Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling

Choongyeun Cho; Benjamin Antin; Sanchit Arora; Shwan Ashrafi; Peilin Duan; Dang The Huynh; Lee James; Hang Tuan Nguyen; Mojtaba Solgi; Cuong Van Than

arXiv · 1809.07895

Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling

Choongyeun Cho·Benjamin Antin·Sanchit Arora·Shwan Ashrafi·Peilin Duan·Dang The Huynh·Lee James·Hang Tuan Nguyen·Mojtaba Solgi·Cuong Van Than

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Abstract

This paper presents the Axon AI's solution to the 2nd YouTube-8M Video Understanding Challenge, achieving the final global average precision (GAP) of 88.733% on the private test set (ranked 3rd among 394 teams, not considering the model size constraint), and 87.287% using a model that meets size requirement. Two sets of 7 individual models belonging to 3 different families were trained separately. Then, the inference results on a training data were aggregated from these multiple models and fed to train a compact model that meets the model size requirement. In order to further improve performance we explored and employed data over/sub-sampling in feature space, an additional regularization term during training exploiting label relationship, and learned weights for ensembling different individual models.

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BibTeX RIS

Choongyeun Cho, Benjamin Antin, Sanchit Arora, Shwan Ashrafi, Peilin Duan, Dang The Huynh, Lee James, Hang Tuan Nguyen, Mojtaba Solgi, Cuong Van Than. 2018-09-21. Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling. https://arxiv.org/abs/1809.07895

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