This paper is concerned with the matching stability problem across different
decoder layers in DEtection TRansformers (DETR). We point out that the unstable
matching in DETR is caused by a multi-optimization path problem, which is
highlighted by the one-to-one matching design in DETR. To address this problem,
we show that the most important design is to use and only use positional
metrics (like IOU) to supervise classification scores of positive examples.
Under the principle, we propose two simple yet effective modifications by
integrating positional metrics to DETR's classification loss and matching cost,
named position-supervised loss and position-modulated cost. We verify our
methods on several DETR variants. Our methods show consistent improvements over
baselines. By integrating our methods with DINO, we achieve 50.4 and 51.5 AP on
the COCO detection benchmark using ResNet-50 backbones under 12 epochs and 24
epochs training settings, achieving a new record under the same setting. We
achieve 63.8 AP on COCO detection test-dev with a Swin-Large backbone. Our code
will be made available at this https URL

探讨 DETR 中跨不同解码器层次的匹配稳定性问题，提出使用位置度量监督正样本的分类得分和集成位置度量到 DETR 的分类损失和匹配代价中的两种方法，并在 COCO 检测基准测试中验证其有效性。