A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision

• URL: http://arxiv.org/abs/2505.10875v1
• Date: Fri, 16 May 2025 05:32:25 GMT
• Title: A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision
• Authors: Alexey Magay, Dhurba Tripathi, Yu Hao, Yi Fang,
• Abstract summary: People with blindness and low vision (pBLV) face significant challenges, struggling to navigate environments and locate objects due to limited visual cues.<n>Current multi-modal large language (MLLM) models for low vision people lack the spatial reasoning capabilities needed to effectively assist in these tasks.<n>We propose a novel spatial enhanced multi-modal large language model based approach for visually impaired individuals.
• Score: 9.057330310306696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: People with blindness and low vision (pBLV) face significant challenges, struggling to navigate environments and locate objects due to limited visual cues. Spatial reasoning is crucial for these individuals, as it enables them to understand and interpret the spatial relationships in their surroundings, enhancing their ability to navigate and interact more safely and independently. Current multi-modal large language (MLLM) models for low vision people lack the spatial reasoning capabilities needed to effectively assist in these tasks. Moreover, there is a notable absence of lightweight, easy-to-use systems that allow pBLV to effectively perceive and interact with their surrounding environment. In this paper, we propose a novel spatial enhanced multi-modal large language model based approach for visually impaired individuals. By fine-tuning the MLLM to incorporate spatial reasoning capabilities, our method significantly improves the understanding of environmental context, which is critical for navigation and object recognition. The innovation extends to a hardware component, designed as an attachment for glasses, ensuring increased accessibility and ease of use. This integration leverages advanced VLMs to interpret visual data and provide real-time, spatially aware feedback to the user. Our approach aims to bridge the gap between advanced machine learning models and practical, user-friendly assistive devices, offering a robust solution for visually impaired users to navigate their surroundings more effectively and independently. The paper includes an in-depth evaluation using the VizWiz dataset, demonstrating substantial improvements in accuracy and user experience. Additionally, we design a comprehensive dataset to evaluate our method's effectiveness in realworld situations, demonstrating substantial improvements in accuracy and user experience.

Related papers

• VLM4D: Towards Spatiotemporal Awareness in Vision Language Models [66.833085504228]
  We introduce V4DLM, the first benchmark specifically designed to evaluate visual language models (VLMs)<n>Our benchmark comprises diverse real-world and synthetic videos accompanied by carefully curated question-answer pairs.<n>We identify significant performance gaps compared to human baselines, highlighting fundamental deficiencies in existing models.
  arXiv  Detail & Related papers  (2025-08-04T06:06:06Z)
• True Multimodal In-Context Learning Needs Attention to the Visual Context [69.63677595066012]
  Multimodal Large Language Models (MLLMs) have enabled Multimodal In-Context Learning (MICL)-adapting to new tasks.<n>Current MLLMs tend to neglect visual cues and over-rely on textual patterns, leading to mere text imitation rather than genuine multimodal adaptation.<n>We introduce Dynamic Attention Reallocation (DARA), an efficient fine-tuning strategy that encourages models to attend to the visual context.
  arXiv  Detail & Related papers  (2025-07-21T17:08:18Z)
• Top-Down Compression: Revisit Efficient Vision Token Projection for Visual Instruction Tuning [70.57180215148125]
  Visual instruction tuning aims to enable large language models to comprehend the visual world.<n>Existing methods often grapple with the intractable trade-off between accuracy and efficiency.<n>We present LLaVA-Meteor, a novel approach that strategically compresses visual tokens without compromising core information.
  arXiv  Detail & Related papers  (2025-05-17T10:22:29Z)
• VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion [5.6565850326929485]
  We propose a novel framework that uses Vision-Language Models to enhance training by providing attentional cues.<n>Our method integrates textual representations into Bird's-Eye-View (BEV) features for semantic supervision.<n>We evaluate VLM-E2E on the nuScenes dataset and demonstrate its superiority over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-02-25T10:02:12Z)
• iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs [4.381263829108405]
  Vision-Language Models (VLMs) are known to struggle with spatial reasoning and visual alignment.<n>We introduce iVISPAR, an interactive multi-modal benchmark designed to evaluate the spatial reasoning capabilities of VLMs acting as agents.
  arXiv  Detail & Related papers  (2025-02-05T14:29:01Z)
• AI-based Wearable Vision Assistance System for the Visually Impaired: Integrating Real-Time Object Recognition and Contextual Understanding Using Large Vision-Language Models [0.0]
  This paper introduces a novel wearable vision assistance system with artificial intelligence (AI) technology to deliver real-time feedback to a user through a sound beep mechanism.<n>The system provides detailed descriptions of objects in the user's environment using a large vision language model (LVLM)
  arXiv  Detail & Related papers  (2024-12-28T07:26:39Z)
• TWIST & SCOUT: Grounding Multimodal LLM-Experts by Forget-Free Tuning [54.033346088090674]
  We introduce TWIST & SCOUT, a framework that equips pre-trained MLLMs with visual grounding ability.<n>To fine-tune the model effectively, we generate a high-quality synthetic dataset we call SCOUT.<n>This dataset provides rich supervision signals, describing a step-by-step multimodal reasoning process.
  arXiv  Detail & Related papers  (2024-10-14T13:35:47Z)
• Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients [0.0]
  This paper presents a novel approach for indoor assistance to the blind by addressing the challenge of small object detection. We propose a technique YOLO NAS Small architecture, a lightweight and efficient object detection model, optimized using the Super Gradients training framework.
  arXiv  Detail & Related papers  (2024-08-28T05:38:20Z)
• Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [61.143381152739046]
  We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach.<n>Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations.<n>We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
  arXiv  Detail & Related papers  (2024-06-24T17:59:42Z)
• Voila-A: Aligning Vision-Language Models with User's Gaze Attention [56.755993500556734]
  We introduce gaze information as a proxy for human attention to guide Vision-Language Models (VLMs) We propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications.
  arXiv  Detail & Related papers  (2023-12-22T17:34:01Z)
• Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning [67.0609518552321]
  We propose to conduct Machine Vision Therapy which aims to rectify the noisy predictions from vision models. By fine-tuning with the denoised labels, the learning model performance can be boosted in an unsupervised manner.
  arXiv  Detail & Related papers  (2023-12-05T07:29:14Z)
• MISAR: A Multimodal Instructional System with Augmented Reality [38.79160527414268]
  Augmented reality (AR) requires seamless integration of visual, auditory, and linguistic channels for optimized human-computer interaction. Our study introduces an innovative method harnessing large language models (LLMs) to assimilate information from visual, auditory, and contextual modalities.
  arXiv  Detail & Related papers  (2023-10-18T04:15:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.