1 citations · 3 across the 38 of their papers we have counts for
5 papers · 1 filter
Consistency of Compositional Generalization across Multiple Levels
Chuanhao Li, Zhen Li, Chenchen Jing +4
Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts. There are multiple levels of novel compositions including phra…
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Pengfei Zhou, Xiaopeng Peng, Jiajun Song +15
Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a ch…
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…
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge
Chuanhao Li, Zhen Li, Chenchen Jing +6
Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources…
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…