Abstract
In this paper, we address the combination of the active learning (AL) and semi-supervised (SSL) learnings, called ASSL, to leverage the strong points of the both learning paradigms for improving the performance of object detection. Considering the pros and cons of the AL and SSL learning methods, ASSL where SSL method provides the incremental improvement of semi-supervised detection performance by combining the concept of diversity imported from AL methods. The proposed method demonstrates outstanding performance compared with state-of-art methods on the challenging Caltech pedestrian detection dataset, reducing the miss rate to 12.2%, which is significantly smaller than current state-of-art. In addition, extensive experiments have been carried out using ILSVRC detection dataset and online evaluation for activity recognition.
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.
