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arXiv · 2603.27250

IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment

Huiyao Zhang·Jin Bai·Rui Guo·JianWen Tan·HongFei Wang·Ye Li
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Abstract

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 into complementary intrinsic prompts that serve as coarse regional anchors. These cues are projected through SAM2's frozen prompt encoder, restoring prompt-guided decoding without external intervention. To suppress background-induced false positives, Prompt-Space Gating (PSG) leverages the intrinsic background prompt as an asymmetric suppressive constraint prior to decoding. Under a deterministic no-external-prompt protocol, IP-SAM achieves state-of-the-art performance across four camouflaged object detection benchmarks with only 21.26M trainable parameters. Furthermore, the proposed conditioning strategy generalizes beyond COD to medical polyp segmentation.

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Huiyao Zhang, Jin Bai, Rui Guo, JianWen Tan, HongFei Wang, Ye Li. 2026-03-28. IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment. https://arxiv.org/abs/2603.27250

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