4 papers
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…
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…
FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models
Rui Hu, Qian He, Gaofeng He +4
Modeling and producing lifelike clothed human images has attracted researchers' attention from different areas for decades, with the complexity from highly articulated and structur…
FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance
Jiedong Zhuang, Jiaqi Hu, Lianrui Mu +4
CLIP has achieved impressive zero-shot performance after pre-training on a large-scale dataset consisting of paired image-text data. Previous works have utilized CLIP by incorporat…