1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CV2024★ 1 cited
MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
Fanqing Meng, Jin Wang, Chuanhao Li +9
The capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LV…
cs.MM2024★ 1 cited
ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
Shuo Liu, Kaining Ying, Hao Zhang +8
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess indivi…