With the proliferation of online services and mobile technologies, the world has stepped into
a multimedia big data era. A vast amount of research work has been done in the multimedia …

In the study of image and vision computing, the generalization capability of an algorithm
often determines whether it is able to work well in complex scenes. The goal of this review …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

Cross-scene crowd counting is a challenging task where no laborious data annotation is
required for counting people in new target surveillance crowd scenes unseen in the training …

Classifiers trained on given databases perform poorly when tested on data acquired in
different settings. This is explained in domain adaptation through a shift among distributions …

In this paper, we address the task of object counting in images. We follow modern learning
approaches in which a density map is estimated directly from the input image. We employ …

In this paper, we propose deformable deep convolutional neural networks for generic object
detection. This new deep learning object detection diagram has innovations in multiple …

This paper presents a novel method for detecting pedestrians under adverse illumination
conditions. Our approach relies on a novel cross-modality learning framework and it is …

Visual ego-motion estimation, or briefly visual odometry (VO), is one of the key building
blocks of modern SLAM systems. In the last decade, impressive results have been …

Abstract Current Domain Adaptation (DA) methods based on deep architectures assume
that the source samples arise from a single distribution. However, in practice most datasets …