Computer Science > Computer Vision and Pattern Recognition
arXiv:2304.07527 (cs)
[Submitted on 15 Apr 2023 (v1), last revised 23 Dec 2024 (this version, v2)]
Title:Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss
View a PDF of the paper titled Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss, by Zhi Cai and 4 other authors
View PDFHTML (experimental)Abstract:DETR has set up a simple end-to-end pipeline for object detection by formulating this task as a set prediction problem, showing promising potential. Despite its notable advancements, this paper identifies two key forms of misalignment within the model: classification-regression misalignment and cross-layer target misalignment. Both issues impede DETR's convergence and degrade its overall performance. To tackle both issues simultaneously, we introduce a novel loss function, termed as Align Loss, designed to resolve the discrepancy between the two tasks. Align Loss guides the optimization of DETR through a joint quality metric, strengthening the connection between classification and regression. Furthermore, it incorporates an exponential down-weighting term to facilitate a smooth transition from positive to negative samples. Align-DETR also employs many-to-one matching for supervision of intermediate layers, akin to the design of H-DETR, which enhances robustness against instability. We conducted extensive experiments, yielding highly competitive results. Notably, our method achieves a 49.3% (+0.6) AP on the H-DETR baseline with the ResNet-50 backbone. It also sets a new state-of-the-art performance, reaching 50.5% AP in the 1x setting and 51.7% AP in the 2x setting, surpassing several strong competitors. Our code is available atthis https URL.
| Comments: | Accepted by BMVC2024 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2304.07527 [cs.CV] |
| (orarXiv:2304.07527v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2304.07527 arXiv-issued DOI via DataCite |
Submission history
From: Zhi Cai [view email][v1] Sat, 15 Apr 2023 10:24:51 UTC (1,250 KB)
[v2] Mon, 23 Dec 2024 11:30:51 UTC (2,694 KB)
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View a PDF of the paper titled Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss, by Zhi Cai and 4 other authors
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