End-to-End Semi-Supervised Object Detection with Soft Teacher

by   Mengde Xu, et al.

This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods. The end-to-end training gradually improves pseudo label qualities during the curriculum, and the more and more accurate pseudo labels in turn benefit object detection training. We also propose two simple yet effective techniques within this framework: a soft teacher mechanism where the classification loss of each unlabeled bounding box is weighed by the classification score produced by the teacher network; a box jittering approach to select reliable pseudo boxes for the learning of box regression. On COCO benchmark, the proposed approach outperforms previous methods by a large margin under various labeling ratios, i.e. 1%, 5% and 10%. Moreover, our approach proves to perform also well when the amount of labeled data is relatively large. For example, it can improve a 40.9 mAP baseline detector trained using the full COCO training set by +3.6 mAP, reaching 44.5 mAP, by leveraging the 123K unlabeled images of COCO. On the state-of-the-art Swin Transformer-based object detector (58.9 mAP on test-dev), it can still significantly improve the detection accuracy by +1.5 mAP, reaching 60.4 mAP, and improve the instance segmentation accuracy by +1.2 mAP, reaching 52.4 mAP, pushing the new state-of-the-art.


page 3

page 7


Unbiased Teacher for Semi-Supervised Object Detection

Semi-supervised learning, i.e., training networks with both labeled and ...

Mind the Gap: Polishing Pseudo labels for Accurate Semi-supervised Object Detection

Exploiting pseudo labels (e.g., categories and bounding boxes) of unanno...

Dynamic Curriculum Learning for Great Ape Detection in the Wild

We propose a novel end-to-end curriculum learning approach that leverage...

3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

3D object detection is an important yet demanding task that heavily reli...

Semi-supervised 3D Object Detection with Proficient Teachers

Dominated point cloud-based 3D object detectors in autonomous driving sc...

Improving Localization for Semi-Supervised Object Detection

Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since,...

Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors

We present a simple and effective learning technique that significantly ...