探讨视觉基元推理与真实世界标注数据的必要性
Absolutely mind-blowing work. 🤯
This reminds me of when we were exploring the concept of 'thinking with visual primitives'. We noticed the model excelled in synthetic scenarios, but real-world generalization for complex tasks still faced bottlenecks. Back then, I kept thinking: "If only someone could provide manual annotations for these real-world scenarios..."
Using point-form visual primitives for reasoning would be powerful for tasks like state tracking, spatial topology, and logical testing, but they may heavily rely on high-fidelity, real-world labeled data. I think it’s the same across other domains too. The journey toward AGI cannot skip the hard, grounded work of human-labelled data. Massive respect to this effort! 🫡
This reminds me of when we were exploring the concept of 'thinking with visual primitives'. We noticed the model excelled in synthetic scenarios, but real-world generalization for complex tasks still faced bottlenecks. Back then, I kept thinking: "If only someone could provide manual annotations for these real-world scenarios..."
Using point-form visual primitives for reasoning would be powerful for tasks like state tracking, spatial topology, and logical testing, but they may heavily rely on high-fidelity, real-world labeled data. I think it’s the same across other domains too. The journey toward AGI cannot skip the hard, grounded work of human-labelled data. Massive respect to this effort! 🫡
@skalskip92 You might not believe it, but I simply manually annotated over 2,000,000 human body parts with ultra-precise detail. Probably no one else could do that. https://t.co/wUYhHH7Td7