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 …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Large language models (LLMs) have demonstrated excellent zero-shot generalization to
new language tasks. However, effective utilization of LLMs for zero-shot visual question …

Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but
has not enjoyed the same level of engagement in terms of data creation. In this paper, we …

Abstract Visual Question Answering (VQA) models often perform poorly on out-of-distribution
data and struggle on domain generalization. Due to the multi-modal nature of this task …

Large language models (LLMs) have demonstrated excellent zero-shot generalization to
new language tasks. However, effective utilization of LLMs for zero-shot visual question …

In recent years, several machine learning models have been proposed. They are trained
with a language modelling objective on large-scale text-only data. With such pretraining …

We propose CX-ToM, short for counterfactual explanations with theory-of-mind, a new
explainable AI (XAI) framework for explaining decisions made by a deep convolutional …

Vision-and-language (V&L) models pretrained on large-scale multimodal data have
demonstrated strong performance on various tasks such as image captioning and visual …

Attention based explanations (viz. saliency maps), by providing interpretability to black box
models such as deep neural networks, are assumed to improve human trust and reliance in …