IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain | Paper Citation Record

Observation 4a47c1c0-a60a-4458-913c-c2890384aa14 · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:32.559516Z

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Observation f9ec990a-61af-44f5-b454-fdc94711534f · outbound

This paper cites Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:02.001663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bca10505-fddd-4c8e-b8f0-b9257883b6e3 · outbound

This paper cites BMAD: Benchmarks for medical anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain BMAD: Benchmarks for medical anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:01.835802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b3d2aec-79e7-45a8-b795-a9c3b5ecc21a · outbound

This paper cites Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.JAMA, 318(22):2199–2210, 2017.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.JAMA, 318(22):2199–2210, 2017

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:01.610297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2949df2c-992d-4826-8f9b-e4ce676fc0a9 · outbound

This paper cites The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection.International Journal of Computer Vision, 129(4):1038–1059, 2021.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection.International Journal of Computer Vision, 129(4):1038–1059, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:01.366632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c91c33d-9158-4bf0-9656-b89cb318bc8a · outbound

This paper cites The liver tumor segmentation benchmark (LiTS).Medical Image Analysis, 84:102680, 2023.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain The liver tumor segmentation benchmark (LiTS).Medical Image Analysis, 84:102680, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:33.173023Z

Source-reported events for the cited work

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Observation c5609c92-8556-44a5-aec7-f5dc2a5d30dc · outbound

This paper cites Deep autoencoders for anomaly detection in textured images using CW-SSIM.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Deep autoencoders for anomaly detection in textured images using CW-SSIM

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:01.135764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b989f988-5945-4bf0-aef3-cfa076554f38 · outbound

This paper cites Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images.Medical Image Analysis, 86:102794, 2023.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images.Medical Image Analysis, 86:102794, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:00.935581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 22dea9a1-5762-4582-b0df-8c650dc70a6f · outbound

This paper cites Informative knowledge distillation for image anomaly segmentation.Knowledge-Based Systems, 248:108846, 2022.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Informative knowledge distillation for image anomaly segmentation.Knowledge-Based Systems, 248:108846, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:00.770989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 114501ed-7062-4436-9b55-7d5d58f6b26f · outbound

This paper cites Segment Any Anomaly without Training via Hybrid Prompt Regularization.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:33.651441Z

Source-reported events for the cited work

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Observation 38dbf0c8-300c-4a4f-9d34-4cffcd5db272 · outbound

This paper cites BiaS: Incorporating biased knowledge to boost unsupervised image anomaly localization.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4):2342–2353, 2024.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain BiaS: Incorporating biased knowledge to boost unsupervised image anomaly localization.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4):2342–2353, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:00.506231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e4df65f-c3a3-4497-90c1-a70de2bb8739 · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:33.889507Z

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Observation 47e17cdc-b646-4e2c-82ac-80ed5344a082 · outbound

This paper cites AdaCLIP: Adapting CLIP with hybrid learnable prompts for zero-shot anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain AdaCLIP: Adapting CLIP with hybrid learnable prompts for zero-shot anomaly detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:00.272836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb3cd3ef-bb64-44ca-8aae-80d9eecefcb0 · outbound

This paper cites Anomaly detection: A survey.ACM Computing Surveys, 41(3):1–58, 2009.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Anomaly detection: A survey.ACM Computing Surveys, 41(3):1–58, 2009

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:00.013105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1316dc7-c99e-4000-89b3-40cb32fcd2ee · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:34.693002Z

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Observation 376c4035-d1a9-4af3-8672-028a59b954ee · outbound

This paper cites CLIP-AD: A language-guided staged dual-path model for zero-shot anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain CLIP-AD: A language-guided staged dual-path model for zero-shot anomaly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:59.742772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb1f4f99-41d2-473a-84bb-03e098d212f7 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embed- ding.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Anomaly detection via reverse distillation from one-class embed- ding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:59.519700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0febdc3c-9c16-4ecd-a394-9802242b9e9c · outbound

This paper cites Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:40.057841Z

Source-reported events for the cited work

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Observation a0a7e368-0dca-47bc-863a-97dfc76c8f97 · outbound

This paper cites Catching both gray and black swans: Open- set supervised anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Catching both gray and black swans: Open- set supervised anomaly detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:59.245997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c9df9b60-0179-48a6-a572-018c6d9af61f · outbound

This paper cites Unsupervised anomaly segmentation for brain lesions using dual semantic-manifold reconstruction.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Unsupervised anomaly segmentation for brain lesions using dual semantic-manifold reconstruction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:59.063896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ece25f38-f321-463a-9b1a-6c0309d4804d · outbound

This paper cites Deep learning for medical anomaly detection–a survey.ACM Computing Surveys, 54(7):1–37, 2021.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Deep learning for medical anomaly detection–a survey.ACM Computing Surveys, 54(7):1–37, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:58.787004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dc50a285-6305-4308-ad5c-c9cbef614712 · outbound

This paper cites Memorizing normality to detect anomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Memorizing normality to detect anomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:58.519661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 73756f89-e5ec-4b14-89df-dd2ef61ca26f · outbound

This paper cites CFlow-AD: Real-time unsupervised anomaly detection with localization via conditional normalizing flows.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain CFlow-AD: Real-time unsupervised anomaly detection with localization via conditional normalizing flows

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:58.288332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 75aa9fff-8ded-4e08-9d31-cb0b3b4d0791 · outbound

This paper cites DiAD: A diffusion-based framework for multi-class anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain DiAD: A diffusion-based framework for multi-class anomaly detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:58.015819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dfd6cd63-3d17-4bad-98e9-10810300763c · outbound

This paper cites Automated segmentation of macular edema in OCT using deep neural networks.Medical Image Analysis, 55:216–227, 2019.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Automated segmentation of macular edema in OCT using deep neural networks.Medical Image Analysis, 55:216–227, 2019

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:57.779628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 48a17bee-bfd3-4aba-b5d9-2141765393fd · outbound

This paper cites Registration based few-shot anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Registration based few-shot anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:57.489054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation afeea7ce-1dd5-4c9c-9ca6-d2dbabddc01c · outbound

This paper cites Adapting visual-language models for generalizable anomaly detection in medical images.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Adapting visual-language models for generalizable anomaly detection in medical images

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:57.270859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 380fdde3-7c8d-4db4-b1db-9edb23f145c7 · outbound

This paper cites WinCLIP: Zero-/few-shot anomaly classification and segmentation.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain WinCLIP: Zero-/few-shot anomaly classification and segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:57.067121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e368fc10-50e3-41c1-a88a-78789e705d12 · outbound

This paper cites A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection.Knowledge-Based Systems, page 110982, 2023.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection.Knowledge-Based Systems, page 110982, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:56.811929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2f3f9b10-4a93-4dba-93e7-9bc575ff4fb9 · outbound

This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning.Cell, 172(5):1122–1131, 2018.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Identifying medical diagnoses and treatable diseases by image-based deep learning.Cell, 172(5):1122–1131, 2018

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:56.591065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f89ef07-9c64-4718-8218-04e3438b7e8a · outbound

This paper cites MaPLe: Multi-modal prompt learning.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain MaPLe: Multi-modal prompt learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:56.419288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 724119de-c395-45d0-bdab-d38c9e155f65 · outbound

This paper cites Segment anything.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Segment anything

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:56.212447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 21ec44e0-3bf5-4368-ba77-3707f2193f7b · outbound

This paper cites MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:55.894636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebd96eec-3420-41b4-a6cd-5f971ae1b6e9 · outbound

This paper cites CutPaste: Self-supervised learning for anomaly detection and localization.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain CutPaste: Self-supervised learning for anomaly detection and localization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:45.250502Z

Source-reported events for the cited work

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Observation d41ddecd-ee84-4bca-aaa2-7add6e1f5442 · outbound

This paper cites PromptAD: Learning prompts with only normal samples for few-shot anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain PromptAD: Learning prompts with only normal samples for few-shot anomaly detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:55.688566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b33e45e-aca2-4ba6-a6bb-a6ff3707e110 · outbound

This paper cites Focal loss for dense object detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Focal loss for dense object detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:55.483224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52a8a4f8-cf40-4a59-845d-13a89181a0b3 · outbound

This paper cites Real3D-AD: A dataset of point cloud anomaly detection.Advances in Neural Information Processing Systems, 36, 2024.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Real3D-AD: A dataset of point cloud anomaly detection.Advances in Neural Information Processing Systems, 36, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:55.366621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1082ddb4-612b-420e-9653-04316fb3e1a2 · outbound

This paper cites Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:55.229401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86868963-1796-4388-86dd-f371b8c3a061 · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (BraTS).IEEE Transactions on Medical Imaging, 34(10):1993–2024, 2014.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain The multimodal brain tumor image segmentation benchmark (BraTS).IEEE Transactions on Medical Imaging, 34(10):1993–2024, 2014

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:55.014864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5b117f3b-708c-42c0-832a-029d1b62a7fb · outbound

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

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain V-Net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:54.694386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e421d93f-43c8-492e-8ba7-1517d8bb5495 · outbound

This paper cites VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:54.482927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b09d1a00-2ef2-4c6a-991a-f4e17d949a25 · outbound

This paper cites Learning transferable visual models from natural language supervision.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Learning transferable visual models from natural language supervision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:54.216193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a992cc9-80a0-4e74-892e-24cb26ee2bad · outbound

This paper cites Towards total recall in industrial anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Towards total recall in industrial anomaly detection

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:46.475250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ea5088-5311-47d0-8afb-6dfca0fed526 · outbound

This paper cites CLIP for all things zero-shot sketch-based image retrieval, fine-grained or not.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain CLIP for all things zero-shot sketch-based image retrieval, fine-grained or not

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:54.023741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 03f59be1-4cd6-407e-ac3c-8227f67004a3 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Multiresolution knowledge distillation for anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:53.784430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8516cf80-835f-492c-be8d-7437f0862d53 · outbound

This paper cites DualCoOp: Fast adaptation to multi-label recognition with limited annotations.Advances in Neural Information Processing Systems, 35:30569–30582, 2022.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain DualCoOp: Fast adaptation to multi-label recognition with limited annotations.Advances in Neural Information Processing Systems, 35:30569–30582, 2022

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:53.590895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b04165a7-1c9b-40a7-a2de-bc132c959faf · outbound

This paper cites Deep learning for unsupervised anomaly localization in industrial images: A survey.IEEE Transactions on Instrumentation and Measurement, 71:1–21, 2022.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Deep learning for unsupervised anomaly localization in industrial images: A survey.IEEE Transactions on Instrumentation and Measurement, 71:1–21, 2022

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:47.002147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c89d84db-c4db-48c5-b4e0-f5b44045cd3a · outbound

This paper cites Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605, 2008.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605, 2008

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:47.130538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d646c9f-3b5e-4726-ac38-1410d41a4e91 · outbound

This paper cites Attention is all you need.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Attention is all you need

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:47.265419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166053e2-9957-45a1-9b85-1e054ebc29c8 · outbound

This paper cites Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature.IEEE Transactions on Industrial Electronics, 69(6):6182–6192, 2022.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature.IEEE Transactions on Industrial Electronics, 69(6):6182–6192, 2022

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:53.376792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05c5f4fe-95c7-4dd8-9e1c-144ce0e001fb · outbound

This paper cites Real-IAD: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Real-IAD: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:53.131551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f55b7e08-9774-434c-879c-0697cc3af525 · outbound

This paper cites ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly- supervised classification and localization of common thorax diseases.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly- supervised classification and localization of common thorax diseases

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:52.854471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 36519035-1bd7-44db-b392-e1f1dbf3ae4b · outbound

This paper cites MedCLIP: Contrastive Learning from Unpaired Medical Images and Text.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:47.915887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e827a93-e25c-47c7-8720-095a0a772a64 · outbound

This paper cites Learning unsupervised Metaformer for anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Learning unsupervised Metaformer for anomaly detection

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:52.608476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d814265e-f090-4ad6-82d8-d1d68fb43df8 · outbound

This paper cites AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:52.317462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10ab9c9e-8e9e-4de7-a853-465472f65947 · outbound

This paper cites SQUID: Deep feature in-painting for unsupervised anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain SQUID: Deep feature in-painting for unsupervised anomaly detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:52.078995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7aded247-5349-4169-b5b5-19ad17dd94c6 · outbound

This paper cites an unresolved cited work.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:28:51.869867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd5043a2-f4f0-4233-bb65-5b96e7761714 · outbound

This paper cites Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:28:50.256775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 348536b8-f6c4-406f-93e3-5956275ec593 · outbound

This paper cites DSR–a dual subspace re-projection network for surface anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain DSR–a dual subspace re-projection network for surface anomaly detection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:51.648129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae1589d4-fe76-442b-9e04-12d7d8761863 · outbound

This paper cites MediCLIP: Adapting CLIP for few-shot medical image anomaly detection.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain MediCLIP: Adapting CLIP for few-shot medical image anomaly detection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:51.344123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1358a71d-4449-448f-aaf7-0865e1a90ed5 · outbound

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

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Conditional prompt learning for vision-language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:51.059634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 31b2c19e-fee6-461e-a7e9-995004b0b08d · outbound

This paper cites Learning to prompt for vision-language models.International Journal of Computer Vision, 130(9):2337–2348, 2022.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Learning to prompt for vision-language models.International Journal of Computer Vision, 130(9):2337–2348, 2022

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:49.117184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd6828c4-baad-4873-88e7-cdfe30c64080 · outbound

This paper cites Encoding structure-texture relation with P-Net for anomaly detection in retinal images.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Encoding structure-texture relation with P-Net for anomaly detection in retinal images

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:50.806270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fdef666-5c0b-4f5c-8b33-abd8a8d66e7f · outbound

This paper cites AnomalyCLIP: Object- agnostic prompt learning for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain AnomalyCLIP: Object- agnostic prompt learning for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:49.363952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6354c96d-401d-43b3-ad23-3fc51afe3c0e · outbound

This paper cites Towards high-resolution 3D anomaly detection via group-level feature contrastive learning.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain Towards high-resolution 3D anomaly detection via group-level feature contrastive learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:28:50.550756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a83959c-7746-41c3-9531-6595a4570826 · outbound

This paper cites For IQM, the number of attention heads is set to 8, and the number of blocks N is set to 4.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain For IQM, the number of attention heads is set to 8, and the number of blocks N is set to 4

Reference 67

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:28:49.975047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers