Showing cs.CVShow all
2 papers · 1 filter
cs.CV2025
DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs
Zhenhailong Wang, Senthil Purushwalkam, Caiming Xiong +3
We present DyMU, an efficient, training-free framework that dynamically reduces the computational burden of vision-language models (VLMs) while maintaining high task performance. O…
cs.CV2024
BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions
Anas Awadalla, Le Xue, Manli Shu +13
We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthet…