Franca introduces nested Matryoshka clustering and positional disentanglement in a transparent SSL pipeline to deliver open-source vision models competitive with closed proprietary systems.
Masked autoencoders are scalable vision learners
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Seer, a transformer-based PIDM pre-trained on large robotic datasets like DROID, outperforms prior methods on simulation and real-world robotic manipulation benchmarks with gains up to 43%.
PixArt-α matches commercial text-to-image quality with a diffusion transformer trained in 675 A100 GPU days through decomposed training stages, cross-attention text injection, and vision-language model dense captions.
citing papers explorer
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Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
Franca introduces nested Matryoshka clustering and positional disentanglement in a transparent SSL pipeline to deliver open-source vision models competitive with closed proprietary systems.
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Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Seer, a transformer-based PIDM pre-trained on large robotic datasets like DROID, outperforms prior methods on simulation and real-world robotic manipulation benchmarks with gains up to 43%.
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PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
PixArt-α matches commercial text-to-image quality with a diffusion transformer trained in 675 A100 GPU days through decomposed training stages, cross-attention text injection, and vision-language model dense captions.