1 citations · 1 across the 10 of their papers we have counts for
10 papers
VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning
Hengbo Xu, Shengjie Jin, Yanbiao Ma +1
With the rapid advancement of large multimodal models (LMMs), inference-time overhead has become a key bottleneck for real-world deployment. Existing methods typically prune visual…
Osmosis Distillation: Model Hijacking with the Fewest Samples
Yuchen Shi, Huajie Chen, Heng Xu +6
Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources. Meanwhile, dataset distillation has eme…
Hide&Seek: Remove Image Watermarks with Negligible Cost via Pixel-wise Reconstruction
Huajie Chen, Tianqing Zhu, Hailin Yang +7
Watermarking has emerged as a key defense against the misuse of machine-generated images (MGIs). Yet the robustness of these protections remains underexplored. To reveal the limits…
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
Heng Xu, Tianqing Zhu, Dayong Ye +3
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extens…
Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications
Heng Xu, Tianqing Zhu, Wanlei Zhou
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. While extensive…
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine…
Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, ha…
Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
Heng Xu, Tianqing Zhu, Lefeng Zhang +1
Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategie…
Don't Forget Too Much: Towards Machine Unlearning on Feature Level
Heng Xu, Tianqing Zhu, Wanlei Zhou +1
Machine unlearning enables pre-trained models to remove the effect of certain portions of training data. Previous machine unlearning schemes have mainly focused on unlearning a clu…
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
Wenhan Chang, Tianqing Zhu, Heng Xu +2
Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This capability is particularly impo…