  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.
