Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large
number of vehicle images, remains a great challenge in computer vision. Most existing …

Third generation surveillance systems are largely requested for intelligent surveillance of
different scenarios such as public areas, urban traffic control, smart homes and so on. They …

We introduce in this paper a novel active learning algorithm for satellite image change
detection. The proposed solution is interactive and based on a question and answer model …

Change detection, in multi-temporal satellite imagery, seeks to discover relevant changes
and to discard irrelevant ones. This task is usually achieved by modeling accurate decision …

In this paper, we devise a novel lightweight Graph Convolutional Network (GCN) design
dubbed as Multi-Rate Magnitude Pruning (MRMP) that jointly trains network topology and …

Change detection in satellite imagery seeks to find occurrences of targeted changes in a
given scene taken at different instants. This task has several applications ranging from land …

Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular
domains, namely graphs. Their success is highly dependent on how the topology of input …

We introduce a novel interactive satellite image change detection algorithm based on active
learning. The proposed method is iterative and consists in frugally probing the user (oracle) …

Canonical correlation analysis (CCA) is a statistical learning method that seeks to build view-
independent latent representations from multi-view data. This method has been successfully …

Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity
directly from re-identification (re-ID) feature distances. However, we argue that it might not …