Saketh Rambhatla

I am a Ph.D. student in the department of ECE at University of Maryland (UMD), College Park, where I work on computer vision and deep learning. I work with Dr. Rama Chellappa and Dr. Abhinav Shrivastava.

I completed my bachelor's and master's at Indian Institute of Technology, Kharagpur. During my master's I worked on SLAM and pose recognition.

Email  /  CV  /  Google Scholar  

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Research

I'm interested in computer vision, machine and deep learning. Much of my research is about training models with little to no supervision.

Sparsely annotated object detection: A region-based Semi-supervised approach
Sai Saketh Rambhatla*, Saksham Suri*, Rama Chellappa, Abhinav Shrivastava
Under Review
arXiv

Improve performance of detectors trained using missing annotations by posing the problem as region-based semi-supervised learning.

The Pursuit of Knowledge: Discovering and Localizing new concepts using Dual Memory
Sai Saketh Rambhatla, Rama Chellappa, Abhinav Shrivastava
ICCV, 2021
Project Page | arXiv | Poster

Equip machines with capabilities to automatically discover and learn models for new categories from a large unlabeled dataset.

Towards Discovery and Attribution of Open-world GAN Generated Images.
Saksham Suri*, Sharath Girish*, Sai Saketh Rambhatla, Abhinav Shrivastava
ICCV, 2021
Project Page | arXiv

Automatically discover and attribute open-world GAN images.

Self-Denoising neural networks for few shot learning
Steven Schwarcz, Sai Saketh Rambhatla, Rama Chellappa
Under Review
arXiv

Novel architecture based on denoising auto-encoders to improve few shot learning.

An Empirical analysis of Boosting Deep Networks
Sai Saketh Rambhatla, Michael Jones, Rama Chellappa
IJCNN, 2022
Paper

Empirical evidence that a single large neural network is usually more accurate than a boosted ensemble of neural networks with the same number of total parameters

Towards Accurate Visual and Natural language-based vehicle retrieval systems.
Pirazh Khorramshahi*, Sai Saketh Rambhatla*, Rama Chellappa
NVIDIA AI City Challenge, CVPR Workshops, 2021
Paper

Proposed a real-time system for image-based vehicle re-identification and natural language-based vehicle retrieval.

Towards Real-Time Systems for Vehicle Re-Identification, Multi-Camera Tracking, and Anomaly Detection.
Neehar Peri*, Pirazh Khorramshahi*, Sai Saketh Rambhatla*, Vineet Shenoy, Saumya Rawat Jun-Cheng Chen, Rama Chellappa
NVIDIA AI City Challenge, CVPR Workshops, 2020
Paper

Proposed a Real-Time system for Vehicle Re-identification, Multi-Camera Tracking, and Anomaly Detection in a network of traffic cameras.

Detecting Human-Object Interactions via Functional Generalization.
Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa
AAAI, 2020
project page / arXiv

Humans interact with functionally similar objects in a similar manner.

Spatial Priming for Detecting Human-Object Interactions
Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa
arxiv
project page / arXiv

A method for exploiting the spatial layout information of a human and an object for detecting HOIs in images.

A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification
Pirazh Khorramshahi*, Amit Kumar Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, Rama Chellappa
ICCV, 2019   (Oral Presentation)
arXiv

Proposed a novel dual-path adaptive attention model for Vehicle re-identification.

Body Part Alignment and Temporal Attention Pooling for Video-Based Person ReIdentification
Sai Saketh Rambhatla, Michael Jones
BMVC, 2019
Paper

Training deep networks with the ability to align features achieves state of the art performance on Person Re-identification.

Service

Reviewer, Pattern Recognition Letters

Reviewer, IEEE Access

Reviewer, ECCV 2020

Reviewer, AAAI 2021

cs188

Graduate Teaching Assistant, ENEE222 Fall 2016

Graduate Teaching Assistant, ENEE324 Spring 2017

Graduate Teaching Assistant, ENEE425 Fall 2017

Graduate Teaching Assistant, ENEE630 Fall 2017


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