Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
This work addresses a specific problem in computer vision for researchers and practitioners by offering an incremental improvement over existing methods through a novel architectural approach.
The paper tackles the performance bottleneck in salient object detection caused by homogenized deep features in deeper networks by proposing a wider network architecture with parallel sub-networks to increase feature diversity and complementarity, achieving superior performance on several benchmarks.
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.