Negative Space Is a Label
A car mask can pass review and still teach the model to keep the wrong pixels. The outline looks clean. The bumper is inside. The wheels are inside. Then the trained network holds onto the dark patch under the tires, because the label treated that patch as part of the vehicle's visual neighborhood. Training stays quiet. Production gets loud the first time a listing photo drags a strip of the old lot onto a new backdrop. AutoLensAI turns dealer photography into listing-ready vehicle media. This installment follows the earlier pieces on segmentation and image provenance, then narrows to one question: how do I teach a matting model that the shadow touching a tire is evidence against foreground rather than a faint version of it? 1. The failure arrives without an error message Vehicle matting estimates which pixels belong to the vehicle, at finer boundary resolution than segmentation gives. Tires, rocker panels, glossy showroom floors, and the halo under a lowered front lip are where a pretty binary mask does its damage. Two cases cause most of it. A cast shadow can touch rubber and still sit outside the object. A reflection can match paint color exactly and still belong to the floor. Both look like they belong to the car in a thumbnail. Neither belongs to it in geometry. A binary target has no vocabulary for that distinction. Every pixel is in or out, so the annotator's only lever is where to put the line. Push the line outward and shadow becomes vehicle. Pull it inward and the wheel arch loses its edge. Neither answer says the thing that matters, which is that some exterior pixels are ordinary background and some are adversarial background sitting one pixel from the object. The model learns the difference anyway. It learns it wrong, because nothing in the supervision ever separated the two. 2. Three states, not two The supervision contract uses three: state meaning training treatment vehicle body, glass, wheels, trim, and visible geometry foreground loss hard negative