Although Large Language Models (LLMs) and Large Multimodal Models (LMMs) exhibit
impressive skills in various domains, their ability for mathematical reasoning within visual …

The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered
unparalleled attention, due to their superior performance in visual contexts. However, their …

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital
aspect of their development and application in the pursuit of Artificial General Intelligence …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

While significant progress has been made in natural language processing (NLP), existing
methods exhibit limitations in effectively interpreting and processing diverse mathematical …

We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …

We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …

Visual language such as charts and plots is ubiquitous in the human world. Comprehending
plots and charts requires strong reasoning skills. Prior state-of-the-art (SOTA) models …

The emergence of multimodal large models (MLMs) has significantly advanced the field of
visual understanding, offering remarkable capabilities in the realm of visual question …

In this paper, we present a novel approach for the task of eXplainable Question Answering
(XQA), ie, generating natural language (NL) explanations for the Visual Question Answering …