Co-Salient Object Detection with Co-Representation Purification

• URL: http://arxiv.org/abs/2303.07670v1
• Date: Tue, 14 Mar 2023 07:23:27 GMT
• Title: Co-Salient Object Detection with Co-Representation Purification
• Authors: Ziyue Zhu, Zhao Zhang, Zheng Lin, Xing Sun, Ming-Ming Cheng
• Abstract summary: Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. The current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. We propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation.
• Score: 80.2644026634024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. Such irrelevant information in the co-representation interferes with its locating of co-salient objects. In this paper, we propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation. We search a few pixel-wise embeddings probably belonging to co-salient regions. These embeddings constitute our co-representation and guide our prediction. For obtaining purer co-representation, we use the prediction to iteratively reduce irrelevant embeddings in our co-representation. Experiments on three datasets demonstrate that our CoRP achieves state-of-the-art performances on the benchmark datasets. Our source code is available at https://github.com/ZZY816/CoRP.

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