DC-DiT learns dynamic chunking to allocate fewer tokens to smooth or noisy regions and more to detailed or late-stage areas, cutting inference FLOPs up to 36.8% while improving FID up to 37.8% on class-conditional ImageNet generation.
Dynamicvit: Efficient vision transformers with dynamic token sparsification.arXiv preprint arXiv:2106.02034
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S2 improves generalization in vision-language-action models by using goal-preserving refined language guidance and explicit visual evidence budgets, raising mean subtask success from 54.2% to 79.0% on eight real-robot tasks compared to pi0.5.
MobileViT is a lightweight vision transformer that reports 78.4% top-1 accuracy on ImageNet-1k with ~6M parameters, outperforming MobileNetv3 by 3.2% and DeIT by 6.2% at similar size, plus gains on MS-COCO detection.
citing papers explorer
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DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking
DC-DiT learns dynamic chunking to allocate fewer tokens to smooth or noisy regions and more to detailed or late-stage areas, cutting inference FLOPs up to 36.8% while improving FID up to 37.8% on class-conditional ImageNet generation.
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See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs
S2 improves generalization in vision-language-action models by using goal-preserving refined language guidance and explicit visual evidence budgets, raising mean subtask success from 54.2% to 79.0% on eight real-robot tasks compared to pi0.5.
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MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
MobileViT is a lightweight vision transformer that reports 78.4% top-1 accuracy on ImageNet-1k with ~6M parameters, outperforming MobileNetv3 by 3.2% and DeIT by 6.2% at similar size, plus gains on MS-COCO detection.