u/dimfot333

I built a small computer vision project based on INSID3, the CVPR 2026 training-free in-context segmentation method using DINOv3.
My version flips the idea: instead of providing a foreground reference, you provide background or normal examples. The algorithm removes background-like regions and segments the remaining object/anomaly.
It supports multiple background sources and can also turn coarse boxes into more precise masks. Other applications are possible like zero-shot anomaly detection.

Would love feedback or test cases: https://github.com/dimfot3/Inverse-INSID3