Paper Citation Record · LEDGER

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 2 inbound Pith citation observations for arXiv:2505.21152.

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

pith.paper-citation-record.v1
2505.21152 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:43:21.599951Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:14:49.787243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T21:15:03.940270Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy27
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a20af81f-219f-4491-af2c-815c5e1b1538 · outbound

This paper cites Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:25.579175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b59b969f-98dc-4850-9f4d-a4ccb51c721f · outbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Correcting deviations from normality: A reformulated diffusion model for multi-class unsupervised anomaly detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:25.436387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4cc19f72-eab0-4ba0-94d8-8e5ae6d0ce45 · outbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:25.335746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bfc705f3-9a44-40b2-a0f7-21b20f850b2c · outbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:19.564441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f38b8a91-1168-4867-a14e-f9b2376b8c75 · outbound

This paper cites A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:25.184340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 421933a9-c2c0-430c-9df4-b33f5f45bdc9 · outbound

This paper cites Vision transformers need registers.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Vision transformers need registers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:25.013395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 072ad404-932b-4538-b92d-ad59d779e240 · outbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Anomaly detection via reverse distillation from one-class embedding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.884975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c3adf2c-bbd7-4f35-bac0-e8ca891e0b77 · outbound

This paper cites Transfusion–a transparency-based diffusion model for anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Transfusion–a transparency-based diffusion model for anomaly detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.788622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92070264-85e7-4a9b-8196-0bf3d0d8f8be · outbound

This paper cites Encoder-decoder contrast for unsupervised anomaly detection in medical images.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Encoder-decoder contrast for unsupervised anomaly detection in medical images

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.668521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a96a9534-5418-4736-ade3-1a5ee4952f8a · outbound

This paper cites Dinomaly: The less is more philosophy in multi-class unsupervised anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Dinomaly: The less is more philosophy in multi-class unsupervised anomaly detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.511560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dcc1d8b8-7bd3-4845-9589-680d34bdf67d · outbound

This paper cites Mambaad: Exploring state space models for multi-class unsupervised anomaly detec- tion.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Mambaad: Exploring state space models for multi-class unsupervised anomaly detec- tion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.383761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7193a0d7-83c7-49ac-8621-b0c57f21e4f6 · outbound

This paper cites The mvtec ad 2 dataset: Ad- vanced scenarios for unsupervised anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images The mvtec ad 2 dataset: Ad- vanced scenarios for unsupervised anomaly detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:20.257123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 908506d1-981b-4bd0-bedc-ce5a9899511b · outbound

This paper cites Anomalyncd: Towards novel anomaly class discovery in industrial scenarios.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Anomalyncd: Towards novel anomaly class discovery in industrial scenarios

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.255646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ed7d8606-cb80-4ac1-a1fd-7a87a3286db5 · outbound

This paper cites Segment any- thing.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Segment any- thing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.169929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ec76a989-bf2a-47dc-9848-d945ef5aaa01 · outbound

This paper cites Reducing boundary artifacts in image deconvolution.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Reducing boundary artifacts in image deconvolution

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.026294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 863a7251-aea3-4b5b-aae2-0014b38fb332 · outbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Simplenet: A simple network for image anomaly detection and localization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.889127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 264f4044-c393-4aec-a0c7-52b7ad64bcc2 · outbound

This paper cites Explor- ing intrinsic normal prototypes within a single image for universal anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Explor- ing intrinsic normal prototypes within a single image for universal anomaly detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.790557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea0891c9-610f-4df7-872f-251d228d5585 · outbound

This paper cites Abnormal be- havior recognition for intelligent video surveillance systems: A review.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Abnormal be- havior recognition for intelligent video surveillance systems: A review

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.648762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0e232e0d-230f-4afd-ba34-de4a60e2d102 · outbound

This paper cites A threshold selection method from gray- level histograms.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images A threshold selection method from gray- level histograms

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.554800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 17619b96-edeb-4ea5-beea-eb9c76d01125 · outbound

This paper cites Towards total recall in industrial anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Towards total recall in industrial anomaly detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.408656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cc8b1757-5e22-4b3a-9395-0829947888b7 · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Revisiting reverse distillation for anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.279897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c6b27f7c-94fa-4e49-a9ea-d6ccb94273d5 · outbound

This paper cites Stable and low- precision training for large-scale vision-language models.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Stable and low- precision training for large-scale vision-language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.102845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b3006fc-6562-412b-a13b-6e89c687d832 · outbound

This paper cites Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffu- sion models.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffu- sion models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.934333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d09a41f7-015e-403f-a3ee-4244d10ddd34 · outbound

This paper cites Im-iad: Indus- trial image anomaly detection benchmark in manufacturing.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Im-iad: Indus- trial image anomaly detection benchmark in manufacturing

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.768466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation db18170b-278f-4979-99d2-13cd642001f6 · outbound

This paper cites Dual- level adaptive self-labeling for novel class discovery in point cloud segmentation.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Dual- level adaptive self-labeling for novel class discovery in point cloud segmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.586288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bee66b26-fe84-4029-a2f9-dbb99b14a46b · outbound

This paper cites Glad: towards better reconstruction with 6 global and local adaptive diffusion models for unsupervised anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Glad: towards better reconstruction with 6 global and local adaptive diffusion models for unsupervised anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.389058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13409bbc-e174-410f-9245-40cffa88c41c · outbound

This paper cites Dsr–a dual subspace re-projection network for surface anomaly de- tection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Dsr–a dual subspace re-projection network for surface anomaly de- tection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.212061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 084e8cb1-3cbe-4cec-aba9-ee0500496b97 · outbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.050238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fa25672-dc25-42b2-bc81-9207223877fe · outbound

This paper cites Msflow: Multiscale flow-based framework for unsupervised anomaly detection.

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images Msflow: Multiscale flow-based framework for unsupervised anomaly detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.894918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation db3d77b2-59e9-4e26-8d19-8a43f5503d56 · inbound

MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models cites this paper.

MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:03:20.080409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 90154ced-fca4-4297-a8bd-10baf977df6d · inbound

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track cites this paper.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:03.942188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.