# Trained Model Fusion for Object Detection using Gating Network Source: [Trained Model Fusion for Object Detection using Gating Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:08:20.129Z Authors: Tetsuo Inoshita, Yuichi Nakatani, Katsuhiko Takahashi, Asuka Ishii, Gaku Nakano Published: 2020-03-03T01:38:20.000Z ## Abstract The major approaches of transfer learning in computer vision have tried to adapt the source domain to the target domain one-to-one. However, this scenario is difficult to apply to real applications such as video surveillance systems. As those systems have many cameras installed at each location regarded as source domains, it is difficult to identify the proper source domain. In this paper, we introduce a new transfer learning scenario that has various source domains and one target domain, assuming video surveillance system integration. Also, we propose a novel method for automatically producing a high accuracy model by fusing models trained at various source domains. In particular, we show how to apply a gating network to fuse source domains for object detection tasks, which is a new approach. We demonstrate the effectiveness of our method through experiments on traffic surveillance datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()