Learning to Hash-tag Videos with Tag2Vec
Aditya Singh, Saurabh Saini, Rajvi Shah, PJ Narayanan

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.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications · Video Analysis and Summarization
