PLaMo 2.1-VL Technical Report
📰 ArXiv cs.AI
Learn about PLaMo 2.1-VL, a lightweight Vision Language Model for autonomous devices, and its applications in Visual Question Answering and Visual Grounding
Action Steps
- Evaluate PLaMo 2.1-VL's performance on Visual Question Answering tasks using the provided 8B and 2B variants
- Apply PLaMo 2.1-VL to real-world scenarios such as factory task analysis via tool recognition
- Configure PLaMo 2.1-VL for edge deployment on autonomous devices with Japanese-language operation
- Test PLaMo 2.1-VL's Visual Grounding capabilities for infrastructure anomaly detection
- Compare the performance of PLaMo 2.1-VL with other VLMs on similar tasks
Who Needs to Know This
Computer vision engineers and researchers can benefit from this technical report to develop and deploy VLMs for edge devices, particularly for applications in factory task analysis and infrastructure anomaly detection
Key Insight
💡 PLaMo 2.1-VL is a compact and efficient VLM suitable for edge deployment, enabling applications such as factory task analysis and infrastructure anomaly detection
Share This
🚀 Introducing PLaMo 2.1-VL, a lightweight Vision Language Model for autonomous devices! 🤖
Key Takeaways
Learn about PLaMo 2.1-VL, a lightweight Vision Language Model for autonomous devices, and its applications in Visual Question Answering and Visual Grounding
Full Article
Title: PLaMo 2.1-VL Technical Report
Abstract:
arXiv:2604.19324v1 Announce Type: cross Abstract: We introduce PLaMo 2.1-VL, a lightweight Vision Language Model (VLM) for autonomous devices, available in 8B and 2B variants and designed for local and edge deployment with Japanese-language operation. Focusing on Visual Question Answering (VQA) and Visual Grounding as its core capabilities, we develop and evaluate the models for two real-world application scenarios: factory task analysis via tool recognition, and infrastructure anomaly detection
Abstract:
arXiv:2604.19324v1 Announce Type: cross Abstract: We introduce PLaMo 2.1-VL, a lightweight Vision Language Model (VLM) for autonomous devices, available in 8B and 2B variants and designed for local and edge deployment with Japanese-language operation. Focusing on Visual Question Answering (VQA) and Visual Grounding as its core capabilities, we develop and evaluate the models for two real-world application scenarios: factory task analysis via tool recognition, and infrastructure anomaly detection
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