Multi-Anchor Active Domain Adaptation for Semantic Segmentation
- URL: http://arxiv.org/abs/2108.08012v1
- Date: Wed, 18 Aug 2021 07:33:13 GMT
- Title: Multi-Anchor Active Domain Adaptation for Semantic Segmentation
- Authors: Munan Ning, Donghuan Lu, Dong Wei, Cheng Bian, Chenglang Yuan, Shuang
Yu, Kai Ma, Yefeng Zheng
- Abstract summary: Unsupervised domain adaption has proven to be an effective approach for alleviating the intensive workload of manual annotation.
We propose to introduce a novel multi-anchor based active learning strategy to assist domain adaptation regarding the semantic segmentation task.
- Score: 25.93409207335442
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised domain adaption has proven to be an effective approach for
alleviating the intensive workload of manual annotation by aligning the
synthetic source-domain data and the real-world target-domain samples.
Unfortunately, mapping the target-domain distribution to the source-domain
unconditionally may distort the essential structural information of the
target-domain data. To this end, we firstly propose to introduce a novel
multi-anchor based active learning strategy to assist domain adaptation
regarding the semantic segmentation task. By innovatively adopting multiple
anchors instead of a single centroid, the source domain can be better
characterized as a multimodal distribution, thus more representative and
complimentary samples are selected from the target domain. With little workload
to manually annotate these active samples, the distortion of the target-domain
distribution can be effectively alleviated, resulting in a large performance
gain. The multi-anchor strategy is additionally employed to model the
target-distribution. By regularizing the latent representation of the target
samples compact around multiple anchors through a novel soft alignment loss,
more precise segmentation can be achieved. Extensive experiments are conducted
on public datasets to demonstrate that the proposed approach outperforms
state-of-the-art methods significantly, along with thorough ablation study to
verify the effectiveness of each component.
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