Most existing salient object detection methods mostly use U-Net or feature
pyramid structure, which simply aggregates feature maps of different scales,
ignoring the uniqueness and interdependence of them and their respective
contributions to the final prediction. To overcome these, we propose the
M$^3$Net, i.e., the Multilevel, Mixed and Multistage attention network for
Salient Object Detection (SOD). Firstly, we propose Multiscale Interaction
Block which innovatively introduces the cross-attention approach to achieve the
interaction between multilevel features, allowing high-level features to guide
low-level feature learning and thus enhancing salient regions. Secondly,
considering the fact that previous Transformer based SOD methods locate salient
regions only using global self-attention while inevitably overlooking the
details of complex objects, we propose the Mixed Attention Block. This block
combines global self-attention and window self-attention, aiming at modeling
context at both global and local levels to further improve the accuracy of the
prediction map. Finally, we proposed a multilevel supervision strategy to
optimize the aggregated feature stage-by-stage. Experiments on six challenging
datasets demonstrate that the proposed M$^3$Net surpasses recent CNN and
Transformer-based SOD arts in terms of four metrics. Codes are available at
this https URL

提出了用于显著性目标检测的多层次、混合和多阶段注意力网络（M$^3$Net），包括多尺度交互块和混合注意力块，并引入多层次监督策略进行聚合特征的优化。在六个具有挑战性的数据集上的实验证明，所提出的 M$^3$Net 在四个指标上优于最近的 CNN 和基于 Transformer 的显著性目标检测方法。