Public articles linked to the same research event.
Intelligent Computing 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
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