5 papers · 1 filter
Evaluation of Safety Cognition Capability in Vision-Language Models for Autonomous Driving
Enming Zhang, Peizhe Gong, Xingyuan Dai +3
Ensuring the safety of vision-language models (VLMs) in autonomous driving systems is of paramount importance, yet existing research has largely focused on conventional benchmarks…
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous Driving
Enming Zhang, Xingyuan Dai, Min Huang +2
Vision-language models (VLMs) serve as general-purpose end-to-end models in autonomous driving, performing subtasks such as prediction, planning, and perception through question-an…
First Multi-Dimensional Evaluation of Flowchart Comprehension for Multimodal Large Language Models
Enming Zhang, Ruobing Yao, Huanyong Liu +2
With the development of Multimodal Large Language Models (MLLMs) technology, its general capabilities are increasingly powerful. To evaluate the various abilities of MLLMs, numerou…
Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language Models
Enming Zhang, Bingke Zhu, Yingying Chen +3
Vision-Language Models (VLMs), such as CLIP, play a foundational role in various cross-modal applications. To fully leverage VLMs' potential in adapting to downstream tasks, contex…
PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model
Zheng Zhang, Yeyao Ma, Enming Zhang +1
PSALM is a powerful extension of the Large Multi-modal Model (LMM) to address the segmentation task challenges. To overcome the limitation of the LMM being limited to textual outpu…