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16th European Conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Towards Accurate Scene Text recognition With Semantic Reasoning Networks

Towards Accurate Scene Text Recognition With Semantic Reason...

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Conference on computer vision and pattern recognition (CVPR)

作者： Deli Yu Xuan Li Chengquan Zhang Tao Liu Junyu Han Jingtuo Liu Errui Ding School of Artificial Intelligence University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Vision Technology(VIS) Baidu Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to assist text recognition attracts less attention, only RNN-like structures are explored to implicitly model semantic information. However, we observe that RNN based methods have some obvious shortcomings, such as time-dependent decoding manner and one-way serial transmission of semantic context, which greatly limit the help of semantic information and the computation efficiency. To mitigate these limitations, we propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission. The state-of-the-art results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method. In addition, the speed of SRN has significant advantages over the RNN based methods, demonstrating its value in practical use.

关键词： Semantics Visualization Text recognition Cognition Feature extraction Decoding Robustness

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ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents recognition and Verification (SASIGCOM 2020)

ICFHR 2020 Competition on Short answer ASsessment and Thai s...

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International Workshop on Frontiers in Handwriting recognition

作者： Abhijit Das Hemmaphan Suwanwiwat Umapada Pal Michael Blumenstein Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Information Technology Academy James Cook University Cairns Australia School of Software University of Technology Sydney Australia

ISBN: (数字)9781728199665

ISBN: (纸本)9781728199672

This paper describes the results of the competition on Short answer ASsessment and Thai student SIGnature and Name COMponents recognition and Verification (SASIGCOM 2020) in conjunction with the 17th International Conference on Frontiers in Handwriting recognition (ICFHR 2020). The competition was aimed to automate the evaluation process short answer-based examination and record the development and gain attention to such system. The proposed competition contains three elements which are short answer assessment (recognition and marking the answers to short-answer questions derived from examination papers), student name components (first and last names) and signature verification and recognition. Signatures and name components data were collected from 100 volunteers. For the Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. With Thai name components dataset, there are 30 genuine and 12 skilfully forged name components for each writer. There are 104 exam papers in the short answer assessment dataset, 52 of which were written with cursive handwriting; the rest of 52 papers were written with printed handwriting. The exam papers contain ten questions, and the answers to the questions were designed to be a few words per question. Three teams from distinguished labs submitted their systems. For short answer assessment, word spotting task was also performed. This paper analysed the results produced by their algorithms using a performance measure and defines a way forward for this subject of research. Both the datasets, along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.

关键词： Task analysis Handwriting recognition Training Particle measurements Atmospheric measurements Information technology Writing

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Self-slimmed vision Transformer

arXiv

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arXiv 2021年

作者： Zong, Zhuofan Li, Kunchang Song, Guanglu Wang, Yali Qiao, Yu Leng, Biao Liu, Yu School of Computer Science and Engineering Beihang University China SenseTime Research China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Shanghai AI Laboratory China

vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping insignificant tokens to reduce the computational cost of ViTs. But when the dropping ratio increases, this hard manner will inevitably discard the vital tokens, which limits its efficiency. To solve the issue, we propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation. As a general method of token hard dropping, our TSM softly integrates redundant tokens into fewer informative ones. It can dynamically zoom visual attention without cutting off discriminative token relations in the images, even with a high slimming ratio. Furthermore, we introduce a concise Feature Recalibration Distillation (FRD) framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the unstructured token in a flexible auto-encoder manner. Due to the similar structure between teacher and student, our FRD can effectively leverage structure knowledge for better convergence. Finally, we conduct extensive experiments to evaluate our SiT. It demonstrates that our method can speed up ViTs by 1.7× with negligible accuracy drop, and even speed up ViTs by 3.6× while maintaining 97% of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet. Code is available at https://***/Sense-X/SiT. © 2021, CC BY.

关键词： Distillation

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MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

arXiv

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arXiv 2021年

作者： Srivastava, Abhishek Jha, Debesh Chanda, Sukalpa Pal, Umapada Johansen, Håvard D. Johansen, Dag Riegler, Michael A. Ali, Sharib Halvorsen, Pål Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India SimulaMet Oslo Norway UiT The Arctic University of Norway Tromsø Norway Østfold University College Halden Norway Indian Statistical Institute Kolkata India The Department of Engineering Science University of Oxford Oxford NIHR Biomedical Research Centre Oxford United Kingdom Oslo Metropolitan University Oslo Norway

Methods based on convolutional neural networks have improved the performance of biomedical image segmentation However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, they usually use complex models that are more suitable for general semantic segmentation problems In this paper, we propose a novel architecture called Multi-Scale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation The proposed MSRF-Net is able to exchange multi-scale features of varying receptive fields using a Dual-Scale Dense Fusion (DSDF) block Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion This allows the preservation of resolution, improved information flow and propagation of both high-and low-level features to obtain accurate segmentation maps The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets Extensive experiments on MSRF-Net demonstrate that the proposed method outperforms the cutting-edge medical image segmentation methods on four publicly available datasets We achieve the Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824 on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl dataset, and ISIC-2018 skin lesion segmentation challenge dataset respectively We further conducted generalizability tests and achieved DSC of 0.7921 and 0.7575 on CVC-ClinicDB and Kvasir-SEG, respectively. © 2021, CC BY.

关键词： Image enhancement

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ICDAR 2019 CROHME + TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection 15

ICDAR 2019 CROHME + TFD: Competition on recognition of handw...

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15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Mahdavi, Mahshad Zanibbi, Richard Mouchere, Harold Viard-Gaudin, Christian Garain, Utpal Document and Pattern Recognition Lab Rochester Institute of Technology RochesterNY United States Christian Viard-Gaudin LS2N-UMR CNRS 6004 University of Nantes Nantes France Computer Vision and Pattern Recognition Unit Centre for Artif. Intel. and Mach. Leaning Indian Statistical Institute Kolkata India

ISBN: (纸本)9781728128610

We summarize the tasks, protocol, and outcome for the 6th Competition on recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME + TFD 2019, participants chose between two tasks for recognizing handwritten formulas from 1) online stroke data, or 2) images generated from the handwritten strokes. To compare LATEX strings and the labeled directed trees over strokes (label graphs) used in previous CROHMEs, we convert LATEX and stroke-based label graphs to label graphs defined over symbols (symbol-level label graphs, or symLG). More than thirty (33) participants registered for the competition, with nineteen (19) teams submitting results. The strongest formula recognition results were produced by the USTC-iFLYTEK research team, for both stroke-based (81%) and image-based (77%) input. For the new typeset formula detection task, the Samsung R&D institute Ukraine (Team 2) obtained a very strong F-score (93%). System performance has improved since the last CROHME-still, the competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task. © 2019 IEEE.

关键词： Trees (mathematics)

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Fast Texture Synthesis via Pseudo Optimizer

Fast Texture Synthesis via Pseudo Optimizer

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Conference on computer vision and pattern recognition (CVPR)

作者： Wu Shi Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Texture synthesis using deep neural networks can generate high quality and diversified textures. However, it usually requires a heavy optimization process. The following works accelerate the process by using feed-forward networks, but at the cost of scalability. diversity or quality. We propose a new efficient method that aims to simulate the optimization process while retains most of the properties. Our method takes a noise image and the gradients from a descriptor network as inputs, and synthesize a refined image with respect to the target image. The proposed method can synthesize images with better quality and diversity than the other fast synthesis methods do. Moreover, our method trained on a large scale dataset can generalize to synthesize unseen textures.

关键词： Neural networks Training Transforms Scalability Optimization methods Convolution

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Face recognition - A one-shot learning perspective 15

Face recognition - A one-shot learning perspective

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15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019

作者： Chanda, Sukalpa Gv, Asish Chakrapani Brun, Anders Hast, Anders Pal, Umapada Doermann, David Department of Information Technology Østfold University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Centre for Image Analysis Uppsala University Sweden Computer Science and Engineering University at Buffalo United States

ISBN: (纸本)9781728156866

Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems. © 2019 IEEE.

关键词： Face recognition

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Smallbignet: Integrating core and contextual views for video classification

arXiv

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arXiv 2020年

作者： Li, Xianhang Wang, Yali Zhou, Zhipeng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of learning video representation. To alleviate this problem, we propose a concise and novel SmallBig network, with the cooperation of small and big views. For the current time step, the small view branch is used to learn the core semantics, while the big view branch is used to capture the contextual semantics. Unlike traditional temporal convolution, the big view branch can provide the small view branch with the most activated video features from a broader 3D receptive field. Via aggregating such big-view contexts, the small view branch can learn more robust and discriminative spatio-temporal representations for video classification. Furthermore, we propose to share convolution in the small and big view branch, which improves model compactness as well as alleviates overfitting. As a result, our SmallBigNet achieves a comparable model size like 2D CNNs, while boosting accuracy like 3D CNNs. We conduct extensive experiments on the large-scale video benchmarks, e.g., Kinetics400, Something-Something V1 and V2. Our SmallBig network outperforms a number of recent state-of-the-art approaches, in terms of accuracy and/or efficiency. The codes and models will be available on https://***/xhl-video/SmallBigNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Collaborative Multi-View Convolutions With Gating For Accurate And Fast Volumetric Medical Image Segmentation

Collaborative Multi-View Convolutions With Gating For Accura...

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IEEE International Symposium on Biomedical Imaging

作者： Cheng Li Jin Ye Junjun He Shanshan Wang Lixu Gu Yu Qiao Paul C. Lauterbur Research Center for Biomedical Imaging SIAT CAS Shenzhen China Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab SIAT CAS Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Biomedical Engineering/the Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665412469;9781665429474

Due to their high capacity in capturing 3D spatial information, 3D Fully Convolutional Neural Networks (3D FCNs), especially 3D U-Net, are prevalent for volumetric medical image segmentation. However, 3D convolutions are much more computationally complex than 2D convolutions and thus, are more prone to overfitting. This paper proposes Collaborative Multi-View convolutions (CMV convs) that can keep the model complexity similar to those employing 2D convolutions while capturing the 3D spatial context like 3D convolutions. Specifically, CMV convs simultaneously extract information from three orthogonal views with three parameter-shared 2D convolutions. A Global-Guided Gating mechanism (3G) is further designed that selectively passes information from CMV convs to the next stage. Combined with 3G, a CMV conv become a G-CMV conv that constitutes a plug-and-play module, which can be easily integrated into various 3D CNNs for image segmentation. Extensive experiments utilizing BraTS18 dataset have been conducted. Our method achieves competitive results compared to state-of-the-art methods with over 10× fewer parameters than 3D-UNet.

关键词： Solid modeling Image segmentation Interpolation Three-dimensional displays Computational modeling Biological system modeling Collaboration

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