TFGNet: Frequency-guided saliency detection for complex scenes

Yi Wang, Ruili Wang, Juncheng Liu, Rui Xu, Tianzhu Wang, Feng Hou, Bin Liu, Na Lei

Abstract: Highlights•Saliency detection struggles with boundary identification in complex scenes due to errors in multiscale feature fusion.•TFGNet enhances boundary and inner region detection by learning high and low spatial frequency features separately.•TFGNet employs pixel and mask-level decoders to obtain more comprehensive saliency features.•A histogram dissimilarity loss ensures frequency distribution consistency between ground truth and predicted saliency maps.•TFGNet surpasses leading methods with more accurate and complete boundaries in complex scenes.

External IDs:doi:10.1016/j.asoc.2024.112685