Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework | Paper Citation Record

Observation 3d0dec2d-c973-40a3-9354-8cb51fab9d35 · outbound

This paper cites Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs

Reference 1

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Observation 54d3bbd1-46c2-4633-9f50-467fa9a082a9 · outbound

This paper cites ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection

Reference 2

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Observation ab48603a-ba87-4182-a4c5-8e71b5985325 · outbound

This paper cites Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation.Medical Image Analysis, 98:103310, 2024.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation.Medical Image Analysis, 98:103310, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:20.307538Z

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Observation 2cd06578-4528-4204-a87f-cdddd591273b · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:06.442490Z

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Observation c09a9219-7676-49c5-8fdc-28dcc9812ce6 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology.Nature Medicine, 30(3):850–862, 2024.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Towards a general-purpose foundation model for computational pathology.Nature Medicine, 30(3):850–862, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:20.059345Z

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Observation cf65123f-2066-4cbe-b91e-8a6cd3d24df1 · outbound

This paper cites Adapt- former: Adapting vision transformers for scalable visual recognition.Advances in Neural Information Processing Systems, 35:16664–16678, 2022.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Adapt- former: Adapting vision transformers for scalable visual recognition.Advances in Neural Information Processing Systems, 35:16664–16678, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:19.870660Z

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Observation 19b0f65d-12e3-4313-95bf-e6a6b9afbea4 · outbound

This paper cites SAM-Med2D.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework SAM-Med2D

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:06.764456Z

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Observation 5079cfcf-fc8b-4503-954a-b7e4843c51e3 · outbound

This paper cites Unleashing the potential of sam for medical adaptation via hierarchical decoding.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Unleashing the potential of sam for medical adaptation via hierarchical decoding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:19.667177Z

Source-reported events for the cited work

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Observation 8df79c19-3150-4f26-bb63-96758fca0a4d · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:07.034547Z

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Observation 1e65668e-9e85-4f15-9fbf-824caa61311c · outbound

This paper cites OpenPrompt: An Open-source Framework for Prompt-learning.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework OpenPrompt: An Open-source Framework for Prompt-learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:07.131166Z

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Observation 011c1161-1626-4b46-84d1-60cad01301a2 · outbound

This paper cites Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:07.243405Z

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Observation 4ef627cc-0383-4701-a5e9-62a1ae216d1c · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Pranet: Parallel reverse attention network for polyp segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:19.442126Z

Source-reported events for the cited work

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Observation 12a28125-2be4-4f88-8f50-e7e8866c5b6c · outbound

This paper cites Making Pre-trained Language Models Better Few-shot Learners.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Making Pre-trained Language Models Better Few-shot Learners

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:07.442968Z

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Observation f51d25cb-4dd0-4e07-b207-decc34a97d96 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:07.554949Z

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Observation 0f4d75d4-6a47-421b-b86a-61d64cc57739 · outbound

This paper cites H2former: An efficient hierarchical hybrid transformer for medical image segmentation.IEEE Transactions on Medical Imaging, 42(9):2763–2775, 2023.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework H2former: An efficient hierarchical hybrid transformer for medical image segmentation.IEEE Transactions on Medical Imaging, 42(9):2763–2775, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:19.195006Z

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Observation d52f1dc5-7646-4d28-87cf-91d62dc38481 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:07.745393Z

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Observation b12dd4b7-5b69-4291-bde7-052a2347d9d4 · outbound

This paper cites Att-unet: pixel-wise staircase attention for weed and crop detection.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Att-unet: pixel-wise staircase attention for weed and crop detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:18.928212Z

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 cc0ffedf-12c2-4f51-8f27-b820e92193a9 · outbound

This paper cites Segment anything model for medical images?Medical Image Analysis, 92:103061, 2024.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Segment anything model for medical images?Medical Image Analysis, 92:103061, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:08.020594Z

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Observation d524ce18-14e7-482b-b379-e4bd83a20716 · outbound

This paper cites Jaeger, Simon A.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Jaeger, Simon A

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:18.707218Z

Source-reported events for the cited work

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Observation f9bd7a98-7132-479b-99c8-a15cbb53896c · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Kvasir-seg: A segmented polyp dataset

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:18.474190Z

Source-reported events for the cited work

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Observation bb18eb40-c7da-4c6f-a5dd-ddb51ab5effb · outbound

This paper cites Zept: Zero-shot pan-tumor segmentation via query-disentangling and self-prompting.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Zept: Zero-shot pan-tumor segmentation via query-disentangling and self-prompting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:18.211360Z

Source-reported events for the cited work

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Observation 078453af-74a1-40ba-aa88-f4fe8f683571 · outbound

This paper cites How can we know what language models know?Transactions of the Association for Computational Linguistics, 8:423–438, 2020.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework How can we know what language models know?Transactions of the Association for Computational Linguistics, 8:423–438, 2020

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:08.451414Z

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Observation 83ff06d2-b8e8-4e22-a4c7-5d7187475f22 · outbound

This paper cites Maple: Multi-modal prompt learning.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Maple: Multi-modal prompt learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:08.540825Z

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Observation 8b7af166-49e1-41ff-9f6d-48eaefbd4520 · outbound

This paper cites Uacanet: Uncertainty augmented context attention for polyp segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Uacanet: Uncertainty augmented context attention for polyp segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:17.981857Z

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 d603a40b-8fef-45ce-8185-3820aae57e50 · outbound

This paper cites Segment anything.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Segment anything

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:08.749259Z

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Observation eb31cb7e-5482-4b95-9456-bd2748fa808e · outbound

This paper cites Prompt learning in computer vision: a survey.Frontiers of Information Technology & Electronic Engineering, 25(1):42–63, 2024.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Prompt learning in computer vision: a survey.Frontiers of Information Technology & Electronic Engineering, 25(1):42–63, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:17.770513Z

Source-reported events for the cited work

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Observation 52cd23ad-ee45-4102-b208-84d49bdafa92 · outbound

This paper cites Segment anything in medical images.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Segment anything in medical images

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:08.995610Z

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Observation 8eacfd90-4204-4163-8ba0-a5716a683027 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:17.577910Z

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 b6271624-dbbf-4870-9aa2-58c1837a2cff · outbound

This paper cites Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi-scale attention.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi-scale attention

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:17.298076Z

Source-reported events for the cited work

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Observation e6c575d5-0c31-4d8a-bf0d-15954945c91f · outbound

This paper cites Hammerla, Bernhard Kainz, Ben Glocker, and Daniel Rueckert.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Hammerla, Bernhard Kainz, Ben Glocker, and Daniel Rueckert

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:16.990673Z

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 02f905a6-8a2a-4d63-9232-f4f281fc3313 · outbound

This paper cites Causality- inspired single-source domain generalization for medical image segmentation.IEEE Transactions on Medical Imaging, 42(4):1095–1106, 2022.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Causality- inspired single-source domain generalization for medical image segmentation.IEEE Transactions on Medical Imaging, 42(4):1095–1106, 2022

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:16.766501Z

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 a774b45a-ee66-4744-a14b-f57ac9d48bfa · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework U-net: Convolutional networks for biomedical image segmentation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:09.560533Z

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Observation 54d06e6c-9e71-44bd-8e36-2910591c9257 · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:09.649773Z

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Observation 7fa5d778-6600-418b-8edb-191b6ef7f0c7 · outbound

This paper cites Malunet: A multi-attention and light-weight unet for skin lesion segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Malunet: A multi-attention and light-weight unet for skin lesion segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:16.513003Z

Source-reported events for the cited work

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Observation d7bd2d2e-d4d6-4bd1-95e4-f9f7b808806c · outbound

This paper cites Ege-unet: an efficient group enhanced unet for skin lesion segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Ege-unet: an efficient group enhanced unet for skin lesion segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:16.267308Z

Source-reported events for the cited work

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Observation 3cb8a5c5-3f83-47c3-8d51-b9362fc3757e · outbound

This paper cites Attention gated networks: Learning to leverage salient regions in medical images.Medical image analysis, 53:197–207, 2019.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Attention gated networks: Learning to leverage salient regions in medical images.Medical image analysis, 53:197–207, 2019

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:16.042917Z

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 d06839a7-4389-4e98-adfa-53a9312ecf24 · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer.International journal of computer assisted radiology and surgery, 9:283–293, 2014.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer.International journal of computer assisted radiology and surgery, 9:283–293, 2014

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:15.799969Z

Source-reported events for the cited work

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Observation eeaf33d0-c7d0-455f-aa1b-c9c65561e986 · outbound

This paper cites SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration

Reference 38

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Observation e41fa311-b2db-46df-91ba-f0a29e63a776 · outbound

This paper cites Automated polyp detection in colonoscopy videos using shape and context information.IEEE transactions on medical imaging, 35(2):630–644, 2015.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Automated polyp detection in colonoscopy videos using shape and context information.IEEE transactions on medical imaging, 35(2):630–644, 2015

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:15.584273Z

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Observation 2484870b-88dc-464f-a837-2658eb05f3db · outbound

This paper cites A benchmark for endoluminal scene segmentation of colonoscopy images.Journal of healthcare engineering, 2017(1):4037190, 2017.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework A benchmark for endoluminal scene segmentation of colonoscopy images.Journal of healthcare engineering, 2017(1):4037190, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:15.308644Z

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

Observation 24889d44-d7a0-49a7-9e11-4468c41a56ba · outbound

This paper cites RepViT-SAM: Towards Real-Time Segmenting Anything.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework RepViT-SAM: Towards Real-Time Segmenting Anything

Reference 41

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Observation 2442b3ff-7284-45ae-a214-01ec91700f6c · outbound

This paper cites Vilt-clip: Video and language tuning clip with multimodal prompt learning and scenario-guided optimization.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Vilt-clip: Video and language tuning clip with multimodal prompt learning and scenario-guided optimization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:15.094484Z

Source-reported events for the cited work

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Observation b887a8d4-8402-44e3-8c14-1605e52a62ec · outbound

This paper cites Mcpl: Multi-modal collaborative prompt learning for medical vision-language model.IEEE Transactions on Medical Imaging, 2024.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Mcpl: Multi-modal collaborative prompt learning for medical vision-language model.IEEE Transactions on Medical Imaging, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:14.887050Z

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

Observation 535e4b31-10ab-405b-ae62-9e97dd096593 · outbound

This paper cites Medical image segmentation using deep learning: A survey.IET image processing, 16(5):1243–1267, 2022.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Medical image segmentation using deep learning: A survey.IET image processing, 16(5):1243–1267, 2022

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:10.665303Z

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Observation 5581926c-499a-4931-816d-dde23b4b8214 · outbound

This paper cites Learning to prompt for continual learning.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Learning to prompt for continual learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:14.639557Z

Source-reported events for the cited work

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Observation 70c2ddbc-5bce-49a1-8c5f-a15c7a8b4bda · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation.Medical image analysis, 102:103547, 2025.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Medical sam adapter: Adapting segment anything model for medical image segmentation.Medical image analysis, 102:103547, 2025

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:10.869421Z

Source-reported events for the cited work

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Observation 82245986-a000-4827-9b4d-bc00e54cfd20 · outbound

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

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:10.979366Z

Source-reported events for the cited work

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Observation 1de72b2c-2646-4358-9b40-118eac5dc32d · outbound

This paper cites MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:11.051172Z

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Observation 8ba06dd7-8f72-42fe-87fb-12410b37014e · outbound

This paper cites Efficientsam: Leveraged masked image pretraining for efficient segment anything.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Efficientsam: Leveraged masked image pretraining for efficient segment anything

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:11.133326Z

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Observation 346b56a2-c4a1-4b56-b126-2f40b1987f2a · outbound

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

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:11.221739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32935f2d-4604-491c-a64b-34461a69db5a · outbound

This paper cites Prompt learns prompt: Exploring knowledge-aware generative prompt collaboration for video captioning.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Prompt learns prompt: Exploring knowledge-aware generative prompt collaboration for video captioning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:14.426567Z

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 33c4976a-2f81-4557-942d-cb77d13c8f6e · outbound

This paper cites Text prompt with normality guidance for weakly supervised video anomaly detection.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Text prompt with normality guidance for weakly supervised video anomaly detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:14.151074Z

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 0dfe00e4-54cd-4e6c-88d7-d28294a57d14 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:11.467988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24fb30fe-f4e7-4ecb-ab0d-dea6bec97dc5 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Customized Segment Anything Model for Medical Image Segmentation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:11.534015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b61233e3-f892-4628-8bbb-915d890575fd · outbound

This paper cites Transfuse: Fusing transformers and cnns for medical image segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Transfuse: Fusing transformers and cnns for medical image segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:13.899660Z

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 ee038438-8ae7-4316-b987-aaa63ec23b2d · outbound

This paper cites Efficientvit-sam: Accelerated segment anything model without performance loss.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Efficientvit-sam: Accelerated segment anything model without performance loss

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:13.604676Z

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 170d0144-9273-4e2a-93ba-a565fedf0dcc · outbound

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

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:11.788079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7496cfdd-3fe7-4870-8a5a-68ebb96612d8 · outbound

This paper cites Sam-sp: Self-prompting makes sam great again, 2024.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Sam-sp: Self-prompting makes sam great again, 2024

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:13.351235Z

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 dafcf883-c868-40e7-8a08-b6ea179c5807 · outbound

This paper cites SAM-SP: Self-Prompting Makes SAM Great Again.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework SAM-SP: Self-Prompting Makes SAM Great Again

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:12.009357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe0da5fe-77bd-498a-9e5f-9de03640f4ac · outbound

This paper cites Conditional prompt learning for vision- language models.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Conditional prompt learning for vision- language models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:12.092250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afac61bf-f5b9-41e3-b625-dce2fe68f0a7 · outbound

This paper cites Conditional prompt learning for vision-language models.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Conditional prompt learning for vision-language models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:12.236781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f6cdc55-098a-49a8-98c0-b814ecb564ed · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Unet++: A nested u-net architecture for medical image segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:13.138332Z

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 e0e00c0c-54be-4989-9440-b6a22277a348 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation.

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework Unet++: A nested u-net architecture for medical image segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:19:12.916127Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers