# Interactive Segmentation

MEOW converts non-interactive 3D segmentation backbones into open-world interactive segmentation systems without updating any learnable parameters. Interactive segmentation had been confined to the open-set setting, able to predict or reject unknown structures but not to accumulate those predictions. MEOW pairs a frozen feature extractor with parameter-free transductive reasoning and memory consolidation, storing user-confirmed pseudo-labels in memory for later queries to retrieve; on classes never seen in training it keeps improving its own predictions as memory grows, and it transfers zero-shot to micro-CT and other imaging modalities.

MEOW converts non-interactive 3D segmentation backbones into open-world interactive segmentation systems without updating any learnable parameters. Interactive segmentation had been confined to the open-set setting, able to predict or reject unknown structures but not to accumulate those predictions. MEOW pairs a frozen feature extractor with parameter-free transductive reasoning and memory consolidation, storing user-confirmed pseudo-labels in memory for later queries to retrieve; on classes never seen in training it keeps improving its own predictions as memory grows, and it transfers zero-shot to micro-CT and other imaging modalities.

The interactive segmentation model SEEM performs poorly on OCTA retinal vessels out of the box, reaching only 75.83% Dice on large vessels with a frozen encoder, and every candidate mask it can produce is read from the same quarter-resolution feature maps, too coarse for vessels one to three pixels wide. The Read-out Basis Adapter (RBA) adds 32 half-resolution basis maps at the read-out, which each candidate accesses through a zero-initialised projection, for 0.47M trainable parameters. On OCTA-500 3 mm large vessels, averaged over three seeds, it reaches 91.93% Dice and 85.16% IoU, 1.72 and 2.90 points above Conv-LoRA, with gains concentrated on thin vessels and edges and no significant difference on the FAZ.

The interactive segmentation model SEEM performs poorly on OCTA retinal vessels out of the box, reaching only 75.83% Dice on large vessels with a frozen encoder, and every candidate mask it can produce is read from the same quarter-resolution feature maps, too coarse for vessels one to three pixels wide. The Read-out Basis Adapter (RBA) adds 32 half-resolution basis maps at the read-out, which each candidate accesses through a zero-initialised projection, for 0.47M trainable parameters. On OCTA-500 3 mm large vessels, averaged over three seeds, it reaches 91.93% Dice and 85.16% IoU, 1.72 and 2.90 points above Conv-LoRA, with gains concentrated on thin vessels and edges and no significant difference on the FAZ.

This survey systematically reviews prompt engineering research for SAM and its growing ecosystem, proposing a hierarchical taxonomy that organizes methods into geometric prompts, textual semantic prompts, and multimodal fusion prompts, further tracing the transition from manually crafted prompts to automated generation based on detector outputs, prototype learning, reinforcement learning, and vision-language models, while tracing how prompt engineering enables cross-domain generalization in medical imaging, remote sensing, industrial inspection, and anomaly detection, and identifying key challenges such as prompt sensitivity, cross-modal misalignment, and computational inefficiency alongside future directions including causal prompt reasoning, collaborative multi-agent prompting, and diffu

This survey systematically reviews prompt engineering research for SAM and its growing ecosystem, proposing a hierarchical taxonomy that organizes methods into geometric prompts, textual semantic prompts, and multimodal fusion prompts, further tracing the transition from manually crafted prompts to automated generation based on detector outputs, prototype learning, reinforcement learning, and vision-language models, while tracing how prompt engineering enables cross-domain generalization in medical imaging, remote sensing, industrial inspection, and anomaly detection, and identifying key challenges such as prompt sensitivity, cross-modal misalignment, and computational inefficiency alongside future directions including causal prompt reasoning, collaborative multi-agent prompting, and diffu