3 accepted papers
We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this probl…
Table-to-text generation has been widely studied in the Natural Language Processing community in the recent years. We give a new perspective to this problem by incorporating signals from both tables as well as associated images to generate relevant text. While tables contain a structured list of fac…
In this paper we present a symmetric KL divergence based agglomerative clustering framework to segment multiple levels of depigmentation in Vitiligo images. The proposed framework starts with a simple merge cost based on symmetric KL divergence. We extend the recent body of work related to Bregman d…