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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)

Abstract

Generating models to handle new visual tasks requires additional datasets, which take considerable effort to create. We propose a 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 glue layers and a generative model, which feeds latent features to the glue layers to train them without an additional dataset. We also propose a generative model that is created by distilling knowledge from pre-trained models. This enables the dataset to be reused 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 model comprises two models, Learning-to-See-in-the-Dark model and YOLO. We present the proposed method and report the result of domain adaptation to detect objects from RAW short-exposure low-light images. The YOLO-in-the-Dark model uses fewer computing resources than the naive approach.

Keywords

Knowledge distillation Domain adaptation Object detection 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Socionext Inc.KyotoJapan
  2. 2.Osaka UniversityOsakaJapan

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