Abstract:
Few-shot learning is the mainstream method for few-shot object detection, but it has serious shortcomings. 1) The extreme lack of new class samples leads to biased distribution of new class features; 2) Due to the robustness assumption during the fine-tuning process not necessarily applicable to new class samples, the feature extraction network is unable to extract unbiased new class sample features. To deal with the above two issues, three-stage fine-tuning few-shot object detection method based on cross-module knowledge distillation is here proposed. Firstly, the feature distribution calibration strategy is designed to calibrate the feature distribution of new class during the two-step fine-tuning process. Secondly, the proposed first bias reduction strategy, effectively alleviate the bias estimation problem of weight parameters in the linear probing (the first stage of the fine-tuning process), and the proposed inverse first bias reduction effectively alleviates the over-fitting problem of feature extraction network during overall fine-tuning (the second stage of the fine-tuning process). Finally, the proposed cross-module knowledge distillation strategy is utilized to guide the shallow modules of the model to learn deep features to capture more discriminative new class features. A large number of experimental results show that the proposed three-stage fine-tuning few-shot object detection method effectively improves the accuracy and robustness of few-shot object detection.