Making Few-Shot Object Detection Simpler and Less Frustrating

Abstract

Few-shot object detection is useful in order to extend object detection capabilities in media production and archiving applications with specific object classes of interest for a particular organization or production context. While recent approaches for few-shot object detection have advanced the state of the art, they still do not fully meet the requirements of practical workflows, e.g., in media production and archiving. In these applications, annotated samples for novel classes are drawn from different data sources, they differ in numbers and it may be necessary to add a new class quickly to cover the requirements of a specific production. In contrast, current frameworks for few-shot object detection typically assume a static dataset, which is split into the base and novel classes. We propose a toolchain to facilitate training for few-shot object detection, which takes care of data preparation when using heterogeneous training data and setup of training steps. The toolchain also creates annotation files to use combined data sets as new base models, which facilitates class-incremental training. We also integrated the toolchain with an annotation UI.