  Vision-Language (VL) models have gained significant research focus, enabling
remarkable advances in multimodal reasoning. These architectures typically
comprise a vision encoder, a Large Language Model (LLM), and a projection
module that aligns visual features with the LLM's representation space. Despite
their success, a critical limitation persists: the vision encoding process
remains decoupled from user queries, often in the form of image-related
questions. Consequently, the resulting visual features may not be optimally
attuned to the query-specific elements of the image. To address this, we
introduce QA-ViT, a Question Aware Vision Transformer approach for multimodal
reasoning, which embeds question awareness directly within the vision encoder.
This integration results in dynamic visual features focusing on relevant image
aspects to the posed question. QA-ViT is model-agnostic and can be incorporated
efficiently into any VL architecture. Extensive experiments demonstrate the
effectiveness of applying our method to various multimodal architectures,
leading to consistent improvement across diverse tasks and showcasing its
potential for enhancing visual and scene-text understanding.
