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Keyword: local feature aggregation ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   VOLO: Vision Outlooker for Visual Recognition

   Yuan, Hou, Jiang, Feng and Yan · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2022

   The paper introduces Vision Outlooker (VOLO), built on a new outlook attention, and reports 87.1% top-1 ImageNet-1K accuracy without extra training data, plus good transfer to semantic segmentation.

   Unchecked2 claims
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   1. UncheckedOutlook attention encodes finer-level features and context into tokens, which the paper says helps recognition and self-attention largely overlooks.“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.”
   2. UncheckedThe VOLO model reaches 87.1% top-1 accuracy on ImageNet-1K without extra training data, and its pre-trained version transfers well to semantic segmentation.“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 seg…”