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Unsupervised Domain Adaptation of Object Detectors: A Survey
[article]
2021
arXiv
pre-print
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
arXiv:2105.13502v2
fatcat:ozzbbvoflfdvjdewjnjmfajlpa