10 papers
Look Where It Matters: Adaptive Visual Refinement for Vision-Language-Action Models
Jin Cui, Yanbin Hu, Xinyue Long +3
Visual representations of VLA models remain unreliable for spatially precise robotic manipulation. We uncover that vision encoders in VLAs also exhibit attention artifacts previous…
HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models
Jin Cui, Chuanchang Su, Jiayi Lu +3
Vision-language models (VLMs) remain unreliable when predictions require fine-grained visual evidence. We identify a previously overlooked cause: spectral response rigidity. Despit…
Distill What RGB Can Recover: Privileged 3D Evidence for RGB-Only Vision-Language Models
Yanbin Hu, Jin Cui, Jun Ye +4
3D scene understanding requires reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient 3D cues. Existing 3D-VLMs co…
"The Whole Is Greater Than the Sum of Its Parts": A Compatibility-Aware Multi-Teacher CoT Distillation Framework
Jin Cui, Jiaqi Guo, Ruixuan Yang +6
Chain-of-Thought (CoT) reasoning empowers Large Language Models (LLMs) with remarkable capabilities but typically requires prohibitive parameter scales. CoT distillation has emerge…
CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection
Boran Zhao, Hetian Liu, Zhenxian Hu +3
The training of large multimodal models fundamentally relies on massive image-text datasets, which inevitably incur prohibitive computational overhead. Dataset selection offers a p…
ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
Yanbin Hu, Jin Cui, Jiayi Lu +6
Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks. Existing memory-augmented architectures…