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Paper Citation Record · LEDGER

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects

As of 22 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 2 inbound Pith citation observations for arXiv:2412.04867.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.04867 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:16:26.969895Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:03:13.215004Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T18:34:48.365451Z

Reference resolution

61 of 61 outbound references displayed

  • verified exact0
  • verified fuzzy57
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe79e306-020e-417f-a710-d9431223f1e4 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Flamingo: a visual language model for few-shot learning

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 19173f32-51b6-4c2b-a196-b9a70958f14d · outbound

This paper cites Vqa: Visual question answering.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Vqa: Visual question answering

Reference 2

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 70b1bc84-7330-45cb-b134-090332d94c2e · outbound

This paper cites To- wards a theory of declarative knowledge.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects To- wards a theory of declarative knowledge

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 68e0c39c-5900-4562-8e6b-0bc96e932e45 · outbound

This paper cites Pni: Industrial anomaly detection using position and neighborhood infor- mation.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Pni: Industrial anomaly detection using position and neighborhood infor- mation

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.936114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.699086Z digest=sha256:c1d5d2340494ce45de4f8f8aae8a13cef5225b823e4d389c5fb2fa09e8d73df1

Observation 7f301336-ceda-41d7-85c8-0e52549b0942 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 5

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation f77bf603-7155-47ac-8f70-e172321ebbb2 · outbound

This paper cites Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.709006Z digest=sha256:b34c076b2d0508a21f971422eb016e2b24e6a60a040f8986e626d9bcec901c50

Observation 551a70d7-f3a7-4786-8fcb-d50199d5602e · outbound

This paper cites The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization

Reference 7

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.714121Z digest=sha256:90845fc3fa5f4c454cdb958d4698904b610e9f8b634ed6d1f6a556079b568560

Observation 086d84d7-263b-4770-85aa-86ec3e49df87 · outbound

This paper cites Collaborative discrepancy optimization for reliable image anomaly localization.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Collaborative discrepancy optimization for reliable image anomaly localization

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.719548Z digest=sha256:8bb7a7d5ef6f1b8bd4c9da4be3ff29179359d649ffe351e43ce6a037b2f07b7f

Observation 921227cf-e6b6-48f0-b8a5-942439548cf3 · outbound

This paper cites Defect detection in sem images of nanofi- brous materials.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Defect detection in sem images of nanofi- brous materials

Reference 9

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.724398Z digest=sha256:7938025d2bd0ed6ffe6e1edee9171d492a747aef020b364642400e4e0866781c

Observation 7177a22c-2f43-4e31-bd09-260bceb9ac26 · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ff0d1d29-025d-4708-b1c4-1319ee74ab5f · outbound

This paper cites Anomaly detection via re- verse distillation from one-class embedding.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Anomaly detection via re- verse distillation from one-class embedding

Reference 11

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 84ab245c-0079-4489-aa70-0f4e44753061 · outbound

This paper cites Grainspace: A large-scale dataset for fine-grained and domain-adaptive recognition of cereal grains.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Grainspace: A large-scale dataset for fine-grained and domain-adaptive recognition of cereal grains

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.807563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.738787Z digest=sha256:48616fceaea44c869aebd54ff518b2d067de82fb7f82c0a841f6501422fe482c

Observation bfdc7375-290d-49e3-bf94-f7ac2d3dd9a4 · outbound

This paper cites Identifying the defective: De- tecting damaged grains for cereal appearance inspection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Identifying the defective: De- tecting damaged grains for cereal appearance inspection

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.792075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.743045Z digest=sha256:caf22330a0d458e0a225355f7f8528835a4b2c6c1e60d8f0ce9bec2e88e370c4

Observation 98997d33-ee87-4a07-b375-d1c53df89cd2 · outbound

This paper cites Template-guided hierarchical feature restoration for anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Template-guided hierarchical feature restoration for anomaly detection

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.776049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.747141Z digest=sha256:8afe9f7105fed80cefac3032011f3efc7721d1f922afff56bd0039fe05fd03e0

Observation 23f539fc-9665-4dc8-899a-ca44248c1573 · outbound

This paper cites Adbench: Anomaly detection benchmark.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Adbench: Anomaly detection benchmark

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.759582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.751748Z digest=sha256:7227bfa040381dd92d39ca25fbb6b1934b9e0dead7b578aa5b82bb07f4dd45eb

Observation 91fff102-2803-4463-b16b-4665fb0bcfd5 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects A diffusion-based framework for multi-class anomaly detection

Reference 16

Resolution
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raw_fallback, observed 2026-08-11T21:16:27.743481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.755861Z digest=sha256:2856b75e223e06bab715ea36ffe11294246bfa19a1f5837c827b72943c07ff1c

Observation 02ebfba6-ca2b-48e3-add6-8de092ac4f99 · outbound

This paper cites Deep residual learning for image recognition.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Deep residual learning for image recognition

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:16:26.760161Z digest=sha256:adf986ace05fc3bd47989ffeb2ee67c91b210f689c0dc73d34ee7bf831e6420f

Observation e6a02001-3d1e-436a-b3c6-6bcae254093c · outbound

This paper cites Self-supervised anomaly detection in computer vision and beyond: A survey and outlook.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Self-supervised anomaly detection in computer vision and beyond: A survey and outlook

Reference 18

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raw_fallback, observed 2026-08-11T21:16:27.717640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.764272Z digest=sha256:02e8a4f0ed2b4b3e8432917fd25033fbd84588f07143d8a5b0db38ac19ba3e2d

Observation f90899af-9935-4190-9ac1-711706ddc641 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Lora: Low-rank adaptation of large language models

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.702156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.768341Z digest=sha256:7f9ba622bf84112fc9c7cb4892b3d903f9ab4306019635e40984bf664cae6d7a

Observation 4af84dbb-a095-4126-8536-5c0953cb4c68 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Adapting visual-language models for generalizable anomaly detection in medical im- ages

Reference 20

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raw_fallback, observed 2026-08-11T21:16:27.686111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.772753Z digest=sha256:88d11d722c7f9e1bd9563df21836579595e7756dcd71c65d6109ff420b146ac6

Observation d83d07de-8924-43a8-bb97-b920b909e1e7 · outbound

This paper cites Surface defect saliency of magnetic tile.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Surface defect saliency of magnetic tile

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.670544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation d8ad7fac-5111-40e3-a205-430e61297721 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Winclip: Zero-/few-shot anomaly classification and segmentation

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.654990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.782056Z digest=sha256:53a8b42ffbbde7cc53b4455446415eed844a68ad3ea8bf039dfe98954e46cc1a

Observation 4251deae-3bc3-4ba8-951e-08fb36c4055a · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.636959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.786618Z digest=sha256:975096bd412027a0da757dcfc985957230b08ec861b96ec4f6d563ea6fb097de

Observation 03adcf4f-74e4-4a45-a52d-49ee2a6f9ac5 · outbound

This paper cites Woo, and Jong Hwan Ko.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Woo, and Jong Hwan Ko

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.622047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 2ca7f81a-0a6d-433c-b4bd-33838c6d4a0b · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localiza- tion.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Cutpaste: Self-supervised learning for anomaly detection and localiza- tion

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.607396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.795706Z digest=sha256:e0f219f997a9bc4fc2026602e0b853dde9e34ee2e5b125720ed89d01c76ceec3

Observation 72c38a81-91cb-4243-9c1e-142be1b56a9b · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 26

Resolution
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raw_fallback, observed 2026-08-11T21:16:27.593050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.800174Z digest=sha256:319f5b72dd37eca32864a37ba99160b901e39dfe5fa5cc28c1cb06e3c3444b30

Observation 071a1b9e-efc6-452e-bebd-72565ec1df38 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.577641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.804671Z digest=sha256:08bd439df0e8eda211fd5c436d080d4b33f6099ae7960911d909df34336955d5

Observation 9fc6609e-32ba-47eb-a898-4385aa3e7832 · outbound

This paper cites Unsupervised continual anomaly detection with contrastively-learned prompt.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Unsupervised continual anomaly detection with contrastively-learned prompt

Reference 28

Resolution
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raw_fallback, observed 2026-08-11T21:16:27.561810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.810201Z digest=sha256:7021c26205e3df2b75dfbb0a609bcbfa174253bc92acbfb8f26662e65ab3fce2

Observation ced2665e-0a72-4d19-9371-8f73b8fe4069 · outbound

This paper cites Real3d-ad: A dataset of point cloud anomaly detection.NeurIPS, 36, 2024.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Real3d-ad: A dataset of point cloud anomaly detection.NeurIPS, 36, 2024

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.546633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.814589Z digest=sha256:ff7faf7b3edf33355c2e9d101af6926adfc920fee921fa213bac350d80056e82

Observation cefdc2b9-1757-46c3-b8ff-4d4e769cc33c · outbound

This paper cites Deep industrial image anomaly detection: A survey.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Deep industrial image anomaly detection: A survey

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.530718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.819155Z digest=sha256:32a14bd900dfaebfbb34bb2ea3c9d13be815847fea7e8c8f3c091ab7e96edd99

Observation 03534171-8141-4a01-8a12-123cce90dc07 · outbound

This paper cites Diversity-measurable anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Diversity-measurable anomaly detection

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.515355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.823659Z digest=sha256:acfb9b7a4e93a2619423ac56981d33e2df59e814803606b230f87cb519c16a7c

Observation 846ed698-662d-4f78-a7b9-861a779b1c2b · outbound

This paper cites A survey and performance evaluation of deep learning methods 9 for small object detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects A survey and performance evaluation of deep learning methods 9 for small object detection

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.499294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.829089Z digest=sha256:fa1f49532d580fdf91c9682599490c576573d09ab26a81263ebae2b4740e36a5

Observation b0add142-ecc8-413f-85bc-6dade72e01d6 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Simplenet: A simple network for image anomaly detection and localization

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.483355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.834579Z digest=sha256:8084299a8206ebc8d605371897d4f12da900d23acb30a9847c3e861d5ec636fe

Observation 5eabc2d9-1270-474a-9f0d-84ac3b656144 · outbound

This paper cites Gdxray: The database of x-ray images for nondestructive testing.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Gdxray: The database of x-ray images for nondestructive testing

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.467935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.839093Z digest=sha256:ba4308fab793b97c0888f44ea225afa6210f7efd67fbb39d9a8b49cc65a7abe1

Observation d065d924-425d-4421-838d-432d55bf2f49 · outbound

This paper cites Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.452500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.843873Z digest=sha256:c9dc67c7b08291ec1e97cdba51bf98c3ffa90bb1a4ab874cd77de6b643a22f2c

Observation 494b9ed6-df35-48d3-8e5a-ddcfad715d88 · outbound

This paper cites Social constructivist perspectives on teaching and learning.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Social constructivist perspectives on teaching and learning

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.435772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.848654Z digest=sha256:3b422f5881ef3ccd923c81815006b027af8b008d1db92fd574fedf568f19e570

Observation 0f2d4d2e-2f18-4e41-804d-489bdf998103 · outbound

This paper cites Deep learning for anomaly detection: A review.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Deep learning for anomaly detection: A review

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.420449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.853107Z digest=sha256:5baef54e4edfde03234249c65ff21b896faae86a124d8f0b1695cf00f12e0ac2

Observation 011167bb-e2c7-4eb9-8ee1-737572ca01f4 · outbound

This paper cites Vcp-clip: A visual context prompting model for zero-shot anomaly segmenta- tion.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Vcp-clip: A visual context prompting model for zero-shot anomaly segmenta- tion

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.404366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.857831Z digest=sha256:311222dc4385c2c73b55ab3b0f7f3b3b9302a5246787f0eaf7e4ea215640fb31

Observation bd2ff331-3ac4-4904-ab35-b4e4a772aee2 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Learn- ing transferable visual models from natural language super- vision

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.387973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.862454Z digest=sha256:aac420c8e500816a32ba2926cdafa783add2d6472bdf87d6585e936af55ff858

Observation f7b2285a-46ad-4acb-8437-3ba490b267f2 · outbound

This paper cites Self-supervised predictive con- volutional attentive block for anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Self-supervised predictive con- volutional attentive block for anomaly detection

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.371175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.867427Z digest=sha256:b06731e1586cc122a08a2f39fedcd978903ae2d6df986c090c039005c29dfe1d

Observation 54d4a2e8-af9d-4337-be33-da2ade6490a5 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects U-net: Convolutional networks for biomedical image segmentation

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T21:16:26.872091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:16:26.872091Z digest=sha256:fe9a0fdeab555e61a3d35506dc71a6b1cb2641aeb3995942c234b1c6a3f30965

Observation db0cb70e-c344-400f-816a-9429a561f51d · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Towards to- tal recall in industrial anomaly detection

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.343992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.877349Z digest=sha256:806df5d16e8b4f4bd911f13c240efcdee6d96265d4acf655958c51e3803b8034

Observation e00dc7b4-2cef-4d0f-9dfa-15fda235f175 · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Towards to- tal recall in industrial anomaly detection

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.328963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.882049Z digest=sha256:09392cf06820150cd4e6b128d12ab7fd703885c2e57c326a29e170b33a425378

Observation 65057736-36ae-42bb-901e-c1c4eef2ae3a · outbound

This paper cites Deep one-class classifi- cation.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Deep one-class classifi- cation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.312721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.886992Z digest=sha256:e0df444f55cb8174b400fdfc9bb248d983cd479b9ccfaa1988b4a8cdc0245577

Observation 2bdb6ba1-615e-4b91-924e-6239f8b60731 · outbound

This paper cites Prompt- guided zero-shot anomaly action recognition using pre- trained deep skeleton features.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Prompt- guided zero-shot anomaly action recognition using pre- trained deep skeleton features

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.296526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.891521Z digest=sha256:e132cb17df5437d2c345be271ea932799e2150ca89fcdc614c262b36c2c25655

Observation 46318638-0a8e-43bd-b7fe-a9224a947081 · outbound

This paper cites Anomaly detection in time series: a comprehensive evalu- ation.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Anomaly detection in time series: a comprehensive evalu- ation

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.278521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.896094Z digest=sha256:41a1462e374ae05de8e224d239db97b4065bd563239bbd99f6cf91233b4b3db5

Observation 6e87f989-3a0a-4c80-bb8d-e0dc1ab9956c · outbound

This paper cites Hy- perspectral anomaly detection: A survey.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Hy- perspectral anomaly detection: A survey

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.237165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.900457Z digest=sha256:ee6767628e3cdc4f9c0fc912daea34c5c6f58f7d0066c4d1e36cde64ed9409dc

Observation 3dd25ae6-d217-4ddf-9529-a721b5a84b8c · outbound

This paper cites Segmentation-based deep-learning approach for surface-defect detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Segmentation-based deep-learning approach for surface-defect detection

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.214518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.905017Z digest=sha256:8bc82511f77c0ed1e6542e7b5feb50524d3f70de77953d5803200e68fe23b1c9

Observation d927f112-d5f9-4133-824c-725d9fe4c824 · outbound

This paper cites Deep learning-based detection from the perspective of small or tiny objects: A survey.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Deep learning-based detection from the perspective of small or tiny objects: A survey

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.198400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.909750Z digest=sha256:2da2a3a1d9e6aa0daac27cd5de79073e67f5195c51e370d1872944e8029301d7

Observation b55658f0-d1f5-4cdf-a16e-d8821d0c01b1 · outbound

This paper cites Mass- producing failures of multimodal systems with language models.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Mass- producing failures of multimodal systems with language models

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.182490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.914991Z digest=sha256:0ed793ce297e99f55d7718fbb9dcb5647d41659b1ba30850ce62728099ed21c9

Observation 1ce7c6a2-ba65-4602-8919-38b6db987c69 · outbound

This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Eyes wide shut? exploring the visual shortcomings of multimodal llms

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.165721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.920555Z digest=sha256:f3c74cc75afa400852dd6143cc87837a16ad9eb4149a74b6ccb77d3e619426ee

Observation d37ec369-1ba5-42e8-9917-a7a7ecbe4423 · outbound

This paper cites Computer Vision Annotation Tool (CV AT), 2024.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Computer Vision Annotation Tool (CV AT), 2024

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.149006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.925293Z digest=sha256:e11e2df0d5e76c6dd4da07948456ba308524de07325f45d140c3dd26d3262818

Observation d90abf41-9897-4d5a-ba9c-e746e7422865 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.133548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.930066Z digest=sha256:2f45aad1cf09debbb62953b20da758ca9dabb4ca53f4c6222d0fdeb03a89ef19

Observation 7a56000f-03f9-4729-b76f-09596610aa89 · outbound

This paper cites Hierarchical gaussian mixture normaliz- ing flows modeling for unified anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Hierarchical gaussian mixture normaliz- ing flows modeling for unified anomaly detection

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.117871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.935630Z digest=sha256:afd4215dc78f78be8e522b6358e744225c1b49127065dfb84d2c820619ab3b5d

Observation f339677f-c2fd-4cd5-82a4-877d9367bee4 · outbound

This paper cites A unified model for multi-class anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects A unified model for multi-class anomaly detection

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.101607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.940775Z digest=sha256:c002d303a1fe98f6ef457d6c90953890bcd8f35bc5690da1183a6481b66c44ad

Observation 58ec6a92-9202-46b2-b70a-b13ed8407efd · outbound

This paper cites Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.086853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.945591Z digest=sha256:390966c8fdc62ac4964ca3eec29ca3045121552e916a26f94faeed3907aadb05

Observation bdf223b2-006b-48ae-9b9a-a2d80eb2155c · outbound

This paper cites Pku-goodsad: A supermarket goods dataset for unsupervised anomaly detection and segmentation.IEEE Robotics and Au- tomation Letters, 2024.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Pku-goodsad: A supermarket goods dataset for unsupervised anomaly detection and segmentation.IEEE Robotics and Au- tomation Letters, 2024

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-11T21:16:26.950172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:16:26.950172Z digest=sha256:15edfa2065e9b2644655385409ca7c505336766c038a3c1a8e9cb9779159d550

Observation b15f5861-ace4-4517-8c32-7c6a0200dafb · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects OPT: Open Pre-trained Transformer Language Models

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-11T21:16:26.955162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:16:26.955162Z digest=sha256:c39eb437f64af24e091e79b7d55b13dc52ea92d6258d42293ea1fd19be212a0e

Observation 95f96881-d181-43dd-956f-1c300bfc56e0 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.059628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.960064Z digest=sha256:491e9f7c5d60f07502ce49733d3210f89f2a4579efc0196f5808a786f961c894

Observation 76feaa53-5594-4672-869d-db5c01d7a23d · outbound

This paper cites Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts.

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.042663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.964823Z digest=sha256:bdb37448e9517bc3451166b8695e42d05d6f5ca79cc638bdd1b08cedc8328532

Observation 28b670a3-715c-4b28-9ad4-21eba9398831 · outbound

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

MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:16:27.025625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T21:16:26.969895Z digest=sha256:5f5be97b512f2d6f8136a8239e045ea8f243e62c167784aa85100e3894810745

Pith citing papers

Observation 5c44d978-5790-4865-a33f-85bf8bdb35ba · inbound

Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark cites this paper.

Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-15T21:03:13.215004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:03:13.215004Z digest=sha256:07b2b97298d354767c8a44f86245a844f2328cbe4066c5fa1ce18bc19fe840e1

Observation 7dcb9137-0588-4a6a-9983-d932ecbdcc89 · inbound

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment cites this paper.

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:34:48.370506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T18:34:47.937428Z digest=sha256:74fe3c6dd28a99f388ca84450a2f33c6ed101f7a411424ce00fd859308c2a236