Individuals with visual impairments, encompassing both partial and total difficulties in visual perception, are referred to as visually impaired (VI) people. An estimated 2.2 billion individuals worldwide are affected by visual impairments. Recent advancements in multi-modal large language models (MLLMs) have showcased their extraordinary capabilities across various domains. It is desirable to help VI individuals with MLLMs' great capabilities of visual understanding and reasoning. However, it is challenging for VI people to use MLLMs due to the difficulties in capturing the desirable images to fulfill their daily requests. For example, the target object is not fully or partially placed in the image. This paper explores how to leverage MLLMs for VI individuals to provide visual-question answers. VIAssist can identify undesired images and provide detailed actions. Finally, VIAssist can provide reliable answers to users' queries based on the images. Our results show that VIAssist provides +0.21 and +0.31 higher BERTScore and ROUGE scores than the baseline, respectively.

New technological advances could have important implications for those affected by disabilities, offering valuable assistance throughout their everyday lives. One key example of this is the guidance that technological tools could provide to the visually impaired (VI), individuals that are either partially or entirely blind. With this in mind, researchers at CloudMinds Technologies Inc., in China, have recently created a new deep learning-powered wearable assistive system for VI individuals. This system, presented in a paper pre-published on arXiv, consists of a wearable terminal, a powerful processor and a smartphone. The wearable terminal has two key components, an RGBD camera and an earphone.