Nyle Siddiqui

I'm a 3rd-year Ph.D student at the Center for Research in Computer Vision at the University of Central Florida advised by Dr. Mubarak Shah. I am also a NSF GRFP 2024 Honorable Mention.

Email  /  CV  /  Scholar  /  Github  /  LinkedIn

profile photo

Research

My research broadly spans the field of computer vision, with specific interests including generative AI, diffusion models, action recognition, person recognition, and representation learning. You can check out my selected papers below, with important papers highlighted.

DVANet: Disentantgling View and Action Features for Multi-View Action Recognition
Nyle Siddiqui, Praveen Tirupattur, Mubarak Shah.
AAAI Conference on Artificial Intelligence, Main Technical Track (AAAI), 2024
paper  /  code  /  project page

We propose a novel transformer decoder-based architecture in tandem with two supervised contrastive losses for multi-view action recognition. By disentangling the view-relevant features from action-relevant features, we enable our model to learn action features that are robust to change in viewpoints. We show that changes in viewpoint impart perturbations on learned action features, and thus, disentangling these perturbations improves overall action recognition performance.

DLCR: Leveraging Diffusion and Large Language Models for Effective Clothes-Changing Re-Identification
Nyle Siddiqui, Alin Croitoru, Gaurav Kumar Nayak, Radu Tudor Ionescu, Mubarak Shah.
IEEE/CVF Winter Conference on Applications of Computer Vision, Main Algorithms Track (WACV), 2025

We propose a generative data expansion framework via diffusion for clothes-changing person Re-ID, which leverages pre-trained diffusion models and large language models to accurately generate images of individuals with different clothing attires. We address the challenges faced by CC-ReID models due to the limited clothing diversity in current CC-ReID datasets by genereating additional synthetic data that increases clothing diversity while preserving important personal features in the generated images. We also introduce two novel CC-ReID training strategies: progressive learning and test-time prediction refinement. Notably, training certain models with data generated by DLCR on the PRCC dataset resulted in improvements of up to 11.3% improvement in top-1 accuracy, with additonal enhanced performance on out-of-distribution test data.

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication
Nyle Siddiqui, Rushit Dave, Mounika Vanamala, Naeem Seliya.
MDPI Journal of Machine Learning and Knowledge Extraction (MAKE), 2022
(4th most cited MAKE paper in 2022)
paper    

StretchySnake: A Flexible VideoMamba for Short and Long Form Action Recognition
Nyle Siddiqui, Rohit Gupta, Swetha Sirnam, Mubarak Shah.
Under Review

Currenly under review. We instill VideoMamba with spatio-temporal flexibility and shows it performs better on a variety of action recognition tasks.

Awards / Recognitions
Trophy Image
ORCGS Doctoral Fellowship, 2022-2026

Professional Reviewing Experience
Reviewer, ICLR 2025
Reviewer, NeurIPS 2024
Reviewer, ICCV 2023


Mentor in NSF-REU
NSF Image Philomina Ekezie, REU 2023
Joseph Ho, REU 2024
Franco Vidal, REU 2024

Feel free to steal this website's source code. Do not scrape the HTML from this page itself, as it includes analytics tags that you do not want on your own website — use the github code instead. Also, consider using Leonid Keselman's Jekyll fork of this page.