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arXiv 2025年

作者： Guo, Juncen Liu, Yang Zhu, Xiaogunag Sun, Lianlong Teng, Liangyu Wu, Jingyi Li, Di Zhou, Wei Song, Liang Academy for Engineering and Technology Fudan University Shanghai200433 China DataLab: Data Science and Informatics University of California DavisCA95616 United States University of Rochester New York14627 United States Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Academy for Computer Science and Informatics Cardiff University Wales CF24 4AG United Kingdom

Class-Incremental Learning (CIL) enables models to continuously learn new class knowledge while retaining previous classes, facilitating adaptation and evolution in dynamic, real-world environments. Traditional CIL methods primarily rely on visual features, which limits their effectiveness in complex, multimodal scenarios. In contrast, VLMs show promising potential for enhancing CIL by leveraging pre-trained knowledge and integrating multi-modal semantic cues such as text and vision. However, existing approaches struggle to mitigate catastrophic forgetting while preserving the generalization strengths of VLMs across diverse modalities. To address these challenges, we propose a Feature Calibration Enhanced Parameter Synthesis (FCPS) framework. Specifically, FCPS introduces a dynamic parameter adjustment mechanism that iteratively calibrates the contribution of original visual features to the final class decision, thus preserving the model’s intrinsic generalization capability across modalities. Simultaneously, parameter integration enables effective knowledge transfer, maintaining a balance between acquiring new class representations and preserving old knowledge. Experimental results on popular benchmarks (e.g., CIFAR100 and ImageNet100) validate the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge transfer

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Privacy-Preserving Video Anomaly Detection: A Survey

arXiv

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arXiv 2024年

作者： Liu, Jing Liu, Yang Zhu, Xiaoguang Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada School of Information Science and Technology Fudan University Shanghai200433 China The Department of Computer Science University of Toronto ONM5S 1A1 Canada The DataLab: Data Science and Informatics University of California DavisCA95616 United States

Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The lack of transparency and interpretability in video transmission and usage raises public concerns about privacy and ethics, limiting the real-world application of VAD. Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives including data, features, and systems, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in the AI community. However, current research in P2VAD is fragmented, and prior reviews have mostly focused on methods using RGB sequences, overlooking privacy leakage and appearance bias considerations. To address this gap, this article systematically reviews the progress of P2VAD for the first time, defining its scope and providing an intuitive taxonomy. We outline the basic assumptions, learning frameworks, and optimization objectives of various approaches, analyzing their strengths, weaknesses, and potential correlations. Additionally, we provide open access to research resources such as benchmark datasets and available code. Finally, we discuss key challenges and future opportunities from the perspectives of AI development and P2VAD deployment, aiming to guide future work in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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