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This paper introduces MIMONet, a novel architecture for salient object detection that employs multi-scale inputs and outputs to enhance the model's ability to recognize objects of varying sizes. By utilizing three encoder branches with images of different resolutions and implementing a Multi-scale Perception (MSP) module, MIMONet facilitates knowledge exchange between branches, improving the learning of size variations. Experimental results demonstrate that MIMONet outperforms existing methods, achieving superior detection capabilities and evaluation scores across multiple datasets.
MIMONet significantly enhances salient object detection by effectively learning size variations through multi-scale input and output strategies.
The existing methods for saliency detection task focus on the application of multi-level features, aiming to take advantage of the respective strengths of high- and low-level features. However, because the inputs of these models are single-size images, their multi-level features have difficulty in learning the knowledge of size variations of salient objects. Object-scale variation learning has great potential for detecting multi-scale objects, which has not been fully explored by existing methods. To improve the recognition ability of a model for objects with different sizes, we are inspired by the image pyramid to propose a Multi-scale Input and Multi-scale Output Network (MIMONet). In MIMONet, we extract multi-level features for three images with different resolutions to form three encoder branches, and information will be exchanged between the branches. The advantage of this approach is that the features of one branch can learn the knowledge of target size variation from the features of the other two branches. In addition, we design a Multi-scale Perception (MSP) module, in which the input feature layer is divided into several sub-layers with different resolutions. Capturing the multi-level structure information of the objects in these sub-layers can make the objects more fully perceived. For network training, we propose a Joint Saliency Loss (JSL), which can constrain multiple saliency maps output by the network to identify the same foreground objects, and induce their boundaries to be preserved clearly. Experimental results show that MIMONet has stronger detection capabilities and harvests better evaluation scores on multiple datasets compared to existing models. The code of our model will be released.
