SoftMoR mixes all recursion-step outputs via per-token weights so recursive Vision Transformers gain accuracy from added depth with only ~1.7M extra parameters on ImageNet-1K.
Scaling vision transformers
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
citing papers explorer
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Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers
SoftMoR mixes all recursion-step outputs via per-token weights so recursive Vision Transformers gain accuracy from added depth with only ~1.7M extra parameters on ImageNet-1K.
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Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
- AsymLoc: Towards Asymmetric Feature Matching for Efficient Visual Localization