Areas of specialism

Computer Vision; Machine Learning; Representation Learning; Learning from Fewer Labels; Structured Representation Learning

My qualifications

Postgraduate Certification in Academic Practice (PCAP / FHEA)
University of Exeter
PhD in Computer Science
Autonomous University of Barcelona
MSc in Computer Vision and Artificial Intelligence
Autonomous University of Barcelona
MCA in Computer Application
Maulana Abul Kalam Azad University of Technology
BSc in Mathematics (Honours)
University of Calcutta

Previous roles

2019 - 2022
Lecturer of Computer Vision & Machine Learning
University of Exeter
2017 - 2019
Marie Curie Fellow
Computer Vision Centre
2016 - 2017
Postdoctoral Researcher
Computer Vision Centre
2014 - 2015
Postdoctoral Researcher
Télécom ParisTech


Stephan Alaniz, Massimiliano Mancini, Anjan Dutta, Diego Marcos, Zeynep Akata (2022)Abstracting Sketches through Simple Primitives

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.