Approach for Video Classification with Multi-label on YouTube-8M Dataset
Kwangsoo Shin, Junhyeong Jeon, Seungbin Lee, Boyoung Lim, Minsoo, Jeong, Jongho Nang
TL;DR
This paper presents a method for multi-label video classification on YouTube-8M using NetVLAD and NetFV models, achieving a high GAP score through hyperparameter optimization.
Contribution
It introduces the application of NetVLAD and NetFV with Huber loss for improved multi-label video classification on a large-scale dataset.
Findings
Achieved a GAP score of 0.8668 on YouTube-8M.
Optimized hyperparameters for better performance.
Validated effectiveness of NetVLAD and NetFV models.
Abstract
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.
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Text and Document Classification Technologies · Video Analysis and Summarization
MethodsHuber loss