Deep learning based salient object detection methods have recently received significant attention. However, current methods still suffer from shortcomings such as informative background information being ignored which a significant problem for image saliency understanding. Additionally, it is also a challenge to suppress the noisy features in the network. By analyzing the difference between high-level and low-level features from ResNet-50, we utilize a Bilateral Feature Fusion (BFF) module to deal with the problem caused by ignoring informative background information. Benefitting from the BFF module, our proposed network can capture more meaningful foreground and background cues, which helps to get a more accurate saliency map. Moreover, we adopt a Score Sorting Attention (SSA) module which suppresses noisy and irrelevant features. Experimental results on five benchmark datasets demonstrate that our proposed method performs better than other state-of-the-art methods. The ablation studies also prove our contributions.
|Title of host publication||Proceedings 2022 IEEE International Conference on Multimedia and Expo (ICME)|
|Publisher||IEEE Computer Society|
|Publication status||Published online - 26 Aug 2022|
|Event||2022 IEEE International Conference on Multimedia and Expo, ICME 2022 - Taipei, Taiwan, Republic of China|
Duration: 18 Jul 2022 → 22 Jul 2022
|Name||Proceedings - IEEE International Conference on Multimedia and Expo|
|Conference||2022 IEEE International Conference on Multimedia and Expo, ICME 2022|
|Country/Territory||Taiwan, Republic of China|
|Period||18/07/22 → 22/07/22|
Bibliographical noteFunding Information:
∗Corresponding Author. This work was supported by Open Research Projects of Zhejiang Lab(No.2019KD0AD01/006), Major Science and Technology Projects of Liaoning Province(No.2021JH1/10400049), Fundation of Key Laboratory of Equipment Reliability(No.WD2C20205500306), Fundation of Key Laboratory of Aerospace System Simulation(No.6142002200301), National Natural Science Foundation of China (No. 61906176) and Fundamental Research Funds for the Central Universities (N2004022).
© 2022 IEEE.
- bilateral feature fusion
- salient object detection
- score sorting attention