Disclaimer If you're planing to use information provided on this site, please keep in mind that all numbers and papers are added by authors without double checking. We of course try to keep results as accurate as possible, and whenever we got notice of an error it will be fixed, but this does not release you from the obligation of reading the papers and double checking the numbers listed here before using them.
Charades
Paper : Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Author : Gunnar A. Sigurdsson and G{"u}l Varol and Xiaolong Wang and Ali Farhadi and Ivan Laptev and Abhinav Gupta
Description : Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence. The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos.
Number of Videos : 9848
Number of Classes : 157
Evaluation: Video classification
Description: Video action classification performance is evaluated with the standard mean average precision (mAP) measure.
Results
If you want to add this result data into your web page, please insert the following HTML code on your web page:
Evaluation: Temporal localization
Description: Temporal localization/segmentation performance is evaluated with the standard mean average precision (mAP) measure
Results
| Result | Paper | Description | URL | Peer Reviewed | Year |
|---|---|---|---|---|---|
| Result | Paper | Description | URL | Peer Reviewed | Year |
| 12.8 | Asynchronous Temporal Fields for Action Recognition[Gunnar A. Sigurdsson, Santosh Divvala, Ali Farhadi, Abhinav Gupta] | Asynchronous Temporal Field | URL | Yes | 2017 |
| 12.7 | R-C3D: Region Convolutional 3D Network for Temporal Activity Detection[Huijuan Xu,Abir Das,Kate Saenko] | Region Convolutional 3D Network | URL | Yes | 2017 |
| 8.9 | Predictive-Corrective Networks for Action Detection[Achal Dave,Olga Russakovsky,Deva Ramanan] | Predictive-Corrective Networks (RGB Only) | URL | Yes | 2017 |
| 8.9 | Asynchronous Temporal Fields for Action Recognition[Gunnar A. Sigurdsson, Santosh Divvala, Ali Farhadi, Abhinav Gupta] | Two-Stream baseline | URL | Yes | 2017 |
| 1.98 | Temporal Dynamic Graph LSTM for Action-driven Video Object Detection[Yuan Yuan, Xiaodan Liang, Xiaolong Wang, Dit-Yan Yeung, Abhinav Gupta] | TD-Graph LSTM | URL | Yes | 2017 |
If you want to add this result data into your web page, please insert the following HTML code on your web page: