Priyank Pathak

I am a third-year Ph.D. student in Computer Vision at the Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), where I am fortunate to be advised by Prof. Yogesh S. Rawat. My research focuses on Low-Resolution Recognition, Person Re-Identification (ReID), and Robustness in Deep Learning. Prior to joining UCF, I earned my Master’s in Computer Science from New York University (NYU) Courant Institute, where I completed my thesis under the guidance of Prof. Rob Fergus. I hold dual Bachelor of Technology (B.Tech) degrees in Electrical Engineering and Computer Science from the Indian Institute of Technology (IIT) Kanpur.

Email: priyank@ucf.edu  /  pp1953@nyu.edu

profile photo
Research

My research focuses on developing efficient training techniques for computer vision models, with a particular emphasis on understanding the adverse effect of noise and enhancing the robustness of deep learning architectures in real-world scenarios. As a Ph.D. student, my work investigates how various types of noise—such as low-resolution, and environmental distortions—impact the performance of deep learning models, including transformers and vision-language models (VLMs).   Previously, my Master's thesis centered on Person Re-Identification (ReID) in surveillance settings, a domain inherently affected by low-resolution and noisy visual data.

Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement
Priyank Pathak, Yogesh S. Rawat,
ICCV, 2025
Paper / Code

We propose a lightweight, annotation-free proxy for mitigating appearance bias in ReID models, when clothing annotations arent avaialble. We propose Colors See, Colors Ignore (CSCI), a RGB-only method that leverages color information directly from raw images or video frames. CSCI efficiently captures colorrelated appearance bias (‘Color See’) while disentangling it from identity-relevant ReID features (‘Color Ignore’).

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models
Priyank Pathak, Shyam Marjit, Shruti Vyas, Yogesh S. Rawat,
ICLR, 2025
Paper / Project Page / Code

We introduce LR0.FM, a benchmark evaluating the impact of low resolution on the zero-shot classification performance of VLMs. We propose a novel metric, Weighted Aggregated Robustness, to address the limitations of existing metrics and LR-TK0, to enhance the robustness of models without compromising their pre-trained weights. We demonstrate the effectiveness of LR-TK0 for robustness against low-resolution across several datasets and its generalization capability across backbones and other approaches.

Video person re-id: Fantastic techniques and where to find them (student abstract)
Priyank Pathak, Amir Erfan Eshratifar, Michael Gormish,
AAAI, 2020
Paper / Code

One of the first works to solve Person Reid exploring multiple techniques to improve accuracy on video ReID model.

Service Reviewer, Neurips 2025
Reviewer, BMVC 2025
Reviewer, CVPR 2025 (Outstanding Reviewer)
Reviewer, ICLR 2025 (Outstanding Reviewer)
Pre-Prints Fine-Grained Re-Identification (Master's Thesis)
Local Learning on Transformers via Feature Reconstruction
Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID
Misc Projects

I sometime release implementations for papers.

Success of Filmmaking (Javascript & novel visualization) [Course Highlight Project] [GIF]
Faster RCNN tutorial [Githhub]
Reinforcement learning tutorial[Web Page]
Spatial Transformers for traffic signal detection[Github]
Video-Action-Transformer-Network [Github]
Online softmining loss [Github]

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