The main objective of few-shot semantic segmentation (FSSS) is to segment novel objects within query images by leveraging a limited set of support images. Being capable of segmenting the novel classes plays an essential role in the development of perception functions for automated vehicles. However, existing few-shot semantic segmentation work strives to improve the performance of the models on object-centric datasets. In our work, we evaluate the few-shot semantic segmentation on the more challenging driving scene understanding tasks. As a use case specific study, we give a systematic analysis of the disparity between commonly used FSSS datasets and driving datasets. Based on that, we proposed methodologies to integrate knowledge from the class hierarchy of the datasets, utilize more effective feature extraction, and choose more representative support images during inference. These approaches are evaluated extensively on the Cityscapes and Mapillary datasets to indicate their effectiveness. We point out the remaining challenges of training, evaluating, and employing FSSS models for complex road scenes in real practice.
Few-Shot Semantic Segmentation for Complex Driving Scenes
2024-06-02
4830485 byte
Conference paper
Electronic Resource
English
Waterfall Segmentation of Complex Scenes
British Library Conference Proceedings | 2006
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