Video Caption Based Searching Using End-to-End Dense Captioning and Sentence Embeddings

• Published in: Symmetry (Basel) Vol. 12; no. 6; p. 992
• Main Authors: Aggarwal, Akshay, Chauhan, Aniruddha, Kumar, Deepika, Mittal, Mamta, Roy, Sudipta, Kim, Tai-hoon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2020
• Subjects: Algorithms; Coders; deep learning; Embedding; embeddings; Experimentation; Natural language; Neural networks; Queries; Researchers; Searching; Semantics; sentence embeddings; video captioning
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym12060992

Traditionally, searching for videos on popular streaming sites like YouTube is performed by taking the keywords, titles, and descriptions that are already tagged along with the video into consideration. However, the video content is not utilized for searching of the user’s query because of the difficulty in encoding the events in a video and comparing them to the search query. One solution to tackle this problem is to encode the events in a video and then compare them to the query in the same space. A method of encoding meaning to a video could be video captioning. The captioned events in the video can be compared to the query of the user, and we can get the optimal search space for the videos. There have been many developments over the course of the past few years in modeling video-caption generators and sentence embeddings. In this paper, we exploit an end-to-end video captioning model and various sentence embedding techniques that collectively help in building the proposed video-searching method. The YouCook2 dataset was used for the experimentation. Seven sentence embedding techniques were used, out of which the Universal Sentence Encoder outperformed over all the other six, with a median percentile score of 99.51. Thus, this method of searching, when integrated with traditional methods, can help improve the quality of search results.