We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.

探讨关于面部分析和人类行为识别中视频分类的问题，提出了一种新颖的弱监督学习方法，通过模拟自动挖掘的明显子事件序列（例如“微笑”的起始和停止阶段，“跑步”和“跳跃”）来建模视频，将其扩展到顺序方面，从而在四个面部分析数据集和三个人类动作数据集上获得了一致改进。

用于视频分类的判别式训练潜在顺序模型