Positive-Incentive Pseudo Label Optimization for Salient Object Detection

Abstract

We present a salient object detection method in this paper. It uses a lightweight network based on multi-scale information aggregation (MIA) module and bidirectional cross-stage feature fusion (BCF2) module to speed up the reasoning process. Besides, it designs a stepwise coupling optimization strategy based on positive-incentive pseudo label to alleviate the miss detection rate for small objects and the false detection rate for big objects. It is worth noting that the positive-incentive pseudo label generation mechanism can increase the ratio of salient pixels in small objects to alleviate the severe category imbalance problem, while the stepwise coupling optimization strategy can avoid the misguidance of pseudo label to big objects. In addition, the experimental results demonstrate that our method is fairly efficient, its frame rate in reasoning phase reaches 275.6 FPS on NVIDIA RTX3080 GPU when the input resolution is set to $336\times 336$ .

Keywords

• Salient object detection
• high-efficiency
• positive-incentive pseudo label
• lightweight network