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cs.CV2024
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks
Jiannan Wu, Muyan Zhong, Sen Xing +10
We present VisionLLM v2, an end-to-end generalist multimodal large model (MLLM) that unifies visual perception, understanding, and generation within a single framework. Unlike trad…
cs.CV2024
Asymmetric Masked Distillation for Pre-Training Small Foundation Models
Zhiyu Zhao, Bingkun Huang, Sen Xing +3
Self-supervised foundation models have shown great potential in computer vision thanks to the pre-training paradigm of masked autoencoding. Scale is a primary factor influencing th…