In the contemporary landscape, where a huge amount of data plays a vital role, the
importance of strong and robust cybersecurity measures has become increasingly …

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 …

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 methods for efficient image retrieval aim at learning hash functions that map similar
images to semantically correlated binary codes in the Hamming space with similarity well …

Person re-identification (re-ID) has become increasingly popular in the community due to its
application and research significance. It aims at spotting a person of interest in other …

Abstract Person Re-identification (ReID) is to identify the same person across different
cameras. It is a challenging task due to the large variations in person pose, occlusion …

In this paper, we present a new hashing method to learn compact binary codes for highly
efficient image retrieval on large-scale datasets. While the complex image appearance …

Learning generic and robust feature representations with data from multiple domains for the
same problem is of great value, especially for the problems that have multiple datasets but …

Learning discrete representations of data is a central machine learning task because of the
compactness of the representations and ease of interpretation. The task includes clustering …

Is strong supervision necessary for learning a good visual representation? Do we really
need millions of semantically-labeled images to train a Convolutional Neural Network …