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Deep learning for visual semantic segmentation

Abstract : In this thesis, we are interested in Visual Semantic Segmentation, one of the high-level task that paves the way towards complete scene understanding. Specifically, it requires a semantic understanding at the pixel level. With the success of deep learning in recent years, semantic segmentation problems are being tackled using deep architectures. In the first part, we focus on the construction of a more appropriate loss function for semantic segmentation. More precisely, we define a novel loss function by employing a semantic edge detection network. This loss imposes pixel-level predictions to be consistent with the ground truth semantic edge information, and thus leads to better shaped segmentation results. In the second part, we address another important issue, namely, alleviating the need for training segmentation models with large amounts of fully annotated data. We propose a novel attribution method that identifies the most significant regions in an image considered by classification networks. We then integrate our attribution method into a weakly supervised segmentation framework. The semantic segmentation models can thus be trained with only image-level labeled data, which can be easily collected in large quantities. All models proposed in this thesis are thoroughly experimentally evaluated on multiple datasets and the results are competitive with the literature.
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Submitted on : Wednesday, December 1, 2021 - 4:14:39 PM
Last modification on : Saturday, July 9, 2022 - 3:26:49 AM
Long-term archiving on: : Wednesday, March 2, 2022 - 7:56:14 PM


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  • HAL Id : tel-03462101, version 1


Yifu Chen. Deep learning for visual semantic segmentation. Machine Learning [cs.LG]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS200⟩. ⟨tel-03462101⟩



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