Incremental Learning in Semantic Segmentation from Image Labels

• URL: http://arxiv.org/abs/2112.01882v1
• Date: Fri, 3 Dec 2021 12:47:12 GMT
• Title: Incremental Learning in Semantic Segmentation from Image Labels
• Authors: Fabio Cermelli, Dario Fontanel, Antonio Tavera, Marco Ciccone, Barbara Caputo
• Abstract summary: Existing semantic segmentation approaches achieve impressive results, but struggle to update their models incrementally as new categories are uncovered. This paper proposes a novel framework for Weakly Incremental Learning for Semantics, that aims at learning to segment new classes from cheap and largely available image-level labels. As opposed to existing approaches, that need to generate pseudo-labels offline, we use an auxiliary classifier, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally.
• Score: 18.404068463921426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This paper proposes a novel framework for Weakly Incremental Learning for Semantic Segmentation, that aims at learning to segment new classes from cheap and largely available image-level labels. As opposed to existing approaches, that need to generate pseudo-labels offline, we use an auxiliary classifier, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally. We cope with the inherent noise in the process by using soft-labels generated by the auxiliary classifier. We demonstrate the effectiveness of our approach on the Pascal VOC and COCO datasets, outperforming offline weakly-supervised methods and obtaining results comparable with incremental learning methods with full supervision.

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