1 code implementation • 24 Jun 2022 • Arjun Majumdar, Gunjan Aggarwal, Bhavika Devnani, Judy Hoffman, Dhruv Batra
We present a scalable approach for learning open-world object-goal navigation (ObjectNav) -- the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e. g., "find a sink").
no code implementations • 4 Jul 2020 • Gunjan Aggarwal, Devi Parikh
There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and symbolic or algorithmic, where an artist designs the primary parameters and an autonomous system generates samples within these constraints.
no code implementations • 4 May 2020 • Gunjan Aggarwal, Abhishek Sinha, Nupur Kumari, Mayank Singh
In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
no code implementations • 6 Dec 2019 • Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves the texture of one and the shape of the other input.