52 citations · 73 across the 6 of their papers we have counts for
6 papers
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
Bohao Li, Yuying Ge, Yi Chen +3
Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real…
Supervised Fine-tuning in turn Improves Visual Foundation Models
Xiaohu Jiang, Yixiao Ge, Yuying Ge +3
Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning…
Making LLaMA SEE and Draw with SEED Tokenizer
Yuying Ge, Sijie Zhao, Ziyun Zeng +4
The great success of Large Language Models (LLMs) has expanded the potential of multimodality, contributing to the gradual evolution of General Artificial Intelligence (AGI). A tru…
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Bohao Li, Rui Wang, Guangzhi Wang +3
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable…
Bridging Video-text Retrieval with Multiple Choice Questions
Yuying Ge, Yixiao Ge, Xihui Liu +4
Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separa…
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples
Yuying Ge, Ruimao Zhang, Ping Luo
Recent advanced methods for fashion landmark detection are mainly driven by training convolutional neural networks on large-scale fashion datasets, which has a large number of anno…