Abstract
There has been a tremendous success in individual domains of Computer Vision, Natural Language Processing, and Knowledge Representation. Videos are a rich source of information with the multi-modal data forms of images, audio, and optionally subtitles blended. Current research is going on in combining these individual domains which have given rise to topics such as image captioning, visual question answering, and video question answering. Video Question Answering is a model which combines research topics like object detection and recognition, temporal information processing, visual attention, and natural language processing. In this paper, we propose a model with Attention Mechanism for Video Question Answering that assigns varying weights to the many pieces of information the video encompasses. The model combines the question with 3 streams i.e., video's frames, subtitles, and objects to get the most probable answer. The model also receives the set of answer candidates as input and predicts one of them as the most probable answer since it has been trained and tested on the TVQA dataset.
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.
