Abstract
Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification, object detection, etc. However, few-shot object detection methods suffer from an intrinsic defect that the limited training data makes the model cannot sufficiently explore semantic information. To tackle this, we introduce knowledge distillation to the few-shot object detection learning paradigm. We further run a motivating experiment, which demonstrates that in the process of knowledge distillation the empirical error of the teacher model degenerates the prediction performance of the few-shot object detection model, as the student. To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model. Following the theoretical guidance, we propose a backdoor adjustment-based knowledge distillation method for the few-shot object detection task, namely Disentangle and Remerge (D&R), to perform conditional causal intervention toward the corresponding Structural Causal Model. Theoretically, we provide an extended definition, i.e., general backdoor path, for the backdoor criterion, which can expand the theoretical application boundary of the backdoor criterion in specific cases. Empirically, the experiments on multiple benchmark datasets demonstrate that D&R can yield significant performance boosts in few-shot object detection.
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URL
https://arxiv.org/abs/2208.12681
