The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ...

Deep Learning-based object category understanding is an important and active area of research in Computer Vision. Most work in this area has predominantly focused on the portion of depiction spectrum consisting of ...

Analyzing humans and their activities takes a central role in computer vision. This requires machine learning models to encapsulate both the diverse poses and appearances exhibited by humans. Estimating the 3D poses of ...

The exponential rise in the availability of data over the past decade has fuelled research in deep learning. While supervised deep learning models achieve near-human performance using annotated data, it comes with an ...

Single image super-resolution (SR) has been a topic of great interest in the computer vision and deep learning community and has found applications in many areas including quality enhancement of satellite images. As the ...

Computed Tomography (CT) Perfusion imaging is a non-invasive medical imaging modality that has also established itself as a fast and economical imaging modality for diagnosing cerebrovascular diseases such as acute ischemia, ...

With rapid development in technology and ubiquitous presence of diverse types of sensors, a large amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/ object/event has ...

Deep Neural Networks achieve near-human performance on several benchmark datasets, yet they are not as robust as humans. Their success relies on the proximity of test samples to the distribution of training data, resulting ...