An embedded scaling-based arbitrary shape region-of-interest coding method for JPEG2000. - Mahesh Subedar; Lina J. Karam; Glen P. Abousleman (ICASSP (3), 2004)

JPEG2000-based shape adaptive algorithm for the efficient coding of multiple regions-of interest. - Mahesh Subedar; Lina J. Karam; Glen P. Abousleman (ICIP, 2004)

A Comparative Study of Color and Contrast Enhancement for Still Images and Consumer Video Applications. - Abhijit Sarkar; Mark D. Fairchild; Jorge E. Caviedes; Mahesh Subedar (CIC, 2008)

Study of blur discrimination for 3D stereo viewing. - Mahesh Subedar; Lina J. Karam (SD&A, 2014)

A subjective study and an objective metric to quantify the granularity level of textures. - Mahesh M. Subedar; Lina J. Karam (Human Vision and Electronic Imaging, 2015)

A no reference texture granularity index and application to visual media compression. - Mahesh M. Subedar; Lina J. Karam (ICIP, 2015)

Visual Quality with a Focus on 3D Blur Discrimination and Texture Granularity. - Mahesh Subedar (2015)

3D Blur Discrimination. - Mahesh M. Subedar; Lina J. Karam (ACM Trans. Appl. Percept., 2016)

A Factorization Approach for Enabling Structure-from-Motion/SLAM Using Integer Arithmetic. - Nilesh A. Ahuja; Mahesh Subedar; Omesh Tickoo; Yeongseon Lee (ICCV Workshops, 2017)

BAR: Bayesian Activity Recognition using variational inference. - Ranganath Krishnan; Mahesh Subedar; Omesh Tickoo (CoRR, 2018)

Uncertainty aware multimodal activity recognition with Bayesian inference. - Mahesh Subedar; Ranganath Krishnan; Paulo Lopez-Meyer; Omesh Tickoo; Jonathan Huang (CoRR, 2018)

Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference. - Mahesh Subedar; Ranganath Krishnan; Paulo Lopez-Meyer; Omesh Tickoo; Jonathan Huang (CVPR Workshops, 2019)

Efficient Priors for Scalable Variational Inference in Bayesian Deep Neural Networks. - Ranganath Krishnan; Mahesh Subedar; Omesh Tickoo (ICCV Workshops, 2019)

MOPED: Efficient priors for scalable variational inference in Bayesian deep neural networks. - Ranganath Krishnan; Mahesh Subedar; Omesh Tickoo (CoRR, 2019)

Deep Probabilistic Models to Detect Data Poisoning Attacks. - Mahesh Subedar; Nilesh A. Ahuja; Ranganath Krishnan; Ibrahima J. Ndiour; Omesh Tickoo (CoRR, 2019)

Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes. - Ranganath Krishnan; Mahesh Subedar; Omesh Tickoo (AAAI, 2020)

B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning. - Shashank Bujimalla; Mahesh Subedar; Omesh Tickoo (CoRR, 2020)

Data augmentation to improve robustness of image captioning solutions. - Shashank Bujimalla; Mahesh Subedar; Omesh Tickoo (CoRR, 2021)

Partially-supervised novel object captioning leveraging context from paired data. - Shashank Bujimalla; Mahesh Subedar; Omesh Tickoo (CoRR, 2021)

Mitigating Sampling Bias and Improving Robustness in Active Learning. - Ranganath Krishnan; Alok Sinha; Nilesh A. Ahuja; Mahesh Subedar; Omesh Tickoo; Ravi Iyer 001 (CoRR, 2021)

Improving Robustness and Efficiency in Active Learning with Contrastive Loss. - Ranganath Krishnan; Nilesh A. Ahuja; Alok Sinha; Mahesh Subedar; Omesh Tickoo; Ravi Iyer 001 (CoRR, 2021)

Partially-Supervised Novel Object Captioning Using Context from Paired Data. - Shashank Bujimalla; Mahesh Subedar; Omesh Tickoo (BMVC, 2022)

Learning A Continuous and Reconstructible Latent Space for Hardware Accelerator Design. - Qijing Huang 001; Charles Hong; John Wawrzynek; Mahesh Subedar; Yakun Sophia Shao (ISPASS, 2022)

Quantization for Bayesian Deep Learning: Low-Precision Characterization and Robustness. - Jun-Liang Lin; Ranganath Krishnan; Keyur Ruganathbhai Ranipa; Mahesh Subedar; Vrushabh Sanghavi; Meena Arunachalam; Omesh Tickoo; Ravishankar Iyer 001; Mahmut Taylan Kandemir (IISWC, 2023)

DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators. - Charles Hong; Qijing Huang 001; Grace Dinh; Mahesh Subedar; Yakun Sophia Shao (MICRO, 2023)

Multi-Objective Software-Hardware Co-Optimization for HD-PIM via Noise-Aware Bayesian Optimization. - Chien-Yi Yang; Minxuan Zhou; Flavio Ponzina; Suraj Sathya Prakash; Raid Ayoub; Pietro Mercati; Mahesh Subedar; Tajana Rosing (ICCAD, 2024)

Parameter-Efficient Active Learning for Foundational models. - Athmanarayanan Lakshmi Narayanan; Ranganath Krishnan; Amrutha Machireddy; Mahesh Subedar (CoRR, 2024)

FUVAS: Few-shot Unsupervised Video Anomaly Segmentation via Low-Rank Factorization of Spatio-Temporal Features. - Jiaxiang Jiang; Ibrahima J. Ndiour; Mahesh Subedar; Omesh Tickoo (ICASSP, 2025)