Abstract
We present a novel model for multiple object recognition. Our model combines current deep learning recognition systems with object category relation information. This model is mainly inspired by spatial memory network [1], which treats multiple object recognition task as an iterative process, reusing some region feature as context information. We extend this work by presenting a statistical-based category relation model to measure object category semantic relevance. With the spatial memory and category relation model as relation reasoning modules, our model takes in context information. Our model achieves 4.4% per-instance and 4.9% per-class absolute improvement of average classification accuracy over plain convolutional neural networks on ADE dataset.
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.
