Hybrid Classification-Regression Adaptive Loss for Dense Object Detection
<|reference_start|>Hybrid Classification-Regression Adaptive Loss for Dense Object Detection: For object detection detectors, enhancing model performance hinges on the ability to simultaneously consider inconsistencies across tasks and focus on difficult-to-train samples. Achieving this necessitates incorporating information from both the classification and regression tasks. However, prior work tends to either emphasize difficult-to-train samples within their respective tasks or simply compute classification scores with IoU, often leading to suboptimal model performance. In this paper, we propose a Hybrid Classification-Regression Adaptive Loss, termed as HCRAL. Specifically, we introduce the Residual of Classification and IoU (RCI) module for cross-task supervision, addressing task inconsistencies, and the Conditioning Factor (CF) to focus on difficult-to-train samples within each task. Furthermore, we introduce a new strategy named Expanded Adaptive Training Sample Selection (EATSS) to provide additional samples that exhibit classification and regression inconsistencies. To validate the effectiveness of the proposed method, we conduct extensive experiments on COCO test-dev. Experimental evaluations demonstrate the superiority of our approachs. Additionally, we designed experiments by separately combining the classification and regression loss with regular loss functions in popular one-stage models, demonstrating improved performance.<|reference_end|>
@article{huang2024hybrid,
    title={Hybrid Classification-Regression Adaptive Loss for Dense Object
  Detection},
    author={Yanquan Huang and Liu Wei Zhen and Yun Hao and Mengyuan Zhang and
  Qingyao Wu and Zikun Deng and Xueming Liu and Hong Deng},
    journal={arXiv preprint arXiv:2408.17182},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17182},
    primaryClass={cs.CV}
}