86 citations · 104 across the 6 of their papers we have counts for
7 papers
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR
Bharat Singh, Viveka Kulharia, Luyu Yang +3
Multimodal synthetic data generation is crucial in domains such as autonomous driving, robotics, augmented/virtual reality, and retail. We propose a novel approach, GenMM, for join…
A Revised Generative Evaluation of Visual Dialogue
Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania +2
Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge. The current evaluation scheme of the VisDi…
Calibrating Deep Neural Networks using Focal Loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal +3
Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to…
Domain Partitioning Network
Botos Csaba, Adnane Boukhayma, Viveka Kulharia +2
Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game. However, even if the players converge to an equilibrium, the gen…
Multi-Agent Diverse Generative Adversarial Networks
Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri +2
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First…
Message Passing Multi-Agent GANs
Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri
Communicating and sharing intelligence among agents is an important facet of achieving Artificial General Intelligence. As a first step towards this challenge, we introduce a novel…
Contextual RNN-GANs for Abstract Reasoning Diagram Generation
Arnab Ghosh, Viveka Kulharia, Amitabha Mukerjee +2
Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better…