Few-Shot Object Detection via Variational Feature Aggregation
Few-Shot Object Detection via Variational Feature Aggregation
As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples. To address this issue, we propose a meta-learning framework with two novel feature aggregation schemes. More …