  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. Empirically, the experiments on
benchmarks demonstrate that D&R can yield significant performance boosts in
few-shot object detection. Code is available at
https://github.com/ZYN-1101/DandR.git.
