@inproceedings{baghel-etal-2025-towards,
    title = "Towards Blind and Low-Vision Accessibility of Lightweight {VLM}s and Custom {LLM}-Evals",
    author = "Baghel, Shruti Singh  and
      Rathore, Yash Pratap Singh  and
      Pradhan, Anurag  and
      Jena, Sushovan  and
      Bhavsar, Arnav  and
      Shukla, Amit  and
      Goyal, Pawan",
    editor = "Shukla, Ankita  and
      Kumar, Sandeep  and
      Bedi, Amrit Singh  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 1st Workshop on Multimodal Models for Low-Resource Contexts and Social Impact (MMLoSo 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.mmloso-1.8/",
    pages = "86--94",
    ISBN = "979-8-89176-311-1",
    abstract = "Large Vision-Language Models (VLMs) excel at understanding and generating video descriptions but their high memory, computation, and deployment demands hinder practical use particularly for blind and low-vision (BLV) users who depend on detailed, context-aware descriptions. To study the effect of model size on accessibility-focused description quality, we evaluate SmolVLM2 variants with 500M and 2.2B parameters across two diverse datasets: AVCaps (outdoor), and Charades (indoor). In this work, we introduce two novel evaluation frameworks specifically designed for BLV accessibility assessment: the Multi-Context BLV Framework evaluating spatial orientation, social interaction, action events, and ambience contexts; and the Navigational Assistance Framework focusing on mobility-critical information. Additionally, we conduct a systematic evaluation of four different prompt design strategies and deploy both models on a smartphone, evaluating FP32 and INT8 precision variants to assess real-world performance constraints on resource-limited mobile devices."
}