Summary of Wordvis: a Color Worth a Thousand Words, by Umar Khan et al.
WordVIS: A Color Worth A Thousand Words
by Umar Khan, Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed
First submitted to arxiv on: 13 Dec 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 The paper proposes a novel approach to document classification by directly embedding textual features into visual space, enabling lightweight image-based classifiers to achieve state-of-the-art results using small-scale datasets. This is achieved without requiring extensive training data or computational power. The authors test their approach on the Tobacco-3482 dataset and report an improvement of 4.64% using ResNet50 with no document pre-training, setting a new record for the best accuracy with a score of 91.14% using the image-based DocXClassifier. The simplicity and efficiency of the approach make it a promising solution for industrial use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines better understand documents by combining text and images in a special way. This makes it possible to train simple computers to classify documents quickly and accurately, even with limited data. The authors tested their idea on a standard dataset called Tobacco-3482 and got impressive results. They showed that their method can improve document classification accuracy by 4.64% and achieve a score of 91.14%, which is the best ever recorded for this task. |
Keywords
» Artificial intelligence » Classification » Embedding
