Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework,  propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality.  use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows.  argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations.  demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.
['Zelun Luo', 'Boya Peng', 'De-An Huang', 'Alexandre Alahi', 'Li Fei-Fei']
I am writing a research paper on a idea Unsupervised Learning of Long-Term Motion Dynamics for Videos
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised Learning of Long-Term Motion Dynamics for Videos"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework,  propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality.  use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows.  argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations.  demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos."}]