Effective full-scale detection for salient object based on condensing-and-filtering network

Highlights：

• There are still two challenges in the field of salient object detection: 1) The lack of rich features extracted from multiple perspectives at different encoder levels results in the omission of salient objects with varying scales. 2) The ineffective fusion of multi-level features during decoding dilutes the saliency features, which destroys the purity of the predicted maps.

• To solve the above two problems, we propose a Condensing-and-Filtering Network (CFNet), in which a saliency pyramid condensing module (SPCM) and a saliency filtering module (SFM) are proposed to achieve an effective full-scale detection for salient objects.

• Experimental results demonstrate that the proposed method outperforms 23 state-of-the-art methods with a real-time speed and considerable computation on five benchmark datasets.