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

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.

We introduce Robustness against Low-Quality (RLQ) in clothes changing real-world ReID to make the model robust against low-quality artifacts like pixelation, out-of-focus blur, and motion blur.

Lightweight, annotation-free proxy for mitigating appearance bias in ReID models, when "expensive" clothing annotations aren't available. Colors See, Colors Ignore (CSCI), an efficient RGB-only method leverages color information directly from raw images or video frames.

LR0.FM benchmark evaluates the impact of low resolution on the zero-shot performance of VLMs, via novel Weighted Aggregated Robustness metric. LR-TK0 enhances the robustness of VLMs against low-resolution across several datasets without compromising their pre-trained weights.

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

Removing bells and whistles of SOTA Zero-Shot Object Detectors under resolution degradation (`pixelation'), exposing the true vunerability of "backbones", and "dataset" (certain type of image are not affected by pixelation) with minimal impact of "lanugage".

CosPlan benchmarks evaluates VLMs on "error-prone" sequence completion tasks, testing abilities like Error Detection and Sequence Completion ability. Novel SGI, incrementally updates Scene Graphs better handle decision making.

I sometimes release implementations for others' papers.

"Aloo has no plaace in Biryani" - Every Desi Foodie

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