34 citations · 155 across the 18 of their papers we have counts for
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cs.CV2017
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition
Jingxuan Hou, Tae Soo Kim, Austin Reiter
Despite the growing discriminative capabilities of modern deep learning methods for recognition tasks, the inner workings of the state-of-art models still remain mostly black-boxes…
cs.CV2017
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
Tae Soo Kim, Austin Reiter
The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent. In conjunction with the recent resurgence of 3D human action repr…
cs.CV2017★ 15 cited
Regularizing Face Verification Nets For Pain Intensity Regression
Feng Wang, Xiang Xiang, Chang Liu +6
Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is l…