Papers › Progressive Mirror Detection
Progressive Mirror Detection
Jiaying Lin, Guodong Wang, Rynson W.H. Lau
The mirror detection problem is important as mirrors can affect the performances of many vision tasks. It is a difficult problem as it requires an understanding of global scene semantics. Recently, a method was proposed to detect mirrors by learning multi-level contextual contrasts between inside and outside of mirrors, which helps locate mirror edges implicitly. We observe that the content of a mirror reflects the content of its surrounding, separated by the edge of the mirror. Hence, we propose a model in this paper to progressively learn the content similarity between the inside and outside of the mirror while explicitly detecting the mirror edges. Our work has two main contributions. First, we propose a new relational contextual contrasted local (RCCL) module to extract and compare the mirror features with its corresponding context features, and an edge detection and fusion (EDF) module to learn the features of mirror edges in complex scenes via explicit supervision. Second, we construct a challenging benchmark dataset of 6,461 mirror images. Unlike the existing MSD dataset, which has limited diversity, our dataset covers a variety of scenes and is much larger in scale. Experimental results show that our model outperforms relevant state-of-the-art methods.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Segmentation | MSD (Mirror Segmentation Dataset) | PMD | F-measure | 0.892 | #3 of 5 | Archive leaderboard | report |
| Image Segmentation | MSD (Mirror Segmentation Dataset) | PMD | IoU | 0.815 | #3 of 5 | Archive leaderboard | report |
| Image Segmentation | MSD (Mirror Segmentation Dataset) | PMD | MAE | 0.047 | #3 of 5 | Archive leaderboard | report |
| Image Segmentation | PMD | PMD | F-measure | 0.794 | #4 of 5 | Archive leaderboard | report |
| Image Segmentation | PMD | PMD | IoU | 0.660 | #4 of 5 | Archive leaderboard | report |
| Image Segmentation | PMD | PMD | MAE | 0.032 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections