Summary of Haap: Vision-context Hierarchical Attention Autoregressive with Adaptive Permutation For Scene Text Recognition, by Honghui Chen et al.
HAAP: Vision-context Hierarchical Attention Autoregressive with Adaptive Permutation for Scene Text Recognition
by Honghui Chen, Yuhang Qiu, Jiabao Wang, Pingping Chen, Nam Ling
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a new method called Hierarchical Attention autoregressive Model with Adaptive Permutation (HAAP), which enhances location-context-image interaction capabilities to improve autoregressive generalization using internal Language Models (LMs). The HAAP combines two key innovations: Implicit Permutation Neurons (IPN) and Cross-modal Hierarchical Attention mechanism (CHA). IPN generates adaptive attention masks to exploit token dependencies, reducing training overhead and fit oscillations. CHA couples context and image features to establish rich positional semantic dependencies while avoiding Iterative Refinement (IR) operations. The proposed method achieves state-of-the-art performance in terms of accuracy, complexity, and latency on several benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to understand images by combining two ideas: Hierarchical Attention autoregressive Model with Adaptive Permutation (HAAP). HAAP helps machines recognize pictures better. It does this by using two special tools: Implicit Permutation Neurons (IPN) and Cross-modal Hierarchical Attention mechanism (CHA). IPN makes sure the computer pays attention to important details in images, while CHA connects what’s happening in the image with what’s being said about it. This helps machines get better at understanding pictures. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Generalization » Token
