74 citations · 186 across the 13 of their papers we have counts for
5 papers · 1 filter
DataComp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang +31
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model arch…
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
Wanrong Zhu, Jack Hessel, Anas Awadalla +7
In-context vision and language models like Flamingo support arbitrarily interleaved sequences of images and text as input. This format not only enables few-shot learning via interl…
Does progress on ImageNet transfer to real-world datasets?
Alex Fang, Simon Kornblith, Ludwig Schmidt
Does progress on ImageNet transfer to real-world datasets? We investigate this question by evaluating ImageNet pre-trained models with varying accuracy (57% - 83%) on six practical…
Neural Radiance Field Codebooks
Matthew Wallingford, Aditya Kusupati, Alex Fang +4
Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representa…
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman +4
Contrastively trained language-image models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts. Since the…