@inproceedings{zhong2021dap,
author = {Zhong, Yuanyi and Wang, Jianfeng and Wang, Lijuan and Peng, Jian and Wang, Yu-Xiong and Zhang, Lei},
title = {DAP: Detection-Aware Pre-training with Weak Supervision},
booktitle = {CVPR 2021},
year = {2021},
month = {June},
abstract = {This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is specifically tailored to benefit object detection tasks. In contrast to the widely used image classification-based pre-training (e.g., on ImageNet), which does not include any location-related training tasks, we transform a classification dataset into a detection dataset through a weakly supervised object localization method based on Class Activation Maps to directly pre-train a detector, making the pre-trained model location-aware and capable of predicting bounding boxes. We show that DAP can outperform the traditional classification pre-training in terms of both sample efficiency and convergence speed in downstream detection tasks including VOC and COCO. In particular, DAP boosts the detection accuracy by a large margin when the number of examples in the downstream task is small.},
url = {http://approjects.co.za/?big=en-us/research/publication/dap-detection-aware-pre-training-with-weak-supervision/},
}