Search, Estimate, and Predict: Efficient Weakly-Supervised Learning

Most of state-of-the-arts region-based detectors are suffered from the requirement of training for a large volume of bounding box annotated data. In this paper, we propose an innovative weakly-supervised architecture, namely SEP-Net, that employs an efficient proposal searching method on feature maps to generate region proposals via transforming CNN classifiers to object detectors without bounding box annotations. We further show the proposed framework can be used for real-time detection, in which high computation efficiency is a prerequisite. Moreover, the training procedure is exactly the same as what’s for CNN classifiers, so is easy to transfer a pre-trained CNN to our weakly-supervised detector as well. Experimental validation on ImageNet 2012 dataset show that the novel model outperforms classical and most recent state-of-the-art weakly-supervised method by a wide margin in terms of both accuracy and efficiency.