Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract:  propose an approach to learn spatio-temporal features in videos from intermediate visual representations  call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend to have a low-spatial resolution. Low-level percepts, on the other hand, preserve a higher spatial resolution from which  can model finer motion patterns. Using low-level percepts can leads to high-dimensionality video representations. To mitigate this effect and control the model number of parameters,  introduce a variant of the GRU model that leverages the convolution operations to enforce sparse connectivity of the model units and share parameters across the input spatial locations. #R##N# empirically validate our approach on both Human Action Recognition and Video Captioning tasks. In particular,  achieve results equivalent to state-of-art on the YouTube2Text dataset using a simpler text-decoder model and without extra 3D CNN features.
['Nicolas Ballas', 'Li Yao', 'Christopher J. Pal', 'Aaron C. Courville']
I am writing a research paper on a idea Delving Deeper into Convolutional Networks for Learning Video Representations
[{"from": "human", "value": "I am writing a research paper on a idea Delving Deeper into Convolutional Networks for Learning Video Representations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract:  propose an approach to learn spatio-temporal features in videos from intermediate visual representations  call \"percepts\" using Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend to have a low-spatial resolution. Low-level percepts, on the other hand, preserve a higher spatial resolution from which  can model finer motion patterns. Using low-level percepts can leads to high-dimensionality video representations. To mitigate this effect and control the model number of parameters,  introduce a variant of the GRU model that leverages the convolution operations to enforce sparse connectivity of the model units and share parameters across the input spatial locations. #R##N# empirically validate our approach on both Human Action Recognition and Video Captioning tasks. In particular,  achieve results equivalent to state-of-art on the YouTube2Text dataset using a simpler text-decoder model and without extra 3D CNN features."}]