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Recurrent Vision Transformer Achieves Full-Depth Encoder Accuracy with Single Block

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel recurrent Vision Transformer (reViT) that utilizes a single Transformer block applied recurrently to achieve accuracy comparable to full-depth vision encoders. This approach represents the feed-forward network (FFN) at each recurrent depth as a combination of shared experts, programmed by a normalized-depth coordinate. The reViT model demonstrates strong performance in both supervised training on ImageNet-1k and distillation from a DINOv2 teacher, outperforming other Mixture-of-Experts (MoE) families and achieving DeiT III accuracy with significantly fewer parameters. AI

IMPACT This research could lead to more efficient vision models by reducing parameter count while maintaining high accuracy.

RANK_REASON The cluster describes a new research paper detailing a novel model architecture.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Recurrent Vision Transformer Achieves Full-Depth Encoder Accuracy with Single Block

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos ·

    One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts

    arXiv:2610.12448v1 Announce Type: cross Abstract: In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inference FLOPs without intermediate feature distillation. reViT restores depth-specif…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts

    In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inference FLOPs without intermediate feature distillation. reViT restores depth-specific transformations by representing the FFN at each…

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