Ciao👋!! I am a first-year ELLIS doctoral student in the VANDAL lab at Politecnico di Torino, Italy, under the supervision of Prof. Barbara Caputo and co-supervision of Prof. Zeynep Akata. My current areas of interest lie in the amalgamation of semantic segmentation, open-world detection, and federated learning.
I completed my master at IIIT Hyderabad, where I was under the joint supervision of Prof. C.V. Jawahar, Prof. Vineeth N Balasubramanian, and Dr. Anbumani Subramanian on the problems of image restoration in adverse weather conditions. Previously, I was an undergrad honors student at IIIT, Sricity, where I worked on the problems related to Cartoon Image Understanding in collaboration with Prof C.V. Jawahar and Prof. Anand Mishra.
PhD in Artificial Intelligence
VANDAL, Politecnico di Torino, Italy
M.S. (Research) in Computer Science, 2020
CVIT, International Institute of Information Technology, Hyderabad
B.Tech (Hons.) in Electronics and Communication Engineering, 2017
Indian Institute of Information Technology, Sri City
Developed an interactive computer vision application using Qt and Opencv, to detect misalignment in CAD Images.
We used pre-trained model VGG Face for extracting features and used SVM for classification with different kernels. In addition, we used a unified model which fuses the CNN features and HOG feature giving higher accuracy than other models.
Implemented different feature based methods to identify gender from facial images. Extended the method to cross modal gender identification between the real face and its cartoon and caricature modalities.
Multi-scale architecture based on a laplacian pyramid approach to improve semantic segmentation.
Implementation of MAD-GAN which addresses the problem of mode collapse.
Posed reading comprehension as a sentence classification task. Instead of sequential models, we used CNN models for classification and extended to a siamese variation using contrastive loss.
Objective of this project is to find a strong correlation between personality of an individual and their music preferences.
Implementation of Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) which significantly improves the diversity of class-conditional image generation.