Salient object detection (SOD) extracts meaningful contents from an input
image. RGB-based SOD methods lack the complementary depth clues; hence,
providing limited performance for complex scenarios. Similarly, RGB-D models
process RGB and depth inputs, but the depth data availability during testing
may hinder the model's practical applicability. This paper exploits only RGB
images, estimates depth from RGB, and leverages the intermediate depth
features. We employ a pyramidal attention structure to extract multi-level
convolutional-transformer features to process initial stage representations and
further enhance the subsequent ones. At each stage, the backbone transformer
model produces global receptive fields and computing in parallel to attain
fine-grained global predictions refined by our residual convolutional attention
decoder for optimal saliency prediction. We report significantly improved
performance against 21 and 40 state-of-the-art SOD methods on eight RGB and
RGB-D datasets, respectively. Consequently, we present a new SOD perspective of
generating RGB-D SOD without acquiring depth data during training and testing
and assist RGB methods with depth clues for improved performance. The code and
trained models are available at
this https URL

该研究利用 RGB 图像估计深度信息，使用金字塔式的注意力机制从中提取多层级卷积变换的特征，综合使用残差卷积注意力解码器进行显著性预测，取得了比 21 种 RGB SOD 方法和 40 种 RGB-D SOD 方法更为优异的性能表现。