MMLAB@CUHK, Shatin, Hong Kong
WIDER is a dataset for complex event recognition from static images. As of v0.1, it contains 61 event categories and around 50574 images annotated with event class labels. We provide a split of 50% for training and 50% for testing.
Please use this link for downloading.
This section list the experimental results on WIDER.
Under this setting, the training set of 25275 images will be used to train a event recognition system which classifies an input image into 61 event classes. The test set will be used to evaluate the performance of the system based on its mean recognition accuracy.
| Method | Mean Accuracy (%) | Per-class Results | Models |
|---|---|---|---|
| Baseline CNN* | 39.67 | Link | Caffe Models |
| CNN Deep Channel Fusing | 42.42 | Link | Caffe Models |
* the Baseline CNN is finetuned from a well-known AlexNet model pretrained on ImageNet.
You are welcome to submit your results to be listed here.
Please cite the following paper in you publication if WIDER helps your research
@inproceedings{xiong2015wider,
title={Recognize Complex Events from Static Images by Fusing Deep Channels},
author={Xiong, Yuanjun and Zhu, Kai and Lin, Dahua and Tang, Xiaoou},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on},
year={2015},
organization={IEEE}
}
For questions and result submission, please contact Yuanjun Xiong at yjxiong@ie.cuhk.edu.hk