Search
Now showing items 11-16 of 16
A study on Deep Learning Approaches, Architectures and Training Methods for Crowd Analysis
Analyzing large crowds quickly is one of the highly sought-after capabilities nowadays. Especially in terms of public security and planning, this assumes prime importance. But automated reasoning of crowd images or videos ...
Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation
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 ...
On the Optimality of Generative Adversarial Networks — A Variational Perspective
Generative adversarial networks (GANs) are a popular learning framework to model the underlying distribution of images. GANs comprise a min-max game between the generator and the discriminator. While the generator transforms ...
Efficient and Effective Algorithms for Improving the Robustness of Deep Neural Networks
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 ...
Development of Novel Deep Learning Models for Quantitative Medical Image Analysis
Medical imaging provides a non-invasive way to visualize tissues and diseases, enabling
both qualitative and quantitative assessments that are needed in diagnosing and monitoring
a wide range of conditions. Modalities ...
Development of Perception Systems to Enhance Robustness for Robotic Applications
Robotic systems are composed of interconnected modules such as planning, control, and perception, where inaccuracies in the perception module can trigger cascading failures throughout the entire pipeline. To achieve reliable ...