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LocalViT: Analyzing Locality in Vision Transformers

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arxiv 2104.05707 v2 pith:D4CRNYL5 submitted 2021-04-12 cs.CV

Yawei Li , Kai Zhang , Jiezhang Cao , Radu Timofte , Michele Magno , Luca Benini , Luc Van Gool This is my paper
classification cs.CV
keywords localitytransformersmechanismvisionmechanismsavailablechoicesfeed-forward
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The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for information exchange within a local region. In this paper, locality mechanism is systematically investigated by carefully designed controlled experiments. We add locality to vision transformers into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept. For ImageNet2012 classification, the locality-enhanced transformers outperform the baselines Swin-T, DeiT-T, and PVT-T by 1.0%, 2.6% and 3.1% with a negligible increase in the number of parameters and computational effort. Code is available at https://github.com/ofsoundof/LocalViT.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution

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  3. DuoFormer: Leveraging Hierarchical Representations by Local and Global Attention Vision Transformer

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  4. DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction

    cs.CV 2025-07 reject novelty 3.0 of 10

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