  Building correspondences across different modalities, such as video and
language, has recently become critical in many visual recognition applications,
such as video captioning. Inspired by machine translation, recent models tackle
this task using an encoder-decoder strategy. The (video) encoder is
traditionally a Convolutional Neural Network (CNN), while the decoding (for
language generation) is done using a Recurrent Neural Network (RNN). Current
state-of-the-art methods, however, train encoder and decoder separately. CNNs
are pretrained on object and/or action recognition tasks and used to encode
video-level features. The decoder is then optimised on such static features to
generate the video's description. This disjoint setup is arguably sub-optimal
for input (video) to output (description) mapping. In this work, we propose to
optimise both encoder and decoder simultaneously in an end-to-end fashion. In a
two-stage training setting, we first initialise our architecture using
pre-trained encoders and decoders -- then, the entire network is trained
end-to-end in a fine-tuning stage to learn the most relevant features for video
caption generation. In our experiments, we use GoogLeNet and
Inception-ResNet-v2 as encoders and an original Soft-Attention (SA-) LSTM as a
decoder. Analogously to gains observed in other computer vision problems, we
show that end-to-end training significantly improves over the traditional,
disjoint training process. We evaluate our End-to-End (EtENet) Networks on the
Microsoft Research Video Description (MSVD) and the MSR Video to Text (MSR-VTT)
benchmark datasets, showing how EtENet achieves state-of-the-art performance
across the board.
