Cross-Dataset Collaborative Learning for Semantic Segmentation

• URL: http://arxiv.org/abs/2103.11351v1
• Date: Sun, 21 Mar 2021 09:59:47 GMT
• Title: Cross-Dataset Collaborative Learning for Semantic Segmentation
• Authors: Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan
• Abstract summary: We present a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Learning (CDCL) Given multiple labeled datasets, we aim to improve the generalization and discrimination of feature representations on each dataset. We conduct extensive evaluations on four diverse datasets, i.e., Cityscapes, BDD100K, CamVid, and COCO Stuff, with single-dataset and cross-dataset settings.
• Score: 17.55660581677053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work attempts to improve semantic segmentation performance by exploring well-designed architectures on a target dataset. However, it remains challenging to build a unified system that simultaneously learns from various datasets due to the inherent distribution shift across different datasets. In this paper, we present a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Learning (CDCL). Given multiple labeled datasets, we aim to improve the generalization and discrimination of feature representations on each dataset. Specifically, we first introduce a family of Dataset-Aware Blocks (DAB) as the fundamental computing units of the network, which help capture homogeneous representations and heterogeneous statistics across different datasets. Second, we propose a Dataset Alternation Training (DAT) mechanism to efficiently facilitate the optimization procedure. We conduct extensive evaluations on four diverse datasets, i.e., Cityscapes, BDD100K, CamVid, and COCO Stuff, with single-dataset and cross-dataset settings. Experimental results demonstrate our method consistently achieves notable improvements over prior single-dataset and cross-dataset training methods without introducing extra FLOPs. Particularly, with the same architecture of PSPNet (ResNet-18), our method outperforms the single-dataset baseline by 5.65\%, 6.57\%, and 5.79\% of mIoU on the validation sets of Cityscapes, BDD100K, CamVid, respectively. Code and models will be released.

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