Few-shot object detection has gained significant attention in recent years as it has the potential to greatly reduce the reliance on large amounts of manually annotated bounding boxes. While most existing few-shot object detection literature primarily focuses on bounding box classification by obtaining as discriminative feature embeddings as possible, we emphasize the necessity of handling the lack of intersection-over-union (IoU) variations induced by a biased distribution of novel samples. In this paper, we analyze the IoU imbalance that is caused by the relatively high number of low-quality region proposals, and reveal that it plays a critical role in improving few-shot learning capabilities. The well-known two stage fine-tuning technique causes insufficient quality and quantity of the novel positive samples, which hinders the effective object detection of unseen novel classes. To alleviate this issue, we present a few-shot object detection model with proposal balance refinement, a simple yet effective approach in learning object proposals using an auxiliary sequential bounding box refinement process. This process enables the detector to be optimized on the various IoU scores through additional novel class samples. To fully exploit our sequential stage architecture, we revise the fine-tuning strategy and expose the Region Proposal Network to the novel classes in order to provide increased learning opportunities for the region-of-interest (RoI) classifiers and regressors. Our extensive assessments on PASCAL VOC and COCO demonstrate that our framework substantially outperforms other existing few-shot object detection approaches.
|Title of host publication||2022 26th International Conference on Pattern Recognition, ICPR 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2022|
|Event||26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada|
Duration: 2022 Aug 21 → 2022 Aug 25
|Name||Proceedings - International Conference on Pattern Recognition|
|Conference||26th International Conference on Pattern Recognition, ICPR 2022|
|Period||22/8/21 → 22/8/25|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01371, Development of brain-inspired AI with human like intelligence & No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University).
© 2022 IEEE.
- few-shot learning
- object detection
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition