Name
Title | ||
|---|---|---|
SGINet: Toward Sufficient Interaction Between Single Image Deraining and Semantic Segmentation |
Abstract | ||
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ABSTRACTData-driven single image deraining (SID) models have achieved greater progress by simulations, but there is still a large gap between current deraining performance and practical high-level applications, since high-level semantic information is usually neglected in current studies. Although few studies jointly considered high-level tasks (e.g., segmentation) to enable the model to learn more high-level information, there are two obvious shortcomings. First, they require the segmentation labels for training, limiting their operations on other datasets without high-level labels. Second, high- and low-level information are not fully interacted, hence having limited improvement in both deraining and segmentation tasks. In this paper, we propose a Semantic Guided Interactive Network (SGINet), which considers the sufficient interaction between SID and semantic segmentation using a three-stage deraining manner, i.e., coarse deraining, semantic information extraction, and semantics guided deraining. Specifically, a Full Resolution Module (FRM) without down-/up-sampling is proposed to predict the coarse deraining images without context damage. Then, a Segmentation Extracting Module (SEM) is designed to extract accurate semantic information. We also develop a novel contrastive semantic discovery (CSD) loss, which can instruct the process of semantic segmentation without real semantic segmentation labels. Finally, a triple-direction U-net-based Semantic Interaction Module (SIM) takes advantage of the coarse deraining images and semantic information for fully interacting low-level with high-level tasks. Extensive simulations on the newly-constructed complex datasets Cityscapes_syn and Cityscapes_real demonstrated that our model could obtain more promising results. Overall, our SGINet achieved SOTA deraining and segmentation performance in both simulation and real-scenario data, compared with other representative SID methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3503161.3548241 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanyan Wei | 1 | 0 | 0.34 |
| Zhao Zhang | 2 | 938 | 65.99 |
| Huan Zheng | 3 | 0 | 0.34 |
| Richang Hong | 4 | 4791 | 176.47 |
| Yi Yang | 5 | 6873 | 271.72 |
| Meng Wang | 6 | 3094 | 167.38 |