Abstract
Semantic segmentation is a classical task in computer vision. In this paper, we target to address the low confidence regions which traditional CNN can not solve very well in semantic segmentation task. Depending on different characteristics of low confidence regions, an adaptive and robust attention mechanism is important to focus on the informative regions but ignore the noisy parts in the image. Intuitively, one attention map only is not sufficient to model the interaction between the low confidence regions and its surrounding patches. Thus, in this paper, we propose an Attention Forest structure, a novel and robust attention mechanism, to handle the low confidence regions. Each Attention Tree structure can capture more interactions between current patches with its adjacent regions. Experiments on PASCAL VOC 2012 Dataset validate the effectiveness of our proposed algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
