Summary of Quota: Quantifying Objects with Text-to-image Models For Any Domain, by Wenfang Sun et al.
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain
by Wenfang Sun, Yingjun Du, Gaowen Liu, Cees G. M. Snoek
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A generative text-to-image model is used to quantify the number of objects in an image. Instead of retraining the model for each new domain of interest, which can be computationally expensive and limited in scalability, a domain-agnostic approach is proposed. The optimization framework, called QUOTA, uses a dual-loop meta-learning strategy to optimize a domain-invariant prompt that enables effective object quantification across unseen domains without retraining. This method integrates learnable counting, domain tokens, and prompt learning to capture stylistic variations and maintain accuracy, even for object classes not encountered during training. The performance of QUOTA is evaluated using a new benchmark specifically designed for object quantification in domain generalization, which assesses both accuracy and adaptability across unseen domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A text-to-image model helps count objects in pictures. Instead of retraining the model every time we want to use it for a new type of image, researchers created QUOTA, an optimization framework that can work with any kind of image without needing to be retrained. QUOTA uses a special kind of learning to find a prompt that works across different types of images, even if the model has never seen some of those objects before. This makes it more accurate and faster than other models. |
Keywords
» Artificial intelligence » Domain generalization » Meta learning » Optimization » Prompt
