Summary of Post-hoc Probabilistic Vision-language Models, by Anton Baumann et al.
Post-hoc Probabilistic Vision-Language Models
by Anton Baumann, Rui Li, Marcus Klasson, Santeri Mentu, Shyamgopal Karthik, Zeynep Akata, Arno Solin, Martin Trapp
First submitted to arxiv on: 8 Dec 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 The proposed method in this paper focuses on improving the uncertainty estimation capabilities of vision-language models (VLMs) like CLIP and SigLIP, which have shown significant success in tasks such as classification, retrieval, and generation. The current deterministic mapping approach in VLMs fails to capture uncertainties that arise from domain shifts when used in downstream tasks. To address this limitation, the authors introduce a post-hoc uncertainty estimation method for VLMs that does not require additional training. This method leverages Bayesian posterior approximation over the last layers of VLMs and analytically quantifies uncertainties over cosine similarities. The proposed approach is demonstrated to be effective for uncertainty quantification and support set selection in active learning, yielding improved and well-calibrated predictive uncertainties, interpretable uncertainty estimates, and sample-efficient active learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to make vision-language models better at guessing how certain they are about their answers. This is important because these models can be used for things like self-driving cars or medical diagnosis, where it’s crucial to know when you’re not sure. The current way that these models work doesn’t take into account the uncertainty of the input data, which can lead to poor results in real-world applications. The authors developed a new method that doesn’t require retraining the model and improves the accuracy of the uncertainty estimates. |
Keywords
» Artificial intelligence » Active learning » Classification
