Channel-Spatial Mutual Attention Network for 360° Salient Object Detection
Yi Zhang, Wassim Hamidouche, Olivier Déforges · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
In this work, we conduct 360° panoramic salient object detection by taking advantage of both the global and local visual cues of 360° images, with a novel channel-spatial mutual attention network (CSMA-Net). The key component of the CSMA-Net is the proposed CSMA module, which cascades channel-/spatial-weighting-based mutual attentions. The objective of our CSMA module is to refine and fuse the bottleneck features from two separate encoders with different planar representations of 360° panorama as inputs, i.e., equirectangular image and cube map. Our CSMA-Net outperforms 10 state-of-the-art segmentation methods based on the proposed 360° SOD benchmark where multiple fine-tuning and testing strategies are applied to the widely-used 360° datasets. Extensive experimental results illustrate the effectiveness and robustness of the proposed CSMA-Net1.