  With the prevalence of RGB-D cameras, multi-modal video data have become more
available for human action recognition. One main challenge for this task lies
in how to effectively leverage their complementary information. In this work,
we propose a Modality Compensation Network (MCN) to explore the relationships
of different modalities, and boost the representations for human action
recognition. We regard RGB/optical flow videos as source modalities, skeletons
as auxiliary modality. Our goal is to extract more discriminative features from
source modalities, with the help of auxiliary modality. Built on deep
Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks,
our model bridges data from source and auxiliary modalities by a modality
adaptation block to achieve adaptive representation learning, that the network
learns to compensate for the loss of skeletons at test time and even at
training time. We explore multiple adaptation schemes to narrow the distance
between source and auxiliary modal distributions from different levels,
according to the alignment of source and auxiliary data in training. In
addition, skeletons are only required in the training phase. Our model is able
to improve the recognition performance with source data when testing.
Experimental results reveal that MCN outperforms state-of-the-art approaches on
four widely-used action recognition benchmarks.
