Qwen-VL VLMs for zero- and few-shot object detection

The article introduces Vision-Language Models as an evolution of traditional large language models (LLMs), enabling them to interpret and reason across both text and image data. Qwen3-VL models are designed for tasks where users provide an image alongside text instructions, such as “detect all objects in this image and return bounding boxes.” These models can perform well even in zero-shot settings without prior examples, outperforming some state-of-the-art systems in specific cases.

Alatalo explains the differences between zero-shot, one-shot, and few-shot methods, which influence how VLMs adapt to new tasks. Object detection is a fundamental computer vision problem, with industrial applications in manufacturing quality control, where verifying component placement is critical. The practical experimentation used three Qwen3-VL model variants: Instruct and Thinking models with different parameter sizes, tested in Jamk’s computing environment equipped with high-performance GPUs. The article provides technical insights into model deployment, parallel processing, output formats, and observed behaviors.

While smaller models worked well on simpler tasks like single-object detection, more complex scenarios highlighted the need for precise prompting and computation resources. The experiments revealed both the potential and current limitations of Qwen3-VL for industry use cases, including challenges in consistent output formatting and handling visually dense images.

The article concludes that open multimodal models like Qwen3-VL lower the barrier to exploring advanced AI capabilities in real-world settings, but they require significant computational resources and careful prompt engineering. For professionals working with AI-driven automation, manufacturing, and computer vision, these findings offer practical starting points for experimentation and integration. Estimated reading time: 8–10 minutes.