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International Conference on Computer Vision (ICCV)

作者： Zunlei Feng Zhonghua Wang Xinchao Wang Yining Mao Thomas Li Jie Lei Yuexuan Wang Mingli Song Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot Joint Research Institute of Frontier Technologies Alibaba-Zhejiang University National University of Singapore The University of Hong Kong Zhejiang University of Technology

ISBN: (纸本)9781665428132

Detection and segmentation of nuclei are fundamental analysis operations in pathology images, the assessments derived from which serve as the gold standard for cancer diagnosis. Manual segmenting nuclei is expensive and time-consuming. What’s more, accurate segmentation detection of nuclei can be challenging due to the large appearance variation, conjoined and overlapping nuclei, and serious degeneration of histological structures. Supervised methods highly rely on massive annotated samples. The existing two unsupervised methods are prone to failure on degenerated samples. This paper proposes a Mutual-Complementing Framework (MCF) for nuclei detection and segmentation in pathology images. Two branches of MCF are trained in the mutual-complementing manner, where the detection branch complements the pseudo mask of the segmentation branch, while the progressive trained segmentation branch complements the missing nucleus templates through calculating the mask residual between the predicted mask and detected result. In the detection branch, two response map fusion strategies and gradient direction based postprocessing are devised to obtain the optimal detection response. Furthermore, the confidence loss combined with the synthetic samples and self-finetuning is adopted to train the segmentation network with only high confidence areas. Extensive experiments demonstrate that MCF achieves comparable performance with only a few nucleus patches as supervision. Especially, MCF possesses good robustness (only dropping by about 6%) on degenerated samples, which are critical and common cases in clinical diagnosis.

关键词： Image segmentation Pathology Computer vision Manuals Robustness Clinical diagnosis Cancer

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Efficient Medical Image Segmentation Based on Knowledge Distillation

arXiv

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arXiv 2021年

作者： Qin, Dian Bu, Jia-Jun Liu, Zhe Shen, Xin Zhou, Sheng Gu, Jing-Jun Wang, Zhi-Hua Wu, Lei Dai, Hui-Fen Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Ningbo Research Institute School of Software Technology Zhejiang University The Fourth Affiliated Hospital Zhejiang University School of Medicine

Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network. This architecture empowers the lightweight network to get a significant improvement on segmentation capability while retaining its runtime efficiency. We further devise a novel distillation module tailored for medical image segmentation to transfer semantic region information from teacher to student network. It forces the student network to mimic the extent of difference of representations calculated from different tissue regions. This module avoids the ambiguous boundary problem encountered when dealing with medical imaging but instead encodes the internal information of each semantic region for transferring. Benefited from our module, the lightweight network could receive an improvement of up to 32.6% in our experiment while maintaining its portability in the inference phase. The entire structure has been verified on two widely accepted public CT datasets LiTS17 and KiTS19. We demonstrate that a lightweight network distilled by our method has non-negligible value in the scenario which requires relatively high operating speed and low storage usage. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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An End-to-End OCR Text Re-organization Sequence Learning for Rich-Text Detail Image Comprehension 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Liangcheng Gao, Feiyu Bu, Jiajun Wang, Yongpan Yu, Zhi Zheng, Qi Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China Alibaba Group Hangzhou China Alibaba-Zhejiang University Joint Institute of Frontier Technologies Hangzhou China Ningbo Research Institute Zhejiang University Ningbo China

ISBN: (数字)9783030585952

ISBN: (纸本)9783030585945

Nowadays the description of detailed images helps users know more about the commodities. With the help of OCR technology, the description text can be detected and recognized as auxiliary information to remove the visually impaired users’ comprehension barriers. However, for lack of proper logical structure among these OCR text blocks, it is challenging to comprehend the detailed images accurately. To tackle the above problems, we propose a novel end-to-end OCR text reorganizing model. Specifically, we create a Graph Neural Network with an attention map to encode the text blocks with visual layout features, with which an attention-based sequence decoder inspired by the Pointer Network and a Sinkhorn global optimization will reorder the OCR text into a proper sequence. Experimental results illustrate that our model outperforms the other baselines, and the real experiment of the blind users’ experience shows that our model improves their comprehension. © 2020, Springer Nature Switzerland AG.

关键词： Global optimization

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Horizontal-to-vertical video conversion

arXiv

引用

arXiv 2021年

作者： Zhu, Tun Zhang, Daoxin Hu, Yao Wang, Tianran Jiang, Xiaolong Zhu, Jianke Li, Jiawei The College of Computer Science Zhejiang University Hangzhou310027 China The Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Company of Alibaba Youku Cognitive and Intelligent Lab

At this blooming age of social media and mobile platform, mass consumers are migrating from horizontal video to vertical contents delivered on hand-held devices. Accordingly, revitalizing the exposure of horizontal video becomes vital and urgent, which is hereby tackled by our automated horizontal-to-vertical (abbreviated as H2V) video conversion framework. Essentially, the H2V framework performs subject-preserving video cropping instantiated in the proposed Rank-SS module. Rank-SS incorporates object detection to discover the candidate subjects, from which we select the primary subject-to-preserve leveraging location, appearance, and salient cues in a convolutional neural network. In addition to converting horizontal videos vertically by cropping around the selected subject, automatic shot detection and multi-object tracking are integrated into the H2V framework to accommodate long and complex videos. To develop H2V systems, we collect an H2V-142K dataset containing 125 videos (132K frames) and 9,500 cover images annotated with primary subject bounding boxes. On H2V-142K and public object detection datasets, our method demonstrates promising results on the subject selection comparing to the related solutions. Furthermore, our H2V framework is industrially deployed hosting millions of daily active users and exhibits favorable H2V conversion performance. By making this dataset as well as our approach publicly available, we wish to pave the way for more horizontal-to-vertical video conversion research. Our collected H2V-142K dataset is available at H2V-142K website. © 2021, CC BY.

关键词： Video signal processing

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Cross-modal image retrieval with deep mutual information maximization

arXiv

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arXiv 2021年

作者： Gu, Chunbin Bu, Jiajun Zhou, Xixi Yao, Chengwei Ma, Dongfang Yu, Zhi Yan, Xifeng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou310007 China Institute of Marine Sensing and Networking Zhejiang University Hangzhou310058 China Alibaba-Zhejiang University Joint Institute of Frontier Technologies Hangzhou310007 China MOE Key Laboratory of Machine Perception Beijing100871 China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhejiang University Zhoushan316021 China Department of Computer Science University of California Santa BarbaraCA93106 United States

In this paper, we study the cross-modal image retrieval, where the inputs contain a source image plus some text that describes certain modifications to this image and the desired image. Prior work usually uses a three-stage strategy to tackle this task: 1) extract the features of the inputs;2) fuse the feature of the source image and its modified text to obtain fusion feature;3) learn a similarity metric between the desired image and the source image + modified text by using deep metric learning. Since classical image/text encoders can learn the useful representation and common pair-based loss functions of distance metric learning are enough for cross-modal retrieval, people usually improve retrieval accuracy by designing new fusion networks. However, these methods do not successfully handle the modality gap caused by the inconsistent distribution and representation of the features of different modalities, which greatly influences the feature fusion and the similarity learning. To alleviate this problem, we adopt the contrastive self-supervised learning method Deep InforMax (DIM) [1] to our approach to bridge this gap by enhancing the dependence between the text, the image, and their fusion. Specifically, our method narrows the modality gap between the text modality and the image modality by maximizing mutual information between their not exactly semantically identical representation. Moreover, we seek an effective common subspace for the semantically same fusion feature and desired image’s feature by utilizing Deep InforMax between the low-level layer of the image encoder and the high-level layer of the fusion network. Extensive experiments on three large-scale benchmark datasets show that we have bridged the modality gap between different modalities and achieve state-of-the-art retrieval performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Crowdsourcing-based web accessibility evaluation with golden maximum likelihood inference

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Proceedings of the ACM on Human-Computer Interaction 2018年第CSCW期2卷 1-21页

作者： Song, Shuyi Bu, Jiajun Artmeier, Andreas Shi, keyue Wang, Ye Yu, Zhi Wang, Can Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China

Web accessibility evaluation examines how well websites comply with accessibility guidelines which help people with disabilities to perceive, navigate and contribute to the Web. This demanding task usually requires manual assessment by experts with many years of training and experience. However, not enough experts are available to carry out the increasing number of evaluation projects while non-experts often have different opinions about the presence of accessibility barriers. Addressing these issues, we introduce a crowdsourcing system with a novel truth inference algorithm to derive reliable and accurate assessments from conflicting opinions of evaluators. Extensive evaluation on 23,901 complex tasks assessed by 50 people with and without disabilities shows that our approach outperforms state of the art approaches. In addition, we conducted surveys to identify frequent barriers that people with disabilities are facing in their daily lives and the difficulty to access Web pages when they encounter these barriers. The frequencies and severities of barriers correlate with their derived importance in our evaluation project. Copyright © ACM.

关键词： Crowdsourcing

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EagleQR: An Application in Accessing Printed Text for the Elderly and Low Vision People 7th

EagleQR: An Application in Accessing Printed Text for the El...

引用

7th CCF Academic Conference on BigData, CCF BigData 2019

作者： Yu, Zhi Bu, Jiajun Gu, Chunbin Song, Shuyi Li, Liangcheng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China Alibaba-Zhejiang University Joint Institute of Frontier Technologies Hangzhou China MOE Key Laboratory of Machine Perception Beijing China Ningbo Research Institute Zhejiang University Ningbo315100 China

ISBN: (纸本)9789811518980

Most people ignore the difficulties for the elderly and low vision people to access the printed text, which is of critical importance in their daily lives. To resolve this issue, an application named Voiceye is designed to get the audio text by scanning the QR code. But this application has the limitation on the length of the printed text and takes no account of information security. In this paper, we design an advanced application EagleQR that store encrypted URL instead of the whole printed text. On one hand, we convert the printed text into online version and then encrypt and compress its URL into the QR code on the server. On the other hand, we decompress and decrypt the data in the QR code and then get the audio text to the client. This application has significance to broaden the information access to the printed text for the elderly and low vision people. © Springer Nature Singapore Pte Ltd 2019.

关键词： Security of data

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MoRa: A LoRa-Based System for Timely and Energy-Efficient e-Price Tag Update in Markets

MoRa: A LoRa-Based System for Timely and Energy-Efficient e-...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yihui Wang Gonglong Chen Jiajun Bu Wei Dong College of Computer Science Zhejiang Provincial Key Laboratory of Service Robot

ISBN: (纸本)9781728125848

While numerous wireless applications are emerging to simplify our daily life, updating price tags in large-scale markets is still largely performed manually. Considering the potential adoption of electronic price (e-price) tags, we propose MoRa, a LoRa-based system for timely and energy-efficient price update in Markets. By leveraging the category information in the market, we design a hierarchical address scheme. Based on the scheme, we can perform efficient multicast with nearly zero overhead in group joining and activity scheduling. Furthermore, considering LoRa's characteristics and the asymmetry between the gateway and nodes, we let the gateway make fast and repetitive transmissions before the uncovered nodes send light weight NAKs. Extensive testbed experiments and simulation evaluations are conducted. Results demonstrate that MoRa can efficiently improve the performance in terms of update delay and energy consumption.

关键词： E-price tag LoRa Multicast Delay Energy Multicast Markets Mora doorways Energy sources delay Goal Orientation Energy efficiency TAGGING Ganglia

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Awareness and Settlement of IT Field key Challenges for Next Generation Development in World 2019

Awareness and Settlement of IT Field Key Challenges for Next...

引用

Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

作者： Muhammad Bilal Zhi Yu Abid Bashir Muhammad Ali Hussain Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China Information Technology Services Manager MIS / Deputy Director of IT Department of Computer Science National Textile University Faisalabad Pakistan Management Science and Engineering School of Management and Economics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781450376631

Information Technology (IT) is playing a vital role in all aspects of our lives nowadays. IT is usually used for fast communication links and transferring useful information from one place to another. Nevertheless, around the world, unfortunately, IT is not properly handled. Without IT and Information and Communication Technology (ICT) world will become a dark place. Most of the research in the IT field is inadequate. A subjective IT field development framework designed with the assistance of better Internet features utilized properly in all developing countries around the world. The proposed IT development framework can be used innovatively by adopting a wireless-based network in developing country growth around the world. We analyzed wired and wireless systems based on six common key Internet feature parameters for IT field improvement in developing countries around the world. In this empirical research, a total of 72 IT professional specialists included based on the statistical technique judgment sampling. The questionnaire survey results signify that speed, security, and reliability Internet features in the wired network are better than the wireless network. Moreover, mobility, coverage area, and cost relevant Internet features in the wireless network are much better than a wired network. According to expert decision-makers' strategy, the wireless-based network transmission medium is better to develop improvement in the world. Therefore, it finally concluded that developing countries could also play a significant role in the IT field as well as other fields around the world if Internet features are provided in the form of a wireless-based network properly. In the future, security and speed relevant problems will be resolved in the wireless-based system through improving routing algorithms.

关键词： Information Technology Internet Decision-makers Developing countries Transmission medium

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Awareness and settlement of IT field key challenges for next generation development in world 7

Awareness and settlement of IT field key challenges for next...

引用

7th International Conference on Information Technology: IoT and Smart City, ICIT 2019

作者： Bilal, Muhammad Yu, Zhi Bashir, Abid Hussain, Muhammad Ali Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou310027 China Information Technology Services Department of Computer Science National Textile University Faisalabad38000 Pakistan Management Science and Engineering School of Management and Economics University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781450376631

关键词： Developing countries

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