Summary of Single-image Driven 3d Viewpoint Training Data Augmentation For Effective Wine Label Recognition, by Yueh-cheng Huang et al.
Single-image driven 3d viewpoint training data augmentation for effective wine label recognition
by Yueh-Cheng Huang, Hsin-Yi Chen, Cheng-Jui Hung, Jen-Hui Chuang, Jenq-Neng Hwang
First submitted to arxiv on: 12 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 This paper addresses the issue of limited training data for complex image recognition, particularly in the context of wine label recognition. A novel 3D viewpoint augmentation technique is introduced to enhance deep learning model performance by generating realistic training samples from a single real-world wine label image. The method leverages computer vision and image processing strategies to expand the training dataset, overcoming limitations posed by intricate text and logo combinations. Classical GAN methods are inadequate for synthesizing such content combinations, making this approach unique. The proposed technique uses batch-all triplet metric learning on a Vision Transformer (ViT) architecture, enabling one-shot recognition of wine labels in existing or newly collected classes. Experimental results show significant improvements over conventional 2D data augmentation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a big problem in recognizing images, specifically wine labels. The challenge is that we don’t have enough training data to make the computer recognize these labels accurately. To overcome this issue, the researchers developed a new way to create fake training samples from real wine label images. This helps train the computer to recognize different wine labels more effectively. The technique uses clever image processing and computer vision strategies to generate realistic training samples. This leads to better recognition accuracy for existing or new wine labels. The results show that this approach is much better than previous methods. |
Keywords
* Artificial intelligence * Data augmentation * Deep learning * Gan * One shot * Vision transformer * Vit
