Learning to Adapt Structured Output Space for Semantic Segmentation

Code

• github.com/wasidennis/AdaptSegNet
  OfficialIn paperpytorch★ 0
• github.com/KookHoiKim/AdaptSegNet
  pytorch★ 0
• github.com/lym29/DASeg
  pytorch★ 0
• github.com/xiaowillow/AdaptSegNet
  pytorch★ 0
• github.com/NiteshBharadwaj/adaptsegnet-materials
  pytorch★ 0
• github.com/zqwhu/SegDAwithBoundary
  pytorch★ 0
• github.com/jizongFox/ReproduceAdaptSegNet
  pytorch★ 0
• github.com/tanpinquan/EE5934_2
  pytorch★ 0
• github.com/stu92054/Domain-adaptation-on-segmentation
  pytorch★ 0
• github.com/Sshanu/AdaptSegNet
  pytorch★ 0

Abstract

Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality.

Tasks

Benchmark Results

Reproductions