EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers

• URL: http://arxiv.org/abs/2205.03436v1
• Date: Fri, 6 May 2022 18:17:19 GMT
• Title: EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers
• Authors: Junting Pan, Adrian Bulat, Fuwen Tan, Xiatian Zhu, Lukasz Dudziak, Hongsheng Li, Georgios Tzimiropoulos and Brais Martinez
• Abstract summary: Self-attention based vision transformers (ViTs) have emerged as a very competitive architecture alternative to convolutional neural networks (CNNs) in computer vision. We introduce EdgeViTs, a new family of light-weight ViTs that, for the first time, enable attention-based vision models to compete with the best light-weight CNNs.
• Score: 88.52500757894119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-attention based models such as vision transformers (ViTs) have emerged as a very competitive architecture alternative to convolutional neural networks (CNNs) in computer vision. Despite increasingly stronger variants with ever-higher recognition accuracies, due to the quadratic complexity of self-attention, existing ViTs are typically demanding in computation and model size. Although several successful design choices (e.g., the convolutions and hierarchical multi-stage structure) of prior CNNs have been reintroduced into recent ViTs, they are still not sufficient to meet the limited resource requirements of mobile devices. This motivates a very recent attempt to develop light ViTs based on the state-of-the-art MobileNet-v2, but still leaves a performance gap behind. In this work, pushing further along this under-studied direction we introduce EdgeViTs, a new family of light-weight ViTs that, for the first time, enable attention-based vision models to compete with the best light-weight CNNs in the tradeoff between accuracy and on-device efficiency. This is realized by introducing a highly cost-effective local-global-local (LGL) information exchange bottleneck based on optimal integration of self-attention and convolutions. For device-dedicated evaluation, rather than relying on inaccurate proxies like the number of FLOPs or parameters, we adopt a practical approach of focusing directly on on-device latency and, for the first time, energy efficiency. Specifically, we show that our models are Pareto-optimal when both accuracy-latency and accuracy-energy trade-offs are considered, achieving strict dominance over other ViTs in almost all cases and competing with the most efficient CNNs.

Related papers

• Optimizing Vision Transformers with Data-Free Knowledge Transfer [8.323741354066474]
  Vision transformers (ViTs) have excelled in various computer vision tasks due to their superior ability to capture long-distance dependencies. We propose compressing large ViT models using Knowledge Distillation (KD), which is implemented data-free to circumvent limitations related to data availability.
  arXiv  Detail & Related papers  (2024-08-12T07:03:35Z)
• Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition [49.14350399025926]
  We apply pre-trained architectures, originally developed for the ImageNet Large Scale Visual Recognition Challenge, for periocular recognition. Middle-layer features from CNNs and ViTs are a suitable way to recognize individuals based on periocular images.
  arXiv  Detail & Related papers  (2024-07-28T11:52:36Z)
• PriViT: Vision Transformers for Fast Private Inference [55.36478271911595]
  Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. ViTs are ill-suited for private inference using secure multi-party protocols, due to the large number of non-polynomial operations. We propose PriViT, an algorithm to selectively " Taylorize" nonlinearities in ViTs while maintaining their prediction accuracy.
  arXiv  Detail & Related papers  (2023-10-06T21:45:05Z)
• RepViT: Revisiting Mobile CNN From ViT Perspective [67.05569159984691]
  lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency, compared with lightweight Convolutional Neural Networks (CNNs) In this study, we revisit the efficient design of lightweight CNNs from ViT perspective and emphasize their promising prospect for mobile devices.
  arXiv  Detail & Related papers  (2023-07-18T14:24:33Z)
• FasterViT: Fast Vision Transformers with Hierarchical Attention [63.50580266223651]
  We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs.
  arXiv  Detail & Related papers  (2023-06-09T18:41:37Z)
• Rethinking Vision Transformers for MobileNet Size and Speed [58.01406896628446]
  We propose a novel supernet with low latency and high parameter efficiency. We also introduce a novel fine-grained joint search strategy for transformer models. This work demonstrate that properly designed and optimized vision transformers can achieve high performance even with MobileNet-level size and speed.
  arXiv  Detail & Related papers  (2022-12-15T18:59:12Z)
• Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training [29.20567759071523]
  Vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in computer vision. This paper introduces CNNs' inductive biases back to ViTs while preserving their network architectures for higher upper bound. Experiments on CIFAR-10/100 and ImageNet-1k with limited training data have shown encouraging results.
  arXiv  Detail & Related papers  (2021-12-07T07:56:50Z)
• Rethinking the Design Principles of Robust Vision Transformer [28.538786330184642]
  Vision Transformers (ViT) have shown that self-attention-based networks surpassed traditional convolution neural networks (CNNs) in most vision tasks. In this paper, we rethink the design principles of ViTs based on the robustness. By combining the robust design components, we propose Robust Vision Transformer (RVT)
  arXiv  Detail & Related papers  (2021-05-17T15:04:15Z)
• LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference [25.63398340113755]
  We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. We introduce the attention bias, a new way to integrate positional information in vision transformers. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect to the speed/accuracy tradeoff.
  arXiv  Detail & Related papers  (2021-04-02T16:29:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.