Summary of Automated Image Captioning with Cnns and Transformers, by Joshua Adrian Cahyono et al.
Automated Image Captioning with CNNs and Transformers
by Joshua Adrian Cahyono, Jeremy Nathan Jusuf
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 proposed automated image captioning system combines computer vision and natural language processing techniques to generate descriptive captions for input images. The system employs various methods, including CNN-RNN and transformer-based approaches, and is trained on paired image-caption datasets. Performance will be evaluated using established metrics like BLEU, METEOR, and CIDEr. Additionally, the project involves experimenting with advanced attention mechanisms, comparing different architectural choices, and optimizing hyperparameters to refine captioning accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The project aims to create an automated system that can describe images in natural language. It combines two fields: computer vision (helps recognize what’s in an image) and natural language processing (generates text). The system will be trained on pictures with captions, then tested using special metrics. By experimenting with new ideas and adjusting settings, the team hopes to make the system better at describing images. |
Keywords
» Artificial intelligence » Attention » Bleu » Cnn » Image captioning » Natural language processing » Rnn » Transformer
