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 …

The challenge of bias in visual question answering (VQA) has gained considerable attention
in contemporary research. Various intricate bias dependencies, such as modalities and data …

Training generative adversarial networks (GANs) for noise-to-image synthesis is a challenge
task, primarily due to the instability of GANs' training process. One of the key issues is the …

Visual question answering requires a system to provide an accurate natural language
answer given an image and a natural language question. However, it is widely recognized …

Large language models (LLMs) exhibit exceptional reasoning capabilities and have played
significant roles in knowledge-based visual question-answering (VQA) systems. By …

In his classic book Thinking, Fast and Slow (Daniel, 2017), Kahneman points out that human
thinking can be categorized into two main modes of thinking: a system that displays intuition …

Visual question answering (VQA) is a challenging task that requires models to understand
both visual and linguistic inputs and produce accurate answers. However, VQA models often …

Previous studies have pointed out that visual question answering (VQA) models are prone to
relying on language priors for answer predictions. In this context, predictions often depend …

Visual cues and question semantics contribute to final answer predictions from distinct
perspectives. However, inherent language bias confounds the relationship between visual …

Language bias stands as a noteworthy concern in visual question answering (VQA),
wherein models tend to rely on spurious correlations between questions and answers for …