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README.md

Zero-Example Video Search

This tool provides an efficient implementation for zero-example video search. It is derived from our successful system for NIST TRECVID multimedia event detection (MED'15, '16), ad-hoc video search (AVS'16) and MMM video browswer showdown (VBS'17). The tool is capable of both MED and AVS tasks, and also supports interactive search. The implementation has the state-of-the-art performance which can serve as a good baseline. We hope the open source can benefit future research.

We highlight the following features:

  • [ General-purpose, zero-example search ] - Compatible for both simple queries and complex queries (event kits in MED).
  • [ High efficiency ] - Support 10,000+ visual concepts and can finish a search within seconds on a laptop for a corpus size of around 300,000 videos/keyframes.
  • [ Interactive search ] - Support human-in-the-loop. Human efforts can be involved in the concept screening which is an intermediate step where a user has a chance to improve the search result while being kept away from directly seeing the result. Alternatively, a user can also perform interactive search by iteratively refining the result after seeing it.
  • [ State-of-the-art performance and open source ] - Can be used as a standalone tool or embedded as a module with ease.

The package encapsulates three datasets with deep net features and ground truth for benchmarks. The datasets are (1) IACC.3 for AVS'16, (2) MED14Test, and (3) TV2008 search task.

To get started, please follow this GUIDE. Download the release version HERE. Have fun!


If you find this tool helpful, please cite the following work:

@inproceedings{Lu2016Event,
 author = {Lu, Yi-Jie and Zhang, Hao and de Boer, Maaike and Ngo, Chong-Wah},
 title = {Event Detection with Zero Example: Select the Right and Suppress the Wrong Concepts},
 booktitle = {Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval},
 series = {ICMR '16},
 year = {2016},
 location = {New York, NY, USA},
 pages = {127--134},
}

Performance

Both fully automatic and manual runs on TRECVID'16 AVS task benchmark:

AVS'16 performance

Benchmarks of the fully automatic and manual runs on MED'14 test set and fully automatic run on TRECVID'08 Search task:

MED'14 and Search'08 performance