Name
Abstract | ||
|---|---|---|
Recently, significant effort has been devoted to exploring the role of feature fusion and enriching contextual information on detecting multi-scale faces. However, simply integrating features of different levels could lead to introducing significant noise. Moreover, recently proposed approaches of enriching contextual information are not efficient or ignore the gridding artifacts produced by dilated convolution. To tackle these issues, we developed a smoothed attention network (dubbed SANet), which introduces an Attention-guided Feature Fusion Module (AFFM) and a Smoothed Context Enhancement Module (SCEM). In particular, the AFFM applies an attention module to high-level semantic features and fuses attention-focused features with low-level semantic features to reduce the noise of the fused feature map. The SCEM stacks dilated convolution and convolution layers alternately to re-learn the relationship among completely separate sets of units produced by dilated convolution to maintain consistency of local information. The SANet achieves promising results on the WIDER FACE validation and testing datasets and is state-of-the-art on the UFDD dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICB45273.2019.8987285 | 2019 International Conference on Biometrics (ICB) |
Keywords | Field | DocType |
smoothed attention network,single stage face detector,enriching contextual information,multiscale faces,gridding artifacts,dilated convolution,SANet,Attention-guided Feature Fusion Module,AFFM,Smoothed Context Enhancement Module,SCEM,attention module,high-level semantic features,low-level semantic features,fused feature map,convolution layers,local information,testing datasets,attention-focused features | Computer vision,Contextual information,Feature fusion,Pattern recognition,Computer science,Convolution,Attention network,Artificial intelligence,Fuse (electrical),Detector | Conference |
ISSN | ISBN | Citations |
2376-4201 | 978-1-7281-3641-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Shi | 1 | 306 | 55.98 |
| Xiang Xu | 2 | 30 | 5.58 |
| Ioannis A. Kakadiaris | 3 | 1910 | 203.66 |