Shlok Mishra
I am a Ph.D. student at University of Maryland in the department of Computer Science. I am advised by Prof. David Jacobs working on problems in Computer Vision and Machine Learning. I also obtained an MS in Computer Science from University of Maryland, College Park.
I primarily work in Self-Supervised Learning and Generative models in Computer Vision. I have done multiple internships at Google. In Summer of 2022, I worked with Dilip Krishnan on Self-Supervised learning. In Summer 2021 I worked with Christian Haene and Hossam Isack on Generative models. In Summer 2020, I worked with Kuntal Sengupta, Vincent Chu and Sofien Bouaziz on Spoof Detection.
I have also done 4 student research internships and have been working in Google for last 2.5 years.
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Research
I am interested in computer vision, machine learning. Specifically, my current research interests are in Self-Supervised learning, Generative models and building models which can do learn both generative and discriminative features.
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Hyperbolic Contrastive Learning for Visual Representations beyond Objects
Shlok Mishra* Songwei Ge* , Simon Kornblith, Chun-Liang Li, David Jacobs
CVPR 2023
We propose a Hyperbolic loss to better encode scene and objects.
arXiv
code
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A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
Shlok Mishra*, Joshua Robinson* , Huiwen Chang , David Jacobs , Aaron Sarna, Aaron Maschinot, Dilip Krishnan
preprint
We propose a simple and scalable contrastive masked autoencoder method.
arXiv
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HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
Anshul Shah, Aniket Roy*, Ketul Shah*, Shlok Mishra, David Jacobs, Anoop Cherian, Rama Chellappa
CVPR 2023
We hallucinate latent postives for learning skeleton encoders without labels
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Robust Contrastive Learning Using Negative Samples
with Diminished Semantics
Songwei Ge*, Shlok Mishra, Chun-Liang Li , Chun-Liang Li, David Jacobs
Neurips 2021
We show texture based hard negative samples can imporve generalization of contrastive learning method.
paper
code
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Object-Aware Cropping for Self-Supervised Learning
Shlok Mishra, Anshul Shah , Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan
TMLR 12/2022
A novel cropping strategy for SSL on uncurated datasets
arXiv
code
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Generating Annotated Datasets via Keypoints Conditioned StyleGAN
Shlok Mishra, Hossam Isack , Sergio Orts-Escolano, Luca Prasso, Rohit Pandey, Franziska Mueller, Abhimitra Meka, Jonathan Taylor, Dilip Krishnan , David Jacobs , Christian Haene.
Under Submission
We use StyleGAN to generate labelled data for keypoint estimation task.
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Learning Visual Representations for Transfer Learning by Suppressing Texture
Shlok Mishra, Anshul Shah , Ankan Bansal, Abhyuday Jagannatha, Jonghyun Choi, Abhinav Shrivastava, Abhishek Sharma, David Jacobs
BMVC 2022
Anisotropic diffusion based augmentation to reduce texture bias in supervised and self-supervised approaches
paper
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Improved Presentation Attack Detection Using Image Decomposition
Shlok Mishra, Kuntal Sengupta, Wen-Sheng Chu , Max Horowitz-Gelb, Sofien Bouaziz , David Jacobs
IJCB 2022 (ORAL)
We show albedo is a strong cue for spoof detection.
arXiv
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Pose and Joint-Aware Action Recognition
Anshul Shah , Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava
WACV 2022
We present a new model and loss for Pose-based action recognition
arXiv /
video /
code
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Service
Serving as reviewer for major machine learning and computer vision conferences (CVPR, Neurips, ICLR, AAAI, ECCV, ICCV, etc) and
journals (TMLR, TIP , CVIU, IJCV). In the past I have also served in NLP conferences (ACL, EMNLP).
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