Summary of Debiasing Vison-language Models with Text-only Training, by Yunfan Yang et al.
Debiasing Vison-Language Models with Text-Only Training
by Yunfan Yang, Chaoquan Jiang, Zhiyu Lin, Jinlin Xiao, Jiaming Zhang, Jitao Sang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A recent paper proposes a novel approach to mitigate visual biases in pre-trained vision-language models (VLMs) like CLIP. The authors identify that existing debiasing methods struggle to obtain sufficient image samples for minority groups, which leads to biased models. To address this issue, they introduce the Text-Only Debiasing framework (TOD), which leverages a text-as-image training paradigm and utilizes large language models to generate balanced text datasets. Additionally, they develop a Multi-Target Prediction task to encourage the model to focus on complex contexts and distinguish between target and biased information. The authors demonstrate state-of-the-art results among image-free methods and competitive performance compared to image-supervised methods on the Waterbirds and CelebA datasets. The proposed method shows strong generalization and robustness, making it suitable for challenging scenarios with multiple or unknown bias attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make computer vision models less biased. These models are trained using lots of images and text, but they can still be unfair because they’re not exposed to many examples from certain groups. The authors came up with a new way to fix this problem that only uses text data. They tested it on two different datasets and found that it worked really well. This is important because we need our computer models to be fair and accurate, especially when we’re dealing with images of people. |
Keywords
» Artificial intelligence » Generalization » Supervised
