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New SoftMoR technique enables deeper, more efficient Vision Transformers

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Soft Mixture-of-Recursions (SoftMoR), a novel approach to enhance Vision Transformers (ViTs) by enabling them to leverage intermediate representations from all recursion steps. This method, instantiated as the Soft Recursive Vision Transformer (SR-ViT), allows for the creation of deeper and more powerful ViTs with minimal parameter increases. Experiments on ImageNet-1K showed that increasing recursion depth in SR-ViT-S from one to four improved top-1 accuracy from 79.83% to 82.48%, while using significantly fewer parameters than larger models like DeiT-B. AI

IMPACT This research offers a parameter-efficient method for developing more capable Vision Transformers, potentially impacting computer vision applications.

RANK_REASON The cluster contains an academic paper detailing a new method for improving Vision Transformers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New SoftMoR technique enables deeper, more efficient Vision Transformers

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The cluster contains an academic paper detailing a new method for improving Vision Transformers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sang In Lee, Jihun Park ·

    Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers

    arXiv:2607.00774v1 Announce Type: cross Abstract: Recent recursive Transformer studies have primarily reused shared parameters across computation steps to construct compact, parameter-efficient models. In this work, we leverage recursion to build effectively deeper Transformers w…

  2. arXiv cs.LG TIER_1 English(EN) · Jihun Park ·

    Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers

    Recent recursive Transformer studies have primarily reused shared parameters across computation steps to construct compact, parameter-efficient models. In this work, we leverage recursion to build effectively deeper Transformers with stronger representational capacity. However, i…

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