Group-wise Deep Co-saliency Detection

Group-wise Deep Co-saliency Detection

Lina Wei, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li, Fei Wu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3041-3047. https://doi.org/10.24963/ijcai.2017/424

In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation, and model this in a collaborative learning framework. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
Keywords:
Machine Learning: Neural Networks
Robotics and Vision: Vision and Perception