在培训中,我们还提出了一个即插即用的对比证据去偏(CED)模块,以消除视频中人类行为的表现。 3.1. Deep Evidential Action Recognition 首先我们介绍证据深度学习。现有的基于深度学习的模型通常在深度神经网络(dnn)之上使用softmax层进行分类。然而,这些基于softmax的dnn无法估计分类问题的预测不确定性,因为softmax分数...
[2] Two-stream convolutional networks for action recognition in videos [3] Beyond Short Snippets: Deep Networks for Video Classification [4] Learning Spatiotemporal Features with 3D Convolutional Networks [5] https://clarifai.com/
"Sequential deep learning for human action recognition." International Workshop on Human Behavior Understanding. Springer Berlin Heidelberg, (2011)M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, "Sequential deep learning for hu- man action recognition," International Workshop on...
[2] Two-stream convolutional networks for action recognition in videos [3] Beyond Short Snippets: Deep Networks for Video Classification [4] Learning Spatiotemporal Features with 3D Convolutional Networks [5]https://clarifai.com/
模型可由Evidential Deep Learning Loss(EDLLoss)进行训练,并通过本文所提出的Evidential Uncertainty Calibration (EUC)进行正则化。在训练中作者还提出了一种即插即用的Contrastive Evidence Debiasing (CED) 模块对视频人类动作表示进行去偏。 Fig3 : The proposed DEAR method...
Deep Learning on Lie Groups for Skeleton-based Action Recognition T., Van Gool, L., Deep learning on lie groups for skeleton-based action recognition, in IEEE Conference on Computer Vision and Pattern Recognition, 2016... Z Huang,C Wan,T Probst,... - IEEE Computer Society 被引量: 62...
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning 使用多任务深度学习进行2D / 3D姿态估计和动作识别 1.动作识别和人体姿势结合进行动作的2D和3D姿态估计。 2.两者联合做的姿势估计准确率优于单一的专用方法。 使用了四个数据集(MPII,Human3.6M,Penn Action和NTU) ...
action recognition. They use a hierarchical partwise bag-of-words representation to encode local and global features and then formulate a part-regularized multitask structural learning framework for action classification. Hou et al.22used skeleton data for encoding the spatial-temporal information into ...
Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition motivation 对于不同的视频序列,挑出最有代表性的帧的方法是不同的,因此,本文提出用深度增强学习来将帧的选择模拟为一个不断进步的progressive process。 强化学习是通过优化选择actions的policy来最大化agent从environment所获得的rewards。文章...
D.Tran,L.Bourdev,R.Fergus,L.Torresani,andM.Paluri. Learning spatiotemporal features with 3Dconvolutional networks. In Proc. ICCV, 2015. J. Carreira and A. Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In Proc. CVPR, 2017. ...