Abstract
Few-Shot Object Detection (FSOD) task is widely used in various data-scarce scenarios, aiming to expand the object detector with a few novel class samples. The current mainstream FSOD models improve the accuracy by mining novel class instances in the training set and fine-tuning the detector with mined pseudo set. Substantial progress has been made using pseudo-label approaches, but the impact of pseudo-labels diversity on FSOD tasks has not been explored. In our work, for the purpose of fully utilizing the pseudo-label set and exploring their diversity, we propose a new framework mainly including Novel Instance Bank (NIB) and Correlation-Guided Loss Correction (CGLC). Dynamically updated NIB stores the novel class instances to increase the diversity of novel instances in each batch. Moreover, to better exploit the pseudo-label diversity, CGLC adaptively employs k-shot samples to guide correct and incorrect pseudo-labels to pull away from each other. Experimental results on the MS-COCO dataset demonstrate the effectiveness of our method, which does not require any additional training samples or parameters. Our code is available at: https://github.com/lotuser1/PDE.
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Acknowledgments
This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY20F030005) and National Natural Science Foundation of China (No. 61603202). (Corresponding Author: Chong Wang).
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Chen, S., Wang, C., Liu, W., Ye, Z., Deng, J. (2023). Pseudo-label Diversity Exploitation for Few-Shot Object Detection. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_24
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