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REViT-v2: New Equivariant Vision Transformer Scales to Millions of Parameters

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced REViT-v2, a novel vision transformer architecture designed for equivariant feature extraction. This model utilizes windowed group-convolutional self-attention and a hierarchical feature design, enabling it to scale effectively to millions of parameters and large datasets like ImageNet. The associated code and pre-trained weights for REViT-v2 are publicly available. AI

IMPACT Introduces a new architecture for equivariant feature extraction that scales to large datasets and models.

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

Read on arXiv cs.LG →

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

REViT-v2: New Equivariant Vision Transformer Scales to Millions of Parameters

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

  1. arXiv cs.LG TIER_1 English(EN) · Sheir A. Zaheer, Jihwan Moon, Chan Y. Park ·

    REViT-v2: Hierarchical Windowed Roto-reflection Equivariant ViT for Equivariant Feature Extraction

    arXiv:2610.07585v1 Announce Type: cross Abstract: We propose a scalable roto-reflection-group-equivariant vision transformer based on windowed group-convolutional self-attention and a hierarchical feature architecture. We demonstrate that our approach can be scaled to group-equiv…

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