Summary of Bayesian Exploration Of Pre-trained Models For Low-shot Image Classification, by Yibo Miao et al.
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification
by Yibo Miao, Yu Lei, Feng Zhou, Zhijie Deng
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a probabilistic model ensemble framework based on Gaussian processes to improve low-shot image classification using CLIP-based methods. The proposed approach integrates prior knowledge from various pre-trained models by specifying the mean function with CLIP and the kernel function with an ensemble of deep kernels. This allows for analytical inference, uncertainty quantification, and hyper-parameter tuning. Experimental results show that the method outperforms competitive baselines in predictive performance on standard benchmarks, while also demonstrating robustness and quality uncertainty estimates on out-of-distribution datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better recognize images even when they don’t have many examples to learn from. It uses a new way to combine information from different models that know different things about pictures. This lets the computer make more accurate predictions and understand how sure it is of its answers. The results show that this approach works well on normal datasets, but also does well on datasets that are a little different. Overall, this paper makes computers better at understanding images. |
Keywords
» Artificial intelligence » Image classification » Inference » Probabilistic model
