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.
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Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement
Priyank Pathak,
Yogesh S. Rawat,
ICCV, 2025
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Project Page /
Code
We propose a lightweight, annotation-free proxy for mitigating appearance bias in ReID models, when clothing annotations aren't available.
We propose Colors See, Colors Ignore (CSCI), an RGB-only method that leverages color information directly from raw images or video frames. CSCI efficiently captures color-related appearance bias (‘Color See’) while disentangling it from identity-relevant ReID features (‘Color Ignore’).
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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
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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.
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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 the Video ReID model.
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Pixel Onion: Peeling Layers of Zero-Shot Object Detection in Pixelation
Priyank Pathak*,
Mukilan Karuppasamy*,
Aaditya Baranwal,
Shyam Marjit,
Shruti Vyas,
Yogesh S. Rawat,
Under Review
* equal contribution
We critically examine the SOTA Zero-Shot Object Detectors' vulnerability to resolution degradation (`pixelation'). Two key takeaways are
1) The choice of backbone alone can substantially enhance robustness, while prompting (language) plays a minimal role.
2) Robustness is also a function of the dataset/domain of images at inference. A certain type of image are not affected by pixelation.
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Coarse Attribute Prediction with Task Agnostic Distillation for Real World Clothes Changing ReID
Priyank Pathak,
Yogesh S. Rawat,
Under Review
Arxiv
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.
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Research Assistant, National University of Singapore
Internship
Aug'22 - Dec'2022
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Research Engineer, Amobee
Full-time
June'20 - Aug'2021
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Deep Learning Research Intern, Clarifai,
Internship
May'19 - Sept'19
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Deep Learning Research Intern, Rice University,
Internship
May'17 - Aug'17
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"Aloo has no plaace in Biryani" - Every Desi Foodie
Built upon Jon Barron's
template, and modified upon Rohit Gupta
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