5 papers
CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition
Lai Wei, Chengqi Li, Jiapeng Li +3
Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge. While recent work has highlighted this capability as co…
Attend to Evidence: Evidence-Anchored Spatial Attention Supervision for Multimodal RLVR
Ruina Hu, Chen Wang, Lai Wei +5
Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only re…
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…