Empirical tests show that factorized world-model with hard-region-weighted latent dynamics improves ImageNet-100 by 5.92 and SSv2 by 3.21 points over baseline in mixed-dataset pretraining while staying within 0.3 points on Diving-48.
A simple framework for contrastive learning of visual representations
2 Pith papers cite this work. Polarity classification is still indexing.
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UniSplat learns consistent 3D geometry, appearance, and semantics from unposed images using dual masking, progressive Gaussian splatting, and recalibration to align predictions across tasks.
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Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives
Empirical tests show that factorized world-model with hard-region-weighted latent dynamics improves ImageNet-100 by 5.92 and SSv2 by 3.21 points over baseline in mixed-dataset pretraining while staying within 0.3 points on Diving-48.
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Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images
UniSplat learns consistent 3D geometry, appearance, and semantics from unposed images using dual masking, progressive Gaussian splatting, and recalibration to align predictions across tasks.