Group-wise Deep Co-saliency Detection
This addresses the problem of detecting common salient objects across multiple images for computer vision applications, but it appears incremental as it builds on existing deep learning frameworks.
The paper tackles co-salient object discovery by proposing an end-to-end group-wise deep co-saliency detection approach using a fully convolutional network with group input and output, which captures group-wise interaction and collaborative relationships, and experimental results show it outperforms state-of-the-art methods.
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.