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bViT uses single-block recurrence for parameter-efficient vision transformers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed bViT, a novel Vision Transformer architecture that utilizes a single transformer block applied repeatedly for image recognition. This recurrent approach achieves accuracy comparable to standard ViTs on ImageNet-1K with significantly fewer parameters. The study suggests that a substantial portion of a ViT's depth can be achieved through recurrent computation, especially when the representation space is wide, enabling parameter-efficient fine-tuning for downstream tasks. AI

IMPACT Introduces a parameter-efficient architecture for vision transformers, potentially reducing computational costs for image recognition tasks.

RANK_REASON The cluster contains an academic paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

bViT uses single-block recurrence for parameter-efficient vision transformers

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The cluster contains an academic paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Alberto Presta ·

    bViT: Investigating Single-Block Recurrence in Vision Transformers for Image Recognition

    Vision Transformers (ViTs) are built by stacking independently parameterized blocks, but it remains unclear how much of this depth requires layer specific transformations and how much can be realized through recurrent computation. We study this question with bViT, a single-block …

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