Summary of Segxal: Explainable Active Learning For Semantic Segmentation in Driving Scene Scenarios, by Sriram Mandalika et al.
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios
by Sriram Mandalika, Athira Nambiar
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 Most AI models require large amounts of annotated data and significant training to achieve high performance, but they often struggle in real-world scenarios due to inefficient use of unlabeled data, lack of human expertise, and unclear results. The proposed Explainable Active Learning (XAL) model, SegXAL, addresses these challenges by effectively utilizing unlabeled data, facilitating human-in-the-loop paradigm, and providing interpretable results for semantic segmentation tasks. Specifically, SegXAL uses explainable AI and uncertainty measures to propose image regions requiring labeling assistance from an oracle in a weakly-supervised manner. The model incorporates Proximity-aware Explainable-AI (PAE) and Entropy-based Uncertainty (EBU) modules to generate an Explainable Error Mask, enabling machine teachers/human experts to provide intuitive reasoning behind results and solicit feedback via active learning. This collaborative intelligence bridges the semantic gap between humans and machines. A novel high-confidence sample selection technique based on the DICE similarity coefficient is also presented within the SegXAL framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models need lots of data and training to work well, but they often struggle in real-life situations because they don’t use unlabeled data efficiently, don’t involve humans enough, and don’t provide clear results. A new type of AI model called SegXAL tries to fix these problems by using unlabeled data better, working with humans more effectively, and providing clearer results for tasks like image segmentation. |
Keywords
* Artificial intelligence * Active learning * Image segmentation * Mask * Semantic segmentation * Supervised
