# Deep Domain Adaptive Object Detection: a Survey

Source: [Deep Domain Adaptive Object Detection: a Survey](<https://www.sota2.com/research/paper/68fa68bf1e209131cea1e062>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:44:52.991Z

Authors: Wanyi Li, Fuyu Li, Yongkang Luo, Peng Wang, Jia sun

Published: 2020-02-17T06:40:19.000Z

## Abstract

Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented.

## 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](<https://arxiv.org/abs/2002.06797>)

[PDF](<https://arxiv.org/pdf/2002.06797.pdf>)