Revisiting the Loss Weight Adjustment in Object Detection
This addresses a specific imbalance issue in object detection for computer vision applications, representing an incremental improvement.
The paper tackles the problem of classification loss dominating multi-task optimization in anchor-based object detection methods, proposing Adaptive Loss Weight Adjustment (ALWA) which achieves significant performance gains on PASCAL VOC and MS COCO datasets.
Object detection is a typical multi-task learning application, which optimizes classification and regression simultaneously. However, classification loss always dominates the multi-task loss in anchor-based methods, hampering the consistent and balanced optimization of the tasks. In this paper, we find that shifting the bounding boxes can change the division of positive and negative samples in classification, meaning classification depends on regression. Moreover, we summarize three important conclusions about fine-tuning loss weights, considering different datasets, optimizers and regression loss functions. Based on the above conclusions, we propose Adaptive Loss Weight Adjustment(ALWA) to solve the imbalance in optimizing anchor-based methods according to statistical characteristics of losses. By incorporating ALWA into previous state-of-the-art detectors, we achieve a significant performance gain on PASCAL VOC and MS COCO, even with L1, SmoothL1 and CIoU loss. The code is available at https://github.com/ywx-hub/ALWA.