Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the problem of video classification for facial analysis and human action recognition.  propose a novel akly 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.  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.  also validate the method with qualitative results and show that they largely support the intuitions behind the method.
['Karan Sikka', 'Gaurav Sharma']
I am writing a research paper on a idea Discriminatively Trained Latent Ordinal Model for Video Classification
[{"from": "human", "value": "I am writing a research paper on a idea Discriminatively Trained Latent Ordinal Model for Video Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the problem of video classification for facial analysis and human action recognition.  propose a novel akly 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.  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.  also validate the method with qualitative results and show that they largely support the intuitions behind the method."}]