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.

中文翻译：

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具有共同表示净化的共同显着目标检测

共同显着目标检测（Co-SOD）旨在发现一组相关图像中的共同目标。挖掘共同表示对于定位共同显着对象至关重要。不幸的是，当前的 Co-SOD 方法没有足够重视将与共同显着对象无关的信息包含在共同表示中。共同表示中的此类不相关信息会干扰其对共同显着对象的定位。在本文中，我们提出了一种旨在搜索无噪声共同表示的共同表示净化（CoRP）方法。我们搜索了一些可能属于共同显着区域的像素嵌入。这些嵌入构成了我们的共同表示并指导我们的预测。为了获得更纯粹的共同表现，我们使用预测来迭代地减少我们共同表示中不相关的嵌入。在三个数据集上进行的实验表明，我们的 CoRP 在基准数据集上实现了最先进的性能。我们的源代码可在 https://github.com/ZZY816/CoRP 获得。