  Models based on deep convolutional networks have dominated recent image
interpretation tasks; we investigate whether models which are also recurrent,
or "temporally deep", are effective for tasks involving sequences, visual and
otherwise. We develop a novel recurrent convolutional architecture suitable for
large-scale visual learning which is end-to-end trainable, and demonstrate the
value of these models on benchmark video recognition tasks, image description
and retrieval problems, and video narration challenges. In contrast to current
models which assume a fixed spatio-temporal receptive field or simple temporal
averaging for sequential processing, recurrent convolutional models are "doubly
deep"' in that they can be compositional in spatial and temporal "layers". Such
models may have advantages when target concepts are complex and/or training
data are limited. Learning long-term dependencies is possible when
nonlinearities are incorporated into the network state updates. Long-term RNN
models are appealing in that they directly can map variable-length inputs
(e.g., video frames) to variable length outputs (e.g., natural language text)
and can model complex temporal dynamics; yet they can be optimized with
backpropagation. Our recurrent long-term models are directly connected to
modern visual convnet models and can be jointly trained to simultaneously learn
temporal dynamics and convolutional perceptual representations. Our results
show such models have distinct advantages over state-of-the-art models for
recognition or generation which are separately defined and/or optimized.
