Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have
become dominant in image retrieval due to their discriminative power, compactness of …

Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …

This paper addresses deep face recognition (FR) problem under open-set protocol, where
ideal face features are expected to have smaller maximal intra-class distance than minimal …

Convolutional neural networks (CNNs) have been widely used in computer vision
community, significantly improving the state-of-the-art. In most of the available CNNs, the …

This work provides a framework for addressing the problem of supervised domain
adaptation with deep models. The main idea is to exploit adversarial learning to learn an …

Self-supervised monocular depth estimation has been widely studied, owing to its practical
importance and recent promising improvements. However, most works suffer from limited …