Scaling Graph Convolutions for Mobile Vision

• URL: http://arxiv.org/abs/2406.05850v1
• Date: Sun, 9 Jun 2024 16:49:19 GMT
• Title: Scaling Graph Convolutions for Mobile Vision
• Authors: William Avery, Mustafa Munir, Radu Marculescu,
• Abstract summary: This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency.
• Score: 6.4399181389092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 $AP^{box}$ and 0.7 $AP^{mask}$, and MobileViGv2-B outperforms MobileViG-B by 1.0 $AP^{box}$ and 0.7 $AP^{mask}$. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% $mIoU$ and MobileViGv2-B achieves 44.3% $mIoU$. Our code can be found at \url{https://github.com/SLDGroup/MobileViGv2}.

Related papers

• GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs [5.895049552752008]
  Vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. We propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN. We also propose a novel CNN-GNN architecture, GreedyViG, which uses DAGC.
  arXiv  Detail & Related papers  (2024-05-10T23:21:16Z)
• MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices [73.46317110474064]
  MobileVLM is a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion.
  arXiv  Detail & Related papers  (2023-12-28T08:21:24Z)
• Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts [55.282613372420805]
  We explore the use of sparse MoEs to scale-down Vision Transformers (ViTs) to make them more attractive for resource-constrained vision applications. We propose a simplified and mobile-friendly MoE design where entire images rather than individual patches are routed to the experts. We empirically show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off between performance and efficiency than the corresponding dense ViTs.
  arXiv  Detail & Related papers  (2023-09-08T14:24:10Z)
• 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)
• MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications [7.2210216531805695]
  Vision graph neural networks (ViGs) provide a new avenue for exploration. ViGs are computationally expensive due to the overhead of representing images as graph structures. We propose a new graph-based sparse attention mechanism, Sparse Vision Graph Attention (SVGA), that is designed for ViGs running on mobile devices.
  arXiv  Detail & Related papers  (2023-07-01T17:49:12Z)
• A Close Look at Spatial Modeling: From Attention to Convolution [70.5571582194057]
  Vision Transformers have shown great promise recently for many vision tasks due to the insightful architecture design and attention mechanism. We generalize self-attention formulation to abstract a queryirrelevant global context directly and integrate the global context into convolutions. With less than 14M parameters, our FCViT-S12 outperforms related work ResT-Lite by 3.7% top1 accuracy on ImageNet-1K.
  arXiv  Detail & Related papers  (2022-12-23T19:13:43Z)
• MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features [0.0]
  MobileViT (MobileViTv1) combines convolutional neural networks (CNNs) and vision transformers (ViTs) to create light-weight models for mobile vision tasks. We propose changes to the fusion block that are simple and effective to create MobileViTv3-block. Our proposed MobileViTv3-block used to create MobileViTv3-XXS, XS and S models outperform MobileViTv1 on ImageNet-1k, ADE20K, COCO and PascalVOC2012 datasets.
  arXiv  Detail & Related papers  (2022-09-30T01:04:10Z)
• Separable Self-attention for Mobile Vision Transformers [34.32399598443582]
  This paper introduces a separable self-attention method with linear complexity, i.e. $O(k)$. The improved model, MobileViTv2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection.
  arXiv  Detail & Related papers  (2022-06-06T15:31:35Z)
• MoCoViT: Mobile Convolutional Vision Transformer [13.233314183471213]
  We present Mobile Convolutional Vision Transformer (MoCoViT), which improves in performance and efficiency by transformer into mobile convolutional networks. MoCoViT is carefully designed for mobile devices and is very lightweight, accomplished through two primary modifications. Comprehensive experiments verify that our proposed MoCoViT family outperform state-of-the-art portable CNNs and transformer on various vision tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:21:57Z)
• TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation [111.8342799044698]
  We present a mobile-friendly architecture named textbfToken textbfPyramid Vision Transtextbfformer (textbfTopFormer) The proposed textbfTopFormer takes Tokens from various scales as input to produce scale-aware semantic features, which are then injected into the corresponding tokens to augment the representation. On the ADE20K dataset, TopFormer achieves 5% higher accuracy in mIoU than MobileNetV3 with lower latency on an ARM-based mobile device.
  arXiv  Detail & Related papers  (2022-04-12T04:51:42Z)
• MobileDets: Searching for Object Detection Architectures for Mobile Accelerators [61.30355783955777]
  Inverted bottleneck layers have been the predominant building blocks in state-of-the-art object detection models on mobile devices. Regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators. We obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.
  arXiv  Detail & Related papers  (2020-04-30T00:21:30Z)

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.