How do vision-language models (VLMs) perform on object detection tasks? In this video, Machine Learning Engineer Matvei Popov explores findings from research into the object detection capabilities of large, pre-trained VLMs like Gemini 2.5 Pro, Qwen 2.5 VL, GroundingDINO, and more.

What you’ll see:
00:00 Introduction: Do VLMs Struggle to Generalize on Object Detection Tasks?
03:28 Understanding Pre-Trained VLMs vs. Task-Specific Vision Models
04:54 Why Even Use VLMs for Object Detection?
09:48 Can We Leverage VLMs Pre-Training Data for Zero-Shot Detections?
12:18 Introducing RF100-VL: Object Detection Benchmark for VLMs
17:52 How to Evaluate Object Detection Capabilities in VLMs
21:46 Example: Comparing Evaluation Performance
25:34 Prompting Strategies for Object Detection Tests
30:10 Results! Comparing VLMs Object Detection Scores
37:43 Conclusion, Takeaways, and Looking Forward

💡 Learn more about RF100-VL: https://rf100-vl.org/

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