Dense scaling-law fits across model sizes, datasets and tasks show that MaMMUT (contrastive plus captioning loss) outperforms standard CLIP at large compute scales, with a consistent crossover around 1e10 to 1e11 GFLOPs.
Learning transferable visual models from natural language supervision
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Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
Dense scaling-law fits across model sizes, datasets and tasks show that MaMMUT (contrastive plus captioning loss) outperforms standard CLIP at large compute scales, with a consistent crossover around 1e10 to 1e11 GFLOPs.