Placing objects in context via inpainting for out-of-distribution segmentation
When deploying a semantic segmentation model into the real world, it will inevitably be confronted with semantic classes unseen during training. Thus, to safely deploy such systems, it is crucial to accurately evaluate and improve their anomaly segmentation capabilities. However, acquiring and label...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2024
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