2 citations · 3 across the 6 of their papers we have counts for
6 papers
Distorted or Fabricated? A Survey on Hallucination in Video LLMs
Yiyang Huang, Yitian Zhang, Yizhou Wang +4
Despite significant progress in video-language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid-LLMs), referring to outputs that appear pl…
IIR-VLM: In-Context Instance-level Recognition for Large Vision-Language Models
Liang Shi, Wei Li, Kevin M Beussman +2
Instance-level recognition (ILR) concerns distinguishing individual instances from one another, with person re-identification as a prominent example. Despite the impressive visual…
SHIELD: Suppressing Hallucinations In LVLM Encoders via Bias and Vulnerability Defense
Yiyang Huang, Liang Shi, Yitian Zhang +2
Large Vision-Language Models (LVLMs) excel in diverse cross-modal tasks. However, object hallucination, where models produce plausible but inaccurate object descriptions, remains a…
ExpertGen: Training-Free Expert Guidance for Controllable Text-to-Face Generation
Liang Shi, Yun Fu
Recent advances in diffusion models have significantly improved text-to-face generation, but achieving fine-grained control over facial features remains a challenge. Existing metho…
Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation
Liang Shi, Jie Zhang, Shiguang Shan
Text-to-image diffusion models, such as Stable Diffusion, generate highly realistic images from text descriptions. However, the generation of certain content at such high quality r…
Real Face Foundation Representation Learning for Generalized Deepfake Detection
Liang Shi, Jie Zhang, Shiguang Shan
The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is now of great significance to deve…