Learning to Hash-tag Videos with Tag2Vec

TL;DR

This paper introduces Tag2Vec, a method that learns to generate relevant hash-tags for short videos by mapping video features into a learned tag embedding space, enabling efficient tag retrieval.

Contribution

The paper proposes a novel two-step training approach combining NLP-based tag embedding with video feature mapping for hash-tag generation.

Findings

Effective tag retrieval demonstrated through qualitative analysis.

Quantitative validation shows high relevance of generated tags.

System works across 29 video categories.

Abstract

User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash-tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data-driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study.