Summary of Harlequin: Color-driven Generation Of Synthetic Data For Referring Expression Comprehension, by Luca Parolari et al.
Harlequin: Color-driven Generation of Synthetic Data for Referring Expression Comprehension
by Luca Parolari, Elena Izzo, Lamberto Ballan
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 framework to generate synthetic labeled data for Referring Expression Comprehension (REC), an important topic in visual language understanding. The framework creates variations of existing annotations and generates images using altered guidance, resulting in a new dataset called Harlequin with over 1M queries. This approach eliminates manual data collection and annotation, enabling scalability and facilitating complex tasks. The authors pre-train three REC models on Harlequin, fine-tune them on human-annotated datasets, and demonstrate the benefits of pre-training on artificial data for improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers understand what we mean when we talk about objects in a picture. Currently, it’s hard to make these systems work without labeling lots of pictures manually. The authors developed a new way to create fake labeled pictures that can help train computer models to do this job better. This can be very useful because it means we don’t have to label so many pictures by hand. |
Keywords
» Artificial intelligence » Language understanding
