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 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’).

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.

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

I sometime release implementations for papers.

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