YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models -

Yukihiro Sasagawa, Hajime Nagahara         ;

Generating new visual tasks requires additional datasets, and it costs a considerable effort. We propose a new method of domain adaptation for merging multiple models with less effort than creating an additional dataset. This method merges pre-trained models in different domains using the glue layers and the generative model, which feeds latent features to train the glue layers without an additional dataset. We also propose the generative model created by knowledge distillation from pre-trained models. It also allows reusing the dataset to create latent features for training the glue layers. We apply this method to object detection in a low-light situation. The “YOLO in the Dark” contains two models, “Learning to See in the Dark” and YOLO. We report the new method and the result of domain adaptation that detect objects from raw short-exposure low-light images. The “YOLO in the Dark” costs fewer computing resources compared to the naive approach."