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Visible-Thermal Pedestrian Detection via Unsupervised Transfer Learning
2021
2021 the 5th International Conference on Innovation in Artificial Intelligence
Recently, pedestrian detection using visible-thermal pairs plays a key role in around-the-clock applications, such as public surveillance and autonomous driving. However, the performance of a well-trained pedestrian detector may drop significantly when it is applied to a new scenario. Normally, to achieve a good performance on the new scenario, manual annotation of the dataset is necessary, while it is costly and unscalable. In this work, an unsupervised transfer learning framework is proposed
doi:10.1145/3461353.3461369
fatcat:qwvzmqd5cjd2dhso3z3cvc7fyq