5 citations · 8 across the 8 of their papers we have counts for
9 papers
Love Me, Love My Label: Rethinking the Role of Labels in Prompt Retrieval for Visual In-Context Learning
Tianci Luo, Haohao Pan, Jinpeng Wang +5
Visual in-context learning (VICL) enables visual foundation models to handle multiple tasks by steering them with demonstrative prompts. The choice of such prompts largely influenc…
Imagine Before Concentration: Diffusion-Guided Registers Enhance Partially Relevant Video Retrieval
Jun Li, Xuhang Lou, Jinpeng Wang +4
Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos based on text queries that describe only partial events. Existing methods suffer from incomplete global…
Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection
Wenjie Li, Siying Gu, Yiming Li +4
Backdoor detection is currently the mainstream defense against backdoor attacks in federated learning (FL), where a small number of malicious clients can upload poisoned updates to…
Holmes: Towards Effective and Harmless Model Ownership Verification to Personalized Large Vision Models via Decoupling Common Features
Linghui Zhu, Yiming Li, Haiqin Weng +4
Large vision models (LVMs) achieve remarkable performance in various downstream tasks, primarily by personalizing pre-trained models through fine-tuning with private and valuable l…
Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models
Kuofeng Gao, Yufei Zhu, Yiming Li +4
Text-to-image (T2I) diffusion models enable high-quality image generation conditioned on textual prompts. However, fine-tuning these pre-trained models for personalization raises c…
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Yiming Li, Kaiying Yan, Shuo Shao +5
Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such infringement is difficult to detect or…
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
Mingyan Zhu, Yiming Li, Junfeng Guo +3
Currently, sample-specific backdoor attacks (SSBAs) are the most advanced and malicious methods since they can easily circumvent most of the current backdoor defenses. In this pape…
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
Junfeng Guo, Yiming Li, Lixu Wang +4
The prosperity of deep neural networks (DNNs) is largely benefited from open-source datasets, based on which users can evaluate and improve their methods. In this paper, we revisit…
Towards Robust Model Watermark via Reducing Parametric Vulnerability
Guanhao Gan, Yiming Li, Dongxian Wu +1
Deep neural networks are valuable assets considering their commercial benefits and huge demands for costly annotation and computation resources. To protect the copyright of DNNs, b…