  Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced
data, while imbalanced data with long-tail distribution is more general in
real-world. Several long-tail hashing methods have been proposed but they can
not adapt for multi-modal data, due to the complex interplay between labels and
individuality and commonality information of multi-modal data. Furthermore, CMH
methods mostly mine the commonality of multi-modal data to learn hash codes,
which may override tail labels encoded by the individuality of respective
modalities. In this paper, we propose LtCMH (Long-tail CMH) to handle
imbalanced multi-modal data. LtCMH firstly adopts auto-encoders to mine the
individuality and commonality of different modalities by minimizing the
dependency between the individuality of respective modalities and by enhancing
the commonality of these modalities. Then it dynamically combines the
individuality and commonality with direct features extracted from respective
modalities to create meta features that enrich the representation of tail
labels, and binaries meta features to generate hash codes. LtCMH significantly
outperforms state-of-the-art baselines on long-tail datasets and holds a better
(or comparable) performance on datasets with balanced labels.
