2021/05/27 by Poojan Oza, Vishwanath A. Sindagi, Oza, Poojan +5 · 20 citations
Computer Science · Medicine · #Adaptation (eye) #Advanced Neural Network Applications #Artificial intelligence #COVID-19 diagnosis using AI #Cognitive neuroscience of visual object recognition #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Domain (mathematical analysis) #Domain Adaptation and Few-Shot Learning #Domain adaptation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine learning #Object (grammar) #Object detection #Scale (ratio) #Segmentation #Task (project management) #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.13502
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/05/27 · arxiv created 2021/07/04 · arxiv updated 2021/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 is a plethora of works to adapt classification and segmentation models to label-scarce target datasets 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.