Summary of Recurrent Few-shot Model For Document Verification, by Maxime Talarmain et al.
Recurrent Few-Shot model for Document Verification
by Maxime Talarmain, Carlos Boned, Sanket Biswas, Oriol Ramos
First submitted to arxiv on: 3 Oct 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 recurrent-based model that can detect forged travel documents in a few-shot scenario, addressing the challenge of low-resolution images and limited training data. The model’s recurrent architecture makes it robust to document resolution variability, enabling it to perform well even with unseen document classes. Preliminary results on SIDTD and Findit datasets show promising performance for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in checking travel documents by creating a better way to recognize fake ones. The current methods aren’t good enough because they struggle with low-quality images and limited training data. This new model can handle these challenges and work well even when it’s never seen certain types of documents before. |
Keywords
» Artificial intelligence » Few shot
