IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment
Prompt-conditioned foundation segmenters have emerged as a dominant paradigm for image segmentation, where explicit spatial prompts(e.g., points, boxes, masks) guide mask decoding. However, many real-world deployments require fully automatic segmentation, creating a structural mismatch: the decoder expects prompts that are unavailable at inference. Existing adaptations typically modify intermediate features, inadvertently bypassing the model's native prompt interface and weakening prompt-conditioned decoding. We propose IP-SAM, which revisits adaptation from a prompt-space perspective through prompt-space conditioning. Specifically, a Self-Prompt Generator (SPG) distills image context int...
| Authors | Huiyao Zhang, Jin Bai, Rui Guo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 March 2026 |
| Last updated at source | 11 July 2026 |
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