  We propose a methodology to address the challenge of instance segmentation in
autonomous systems, specifically targeting accessibility and inclusivity. Our
approach utilizes a non-hierarchical Vision Transformer variant, EVA-02,
combined with a Cascade Mask R-CNN mask head. Through fine-tuning on the AVA
instance segmentation challenge dataset, we achieved a promising mean Average
Precision (mAP) of 52.68\% on the test set. Our results demonstrate the
efficacy of ViT-based architectures in enhancing vision capabilities and
accommodating the unique needs of individuals with disabilities.
