Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

We introduce the task of 3D object localization in RGB-D scans using natural language
descriptions. As input, we assume a point cloud of a scanned 3D scene along with a free …

People learn throughout life. However, incrementally updating conventional neural networks
leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the …

Language grounded image understanding tasks have often been proposed as a method for
evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of …

Abstract Visual Question Answering (VQA) research is split into two camps: the first focuses
on VQA datasets that require natural image understanding and the second focuses on …

Humans can incrementally learn to do new visual detection tasks, which is a huge challenge
for today's computer vision systems. Incrementally trained deep learning models lack …

Abstract Existing Visual Question Answering (VQA) methods tend to exploit dataset biases
and spurious statistical correlations, instead of producing right answers for the right reasons …

Chart question answering (CQA) is a newly proposed visual question answering (VQA) task
where an algorithm must answer questions about data visualizations, eg bar charts, pie …

Pre-trained deep neural networks (DNNs) are being widely deployed by industry for making
business decisions and to serve users; however, a major problem is model decay, where the …

With the advent of multi-modal large language models (MLLMs), datasets used for visual
question answering (VQA) and referring expression comprehension have seen a …