Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …

Cross-modal retrieval aims to retrieve relevant data from another modality when given a
query of one modality. Although most existing methods that rely on the label information of …

Cross-modal hash has become a key technology for large datasets retrieval. However, some
challenges still need to be tackled: 1) How to effectively embed semantic information into …

The similarity calculation is too simple in most cross-modal hash retrieval methods, which do
not consider the impact of the relations between instances. To solve this problem, this paper …

Existing cross-modal hash retrieval methods can simultaneously enhance retrieval speed
and reduce storage space. However, these methods face a major challenge in determining …

Cross-modal retrieval aims at retrieving highly semantic relevant information among multi-
modalities. Existing cross-modal retrieval methods mainly explore the semantic consistency …

Cross-modal retrieval tasks, which are more natural and challenging than traditional
retrieval tasks, have attracted increasing interest from researchers in recent years. Although …

Deep cross-modal hash retrieval (DCMHR) methods can effectively analyze the correlation
of multimodal data while maintaining efficiency. However, to pursue better accuracy, most …

Recently, significant progress has been made in graph-based hashing methods for the
purpose of learning hash codes that can preserve semantic similarity. Many approaches …

To solve the information overload problem of multimodal answers in community question
answering (CQA), this paper proposes a multimodal representative answer extraction …