Summary of Positive-augmented Contrastive Learning For Vision-and-language Evaluation and Training, by Sara Sarto et al.
Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Training
by Sara Sarto, Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 9 Oct 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 paper proposes a new evaluation metric for caption generation, called PAC-S++. This metric uses the CLIP model to leverage both web-collected and cleaned data, as well as regularized through additional pairs of generated visual and textual positive samples. The authors demonstrate the effectiveness of PAC-S++ compared to popular metrics, such as its sensitivity to object hallucinations. Furthermore, they show that integrating PAC-S++ into the fine-tuning stage of a captioning model results in semantically richer captions with fewer repetitions and grammatical errors. Evaluations on out-of-domain benchmarks further demonstrate the efficacy of their fine-tuning approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to measure how good a computer-generated description of an image or video is. Right now, we use simple methods that don’t really capture what makes a great caption. The authors propose a new method called PAC-S++ that uses a special kind of AI model trained on lots of data. They tested their approach and found it worked better than other methods, making the captions more detailed and accurate. |
Keywords
» Artificial intelligence » Fine tuning
