Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges | Paper Citation Record

Observation 15ad3b6f-bcf1-40fd-ba1a-3cfe858c8377 · outbound

This paper cites BioSAM: Generating SAM Prompts From Superpixel Graph for Biological In- stance Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges BioSAM: Generating SAM Prompts From Superpixel Graph for Biological In- stance Segmentation

Reference 1

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Observation 436c2634-dac8-4417-a980-aaf8aac02935 · outbound

This paper cites Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Personalizing Vision-Language Models With Hybrid Prompts for Zero-Shot Anomaly Detection

Reference 2

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Observation 21cc5df1-8f53-48ed-b5b3-228a34ffca1a · outbound

This paper cites RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

Reference 3

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Observation d37862e8-26e5-4aac-b56c-7de7f0e5a73e · outbound

This paper cites SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.140111Z

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Observation 14690656-2672-4117-ae62-d01f27879b63 · outbound

This paper cites Segmentation by registration-enabled SAM prompt engineering using five reference images.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Segmentation by registration-enabled SAM prompt engineering using five reference images

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.550396Z

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Observation 7ea8613b-864a-461f-86bc-a2e50a123db4 · outbound

This paper cites All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.112400Z

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Observation 4595d20f-a178-4441-b39f-9a356ffca704 · outbound

This paper cites SAMAug: Point Prompt Augmentation for Segment Anything Model.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAMAug: Point Prompt Augmentation for Segment Anything Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.562790Z

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Observation a731a88c-e06f-42f2-b7fa-5bdd12cb1df7 · outbound

This paper cites Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.087584Z

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Observation 84889892-16e0-4231-a0d6-2c02d2d28ebe · outbound

This paper cites SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.073852Z

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Observation 6089519b-2b26-4acb-a0c0-fea62ce03576 · outbound

This paper cites K-SAM: A Prompting Method Using Pretrained U-Net to Improve Zero Shot Performance of SAM on Lung Segmentation in CXR Images.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges K-SAM: A Prompting Method Using Pretrained U-Net to Improve Zero Shot Performance of SAM on Lung Segmentation in CXR Images

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.059830Z

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Observation 15997316-3d74-47b0-b62e-c9247c205b2d · outbound

This paper cites Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.045944Z

Source-reported events for the cited work

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Observation d9b587d8-d065-4da3-8b59-306c29690b89 · outbound

This paper cites Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.031591Z

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Observation 12a1a84e-2992-47d4-8179-b14f251ea585 · outbound

This paper cites Lite Class-Prompt Tiny-VIT for Multi-modality Medical Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Lite Class-Prompt Tiny-VIT for Multi-modality Medical Image Segmentation

Reference 13

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Observation e4d0af7c-3cd0-422a-b0ca-d208cf8c9bec · outbound

This paper cites APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.588317Z

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Observation 567284f3-253c-420a-9727-cb72e63bd824 · outbound

This paper cites Relax Image-Specific Prompt Require- ment in SAM: A Single Generic Prompt for Segment- ing Camouflaged Objects.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Relax Image-Specific Prompt Require- ment in SAM: A Single Generic Prompt for Segment- ing Camouflaged Objects

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.594919Z

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Observation 4108d9f1-12bb-41ec-bcd1-7a9a68f854fa · outbound

This paper cites APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.015659Z

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Observation a04d7fa8-bb61-4ed6-8f56-1259d326a78a · outbound

This paper cites Diffusion-empowered AutoPrompt MedSAM.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Diffusion-empowered AutoPrompt MedSAM

Reference 17

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Observation fcaae867-d249-48a2-99f8-088839477017 · outbound

This paper cites Learning to Prompt Segment Anything Models.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Learning to Prompt Segment Anything Models

Reference 18

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Observation c2903221-8b7a-405f-abc0-1c8b44a12666 · outbound

This paper cites Robust Box Prompt based SAM for Medical Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Robust Box Prompt based SAM for Medical Image Segmentation

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.982942Z

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Observation cd32f2aa-e2a2-4c26-acec-61f41f27ba2e · outbound

This paper cites Optimizing Efficiency and Effec- tiveness in Sequential Prompt Strategy for SAM us- ing Reinforcement Learning.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Optimizing Efficiency and Effec- tiveness in Sequential Prompt Strategy for SAM us- ing Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.606107Z

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Observation 289f5e13-0aa5-4ce2-9648-8522fff6928f · outbound

This paper cites SAM2 for Image and Video Segmentation: A Comprehensive Survey.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAM2 for Image and Video Segmentation: A Comprehensive Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.617338Z

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Observation b112e6ad-4eaf-4e5b-86ad-89e480845df2 · outbound

This paper cites TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.967828Z

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Observation ade55baa-af27-45b6-85dd-1fcaa6b0f58d · outbound

This paper cites Segment Anything.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Segment Anything

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.625174Z

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Observation 3735bddb-bff8-4e63-b979-5b2c9c7c4868 · outbound

This paper cites Ul- tralytics YOLOv8.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Ul- tralytics YOLOv8

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.621306Z

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Observation 88b9d180-4932-4cf0-aa7f-acb343543d6e · outbound

This paper cites Grounded Language-Image Pre-training.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Grounded Language-Image Pre-training

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.635888Z

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Observation 32749c5c-320b-4e7d-af05-98f4bbffe3cb · outbound

This paper cites AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.628800Z

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Observation 5ea382ed-e060-4ca9-8108-5dab0216cfab · outbound

This paper cites A Closer Look at the Explainability of Contrastive Language-Image Pre-training.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.642271Z

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Observation a03b3165-40be-40ee-97c9-7a4762714936 · outbound

This paper cites AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-06T17:56:04.853660Z

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Observation 6615a959-794e-4b0b-828e-02ede25f15c8 · outbound

This paper cites ClipSAM: CLIP and SAM collabo- ration for zero-shot anomaly segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges ClipSAM: CLIP and SAM collabo- ration for zero-shot anomaly segmentation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.639428Z

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Observation 2acd78b7-37ca-432a-bf77-e750333dcadb · outbound

This paper cites SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.652348Z

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Observation 45ec1059-57d9-439a-98e3-ec592b5863db · outbound

This paper cites Training- Free Open-Ended Object Detection and Segmentation via Attention as Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Training- Free Open-Ended Object Detection and Segmentation via Attention as Prompts

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.655364Z

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Observation 5218031c-c5ee-477a-a71d-f6dc6c057fe1 · outbound

This paper cites SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.777374Z

Source-reported events for the cited work

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Observation e8731712-80b3-4052-b17e-4d8817514c62 · outbound

This paper cites Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.736805Z

Source-reported events for the cited work

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Observation b47fcfa1-a7ef-49ad-affa-122558501eb0 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.667197Z

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Observation 4370c5d7-1f32-4048-b13a-aa88c07f8ed0 · outbound

This paper cites Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.751899Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 82db0b4e-17c5-4ec3-98b3-fde59be56eab · outbound

This paper cites Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Feature-prompting GBMSeg: One-Shot Reference Guided Training-Free Prompt Engineering for Glomerular Basement Membrane Segmentation

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.697030Z

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Observation a39dcf39-ed16-42bb-b23f-78ec5dc83d2d · outbound

This paper cites SAM-RSIS: Progressively Adapt- ing SAM With Box Prompting to Remote Sensing Im- age Instance Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAM-RSIS: Progressively Adapt- ing SAM With Box Prompting to Remote Sensing Im- age Instance Segmentation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.677294Z

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Observation 23782cd2-f165-4de0-8f7a-b804dcce4a1f · outbound

This paper cites Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.711231Z

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Observation 604e03be-f9b1-4d51-a5ba-8b31db9a6103 · outbound

This paper cites CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.523720Z

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Observation 0756f776-404d-4cdb-9da9-1c80605c9b28 · outbound

This paper cites Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.507057Z

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Observation f89d6282-715d-4bff-bfb1-0c0fbb06fc71 · outbound

This paper cites an unresolved cited work.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.680295Z

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Observation fddd6059-67d3-470d-b596-797a21b563b1 · outbound

This paper cites GroupPrompter: A Prompting Method for Semantic Segmentation Based on SAM.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges GroupPrompter: A Prompting Method for Semantic Segmentation Based on SAM

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.683298Z

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Observation fdeabbb9-def9-4d37-9bfd-f5a0d34aaee8 · outbound

This paper cites Hypercorrelation Squeeze for Few-Shot Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Hypercorrelation Squeeze for Few-Shot Segmentation

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.476087Z

Source-reported events for the cited work

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Observation 4b9185cd-8d8e-427e-ae0f-cf21d77d3380 · outbound

This paper cites CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.704048Z

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Observation 91aa0fcc-e747-47cc-8e27-ac5d7377d437 · outbound

This paper cites Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.491236Z

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Observation aa9027e9-a4da-4d77-aceb-b497cbf9ed5e · outbound

This paper cites Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medi- cal Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medi- cal Image Segmentation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.697265Z

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Observation eb015687-a4e5-4da5-a72c-dfb8a592e292 · outbound

This paper cites Benchmarking Human and Au- tomated Prompting in the Segment Anything Model.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Benchmarking Human and Au- tomated Prompting in the Segment Anything Model

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.714928Z

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Observation 14034ff9-b270-41bd-81ab-ddab08b7fac0 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Learning Transferable Visual Models From Natural Language Supervision

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.723010Z

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Observation 2288b4cb-de84-4c05-a836-9333a146de69 · outbound

This paper cites Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.707859Z

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Observation f5c9f04c-3cca-460c-9352-ba3d276a36bc · outbound

This paper cites SAMIC: Segment Anything with In-Context Spatial Prompt Engineering.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAMIC: Segment Anything with In-Context Spatial Prompt Engineering

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.443171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75fd4680-1a32-44e2-b7a6-5da3903e592a · outbound

This paper cites Temporally-Extended Prompts Optimization for SAM in Interactive Medical Im- age Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Temporally-Extended Prompts Optimization for SAM in Interactive Medical Im- age Segmentation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.732813Z

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Unavailable: canonical work link unavailable.

Observation 24d0ad99-ef17-4717-9b2a-3cb5c8d330ba · outbound

This paper cites Benchmarking Human and Automated Prompting in the Segment Anything Model.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Benchmarking Human and Automated Prompting in the Segment Anything Model

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.426913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03eefa40-662b-46e1-8b6a-c7671f9d633e · outbound

This paper cites EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.739877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b84c285-32b4-4572-af13-8b110c880b48 · outbound

This paper cites PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.400579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95cf200c-2baa-42b4-a440-6ceecb325b54 · outbound

This paper cites Vision and Language Reference Prompt into SAM for Few-shot Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Vision and Language Reference Prompt into SAM for Few-shot Segmentation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.729432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a6808a4-3766-4812-9a1e-7714c19f2d50 · outbound

This paper cites Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.750299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64963b31-13f7-469d-8933-d4f691d65deb · outbound

This paper cites Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.321932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fcc94408-edc4-4101-ae27-11c4c45e3098 · outbound

This paper cites CogVLM: Visual Expert for Pre- trained Language Models.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges CogVLM: Visual Expert for Pre- trained Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.760452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42646093-a1c2-4d38-8545-8b951bcc61ef · outbound

This paper cites Deep High-Resolution Representation Learning for Human Pose Estimation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Deep High-Resolution Representation Learning for Human Pose Estimation

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.743278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5d8743d-c446-4aa2-95c8-d8c46d9a0dae · outbound

This paper cites On Efficient Variants of Segment Anything Model: A Survey.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges On Efficient Variants of Segment Anything Model: A Survey

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.746472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8bc9b41-a176-45d1-a94b-bf0ef87e5a4a · outbound

This paper cites TinyViT: Fast Pretraining Distillation for Small Vision Transformers.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges TinyViT: Fast Pretraining Distillation for Small Vision Transformers

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.770740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c943913a-4cec-4eda-a1cc-6b441129a33e · outbound

This paper cites Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.287615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fdebbcd3-9203-48e1-a8e5-7d035ef15139 · outbound

This paper cites Auto-Prompting SAM for Weakly Supervised Landslide Extraction.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Auto-Prompting SAM for Weakly Supervised Landslide Extraction

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.273086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bdc40d2b-fd8d-4adc-8a55-02889471cecb · outbound

This paper cites Self-Prompt SAM: Medical Image Seg- mentation via Automatic Prompt SAM Adaptation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Self-Prompt SAM: Medical Image Seg- mentation via Automatic Prompt SAM Adaptation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:56:05.331248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78dd3b14-746a-4886-92de-99dcaa8b78ec · outbound

This paper cites CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.259814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72d8c4a3-2589-4c46-b721-73249c6ecfcf · outbound

This paper cites Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.244527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99146d75-9310-4b65-b881-44323d1dc1e5 · outbound

This paper cites De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.792775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3d198ab-0394-424d-866f-fd67eaa15ef9 · outbound

This paper cites Self- Prompting Large Vision Models for Few-Shot Med- ical Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Self- Prompting Large Vision Models for Few-Shot Med- ical Image Segmentation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.773821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59e54dff-fab0-49c7-831b-a87772afc910 · outbound

This paper cites Integrating multi-scale informa- tion and diverse prompts in large model SAM-Med2D for accurate left ventricular ejection fraction estima- tion.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Integrating multi-scale informa- tion and diverse prompts in large model SAM-Med2D for accurate left ventricular ejection fraction estima- tion

Reference 69

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T17:56:05.346248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 463fb685-a52d-4916-896a-d73e58d8ee0f · outbound

This paper cites PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.803147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90c93ea0-ca7c-4711-96fb-7d221a108d7c · outbound

This paper cites TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.143341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff199018-7170-401f-945e-7776b9363f61 · outbound

This paper cites Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.207661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aac91243-ab07-4011-afe8-9673e62f9d9d · outbound

This paper cites SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.789703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24b2c8de-2640-4836-b7e8-210d704f6dc6 · outbound

This paper cites SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.111852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4fcfca8c-a9ab-4f51-88dc-dc4c55165568 · outbound

This paper cites SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.176326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c43331d7-32b4-49a9-b036-c96b36276fa0 · outbound

This paper cites ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.799771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11ecc90a-50e5-4d6a-9ddb-360ec1187ab4 · outbound

This paper cites COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.087049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0eed46f3-8f41-4750-bb05-bdc70fcb0d67 · outbound

This paper cites UV-SAM: Adapting Segment Anything Model for Urban Village Identification.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges UV-SAM: Adapting Segment Anything Model for Urban Village Identification

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.072001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd5da4fd-8bf4-4d08-b59e-0d8c6d0d5889 · outbound

This paper cites Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization

Reference 79

Resolution: verified exact; doi, observed 2026-08-06T17:56:03.894626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10715ede-db99-4703-8881-a371319c2727 · outbound

This paper cites SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.127348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d67ca4de-c1c1-4530-92be-91df47bfe18d · outbound

This paper cites Automatic Seg- mentation Annotation of Space Target Using Segment Anything Model and Object Detection Prompts.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Automatic Seg- mentation Annotation of Space Target Using Segment Anything Model and Object Detection Prompts

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:56:05.306908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6cbe4cec-e8fd-4ac2-b211-ee1154ca50d3 · outbound

This paper cites A Survey on Segment Any- thing Model (SAM): Vision Foundation Model Meets Prompt Engineering.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges A Survey on Segment Any- thing Model (SAM): Vision Foundation Model Meets Prompt Engineering

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:56:05.319197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c4ee73d-5102-4752-ba2e-58d8b1209621 · outbound

This paper cites A Comprehensive Survey on Segment Anything Model for Vision and Beyond.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.824585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 477c555b-863c-4d69-89ef-d81d3f2f58dd · outbound

This paper cites Curriculum Prompting Foundation Models for Medical Image Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Curriculum Prompting Foundation Models for Medical Image Segmentation

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:03.966270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b448b334-fcd0-4656-93e6-de55560d783f · outbound

This paper cites EdgeSAM: Prompt-In-the-Loop Distillation for SAM.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.855214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5212793-1c11-4e3b-ae40-15e19fe40275 · outbound

This paper cites Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.835409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f88cf439-37fe-4eda-9b9b-7d550bb4b553 · outbound

This paper cites Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.838493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e008236-00e6-4e64-8d6f-cc78ffb88613 · outbound

This paper cites Segment Everything Everywhere All at Once.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Segment Everything Everywhere All at Once

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.866876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d361070-cdfa-4a80-9a83-c437165454c3 · outbound

This paper cites Semantic-Enhanced Point-Box Joint Prompting for Video Object Segmentation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Semantic-Enhanced Point-Box Joint Prompting for Video Object Segmentation

Reference 89

Resolution: malformed identifier; no resolver link, observed 2026-08-06T17:56:03.845024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97323e76-0230-4f2c-9d8b-69ce71a072c3 · outbound

This paper cites Fast Segment Anything.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Fast Segment Anything

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.848050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 720937dc-a082-4fbe-bf8e-34b49107847b · outbound

This paper cites MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM

Reference 93

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:03.941307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 693d6196-8f77-4f8b-8a6c-a0382fd50ecd · outbound

This paper cites ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.863141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 585da220-c1a7-46dd-8828-5b48ff51dcc4 · outbound

This paper cites Segmentation by registration-enabled SAM prompt engineering using five reference images.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Segmentation by registration-enabled SAM prompt engineering using five reference images

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:05.127452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0358123a-c8d1-4b95-8da5-fcf19de2880d · outbound

This paper cites Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

Reference 2025

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:56:04.221739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c285a3da-ee4f-4e08-8881-3b16ecc97c33 · outbound

This paper cites an unresolved cited work.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Unresolved cited work

Reference 3580

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.632855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers