A Universal Multi-View Guided Network for Salient Object and Camouflaged Object Detection
Xiaogang Song, Pengfei Zhang, Xiaofeng Lu, Xinhong Hei, Rongrong Liu
Abstract
Salient object detection and camouflaged object detection have attracted increasing attention due to their significant practical applications. While these two domains share similarities in recognition methods and object characteristics, they also exhibit distinctions. In this paper, we propose a novel multi-view guided network for camouflaged and salient object detection, utilizing the Transformer as the backbone network for feature extraction. Capitalizing on shared characteristics, we introduce a CNN-based multi-view encoder and a multi-view fusion module, enhancing the acquisition of multi-perspective information while minimizing the increase in computational cost. Moreover, recognizing domain differences, we incorporate an attention exploration module, seamlessly integrating multi-view features with globally extracted features from the backbone network. This integration involves simultaneous exploration from both positional and color perspectives, unearthing valuable information to identify salient and camouflaged objects. Our approach maximizes shared characteristics between the two tasks while effectively addressing their differences, leading to precise object identification—be it for camouflaged or salient objects. Extensive experiments on nine challenging benchmark datasets demonstrate the superior performance of our method across four widely used evaluation metrics, outperforming 34 state-of-the-art methods. Furthermore, we applied our method to other visually-related tasks, such as polyp segmentation and defect detection. The results further demonstrate the versatility of our model. The source code and results of our method are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/1900zpf/MVGNet</uri>.