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. Author manuscript; available in PMC: 2021 Mar 3.
Published in final edited form as: Int J Comput Vis. 2019 Aug 31;128(1):1–25. doi: 10.1007/s11263-019-01215-y

Table 3.

Dimensional emotion regression and categorical emotion classification performance on the test set

Model Regression Classification ERS
mR2 mAP mRA
A random method based on priors
 Chance 0 10.55 50 0.151
Learning from skeleton
 ST-GCN 0.044 12.63 55.96 0.194
 LMA 0.075 13.59 57.71 0.216
Learning from pixels
 TF −0.008 10.93 50.25 0.149
 TS-ResNet101 0.084 17.04 62.29 0.240
 I3D 0.098 15.37 61.24 0.241
 TSN 0.095 17.02 62.70 0.247
 TSN-Spatial 0.048 15.34 60.03 0.212
 TSN-Flow 0.098 15.78 61.28 0.241

Best performance for each evaluation metric under each modality is highlighted in bold

mR2 = mean of R2 over dimensional emotions, mAP(%) = average precision/area under precision recall curve (PR AUC) over categorical emotions, mRA(%) = mean of area under ROC curve (ROC AUC) over categorical emotions, and ERS = emotion recognition score. Baseline methods: ST-GCN (Yan et al. 2018), TF (Kantorov and Laptev 2014), TS-ResNet101 (Simonyan and Zisserman 2014), I3D (Carreira and Zisserman 2017), and TSN (Wang et al. 2016)