Shishira R Maiya

I am a PhD Student at University of Maryland, under Prof Abhinav Shrivastava where I work on computer vision and machine learning. Prior to this I earned my Masters in Computer Science from University of Maryland as well.

Previously, I finished my undergrad from MSRIT Bangalore. My first brush with machine learning was as an intern at Stride where I worked on text classification and other NLP tasks. I later went on to work under Prof AG Ramakrishnan on super resolution of document images at MILE lab at IISc. I continued working in related domains with an internship at Hyperverge under Adarsh Tadimari, where I worked on scene text recognition and OCR.

After my undergrad, I worked as a research assistant at RBCCPS at IISc, under Prof Raghu Krishnapuram. Here, I worked on a wide variety of problems ranging from multi-camera multi-object tracking, segmentation and vision guidance for drones. During my Masters, I had the opportunity of working as a research intern for SIML team at Apple, under Rui Shen.

I recently worked as a research intern at Snap Inc, where I worked on developing an efficient and scalable implicit neural representation for videos.

Currently my research spans model, data compression techniques and understanding adversarial examples for vision tasks.

Email / CV / Google Scholar / Github / Substack

Research

	Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics Shishira R Maiya, Anubhav Gupta , Matt Gwilliam , Max Ehrlich , Abhinav Shrivastava Under Review , 2024 We can now use INRs for downstream tasks !! Project Page \| Paper
	NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling Shishira R Maiya, Sharath Girish , Max Ehrlich , Hanyu Wang, Kwot Sin Lee, Patrick Poirson Pengxiang Wu Chen Wang, Abhinav Shrivastava IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2023 An efficient and scalable INR for Videos! Project Page \| Paper
	A Frequency Perspective of Adversarial Robustness Shishira R Maiya, Max Ehrlich , Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava Under Review , 2021 A study of adversarial robustness from the perspective of frequency analysis. Project Page \| Paper
	The Lottery Ticket Hypothesis for Object Recognition Sharath Girish* , *Shishira R Maiya,** Kamal Gupta , Hao Chen, Larry Davis, Abhinav Shrivastava IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021 Explored the nuances of applying the Lottery ticket Hypothesis for Object recognition models. Project Page \| Paper \| Code
	Membership Inference Attacks on Lottery Ticket Networks Aadesh Bagmar* , *Shishira R Maiya,** Shruti Bidwalkar , Amol Deshpande International Conference on Machine Learning (ICML) workshop: A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning , 2021 Are lottery ticket networks vulnerable to Membership inference attacks due to their sparse nature? Paper \| Code
	Rethinking Retinal Landmark Localization As Pose Estimation: Naive Single Stacked Network For Optic Disk And Fovea Detection *Shishira R Maiya,** Puneet Mathur* IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020 Can we use pose models for medical imaging problems ? Paper
	Slum Segmentation and Change Detection: A Deep Learning Approach *Shishira R Maiya,** Sudharshan Chandra Babu* Neural Information Processing Systems (NeurIPS): ML4D workshop , 2018 Detecting and tracking growth of slums in the city of Mumbai using deep learning. Project Page \| Paper \| Code
	Relation Networks for Optic Disc and Fovea Localization in Retinal Images Sudharshan Chandra Babu* , Shishira R Maiya* Neural Information Processing Systems (NeurIPS): ML4Health workshop , 2018 Utilizing context for detection in medical imaging. Paper
	A new approach for upscaling document images for improving their quality Rama Krishna Pandey, Shishira R Maiya, A G Ramakrishnan IEEE India Conference (INDICON) , 2017 Method to upsample Document Images. Paper

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