In this talk, I will cover my group's recent works on compositional learning for object-centric attributes and relations. We will briefly discuss learning disentangled representations for objects and attributes, and our efforts to create large-scale datasets and scalable approaches for open vocabulary recognition. I will also highlight some of our latest contributions in compositional image generation.
Furthermore, I will offer a glimpse into other research areas we work on, including concept discovery, diffusion-based generative models, neural representations for videos, and predictive perception and robotics.
Abhinav Shrivastava is an Assistant Professor of Computer Science at the University of Maryland, College Park, with an appointment in UMIACS and an affiliate appointment in ECE. His research focuses on computer vision and machine learning. Abhinav's past work has received best paper awards at WACV and best paper finalist at CVPR. His research is supported by NSF, DARPA, IARPA, and gifts from Honda, Facebook, Google, and Adobe. He has received the NSF CAREER and the Amazon Research Award. Before Maryland, he got his PhD in Robotics from CMU and spent a year at Google Research.