Pengchuan Zhang, AI researcher at Meta Superintelligence / FAIR, discusses the release of SAM 3 and the long-term utility of the accompanying benchmark for tracking AI vision capabilities.
Insight
Zhang: Superhuman computer vision requires RLHF rather than human SFT data
“But if you only do SFT and the SFT data is annotated by human, then your performance is funded by human. You cannot get, kind of, superhuman performance just by, kind of, this kind of data engine approach to use human annotated data and then learn from that. Y…”
Prediction Not checkable as stated
Zhang: AI models will handle simple vision natively, using tools for complexity
“I think at least I want to bet on, you know, running their work natively together, the future for simple, I would say for simple or even intermediate difficult vision tasks. For example, kind of counting with less than 20 objects. I think for this kind of simp…”
Assertion Partly supported
Zhang: Fine-Tuned Llama 3.2 Achieved Superhuman Vision Verification Performance
“We kind of fine-tune our, kind of, for example, NAMA's 3.2 with our, kind of, verification, human annotated verification data. We get, kind of, superhuman performance on these two verification tasks, and then we do not need human on these two tasks. Let's furt…”
Assertion Supported
Zhang: SAM 3 achieves real-time tracking across objects via multi-GPU parallelism
“Even for video, if you can't afford the kind of GPUs, pretty many, very kind of, do the kind of parallel inference algorithm. So even you have a lot of object to track, you can still get real-time tracking performance as long as you scale up the GPUs there.”
Assertion Not checkable as stated
Zhang: Video AI models still exhibit a large gap versus human performance
“Video is still far from, I would say, have a big gap from human performance. Right now, there's kind of still, kind of, a lot of research needs to be done there, how to do end-to-end training with video.”
Disclosure
Zhang: Meta intentionally avoided OCR-heavy images during SAM 3 training data sampling
“In fact, during our data engine, we intentionally do not sample OCR-heavy images.”