Agentic computer vision // not your grandma CV
What’s difference between Agentic Object Detection and Large Vision Models object detection?
A cool concept is proposed, but at very early stages, a very long answer — not applicable to many CV real-time apps.
Agentic Object Detection revolutionizes traditional object detection in computer vision by eliminating the need for time-consuming data labeling and model training. Instead of drawing bounding boxes and training neural networks, users simply write a prompt (e.g., “unripe strawberries”) and a Visual AI agent reasons about the task to deliver accurate results. This approach requires zero labeled training data and leverages agentic systems — AI systems that use reflection, tool use, planning, and multi-agent collaboration to produce high-quality outputs.
Unlike traditional methods or large multimodal models (LLMs) that quickly “glance” at images, Agentic Object Detection takes 20–30 seconds to reason deeply about an image, similar to how advanced text models like OpenAI’s o1 and DeepSeek-R1 operate. This results in significantly better performance, as demonstrated in internal benchmarks.
While the current processing time is a limitation, the team is actively working to improve speed. Developers and users can explore the demo and API to build innovative applications. This new workflow simplifies visual AI tasks, allowing users to “just say what you want and get a result,” making it faster and more accessible than ever before.
VS Vision Models: Qwen2.5-VL, DeepSeek-VL2, Janus-Pro
