Aniket Roy

I am a Ph.D. student at Johns Hopkins University in the department of Computer Science under the guidance of Prof. Rama Chellappa. I am currently working on Computer Vision and Machine Learning, specifically on a few-shot learning, multimodal learning, generative AI, including diffusion models and large language models. Prior to that, I obtained M.S.(by Research) degree from Dept. of Computer Science, Indian Institute of Technology Kharagpur. I am honored to be awarded as an Amazon Fellow.

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• 12/2023: Presented Invited talk on Advances in few-shot learning at Indian Statistical Institute Kolkata
• 10/2023: Awarded as an Amazon Fellow as a part of JHU + Amazon initiative for Interactive AI!
• 10/2023: Certified Margin Maximization got accepted in NeurIPS'23
• 09/2023: Halp got accepted in CVPR'23
• 06/2023: Started Research Internship at SRI International
• 08/2022: Awarded Murkos Thomas Memorial Award for Best Research Paper in CSE from IIT Kharagpur.
• 08/2022: FeLMi got accepted in NeurIPS'22
• 07/2022: MuLOT got accepted in WACV'22
• 06/2022: Started Research Internship at Amazon AWS AI
• 06/2021: PASS got accepted in ICCV'21
• 06/2021: Started as a Research Intern at MERL

My broad research interest lies in the intersection of Machine Learning and Computer Vision. Specifically, I have worked on few-shot learning, multi-modal learning, generative models, foundational models, and Large Language models.

We propose a kurtosis loss to generate better quality images

We use pretrained caption and diffusion model to generate semantic augmentations.

We propose hard mixup to improve few shot learning

We hallucinate latent postives for learning skeleton encoders without labels

We leverage the recent success of the generative models for few-shot learning

We propose optimal transport for multimodal fusion

We propose an adversarial framework for gender and racial bias mitigation

We propose a knowledge distillation based framework for gender and racial bias mitigation

The book covers topics in digital image forensics

We performed theoretical analysis of Prediction Error Expansion based Reversible Image Watermarking

We propose a deep convolutional recurrent attention model for classifying computer generated and natural images.

We propose to use Discrete Cosine Transform Residual Feature Based Filtering Forgery and Splicing Detection

We propose to use Discrete Cosine Transform Residual Feature for Camera Source Identification

We use Rotated Local Binary Pattern features for similar but genuine object based forgery

We detect and localize JPEG image forgery based on noise correlation

We performed theoretical analysis of Prediction Error Expansion Based Reversible Watermarking

We proposed a human visual system inspired watermarking scheme and performed robustness analysis

We propose a reversible color image watermarking scheme and perform information theoretic analysis

• Awarded as an Amazon Fellow as a part of JHU + Amazon initiative for Interactive AI!
• Awarded Murkos Thomas Memorial Award for Best Research Paper in CSE from IIT Kharagpur.
• Recipient of Best Paper Award in International Workshop on Digital-forensics and Watermarking (IWDW)
• Awarded IEEE Signal Processing Society Travel Grant for attending ICIP 2017
• Awarded Microsoft Travel Grant for attending CVPR 17 and IWDW 2016.
• Awarded with SCHEME OF SCHOLARSHIP FOR COLLEGE AND UNIVERSITY STUDENTS during Bachelor's degree course.

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