Open World Recognition
Abhijit Bendale · Digital Collections of Colorado (Colorado State University) · 2015
As humans, we encounter countless objects daily. We effortlessly recognize object across variations despite the fact that the objects might vary in size, scale, translation or rotation. Humans can identify previously seen objects and posses the ability to learn new instances with minimal or no supervision. Human visual system continues to learn and adapt to ever changing surroundings. In recent years, there have been significant advances in the field of computer based recognition systems. While significant strides have been made towards building automated recognition systems, these systems face multiple challenges when operating in evolving environments. Operational issues such as changing data distributions, perturbations in input/output conditions and ever changing requirements of the system users, pose challenges in operational environments. In this work we highlight specific operational challenges such as handling partial information, incremental model adaptation, large-scale classification and propose solutions towards addressing these challenges iii Dedication This thesis is dedicated to my family for their love, constant support, encouragement and patience. iv Acknowledgements