Summary of Revisiting Image Captioning Training Paradigm Via Direct Clip-based Optimization, by Nicholas Moratelli et al.
Revisiting Image Captioning Training Paradigm via Direct CLIP-based Optimization
by Nicholas Moratelli, Davide Caffagni, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 Direct CLIP-Based Optimization (DiCO) training paradigm optimizes a reward model distilled from a learnable captioning evaluator to produce fluent and informative captions. This approach jointly learns and optimizes the reward model, solving a weighted classification problem directly inside the captioner while preventing divergence from the original model. DiCO exhibits improved stability and enhanced quality in generated captions, aligns with human preferences in modern metrics, and maintains competitive performance in traditional metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to train machines to describe images. This method is called Direct CLIP-Based Optimization (DiCO). It helps the machine understand what makes good captions by learning from a special evaluator that agrees with humans on how well captions match an image. This approach keeps the machine’s captions fluent and easy to read, and it does better than other methods in making captions that people like. The code and trained models are available for others to use. |
Keywords
» Artificial intelligence » Classification » Optimization
