# Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos

arXiv: https://arxiv.org/abs/1512.00818 · HTML: https://mlsanity.com/paper/1512.00818/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2015.

## Built on by (most cited first, 2 of 2)

- [MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference](https://mlsanity.com/paper/2209.13883/) (2022)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)

## Abstract (arXiv)

We propose a new zero-shot Event Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e.g., "changing a vehicle tire") based on their content. We embed videos into a distributional semantic space and then measure the similarity between videos and the event query in a free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-of-the-art that uses big descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.