TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank | Paper Citation Record

Observation e1fe2e89-1e69-4df3-bb28-c7c29e9e8d5c · outbound

This paper cites Deep anomaly detection using geometric transformations,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Deep anomaly detection using geometric transformations,

Reference 1

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Observation 711a5257-e792-4665-b5fd-7e4a82daae79 · outbound

This paper cites Towards total recall in industrial anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Towards total recall in industrial anomaly detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:46.038511Z

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Observation e0361a9a-c0fa-4892-afb2-402c6b8e00ba · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Promptad: Learning prompts with only normal samples for few-shot anomaly detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:46.028300Z

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Observation 11635f1c-d081-4871-8b8b-21a072880aca · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:46.017754Z

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Observation 29529d45-4a9e-482c-b26c-cb68da92e2f2 · outbound

This paper cites Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:46.005851Z

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Observation d08fe6c0-65df-4cfb-988f-f3d469fc6e8f · outbound

This paper cites Be- yond dents and scratches: Logical constraints in unsupervised anomaly detection and localization,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Be- yond dents and scratches: Logical constraints in unsupervised anomaly detection and localization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.995315Z

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Observation 82977ecf-1782-44ab-bc28-63653719dae4 · outbound

This paper cites Correcting deviations from normality: A reformulated diffusion model for multi- class unsupervised anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Correcting deviations from normality: A reformulated diffusion model for multi- class unsupervised anomaly detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.984749Z

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Observation 9240be01-eafe-43eb-9244-e0876be237f4 · outbound

This paper cites A cognitive memory-augmented network for visual anomaly detection.,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank A cognitive memory-augmented network for visual anomaly detection.,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.972905Z

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Observation e521775f-218b-4417-a53c-be7916b4b6dd · outbound

This paper cites Divide- and-assemble: Learning block-wise memory for unsupervised anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Divide- and-assemble: Learning block-wise memory for unsupervised anomaly detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.962699Z

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Observation b97690d9-81a8-44e1-838c-edc0ac356493 · outbound

This paper cites Rethinking autoencoders for medical anomaly detection from a theoretical perspective,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Rethinking autoencoders for medical anomaly detection from a theoretical perspective,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.952568Z

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Observation dab56856-6c44-427e-bf1b-9e4849dfa81e · outbound

This paper cites Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.942789Z

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Observation 815d9be0-75d2-4291-b9dc-f8d1aa5db637 · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank A diffusion-based framework for multi-class anomaly detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.932831Z

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Observation d0d40bd1-393d-45db-b274-a7b0021c0e17 · outbound

This paper cites Residual denoising diffusion models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Residual denoising diffusion models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.923098Z

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Observation 3d3593f4-08ab-46db-9e80-dc1e817cdf43 · outbound

This paper cites Glad: Towards better reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Glad: Towards better reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.912052Z

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Observation 7729c6e1-f0dd-445c-b1fe-9f9cf79673b3 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Anomaly detection via reverse distillation from one-class embedding,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.900400Z

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Observation 9aebd1ca-09a3-4c91-a2e5-b4a4d0eb0e64 · outbound

This paper cites Dual-modeling decouple distillation for unsupervised anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Dual-modeling decouple distillation for unsupervised anomaly detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.888954Z

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Observation 40470317-f0de-429d-a704-5f50c136b934 · outbound

This paper cites Feature-constrained and attention-conditioned distillation learning for visual anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Feature-constrained and attention-conditioned distillation learning for visual anomaly detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.878458Z

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Observation 10ffed13-a929-4b74-a59e-e1020781110e · outbound

This paper cites Aekd: Unsupervised auto- encoder knowledge distillation for industrial anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Aekd: Unsupervised auto- encoder knowledge distillation for industrial anomaly detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.868290Z

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Observation 706bc590-2f1c-4e38-9574-f32083b97895 · outbound

This paper cites Pushing the limits of fewshot anomaly detection in industry vision: Graphcore,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Pushing the limits of fewshot anomaly detection in industry vision: Graphcore,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.859042Z

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Observation eb3fab2b-e7b1-4456-9a8f-f6343f4d7dbc · outbound

This paper cites Pni: industrial anomaly detection using position and neighborhood information,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Pni: industrial anomaly detection using position and neighborhood information,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.848512Z

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Observation a6a55bf0-49ab-4b15-96ce-e720ee3425c9 · outbound

This paper cites Progressive bound- ary guided anomaly synthesis for industrial anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Progressive bound- ary guided anomaly synthesis for industrial anomaly detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.838479Z

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Observation 2dea5e79-c2a0-4d39-a679-219346543825 · outbound

This paper cites Inter-realization channels: Unsupervised anomaly detection beyond one-class classification,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Inter-realization channels: Unsupervised anomaly detection beyond one-class classification,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.828005Z

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Observation ab44e019-144e-4853-bb96-a82c880e0c53 · outbound

This paper cites Reconpatch: Contrastive patch representation learning for industrial anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Reconpatch: Contrastive patch representation learning for industrial anomaly detection,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.817423Z

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Observation ada09d41-7e72-498f-96b8-8e36fea2360d · outbound

This paper cites A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.806034Z

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Observation d6358c83-76ad-43e9-b016-37d0380bf1ab · outbound

This paper cites Towards training-free anomaly detection with vision and language foundation models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Towards training-free anomaly detection with vision and language foundation models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.795502Z

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Observation 7bbdb976-36d9-4215-be70-119cad7b3495 · outbound

This paper cites Space: Spatial- aware consistency regularization for anomaly detection in industrial applications,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Space: Spatial- aware consistency regularization for anomaly detection in industrial applications,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.783684Z

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Observation 33c0f4ae-3cbb-4df8-9637-254530400e05 · outbound

This paper cites Contextual affinity distillation for image anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Contextual affinity distillation for image anomaly detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.772392Z

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Observation 9407b804-8367-4aec-8fad-c6b69b25ff71 · outbound

This paper cites Efficientad: Accurate visual anomaly detection at millisecond-level latencies,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Efficientad: Accurate visual anomaly detection at millisecond-level latencies,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.762076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6feed949-31bb-4daf-a9d3-8705dd281b65 · outbound

This paper cites Few shot part segmentation reveals compositional logic for industrial anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Few shot part segmentation reveals compositional logic for industrial anomaly detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.751452Z

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Observation 00fafb0d-dc01-44c9-bf09-eae151d2bac5 · outbound

This paper cites Univad: A training-free unified model for few-shot visual anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Univad: A training-free unified model for few-shot visual anomaly detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.741130Z

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Observation 7a3776d8-3078-4e99-adfc-4a51d159c4af · outbound

This paper cites Sam- lad: Segment anything model meets zero-shot logic anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Sam- lad: Segment anything model meets zero-shot logic anomaly detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.730085Z

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Observation c76563b5-e380-4182-8de6-f067500cb352 · outbound

This paper cites Visual anomaly detection via partition memory bank module and error estimation,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Visual anomaly detection via partition memory bank module and error estimation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.717723Z

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Observation 396ed6cf-916c-427b-a6e3-62dc5a101236 · outbound

This paper cites Outlier-probability-based feature adaptation for robust unsupervised anomaly detection on contaminated training data,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Outlier-probability-based feature adaptation for robust unsupervised anomaly detection on contaminated training data,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.707617Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b7c8f99-b728-4b23-9985-63eea641fba2 · outbound

This paper cites Gaussian mixture models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Gaussian mixture models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.696464Z

Source-reported events for the cited work

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Observation 75397760-5222-464c-b851-ffed40efc653 · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Anomalygpt: Detecting industrial anomalies using large vision-language models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.686723Z

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Observation 5b51e9dc-3ad4-4f50-a293-69e00966813b · outbound

This paper cites Focusclip: Focusing on anomaly regions by visual-text discrepancies,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Focusclip: Focusing on anomaly regions by visual-text discrepancies,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.675961Z

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Observation 02a559a1-d0a9-4e67-90e7-c06a1fbaf7fa · outbound

This paper cites Crepe: Can vision-language foundation models reason compositionally?,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Crepe: Can vision-language foundation models reason compositionally?,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.666242Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b2fc0584-8aeb-4243-9e45-0f3765b79a0a · outbound

This paper cites Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.656732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76c86c18-f420-429a-9c76-fd0f450efb9c · outbound

This paper cites Compositional chain- of-thought prompting for large multimodal models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Compositional chain- of-thought prompting for large multimodal models,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.647917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6b4ff05-b977-4f1a-9545-f2e4e0d9e554 · outbound

This paper cites Logicqa: Logical anomaly detection with vision language model generated questions,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Logicqa: Logical anomaly detection with vision language model generated questions,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.638559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5636002e-f810-4887-a027-dd21f51c7885 · outbound

This paper cites Inves- tigating compositional challenges in vision-language models for visual grounding,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Inves- tigating compositional challenges in vision-language models for visual grounding,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.617505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8d4b6b6-ebc3-4d02-bc43-af2dfefaa10a · outbound

This paper cites Segment anything,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Segment anything,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.413613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 067a52a1-b60f-4da4-86ff-d82bc1039fe2 · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Learning transferable visual models from natural language supervision,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:45.114146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fdf19a75-9c50-4cbd-95d3-31f1ff1fec0f · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:39.539111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a68e2ee9-8dad-4a2c-ba88-e41773b8fedb · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:39.654341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db2cd765-822b-4775-95e7-eacd48b9af95 · outbound

This paper cites Towards zero- shot anomaly detection and reasoning with multimodal large language models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Towards zero- shot anomaly detection and reasoning with multimodal large language models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:44.892636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 555f6815-e6c5-43c3-9720-cf2218c75d22 · outbound

This paper cites GPT-4o System Card.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank GPT-4o System Card

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:39.867318Z

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Unavailable: canonical work link unavailable.

Observation 5ff0b8a0-6f68-4cc9-8b88-60bbb231d64a · outbound

This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Mitigating hallucination in large multi-modal models via robust instruction tuning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:44.710663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 33de0a48-1291-492a-9dc9-a3bad3605ee6 · outbound

This paper cites Dinov2: Learning robust visual features without supervision,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Dinov2: Learning robust visual features without supervision,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:44.560367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a92a6dea-f979-4319-b35a-d63074e2910a · outbound

This paper cites From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:40.129707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0597b7d-99b5-4678-960c-5af1e9a9d0f4 · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current meth- ods in complex conditions,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Deep learning-based defect detection of metal parts: evaluating current meth- ods in complex conditions,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:44.423332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a79ee202-2721-42a2-b166-1dcf528008fe · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:40.328658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 307003f2-c282-42c7-a087-ccbf053cc75b · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:44.236849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 540b938e-f256-477e-92f8-94e1f080e9c6 · outbound

This paper cites Automated segmentation of macular edema in oct using deep neural networks,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Automated segmentation of macular edema in oct using deep neural networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:44.088247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0f0f56ff-ee54-42e0-b8b1-1c8d632627a6 · outbound

This paper cites Gestalt pattern matching.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Gestalt pattern matching

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.908867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 525725f3-b076-4fb7-be6b-592bffc41c32 · outbound

This paper cites Logical: Towards logical anomaly synthesis for unsupervised anomaly localization,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Logical: Towards logical anomaly synthesis for unsupervised anomaly localization,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.768418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91bb5ee8-05de-4f44-b680-38860d7c3285 · outbound

This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.647309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 082b8e9b-3292-417a-8e3f-ddd1c023468c · outbound

This paper cites Omnial: A unified cnn framework for unsupervised anomaly localization,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Omnial: A unified cnn framework for unsupervised anomaly localization,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.511866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 62045f19-d5e6-472a-8a84-a55d31972038 · outbound

This paper cites Generalad: Anomaly detection across domains by attending to distorted features,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Generalad: Anomaly detection across domains by attending to distorted features,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.366715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 18f3dc6b-491d-4e64-848c-99f05ffbf632 · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Winclip: Zero-/few-shot anomaly classification and segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.199432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70198286-67da-4ce5-a02a-93fc25977503 · 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.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank 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 61

Resolution: unresolved; no resolver link, observed 2026-08-05T14:01:40.849367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e0c80f1-bbe7-421e-8bdd-1804c835419b · outbound

This paper cites Anomalyclip: Object- agnostic prompt learning for zero-shot anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Anomalyclip: Object- agnostic prompt learning for zero-shot anomaly detection,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:43.045923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a1375852-3841-417b-91b7-89e1fc25b1e8 · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Adapting visual-language models for generalizable anomaly detection in medical images,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.872108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 01ad627b-e531-4cb3-92b1-4b53fd5f7b17 · outbound

This paper cites Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.790919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 681cdad2-9055-4bf3-829e-e3e865893f98 · outbound

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

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.646141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f928faa-d09b-44c5-acf7-c3cc18b3c143 · outbound

This paper cites Simplenet: A simple network for image anomaly detection and localization,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Simplenet: A simple network for image anomaly detection and localization,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.564177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 43e60add-9ace-46cd-ae41-cf799628bacc · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.459044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a4d3ea3-5a4a-4701-a15f-da5edaf12f37 · outbound

This paper cites Detecting human- object contact in images,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Detecting human- object contact in images,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.310817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ba0dcbe9-0a58-4ba7-93cf-0a0413a3ca34 · outbound

This paper cites Interactvlm: 3d interaction reasoning from 2d foundational models,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Interactvlm: 3d interaction reasoning from 2d foundational models,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.208577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d0ad3774-0c82-47d8-8e31-1c8964e9dbb3 · outbound

This paper cites Product quantization for nearest neighbor search,.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Product quantization for nearest neighbor search,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:42.105552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 90732f3f-36cd-4b95-a434-a695a5c2aae5 · outbound

This paper cites Her research interests include deep learning on image processing and medical image processing and applications.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank Her research interests include deep learning on image processing and medical image processing and applications

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:41.779090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation deaf45ed-0c2f-411e-ab6b-34943de3eb80 · outbound

This paper cites His research interests in- clude deep learning, illumination processing, image restoration, shadow removal, anomaly detection, and diffusion models.

TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank His research interests in- clude deep learning, illumination processing, image restoration, shadow removal, anomaly detection, and diffusion models

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:01:41.969727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

Pith citing papers