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Data-efficient Deep Learning Algorithms for Computer Vision Applications
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 ...
Lesion Synthesis using Physics-Based Noise Models for Low-Data Medical Imaging Regime applications
Lesion segmentation and their progression prediction in medical imaging relies critically on the availability of manually annotated, heterogeneous large pathological datasets. Acquiring such diverse large datasets is also ...
Development of Novel Deep Learning Models with Improved Generalizability for Medical Image Analysis
Medical imaging is a process of visualization of disease/tissue in a non-invasive manner. Several imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ...
Deep Learning for Hand-drawn Sketches: Analysis, Synthesis and Cognitive Process Models
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 ...
Self-Supervised Domain Adaptation Frameworks for Computer Vision Tasks
There is a strong incentive to build intelligent machines that can understand and adapt to changes in the visual world without human supervision. While humans and animals learn to perceive the world on their own, almost ...
Learning to Perceive Humans From Appearance and Pose
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 ...
Landmark Estimation and Image Synthesis Guidance using Self-Supervised Networks
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 ...
Deep Learning in Computer Vision: Studies in Neuro-image Segmentation and Satellite Image Super-resolution
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 ...
Advances in High Dynamic Range Imaging Using Deep Learning
Natural scenes have a wide range of brightness, from dark starry nights to bright sunlit beaches. Our human eyes can perceive such a vast range of illumination through various adaptation techniques, thus allowing us to ...
Novel Deep Learning Methods for Improving Low-Dose Computed Tomography Perfusion Imaging of Brain
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, ...