Active learning for deep object detection by fully exploiting unlabeled data
Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. However, labeling huge amounts of data is expensive, making it difficult to train a good detector with limited labeled data. Existing approaches mitigate this issue via active learni...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2195596 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. However, labeling huge amounts of data is expensive, making it difficult to train a good detector with limited labeled data. Existing approaches mitigate this issue via active learning or semi-supervised learning, but there is still room for improvement. In this paper, we propose a novel active learning method for deep object detection that fully exploits unlabeled data by combining the benefits of active learning and semi-supervised learning. Our method first trains an initial model using limited labeled data, then uses self-training and data augmentation strategies to train a semi-supervised model using labeled and unlabeled data. We then select query samples based on informativeness and representativeness from the unlabeled data to further improve the model through semi-supervised training. Experimental results on commonly used object detection datasets demonstrate the effectiveness of our approach, outperforming state-of-the-art methods. |
|---|---|
| ISSN: | 0954-0091 1360-0494 |