1 code implementation • 12 Apr 2024 • Dvij Kalaria, Shreya Sharma, Sarthak Bhagat, Haoru Xue, John M. Dolan
Off-road navigation is a challenging problem both at the planning level to get a smooth trajectory and at the control level to avoid flipping over, hitting obstacles, or getting stuck at a rough patch.
no code implementations • 26 Mar 2024 • Samuel Li, Sarthak Bhagat, Joseph Campbell, Yaqi Xie, Woojun Kim, Katia Sycara, Simon Stepputtis
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments.
no code implementations • 12 Sep 2023 • Sarthak Bhagat, Simon Stepputtis, Joseph Campbell, Katia Sycara
This work focuses on anticipating long-term human actions, particularly using short video segments, which can speed up editing workflows through improved suggestions while fostering creativity by suggesting narratives.
1 code implementation • 15 Jun 2023 • Sarthak Bhagat, Simon Stepputtis, Joseph Campbell, Katia Sycara
Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a limited number of examples are provided.
1 code implementation • 21 Mar 2023 • Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Yi Yu, Yifang Yin, Rajiv Ratn Shah
To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing.
Ranked #1 on Talking Face Generation on CREMA-D
no code implementations • ACM Multimedia Asia 2022 • Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Dhroov Goel, Sakshat Mali, Yi Yu, Yifang Yin, Rajiv Ratn Shah
Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields.
no code implementations • 28 May 2022 • Devansh Gupta, Aditya Saini, Drishti Bhasin, Sarthak Bhagat, Shagun Uppal, Rishi Raj Jain, Ponnurangam Kumaraguru, Rajiv Ratn Shah
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification.
no code implementations • 19 Oct 2020 • Shagun Uppal, Sarthak Bhagat, Devamanyu Hazarika, Navonil Majumdar, Soujanya Poria, Roger Zimmermann, Amir Zadeh
Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data.
1 code implementation • 21 Jul 2020 • Sarthak Bhagat, Sujit PB
Persistent target tracking in urban environments using UAV is a difficult task due to the limited field of view, visibility obstruction from obstacles and uncertain target motion.
Robotics Systems and Control Systems and Control
1 code implementation • 10 Jun 2020 • Sarthak Bhagat, Vishaal Udandarao, Shagun Uppal
Disentangling the underlying feature attributes within an image with no prior supervision is a challenging task.
1 code implementation • 15 May 2020 • Shagun Uppal, Anish Madan, Sarthak Bhagat, Yi Yu, Rajiv Ratn Shah
In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.
1 code implementation • ECCV 2020 • Sarthak Bhagat, Shagun Uppal, Zhuyun Yin, Nengli Lim
Our experiments show that the combination of the improved representations with the novel loss function enable MGP-VAE to outperform the baselines in video prediction.
Ranked #1 on Video Prediction on Colored dSprites
no code implementations • 22 Jul 2019 • Ankita Shukla, Sarthak Bhagat, Shagun Uppal, Saket Anand, Pavan Turaga
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation.
no code implementations • 19 Feb 2019 • Ankita Shukla, Shagun Uppal, Sarthak Bhagat, Saket Anand, Pavan Turaga
We use several metrics to compare the properties of latent spaces of disentangled representation models in terms of class separability and curvature of the latent-space.