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Recursive multi-model complementary deep fusion for robust salient object detection via parallel sub-networks
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DOI：10.1016/j.patcog.2021.108212.png)
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摘要
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Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional deeper schemes, this paper proposes a wider network architecture which consists of parallel sub-networks with totally different network architectures. In this way, those deep features obtained via these two sub-networks will exhibit large diversity, which will have large potential to be able to complement with each other. However, a large diversity may easily lead to the feature conflictions, thus we use the dense short-connections to enable a recursively interaction between the parallel sub-networks, pursuing an optimal complementary status between multi-model deep features. Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Salient object detection
Deep learning
Multi-model fusion
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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