Name
Title | ||
|---|---|---|
Learning scene-adaptive pseudo annotations for pedestrian detection in semi-supervised scenarios |
Abstract | ||
|---|---|---|
Sufficient labeled training data may not be available for pedestrian detection in many real-world scenes. Semi-supervised settings naturally apply for the case where an adequate number of images are collected in a target scene but only a small proportion of them can be manually annotated. A common strategy is to adopt a detector trained on a well-established dataset (source data) or the limited annotated data to pseudo-annotate unannotated images. However, the domain gap and the lack of supervision in the target scene may lead to low-quality pseudo annotations. In this paper, we propose a Scene-adaptive Pseudo Annotation (SaPA) approach, which aims at exploiting two types of training data: source data providing sufficient supervision and unannotated target data offering domain-specific information. To utilize the source data, an Annotation Network (AnnNet) competes with a domain discriminator to learn domain-invariant features. To exploit the unannotated data, we temporally aggregate the parameters of AnnNet to build a more robust network, which is able to provide training goals for AnnNet. This new approach improves the generalization performance of AnnNet, which eventually leads to high-quality pseudo annotations to the unannotated data. Both manual and pseudo annotations are leveraged to train a more precise and scene-specific detector. We perform extensive experiments on multiple benchmarks to verify the effectiveness and superiority of SaPA. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2022.108439 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Pedestrian detection,Semi-supervised learning,Domain adaptation,Collaborative training | Journal | 243 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
26 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhao Wu | 1 | 0 | 0.34 |
| Qianfen Jiao | 2 | 1 | 1.70 |
| Hau-San Wong | 3 | 1008 | 86.89 |
| Gaozhe Li | 4 | 0 | 0.34 |
| Si Wu | 5 | 148 | 16.73 |