While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach. Specifically, the pseudo-label method tends to amplify the inherent strengths of the detector while accentuating its weaknesses, which is manifested in the missed detection of pseudo-labels, particularly for small and tail category objects. To overcome these challenges, this paper proposes Mixed Pseudo Labels (MixPL), consisting of Mixup and Mosaic for pseudo-labeled data, to mitigate the negative impact of missed detections and balance the model's learning across different object scales. Additionally, the model's detection performance on tail categories is improved by resampling labeled data with relevant instances. Notably, MixPL consistently improves the performance of various detectors and obtains new state-of-the-art results with Faster R-CNN, FCOS, and DINO on COCO-Standard and COCO-Full benchmarks. Furthermore, MixPL also exhibits good scalability on large models, improving DINO Swin-L by 2.5% mAP and achieving nontrivial new records (60.2% mAP) on the COCO val2017 benchmark without extra annotations.

翻译：尽管伪标签方法在半监督目标检测任务中取得了显著成功，但本文揭示了该方法存在的明显局限性。具体而言，伪标签方法会放大检测器自身优势的同时加剧其劣势，表现为伪标签的漏检现象，尤其对小目标和尾类别物体更为显著。为克服这些挑战，本文提出混合伪标签方法（MixPL），该方法结合Mixup与Mosaic增强技术处理伪标签数据，从而缓解漏检带来的负面影响并平衡模型对不同尺度目标的学习。此外，通过使用相关实例对标注数据进行重采样，提升了模型在尾类别上的检测性能。值得注意的是，MixPL能够持续提升各类检测器的性能，并在COCO-Standard和COCO-Full基准上基于Faster R-CNN、FCOS和DINO取得了新的最优结果。进一步地，MixPL在大规模模型上展现出良好可扩展性，使DINO Swin-L在COCO val2017基准上的平均精度（mAP）提升2.5%，并在无需额外标注的情况下达到60.2% mAP这一具有突破性的新纪录。