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Shishira R. Maiya

5 accepted papers

2024

Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions

Namitha Padmanabhan, Matthew Gwilliam, Pulkit Kumar, Shishira R Maiya, Max Ehrlich, Abhinav Shrivastava
CVPR 2024poster

The many variations of Implicit Neural Representations (INRs) where a neural network is trained as a continuous representation of a signal have tremendous practical utility for downstream tasks including novel view synthesis video compression and image super-resolution. Unfortunately the inner worki…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation

Archana Swaminathan*, Anubhav Gupta, Kamal Gupta, Shishira R Maiya, Vatsal Agarwal, Abhinav Shrivastava
ECCV 2024poster

"Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on pa…

Cited by 7Source ↗PDF ↗Scholar ↗
2023

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 +3
CVPR 2023poster

Implicit Neural Representations (INR) have recently shown to be powerful tool for high-quality video compression. However, existing works are limiting as they do not explicitly exploit the temporal redundancy in videos, leading to a long encoding time. Additionally, these methods have fixed architec…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2021

The Lottery Ticket Hypothesis for Object Recognition

Sharath Girish, Shishira R Maiya, Kamal Gupta, Hao Chen, Larry S. Davis, Abhinav Shrivastava
CVPR 2021poster

Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep networks that are computationally expensive and have huge memory footprints. This makes it exceedingly difficult to deploy thes…

Cited by 76PDF ↗code ↗Scholar ↗
2020

Rethinking Retinal Landmark Localization as Pose Estimation: Naïve Single Stacked Network for Optic Disk and Fovea Detection

Shishira R. Maiya, Puneet Mathur
ICASSP 2020accepted

Automatic detection of optic disk and fovea, the two fundamental biological landmarks of the retinal system, is crucial to track the disease progression in a diabetic patient. Recent advances in this direction were mostly limited to applying CNN based networks to aggressively extract visual geometri…

Cited by 0Source ↗Scholar ↗