Summary of Hsvlt: Hierarchical Scale-aware Vision-language Transformer For Multi-label Image Classification, by Shuyi Ouyang et al.
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification
by Shuyi Ouyang, Hongyi Wang, Ziwei Niu, Zhenjia Bai, Shiao Xie, Yingying Xu, Ruofeng Tong, Yen-Wei Chen, Lanfen Lin
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 The proposed Hierarchical Scale-Aware Vision-Language Transformer (HSVLT) addresses limitations in existing transformer-based methods for multi-label image classification by introducing a hierarchical multi-scale architecture and an Interactive Visual-Linguistic Attention mechanism. The approach leverages joint multi-modal features extracted from multiple scales to recognize objects of varying sizes and appearances, and enables the tight integration of cross-modal interaction for updating visual, linguistic, and multi-modal features. Evaluation on three benchmark datasets demonstrates that HSVLT surpasses state-of-the-art methods with lower computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers understand images is proposed in this paper. This method is called Hierarchical Scale-Aware Vision-Language Transformer (HSVLT). It’s good at recognizing multiple objects in one image, even if they are different sizes or have different appearances. The method works by using a special kind of computer vision that looks at the same image from different angles and combines this information to get better results. This makes it better than other methods at finding objects in images. |
Keywords
» Artificial intelligence » Attention » Image classification » Multi modal » Transformer
