Summary of A Sober Look at the Robustness Of Clips to Spurious Features, by Qizhou Wang et al.
A Sober Look at the Robustness of CLIPs to Spurious Features
by Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 new research paper proposes a novel approach to evaluating the robustness of large vision language models like CLIP to realistic spurious features. The study argues that existing benchmarking datasets may not accurately reflect the extent to which these models are robust to spurious correlations within their training data, such as LAION. To address this limitation, the authors create a new challenging dataset called CounterAnimal, designed to reveal the reliance of CLIP models on realistic spurious features. CounterAnimal is crafted by splitting animal photos into groups based on backgrounds and identifying pairs where a CLIP model shows significant performance drops across the two groups. The study finds that the spurious features captured by CounterAnimal are generically learned by CLIP models with different backbones and pre-train data, but have limited influence for ImageNet models. The authors provide theoretical insights suggesting that the CLIP objective does not offer additional robustness against spurious features. They also re-evaluate strategies such as scaling up parameters and using high-quality pre-trained data, finding that these approaches still help mitigate the impact of spurious features, providing a promising path for future developments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision language models like CLIP are very good at recognizing things in pictures, but they can be fooled by fake or misleading information. This paper looks into how well these models do when faced with realistic and fake features that might confuse them. The researchers created a new test dataset called CounterAnimal to see if the models would still work well even when these confusing features were present. The study found that while the models did struggle with the confusing features, they were still good at recognizing things in pictures overall. The authors also looked into why this was happening and suggested that the way the models are trained might not be helping them to avoid getting fooled by fake information. This research could help us understand how we can make these language models even better at recognizing things in pictures without being tricked by confusing features. |
