WHAT COMPUTER VISION STILL CAN'T DO IN 2025
Computer vision models are remarkably capable at pattern recognition in controlled distributions. They remain fundamentally brittle everywhere else.
The engineering challenge isn't building a model that works — it's building a system that knows when it doesn't work, and communicates that uncertainty in a way humans can act on. Domain shift, lighting variation, adversarial inputs: each one is an argument for better system design, not better architecture.
Foundation models have dramatically raised the floor for computer vision capabilities. You can fine-tune a CLIP variant on a few hundred examples and get impressive zero-shot generalization to new object categories. The benchmark numbers are genuinely remarkable. The production deployment stories are more sobering.
The Distribution Gap
Every computer vision system I've deployed has had a distribution gap problem. The model performs well on the data it was trained and evaluated on, then encounters real-world variation that the training set didn't capture. Seasonal lighting changes. Camera angle variation. Sensor degradation. Objects that look nothing like anything in the training set but should still be handled gracefully.
The models don't know they're out of distribution. They produce confident outputs regardless. This is the fundamental problem: a model that fails silently is worse than a model that fails loudly, because silent failures propagate through downstream systems before anyone notices.
Systems That Fail Gracefully
The solution isn't a better model — it's better system design. Uncertainty quantification tells you when the model is operating outside its competence. Ensemble methods surface disagreement between predictions. Human-in-the-loop escalation catches the cases that automated systems can't handle. None of this is glamorous. All of it is necessary.