Salient Object Detection via Deep Hierarchical Context Aggregation and Multi-Layer Supervision
Chao Zhang, Zhiguo Cao, Xin Xiong, Ke Xian, Xinyuan Qi · 2019
The aggregation of hierarchical information is vital for saliency detection. To achieve this, most existing saliency detectors apply various network structures to fuse features. But most of them utilize shallow skip connections and only concentrate on the final results, which can not guarantee the model to learn the rich and accurate contextual information. To address these problems, we propose a network with deep layer aggregation and multi-layer intermediate supervision. We utilize deep layer aggregation to fuse features iteratively and hierarchically across layers to obtain richer information. Then we add multi-layer intermediate supervision on each side-output layer to capture more accurate contextual information. We evaluate our method on six benchmark datasets under various metrics and it achieves the new state-of-the-art.