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ProgResViT: Progressive Resolution and Width for Adaptive Vision Transformers
Vision Transformers (ViTs) typically process every image using a fixed input resolution and model width, even though many images can be classified with substantially less computation. We introduce ProgResViT, an input-adaptive ViT that performs inference progressively across multiple rounds. The first round processes a low-resolution image with a narrow subnetwork. Inference terminates when the prediction is sufficiently confident; otherwise, the model reuses the representations produced in the current round and proceeds with a higher-resolution input and a wider subnetwork to refine its predi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T23:10:56.000Z
First collected: 2026-09-21T05:11:56.580Z. This is not the publication date.