no code implementations • ECCV 2020 • Viveka Kulharia, Siddhartha Chandra, Amit Agrawal, Philip Torr, Ambrish Tyagi
We propose a weakly supervised approach to semantic segmentation using bounding box annotations.
1 code implementation • 20 Apr 2020 • Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge.
2 code implementations • NeurIPS 2020 • Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania
To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function.
no code implementations • 25 Sep 2019 • Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania
When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.
no code implementations • 21 Feb 2019 • Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr
Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game.
1 code implementation • CVPR 2018 • Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, Puneet K. Dokania
Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample.
no code implementations • 5 Dec 2016 • Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri
As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs).
no code implementations • 29 Sep 2016 • Arnab Ghosh, Viveka Kulharia, Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal
Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence.