Have you ever tried to detect a street sign in a low-resolution image? It’s a tough task, even for humans. But what if I told you that a reasoning system, combined with basic tools like zoom and crop, can outperform state-of-the-art object detectors on hard cases? It’s true! A recent experiment showed that by giving a reasoning system (o3) access to these tools, it can successfully detect the street sign, even when other models struggle. The best part? No training or fine-tuning was required – just a single prompt. This approach is not perfect, it’s slow and brittle, but it unlocks a new capability that wasn’t possible before. The potential for future research is vast, from improving tokenization to developing better decoders and tool use. This could be a paradigm shift in object detection. Want to try it out for yourself? Check out the demo at spatial-reasoning.com and explore the code on GitHub. What do you think? Is this the future of object detection?
