6 papers
ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models
Zifu Wan, Ce Zhang, Silong Yong +6
Recent Large Vision-Language Models (LVLMs) have introduced a new paradigm for understanding and reasoning about image input through textual responses. Although they have achieved…
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning
Zifu Wan, Yaqi Xie, Ce Zhang +5
Large multimodal foundation models, particularly in the domains of language and vision, have significantly advanced various tasks, including robotics, autonomous driving, informati…
Spectral-Aware Global Fusion for RGB-Thermal Semantic Segmentation
Ce Zhang, Zifu Wan, Simon Stepputtis +2
Semantic segmentation relying solely on RGB data often struggles in challenging conditions such as low illumination and obscured views, limiting its reliability in critical applica…
VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models
Ce Zhang, Kaixin Ma, Tianqing Fang +5
Recent Large Vision-Language Models (LVLMs) have advanced multi-modal understanding by incorporating finer-grained visual perception and encoding. However, such methods incur signi…
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs
Zhehan Kan, Ce Zhang, Zihan Liao +7
Large Vision-Language Model (LVLM) systems have demonstrated impressive vision-language reasoning capabilities but suffer from pervasive and severe hallucination issues, posing sig…
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation
Shuowen Liang, Sisi Li, Qingyun Wang +3
Pose skeleton images are an important reference in pose-controllable image generation. In order to enrich the source of skeleton images, recent works have investigated the generati…