Computer Science > Computer Vision and Pattern Recognition
arXiv:2312.07190 (cs)
[Submitted on 12 Dec 2023 (v1), last revised 14 Mar 2024 (this version, v2)]
Title:Shifted Autoencoders for Point Annotation Restoration in Object Counting
View a PDF of the paper titled Shifted Autoencoders for Point Annotation Restoration in Object Counting, by Yuda Zou and 5 other authors
View PDFHTML (experimental)Abstract:Object counting typically uses 2D point annotations. The complexity of object shapes and the subjectivity of annotators may lead to annotation inconsistency, potentially confusing counting model training. Some sophisticated noise-resistance counting methods have been proposed to alleviate this issue. Differently, we aim to directly refine the initial point annotations before training counting models. For that, we propose the Shifted Autoencoders (SAE), which enhances annotation consistency. Specifically, SAE applies random shifts to initial point annotations and employs a UNet to restore them to their original positions. Similar to MAE reconstruction, the trained SAE captures general position knowledge and ignores specific manual offset noise. This allows to restore the initial point annotations to more general and thus consistent positions. Extensive experiments show that using such refined consistent annotations to train some advanced (including noise-resistance) object counting models steadily/significantly boosts their performances. Remarkably, the proposed SAE helps to set new records on nine datasets. We will make codes and refined point annotations available.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2312.07190 [cs.CV] |
| (orarXiv:2312.07190v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2312.07190 arXiv-issued DOI via DataCite |
Submission history
From: Xin Xiao [view email][v1] Tue, 12 Dec 2023 11:53:23 UTC (20,199 KB)
[v2] Thu, 14 Mar 2024 13:40:56 UTC (17,985 KB)
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View a PDF of the paper titled Shifted Autoencoders for Point Annotation Restoration in Object Counting, by Yuda Zou and 5 other authors
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