2 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning
Qinghao Ye, Xianhan Zeng, Fu Li +2
Image captioning has long been a pivotal task in visual understanding, with recent advancements in vision-language models (VLMs) significantly enhancing the ability to generate det…
Classification Done Right for Vision-Language Pre-Training
Zilong Huang, Qinghao Ye, Bingyi Kang +2
We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text…
LLaVA-Critic: Learning to Evaluate Multimodal Models
Tianyi Xiong, Xiyao Wang, Dong Guo +5
We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator to assess performance across a wide range of multimodal tasks. LLaV…