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 …

Despite the advances in machine learning techniques, similarity assessment among
multimedia data remains a challenging task of broad interest in computer science …

Instance-level image retrieval is the task of searching in a large database for images that
match an object in a query image. To address this task, systems usually rely on a retrieval …

Low-level features and deep features each have their own advantages and disadvantages
in image representation. However, combining their advantages within a CBIR framework …

In content-based image retrieval, the first-round retrieval result by simple visual feature
comparison may be unsatisfactory, which can be refined by visual re-ranking techniques. In …

Identifying the target object in an image can produce more accurate and discriminative
feature representations, which can significantly improve large-scale instance-level image …

Applying deep convolutional features to image retrieval has become the mainstream method
in the field of image retrieval. However, the discriminative power of deep convolutional …

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based
on semantic similarities. Prior work usually focuses on the pairwise relations (ie, whether a …

Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this
paper, we address this paradigm within content based image retrieval (CBIR). We propose …

Significant progress has been made in instance image retrieval based on deep feature
aggregation. However, existing approaches are limited by two issues: 1) The inability of …