Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective. (arXiv:2208.12681v1 [cs.CV])

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 …

arxiv cv detection distillation knowledge perspective