33 citations · 99 across the 8 of their papers we have counts for
6 papers · 1 filter
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin +9
As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Build…
Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
Kaichen Zhang, Yifei Shen, Bo Li +1
Recent advances in Large Multimodal Models (LMMs) lead to significant breakthroughs in both academia and industry. One question that arises is how we, as humans, can understand the…
LLaVA-Video: Video Instruction Tuning With Synthetic Data
Yuanhan Zhang, Jinming Wu, Wei Li +4
The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we pro…
LLaVA-OneVision: Easy Visual Task Transfer
Bo Li, Yuanhan Zhang, Dong Guo +8
We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT…
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
Feng Li, Renrui Zhang, Hao Zhang +5
Visual instruction tuning has made considerable strides in enhancing the capabilities of Large Multimodal Models (LMMs). However, existing open LMMs largely focus on single-image t…
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
Kaichen Zhang, Bo Li, Peiyuan Zhang +8
The advances of large foundation models necessitate wide-coverage, low-cost, and zero-contamination benchmarks. Despite continuous exploration of language model evaluations, compre…