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.
Decoupled weight decay regularization
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Lightweight LLIE framework pairs frozen distribution-normalizing preprocessing with a depthwise U-Net and reports 3rd place in the 2026 NTIRE Efficient Low-Light challenge.
citing papers explorer
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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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Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net
Lightweight LLIE framework pairs frozen distribution-normalizing preprocessing with a depthwise U-Net and reports 3rd place in the 2026 NTIRE Efficient Low-Light challenge.