no code implementations • 5 Dec 2023 • Ananye Agarwal, Shagun Uppal, Kenneth Shaw, Deepak Pathak
However, this task requires both a complex understanding of functional affordances as well as precise low-level control.
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.
1 code implementation • 11 Nov 2021 • I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert, Youngwoon Lee
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations.
2 code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent
We further improve the performance by using a joint-objective for classification and textual entailment.
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 • 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 • 4 Nov 2019 • Jagriti Sikka, Kushal Satya, Yaman Kumar, Shagun Uppal, Rajiv Ratn Shah, Roger Zimmermann
Predicting the runtime complexity of a programming code is an arduous task.
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.