Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

Hashing is an efficient method for nearest neighbor search in large-scale data space by
embedding high-dimensional feature descriptors into a similarity preserving Hamming …

Hashing-based cross-modal search which aims to map multiple modality features into binary
codes has attracted increasingly attention due to its storage and search efficiency especially …

Cross-modal hashing has garnered considerable attention and gained great success in
many cross-media similarity search applications due to its prominent computational …

Because of the rapid growth of multimodal data from the internet and social media, a cross-
modal retrieval has become an important and valuable task in recent years. The purpose of …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

The focus of this survey is on the analysis of two modalities of multimodal deep learning:
image and text. Unlike classic reviews of deep learning where monomodal image classifiers …

Person re-identification (Re-ID) aims to retrieve images of the same person across disjoint
camera views. Most Re-ID studies focus on pedestrian images captured by visible cameras …

Deep hashing has been widely used for large-scale cross-modal retrieval benefited from the
low storage cost and fast search speed. However, most existing deep supervised methods …