Recursive Contour Saliency Blending Network for Accurate Salient Object Detection
This work addresses improved edge quality in salient object detection, which is an incremental advancement for computer vision applications.
The paper tackled the problem of excessive false positives in contour-based salient object detection by proposing a network with a contour-saliency blending module, recursive CNN, and new loss functions, achieving competitive state-of-the-art performance on five benchmark datasets.
Contour information plays a vital role in salient object detection. However, excessive false positives remain in predictions from existing contour-based models due to insufficient contour-saliency fusion. In this work, we designed a network for better edge quality in salient object detection. We proposed a contour-saliency blending module to exchange information between contour and saliency. We adopted recursive CNN to increase contour-saliency fusion while keeping the total trainable parameters the same. Furthermore, we designed a stage-wise feature extraction module to help the model pick up the most helpful features from previous intermediate saliency predictions. Besides, we proposed two new loss functions, namely Dual Confinement Loss and Confidence Loss, for our model to generate better boundary predictions. Evaluation results on five common benchmark datasets reveal that our model achieves competitive state-of-the-art performance.