Weight-based multi-stream model for Multi-Modal Video Question Answering





Video Question Answering, Attention Mechanism, Computer Vision, Natural language Processing, Neural Networks, Pretrained models, Transfer Learning, Weight-based multi-stream model, TVQA Dataset, CLIP, Vision transformers, DeBERTa, Multimedia, Multi-modal


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.




How to Cite

Rajesh, M., Sridhar, S., Kulkarni, C., Shah, A., & S, N. (2023). Weight-based multi-stream model for Multi-Modal Video Question Answering. The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133306



Main Track Proceedings