2 papers
cs.CV2024
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Bowen Shi, Peisen Zhao, Zichen Wang +8
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual…
cs.CV2024
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
Dongsheng Jiang, Yuchen Liu, Songlin Liu +6
Multi-modal Large Language Models (MLLMs) have made significant strides in expanding the capabilities of Large Language Models (LLMs) through the incorporation of visual perception…