YouTube-8M Video Understanding Challenge Approach and Applications
Edward Chen
TL;DR
This paper details the approach and results of participating in the YouTube-8M Video Understanding Challenge, exploring various models, ensemble techniques, and discussing future research directions and applications.
Contribution
It presents a comprehensive experimentation with multiple models and ensemble methods for large-scale video understanding, highlighting practical improvements and future prospects.
Findings
Ensemble learning significantly improved scores
Various models were tested for effectiveness
Future applications of video understanding are promising
Abstract
This paper introduces the YouTube-8M Video Understanding Challenge hosted as a Kaggle competition and also describes my approach to experimenting with various models. For each of my experiments, I provide the score result as well as possible improvements to be made. Towards the end of the paper, I discuss the various ensemble learning techniques that I applied on the dataset which significantly boosted my overall competition score. At last, I discuss the exciting future of video understanding research and also the many applications that such research could significantly improve.
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Taxonomy
TopicsHuman Pose and Action Recognition · Video Analysis and Summarization · Multimodal Machine Learning Applications