Unsupervised Domain Adaptation of Object Detectors: A Survey

By Poojan Oza, Vishwanath A. Sindagi, Vibashan Vs, Vishal M. Patel. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

314 citations recorded by the source corpus.

Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images, termed as domain adaptation problem. There are a plethora of works to adapt classification and segmentation models to label-scarce target dataset through unsupervised domain adaptation. Considering that detection is a fundamental task in computer vision, many recent works have focused on developing novel domain adaptive detection techniques. Here, we describe in detail the domain adaptation problem for detection and present an extensive survey of the various methods. Furthermore, we highlight strategies proposed and the associated shortcomings. Subsequently, we identify multiple aspects of the problem that are most promising for future research. We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/96bb5e0ee886b499e5d6d64b4636bc7be343ccc0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chaehwa Yoo
2. Je-Won Kang
3. Xiaofeng Liu
4. Hyejin Oh
5. Jonghye Woo
6. Fangxu Xing
7. Georges El Fakhri

In Computer Vision and Pattern Recognition

1. Chaehwa Yoo
2. Je-Won Kang
3. Xiaofeng Liu
4. Hyejin Oh
5. Jonghye Woo
6. Fangxu Xing
7. Georges El Fakhri

Citations in the corpus

• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives (2022, background): “Moreover, some of them only focus on limited application areas, such as visual data analytics (Wang and Deng, 2018; Csurka, 2017; Oza et al., 2021) or natural language processing (NLP) (Ramponi and Plank, 2020).”