Jeya Maria Jose

Hi!, I am a 4th year Ph.D. student at Johns Hopkins University, in the Department of Electrical and Computer Engineering where I am working in Vision and Image Understanding Lab , advised by Dr. Vishal M Patel .

My research interests are in Computer Vision and Machine Learning. I have worked on developing effective backbones and transfer learning approaches for large scale computer vision and medical imaging tasks like segmentation and enhancement.

I am glad to have the opportunity of interning and collaborating with research teams at Google, NVIDIA , and Adobe during my Ph.D.

Previously, I received my Bachelor's from NIT Trichy, India in 2019 while also working closely with National University of Singapore.

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

News

  • September, 2022 - Received Young Scientist Impact Award - Finalist at MICCAI 2022.
  • August, 2022 - 1 paper accepted at WACV 2023.
  • August, 2022 - Received NIH MICCAI Award 2022, which rewards early career scientists.
  • June, 2022 - 3 papers accepted at MICCAI 2022.
  • June, 2022 - Invited Talk at IEEE SPS Summer School on AI in Healthcare.
  • May, 2022 - Awarded Outstanding Automation Paper Finalist (top 3) at ICRA 2022.
  • March, 2022 - Presented a Tutorial at ISBI 2022 on medical image segmentation.
  • March, 2022 - 1 paper accepted at CVPR 2022.
  • January, 2022 - 1 paper accepted at ICRA 2022.
  • November, 2021 - 1 paper accepted in IEEE Transactions on Medical Imaging.
  • June, 2021 - 2 papers accepted at MICCAI 2021.
  • May, 2021 - Joined Adobe as a Research Intern.
  • May, 2021 - 1 paper accepted at ICIP 2021 .
  • November, 2020 - 1 paper accepted in IEEE Journal of Selected Topics in Signal Processing .
  • November, 2020 - 1 paper accepted at WACV 2021 .
  • July, 2020 - Recipient of MICCAI Student Travel Award 2020, which rewards first author students of the highest-quality MICCAI papers.
  • May, 2020 - 1 paper accepted in the IEEE Journal of Selected Topics in Signal Processing .
  • May, 2020 - 1 paper accepted at MICCAI 2020 (Early Acceptance).
  • September, 2019 - Awarded Best Student Paper award at CVIP 2019 .
  • August, 2019 - Joined Johns Hopkins University for my Ph.D with ECE fellowship.



Teaching

Teaching Assistant: Deep Learning EN.520.638.01.SP22, Spring 2022, Johns Hopkins University

Teaching Assistant: Deep Learning EN.520.638.01.SP21, Spring 2021, Johns Hopkins University

Research

Interactive Portrait Harmonization
Preprint

Jeya Maria Jose, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin , Jose Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, and Vishal M. Patel

Paper | Code | Project

Fine-Context Shadow Detection using Shadow Removal
WACV 2023

Jeya Maria Jose and Vishal M. Patel

Paper

TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions
CVPR 2022

Jeya Maria Jose, Rajeev Yasarla and Vishal M. Patel

Paper | Code | Project

UNeXt: MLP-based Rapid Medical Image Segmentation Network
MICCAI 2022

Jeya Maria Jose and Vishal M. Patel

Paper | Code | Project

Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency
MICCAI 2022

Aimon Rahman, Jeya Maria Jose, Ilker H, and Vishal M. Patel

Paper

Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
MICCAI 2022

Aimon Rahman, Chaminda Bandara, Jeya Maria Jose, Ilker H, and Vishal M. Patel

Paper

On-the-Fly Test-time Adaptation for Medical Image Segmentation
Preprint

Jeya Maria Jose , Pengfei Guo, Vibashan VS, and Vishal M. Patel

Paper | Code

Target and Task specific Source-Free Domain Adaptive Image Segmentation
Preprint

Vibashan VS*, Jeya Maria Jose* , and Vishal M. Patel

*equal contribution

Paper | Code

SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
ICRA 2022 (Outstanding Automation Paper Finalist)

Chaminda Bandara, Jeya Maria Jose, and Vishal M. Patel

Paper | Code

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
MICCAI 2021

Jeya Maria Jose, Poojan Oza, Ilker Hacihaliloglu, Vishal M. Patel

Paper | Code

Over-and-Under Complete Convolutional RNN for MRI Reconstruction
MICCAI 2021

Pengfei Guo, Jeya Maria Jose, Puyang Wang, Jinyuan Zhou,Shanshan Jiang, Vishal M. Patel

Paper | Code

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
IEEE Transactions on Medical Imaging


Jeya Maria Jose, Vishwanath Sindagi, Ilker Hacihaliloglu, Vishal M. Patel

Paper | Code | Project

Overcomplete Deep Subspace Clustering Networks
WACV 2021

Jeya Maria Jose and Vishal M. Patel

Paper | Code

Overcomplete Representations against Adversarial Videos
ICIP 2021

Shao-Yuan Lo, Jeya Maria Jose, and Vishal M. Patel

Paper

Exploring Overcomplete Representations for Single Image Deraining using CNNs
IEEE Journal of Selected Topics in Signal Processing

Rajeev Yasarla*, Jeya Maria Jose*, Vishal M. Patel

*equal contribution

Paper | Code

KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
MICCAI 2020 (Oral)

Jeya Maria Jose, Vishwanath Sindagi, Ilker Hacihaliloglu, Vishal M. Patel

Paper | Code | Project

Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
IEEE Journal of Selected Topics in Signal Processing

Jeya Maria Jose, Rajeev Yasarla, Puyang Wang, Ilker Hacihaliloglu, Vishal M. Patel

Paper

Tackling Multiple Visual Artifacts: Blind Image Restoration using Conditional Adversarial Networks
CVIP 2019 (Best Student Paper Award)

M Anand, A Ashwin Natraj, Jeya Maria Jose, K Subramanian, S Deivalakshmi

Paper | Code

Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
BraTS, MICCAI Workshop 2019

Mobarakol Islam, Vibashan VS, Jeya Maria Jose, Navodini Wijethilake, Uppal Utkarsh, Hongliang Ren

Paper

Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information
BraTS, MICCAI Workshop 2018

Mobarakol Islam, Jeya Maria Jose, Hongliang Ren

Paper | Poster

Services

Web Chair: AVSS 2021

Reviewer at:

Journals :

TPAMI, TMI, MedIA, TCSVT, TGRS, IJCV, PR, CBM

Conferences :

CVPR, ICCV, ECCV, NeurIPS, ICLR, MICCAI, MIDL, WACV, ICIP

Workshops :

BrainLes, Medical Image Learning with Less Labels and Imperfect Data


source code