Summary of Learning From Synthetic Data For Visual Grounding, by Ruozhen He et al.
Learning from Synthetic Data for Visual Grounding
by Ruozhen He, Ziyan Yang, Paola Cascante-Bonilla, Alexander C. Berg, Vicente Ordonez
First submitted to arxiv on: 20 Mar 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 This paper investigates the use of synthetic training data to improve vision-and-language models for grounding textual descriptions to image regions. The authors explore different strategies for generating image-text pairs and triplets using various pretrained models, under different settings and varying degrees of reliance on real data. Through comparative analyses with synthetic, real, and web-crawled data, the paper identifies factors that contribute to performance differences and proposes SynGround, a pipeline for generating useful synthetic data. The authors find that SynGround improves the localization capabilities of off-the-shelf vision-and-language models and offers potential for arbitrarily large-scale data generation. Specifically, data generated with SynGround increases pointing game accuracy by 4.81% and 17.11% absolute percentage points in ALBEF and BLIP models, respectively, on RefCOCO+ and Flickr30k benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out if fake training data can help improve how well computers understand images and words together. They test different ways of making this fake data using special computer models. By comparing their fake data with real data, they learn what makes the differences between them. They propose a new way of making this fake data called SynGround, which helps make computers better at understanding where things are in an image. This means that computers can get even better at doing tasks like pointing out objects in pictures. The results show that using SynGround improves the computer’s accuracy by a lot. |
Keywords
* Artificial intelligence * Grounding * Synthetic data
