8 citations · 17 across the 13 of their papers we have counts for
14 papers
Symbolic Graph Inference for Compound Scene Understanding
FNU Aryan, Simon Stepputtis, Sarthak Bhagat +4
Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn…
ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition
Samuel Li, Sarthak Bhagat, Joseph Campbell +4
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments. Inspired by the human capability to grasp such objects through intuiti…
Knowledge-Guided Short-Context Action Anticipation in Human-Centric Videos
Sarthak Bhagat, Simon Stepputtis, Joseph Campbell +1
This work focuses on anticipating long-term human actions, particularly using short video segments, which can speed up editing workflows through improved suggestions while fosterin…
Sample-Efficient Learning of Novel Visual Concepts
Sarthak Bhagat, Simon Stepputtis, Joseph Campbell +1
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 lim…
Emotionally Enhanced Talking Face Generation
Sahil Goyal, Shagun Uppal, Sarthak Bhagat +3
Several works have developed end-to-end pipelines for generating lip-synced talking faces with various real-world applications, such as teaching and language translation in videos.…
FaIRCoP: Facial Image Retrieval using Contrastive Personalization
Devansh Gupta, Aditya Saini, Drishti Bhasin +5
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial…
Target-Following Double Deep Q-Networks for UAVs
Sarthak Bhagat, P. B. Sujit
Target tracking in unknown real-world environments in the presence of obstacles and target motion uncertainty demand agents to develop an intrinsic understanding of the environment…
Multimodal Research in Vision and Language: A Review of Current and Emerging Trends
Shagun Uppal, Sarthak Bhagat, Devamanyu Hazarika +4
Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhan…
UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning
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.…
DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors
Sarthak Bhagat, Vishaal Udandarao, Shagun Uppal
Disentangling the underlying feature attributes within an image with no prior supervision is a challenging task. Models that can disentangle attributes well provide greater interpr…
C3VQG: Category Consistent Cyclic Visual Question Generation
Shagun Uppal, Anish Madan, Sarthak Bhagat +2
Visual Question Generation (VQG) is the task of generating natural questions based on an image. Popular methods in the past have explored image-to-sequence architectures trained wi…
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders
Sarthak Bhagat, Shagun Uppal, Zhuyun Yin +1
We introduce MGP-VAE (Multi-disentangled-features Gaussian Processes Variational AutoEncoder), a variational autoencoder which uses Gaussian processes (GP) to model the latent spac…
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Ankita Shukla, Sarthak Bhagat, Shagun Uppal +2
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation. Various learning f…
Geometry of Deep Generative Models for Disentangled Representations
Ankita Shukla, Shagun Uppal, Sarthak Bhagat +2
Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning low-dimensional latent-space variables and an…