Samarth Mishra

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Samarth Mishra,
PhD Student in Computer Science,
Advisors : Prof. Venkatesh Saligrama & Prof. Kate Saenko
Boston University

Education

Master's Advised by Prof. James M. Rehg Georgia Institute of Technology 2017-2019
Bachelor's Advised by Prof. Suyash P. Awate IIT Bombay 2013-2017

Interests

Computer Vision, Machine Learning

Publications

Conferences

  • Samarth Mishra, R. Panda, C.P. Phoo, C.F. Chen, L. Karlinsky, K. Saenko, V. Saligrama, and R.S. Feris.
    Task2Sim: Towards effective pre-training and transfer from synthetic data.
    CVPR 2022 [arxiv] [project page]

  • D. Bashkirova, D. Hendrycks, D. Kim, Samarth Mishra, K. Saenko, K. Saito, P. Teterwak, and B. Usman.
    Visda-2021 competition : Universal domain adaptation to improve performance on out-of-distribution data.
    NeurIPS 2021 Competitions Track. [arxiv] [competition page]

  • Samarth Mishra, K. Saenko, and V. Saligrama.
    Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency.
    BMVC 2021 [arxiv] [github]

  • Samarth Mishra, Z. Zhang, Y. Shen, R. Kumar, V. Saligrama, and B. Plummer.
    Effectively leveraging attributes for visual similarity.
    ICCV 2021 [arxiv] [github]

  • S. Stojanov, Samarth Mishra, N.A. Thai, N. Dhanda,
    A. Humayun, L.B. Smith, C. Yu, and J. M. Rehg.
    Incremental Object Learning from Contiguous Views.
    Oral, Best paper finalist CVPR 2019 [cvf] [project page]

  • K. Chatterjee, B. Kragl, Samarth Mishra, A. Pavlogiannis.
    Faster Algorithms for Weighted Recursive State Machines.
    ESOP 2017 [arxiv]

Workshops

  • D. Kim, K. Saito, Samarth Mishra, S. Sclaroff, K. Saenko, and B.Plummer.
    Self-supervised visual attribute learning for fashion compatibility.
    ICCV 2021 VIPriors Workshop [arxiv]

  • P. Zhu, R. Zhu, Samarth Mishra, and V. Saligrama.
    Low dimensional visual attributes: An interpretable image encoding.
    ICPR 2020 EDL/AI Workshop [springer]

Preprints

  • Samarth Mishra, P. Zhu, V. Saligrama.
    Learning Compositional Representations for Effective Low-Shot Generalization. [arxiv]

Relevant Coursework

Boston University

  • Towards Universal Natural Language Understanding

  • Reinforcement Learning

  • Statistical Learning Theory

Georgia Tech

  • Machine Learning

  • Numerical Linear Algebra

  • Machine Learning Theory

IIT Bombay

  • Advanced Machine Learning (Probabilistic Graphical Models and Deep Learning)

  • Foundations of Intelligent and Learning Agents

  • Algorithms in Medical Image Processing

  • Digital Image Processing

Udacity

  • Computer Vision

  • Deep Learning

Find me on

Google Scholar
Twitter
Github
LinkedIn
Email: samarthm@bu.edu