Papers › VOLO: Vision Outlooker for Visual Recognition

VOLO: Vision Outlooker for Visual Recognition

24 Jun 2021arXiv:2106.13112archive 2025-07-28

Li Yuan, Qibin Hou, Zihang Jiang, Jiashi Feng, Shuicheng Yan

Visual recognition has been dominated by convolutional neural networks (CNNs) for years. Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided. In this work, we try to close the performance gap and demonstrate that attention-based models are indeed able to outperform CNNs. We find a major factor limiting the performance of ViTs for ImageNet classification is their low efficacy in encoding fine-level features into the token representations. To resolve this, we introduce a novel outlook attention and present a simple and general architecture, termed Vision Outlooker (VOLO). Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts into tokens, which is shown to be critically beneficial to recognition performance but largely ignored by the self-attention. Experiments show that our VOLO achieves 87.1% top-1 accuracy on ImageNet-1K classification, which is the first model exceeding 87% accuracy on this competitive benchmark, without using any extra training data In addition, the pre-trained VOLO transfers well to downstream tasks, such as semantic segmentation. We achieve 84.3% mIoU score on the cityscapes validation set and 54.3% on the ADE20K validation set. Code is available at \url{https://github.com/sail-sg/volo}.

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Tasks

Domain GeneralizationImage ClassificationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Generalization VizWiz-Classification VOLO-D5 Accuracy - All Images 57.2 #1 of 90 Archive leaderboard report
Domain Generalization VizWiz-Classification VOLO-D5 Accuracy - Clean Images 59.7 #1 of 90 Archive leaderboard report
Domain Generalization VizWiz-Classification VOLO-D5 Accuracy - Corrupted Images 51.8 #1 of 90 Archive leaderboard report
Image Classification ImageNet VOLO-D5 GFLOPs 412 #107 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D5 Number of params 296M #107 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D5 Top 1 Accuracy 87.1% #107 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D4 GFLOPs 197 #123 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D4 Number of params 193M #123 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D4 Top 1 Accuracy 86.8% #123 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D3 GFLOPs 67.9 #157 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D3 Number of params 86M #157 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D3 Top 1 Accuracy 86.3% #157 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D2 Number of params 59M #180 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D2 Top 1 Accuracy 86% #180 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D1 Number of params 27M #247 of 1060 Archive leaderboard report
Image Classification ImageNet VOLO-D1 Top 1 Accuracy 85.2% #247 of 1060 Archive leaderboard report
Image Classification ImageNet ReaL VOLO-D5 Accuracy 90.6% #15 of 57 Archive leaderboard report
Image Classification ImageNet ReaL VOLO-D4 Accuracy 90.5% #18 of 57 Archive leaderboard report
Image Classification ImageNet V2 VOLO-D5 Top 1 Accuracy 78 #14 of 33 Archive leaderboard report
Image Classification ImageNet V2 VOLO-D4 Top 1 Accuracy 77.8 #15 of 33 Archive leaderboard report
Image Classification VizWiz-Classification VOLO-D5 Accuracy 57.2 #1 of 1 Archive leaderboard report
Semantic Segmentation ADE20K VOLO-D5 Validation mIoU 54.3 #62 of 235 Archive leaderboard report
Semantic Segmentation Cityscapes val VOLO-D4 (MS, ImageNet1k pretrain) mIoU 84.3 #18 of 99 Archive leaderboard report
Semantic Segmentation Graz-02 VOLO-D5 Pixel Accuracy 85 #1 of 2 Archive leaderboard report

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