Approach for Video Classification with Multi-label on YouTube-8M Dataset
This addresses the need for automated video classification for media platforms, but it is incremental as it applies existing methods to a standard dataset.
The paper tackled video classification on the YouTube-8M dataset using NetVLAD and NetFV models with Huber loss, achieving a GAP score of 0.8668.
Video traffic is increasing at a considerable rate due to the spread of personal media and advancements in media technology. Accordingly, there is a growing need for techniques to automatically classify moving images. This paper use NetVLAD and NetFV models and the Huber loss function for video classification problem and YouTube-8M dataset to verify the experiment. We tried various attempts according to the dataset and optimize hyperparameters, ultimately obtain a GAP score of 0.8668.