Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor
search. Deep learning-based hashing methods have proved their efficacy to learn advanced …

Hashing can facilitate efficient retrieval and storage for large-scale images due to the binary
representation. In the real applications, the trade-off between retrieval accuracy and speed …

Extensive advancements have been made in person ReID through the mining of semantic
information. Nevertheless existing methods that utilize semantic-parts from a single image …

Person Re-IDentification (ReID) aims at retrieving images of the same person across
multiple camera views. Despite its popularity in surveillance and public safety, the leakage …

Binary neural networks (BNNs) are variations of artificial/deep neural network (ANN/DNN)
architectures that constrain the real values of weights to the binary set of numbers {− 1, 1} …

Deep incremental hashing has become a subject of considerable interest due to its
capability to learn hash codes in an incremental manner, eliminating the need to generate …

Deep hashing methods have been intensively studied and successfully applied in large-
scale multimedia retrieval. In real-world scenarios, code length can not be set once for all if …

Cross-modal retrieval between videos and texts has attracted growing attention due to the
rapid growth of user-generated videos on the web. To solve this problem, most approaches …

The lack of supervised information is the pivotal problem in unsupervised hashing. Most
methods leverage deep features extracted from pre-trained models to generate semantic …