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arXiv 2023年

作者： Yu, Bruce X.B. Chang, Jianlong Wang, Haixin Liu, Lingbo Wang, Shijie Wang, Zhiyu Lin, Junfan Xie, Lingxi Li, Haojie Lin, Zhouchen Tian, Qi Chen, Chang Wen Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University China Huawei China National Engineering Research Center for Software Engineering Peking University China Peng Cheng Laboratory China College of Computer Science and Engineering Shandong University of Science and Technology China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: fine-tuning, prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning. Copyright © 2023, The Authors. All rights reserved.

关键词： Vision

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Targeting Requirements Violations of Autonomous Driving Systems by Dynamic Evolutionary Search 36

Targeting Requirements Violations of Autonomous Driving Syst...

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36th IEEE/ACM International Conference on Automated software Engineering, ASE 2021

作者： Luo, Yixing Zhang, Xiao-Yi Arcaini, Paolo Jin, Zhi Zhao, Haiyan Ishikawa, Fuyuki Wu, Rongxin Xie, Tao Peking University Ministry of Education Key Lab. of High-Confidence Software Technologies Beijing China Department of Computer Science and Technology School of EECS Peking University Beijing China National Institute of Informatics Tokyo Japan Xiamen University School of Informatics Xiamen China

ISBN: (纸本)9781665403375

Autonomous Driving Systems (ADSs) are complex systems that must satisfy multiple requirements such as safety, compliance to traffic rules, and comfortableness. However, satisfying all these requirements may not always be possible due to emerging environmental conditions. Therefore, the ADSs may have to make trade-offs among multiple requirements during the ongoing operation, resulting in one or more requirements violations. For ADS engineers, it is highly important to know which combinations of requirements violations may occur, as different combinations can expose different types of failures. However, there is currently no testing approach that can generate scenarios to expose different combinations of requirements violations. To address this issue, in this paper, we introduce the notion of requirements violation pattern to characterize a specific combination of requirements violations. Based on this notion, we propose a testing approach named EMOOD that can effectively generate test scenarios to expose as many requirements violation patterns as possible. EMOOD uses a prioritization technique to sort all possible patterns to search for, from the most to the least critical ones. Then, EMOOD iteratively includes an evolutionary many-objective optimization algorithm to find different combinations of requirements violations. In each iteration, the targeted pattern is determined by a dynamic prioritization technique to give preferences to those patterns with higher criticality and higher likelihood to occur. We apply EMOOD to an industrial ADS under two common traffic situations. Evaluation results show that EMOOD outperforms three baseline approaches in generating test scenarios by discovering more requirements violation patterns. © 2021 IEEE.

关键词： Iterative methods

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Context-aware Multi-level Question Embedding Fusion for visual question answering

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Information Fusion 2024年 102卷

作者： Li, Shengdong Gong, Chen Zhu, Yuqing Luo, Chuanwen Hong, Yi Lv, Xueqiang School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los Angeles CA90032 United States School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration 100083 China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing Information Science and Technology University 100101 China

Question model has been widely concerned as the cornerstone of constructing Visual Question Answering (VQA) models. Existing question models attempt to exploit word context to extract multi-level concepts for modeling multi-level questions. However, they still have many defects. For example, most question models utilize simple fusion methods to fuse shallow modules and extract parameter-unshared low-level concepts, leading to poor modeling of multi-level questions;although some question models use deep bidirectional Transformer encoder in external knowledge transfer and BERT for multi-level questions, their complexity is still high. To solve these issues, we propose a novel low-complex multi-level contextual question model, termed Context-aware Multi-level Question Embedding Fusion (CMQEF). We formalize its concepts and theories, deduce its modeling process, optimization process and feature extraction process, analyze its low complexity and high expressiveness, and prove that it defines a new way to solve parameter non-sharing for extracting parameter-shared multi-level concepts and optimize the tradeoff between expressiveness and complexity in question models. Extensive experiments on VQAv2 and VQA-CPv2 validate that comparing with the state-of-the-art, our CMQEF outperforms it on SANs and UpDn, reduces the language priors of SANs and UpDn, and has preferable interpretability and applicability. Our code is available at https://***/lsdruc/CMQEF-for-VQA. © 2023 The Author(s)

关键词： Embeddings

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A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Manna, Suvojit Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malayasia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Ultimo Australia

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Deep symmetric adaptation network for cross-modality medical image segmentation

arXiv

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arXiv 2021年

作者： Han, Xiaoting Qi, Lei Yu, Qian Zhou, Ziqi Zheng, Yefeng Shi, Yinghuan Gao, Yang The State Key Laboratory for Novel Software Technology The National Institute for Healthcare Data Science The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University China Southeast University China The School of Data and Computer Science Shandong Women’s University China The Tencent Jarvis Lab China

Unsupervised domain adaptation (UDA) methods have shown their promising performance in the cross-modality medical image segmentation tasks. These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images. However, when there exists a large domain shift between source and target domains, we argue that this asymmetric structure could not fully eliminate the domain gap. In this paper, we present a novel deep symmetric architecture of UDA for medical image segmentation, which consists of a segmentation sub-network, and two symmetric source and target domain translation sub-networks. To be specific, based on two translation sub-networks, we introduce a bidirectional alignment scheme via a shared encoder and private decoders to simultaneously align features 1) from source to target domain and 2) from target to source domain, which helps effectively mitigate the discrepancy between domains. Furthermore, for the segmentation sub-network, we train a pixel-level classifier using not only original target images and translated source images, but also original source images and translated target images, which helps sufficiently leverage the semantic information from the images with different styles. Extensive experiments demonstrate that our method has remarkable advantages compared to the state-of-the-art methods in both cross-modality Cardiac and BraTS segmentation tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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New Moments Based Fuzzy Similarity Measure for Text Detection in Distorted Social Media Images 5th

New Moments Based Fuzzy Similarity Measure for Text Detectio...

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Roy, Soumyadip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783030414030

A trend towards capturing or filming images using cellphone and sharing images on social media is a part and parcel of day to day activities of humans. When an image is forwarded several times in social media it may be distorted a lot due to several different devices. This work deals with text detection from such distorted images. In this work, we consider images pass through three mobile devices on WhatsApp social media, which results in four images (including the original image) Unlike the existing methods that aim at developing new ways, we utilize the results detected by the existing ones to improve performances. The proposed method extracts Hu moments and fuzzy logic from detected texts of images. The similarity between text detection results given by three existing text detection methods is studied for determining the best pair of texts. The same similarity estimation is then used in a novel way to remove extra background or non-texts and restoring missing text information. Experimental results on own dataset and benchmark datasets of natural scene images, namely, MSRA-TD500, ICDAR2017-MLT, Total-Text, CTW1500 dataset and COCO datasets, show that the proposed method outperforms the existing methods. © Springer Nature Switzerland AG 2020.

关键词： Fuzzy logic

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DEER: Distribution Divergence-based Graph Contrast for Partial label Learning on Graphs

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IEEE Transactions on Multimedia 2024年 1-16页

作者： Gu, Yiyang Chen, Zihao Qin, Yifang Mao, Zhengyang Xiao, Zhiping Ju, Wei Chen, Chong Hua, Xian-Sheng Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University Beijing China School of Mathematical Sciences Peking University Beijing China Department of Computer Science University of California Los Angeles USA Terminus Group Beijing China School of Information Technology & Management University of International Business and Economics Beijing China

Graph neural networks (GNNs) have emerged as powerful tools for graph classification tasks. However, contemporary graph classification methods are predominantly studied in fully supervised scenarios, while there could be label ambiguity and noise in real-world applications. In this work, we explore the weakly supervised problem of partial label learning on graphs, where each graph sample is assigned a collection of candidate labels. A novel method called Distribution Divergence-based Graph Contrast (DEER) is proposed to address this issue. At the heart of our DEER is to measure the divergence among the underlying semantic distributions in the hidden space and this metric enables the identification of accurate positive graph pairs for effective graph contrastive learning. Specifically, we generate graph representations of augmented graph views that retain semantics and can be regarded as samples from the underlying semantic distributions. We employ a non-parametric metric to measure distribution divergence, which is then combined with pseudo-labeling to generate unbiased and target-oriented graph pairs. Furthermore, we introduce a label-correction method to eliminate noisy candidate labels, updating target labels using posterior distributions in a soft manner. Comprehensive experiments on various benchmarks demonstrate the superiority of our DEER in different settings compared to a range of state-of-the-art baselines. IEEE

关键词： Semantics

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HERO: On the chaos when PATH meets modules

arXiv

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arXiv 2021年

作者： Wang, Ying Qiao, Liang Xu, Chang Liu, Yepang Cheung, Shing-Chi Meng, Na Yu, Hai Zhu, Zhiliang Software College Northeastern University China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China Southern University of Science and Technology China Hong Kong University of Science and Technology China Virginia Tech United States

Ever since its first release in 2009, the Go programming language (Golang) has been well received by software communities. A major reason for its success is the powerful support of library-based development, where a Golang project can be conveniently built on top of other projects by referencing them as libraries. As Golang evolves, it recommends the use of a new library-referencing mode to overcome the limitations of the original one. While these two library modes are incompatible, both are supported by the Golang ecosystem. The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management (DM) issues, incurring reference inconsistencies and even build failures. Motivated by the problem, we conducted an empirical study to characterize the DM issues, understand their root causes, and examine their fixing solutions. Based on our findings, we developed HERO, an automated technique to detect DM issues and suggest proper fixing solutions. We applied HERO to 19,000 popular Golang projects. The results showed that HERO achieved a high detection rate of 98.5% on a DM issue benchmark and found 2,422 new DM issues in 2,356 popular Golang projects. We reported 280 issues, among which 181 (64.6%) issues have been confirmed, and 160 of them (88.4%) have been fixed or are under fixing. Almost all the fixes have adopted our fixing suggestions. Copyright © 2021, The Authors. All rights reserved.

关键词： Ecosystems

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Non-stationary Online Learning with Memory and Non-stochastic Control

arXiv

引用

arXiv 2021年

作者： Zhao, Peng Yan, Yu-Hu Wang, Yu-Xiang Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science University of California Santa BarbaraCA93106 United States

We study the problem of Online Convex Optimization (OCO) with memory, which allows loss functions to depend on past decisions and thus captures temporal effects of learning problems. In this paper, we introduce dynamic policy regret as the performance measure to design algorithms robust to non-stationary environments, which competes algorithms' decisions with a sequence of changing comparators. We propose a novel algorithm for OCO with memory that provably enjoys an optimal dynamic policy regret in terms of time horizon, non-stationarity measure, and memory length. The key technical challenge is how to control the switching cost, the cumulative movements of player's decisions, which is neatly addressed by a novel switching-cost-aware online ensemble approach equipped with a new meta-base decomposition of dynamic policy regret and a careful design of meta-learner and base-learner that explicitly regularizes the switching cost. The results are further applied to tackle non-stationarity in online non-stochastic control (Agarwal et al., 2019), i.e., controlling a linear dynamical system with adversarial disturbance and convex cost functions. We derive a novel gradient-based controller with dynamic policy regret guarantees, which is the first controller provably competitive to a sequence of changing policies for online non-stochastic control. Copyright © 2021, The Authors. All rights reserved.

关键词： Controllers

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STAR-RIS and UAV Combination in MEC Networks: Simultaneous Task Offloading and Communications

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IEEE Transactions on Communications 2025年

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China University College London Department of Electronic and Electrical Engineering Torrington Place LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks. © 1972-2012 IEEE.

关键词： Convex optimization

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