  The target of image-text clustering (ITC) is to find correct clusters by
integrating complementary and consistent information of multi-modalities for
these heterogeneous samples. However, the majority of current studies analyse
ITC on the ideal premise that the samples in every modality are complete. This
presumption, however, is not always valid in real-world situations. The missing
data issue degenerates the image-text feature learning performance and will
finally affect the generalization abilities in ITC tasks. Although a series of
methods have been proposed to address this incomplete image text clustering
issue (IITC), the following problems still exist: 1) most existing methods
hardly consider the distinct gap between heterogeneous feature domains. 2) For
missing data, the representations generated by existing methods are rarely
guaranteed to suit clustering tasks. 3) Existing methods do not tap into the
latent connections both inter and intra modalities. In this paper, we propose a
Clustering-Induced Generative Incomplete Image-Text Clustering(CIGIT-C) network
to address the challenges above. More specifically, we first use
modality-specific encoders to map original features to more distinctive
subspaces. The latent connections between intra and inter-modalities are
thoroughly explored by using the adversarial generating network to produce one
modality conditional on the other modality. Finally, we update the
corresponding modalityspecific encoders using two KL divergence losses.
Experiment results on public image-text datasets demonstrated that the
suggested method outperforms and is more effective in the IITC job.
