Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data
(eg, images, texts, or data collected from different sensors), feature engineering (eg …

Highlighting particularly relevant regions of an image can improve the performance of vision-
language models (VLMs) on various vision-language (VL) tasks by guiding the model to …

Recent advancements in question-answering systems have significantly enhanced the
capability of computers to understand and respond to queries in natural language. This …

Abstract Existing Visual Question Answering models suffer from the language prior, where
the answers provided by the models overly rely on the correlations between questions and …

The goal of this work is background-robust continuous sign language recognition. Most
existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed …

Abstract Visual Question Answering (VQA) aims to evaluate the reasoning abilities of an
intelligent agent using visual and textual information. However, recent research indicates …

Abstract The Visual Question Answering (VQA) task is an important research direction in the
field of artificial intelligence, which requires a model that can simultaneously understand …

Abstract Visual Question Answering (VQA) models often suffer from bias issues which cause
their predictions to rely on superficial correlations in datasets rather than the intrinsic …

The Visual Question Answering (VQA) task has recently become notorious because models
are prone to predicting well-educated" guesses" as answers rather than deriving them …

Counterfactuals have been shown to be a powerful method in Visual Question Answering in
the alleviation of Visual Question Answering's unimodal bias. However, existing …