| Paper ID | IVMSP-1.3 |
| Paper Title |
DUAL-STREAM NETWORK BASED ON GLOBAL GUIDANCE FOR SALIENT OBJECT DETECTION |
| Authors |
Shuyong Gao, Qianyu Guo, Wei Zhang, Wenqiang Zhang, Fudan University, China; Zhongwei Ji, ZTE Corporation, China |
| Session | IVMSP-1: Object Detection 1 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVCOM] Image & Video Communications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
High-level features can help low-level features eliminate semantic ambiguity, which is crucial for obtaining the precise salient object. Some methods use high-level features to provide global guidance for some layers of the network. However, there remain several problems: (1) the global guidance has not been fully mined, which leads to its limited capacity; (2) the semantic gap between global guidance and low-level features is ignored, and simple merging methods will cause feature aliasing. To remedy the problems, we propose a dual-stream network based on global guidance with two plug-ins, global attention based multi-scale high-level feature extraction module (GAMS) to mine global guidance and scale adaptive global guidance module (SAGG) to seamlessly integrate the global guidance into each decoding layer. Comprehensive experiments on the five largest benchmark datasets demonstrate our method outperforms previous state-of-the-art methods by a large margin. |
