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Transferable Contrastive Network for Generalized Zero-Shot Learning

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

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

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

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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

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

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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

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

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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

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

An insight into acupoints and meridians in human body based on interstitial fluid circulation

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arXiv 2020年

作者： Li, Hongyi Yin, Yajun Hu, Jun Li, Hua Wang, Fang Ji, Fusui Ma, Chao Cardiology Department Beijing Hospital National Center of Gerontology Beijing China Department of Engineering Mechanics Tsinghua University Beijing China Key Lab of Interfacial Physics and Technology Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Key Lab of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Beijing China Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Department of Human Anatomy Histology and Embryology School of Basic Medicine Peking Union Medical College Beijing China Chinese Institute for Brain Research Beijing China

The atlas of human acupoints and meridians has been utilized in clinical practice for almost a millennium although the anatomical structures and functions remain to be clarified. It has recently been reported that a long-distance interstitial fluid (ISF) circulatory pathway may originate from the acupoints in the extremities. As observed in living human subjects, cadavers and animals using magnetic resonance imaging and fluorescent tracers, the ISF flow pathways include at least 4 types of anatomical structures: the cutaneous-, perivenous-, periarterial-, and neural-pathways. Unlike the blood or lymphatic vessels, these ISF flow pathways are composed of highly ordered and topologically connected interstitial fibrous connective tissues that may work as guiderails for the ISF to flow actively over long distance under certain driving forces. Our experimental results demonstrated that most acupoints in the extremity endings connect with one or more ISF flow pathways and comprise a complex network of acupoint-ISF-pathways. We also found that this acupoint-ISF-pathway network can connect to visceral organs or tissues such as the pericardium and epicardium, even though the topographical geometry in human extremities does not totally match the meridian lines on the atlas that is currently used in traditional Chinese medicine. Based on our experimental data, the following working hypotheses are proposed: 1, there are one or more ISF flow pathways, including at least one cutaneous pathway, originated from an acupoint on the body surface. 2, the acupoints on the body surface specifically connect with certain visceral organs or tissues via ISF flow. And 3, the acupoint-originated ISF pathways constitute a complex connective network and can modulate the ISF and bio-signals in the microenvironments around cells in certain visceral organs or tissues from body surfaces. A comprehensive atlas will be constructed to systemically reveal the detailed anatomical structures of the acupoi

关键词： Histology

Automated Selection of Evolutionary Multi-objective Optimization Algorithms

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Automated Selection of Evolutionary Multi-objective Optimiza...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ye Tian Shichen Peng Tobias Rodemann Xingyi Zhang Yaochu Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Honda Research Institute Europe Offenbach/Main Germany Department of Computer Science University of Surrey Guildford Surrey United Kingdom

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In the last two decades, many evolutionary algorithms have shown promising performance in solving a variety of multi-objective optimization problems (MOPs). Since there does not exist an evolutionary algorithm having the best performance on all the MOPs, it is unreasonable to use a single evolutionary algorithm to tackle all the MOPs. Since many real-world MOPs are computationally expensive, selecting the best evolutionary algorithm from multiple candidates via empirical comparisons is also impractical. To address the above issues, this paper proposes an automated algorithm selection method for choosing the most suitable evolutionary algorithm for a given MOP. The proposed method establishes a predictor based on the performance of a set of candidate evolutionary algorithms on multiple benchmark MOPs, where the inputs of the predictor are the explicit and implicit features of an MOP, and the output is the index of the evolutionary algorithm having the best performance on the MOP. Experimental results indicate that the evolutionary algorithm suggested by the proposed method is highly competitive among all the candidate evolutionary algorithms, demonstrating the practical value of the proposed method for engineers to select an evolutionary algorithm for their applications.

关键词： Evolutionary computation Benchmark testing Feature extraction Optimization Prediction algorithms Convergence Linear programming

Weakly Supervised Object Detection With Segmentation Collaboration

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Weakly Supervised Object Detection With Segmentation Collabo...

International Conference on Computer Vision (ICCV)

作者： Xiaoyan Li Meina Kan 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 Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 53.7% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.

关键词： Proposals Object detection Feature extraction Collaboration Task analysis Detectors Image segmentation

MAC-AC: A Novel Distributed MAC Protocol for Accessing Channel in Vehicular Ad Hoc Networks

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MAC-AC: A Novel Distributed MAC Protocol for Accessing Chann...

IEEE Conference on Vehicular technology (VTC)

作者： Baozhu Li Fen Hou Changyue Zhang Shaohua Li Shujuan Ji Shanzhi Chen School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China Faculty of Science and Technology University of Macau Macau China Automotive Research Institute China National Heavy Duty Truck Group Co. Ltd Jinan China Shandong Key Laboratory of Wisdom Mine Information Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Lab of Wireless Mobile Communication Beijing China China Academy of Telecommunication Technology Beijing China

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

关键词： Vehicular and wireless technologies Time division multiple access Network topology Vehicular ad hoc networks Media Access Protocol Topology Vehicle dynamics