Dr Anjan Dutta
Academic and research departmentsSurrey Institute for People-Centred Artificial Intelligence, Centre for Vision, Speech and Signal Processing (CVSSP), School of Veterinary Medicine.
Dr. Anjan Dutta is a Senior Lecturer (Assistant Professor) in Artificial Intelligence at the University of Surrey in United Kingdom. He received a PhD in Computer Science from the Autonomous University of Barcelona (UAB) in 2014, which was awarded with an Excellent Cum Laude (highest grade) qualification with International mention. Moreover, he is a recipient of the Extraordinary PhD Thesis Award for the year 2013-14 by the UAB. Before his PhD, he obtained MSc in Computer Vision and Artificial Intelligence also from the UAB, MCA in Computer Applications from the Maulana Abul Kalam Azad University of Technology and a BSc in Mathematics (Honours) from the University of Calcutta respectively in the year of 2010, 2009 and 2006. His main research interests revolve around computer vision and machine learning. Specifically, he works on deep multi-modal embedding, zero-shot learning, graph neural network for various computer vision tasks.
Areas of specialism
Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and communicate them in an interpretable protocol. Toward equipping machines with such capabilities , we propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primi-tives under the influence of a budget. To solve this task, our Primitive-Matching Network (PMN), learns interpretable abstractions of a sketch in a self supervised manner. Specifically, PMN maps each stroke of a sketch to its most similar primitive in a given set, predicting an affine transformation that aligns the selected primitive to the target stroke. We learn this stroke-to-primitive mapping end-to-end with a distance-transform loss that is minimal when the original sketch is precisely reconstructed with the predicted primitives. Our PMN abstraction empirically achieves the highest performance on sketch recognition and sketch-based image retrieval given a communication budget, while at the same time being highly interpretable. This opens up new possibilities for sketch analysis, such as comparing sketches by extracting the most relevant primitives that define an object category. Code is available at https://github.com/ExplainableML/sketch-primitives.