Jathushan Rajasegaran

I am a Ph.D. student at BAIR advised by Prof. Jitendra Malik. I am broadly interested in Computer Vision and Deep Learning, with a focus on developing models for understanding long videos. Before coming to Berkeley, I was working with Prof. Salman Khan at Inception Institute. I completed my undergraduate study at University of Moratuwa, with a major in Electronic and Telecommunication Engineering. My Bachelor's Thesis was advised by Dr. Ranga Rodigo.

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Cars don't run like cheetahs, Planes don't fly like birds and Machines won't think in a way same as humans. They will do better. --Richard Feynman

Machines should be able to understand the world outside our window. That world may change and evolve, but the machines should perceive trees, cars and spaceships not pixels. --Max Wertheimer, Stan Lee

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My research interests lie in the general area of computer vision and deep learning, particularly in long-term video understanding, deep neural architectures and meta/continual learning.

Tracking People by Predicting 3D Appearance, Location and Pose.
Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik
CVPR, 2022   (Oral Presentation)
arxiv/ project page/ video/ code/

Performing monocular tracking of people by predicting their appearance, pose and location and in 3D.

Tracking People with 3D Representations.
Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik
NeurIPS, 2021
paper/ arxiv/ project page/ video/ code/ poster

Performing monocular tracking of people by lifting them to 3D and then using 3D representations of their appearance, pose and location.

iTAML: An Incremental Task-Agnostic Meta-learning Approach.
Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan
CVPR, 2020
paper/ arxiv/ slides/ video/ code

By learning generic represenatations from past tasks, we can easily adapt to new tasks as well as remember old tasks.

Random Path Selection for Incremental Learning.
Jathushan Rajasegaran, Munawar Hayat, Salman Khan,
Fahad Shahbaz Khan, Ling Shao
NeurIPS, 2019
paper/ arxiv/ poster/ code

We increase the width of a ResNet like model by adding extra skip connections when new tasks are introduced.

DeepCaps: Going Deeper with Capsule Networks
Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara,
Hirunima Jayasekara, Suranga Seneviratne, Ranga Rodrigo
CVPR, 2019   (Oral Presentation)
paper/ poster/ video/ code

Capsule Networks are cool, but they are shallow. We can increase the depth by 3D convolutions and skip connections.

TextCaps: Handwritten Character Recognition with Very Small Datasets
Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Jathushan Rajasegaran,
Suranga Seneviratne, Ranga Rodrigo
WACV, 2019
paper/ arxiv/ poster/ code

Capsule Networks can capture actual variations that are present in human hand writing, so we generate more data and retrain the capsule networks.

A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store
Jathushan Rajasegaran, Naveen Karunanayake, Ashanie Gunathillake,
Suranga Seneviratne, Guillaume Jourjon
WWW, 2019
paper/ arxiv/ poster/

We use content and style representations detect counterfeit apps in playstore.

Website source from Jon Barron here