VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation

Keskeiset käsitteet

Combining lightweight object detection with Large Language Models enhances safety in visual navigation for the visually impaired.

Tiivistelmä

This paper explores the use of Large Language Models (LLMs) in zero-shot anomaly detection for safe visual navigation. The proposed framework leverages real-time object detection and specialized prompts to identify anomalies, provide audio descriptions, and assist in safe navigation. It addresses challenges in dynamic urban environments and emphasizes the importance of vision-language understanding for safety concerns.

Abstract:

• Explores potential of LLMs in zero-shot anomaly detection.
• Utilizes real-time open-world object detection model Yolo-World.
• Emphasizes on safe visual navigation for visually impaired individuals.

Introduction:

• Discusses advancements in accessible technologies due to machine learning.
• Highlights impact of deep learning on object detection and segmentation models.

Methodology:

• Describes a multi-module architecture integrating object detection with LLM capabilities.
• Outlines the process of anomaly alerts and scene descriptions for users.

Experiments:

• Compares proposed system with rule-based anomaly detection.
• Evaluates system optimization and detection accuracy.

Conclusion:

• Demonstrates potential of combining lightweight object detection with LLMs for enhanced accessibility.
• Emphasizes prompt engineering's role in guiding LLM responses.

Tilastot

"Latency: As shown in Table 4, we measured end-to-end system latency and individual module processing times to identify bottlenecks and optimize for real-time performance. Results indicated an average end-to-end latency of 60 ms on the mobile device (e.g., smartphone) with neural engines, ensuring timely feedback."

Lainaukset

Tärkeimmät oivallukset

VisionGPT

by Hao Wang,Jia... klo arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12415.pdf

Syvällisempiä Kysymyksiä

How can prompt engineering be further optimized to enhance the performance of Large Language Models?

Prompt engineering plays a crucial role in guiding Large Language Models (LLMs) to generate relevant and accurate responses. To optimize prompt engineering for enhancing LLM performance, several strategies can be implemented:

Tailored Prompts: Design prompts that are specific to the task at hand and provide clear instructions on what information is required from the model. Tailoring prompts ensures that the LLM focuses on relevant aspects of the input data.

Multi-Modal Inputs: Incorporate multi-modal inputs, such as images or videos, into prompts to provide a more comprehensive understanding of the context. This allows LLMs to generate responses that consider both visual and textual information.

Feedback Loop: Implement a feedback loop mechanism where user feedback is used to refine prompts over time. By analyzing model outputs and adjusting prompts based on user interactions, prompt quality can be continuously improved.

Fine-Tuning Prompt Complexity: Experiment with different levels of prompt complexity to find the optimal balance between providing sufficient guidance for the model while avoiding unnecessary constraints that may limit creativity or flexibility.

Prompt Variability: Introduce variability in prompts by using templates with placeholders for dynamic content insertion. This approach enables diverse inputs while maintaining consistency in prompting style.

Prompt Validation: Validate prompts through pilot testing with target users to ensure they effectively elicit desired responses from LLMs before full-scale deployment.

By implementing these optimization strategies, prompt engineering can significantly enhance the performance of Large Language Models by guiding them towards generating more accurate and contextually appropriate outputs.

What are the potential ethical considerations when implementing AI-driven systems for visually impaired individuals?

Implementing AI-driven systems for visually impaired individuals raises important ethical considerations that must be carefully addressed:

Privacy Concerns: AI systems often rely on collecting and processing personal data, raising concerns about privacy violations if sensitive information is not adequately protected.

Bias and Fairness: Biases present in training data could lead to discriminatory outcomes for visually impaired users if not mitigated properly during system development.

Transparency and Accountability: It's essential for AI systems designed for visually impaired individuals to operate transparently so users understand how decisions are made by algorithms.

4 .Accessibility Equity: Ensuring equitable access to AI technologies among all members of society regardless of socioeconomic status or geographical location is crucial.
5 .User Consent: Obtaining informed consent from visually impaired users regarding data collection, storage practices, and system functionalities is paramount.
6 .Reliability: The reliability of AI-driven systems must be thoroughly tested before deployment as errors or malfunctions could have severe consequences for visually impaired individuals relying on these technologies.

How can integration of computer vision and language models benefit other accessibility technologies beyond visual navigation?

The integration of computer vision technology with language models offers numerous benefits across various accessibility technologies beyond visual navigation:
1 .Enhanced Assistive Technologies: By combining computer vision capabilities with natural language processing, assistive technologies can offer more personalized support tailored specifically towards individual needs.
2 .Improved Communication Aids: Integration allows speech-to-text conversion tools used by people with speech impairments greater accuracy through image recognition cues provided via computer vision technology
3 .Innovative Learning Tools:

For students with learning disabilities like dyslexia,
integrating text recognition software powered
by computer vision enhances reading comprehension
Providing audio descriptions alongside visuals aids
learners who are blind or have low vision
4 .
- Smart Home Automation:
Integrating voice commands enabled by language models
along with object recognition capabilities improves
independent living experiences
5 .
- Healthcare Applications:
Combining medical imaging analysis through
computer vision techniques alongwith patient records
interpretation using large language models enhances diagnostic accuracy
By leveraging this integrated approach across various domains within accessibility technology applications , it opens up new possibilitiesfor creating inclusive solutions cateringto diverse needsand improving overall qualityof lifeforallusers involved..