Name
Abstract | ||
|---|---|---|
In the light of recent analyses on privacy-concerning scene revelation from visual descriptors, we develop descriptors that conceal the input image content. In particular, we propose an adversarial learning framework for training visual descriptors that prevent image reconstruction, while maintaining the matching accuracy. We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors. The experimental results demonstrate that the visual descriptors obtained with our method significantly deteriorate the image reconstruction quality with minimal impact on correspondence matching and camera localization performance. |
Year | Venue | DocType |
|---|---|---|
2022 | IEEE Conference on Computer Vision and Pattern Recognition | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tony Ng | 1 | 0 | 0.34 |
| Hyo Jin Kim | 2 | 0 | 0.68 |
| Vincent Lee | 3 | 0 | 0.34 |
| Daniel DeTone | 4 | 0 | 1.01 |
| Tsun-Yi Yang | 5 | 0 | 1.01 |
| Tianwei Shen | 6 | 0 | 1.01 |
| Eddy Ilg | 7 | 0 | 0.68 |
| Vassileios Balntas | 8 | 50 | 4.39 |
| Krystian Mikolajczyk | 9 | 7280 | 625.08 |
| Chris Sweeney | 10 | 0 | 0.34 |