Summary of Explainable Image Captioning Using Cnn- Cnn Architecture and Hierarchical Attention, by Rishi Kesav Mohan et al.
Explainable Image Captioning using CNN- CNN architecture and Hierarchical Attention
by Rishi Kesav Mohan, Sanjay Sureshkumar, Vignesh Sivasubramaniam
First submitted to arxiv on: 28 Jun 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 proposed paper explores the application of Explainable AI (XAI) techniques to improve the transparency and trustworthiness of image captioning models. Conventional deep learning-based solutions, while effective, operate as “black boxes” that lack interpretability and explanations for their predictions. The authors aim to address this limitation by developing an XAI-enabled approach to image captioning, leveraging a novel architecture featuring a CNN decoder and hierarchical attention mechanism. The resulting model is trained and evaluated on the MSCOCO dataset, with both quantitative and qualitative results presented in the paper. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re looking at a picture of your favorite animal. You can describe what’s happening in the image, but how does a computer program do it? Researchers are working on making these programs better by explaining why they make certain decisions. This helps us understand how computers think and makes them more trustworthy. In this paper, scientists develop a new way to caption images using an approach called Explainable AI. They use a special architecture that combines different techniques to improve the speed and accuracy of caption generation. The results are impressive, showing both accurate captions and explanations for why they were generated. |
Keywords
» Artificial intelligence » Attention » Cnn » Decoder » Deep learning » Image captioning
