Feature Re-Learning with Data Augmentation for Video Relevance Prediction

• URL: http://arxiv.org/abs/2004.03815v1
• Date: Wed, 8 Apr 2020 05:22:41 GMT
• Title: Feature Re-Learning with Data Augmentation for Video Relevance Prediction
• Authors: Jianfeng Dong, Xun Wang, Leimin Zhang, Chaoxi Xu, Gang Yang, Xirong Li
• Abstract summary: Re-learning is realized by projecting a given deep feature into a new space by an affine transformation. We propose a new data augmentation strategy which works directly on frame-level and video-level features.
• Score: 35.87597969685573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image and video convolutional neural network models, deep visual features are widely used for video content representation. However, as how two videos are relevant is task-dependent, such off-the-shelf features are not always optimal for all tasks. Moreover, due to varied concerns including copyright, privacy and security, one might have access to only pre-computed video features rather than original videos. We propose in this paper feature re-learning for improving video relevance prediction, with no need of revisiting the original video content. In particular, re-learning is realized by projecting a given deep feature into a new space by an affine transformation. We optimize the re-learning process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, we propose a new data augmentation strategy which works directly on frame-level and video-level features. Extensive experiments in the context of the Hulu Content-based Video Relevance Prediction Challenge 2018 justify the effectiveness of the proposed method and its state-of-the-art performance for content-based video relevance prediction.

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