Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence

• URL: http://arxiv.org/abs/2004.07967v1
• Date: Thu, 16 Apr 2020 21:12:32 GMT
• Title: Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
• Authors: Huy Manh Nguyen, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi
• Abstract summary: Visual-semantic embedding aims to learn a joint embedding space where related video and sentence instances are located close to each other. A single space is not enough to accommodate various videos and sentences. We propose a novel framework that maps instances into multiple individual embedding spaces.
• Score: 8.602553195689513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual-semantic embedding aims to learn a joint embedding space where related video and sentence instances are located close to each other. Most existing methods put instances in a single embedding space. However, they struggle to embed instances due to the difficulty of matching visual dynamics in videos to textual features in sentences. A single space is not enough to accommodate various videos and sentences. In this paper, we propose a novel framework that maps instances into multiple individual embedding spaces so that we can capture multiple relationships between instances, leading to compelling video retrieval. We propose to produce a final similarity between instances by fusing similarities measured in each embedding space using a weighted sum strategy. We determine the weights according to a sentence. Therefore, we can flexibly emphasize an embedding space. We conducted sentence-to-video retrieval experiments on a benchmark dataset. The proposed method achieved superior performance, and the results are competitive to state-of-the-art methods. These experimental results demonstrated the effectiveness of the proposed multiple embedding approach compared to existing methods.

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