Approach for Video Classification with Multi-label on YouTube-8M Dataset

08/27/2018
by   Kwangsoo Shin, et al.
0

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.

READ FULL TEXT
research
06/24/2017

Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset

YouTube-8M is the largest video dataset for multi-label video classifica...
research
07/02/2021

Misinformation Detection on YouTube Using Video Captions

Millions of people use platforms such as YouTube, Facebook, Twitter, and...
research
06/28/2017

The YouTube-8M Kaggle Competition: Challenges and Methods

We took part in the YouTube-8M Video Understanding Challenge hosted on K...
research
06/26/2017

YouTube-8M Video Understanding Challenge Approach and Applications

This paper introduces the YouTube-8M Video Understanding Challenge hoste...
research
07/13/2017

Large-scale Video Classification guided by Batch Normalized LSTM Translator

Youtube-8M dataset enhances the development of large-scale video recogni...
research
08/27/2021

Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels

We propose a novel framework for predicting the factuality of reporting ...

Please sign up or login with your details

Forgot password? Click here to reset