We propose a novel crowd counting model that maps a given crowd scene to its density.
Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to …

We present a novel deep learning architecture for fusing static multi-exposure images.
Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input …

A class of recent approaches for generating images, called Generative Adversarial Networks
(GAN), have been used to generate impressively realistic images of objects, bedrooms, …

Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art
computer vision systems today. However, recent works have shown that much smaller …

Our work proposes a novel deep learning framework for estimating crowd density from static
images of highly dense crowds. We use a combination of deep and shallow, fully …

Understanding and predicting the human visual attention mechanism is an active area of
research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, …

The emergence of Deep neural networks has seen human-level performance on large scale
computer vision tasks such as image classification. However these deep networks typically …

There is a strong incentive to develop versatile learning techniques that can transfer the
knowledge of class-separability from a labeled source domain to an unlabeled target domain in …

… [35] Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul MV, and R.
Venkatesh Babu. Towards inheritable models for open-set domain adaptation. In CVPR, 2020. …

In this paper, we present a machine learning approach to measure the visual quality of
JPEG-coded images. The features for predicting the perceived image quality are extracted by …