Summary of Selective Prediction For Semantic Segmentation Using Post-hoc Confidence Estimation and Its Performance Under Distribution Shift, by Bruno Laboissiere Camargos Borges et al.
Selective Prediction for Semantic Segmentation using Post-Hoc Confidence Estimation and Its Performance under Distribution Shift
by Bruno Laboissiere Camargos Borges, Bruno Machado Pacheco, Danilo Silva
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 addresses the limitations of semantic segmentation in various computer vision applications by proposing a novel image-level confidence measure for pre-trained models operating under distribution shift. The method leverages selective prediction to mitigate risks and reduce reliance on expert supervision, particularly in low-resource settings where model errors can have significant consequences. By applying post-hoc confidence estimators to pre-trained models, the approach aims to improve the efficacy of semantic segmentation in scenarios with limited labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions when we don’t have enough training data for computer vision tasks like image classification and object detection. Imagine you’re trying to diagnose a medical condition by looking at images, but you only have a small group of examples to work with. That’s where this paper comes in – it shows how to use special kinds of confidence measures to make more informed decisions even when the data is limited. The method uses pre-trained models and then fine-tunes them for specific tasks, which can be really helpful in situations where we don’t have a lot of labeled data. |
Keywords
* Artificial intelligence * Image classification * Object detection * Semantic segmentation
