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Summary of Progressive Semantic-guided Vision Transformer For Zero-shot Learning, by Shiming Chen et al.


Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning

by Shiming Chen, Wenjin Hou, Salman Khan, Fahad Shahbaz Khan

First submitted to arxiv on: 11 Apr 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Machine Learning (cs.LG)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A novel zero-shot learning (ZSL) approach, dubbed ZSLViT, is proposed to tackle the limitations of existing ZSL methods. These methods rely on pre-trained networks to extract visual features, but fail to learn matched visual-semantic correspondences. ZSLViT addresses this issue by introducing semantic-embedded token learning and semantic-guided token attention to discover semantic-related visual representations explicitly. The model then fuses low semantic-visual correspondence tokens to discard semantic-unrelated information for visual enhancement. This progressive learning process enables accurate visual-semantic interactions in ZSL. Empirically, ZSLViT achieves significant performance gains on three benchmark datasets: CUB, SUN, and AWA2.
Low GrooveSquid.com (original content) Low Difficulty Summary
Researchers have been working on a way to teach computers to recognize things they’ve never seen before. This is called zero-shot learning (ZSL). The problem with current methods is that they don’t really understand what the pictures are showing them. To fix this, scientists came up with a new approach called ZSLViT. It uses special techniques to figure out which parts of the picture are important and which aren’t. This helps it learn more accurately about things it’s never seen before. They tested their method on three different sets of pictures and found that it did much better than other methods.

Keywords

» Artificial intelligence  » Attention  » Token  » Zero shot