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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年 PP卷 PP页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Guangdong University of Technology School of Information Engineering Guangzhou China South China Technology Commercialization Center Guangzhou China

Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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DeepFake Videos Detection Based on Texture Features

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Computers, Materials & Continua 2021年第7期68卷 1375-1388页

作者： Bozhi Xu Jiarui Liu Jifan Liang Wei Lu Yue Zhang School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhou510006China Department of Computer Science University of Massachusetts LowellLowell01854MAUSA

In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake ***,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human *** spread of face manipulation videos is very easy to bring fake ***,it is important to develop effective detection methods to verify the authenticity of the *** to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are ***,in this paper,a new method is proposed to detect DeepFake ***,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face ***,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame *** experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.

关键词： DeepFake video tampering tampering detection texture feature

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Progressive Human Motion Generation Based on Text and Few Motion Frames

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Artificial Intelligence Guangdong Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangdong Guangzhou510275 China Sun Yat-sen University School of Computer Science and Engineering China Guangdong Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Peng Cheng Laboratory China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code is available here. © 1991-2012 IEEE.

关键词： Controllable Motion Generation Human Motion Text2Motion Generation

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When Prompt-based Incremental Learning Does Not Meet Strong Pretraining

arXiv

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arXiv 2023年

作者： Tang, Yu-Ming Peng, Yi-Xing Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large. In this work, we develop a learnable Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and prompt learning processes into a learnable prompt generator. Hence, the whole prompting process can be optimized to reduce the negative effects of the gap between tasks effectively. To make our APG avoid learning ineffective knowledge, we maintain a knowledge pool to regularize APG with the feature distribution of each class. Extensive experiments show that our method significantly outperforms advanced methods in exemplar-free incremental learning without (strong) pretraining. Besides, under strong pretraining, our method also has comparable performance to existing prompt-based models, showing that our method can still benefit from pretraining. Codes can be found at https://***/TOM-tym/APG. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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When Prompt-based Incremental Learning Does Not Meet Strong Pretraining

When Prompt-based Incremental Learning Does Not Meet Strong ...

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International Conference on Computer Vision (ICCV)

作者： Yu-Ming Tang Yi-Xing Peng Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Peng Cheng Laboratory Shenzhen China

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AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domai...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yipeng Gao Kun-Yu Lin Junkai Yan Yaowei Wang Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pengcheng Lab

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://***/Hlings/AsyFPD.

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A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement Learning 24

A Sample-driven Selection Framework: Towards Graph Contrasti...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Xiangping Hao, Xiuxin Wu, Bo Bao, Xigang Zhang, Xuan Li, Wei Liang, Xun College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Heilongjiang Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Shandong Qindao China R&d and External Relations Department Xiangjiang Laboratory Hunan Changsha China School of Artificial Intelligence and Advanced Computing Hunan University of Technology and Business Hunan Changsha China Changsha Artificial Intelligence Social Laboratory Hunan Changsha China School of Information Renmin University of China Beijing China Guanghua School of Management Peking University Beijing China Harvest Fund Management Co. Ltd. Beijing China

ISBN: (纸本)9798400706868

Graph Contrastive Learning (GCL) aims to address the issue of label scarcity by leveraging graph structures to propagate labels from a limited set of labeled data to a broader range of unlabeled data. However, recent GCL methods often rely on uniform negative sample selection schemes, such as random sampling, which results in suboptimal performance. To tackle this challenge, we present GraphSaSe, a tailored approach specifically designed for graph contrastive learning. Our method introduces an innovative reinforcement learning strategy that translates the divergence between positive pairs into a reinforcement reward mechanism. This mechanism generates selection probabilities to dynamically guide the selection of negative samples during training. We explore the impact of negative sample selection at different stages in graph contrastive learning and analyze how the discount factor affects the reward mechanism in reinforcement learning. These studies enhance the overall performance of the model. Comprehensive experimentation across diverse real-world datasets validates the effectiveness of our algorithm, positioning it favorably against contemporary state-of-the-art methodologies. © 2024 ACM.

关键词： Contrastive Learning

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Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks

arXiv

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arXiv 2024年

作者： Rao, Jiahua Xie, Jiancong Lin, Hanjing Zheng, Shuangjia Wang, Zhen Yang, Yuedong School of Computer Science and Engineering Sun Yat-sen University China Shanghai Jiao Tong University China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University China

Graph Neural Networks (GNNs) have gained considerable traction for their capability to effectively process topological data, yet their interpretability remains a critical concern. Current interpretation methods are dominated by post-hoc explanations to provide a transparent and intuitive understanding of GNNs. However, they have limited performance in interpreting complicated subgraphs and can’t utilize the explanation to advance GNN predictions. On the other hand, transparent GNN models are proposed to capture critical subgraphs. While such methods could improve GNN predictions, they usually don’t perform well on explanations. Thus, it is desired for a new strategy to better couple GNN explanation and prediction. In this study, we have developed a novel interpretable causal GNN framework that incorporates retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. The framework could semi-parametrically retrieve crucial subgraphs detected by GIB and compress the explanatory subgraphs via a causal module. The framework was demonstrated to consistently outperform state-of-the-art methods, and to achieve 32.71% higher precision on real-world explanation scenarios with diverse explanation types. More importantly, the learned explanations were shown able to also improve GNN prediction performance. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy

arXiv

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arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Wu, Di Sheng, Quan Z. Hu, Miao Xiao, Linchang The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Ministry of Education China The School of Computing Macquarie University NSW2109 Australia

Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are automatically seized by video content providers, potentially leaking users’ privacy. Unfortunately, current protection methods are not well-suited to preserving user request privacy from content providers while maintaining high-quality online video services. To tackle this challenge, we introduce a novel Privacy-Preserving Video Fetching (PPVF) framework, which utilizes trusted edge devices to pre-fetch and cache videos, ensuring the privacy of users’ requests while optimizing the efficiency of edge caching. More specifically, we design PPVF with three core components: (1) Online privacy budget scheduler, which employs a theoretically guaranteed online algorithm to select non-requested videos as candidates with assigned privacy budgets. Alternative videos are chosen by an online algorithm that is theoretically guaranteed to consider both video utilities and available privacy budgets. (2) Noisy video request generator, which generates redundant video requests (in addition to original ones) utilizing correlated differential privacy to obfuscate request privacy. (3) Online video utility predictor, which leverages federated learning to collaboratively evaluate video utility in an online fashion, aiding in video selection in (1) and noise generation in (2). Finally, we conduct extensive experiments using real-world video request traces from Tencent Video. The results demonstrate that PPVF effectively safeguards user request privacy while upholding high video caching performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Video streaming

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IMPROVING WEAKLY SUPERVISED OBJECT LOCALIZATION BY UNCERTAINTY ESTIMATION OF PSEUDO SUPERVISION

IMPROVING WEAKLY SUPERVISED OBJECT LOCALIZATION BY UNCERTAIN...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Chen, Xi Ma, Andy J. Guo, Nanxi Chen, Jiajia School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

Pseudo bounding box supervision is a promising approach for weakly supervised object localization (WSOL) with only image-level labels. However, the generated pseudo bounding boxes may be inaccurate or even completely non-overlapped with the objects of interest. In this paper, we propose to estimate the uncertainty of pseudo bounding boxes such that the negative impact caused by inaccurate estimation of pseudo supervision could be alleviated for better WSOL. The refined bounding boxes and corresponding variance uncertainties are learned by training a neural network regressor to penalize the erroneous estimations. To the best of our knowledge, this is the first work to incorporate uncertainty information of pseudo bounding boxes for WSOL. Experimental results show that our method not only outperforms previous state-of-the-art methods in CUB-200-2011 and ILSVRC datasets but also gives more precise bounding box prediction when the IoU threshold is higher. © 2021 IEEE

关键词： Uncertainty analysis

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