3 papers
cs.CV2026
Q Cache: Visual Attention is Valuable in Less than Half of Decode Layers for Multimodal Large Language Model
Jiedong Zhuang, Lu Lu, Ming Dai +4
Multimodal large language models (MLLMs) are plagued by exorbitant inference costs attributable to the profusion of visual tokens within the vision encoder. The redundant visual to…
cs.CV2024
ST: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming
Jiedong Zhuang, Lu Lu, Ming Dai +4
Multimodal large language models (MLLMs) enhance their perceptual capabilities by integrating visual and textual information. However, processing the massive number of visual token…
cs.CV2024
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding
Xiaoyu Liang, Jiayuan Yu, Lianrui Mu +7
Although Visual-Language Models (VLMs) have shown impressive capabilities in tasks like visual question answering and image captioning, they still struggle with hallucinations. Ana…