Stay Focus on Object: Cross-Domain Detection Using Domain-Invariant Object Representation

Taehoon Kim, Jaemin Na, Joong-Won Hwang, Wonjun Hwang

Abstract: Unsupervised domain adaptation for object detection (UDAOD) aims to reduce the gap between the labeled source domain and the unlabeled target domain. In the driving scenes, there are distinct unique characteristics that differentiate between the objects, both spatially and categorically. These properties largely maintain their invariance across domains, enabling the effective training of object detectors in the target domain. To consider this, we introduce the domain-invariant object concentration framework, which combines instance-level and image-level approaches to efficiently utilizing domain-invariant object knowledge. At the instance-level, we propose a target surrogate selection module. This module leverages the unique information of object categories to match regions of interest (ROIs) between both domains, thereby enabling effective training on the unlabeled target domain. At the image level, we introduce PutMix, which utilizes domain-invariant knowledge of common object positions in driving scenes based on our statistically defined object crowded area. To validate our method, we experiment across four driving scenarios using four different datasets.

External IDs:dblp:conf/icip/KimNHH24