Unbiased Regression Loss for DETRs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909371794980864 |
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| author | Edric Daisuke, Ueta Yukimasa, Kurokawa Jayashree, Karlekar Pranata, Sugiri |
| author_facet | Edric Daisuke, Ueta Yukimasa, Kurokawa Jayashree, Karlekar Pranata, Sugiri |
| contents | In this paper, we introduce a novel unbiased regression loss for DETR-based detectors. The conventional $L_{1}$ regression loss tends to bias towards larger boxes, as they disproportionately contribute more towards the overall loss compared to smaller boxes. Consequently, the detection performance for small objects suffers. To alleviate this bias, the proposed new unbiased loss, termed Sized $L_{1}$ loss, normalizes the size of all boxes based on their individual width and height. Our experiments demonstrate consistent improvements in both fully-supervised and semi-supervised settings using the MS-COCO benchmark dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unbiased Regression Loss for DETRs Edric Daisuke, Ueta Yukimasa, Kurokawa Jayashree, Karlekar Pranata, Sugiri Computer Vision and Pattern Recognition In this paper, we introduce a novel unbiased regression loss for DETR-based detectors. The conventional $L_{1}$ regression loss tends to bias towards larger boxes, as they disproportionately contribute more towards the overall loss compared to smaller boxes. Consequently, the detection performance for small objects suffers. To alleviate this bias, the proposed new unbiased loss, termed Sized $L_{1}$ loss, normalizes the size of all boxes based on their individual width and height. Our experiments demonstrate consistent improvements in both fully-supervised and semi-supervised settings using the MS-COCO benchmark dataset. |
| title | Unbiased Regression Loss for DETRs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.22638 |