3 papers
cs.CV2026
CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs
Xiaoyi Huang, Kejia Zhang, Zhiming Luo
Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language priors dominate insufficient or…
cs.CV2026
TARS: MinMax Token-Adaptive Preference Strategy for Hallucination Reduction in MLLMs
Kejia Zhang, Keda Tao, Zhiming Luo +3
Multimodal large language models (MLLMs) are prone to hallucinations, generating plausible but visually ungrounded outputs, partly because direct preference optimization (DPO) over…
cs.CV2025
MANI-Pure: Magnitude-Adaptive Noise Injection for Adversarial Purification
Xiaoyi Huang, Junwei Wu, Kejia Zhang +2
Adversarial purification with diffusion models has emerged as a promising defense strategy, but existing methods typically rely on uniform noise injection, which indiscriminately p…