Vision-Language Models for Edge Networks: A Comprehensive Survey

• URL: http://arxiv.org/abs/2502.07855v1
• Date: Tue, 11 Feb 2025 14:04:43 GMT
• Title: Vision-Language Models for Edge Networks: A Comprehensive Survey
• Authors: Ahmed Sharshar, Latif U. Khan, Waseem Ullah, Mohsen Guizani,
• Abstract summary: Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare. Their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations.
• Score: 32.05172973290599
• License:
• Abstract: Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations. This survey explores recent advancements in optimizing VLMs for edge environments, focusing on model compression techniques, including pruning, quantization, knowledge distillation, and specialized hardware solutions that enhance efficiency. We provide a detailed discussion of efficient training and fine-tuning methods, edge deployment challenges, and privacy considerations. Additionally, we discuss the diverse applications of lightweight VLMs across healthcare, environmental monitoring, and autonomous systems, illustrating their growing impact. By highlighting key design strategies, current challenges, and offering recommendations for future directions, this survey aims to inspire further research into the practical deployment of VLMs, ultimately making advanced AI accessible in resource-limited settings.

Related papers

• From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing [16.755590790629153]
  This review examines the development and application of multi-modal language models (MLLMs) in remote sensing. We focus on their ability to interpret and describe satellite imagery using natural language. Key applications such as scene description, object detection, change detection, text-to-image retrieval, image-to-text generation, and visual question answering are discussed.
  arXiv  Detail & Related papers  (2024-11-05T12:14:22Z)
• VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use [74.39058448757645]
  We present VipAct, an agent framework that enhances vision-language models (VLMs) VipAct consists of an orchestrator agent, which manages task requirement analysis, planning, and coordination, along with specialized agents that handle specific tasks. We evaluate VipAct on benchmarks featuring a diverse set of visual perception tasks, with experimental results demonstrating significant performance improvements.
  arXiv  Detail & Related papers  (2024-10-21T18:10:26Z)
• On-Device Language Models: A Comprehensive Review [26.759861320845467]
  Review examines the challenges of deploying computationally expensive large language models on resource-constrained devices. Paper investigates on-device language models, their efficient architectures, as well as state-of-the-art compression techniques. Case studies of on-device language models from major mobile manufacturers demonstrate real-world applications and potential benefits.
  arXiv  Detail & Related papers  (2024-08-26T03:33:36Z)
• VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs [102.36953558562436]
  Vision language models (VLMs) are an exciting emerging class of language models (LMs) One understudied capability inVLMs is visual spatial planning. Our study introduces a benchmark that evaluates the spatial planning capability in these models in general.
  arXiv  Detail & Related papers  (2024-07-02T00:24:01Z)
• 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. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
  arXiv  Detail & Related papers  (2024-06-24T17:59:42Z)
• Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models [81.71651422951074]
  Chain-of-Spot (CoS) method is a novel approach that enhances feature extraction by focusing on key regions of interest. This technique allows LVLMs to access more detailed visual information without altering the original image resolution. Our empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content.
  arXiv  Detail & Related papers  (2024-03-19T17:59:52Z)
• MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting [97.52388851329667]
  We introduce Marking Open-world Keypoint Affordances (MOKA) to solve robotic manipulation tasks specified by free-form language instructions. Central to our approach is a compact point-based representation of affordance, which bridges the VLM's predictions on observed images and the robot's actions in the physical world. We evaluate and analyze MOKA's performance on various table-top manipulation tasks including tool use, deformable body manipulation, and object rearrangement.
  arXiv  Detail & Related papers  (2024-03-05T18:08:45Z)
• Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions [11.786387517781328]
  Vision-Language Models (VLMs) are advanced models that can tackle more intricate tasks such as image captioning and visual question answering. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs. We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible.
  arXiv  Detail & Related papers  (2024-02-20T18:57:34Z)
• A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming [26.082980156232086]
  Top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology. The paper highlights the innovative use of these technologies in producing highly realistic videos. In the realm of video streaming, the paper discusses how LLMs contribute to more efficient and user-centric streaming experiences.
  arXiv  Detail & Related papers  (2024-01-30T14:37:10Z)
• Vision-Language Models for Vision Tasks: A Survey [62.543250338410836]
  Vision-Language Models (VLMs) learn rich vision-language correlation from web-scale image-text pairs. This paper provides a systematic review of visual language models for various visual recognition tasks.
  arXiv  Detail & Related papers  (2023-04-03T02:17:05Z)

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.