Summary of Benchmarking Zero-shot Robustness Of Multimodal Foundation Models: a Pilot Study, by Chenguang Wang et al.
Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study
by Chenguang Wang, Ruoxi Jia, Xin Liu, Dawn Song
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 explores the potential of pre-training image representations from raw text about images for zero-shot vision transfer to downstream tasks. The authors demonstrate that multimodal foundation models, such as CLIP, can achieve state-of-the-art performance on various classification tasks without task-specific training. Moreover, these models close the robustness gap by matching the performance of supervised models trained on ImageNet under natural distribution shift. To evaluate the robustness of these zero-shot models, the authors present a comprehensive evaluation based on a large-scale robustness benchmark covering 7 natural, 3 synthetic distribution shifts, and 11 adversarial attacks using CLIP as a pilot study. The results show that while CLIP leads to a significant robustness drop compared to supervised ImageNet models under some conditions, there is still room for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can learn to recognize images just by reading about them. The researchers trained special computer models on huge amounts of text and image data from the internet. They found that these models could do a great job recognizing pictures without needing extra training. This is important because it could make computers better at understanding what they see in the real world. To test how well these models work, the researchers looked at how well they did when the images were changed or distorted. The results show that while these computer models are pretty good, there’s still room for improvement. |
Keywords
* Artificial intelligence * Classification * Supervised * Zero shot
