openreview.net/forum?id=N2dEPuK0vom	https://openreview.net/pdf?id=N2dEPuK0vom	neurips-2021-12	['counterfactual-inference']	['miscellaneous']	[ 4.01568890e-01 8.61707687e-01 -1.01432502e+00 -3.08449149e-01 -2.81092320e-02 -2.77384102e-01 7.66898453e-01 5.46933055e-01 4.55321908e-01 1.14554560e+00 6.29687846e-01 -9.52144742e-01 -4.10541028e-01 -1.49329126e+00 -1.19574022e+00 -1.75211653e-01 -8.10211480e-01 3.32315266e-01 -4.57253028e-03 -1.78481206e-01 -8.08414724e-03 2.20136881e-01 -1.01829791e+00 3.24409485e-01 8.01496387e-01 3.41606259e-01 -1.32680118e-01 3.20043534e-01 -3.11326295e-01 1.22459459e+00 -1.10409752e-01 -8.63905549e-01 1.76719770e-01 -5.25540590e-01 -1.09641254e+00 -1.03019118e-01 3.95480216e-01 -1.72610030e-01 -1.02993226e+00 8.37562144e-01 5.64756431e-02 -1.02625765e-01 7.53406227e-01 -1.73710179e+00 -9.14727151e-01 1.39933217e+00 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d8ce1049-3585-47f2-aa89-d3ef38715c81	gowvis-a-web-application-for-graph-of-words	null	null	https://aclanthology.org/P16-4026	https://aclanthology.org/P16-4026.pdf	GoWvis: A Web Application for Graph-of-Words-based Text Visualization and Summarization	null	['Antoine Tixier', 'Michalis Vazirgiannis', 'Konstantinos Skianis']	2016-08-01	null	null	null	acl-2016-8	['ad-hoc-information-retrieval']	['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 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 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ec0e1549-d11c-433d-979c-8ab8358832d2	co-attention-based-neural-network-for-source-2	1908.01993	null	https://arxiv.org/abs/1908.01993v1	https://arxiv.org/pdf/1908.01993v1.pdf	Co-Attention Based Neural Network for Source-Dependent Essay Scoring	This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.	['Haoran Zhang', 'Diane Litman']	2019-08-06	co-attention-based-neural-network-for-source-1	https://aclanthology.org/W18-0549	https://aclanthology.org/W18-0549.pdf	ws-2018-6	['automated-essay-scoring']	['natural-language-processing']	[-2.39152655e-01 2.12140128e-01 -4.30348068e-01 -8.16862702e-01 -1.03186059e+00 -3.21631193e-01 3.09335649e-01 1.36984482e-01 -5.99221706e-01 7.70736039e-01 8.73095095e-01 -1.20534465e-01 -1.20977998e-01 -8.27961266e-01 -3.95502150e-01 3.94853428e-02 7.55588412e-01 6.50347710e-01 -1.49718774e-02 -6.55332208e-01 1.08383763e+00 5.37456535e-02 -9.92906988e-01 7.77187705e-01 8.59106898e-01 5.72161973e-01 1.42162608e-03 1.14978194e+00 -2.99237788e-01 1.59678376e+00 -1.37478042e+00 -8.92918468e-01 -4.02602367e-02 -6.27745330e-01 -9.40353572e-01 -6.23048186e-01 7.08996892e-01 -4.42220062e-01 -2.30684683e-01 9.37793434e-01 4.32029307e-01 4.95645791e-01 1.01834834e+00 -9.31155503e-01 -1.61332786e+00 8.69177163e-01 -4.15270865e-01 7.48069882e-01 5.87400973e-01 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585ed52c-6356-4113-aab9-9b2188390bba	simulating-single-photon-detector-array	2210.05644	null	https://arxiv.org/abs/2210.05644v1	https://arxiv.org/pdf/2210.05644v1.pdf	Simulating single-photon detector array sensors for depth imaging	Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous systems as they provide guidance and situational awareness. However, to fully exploit the capabilities of SPAD array sensors, it is crucial to establish the quality of depth images they are able to generate in a wide range of scenarios. Given a particular optical system and a finite image acquisition time, what is the best-case depth resolution and what are realistic images generated by SPAD arrays? In this work, we establish a robust yet simple numerical procedure that rapidly establishes the fundamental limits to depth imaging with SPAD arrays under real world conditions. Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing. This procedure has applications in object detection and tracking for autonomous systems and could be easily extended to systems for underwater imaging or for imaging around corners.	['Jonathan Leach', 'Phil Soan', 'Istvan Gyongy', 'Feng Zhu', 'Germán Mora-Martín', 'Stirling Scholes']	2022-10-07	null	null	null	null	['pico']	['natural-language-processing']	[ 6.04892075e-01 -2.91800201e-01 7.36625254e-01 -1.86589032e-01 -6.48536623e-01 -5.82459033e-01 5.50936878e-01 9.49387923e-02 -9.41322446e-01 7.84741104e-01 -5.83644271e-01 -6.01349212e-02 -2.00232953e-01 -9.30761278e-01 -4.17826176e-01 -1.11573589e+00 -1.41991377e-01 5.88253200e-01 8.29976141e-01 -4.63515595e-02 2.99112022e-01 7.66669214e-01 -1.86505198e+00 -2.32995391e-01 5.93507648e-01 1.15790999e+00 4.55212981e-01 7.69507587e-01 1.97548792e-01 2.19503686e-01 -5.52249730e-01 1.89045221e-01 3.88833225e-01 -5.22476673e-01 1.39389351e-01 -1.33413941e-01 4.15394932e-01 -8.15647602e-01 -8.56719986e-02 9.54513371e-01 7.20821142e-01 -2.16945201e-01 6.06565475e-01 -4.88523960e-01 2.17965066e-01 3.96485813e-03 -4.35415655e-01 4.80390906e-01 4.44609851e-01 5.94421029e-01 2.87342757e-01 -5.45565307e-01 3.23599607e-01 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3.10163319e-01 -7.70201266e-01 -3.83786380e-01 -8.46725285e-01 -7.81722367e-01 -2.98376262e-01 5.04692078e-01 -5.20677865e-01 -7.95144856e-01 -3.63871694e-01]	[9.694669723510742, -2.687131881713867]
e70709d5-f8e2-45d5-bad4-a05102a3506b	conditional-infilling-gans-for-data	1807.08093	null	http://arxiv.org/abs/1807.08093v2	http://arxiv.org/pdf/1807.08093v2.pdf	Conditional Infilling GANs for Data Augmentation in Mammogram Classification	Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly available mammography datasets, in large part due to privacy concerns and the high cost of generating expert annotations. Limited dataset size is further exacerbated by substantial class imbalance since "normal" images dramatically outnumber those with findings. Given the rapid progress of generative models in synthesizing realistic images, and the known effectiveness of simple data augmentation techniques (e.g. horizontal flipping), we ask if it is possible to synthetically augment mammogram datasets using generative adversarial networks (GANs). We train a class-conditional GAN to perform contextual in-filling, which we then use to synthesize lesions onto healthy screening mammograms. First, we show that GANs are capable of generating high-resolution synthetic mammogram patches. Next, we experimentally evaluate using the augmented dataset to improve breast cancer classification performance. We observe that a ResNet-50 classifier trained with GAN-augmented training data produces a higher AUROC compared to the same model trained only on traditionally augmented data, demonstrating the potential of our approach.	['William Lotter', 'Kevin Wu', 'Eric Wu', 'David Cox']	2018-07-21	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 9.76294160e-01 1.00709581e+00 4.20003757e-02 -4.95107293e-01 -1.31585646e+00 -4.56533164e-01 4.87117589e-01 1.91741635e-03 -2.99668014e-01 1.03952432e+00 2.88024455e-01 -4.75385040e-01 3.28706056e-01 -9.96332705e-01 -9.64638114e-01 -6.73256516e-01 2.42334269e-02 5.90049386e-01 -8.28726217e-02 6.19973987e-02 -3.07260364e-01 4.07511592e-01 -1.15138423e+00 5.52563012e-01 7.81564355e-01 7.57854760e-01 -1.02848262e-01 9.09438968e-01 3.02217185e-01 8.08747709e-01 -6.79433286e-01 -7.50880003e-01 3.77969652e-01 -1.15519583e+00 -6.46335959e-01 1.99203372e-01 3.90267253e-01 -6.48341000e-01 -3.61896694e-01 8.44529986e-01 4.27194148e-01 -3.78460974e-01 6.27206743e-01 -1.06356883e+00 -6.86846852e-01 5.21198273e-01 -5.11364281e-01 6.01723306e-02 6.16470464e-02 1.88402891e-01 5.22929192e-01 -3.81671369e-01 8.38202834e-01 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8c6573e4-078d-4675-a384-07afecf119c2	detecting-rumours-with-latency-guarantees	2205.06580	null	https://arxiv.org/abs/2205.06580v1	https://arxiv.org/pdf/2205.06580v1.pdf	Detecting Rumours with Latency Guarantees using Massive Streaming Data	Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social networks. Hence, in this paper, we argue for best-effort rumour detection that detects most rumours quickly rather than all rumours with a high delay. To this end, we combine techniques for efficient, graph-based matching of rumour patterns with effective load shedding that discards some of the input data while minimising the loss in accuracy. Experiments with large-scale real-world datasets illustrate the robustness of our approach in terms of runtime performance and detection accuracy under diverse streaming conditions.	['Quoc Viet Hung Nguyen', 'Thai Son Mai', 'Thanh Thi Nguyen', 'Matthias Weidlich', 'Hongzhi Yin', 'Thanh Trung Huynh', 'Thanh Tam Nguyen']	2022-05-13	null	null	null	null	['rumour-detection']	['natural-language-processing']	[ 7.19583854e-02 -4.55295704e-02 -1.71001673e-01 1.19111866e-01 -1.95939824e-01 -5.73791027e-01 5.38773715e-01 1.10756445e+00 -1.79237872e-01 5.53993940e-01 -2.38344204e-02 -2.44825646e-01 -2.85037845e-01 -1.02532256e+00 -3.00407737e-01 -2.67204762e-01 -8.92429173e-01 8.47774327e-01 1.05619621e+00 -5.29176414e-01 5.13406754e-01 5.07673860e-01 -1.37184000e+00 2.38254890e-01 4.78891999e-01 7.41955817e-01 -2.48264238e-01 8.96170139e-01 -1.42695948e-01 1.19938433e+00 -8.69893372e-01 -1.89107820e-01 2.70333719e-02 -5.54736257e-01 -6.82872355e-01 1.12568736e-01 -6.72905147e-02 -6.34360969e-01 -4.14813519e-01 8.94600332e-01 3.54916096e-01 -7.51905665e-02 3.31833959e-01 -1.55587602e+00 1.85736060e-01 8.04662168e-01 -8.69121194e-01 9.15597975e-01 7.23286927e-01 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b9216c2b-7a01-4e73-bddb-97137df4b9e2	vrt-a-video-restoration-transformer	2201.12288	null	https://arxiv.org/abs/2201.12288v2	https://arxiv.org/pdf/2201.12288v2.pdf	VRT: A Video Restoration Transformer	Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally requires to utilize temporal information from multiple adjacent but usually misaligned video frames. Existing deep methods generally tackle with this by exploiting a sliding window strategy or a recurrent architecture, which either is restricted by frame-by-frame restoration or lacks long-range modelling ability. In this paper, we propose a Video Restoration Transformer (VRT) with parallel frame prediction and long-range temporal dependency modelling abilities. More specifically, VRT is composed of multiple scales, each of which consists of two kinds of modules: temporal mutual self attention (TMSA) and parallel warping. TMSA divides the video into small clips, on which mutual attention is applied for joint motion estimation, feature alignment and feature fusion, while self attention is used for feature extraction. To enable cross-clip interactions, the video sequence is shifted for every other layer. Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping. Experimental results on five tasks, including video super-resolution, video deblurring, video denoising, video frame interpolation and space-time video super-resolution, demonstrate that VRT outperforms the state-of-the-art methods by large margins ($\textbf{up to 2.16dB}$) on fourteen benchmark datasets.	['Luc van Gool', 'Radu Timofte', 'Yawei Li', 'Rakesh Ranjan', 'Kai Zhang', 'Yuchen Fan', 'JieZhang Cao', 'Jingyun Liang']	2022-01-28	null	null	null	null	['space-time-video-super-resolution', 'video-super-resolution', 'video-denoising', 'video-restoration']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 5.08427203e-01 -5.20002782e-01 -1.61570877e-01 -9.15171057e-02 -8.77683461e-01 3.37655134e-02 4.85486597e-01 -5.50897121e-01 -2.84410387e-01 7.58803785e-01 6.79862499e-01 1.34064198e-01 -1.67887568e-01 -3.79492491e-01 -8.00028980e-01 -9.88887191e-01 5.73855154e-02 -4.00180548e-01 3.65458846e-01 -2.70830274e-01 3.02554965e-01 1.38975307e-01 -1.54470146e+00 6.21785402e-01 8.40027392e-01 9.83561456e-01 6.68966472e-01 5.15576899e-01 -7.48873129e-02 1.03522789e+00 -3.10261101e-01 -3.90691496e-02 1.26352489e-01 -5.47324240e-01 -5.73898733e-01 2.34728664e-01 3.30585122e-01 -6.82388067e-01 -6.71126008e-01 1.16095591e+00 4.70547825e-01 4.35332119e-01 4.29444462e-02 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d4afac2b-8937-483f-aabc-5edd749ed2e3	framework-for-certification-of-ai-based	2302.11049	null	https://arxiv.org/abs/2302.11049v1	https://arxiv.org/pdf/2302.11049v1.pdf	Framework for Certification of AI-Based Systems	The current certification process for aerospace software is not adapted to "AI-based" algorithms such as deep neural networks. Unlike traditional aerospace software, the precise parameters optimized during neural network training are as important as (or more than) the code processing the network and they are not directly mathematically understandable. Despite their lack of explainability such algorithms are appealing because for some applications they can exhibit high performance unattainable with any traditional explicit line-by-line software methods. This paper proposes a framework and principles that could be used to establish certification methods for neural network models for which the current certification processes such as DO-178 cannot be applied. While it is not a magic recipe, it is a set of common sense steps that will allow the applicant and the regulator increase their confidence in the developed software, by demonstrating the capabilities to bring together, trace, and track the requirements, data, software, training process, and test results.	['Evan Wilson', 'Rob Timpe', 'Brian Shimanuki', 'Maxime Gariel']	2023-02-21	null	null	null	null	['common-sense-reasoning']	['reasoning']	[-1.30115703e-01 2.44245544e-01 3.03137787e-02 -4.47606951e-01 9.04259831e-02 -7.89160728e-01 2.39431202e-01 1.06988706e-01 -4.31418233e-02 4.15938973e-01 -4.76656854e-01 -1.08119845e+00 -5.56618452e-01 -6.52981460e-01 -7.40787089e-01 -2.37461567e-01 5.00230044e-02 4.84169930e-01 -1.72421470e-01 -5.11218846e-01 2.10724577e-01 8.88727903e-01 -1.50640833e+00 4.89456467e-02 7.86952913e-01 8.60423148e-01 -3.88475746e-01 4.54199046e-01 -4.14854176e-02 9.31850135e-01 -7.84574032e-01 -2.11737663e-01 4.78902787e-01 -3.29960883e-01 -6.02390468e-01 -3.40253651e-01 1.73781708e-01 -3.03647846e-01 -6.76697195e-02 9.66454625e-01 4.19283621e-02 -1.49680868e-01 3.29093456e-01 -1.32432783e+00 -3.02192777e-01 5.54955423e-01 6.04417585e-02 -2.41811946e-01 -1.39356805e-02 2.63605326e-01 6.10848010e-01 -3.77308428e-01 1.31403849e-01 7.05052674e-01 7.99776793e-01 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63b8f2e3-e73a-4a18-bcc6-3a413a29aed2	merkel-podcast-corpus-a-multimodal-dataset-1	null	null	https://aclanthology.org/2022.lrec-1.270	https://aclanthology.org/2022.lrec-1.270.pdf	Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel’s Weekly Video Podcasts	We introduce the Merkel Podcast Corpus, an audio-visual-text corpus in German collected from 16 years of (almost) weekly Internet podcasts of former German chancellor Angela Merkel. To the best of our knowledge, this is the first single speaker corpus in the German language consisting of audio, visual and text modalities of comparable size and temporal extent. We describe the methods used with which we have collected and edited the data which involves downloading the videos, transcripts and other metadata, forced alignment, performing active speaker recognition and face detection to finally curate the single speaker dataset consisting of utterances spoken by Angela Merkel. The proposed pipeline is general and can be used to curate other datasets of similar nature, such as talk show contents. Through various statistical analyses and applications of the dataset in talking face generation and TTS, we show the utility of the dataset. We argue that it is a valuable contribution to the research community, in particular, due to its realistic and challenging material at the boundary between prepared and spontaneous speech.	['Timo Baumann', 'Shravan Nayak', 'Debjoy Saha']	null	null	null	null	lrec-2022-6	['face-detection', 'talking-face-generation', 'face-generation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 2.96630353e-01 4.11306262e-01 4.32183534e-01 -5.16761899e-01 -1.07277393e+00 -7.94392645e-01 8.94905150e-01 -3.89200933e-02 -2.36162357e-02 3.98823559e-01 6.16190732e-01 5.13899773e-02 -5.31102866e-02 -6.80154711e-02 -3.60032469e-01 -6.98114932e-01 1.24881957e-02 6.21739805e-01 2.02848613e-02 -1.87680960e-01 2.26833105e-01 4.65237409e-01 -2.14432549e+00 5.97343385e-01 2.68209904e-01 1.01451933e+00 3.65659922e-01 7.79454589e-01 -1.57521456e-01 7.92604685e-01 -6.86827719e-01 -5.95282555e-01 -4.82549034e-02 -3.72686714e-01 -1.10273516e+00 5.06463230e-01 7.89636195e-01 -3.02495778e-01 -1.08811542e-01 5.54248989e-01 9.56470668e-01 1.15476571e-01 2.80925959e-01 -1.17279172e+00 -5.76013550e-02 8.45426559e-01 -1.49681002e-01 1.45738229e-01 7.99522519e-01 8.32881182e-02 9.50785577e-01 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2.90654272e-01 4.44856912e-01 -1.12325251e+00 -1.15112320e-01 -1.36628616e+00 -7.58860946e-01 3.08889210e-01 4.34741825e-01 -4.43490386e-01 -5.86449385e-01 6.02919102e-01]	[14.403573036193848, 5.2171149253845215]
fe7adcf4-887e-4afe-9f5d-29c753fc6b6e	improving-and-analyzing-neural-speaker	2301.04571	null	https://arxiv.org/abs/2301.04571v1	https://arxiv.org/pdf/2301.04571v1.pdf	Improving And Analyzing Neural Speaker Embeddings for ASR	Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated the usage of neural speaker embeddings for an ASR system. In this work, we present our efforts w.r.t integrating neural speaker embeddings into a conformer based hybrid HMM ASR system. For ASR, our improved embedding extraction pipeline in combination with the Weighted-Simple-Add integration method results in x-vector and c-vector reaching on par performance with i-vectors. We further compare and analyze different speaker embeddings. We present our acoustic model improvements obtained by switching from newbob learning rate schedule to one cycle learning schedule resulting in a ~3% relative WER reduction on Switchboard, additionally reducing the overall training time by 17%. By further adding neural speaker embeddings, we gain additional ~3% relative WER improvement on Hub5'00. Our best Conformer-based hybrid ASR system with speaker embeddings achieves 9.0% WER on Hub5'00 and Hub5'01 with training on SWB 300h.	['Hermann Ney', 'Ralf Schlüter', 'Mohammad Zeineldeen', 'Jingjing Xu', 'Christoph Lüscher']	2023-01-11	null	null	null	null	['speaker-verification']	['speech']	[ 3.72400843e-02 3.08502614e-01 1.19667739e-01 -7.39569664e-01 -1.25192714e+00 -3.68279308e-01 4.08386230e-01 1.02823317e-01 -6.14130497e-01 5.13581783e-02 4.86843377e-01 -7.00204372e-01 3.29128146e-01 -2.11590827e-01 -4.45098221e-01 -3.87826502e-01 9.87690613e-02 3.87919307e-01 9.84890293e-03 -4.33620691e-01 -2.49212831e-01 5.11800349e-01 -1.28676617e+00 3.49552631e-02 1.77052766e-01 7.79365420e-01 -4.90226364e-03 1.24063838e+00 -6.29055873e-02 5.14606416e-01 -1.03078783e+00 -3.33533943e-01 3.65730524e-02 -2.11787373e-01 -6.16207123e-01 -2.17167959e-01 5.59073567e-01 -3.74303281e-01 -8.05389047e-01 6.54338241e-01 1.07824695e+00 4.28352863e-01 4.59446549e-01 -8.81187201e-01 -8.06919158e-01 1.24887788e+00 -1.96135998e-01 4.72230345e-01 3.50950986e-01 1.39425442e-01 1.13485074e+00 -1.01758039e+00 2.93854252e-03 1.49118841e+00 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33a7aed9-55a1-49d1-83ae-6c3d89108520	absolute-value-constraint-the-reason-for	2101.10942	null	https://arxiv.org/abs/2101.10942v2	https://arxiv.org/pdf/2101.10942v2.pdf	Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction	Neural networks for stock price prediction(NNSPP) have been popular for decades. However, most of its study results remain in the research paper and cannot truly play a role in the securities market. One of the main reasons leading to this situation is that the prediction error(PE) based evaluation results have statistical flaws. Its prediction results cannot represent the most critical financial direction attributes. So it cannot provide investors with convincing, interpretable, and consistent model performance evaluation results for practical applications in the securities market. To illustrate, we have used data selected from 20 stock datasets over six years from the Shanghai and Shenzhen stock market in China, and 20 stock datasets from NASDAQ and NYSE in the USA. We implement six shallow and deep neural networks to predict stock prices and use four prediction error measures for evaluation. The results show that the prediction error value only partially reflects the model accuracy of the stock price prediction, and cannot reflect the change in the direction of the model predicted stock price. This characteristic determines that PE is not suitable as an evaluation indicator of NNSPP. Otherwise, it will bring huge potential risks to investors. Therefore, this paper establishes an experiment platform to confirm that the PE method is not suitable for the NNSPP evaluation, and provides a theoretical basis for the necessity of creating a new NNSPP evaluation method in the future.	['Yi Wei']	2021-01-10	null	null	null	null	['stock-price-prediction']	['time-series']	[-8.93325150e-01 -6.97523534e-01 -2.26285696e-01 -3.73864383e-01 -3.00538801e-02 -3.28336120e-01 3.88140172e-01 -1.94407314e-01 -4.28618371e-01 7.08363354e-01 5.33161089e-02 -7.15209424e-01 -2.50012755e-01 -1.33614981e+00 -2.76317537e-01 -6.44092441e-01 7.13604912e-02 1.91194057e-01 3.27548593e-01 -5.75487375e-01 7.63137698e-01 5.11867344e-01 -1.15071452e+00 5.86441793e-02 6.34633899e-01 1.58524346e+00 -1.26945198e-01 -2.61215925e-01 -5.10525763e-01 9.75007772e-01 -6.87563837e-01 -5.25640488e-01 8.12988281e-01 -3.41287732e-01 -7.91635737e-02 -5.11462390e-01 -3.77941608e-01 -7.62054265e-01 -7.02161267e-02 1.26771665e+00 3.83945078e-01 -4.46005493e-01 2.79740363e-01 -1.32680357e+00 -8.36279750e-01 1.01748073e+00 -4.88910794e-01 4.98043776e-01 -3.60067070e-01 -4.51407162e-03 1.31636167e+00 -9.34474885e-01 7.14006200e-02 7.98840046e-01 8.02158892e-01 -3.48559171e-02 -6.29766166e-01 -1.21024525e+00 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5d930e54-4f9f-4cd9-9b3d-d5dc29f2b846	mixpro-data-augmentation-with-maskmix-and	2304.12043	null	https://arxiv.org/abs/2304.12043v1	https://arxiv.org/pdf/2304.12043v1.pdf	MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer	The recently proposed data augmentation TransMix employs attention labels to help visual transformers (ViT) achieve better robustness and performance. However, TransMix is deficient in two aspects: 1) The image cropping method of TransMix may not be suitable for vision transformer. 2) At the early stage of training, the model produces unreliable attention maps. TransMix uses unreliable attention maps to compute mixed attention labels that can affect the model. To address the aforementioned issues, we propose MaskMix and Progressive Attention Labeling (PAL) in image and label space, respectively. In detail, from the perspective of image space, we design MaskMix, which mixes two images based on a patch-like grid mask. In particular, the size of each mask patch is adjustable and is a multiple of the image patch size, which ensures each image patch comes from only one image and contains more global contents. From the perspective of label space, we design PAL, which utilizes a progressive factor to dynamically re-weight the attention weights of the mixed attention label. Finally, we combine MaskMix and Progressive Attention Labeling as our new data augmentation method, named MixPro. The experimental results show that our method can improve various ViT-based models at scales on ImageNet classification (73.8\% top-1 accuracy based on DeiT-T for 300 epochs). After being pre-trained with MixPro on ImageNet, the ViT-based models also demonstrate better transferability to semantic segmentation, object detection, and instance segmentation. Furthermore, compared to TransMix, MixPro also shows stronger robustness on several benchmarks. The code will be released at https://github.com/fistyee/MixPro.	['Jun Liu', 'Fan Zhang', 'Wei Hu', 'Yangyu Huang', 'QiHao Zhao']	2023-04-24	null	null	null	null	['image-cropping']	['computer-vision']	[ 2.95564830e-01 5.50236627e-02 -1.47631794e-01 -2.97932699e-02 -3.99800718e-01 -3.13989252e-01 3.73973817e-01 -1.97882533e-01 -3.95978779e-01 3.31876516e-01 -1.19407922e-01 -4.14118081e-01 3.07357848e-01 -7.74984956e-01 -8.73127639e-01 -6.17876828e-01 5.38702071e-01 2.23196000e-01 3.98238659e-01 -1.42926574e-01 4.80201049e-03 1.39937133e-01 -1.27299106e+00 2.99461007e-01 1.15646410e+00 1.21172559e+00 5.67385316e-01 3.17558587e-01 -2.78279155e-01 7.78208494e-01 -5.48646510e-01 -3.53408724e-01 3.36495250e-01 -3.74513686e-01 -8.31223667e-01 3.47797900e-01 7.45744050e-01 -2.88254261e-01 -2.35396579e-01 1.18375754e+00 2.49192894e-01 -5.94300553e-02 4.37604487e-01 -1.40514219e+00 -1.00002325e+00 5.17521620e-01 -7.51433909e-01 2.44990706e-01 -2.38964319e-01 3.88522953e-01 9.54184175e-01 -1.10859907e+00 2.63531148e-01 1.25384533e+00 6.26163602e-01 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-1.15332216e-01 4.00739253e-01 3.22229087e-01 -7.86803067e-01 -1.86694503e-01 2.07127512e-01]	[9.834083557128906, 0.4078010022640228]
e5f76e35-747b-44e2-8f68-7f0e666e8994	deeply-coupled-convolution-transformer-with	2304.14122	null	https://arxiv.org/abs/2304.14122v1	https://arxiv.org/pdf/2304.14122v1.pdf	Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification	Advanced deep Convolutional Neural Networks (CNNs) have shown great success in video-based person Re-Identification (Re-ID). However, they usually focus on the most obvious regions of persons with a limited global representation ability. Recently, it witnesses that Transformers explore the inter-patch relations with global observations for performance improvements. In this work, we take both sides and propose a novel spatial-temporal complementary learning framework named Deeply-Coupled Convolution-Transformer (DCCT) for high-performance video-based person Re-ID. Firstly, we couple CNNs and Transformers to extract two kinds of visual features and experimentally verify their complementarity. Further, in spatial, we propose a Complementary Content Attention (CCA) to take advantages of the coupled structure and guide independent features for spatial complementary learning. In temporal, a Hierarchical Temporal Aggregation (HTA) is proposed to progressively capture the inter-frame dependencies and encode temporal information. Besides, a gated attention is utilized to deliver aggregated temporal information into the CNN and Transformer branches for temporal complementary learning. Finally, we introduce a self-distillation training strategy to transfer the superior spatial-temporal knowledge to backbone networks for higher accuracy and more efficiency. In this way, two kinds of typical features from same videos are integrated mechanically for more informative representations. Extensive experiments on four public Re-ID benchmarks demonstrate that our framework could attain better performances than most state-of-the-art methods.	['Huchuan Lu', 'Pingping Zhang', 'Chenyang Yu', 'Xuehu Liu']	2023-04-27	null	null	null	null	['person-re-identification']	['computer-vision']	[-2.15945661e-01 -5.39822459e-01 -1.69866443e-01 -4.81485814e-01 -3.48808795e-01 -2.63021171e-01 7.31122911e-01 -2.66517729e-01 -4.42944139e-01 4.75513905e-01 7.08234370e-01 1.21624805e-01 -1.88822865e-01 -7.28154004e-01 -5.26143610e-01 -6.01609766e-01 -2.90307007e-03 5.70651665e-02 8.47174376e-02 -3.16344470e-01 -2.35913798e-01 2.94554979e-01 -1.47678137e+00 4.19644147e-01 8.26587915e-01 9.98125970e-01 1.46000590e-02 2.18664348e-01 5.24046309e-02 7.59268701e-01 -3.04901540e-01 -7.89230525e-01 2.22480834e-01 -3.96088183e-01 -8.34177136e-01 -1.54522434e-02 4.49578732e-01 -5.49682021e-01 -1.02631009e+00 8.75935733e-01 7.77080774e-01 4.47584212e-01 2.81783372e-01 -1.14128065e+00 -8.93526852e-01 5.11033356e-01 -5.99625885e-01 6.08232558e-01 5.17920136e-01 3.97457749e-01 8.34490538e-01 -7.22966075e-01 2.12603197e-01 1.31052876e+00 8.96034360e-01 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b50f00ba-5a9e-4ca1-8015-ae85e6f385c8	beyond-weak-perspective-for-monocular-3d	2009.06549	null	https://arxiv.org/abs/2009.06549v1	https://arxiv.org/pdf/2009.06549v1.pdf	Beyond Weak Perspective for Monocular 3D Human Pose Estimation	We consider the task of 3D joints location and orientation prediction from a monocular video with the skinned multi-person linear (SMPL) model. We first infer 2D joints locations with an off-the-shelf pose estimation algorithm. We use the SPIN algorithm and estimate initial predictions of body pose, shape and camera parameters from a deep regression neural network. We then adhere to the SMPLify algorithm which receives those initial parameters, and optimizes them so that inferred 3D joints from the SMPL model would fit the 2D joints locations. This algorithm involves a projection step of 3D joints to the 2D image plane. The conventional approach is to follow weak perspective assumptions which use ad-hoc focal length. Through experimentation on the 3D Poses in the Wild (3DPW) dataset, we show that using full perspective projection, with the correct camera center and an approximated focal length, provides favorable results. Our algorithm has resulted in a winning entry for the 3DPW Challenge, reaching first place in joints orientation accuracy.	['Imry Kissos', 'Mark Kliger', 'Lior Fritz', 'Eduard Oks', 'Omer Meir', 'Matan Goldman']	2020-09-14	null	null	null	null	['monocular-3d-human-pose-estimation']	['computer-vision']	[-1.33655414e-01 3.87223005e-01 -1.61134288e-01 -4.12116796e-01 -7.96181738e-01 -4.62409765e-01 6.42634630e-01 -6.20387256e-01 -6.31919503e-01 4.31375742e-01 4.02433008e-01 2.71953791e-01 2.28299424e-01 -9.43210274e-02 -9.88340855e-01 -2.69069374e-01 -6.47431314e-02 1.16003680e+00 2.58905709e-01 -3.20934430e-02 1.31798029e-01 5.59039652e-01 -1.14425397e+00 -2.09658351e-02 -1.26449242e-01 5.57429373e-01 -3.83659482e-01 1.17543423e+00 7.17184305e-01 3.24585587e-01 -3.39305401e-01 -6.09845757e-01 7.75371969e-01 -7.37080052e-02 -7.59378612e-01 3.56527269e-01 8.12263191e-01 -8.36122036e-01 -2.86010534e-01 4.83122140e-01 6.24665260e-01 7.36562908e-02 5.74923575e-01 -1.20647669e+00 1.72866136e-01 3.98954041e-02 -7.30146527e-01 -3.69706094e-01 9.77705657e-01 1.77155629e-01 9.44447398e-01 -9.79372740e-01 1.08829284e+00 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73ae4340-7ab0-4c5e-bd03-ab3a760e7f53	multi-view-graph-convolutional-networks-for	2005.04955	null	https://arxiv.org/abs/2005.04955v3	https://arxiv.org/pdf/2005.04955v3.pdf	Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction	Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN are concatenated with historical records and then fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge, as well as learn data-driven relationship.	['Cheng-Zhong Xu', 'Juanjuan Zhao', 'Jiexia Ye', 'Kejiang Ye']	2020-05-11	null	null	null	null	['stock-prediction']	['time-series']	[-6.41811669e-01 -3.82702321e-01 -2.75551528e-01 -2.04623327e-01 -1.45557344e-01 -8.03781390e-01 5.83665788e-01 -1.56518042e-01 -1.00098878e-01 6.00820839e-01 5.22564352e-01 -5.15851974e-01 -3.65712605e-02 -1.36921525e+00 -7.03228652e-01 -4.09566045e-01 -4.75903034e-01 2.96536654e-01 3.67149591e-01 -6.92088783e-01 3.74431968e-01 3.15538019e-01 -9.52856839e-01 -2.18904480e-01 6.38372064e-01 1.16748369e+00 5.07905819e-02 1.76950321e-01 -4.07996744e-01 1.28764927e+00 -5.09152353e-01 -6.15306735e-01 7.28495300e-01 -3.80500466e-01 -2.61242121e-01 2.48725310e-01 -1.80626243e-01 -4.25908983e-01 -5.87933660e-01 1.26421106e+00 1.77493677e-01 -4.78560366e-02 4.14730221e-01 -9.95656133e-01 -9.97266293e-01 1.31120813e+00 -7.86891699e-01 7.61666298e-01 -3.11341971e-01 3.62364084e-01 1.62714064e+00 -5.66137969e-01 4.51832503e-01 9.64305520e-01 5.23207307e-01 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9236230f-3f07-43fa-ac34-9c3211259dda	a-recommender-system-approach-for-very-large	2304.04067	null	https://arxiv.org/abs/2304.04067v1	https://arxiv.org/pdf/2304.04067v1.pdf	A Recommender System Approach for Very Large-scale Multiobjective Optimization	We define very large multi-objective optimization problems to be multiobjective optimization problems in which the number of decision variables is greater than 100,000 dimensions. This is an important class of problems as many real-world problems require optimizing hundreds of thousands of variables. Existing evolutionary optimization methods fall short of such requirements when dealing with problems at this very large scale. Inspired by the success of existing recommender systems to handle very large-scale items with limited historical interactions, in this paper we propose a method termed Very large-scale Multiobjective Optimization through Recommender Systems (VMORS). The idea of the proposed method is to transform the defined such very large-scale problems into a problem that can be tackled by a recommender system. In the framework, the solutions are regarded as users, and the different evolution directions are items waiting for the recommendation. We use Thompson sampling to recommend the most suitable items (evolutionary directions) for different users (solutions), in order to locate the optimal solution to a multiobjective optimization problem in a very large search space within acceptable time. We test our proposed method on different problems from 100,000 to 500,000 dimensions, and experimental results show that our method not only shows good performance but also significant improvement over existing methods.	['Kay Chen Tan', 'Qiuzhen Lin', 'Jonathan M. Garibaldi', 'Min Jiang', 'Haokai Hong']	2023-04-08	null	null	null	null	['thompson-sampling', 'multiobjective-optimization']	['methodology', 'methodology']	[-1.17720827e-01 -4.37798589e-01 8.86732638e-02 -1.83444336e-01 -3.30082178e-01 -5.21547258e-01 -2.34337579e-02 4.19263691e-02 -6.13986909e-01 1.06993437e+00 9.28260684e-02 -5.38324900e-02 -7.55751073e-01 -9.73165274e-01 -4.37414080e-01 -7.94417143e-01 -1.79504126e-01 9.85145390e-01 1.88273951e-01 -6.03151023e-01 7.08696187e-01 2.75416821e-01 -2.03946042e+00 8.71720091e-02 1.55811346e+00 8.68640482e-01 4.02155161e-01 7.32737899e-01 -1.53954312e-01 -6.44320846e-02 -8.48076999e-01 -3.34188282e-01 3.80394906e-01 -4.39108402e-01 -6.61554217e-01 2.35904291e-01 -2.12880343e-01 1.70504302e-01 7.12224960e-01 1.12729657e+00 7.59930313e-01 9.26169991e-01 4.27458256e-01 -1.10684764e+00 -9.05484617e-01 5.54176569e-01 -5.81735015e-01 3.71102542e-01 1.39340147e-01 -2.29703143e-01 1.25812948e+00 -8.52990329e-01 5.45354068e-01 1.33528340e+00 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ab880633-dddd-4a79-a636-f5c42fb1731a	implementing-a-wall-in-building-placement-in	1306.4460	null	http://arxiv.org/abs/1306.4460v1	http://arxiv.org/pdf/1306.4460v1.pdf	Implementing a Wall-In Building Placement in StarCraft with Declarative Programming	In real-time strategy games like StarCraft, skilled players often block the entrance to their base with buildings to prevent the opponent's units from getting inside. This technique, called "walling-in", is a vital part of player's skill set, allowing him to survive early aggression. However, current artificial players (bots) do not possess this skill, due to numerous inconveniences surfacing during its implementation in imperative languages like C++ or Java. In this text, written as a guide for bot programmers, we address the problem of finding an appropriate building placement that would block the entrance to player's base, and present a ready to use declarative solution employing the paradigm of answer set programming (ASP). We also encourage the readers to experiment with different declarative approaches to this problem.	['Michal Certicky']	2013-06-19	null	null	null	null	['real-time-strategy-games']	['playing-games']	[-2.38045678e-01 8.67754668e-02 3.11276197e-01 6.86253160e-02 -1.08343638e-01 -9.42543507e-01 2.36370973e-02 1.49834797e-01 -4.67555076e-01 7.68199921e-01 -2.87576854e-01 -7.34322667e-01 -3.45609784e-01 -1.20852208e+00 -6.91374689e-02 -3.25982690e-01 1.44771636e-01 5.38608491e-01 1.00439358e+00 -9.33027804e-01 7.90238440e-01 3.74511242e-01 -1.68060708e+00 1.72525778e-01 4.79216903e-01 3.65271300e-01 1.95437059e-01 7.49556959e-01 -4.45600778e-01 1.36361086e+00 -9.30260003e-01 -7.91549802e-01 3.88584703e-01 -4.79496568e-01 -1.09775841e+00 -3.40873599e-01 -4.57892001e-01 -4.76253569e-01 7.07055107e-02 9.56221461e-01 1.55503303e-01 9.22675580e-02 2.76247352e-01 -1.36465967e+00 3.37048292e-01 5.10855913e-01 -4.69271451e-01 1.04866713e-01 5.20111799e-01 3.14094692e-01 8.64229918e-01 -2.55450934e-01 6.13952994e-01 6.35151684e-01 7.42361903e-01 6.94338262e-01 -8.99182141e-01 -2.62783796e-01 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110f9735-ee7f-4d87-89b0-5a86bbcf3ce3	learning-to-start-for-sequence-to-sequence	1608.05554	null	http://arxiv.org/abs/1608.05554v1	http://arxiv.org/pdf/1608.05554v1.pdf	Learning to Start for Sequence to Sequence Architecture	The sequence to sequence architecture is widely used in the response generation and neural machine translation to model the potential relationship between two sentences. It typically consists of two parts: an encoder that reads from the source sentence and a decoder that generates the target sentence word by word according to the encoder's output and the last generated word. However, it faces to the cold start problem when generating the first word as there is no previous word to refer. Existing work mainly use a special start symbol </s>to generate the first word. An obvious drawback of these work is that there is not a learnable relationship between words and the start symbol. Furthermore, it may lead to the error accumulation for decoding when the first word is incorrectly generated. In this paper, we proposed a novel approach to learning to generate the first word in the sequence to sequence architecture rather than using the start symbol. Experimental results on the task of response generation of short text conversation show that the proposed approach outperforms the state-of-the-art approach in both of the automatic and manual evaluations.	['Wei-Nan Zhang', 'Ting Liu', 'Lianqiang Zhou', 'Qingfu Zhu']	2016-08-19	null	null	null	null	['short-text-conversation']	['natural-language-processing']	[ 9.05920744e-01 3.30250055e-01 2.21875578e-01 -4.76696312e-01 -8.32053542e-01 -3.59005123e-01 7.54051805e-01 1.31507590e-01 -4.62866485e-01 1.08045292e+00 2.91664988e-01 -4.50264931e-01 3.93126875e-01 -6.81242764e-01 -6.83660686e-01 -5.72186053e-01 5.35571098e-01 4.28181142e-01 2.16520175e-01 -6.48536086e-01 5.65364659e-01 -2.86722600e-01 -1.24745417e+00 6.41127467e-01 7.88550556e-01 6.20570958e-01 7.29682446e-01 8.26056063e-01 -5.46556294e-01 7.02404618e-01 -9.45891023e-01 -4.72081274e-01 1.33742169e-01 -1.16641831e+00 -1.10409343e+00 -1.41675338e-01 1.18792206e-02 -1.73714682e-01 1.42823800e-01 1.05085456e+00 6.13060772e-01 -6.05373196e-02 5.59248745e-01 -9.12902117e-01 -7.76513994e-01 9.07681346e-01 -2.45348215e-01 3.33492219e-01 7.32356668e-01 -1.78153198e-02 7.32749701e-01 -8.67435515e-01 4.47122991e-01 1.36579657e+00 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-8.32216978e-01 -1.43112516e+00 5.59539422e-02 3.71597707e-01 2.14475706e-01 -6.87991157e-02 -4.86363508e-02 1.26680404e-01]	[11.988985061645508, 8.96153736114502]
d85f5f79-55cd-4159-a472-f003794a8eac	brain-mri-image-super-resolution-using-phase	1807.11643	null	http://arxiv.org/abs/1807.11643v1	http://arxiv.org/pdf/1807.11643v1.pdf	Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning	A hallucination-free and computationally efficient algorithm for enhancing the resolution of brain MRI images is demonstrated.	['Sifeng He', 'Bahram Jalali']	2018-07-31	null	null	null	null	['medical-super-resolution']	['medical']	[ 2.47427419e-01 -1.14234231e-01 5.14189899e-01 -2.44199365e-01 -4.59950149e-01 1.66752756e-01 2.31462657e-01 -3.20271969e-01 -6.65103555e-01 9.29845989e-01 4.50669199e-01 -2.40996778e-02 -1.51792750e-01 -4.70676869e-01 -3.97606194e-02 -6.65453672e-01 -5.86754858e-01 5.62909663e-01 1.20034568e-01 -3.33724618e-02 2.53578514e-01 9.61207628e-01 -9.99195457e-01 3.94525558e-01 6.84650302e-01 3.60313118e-01 8.65647078e-01 5.52260637e-01 4.08977717e-01 1.13948703e+00 -3.10876667e-01 -5.31068072e-02 -9.46846679e-02 -5.76067507e-01 -1.05060959e+00 4.98469263e-01 -1.95993602e-01 -5.75513184e-01 -5.55922568e-01 1.31927168e+00 9.35259223e-01 5.00743687e-01 6.05128527e-01 -4.49919105e-01 -3.66019338e-01 2.80475795e-01 -3.66516620e-01 1.02894604e+00 3.44819039e-01 -2.28395939e-01 -3.88527215e-01 -1.58877110e+00 9.25461352e-01 7.50074744e-01 2.81887025e-01 5.12857974e-01 -9.97914076e-01 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e8062a97-d532-4ece-a7ba-2010c9f976d8	hit-hierarchical-transformer-with-momentum	2103.15049	null	https://arxiv.org/abs/2103.15049v2	https://arxiv.org/pdf/2103.15049v2.pdf	HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval	Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet. Transformer for video-text learning has attracted increasing attention due to its promising performance. However, existing cross-modal transformer approaches typically suffer from two major limitations: 1) Exploitation of the transformer architecture where different layers have different feature characteristics is limited; 2) End-to-end training mechanism limits negative sample interactions in a mini-batch. In this paper, we propose a novel approach named Hierarchical Transformer (HiT) for video-text retrieval. HiT performs Hierarchical Cross-modal Contrastive Matching in both feature-level and semantic-level, achieving multi-view and comprehensive retrieval results. Moreover, inspired by MoCo, we propose Momentum Cross-modal Contrast for cross-modal learning to enable large-scale negative sample interactions on-the-fly, which contributes to the generation of more precise and discriminative representations. Experimental results on the three major Video-Text Retrieval benchmark datasets demonstrate the advantages of our method.	['Zhongyuan Wang', 'Wenkui Ding', 'Yiru Chen', 'Shengsheng Qian', 'Haoqi Fan', 'Song Liu']	2021-03-28	null	http://openaccess.thecvf.com//content/ICCV2021/html/Liu_HiT_Hierarchical_Transformer_With_Momentum_Contrast_for_Video-Text_Retrieval_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_HiT_Hierarchical_Transformer_With_Momentum_Contrast_for_Video-Text_Retrieval_ICCV_2021_paper.pdf	iccv-2021-1	['video-text-retrieval']	['computer-vision']	[ 6.48420453e-02 -1.04907107e+00 -3.60286891e-01 -3.49878699e-01 -1.29421818e+00 -3.75094891e-01 6.89639390e-01 1.46216288e-01 -4.48284268e-01 1.85883120e-01 3.72293383e-01 2.79717743e-01 -4.02421921e-01 -4.32345033e-01 -3.38302374e-01 -7.39953041e-01 1.28604561e-01 3.34885865e-01 3.27728659e-01 -1.62074357e-01 2.65210360e-01 1.31322676e-02 -1.59737706e+00 7.66159952e-01 7.95354962e-01 1.29202104e+00 2.16267809e-01 2.60093927e-01 -1.88338220e-01 5.93630016e-01 -1.46910861e-01 -4.61614221e-01 2.09313124e-01 -2.15722755e-01 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36a07a63-eff5-4626-b50e-7b73e241f97b	deep-graph-convolutional-encoders-for	1810.09995	null	http://arxiv.org/abs/1810.09995v1	http://arxiv.org/pdf/1810.09995v1.pdf	Deep Graph Convolutional Encoders for Structured Data to Text Generation	Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.	['Laura Perez-Beltrachini', 'Diego Marcheggiani']	2018-10-23	deep-graph-convolutional-encoders-for-1	https://aclanthology.org/W18-6501	https://aclanthology.org/W18-6501.pdf	ws-2018-11	['graph-to-sequence', 'kg-to-text']	['natural-language-processing', 'natural-language-processing']	[ 8.06620061e-01 8.02619457e-01 -2.30511338e-01 -3.53438735e-01 -3.03811908e-01 -6.10773623e-01 7.20082700e-01 1.62457883e-01 -1.37741193e-01 9.06912506e-01 5.62701344e-01 -9.05283332e-01 4.96526927e-01 -1.12661362e+00 -1.07244802e+00 -8.59724581e-02 -1.38187498e-01 2.95115918e-01 -1.01395391e-01 -5.13577521e-01 1.70755863e-01 1.42071605e-01 -1.21065176e+00 6.27221882e-01 4.84984189e-01 4.14340615e-01 1.40626281e-01 1.30968356e+00 -6.45096660e-01 1.23171937e+00 -8.78097832e-01 -5.84549487e-01 1.95985049e-01 -1.12827170e+00 -8.27581942e-01 8.08396041e-02 4.12609339e-01 -2.52355605e-01 -6.47782028e-01 8.20681214e-01 4.68665838e-01 1.24700107e-01 6.22395396e-01 -9.69477594e-01 -1.15529513e+00 1.26007426e+00 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5a2c2c7a-9812-47d0-a287-4cd0c9f12696	improving-adversarially-robust-few-shot-image	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Dong_Improving_Adversarially_Robust_Few-Shot_Image_Classification_With_Generalizable_Representations_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Dong_Improving_Adversarially_Robust_Few-Shot_Image_Classification_With_Generalizable_Representations_CVPR_2022_paper.pdf	Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations	Few-Shot Image Classification (FSIC) aims to recognize novel image classes with limited data, which is significant in practice. In this paper, we consider the FSIC problem in the case of adversarial examples. This is an extremely challenging issue because current deep learning methods are still vulnerable when handling adversarial examples, even with massive labeled training samples. For this problem, existing works focus on training a network in the meta-learning fashion that depends on numerous sampled few-shot tasks. In comparison, we propose a simple but effective baseline through directly learning generalizable representations without tedious task sampling, which is robust to unforeseen adversarial FSIC tasks. Specifically, we introduce an adversarial-aware mechanism to establish auxiliary supervision via feature-level differences between legitimate and adversarial examples. Furthermore, we design a novel adversarial-reweighted training manner to alleviate the imbalance among adversarial examples. The feature purifier is also employed as post-processing for adversarial features. Moreover, our method can obtain generalizable representations to remain superior transferability, even facing cross-domain adversarial examples. Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.	['Xiaohua Xie', 'Jian-Huang Lai', 'YuAn Wang', 'Junhao Dong']	2022-01-01	null	null	null	cvpr-2022-1	['few-shot-image-classification']	['computer-vision']	[ 6.64349377e-01 -1.63085088e-01 -1.21822360e-03 -2.75942236e-01 -9.60998774e-01 -6.42625034e-01 6.90769672e-01 -3.84070933e-01 -2.86993235e-01 6.91201866e-01 3.31986658e-02 8.28163847e-02 5.89516610e-02 -9.93773282e-01 -1.01796782e+00 -8.16300035e-01 3.36195201e-01 5.53268678e-02 2.18540907e-01 -5.46673238e-01 -4.63930890e-02 4.95309204e-01 -1.39818192e+00 3.14649373e-01 1.00642061e+00 1.04775357e+00 -9.95465815e-02 4.81564462e-01 6.53803423e-02 8.73953283e-01 -8.82531881e-01 -7.29069650e-01 5.67355037e-01 -5.97621381e-01 -6.45283937e-01 1.06233157e-01 7.01075673e-01 -5.31417131e-01 -8.03588271e-01 1.36732161e+00 6.27485991e-01 4.71542358e-01 6.60376608e-01 -1.39670074e+00 -1.23952556e+00 2.85509080e-01 -3.98021936e-01 1.35406464e-01 8.47956017e-02 6.83458626e-01 6.96433365e-01 -8.10527861e-01 5.55222571e-01 1.23175836e+00 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bdac168d-251d-4a4e-bc58-d41dd3233cfb	event-extraction-a-survey	2210.03419	null	https://arxiv.org/abs/2210.03419v2	https://arxiv.org/pdf/2210.03419v2.pdf	Event Extraction: A Survey	Extracting the reported events from text is one of the key research themes in natural language processing. This process includes several tasks such as event detection, argument extraction, role labeling. As one of the most important topics in natural language processing and natural language understanding, the applications of event extraction spans across a wide range of domains such as newswire, biomedical domain, history and humanity, and cyber security. This report presents a comprehensive survey for event detection from textual documents. In this report, we provide the task definition, the evaluation method, as well as the benchmark datasets and a taxonomy of methodologies for event extraction. We also present our vision of future research direction in event detection.	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84e27799-f946-41ea-92b1-318db2fb1fd2	improved-latent-tree-induction-with-distant	2109.05112	null	https://arxiv.org/abs/2109.05112v2	https://arxiv.org/pdf/2109.05112v2.pdf	Improved Latent Tree Induction with Distant Supervision via Span Constraints	For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset.	['Andrew McCallum', 'Mohit Iyyer', 'Shilpa Suresh', 'Dylan Finkbeiner', 'Subendhu Rongali', "Tim O'Gorman", 'Jay Yoon Lee', 'Andrew Drozdov', 'Zhiyang Xu']	2021-09-10	null	https://aclanthology.org/2021.emnlp-main.395	https://aclanthology.org/2021.emnlp-main.395.pdf	emnlp-2021-11	['constituency-parsing']	['natural-language-processing']	[ 4.89179343e-01 7.92930305e-01 -2.66995788e-01 -7.15578794e-01 -1.32207859e+00 -7.51649737e-01 2.35979602e-01 6.68704152e-01 -6.25945807e-01 9.26082909e-01 3.27961892e-01 -5.93307793e-01 2.26984665e-01 -6.92759633e-01 -5.50186515e-01 -4.34861511e-01 5.02471579e-03 6.20786309e-01 3.69583517e-01 -1.57283366e-01 -3.45405489e-02 1.59111544e-01 -9.91409123e-01 3.58938456e-01 6.92560554e-01 2.68572032e-01 3.37755233e-01 4.91501451e-01 -4.50566322e-01 6.23294473e-01 -6.88214183e-01 -5.18158913e-01 4.82642613e-02 -4.74722624e-01 -1.14285100e+00 -1.54083028e-01 4.30787444e-01 -1.12202302e-01 2.80361623e-01 8.31254542e-01 4.70861137e-01 -1.90625250e-01 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5642e97b-2d18-43ab-ac38-7d475656f0d4	a-computer-vision-assisted-approach-to	2202.13285	null	https://arxiv.org/abs/2202.13285v1	https://arxiv.org/pdf/2202.13285v1.pdf	A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management	Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of government funding for infrastructure maintenance typically entails heightened risks of potentially fatal vehicular road accidents as a result of inadequate and infrequent manual inspection of road systems for road hazards. To remedy this, a recent research initiative organized by the Institute of Electrical and Electronics Engineers ("IEEE") as part of their 2020 Global Road Damage Detection ("GRDC") Challenge published in May 2020 a novel 21,041 annotated image dataset of various road distresses calling upon academic and other researchers to submit innovative deep learning-based solutions to these road hazard detection problems. Making use of this dataset, we propose a supervised object detection approach leveraging You Only Look Once ("YOLO") and the Faster R-CNN frameworks to detect and classify road distresses in real-time via a vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score experimental results ranking in the top 5 of 121 teams that entered this challenge as of December 2021.	['Philippe Heitzmann']	2022-02-27	null	null	null	null	['road-damage-detection']	['computer-vision']	[ 9.03484672e-02 2.44691044e-01 1.59471348e-01 -2.00105578e-01 -1.07676494e+00 -1.63980260e-01 2.62142986e-01 1.99854940e-01 -2.81776071e-01 3.45639378e-01 1.19597159e-01 -7.09993660e-01 -1.51165411e-01 -1.21223056e+00 -7.35328078e-01 -4.49158072e-01 8.59152973e-02 1.49647063e-02 4.81972247e-01 -3.42531770e-01 4.51021254e-01 9.43816125e-01 -1.96364272e+00 3.66019346e-02 6.54058874e-01 1.01938999e+00 6.93809688e-02 7.53107190e-01 4.70144361e-01 4.87710893e-01 -3.34681004e-01 -1.64584652e-01 1.56850845e-01 4.50154901e-01 -8.14373136e-01 -3.22700471e-01 6.25894129e-01 -7.23513484e-01 -5.96801162e-01 4.98195916e-01 5.50544977e-01 1.27649784e-01 5.79732239e-01 -1.36636353e+00 -2.46798143e-01 -3.01131696e-01 -5.39357424e-01 6.48841441e-01 9.87421125e-02 3.83806467e-01 7.50702381e-01 -1.23231447e+00 2.21441574e-02 9.64172542e-01 9.86713350e-01 2.11312622e-01 -6.42398596e-01 -6.16457045e-01 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2c9f1305-fc64-461a-97ed-2b8f163f4153	deep-learning-for-music	1606.04930	null	http://arxiv.org/abs/1606.04930v1	http://arxiv.org/pdf/1606.04930v1.pdf	Deep Learning for Music	Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNN-RBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone.	['Raymond Wu', 'Allen Huang']	2016-06-15	null	null	null	null	['music-modeling']	['music']	[-9.95645002e-02 1.01843029e-02 2.81812489e-01 9.82354488e-03 -6.95420742e-01 -2.78706670e-01 6.28202856e-01 -6.06868327e-01 -1.29344940e-01 8.32943499e-01 5.29715955e-01 1.15446530e-01 -1.24650441e-01 -1.00248528e+00 -5.25178850e-01 -5.98436296e-01 1.47456989e-01 8.84089351e-01 -1.88412100e-01 -2.94218183e-01 1.33495435e-01 3.11870694e-01 -1.43970597e+00 2.42606238e-01 2.55649716e-01 6.00202620e-01 7.07905665e-02 1.06253004e+00 5.00783511e-02 1.10067439e+00 -7.80023754e-01 -3.47244143e-01 5.23506030e-02 -1.09808791e+00 -6.59944952e-01 -6.00136697e-01 -9.61147174e-02 8.28796104e-02 -2.91846395e-01 7.34238207e-01 8.63039553e-01 7.16237783e-01 9.37564731e-01 -9.26097333e-01 -7.69306302e-01 1.33778059e+00 -6.48411736e-02 -1.53989702e-01 1.99424013e-01 -5.43607809e-02 8.97080004e-01 -7.16503620e-01 4.13781524e-01 8.94756317e-01 1.02804196e+00 9.29771721e-01 -1.35046101e+00 -6.27398908e-01 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8.22493732e-02 -1.43504411e-01 -2.05653712e-01 7.63260722e-02 3.17918099e-02]	[15.985312461853027, 5.501816749572754]
b1c66877-7fc1-4136-93a3-ce1818eb3a4b	deep-reinforcement-learning-for-sequence-to	1805.09461	null	http://arxiv.org/abs/1805.09461v4	http://arxiv.org/pdf/1805.09461v4.pdf	Deep Reinforcement Learning For Sequence to Sequence Models	In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide state-of-the-art performance in a wide variety of tasks such as machine translation, headline generation, text summarization, speech to text conversion, and image caption generation. The underlying framework for all these models is usually a deep neural network comprising an encoder and a decoder. Although simple encoder-decoder models produce competitive results, many researchers have proposed additional improvements over these sequence-to-sequence models, e.g., using an attention-based model over the input, pointer-generation models, and self-attention models. However, such seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently, a completely novel point of view has emerged in addressing these two problems in seq2seq models, leveraging methods from reinforcement learning (RL). In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories. We present some of the most recent frameworks that combine concepts from RL and deep neural networks and explain how these two areas could benefit from each other in solving complex seq2seq tasks. Our work aims to provide insights into some of the problems that inherently arise with current approaches and how we can address them with better RL models. We also provide the source code for implementing most of the RL models discussed in this paper to support the complex task of abstractive text summarization.	['Tian Shi', 'Naren Ramakrishnan', 'Chandan K. Reddy', 'Yaser Keneshloo']	2018-05-24	null	null	null	null	['headline-generation']	['natural-language-processing']	[ 6.74415946e-01 3.36967528e-01 -2.10438251e-01 -1.84352368e-01 -1.08061707e+00 -3.95879418e-01 9.28407073e-01 -5.26570044e-02 -2.62328774e-01 1.17476368e+00 6.96176708e-01 -2.73092687e-01 1.64449736e-01 -6.81106687e-01 -8.61256599e-01 -5.97680986e-01 2.56784827e-01 5.06932259e-01 -4.83311936e-02 -5.32502472e-01 6.74075663e-01 2.08188385e-01 -1.62082970e+00 6.37807727e-01 8.76827717e-01 7.38753617e-01 5.20081878e-01 9.69571650e-01 -4.86332685e-01 1.27073061e+00 -9.59959030e-01 -5.60303867e-01 -1.66203573e-01 -9.97205496e-01 -1.07846498e+00 -3.06952268e-01 3.41690838e-01 -3.93781275e-01 -3.98109376e-01 7.43104577e-01 1.10839581e+00 2.32036009e-01 6.25893772e-01 -9.89134848e-01 -8.92403483e-01 7.13889062e-01 -3.68008167e-01 2.53111511e-01 7.55224228e-01 2.84215599e-01 8.93836379e-01 -6.87848806e-01 5.68610132e-01 1.17716289e+00 5.44356942e-01 9.16540027e-01 -8.99768651e-01 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-2.03312859e-01 7.27593079e-02 4.43378508e-01 4.04746532e-02 6.13249019e-02 1.60504356e-02]	[12.04637622833252, 9.180880546569824]
4ec368de-e41b-4f81-b948-c5c7132de745	triplet-contrastive-learning-for-unsupervised	2301.09498	null	https://arxiv.org/abs/2301.09498v2	https://arxiv.org/pdf/2301.09498v2.pdf	Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification	Part feature learning is critical for fine-grained semantic understanding in vehicle re-identification. However, existing approaches directly model part features and global features, which can easily lead to serious gradient vanishing issues due to their unequal feature information and unreliable pseudo-labels for unsupervised vehicle re-identification. To address this problem, in this paper, we propose a simple Triplet Contrastive Representation Learning (TCRL) framework which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification. Specifically, TCRL devises three memory banks to store the instance/cluster features and proposes a Proxy Contrastive Loss (PCL) to make contrastive learning between adjacent memory banks, thus presenting the associations between the part and global features as a transition of the part-cluster and cluster-global associations. Since the cluster memory bank copes with all the vehicle features, it can summarize them into a discriminative feature representation. To deeply exploit the instance/cluster information, TCRL proposes two additional loss functions. For the instance-level feature, a Hybrid Contrastive Loss (HCL) re-defines the sample correlations by approaching the positive instance features and pushing the all negative instance features away. For the cluster-level feature, a Weighted Regularization Cluster Contrastive Loss (WRCCL) refines the pseudo labels by penalizing the mislabeled images according to the instance similarity. Extensive experiments show that TCRL outperforms many state-of-the-art unsupervised vehicle re-identification approaches.	['Xiangbo Shu', 'Jinhui Tang', 'Liyan Zhang', 'Xiaoyu Du', 'Fei Shen']	2023-01-23	null	null	null	null	['vehicle-re-identification']	['computer-vision']	[-1.26780555e-01 -1.86372221e-01 -4.65509027e-01 -5.85716784e-01 -8.61045361e-01 -3.01462978e-01 7.86721528e-01 2.71464288e-01 -2.97841996e-01 3.35738987e-01 -1.41929582e-01 -5.89332841e-02 -1.14458077e-01 -7.29056120e-01 -6.24829531e-01 -8.87102842e-01 1.93905786e-01 3.70716244e-01 2.49399900e-01 -1.08680740e-01 2.36562118e-01 6.39798880e-01 -1.86067462e+00 2.04950258e-01 9.21389401e-01 1.31974196e+00 1.06281050e-01 3.55311558e-02 -4.24300790e-01 5.46681941e-01 -1.62923813e-01 -3.06765825e-01 3.05706799e-01 -2.72054642e-01 -6.14107192e-01 1.05111107e-01 5.37327111e-01 -1.79871857e-01 -3.69330794e-01 1.30699301e+00 1.09848499e-01 2.80589879e-01 7.63692439e-01 -1.87947094e+00 -4.11065519e-01 2.56601870e-01 -8.49226475e-01 2.32454091e-01 1.59229934e-02 6.04148135e-02 1.01347136e+00 -1.25030971e+00 2.01897472e-01 1.30545473e+00 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92e87f25-64d7-4d44-952a-1a8347970a70	spin-an-empirical-evaluation-on-sharing	2207.10237	null	https://arxiv.org/abs/2207.10237v1	https://arxiv.org/pdf/2207.10237v1.pdf	SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks	Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN). We present a framework to formalize major weight sharing design decisions and perform a comprehensive empirical evaluation of this design space. Guided by our experimental results, we propose a weight sharing strategy to generate a family of models with better overall efficiency, in terms of FLOPs and parameters versus accuracy, compared to traditional scaling methods alone, for example compressing ConvMixer by 1.9x while improving accuracy on ImageNet. Finally, we perform a qualitative study to further understand the behavior of weight sharing in isotropic architectures. The code is available at https://github.com/apple/ml-spin.	['Mohammad Rastegari', 'Maxwell Horton', 'Anurag Ranjan', 'Sachin Mehta', 'Thomas Merth', 'Anish Prabhu', 'Chien-Yu Lin']	2022-07-21	null	null	null	null	['neural-network-compression', 'neural-network-compression']	['methodology', 'miscellaneous']	[ 3.17421198e-01 1.02930740e-01 -3.91374975e-01 -5.50071895e-01 -2.40364149e-01 -4.45851028e-01 7.05024302e-01 -3.95685494e-01 -1.06072938e+00 2.66345412e-01 3.21984291e-01 -7.07192540e-01 -3.50746036e-01 -5.70679069e-01 -6.98343515e-01 -5.29859424e-01 -1.94367170e-02 1.27766788e-01 1.32198572e-01 3.18292469e-01 2.21931309e-01 7.66530931e-01 -1.47092068e+00 2.59805530e-01 3.40350628e-01 1.26690781e+00 2.69318134e-01 8.12424064e-01 6.61912337e-02 1.12314236e+00 -4.37685609e-01 -8.14078391e-01 4.65142578e-01 5.24238423e-02 -7.47350991e-01 -4.47139561e-01 1.10900402e+00 -7.71486759e-01 -7.55242407e-01 9.81847584e-01 5.01083970e-01 -1.58063233e-01 4.77297962e-01 -1.44181931e+00 -5.07124901e-01 1.22966969e+00 -2.72978485e-01 5.29131114e-01 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cbda1a92-4fa6-4a35-8837-267e7819e191	short-term-density-forecasting-of-low-voltage	2204.13939	null	https://arxiv.org/abs/2204.13939v3	https://arxiv.org/pdf/2204.13939v3.pdf	Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows	The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities. Also, they outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.	['Oliver Dürr', 'Mark Nigge-Uricher', 'Beate Sick', 'Marcus Voss', 'Marcel Arpogaus']	2022-04-29	null	null	null	null	['probabilistic-deep-learning', 'probabilistic-time-series-forecasting']	['computer-vision', 'time-series']	[-5.37409484e-01 -3.39739949e-01 -2.48547196e-01 -5.30880332e-01 -5.71540415e-01 -5.56125283e-01 5.11318445e-01 2.68881619e-01 4.83973585e-02 1.24333906e+00 1.41077682e-01 -6.70257270e-01 -3.41573685e-01 -1.24770439e+00 -3.30360800e-01 -9.32797074e-01 -1.91306457e-01 9.00430381e-01 -5.06136119e-01 3.82421523e-01 -2.02088524e-02 7.04695165e-01 -1.04819691e+00 -3.14043432e-01 1.28531492e+00 1.10638297e+00 1.31062254e-01 3.76294076e-01 -7.26747736e-02 2.75169760e-01 -6.77467525e-01 -8.78693536e-02 4.34438325e-02 -8.42613280e-02 -4.21417058e-02 -5.03303230e-01 -5.26346505e-01 -4.55044031e-01 9.34597030e-02 1.10587645e+00 5.61493993e-01 3.18608314e-01 1.37023139e+00 -1.24244666e+00 -6.65398538e-01 7.90163100e-01 -4.51007128e-01 2.18928114e-01 -1.12217657e-01 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a30e61dc-9cc9-48bf-9a4f-b85d64e1a566	safl-a-self-attention-scene-text-recognizer-1	2201.00132	null	https://arxiv.org/abs/2201.00132v1	https://arxiv.org/pdf/2201.00132v1.pdf	SAFL: A Self-Attention Scene Text Recognizer with Focal Loss	In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortions or irregular layout. Most of the existing approaches mainly leverage recurrence or convolution-based neural networks. However, while recurrent neural networks (RNNs) usually suffer from slow training speed due to sequential computation and encounter problems as vanishing gradient or bottleneck, CNN endures a trade-off between complexity and performance. In this paper, we introduce SAFL, a self-attention-based neural network model with the focal loss for scene text recognition, to overcome the limitation of the existing approaches. The use of focal loss instead of negative log-likelihood helps the model focus more on low-frequency samples training. Moreover, to deal with the distortions and irregular texts, we exploit Spatial TransformerNetwork (STN) to rectify text before passing to the recognition network. We perform experiments to compare the performance of the proposed model with seven benchmarks. The numerical results show that our model achieves the best performance.	['Phi Le Nguyen', 'Thanh Hung Nguyen', 'Duc Anh Le', 'Huu Manh Nguyen', 'Thanh Le-Cong', 'Bao Hieu Tran']	2022-01-01	safl-a-self-attention-scene-text-recognizer	https://ieeexplore.ieee.org/document/9356232	https://ieeexplore.ieee.org/document/9356232	null	['scene-text-recognition']	['computer-vision']	[ 4.40768391e-01 -6.43576503e-01 8.60284735e-03 -3.31517845e-01 -1.40557036e-01 -2.01153718e-02 6.10900700e-01 -3.37875485e-02 -5.02964437e-01 3.98647219e-01 1.63200349e-01 -2.94491231e-01 -1.25623971e-01 -6.81925595e-01 -5.95517635e-01 -7.55598307e-01 4.71023947e-01 1.65406987e-02 3.61357778e-01 4.44287546e-02 7.13188827e-01 4.69202757e-01 -1.24423254e+00 1.25310674e-01 1.18993187e+00 1.02628124e+00 5.59737146e-01 3.96162689e-01 -6.17264986e-01 1.20856237e+00 -6.08834684e-01 -3.48819256e-01 1.65457875e-02 -4.09283996e-01 -3.97243619e-01 1.31208256e-01 3.26266438e-01 -3.42738807e-01 -8.88468623e-01 1.20695877e+00 6.80528104e-01 2.63651788e-01 5.30027866e-01 -8.41717422e-01 -9.20443535e-01 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550ec98c-f9b9-4a1e-9f18-3efebc3518cb	pvt-cov19d-pyramid-vision-transformer-for	2206.15069	null	https://arxiv.org/abs/2206.15069v1	https://arxiv.org/pdf/2206.15069v1.pdf	PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis	With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models, then sampled the images in the dataset according to normal distribution, and fed the sampling results into the modified PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.	['Zhaoyan Yan', 'Rui Zhou', 'Tianyi Wang', 'Jiaxin Fan', 'Hanzhang Li', 'Xiaorun Tang', 'Jiaxuan Fang', 'Lilang Zheng']	2022-06-30	null	null	null	null	['covid-19-detection']	['medical']	[-3.65978852e-02 -4.62825835e-01 5.23681343e-02 -2.54749298e-01 -2.30567962e-01 -2.29215682e-01 3.13263535e-01 -3.65152508e-01 -1.94071800e-01 4.69467491e-01 -5.44773117e-02 -4.28632766e-01 -2.89374202e-01 -7.50764132e-01 -1.56528234e-01 -6.97547019e-01 -1.35672927e-01 1.06160712e+00 7.52533972e-01 4.04452950e-01 -2.32376590e-01 4.32787150e-01 -1.02226830e+00 1.59011513e-01 7.08592832e-01 1.14986157e+00 5.97769022e-01 7.12873042e-01 9.52475816e-02 8.61741543e-01 -6.17545068e-01 -3.05824488e-01 2.16171160e-01 -4.65641230e-01 -5.65470695e-01 1.38121173e-01 -4.16013300e-01 -7.29103386e-01 -3.10871005e-01 8.18788409e-01 4.21028465e-01 -1.54133335e-01 1.11511791e+00 -1.29595017e+00 -2.93787688e-01 5.70534170e-02 -6.55297637e-01 5.99457741e-01 -1.51043748e-02 2.38134444e-01 2.94301301e-01 -9.35148299e-01 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5ac2f329-ad99-41e1-9ac9-651ae84f1841	modnet-v-improving-portrait-video-matting-via	2109.11818	null	https://arxiv.org/abs/2109.11818v1	https://arxiv.org/pdf/2109.11818v1.pdf	MODNet-V: Improving Portrait Video Matting via Background Restoration	To address the challenging portrait video matting problem more precisely, existing works typically apply some matting priors that require additional user efforts to obtain, such as annotated trimaps or background images. In this work, we observe that instead of asking the user to explicitly provide a background image, we may recover it from the input video itself. To this end, we first propose a novel background restoration module (BRM) to recover the background image dynamically from the input video. BRM is extremely lightweight and can be easily integrated into existing matting models. By combining BRM with a recent image matting model, MODNet, we then present MODNet-V for portrait video matting. Benefited from the strong background prior provided by BRM, MODNet-V has only 1/3 of the parameters of MODNet but achieves comparable or even better performances. Our design allows MODNet-V to be trained in an end-to-end manner on a single NVIDIA 3090 GPU. Finally, we introduce a new patch refinement module (PRM) to adapt MODNet-V for high-resolution videos while keeping MODNet-V lightweight and fast.	['Rynson W. H. Lau', 'Huchuan Lu', 'Lihe Zhang', 'Zhanghan Ke', 'Jiayu Sun']	2021-09-24	null	null	null	null	['image-matting', 'video-matting']	['computer-vision', 'computer-vision']	[ 4.67782617e-01 -1.64996326e-01 2.37455871e-02 -6.27310649e-02 -3.78031105e-01 -2.88041472e-01 3.22426498e-01 -2.32783407e-01 -3.04728389e-01 4.65885848e-01 -1.39752924e-01 -2.83180654e-01 5.54266870e-01 -8.15519750e-01 -9.71721113e-01 -5.19706249e-01 4.55577105e-01 1.93307593e-01 4.82901931e-01 -1.89594239e-01 -1.81772470e-01 1.68222532e-01 -1.39505255e+00 4.19668376e-01 1.08569670e+00 8.77514958e-01 5.34397423e-01 7.44603872e-01 -1.38133898e-01 1.04813182e+00 -4.05532449e-01 -4.71653461e-01 5.65779150e-01 -4.70608175e-01 -4.88783538e-01 6.72518909e-01 1.24687421e+00 -8.30225110e-01 -6.82308733e-01 1.04430723e+00 1.45323157e-01 6.12910278e-02 2.45712787e-01 -1.27669966e+00 -2.54945308e-01 4.61751580e-01 -9.05513883e-01 2.26449683e-01 2.03660309e-01 4.03657198e-01 4.94258463e-01 -9.06310022e-01 6.92743778e-01 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6187d43f-3241-4c62-b288-0b8c829c9074	multi-stage-fault-warning-for-large-electric	1903.06700	null	http://arxiv.org/abs/1903.06700v1	http://arxiv.org/pdf/1903.06700v1.pdf	Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning	In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electric grid fault detection, classification, subgroup discovery, and visualization. In the first stage, a computationally efficient anomaly detection method based on quartiles detects the presence of a fault in real time. In the second stage, the fault is classified into one of nine pre-defined disaster scenarios. The time series data are first mapped to highly discriminative features by applying dimensionality reduction based on temporal autocorrelation. The features are then mapped through one of three classification techniques: support vector machine, random forest, and artificial neural network. Finally in the third stage, intra-class clustering based on dynamic time warping is used to characterize the fault with further granularity. Results on the Bonneville Power Administration electric grid data show that i) the proposed anomaly detector is both fast and accurate; ii) dimensionality reduction leads to dramatic improvement in classification accuracy and speed; iii) the random forest method offers the most accurate, consistent, and robust fault classification; and iv) time series within a given class naturally separate into five distinct clusters which correspond closely to the geographical distribution of electric grid buses.	['Ernest Fokoué', 'Sanjeev Raja']	2019-03-15	null	null	null	null	['subgroup-discovery']	['methodology']	[-1.71742961e-01 -7.08299339e-01 3.65315199e-01 -8.29259381e-02 -4.26329762e-01 -6.28930867e-01 5.33725739e-01 6.93720818e-01 7.29089901e-02 6.08495772e-01 -5.00603989e-02 -4.57864046e-01 -7.44375050e-01 -9.39412773e-01 2.66655862e-01 -1.07651365e+00 -7.84711123e-01 6.29231453e-01 1.85205892e-01 -9.99826044e-02 4.48745340e-01 1.09540880e+00 -1.47738397e+00 4.85870838e-02 1.18750179e+00 1.36244595e+00 -7.54954666e-02 4.27732229e-01 1.52688622e-01 3.57437044e-01 -9.58731174e-01 6.47580445e-01 2.87017465e-01 -3.90234411e-01 -6.87678576e-01 1.82624012e-01 -5.50822258e-01 -1.17775008e-01 -1.05640821e-01 9.24367547e-01 3.45726252e-01 3.22330832e-01 6.85817242e-01 -1.54414904e+00 1.90796778e-01 2.23013163e-01 -6.20580912e-01 6.66954339e-01 3.95317644e-01 -9.74331573e-02 7.15041578e-01 -7.57779121e-01 1.88620970e-01 6.63018107e-01 5.78912079e-01 -3.46687376e-01 -1.28109848e+00 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4.66882706e-01 3.12592626e-01 -6.69199646e-01 -7.12095022e-01 -1.12606041e-01]	[6.347107410430908, 2.5676074028015137]
f4f2148a-95b8-406b-b105-d0c1700fd188	efficient-neural-architecture-search-for	2303.13653	null	https://arxiv.org/abs/2303.13653v1	https://arxiv.org/pdf/2303.13653v1.pdf	Efficient Neural Architecture Search for Emotion Recognition	Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. Moreover, identifying the minute feature variations in facial regions for both macro and micro-expressions requires expertise in network design. In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition. To the best of our knowledge, this is the first attempt to design a NAS-based solution for both macro and micro-expression recognition. We produce lightweight models with a gradient-based architecture search algorithm. To maintain consistency between macro and micro-expressions, we utilize dynamic imaging and convert microexpression sequences into a single frame, preserving the spatiotemporal features in the facial regions. The EmoNAS has evaluated over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the existing state-of-the-art methods and perform very well in terms of speed and space complexity.	['Santosh Kumar Vipparthi', 'Yashwanth Reddy Meedimale', 'Satish Kumar Reddy', 'Murari Mandal', 'Monu Verma']	2023-03-23	null	null	null	null	['micro-expression-recognition', 'architecture-search']	['computer-vision', 'methodology']	[-2.97860265e-01 -5.54530501e-01 1.56880826e-01 -7.00229228e-01 -3.98003131e-01 -2.17625692e-01 3.46910387e-01 -4.05115545e-01 -4.92584974e-01 6.09057963e-01 -2.07386956e-01 4.21052247e-01 8.92410949e-02 -2.25203738e-01 -3.56016338e-01 -8.06940675e-01 -1.87095344e-01 3.46814208e-02 -3.29369903e-01 -5.05525351e-01 -1.39847770e-01 7.68117309e-01 -1.85853231e+00 4.59187388e-01 4.75099117e-01 1.74907315e+00 -2.70820975e-01 4.42590028e-01 -1.72975808e-01 9.74448085e-01 -4.20497268e-01 -7.62481570e-01 5.03528863e-02 -6.72031939e-01 -5.71649075e-01 -6.21678121e-02 3.97697806e-01 1.15201846e-02 7.97257479e-03 1.10888267e+00 9.44363236e-01 4.37070169e-02 6.41044974e-01 -1.52418411e+00 -2.57456899e-01 -1.45064339e-01 -8.26673448e-01 -1.62486117e-02 2.50611752e-01 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c7402001-821c-4e38-b788-50b7a92bbace	uolo-automatic-object-detection-and	1810.05729	null	http://arxiv.org/abs/1810.05729v1	http://arxiv.org/pdf/1810.05729v1.pdf	UOLO - automatic object detection and segmentation in biomedical images	We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.	['Aurélio Campilho', 'Ana Maria Mendonça', 'Teresa Araújo', 'Adrian Galdran', 'Pedro Costa', 'Guilherme Aresta']	2018-10-09	null	null	null	null	['fovea-detection']	['medical']	[ 5.72471499e-01 3.42576236e-01 -8.34210142e-02 -3.18676591e-01 -7.02731133e-01 -4.69669133e-01 2.84587294e-01 6.02775097e-01 -7.72303462e-01 3.77739936e-01 -3.29162180e-01 -3.03890407e-01 7.53383413e-02 -6.14988387e-01 -5.25908470e-01 -7.01116502e-01 -6.14815503e-02 6.12106383e-01 7.16792703e-01 4.66939121e-01 3.84629101e-01 8.79638791e-01 -1.94996369e+00 4.87706035e-01 9.73427296e-01 1.32282162e+00 4.47189569e-01 9.09252346e-01 -6.80948496e-02 3.98154259e-01 -4.10171181e-01 -9.90383923e-02 2.41872251e-01 -3.18532288e-01 -1.18270302e+00 8.00662637e-01 8.13253522e-01 -8.42329264e-02 3.03443462e-01 1.18153656e+00 5.44265270e-01 -1.40019953e-01 9.76562381e-01 -4.95961756e-01 -8.28631222e-03 1.63848355e-01 -4.39379185e-01 6.68960154e-01 -4.97499518e-02 4.04266000e-01 8.86684239e-01 -8.06176543e-01 8.67012143e-01 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5.47274292e-01 -6.55802011e-01 -2.69203514e-01 -6.41549468e-01 -8.06187391e-01 1.89405844e-01 5.60854554e-01 -5.83691478e-01 -1.29691169e-01 2.50989735e-01]	[15.758807182312012, -3.9084229469299316]
1d2c815b-86d4-4faa-bb79-7f0e6bb5400c	pose-guided-person-image-generation	1705.09368	null	http://arxiv.org/abs/1705.09368v6	http://arxiv.org/pdf/1705.09368v6.pdf	Pose Guided Person Image Generation	This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$^2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way. Extensive experimental results on both 128$\times$64 re-identification images and 256$\times$256 fashion photos show that our model generates high-quality person images with convincing details.	['Luc van Gool', 'Tinne Tuytelaars', 'Xu Jia', 'Qianru Sun', 'Liqian Ma', 'Bernt Schiele']	2017-05-25	pose-guided-person-image-generation-1	http://papers.nips.cc/paper/6644-pose-guided-person-image-generation	http://papers.nips.cc/paper/6644-pose-guided-person-image-generation.pdf	neurips-2017-12	['gesture-to-gesture-translation', 'pose-transfer']	['computer-vision', 'computer-vision']	[ 4.73154843e-01 2.39963025e-01 5.36838830e-01 -2.88875133e-01 -5.52406788e-01 -4.29435909e-01 6.09723926e-01 -5.49040496e-01 -5.05549014e-01 8.89355242e-01 -7.43960664e-02 2.82770723e-01 2.46467084e-01 -9.05597389e-01 -6.99461162e-01 -5.25522709e-01 4.71674725e-02 5.76305389e-01 -1.51767433e-01 -1.69320703e-01 -5.68958707e-02 3.43511343e-01 -1.26255417e+00 -2.04492186e-04 6.30672932e-01 8.85282993e-01 -1.34143129e-01 1.05206621e+00 5.00363052e-01 2.09464073e-01 -1.02354503e+00 -9.45239842e-01 9.19867277e-01 -7.17332959e-01 -5.95218122e-01 5.30722678e-01 9.61651504e-01 -6.12938583e-01 -5.13369262e-01 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65fc5d81-bb22-458a-a7c1-3105b95927cb	a-theoretical-investigation-of-graph-degree	1801.07889	null	http://arxiv.org/abs/1801.07889v3	http://arxiv.org/pdf/1801.07889v3.pdf	A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure	For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of the corresponding row's values in a similarity matrix. The measure is intuitive given the abnormal samples are usually rare and they are dissimilar to the rest of the data. In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem. We show that our analyses guide us to choose fully-connected graphs whose edge weights are calculated via universal kernels. We show that a simple graph degree based unsupervised anomaly detection method with the above properties, achieves higher accuracy compared to other unsupervised anomaly detection methods on average over 10 widely used datasets. We also provide an extensive analysis on the effect of the kernel parameter on the method's accuracy.	['Emre Aksu', 'Lixin Fan', 'Francesco Cricri', 'Caglar Aytekin']	2018-01-24	null	null	null	null	['spectral-graph-clustering']	['graphs']	[ 2.62453891e-02 1.83269918e-01 9.92771424e-03 -2.26423860e-01 2.35263072e-02 -3.59695792e-01 2.73647845e-01 6.71014488e-01 1.06037498e-01 1.17850378e-01 -1.21772185e-01 -2.07350984e-01 -5.70979118e-01 -9.02372539e-01 -1.58859700e-01 -7.96639621e-01 -8.07306111e-01 2.85625130e-01 3.90280157e-01 -5.81209213e-02 3.02196354e-01 7.29944825e-01 -9.95155632e-01 -3.82555902e-01 9.56261337e-01 8.09963703e-01 -6.68143868e-01 6.56288087e-01 -4.01274264e-02 6.83309257e-01 -3.16094518e-01 -5.81437171e-01 3.70352536e-01 -5.10080278e-01 -7.34180570e-01 3.37636709e-01 3.57637137e-01 1.05331764e-01 -4.86819088e-01 1.41666746e+00 2.25239158e-01 1.25165671e-01 1.29681873e+00 -1.64051735e+00 -4.85074997e-01 4.69470948e-01 -8.41583550e-01 4.48652416e-01 4.09436613e-01 -2.21105009e-01 1.07506514e+00 -4.78161663e-01 3.83431107e-01 8.40675056e-01 7.28855073e-01 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