1 citations · 1 across the 5 of their papers we have counts for
6 papers
RAVEL: Asynchronous Rolling Inference for Flow-Based Vision-Language-Action Models
Yuhan Chen, Ke Yu, Pengfei Liu +3
Flow-based vision-language-action (VLA) models are highly effective for generalist robot manipulation, yet their reliance on computationally expensive VLM encoding and multi-step i…
Training-Free Dual Hyperbolic Adapters for Better Cross-Modal Reasoning
Yi Zhang, Chun-Wun Cheng, Junyi He +5
Recent research in Vision-Language Models (VLMs) has significantly advanced our capabilities in cross-modal reasoning. However, existing methods suffer from performance degradation…
Conceptual Codebook Learning for Vision-Language Models
Yi Zhang, Ke Yu, Siqi Wu +1
In this paper, we propose Conceptual Codebook Learning (CoCoLe), a novel fine-tuning method for vision-language models (VLMs) to address the challenge of improving the generalizati…
NODE-Adapter: Neural Ordinary Differential Equations for Better Vision-Language Reasoning
Yi Zhang, Chun-Wun Cheng, Ke Yu +3
In this paper, we consider the problem of prototype-based vision-language reasoning problem. We observe that existing methods encounter three major challenges: 1) escalating resour…
Concept-Guided Prompt Learning for Generalization in Vision-Language Models
Yi Zhang, Ce Zhang, Ke Yu +2
Contrastive Language-Image Pretraining (CLIP) model has exhibited remarkable efficacy in establishing cross-modal connections between texts and images, yielding impressive performa…
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation
Xueting Hu, Ce Zhang, Yi Zhang +3
Pre-trained Vision-Language Models (VLMs), such as CLIP, have shown enhanced performance across a range of tasks that involve the integration of visual and linguistic modalities. W…