How does Multimodal AI change the way we approach Computer Vision

All answers to this question.

Traditional CV is far from dead, especially for "Edge AI" applications. While GPT-4o has incredible reasoning capabilities for images, it’s far too slow for a conveyor belt moving at high speeds. For industrial QC, you need millisecond inference times which only models like YOLOv10 or specialized ViTs (Vision Transformers) can provide locally on a Jetson Orin. However, VLMs are amazing for "zero-shot" classification of rare defects that you don't have enough training data for. We use a hybrid: YOLO handles the fast detection, and any "uncertain" cases are sent to a VLM for a more detailed analysis.

   Answered 2025-09-12 by Amelia Brooks

Are you seeing any issues with VLMs struggling to identify very small, pixel-level artifacts that a high-res specialized model would catch?

   Answered 2025-09-30 by Liam Taylor

• Liam, that’s exactly the problem. Most VLMs downsample images to a specific resolution (like 512x512) before processing. If you’re looking for a hairline crack on a circuit board, the VLM might miss it entirely because the detail gets blurred out during the tokenization phase. For high-precision work, we still rely on traditional tiling methods and high-resolution CNNs because they preserve the spatial features much better than current general-purpose multimodal models do.

     Commented 2025-10-05 by Noah Anderson

Multimodal models are brilliant for descriptive tasks, like "describe what's happening in this video," but they aren't ready for sub-millimeter measurement yet.

   Answered 2025-11-15 by Charlotte King

• Agreed, Charlotte. The reasoning is there, but the spatial accuracy is still lacking. It’s a "brain" without the "precise eyes" needed for manufacturing.

     Commented 2025-11-20 by Amelia Brooks