Abstract

Recognition of human actions in videos is a challenging task, which has received a significant amount of attention in the research community. We introduce an end-to-end multitask model that jointly learns object-action relationships. We compare it with different training objectives, validate its effectiveness for detecting objects-actions in videos, First we fine-tune a Resnet model to detect objects in videos, second a Neural Network model is used for sequence learning to get the object-action correlation. Finally, we apply our multitask architecture to detect visual relationships between objects to recognize activities in videos of the MSR Daily Activity 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 call

Disclaimer: 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.