Saksham Suri

I am a third year Ph.D student in the department of Computer Science at University of Maryland (UMD), College Park, advised by Dr. Abhinav Shrivastava. My research lies in the field of computer vision and machine learning.

I completed my undergrad at IIIT Delhi (2015 - 2019) majoring in Computer Science and Engineering where I worked at IAB Lab and Precog. During the summer of 2018 I got the chance to intern at MCL at USC Viterbi Shool of Engineering. Post my graduation, I went for a short internship at IBM Research.

In the past, I have been fortunate to have worked with Rama Chellappa, Mayank Vatsa, Richa Singh, Ponnurangam Kumaraguru, C. -C. Jay Kuo, Anush Sankaran and many other super-talented people!

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I am interested in solving problems using less supervision. To this end my current research focuses on sparsely annotated object detection where images have missing object annotations.

saod Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach
Saketh Rambhatla*, Saksham Suri*, Rama Chellappa, Abhinav Shrivastava
Under Review

Propose to model sparsely annotated object detection as a semi-supervised object detection problem at a region level.

gan Towards Discovery and Attribution of Open-world GAN Generated Images
Sharath Girish*, Saksham Suri*, Saketh Rambhatla, Abhinav Shrivastava
IEEE/CVF International Conference on Computer Vision (ICCV) , 2021
Project Page | Paper | arXiv

Proposed an iterative algorithm for discovering images generated from GANs in an open world setup. Also show applications in an online never ending discovery.

gan Learned Spatial Representations for Few-shot Talking-Head Synthesis
Moustafa Meshry, Saksham Suri, Larry S. Davis Abhinav Shrivastava
IEEE/CVF International Conference on Computer Vision (ICCV) , 2021
Project Page | Paper | arXiv

We propose a novel framework which disentangles spatial and style information for image synthesis. A latent spatial layout for the target image is generated, which is used to produce per-pixel style modulation parameters for the final synthesis..

prl Improving Face Recognition Performance using TeCS2 Dictionary
Saksham Suri, Anush Sankaran, Mayank Vatsa, Richa Singh
Pattern Recognition Letters, 2020

Incorporating task agnostic color, shape, texture and symmetry attributes to task specific deep learning classifiers for face recognition.

icip An Interpretable Generative Model for Handwritten Digits Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni, Yueru Chen, Jiali Duan, C. -C. Jay Kuo
International Conference on Image Processing (ICIP) , 2019

Propose a non deep learning based approach to handwritten digit synthesis which is more interpretable and does not require back-propogation.

ad Angel or Demon? Characterizing Variations Across Twitter Timeline of Technical Support Campaigners
S. Gupta, G. S. Bhatia, Saksham Suri, D. Kuchhal, P. Gupta, M. Ahamad, M. Gupta, P. Kumaraguru
The Journal of Web Science Vol.6 , 2019

Analyzing and identifying the presence of fake tech support accounts on twitter.

btas On matching faces with alterations due to plastic surgery and disguise
Saksham Suri, Anush Sankaran, Mayank Vatsa, Richa Singh
IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS) , 2018

A novel approach to perform face recognition in the presence of plastic surgery and disguise.

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