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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly availab.e benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image computing and Computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Learning compact target-oriented feature representations for visual tracking

arXiv

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arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

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Reverberant speech recognition with actual cochlear implants: Verifying a pulsatile vocoder simulation method 23

Reverberant speech recognition with actual cochlear implants...

引用

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Kong, Fanhui Wang, Xianren Teng, Xiangbin Zheng, Nengheng Yu, Guangzheng Meng, Qinglin Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering Shenzhen University Shenzhen China Department of Otorhinolaryngology First Affiliated Hospital Sun Yat-Sen University Institute of Otorhinolaryngology Sun Yat-Sen University Guangzhou China Department of Neuroscience Max-Planck-Institute for Empirical Aesthetics Frankfurt Germany Acoustics Lab of School of Physics and Optoelectronics State Key Laboratory of Subtropical Building Science South China University of Technology Guangzhou China

ISBN: (纸本)9783939296157

Classic channel vocoders have been widely used for simulating cochlear implants (CIs). However, some important features of state-of-the-art CI strategies were usually ignored, such as pulsatile current, broad current spread, and low quantization resolution of intensity. To solve this problem, a novel vocoder was proposed, which is combined from Gaussian-shaped acoustic pulses each corresponding to an electric pulse and can directly transformed CI-electrodograms to sounds. Compared with the classic channel vocoders on a reverberant speech recognition task, this new vocoder was considerably more sensitive to the reverberant condition (published in APSIPA-2018). Moreover, its performance is comparable to actual CI users in previous CI-reverberation studies. Here, the same paradigm as in the APSIPA-2018 paper was tested on actual CI users. We found that under reverberant conditions the CI users got comparable scores to the simulated performance from the new vocoder simulation in the NH cohort, while the classic vocoder predicted significantly much higher performance This study demonstrated that the classic vocoders overestimated the task performance and further lent great support to the usage of the novel vocoder for the simulation of reverberant speech recognition in adult post-lingually deafened CI users. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Cochlear implant Reverberation Vocoder

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Transferable Contrastive Network for Generalized Zero-Shot Learning

Transferable Contrastive Network for Generalized Zero-Shot L...

引用

International Conference on Computer Vision (ICCV)

作者： Huajie Jiang Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Institute of Microsystem and Information Technology CAS Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes in generalized zero-shot learning (GZSL) task, which indicates that they learn little knowledge about target classes. To tackle such problem, we propose a novel Transferable Contrastive Network (TCN) that explicitly transfers knowledge from the source classes to the target classes. It automatically contrasts one image with different classes to judge whether they are consistent or not. By exploiting the class similarities to make knowledge transfer from source images to similar target classes, our approach is more robust to recognize the target images. Experiments on five benchmark datasets show the superiority of our approach for GZSL.

关键词： Semantics Visualization Image recognition Robustness Target recognition Task analysis Fuses

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How to write high-quality news on social network? Predicting news quality by mining writing style

arXiv

引用

arXiv 2019年

作者： Yang, Yuting Cao, Juan Lu, Mingyan Li, Jintao Lin, Chia-Wen Key Laboratory of Intelligent Information Processing Center for Advanced Computing Research Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Department of Electrical Engineering National Tsing Hua University Taiwan

Rapid development of Internet technologies promotes traditional newspapers to report news on social networks. However, people on social networks may have different needs which naturally arises the question: whether can we analyze the influence of writing style on news quality automatically and assist writers in improving news quality? It's challenging due to writing style and 'quality' are hard to measure. First, we use 'popularity' as the measure of 'quality'. It is natural on social networks but brings new problems: popularity are also influenced by event and publisher. So we design two methods to alleviate their influence. Then, we proposed eight types of linguistic features (53 features in all) according eight writing guidelines and analyze their relationship with news quality. The experimental results show these linguistic features influence greatly on news quality. Based on it, we design a news quality assessment model on social network (SNQAM). SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to. Copyright © 2019, The Authors. All rights reserved.

关键词： Linguistics

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DeepACE: Automated chromosome enumeration in metaphase cell images using deep convolutional neural networks

arXiv

引用

arXiv 2019年

作者： Xiao, Li Luo, Chunlong Luo, Yufan Yu, Tianqi Tian, Chan Qiao, Jie Zhao, Yi Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China Beijing China

Chromosome enumeration is an important but tedious procedure in karyotyping analysis. In this paper, to automate the enumeration process, we developed a chromosome enumeration framework, DeepACE, based on the region based object detection scheme. Firstly, the ability of region proposal network is enhanced by a newly proposed Hard Negative Anchors Sampling to extract unapparent but important information about highly confusing partial chromosomes. Next, to alleviate serious occlusion problems, we novelly introduced a weakly-supervised mechanism by adding a Template Module into classification branch to heuristically separate overlapped chromosomes. The template features are further incorporated into the NMS procedure to further improve the detection of overlapping chromosomes. In the newly collected clinical dataset, the proposed method outperform all the previous method, yielding an mAP with respect to chromosomes as 99.45, and the error rate is about 2.4%. Copyright © 2019, The Authors. All rights reserved.

关键词： Chromosomes

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A computational framework for Karl Popper's logic of scientific discovery

引用

Science China(information Sciences) 2018年第4期61卷 97-106页

作者： Wei LI Yuefei SUI State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

Belief revision is both a philosophical and logical problem. From Popper's logic of scientific discovery, we know that revision is ubiquitous in physics and other sciences. The AGM postulates and Rcalculus are approaches from logic, where the R-calculus is a Gentzen-type concrete belief revision *** deduction is undecidable in first-order logic, we apply approximate deduction to derive an R-calculus that is computational and has finite injury. We further develop approximation algorithms for SAT problems to derive a feasible R-calculus based on the relation between deduction and satisfiability. In this manner, we provide a full spectrum of belief revision: from philosophical to feasible revision.

关键词： belief revision logic of scientific discovery approximate deduction approximation algorithms feasible computation

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

引用

arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Corrections to "Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning"

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IEEE transactions on neural networks and learning systems 2021年第8期32卷 3784页

作者： Nan Lai Meina Kan Chunrui Han Xingguang Song Shiguang Shan Key Laboratory of Intelligent Information Processing Institute of Computing Technology (ICT) Chinese Academy of Sciences (CAS) Beijing China University of Chinese Academy of Sciences Beijing China Huawei Technologies Company Ltd. Shenzhen China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

In the above article [1], the results of "Fully-supervised (Upper bound)" in Tables III and IV were inadvertently set to intermediate records that were used as placeholders. This error has no effect on any of the interpretations and conclusions. Tables I and II of this amendment show the corrected results (highlighted in italics) of the original Tables III and IV.

关键词： Predictive models Feature extraction Deep learning Learning systems Adaptive systems

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Modeling semantic relationship in multi-turn conversations with hierarchical latent variables

arXiv

引用

arXiv 2019年

作者： Shen, Lei Feng, Yang Zhan, Haolan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It's practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content. However, little work focuses on such hierarchical relationship among utterances. To address this problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model contains latent variables in three hierarchies. The discourse-level one captures the global background, the pair-level one stands for the common topic information between query and response, and the utterance-level ones try to represent differences in content. Experimental results show that our model significantly improves the quality of responses in terms of fluency, coherence and diversity compared to baseline methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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