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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

Dataset URL

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

Result Paper Description URL Peer Reviewed Year
Result Paper Description URL Peer Reviewed Year
18.6 Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding[Gunnar A. Sigurdsson and G{"u}l Varol and Xiaolong Wang and Ali Farhadi and Ivan Laptev and Abhinav Gupta] Combined Baslines URL Yes 2016
22.4 Asynchronous Temporal Fields for Action Recognition[Gunnar A. Sigurdsson, Santosh Divvala, Ali Farhadi, Abhinav Gupta] Asynchronous Temporal Field URL Yes 2017
21 ActionVLAD: Learning spatio-temporal aggregation for action classification[Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, Bryan Russell] ActionVLAD (RGB only, BN-inception) + iDT URL Yes 2017
39.5 Non-local Neural Networks[Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming He] NL I3D (RGB) URL No 2017
24.1 Compressed Video Action Recognition[Chao-Yuan Wu and Manzil Zaheer and Hexiang Hu and R. Manmatha and Alexander J. Smola and Philipp Kraehenbuehl] CoViAR + optical flow URL No 2017
19.5 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
26.7 Video Representation Learning Using Discriminative Pooling[Jue Wang, Anoop Cherian, Fatih Porikli, Stephen Gould] SVMP(VGG+IDT) URL Yes 2018

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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
14.2 Video Representation Learning Using Discriminative Pooling[Jue Wang, Anoop Cherian, Fatih Porikli, Stephen Gould] SVMP(VGG+IDT) URL Yes 2018
19.4 Learning Latent Super-Events to Detect Multiple Activities in Videos[AJ Piergiovanni, Michael S. Ryoo] I3D + super-events URL Yes 2018

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