activity
20242026
most citedPyramidal Flow Matching for Efficient Video Generative Modeling

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models

Songping Wang, Yueming Lyu, Shiqi Liu +5

The rise of customized diffusion models has fueled a boom in personalized visual content creation, but it also introduces serious risks of malicious misuse, thereby posing threats…

cs.CV2026

Exposing and Defending the Achilles' Heel of Video Mixture-of-Experts

Songping Wang, Qinglong Liu, Yueming Lyu +3

Mixture-of-Experts (MoE) has demonstrated strong performance in video understanding tasks, yet its adversarial robustness remains underexplored. Existing attack methods often treat…

cs.CL2025

Understanding the Ability of LLMs to Handle Character-Level Perturbation

Anyuan Zhuo, Xuefei Ning, Ningyuan Li +3

This work investigates the resilience of contemporary large language models (LLMs) against frequent character-level perturbations. We examine three types of character-level perturb…

cs.CV2025

Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis

Songping Wang, Yueming Lyu, Shiqi Liu +4

The rise of customized diffusion models has spurred a boom in personalized visual content creation, but also poses risks of malicious misuse, severely threatening personal privacy…

cs.CV2024★ 2 cited

Pyramidal Flow Matching for Efficient Video Generative Modeling

Yang Jin, Zhicheng Sun, Ningyuan Li +7

Video generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage. To reduce the complexity, the prevailing approache…