46 citations · 98 across the 14 of their papers we have counts for
3 papers · 1 filter
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri +2
Contrastive pretraining of image-text foundation models, such as CLIP, demonstrated excellent zero-shot performance and improved robustness on a wide range of downstream tasks. How…
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding
Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri +6
The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilitie…
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu +2
The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a hybr…