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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

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arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

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Camouflaged Object Tracking: A Benchmark

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arXiv 2024年

作者： Guo, Xiaoyu Zhong, Pengzhi Zhang, Hao Huang, Defeng Shao, Huikai Zhao, Qijun Li, Shuiwang The College of Computer Science and Engineering Guilin University of Technology 541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology 541004 China The School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The College of Computer Science Sichuan University Sichuan610065 China

Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. This challenge is particularly critical in applications such as military, security, agriculture, and marine monitoring, where precise tracking of camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://***/openat25/ HIPTrack-MLS. © 2024, CC BY-NC-ND.

关键词： Object detection

FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models

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arXiv 2024年

作者： Li, Pengxiang Gao, Zhi Zhang, Bofei Yuan, Tao Wu, Yuwei Harandi, Mehrtash Jia, Yunde Zhu, Song-Chun Li, Qing Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China State Key Laboratory of General Artificial Intelligence BIGAI China State Key Laboratory of General Artificial Intelligence Peking University China Department of Electrical and Computer System Engineering Monash University Australia Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Automation Tsinghua University China

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Fireclay

The quantized chemical reaction resonantly driven by multiple MIR-photons:From nature to the artificial

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Nano Research 2021年第12期14卷 4367-4369页

作者： Feng Zhang Bo Song Lei Jiang Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190China School of Future Technology University of Chinese Academy of SciencesBeijing100049China

Biochemical reactions in vivo occur at the temperature usually lower than that in vitro,however the underlying mechanism still remains a *** by our recent studies of adenosine triphosphate(ATP)releasing photons to resonantly drive DNA replication in a quantum way,we propose a quantized chemical reaction driven by multiple mid-infrared(MIR)*** space confinement effect of enzymes on a reactant molecule increases the lifetime of excitation state of its bond vibration,providing a chance for the bond to resonantly absorb multiple *** the energy of each MIR photon is significantly lower than that of chemical bond,the resonant absorption of multiple photons can break the appointed bond of confined *** from the traditional thermochemistry and photochemistry,the quantized chemical reactions could have a high energy efficiency and ultrahigh *** addition,we also suggest a quantum driving source for our quantum-confined superfluid reactions proposed *** quantized chemical reaction resonantly driven by multiple MIR photons holds great promise to develop novel approaches for the chemical engineering in future.

关键词： quantized chemical reaction quantized bio-synthesis quantized chemical engineering multiple mid-infrared(MIR)/far-infrared(FIR)-photon driving high efficiency and ultrahigh selectivity

Data-driven system inference for Caputo discrete fractional chaotic systems

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Nonlinear Dynamics 2025年 1-12页

作者： Yang, Guang Zhu, Wei Wu, Guo-Cheng Chen, Zhang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing People’s Republic of China Key Laboratory of Intelligent Analysis and Decision on Complex System Chongqing University of Posts and Telecommunications Chongqing People’s Republic of China

A data-driven modeling problem of discrete fractional chaotic systems (DFCS) is studied in this paper. Firstly, a sparse identification framework is constructed and augmented by an iterative thresholding method. Then, by the matrix perturbation theory, the sparse matrix’s structure and vector field functions are determined together with the fractional order. A parameter estimation problem is obtained. Finally, the problem is solved by the famous interior point method. With small sample data, discrete fractional Lorenz system and Chua’s circuit models are provided to show the method’s efficiency.

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UniFace: Unified Cross-Entropy Loss for Deep Face Recognition

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UniFace: Unified Cross-Entropy Loss for Deep Face Recognitio...

International Conference on computer Vision (ICCV)

作者： Jiancan Zhou Xi Jia Qiufu Li Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University Aqara Lumi United Technology Co. Ltd. School of Computer Science University of Birmingham UK Alan Turing Institute UK

As a widely used loss function in deep face recognition, the softmax loss cannot guarantee that the minimum positive sample-to-class similarity is larger than the maximum negative sample-to-class similarity. As a result, no unified threshold is available to separate positive sample-to-class pairs from negative sample-to-class pairs. To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones. Our UCE loss can be integrated with margins for a further performance boost. The face recognition model trained with the proposed UCE loss, UniFace, was intensively evaluated using a number of popular public datasets like MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace. Experimental results show that our approach outperforms SOTA methods like SphereFace, CosFace, ArcFace, Partial FC, etc. Especially, till the submission of this work (Mar. 8, 2023), the proposed UniFace achieves the highest TAR@MR-All on the academic track of the MFR-ongoing challenge. $\color{Blue}{\mathbf{Code}}$ is publicly available.

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Towards Personal Data Sharing Autonomy: A Task-driven Data Capsule Sharing system

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arXiv 2024年

作者： Lyu, Qiuyun Zhou, Yilong Ren, Yizhi Wang, Zheng Guo, Yunchuan The School of Computer Science and Technology Hangzhou Dianzi University China Key Laboratory of Data Storage and Transmission Technology of Zhejiang Province Pinghu Digital Technology Innovation Institute Co. Ltd Hangzhou Dianzi University China The School of Cyberspace Hangzhou Dianzi University China Pinghu Digital Technology Innovation Institute Co. Ltd Hangzhou Dianzi University China Dalian University of Technology China Computing Science and Technology Guilin China The Institute of Computing Technology Chinese Academy of Science Beijing China

Personal data custodian services enable data owners to share their data with data consumers in a convenient manner, anytime and anywhere. However, with data hosted in these services being beyond the control of the data owners, it raises significant concerns about privacy in personal data sharing. Many schemes have been proposed to realize fine-grained access control and privacy protection in data sharing. However, they fail to protect the rights of data owners to their data under the law, since their designs focus on the management of system administrators rather than enhancing the data owners’ privacy. In this paper, we introduce a novel task-driven personal data sharing system based on the data capsule paradigm realizing personal data sharing autonomy. It enables data owners in our system to fully control their data, and share it autonomously. Specifically, we present a tamper-resistant data capsule encapsulation method, where the data capsule is the minimal unit for independent and secure personal data storage and sharing. Additionally, to realize selective sharing and informed-consent based authorization, we propose a task-driven data sharing mechanism that is resistant to collusion and EDoS attacks. Furthermore, by updating parts of the data capsules, the permissions granted to data consumers can be immediately revoked. Finally, we conduct a security and performance analysis, proving that our scheme is correct, sound, and secure, as well as revealing more advantageous features in practicality, compared with the state-of-the-art schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Data consistency

A Better Cardinality Estimator with Fewer Bits, Constant Update Time, and Mergeability

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A Better Cardinality Estimator with Fewer Bits, Constant Upd...

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yang Du He Huang Yu-E Sun Kejian Li Boyu Zhang Guoju Gao School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

Cardinality estimation is a fundamental problem with diverse practical applications. HyperLogLog (HLL) has become a standard in practice because it offers good memory efficiency, constant update time, and mergeability. Some recent work achieved better memory efficiency, but typically at the cost of impractical update time or losing mergeability, making them incompatible with applications like network-wide traffic measurement. This work presents SpikeSketch, a better cardinality estimator that reduces memory usage of HLL by 37% without sacrificing other crucial metrics. We adopt a bucket-based data structure to promise constant update time, design a smoothed log 4 ranking and a spike coding scheme to compress cardinality observables into buckets, and propose a lightweight mergeable lossy compression to balance memory usage, information loss, and mergeability. Then we derive an unbiased estimator for recovering cardinality from the lossy-compressed sketch. Theoretical and empirical results show that SpikeSketch can work as a drop-in replacement for HLL because it achieves a near-optimal MVP (memory-variance-product) of 4.08 (37% smaller than HLL) with constant update time and mergeability. Its memory efficiency even defeats ACPC and HLLL, the state-of-the-art lossless-compressed sketches using linear-time compression to reduce memory usage.

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

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IEEE Transactions on Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks