Correlation and Foreground Attention to Improve Object Detection

Object Detection (OD) can be viewed as a multi-objective task to achieve object localization and class recognition. With the rapid development of the deep neural networks (DNNs), on the one hand, the performance of OD has been significantly improved by relying on the high-quality feature extraction and representation of DNNs. On the other hand, it can be challenging to accurately detect and recognize objects with non-salient or confusing features. In this paper, we propose an efficient and pluggable OD method by using attention mechanism to solve these issues from two aspects. Firstly, we exploit the semantic relationship between objects as a prior knowledge to reduce the incorrect recognition of objects with confusing features, where the relationship is encoded as an attention map by using a graph convolutional network, and then this attention map is used to reweight the feature intensities of objects belonging to different classes. Then, based on the feature map extracted from DNNs, we extract a sub-feature map containing foreground information and use this map to generate foreground attention map to improve the feature saliency of the objects. The qualitative and quantitative experimental results well verify the effectiveness of our method.