In recent years a vast amount of visual content has been generated and shared from many
fields, such as social media platforms, medical imaging, and robotics. This abundance of …

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 …

The detection results of many existing co-saliency detection methods are easily interfered by
the unrelated salient objects, which have similar appearance characteristics to co-salient …

Abstract VLAD (Vector of Locally Aggregated Descriptors) has been widely adopted in
image representation. However, the VLAD algorithm seeks for the algebraic sum of the …

Multi-label image classification is a prediction task that aims to identify more than one label
from a given image. This paper considers the semantic consistency of the latent space …

Content-based medical image retrieval (CBMIR) is one of the most challenging and
ambiguous tasks used to minimize the semantic gap between images and human queries in …

Low light images captured in a non-uniform illumination environment usually are degraded
with the scene depth and the corresponding environment lights. This degradation results in …

Deep supervised hashing has emerged as an influential solution to large-scale semantic
image retrieval problems in computer vision. In the light of recent progress, image label is …

Fashion is the manner in which we introduce ourselves to the world and has become
perhaps the biggest industry on the planet. In recent years, fashion-related research has …

Multi-label image classification is a fundamental yet challenging task in computer vision that
aims to identify multiple objects from a given image. Recent studies on this task mainly focus …