Paper Citation Record · LEDGER

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

As of 9 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 3 inbound Pith citation observations for arXiv:2506.01539.

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

pith.paper-citation-record.v1
2506.01539 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:46:58.520722Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:01:15.034294Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T10:17:04.738485Z

Reference resolution

60 of 60 outbound references displayed

verified exact3
verified fuzzy47
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6edc0138-db13-4405-9aca-ae2ce585aa1b · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models High-resolution image synthesis with latent diffusion models,

Reference 1

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Observation a74657a2-11b1-4957-9683-8e2ee3e255f0 · outbound

This paper cites Long-tailed diffu- sion models with oriented calibration,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Long-tailed diffu- sion models with oriented calibration,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:52.057450Z

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Observation 0393e350-a593-4f2d-b596-628c50c0c56c · outbound

This paper cites Sora as an agi world model? a complete survey on text-to-video generation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Sora as an agi world model? a complete survey on text-to-video generation,

Reference 3

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Observation 6cc0718f-736f-4d2e-88e3-93812dc99dc2 · outbound

This paper cites DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

Reference 4

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Observation fd66ee61-64e3-498e-9dcf-a974936bca0a · outbound

This paper cites Image Segmentation in Foundation Model Era: A Survey.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Image Segmentation in Foundation Model Era: A Survey

Reference 5

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Source-reported events for the cited work

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Observation feea392d-3c9b-4d1e-997b-b18db489ecc4 · outbound

This paper cites Segment Anything.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Segment Anything

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:45:38.438764Z

Source-reported events for the cited work

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Observation 4d35ce61-43d3-4fbb-8c45-9872945c53b1 · outbound

This paper cites Attrseg: open-vocabulary semantic segmentation via attribute decomposition-aggregation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Attrseg: open-vocabulary semantic segmentation via attribute decomposition-aggregation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:52.005821Z

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Observation d9d35464-9722-4e1a-bec3-6cf5efeda0f6 · outbound

This paper cites Multi-modal prototypes for open-world semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Multi-modal prototypes for open-world semantic segmentation,

Reference 8

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Observation 65d0df30-f74d-429e-add3-3ba043ab72e6 · outbound

This paper cites Uncovering prototypical knowledge for weakly open-vocabulary semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Uncovering prototypical knowledge for weakly open-vocabulary semantic segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.859288Z

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Observation d873efc8-599a-442c-ad65-7de86a640acf · outbound

This paper cites Probabilistic conformal distillation for enhancing missing modality robustness,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Probabilistic conformal distillation for enhancing missing modality robustness,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.798766Z

Source-reported events for the cited work

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Observation c868f600-09f9-4c60-b1e2-46452801483d · outbound

This paper cites Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.659384Z

Source-reported events for the cited work

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Observation b632aadb-22a4-45cd-b7bf-a55bcaec4faf · outbound

This paper cites Com- plementary patch for weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Com- plementary patch for weakly supervised semantic segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.487755Z

Source-reported events for the cited work

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Observation cb13eb28-d85a-4668-ba5b-f6a5834376b6 · outbound

This paper cites Exploiting class activation value for partial-label learning,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Exploiting class activation value for partial-label learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.261365Z

Source-reported events for the cited work

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Observation 26598a53-7b9b-4d5c-ba3f-7d41eff06210 · outbound

This paper cites Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:46:58.847203Z

Source-reported events for the cited work

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Observation b7b11a74-f1ce-4762-b698-505b6aa20886 · outbound

This paper cites Monte carlo linear clustering with single-point supervision is enough for infrared small target detection,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Monte carlo linear clustering with single-point supervision is enough for infrared small target detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:51.088276Z

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 8597f06b-4b81-4b45-a9e7-de805c8b4e9c · outbound

This paper cites Ddaug: Differentiable data augmentation for weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Ddaug: Differentiable data augmentation for weakly supervised semantic segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.938368Z

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 b24af750-214b-45e2-9421-1d708c23edfc · outbound

This paper cites Audio-visual segmentation via unlabeled frame exploitation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Audio-visual segmentation via unlabeled frame exploitation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.864695Z

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 2d9a4ff9-8e57-45e6-913a-c5facbb2619d · outbound

This paper cites Exploiting counter- examples for active learning with partial labels,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Exploiting counter- examples for active learning with partial labels,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.761097Z

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 dd2d4e4a-68cc-486e-b761-37e676b49b1c · outbound

This paper cites Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Reference 19

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Source-reported events for the cited work

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Observation 28e2a6fc-e5f7-4729-b5ea-3c9c68ddeb81 · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Open-vocabulary panoptic segmentation with text-to-image diffusion models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.667651Z

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 4c624c87-43c0-4198-8ea1-ed47307f0be5 · outbound

This paper cites Your diffusion model is secretly a zero-shot classifier,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Your diffusion model is secretly a zero-shot classifier,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.586901Z

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 39038d05-c417-444f-81cb-5e19a3ee2aba · outbound

This paper cites Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.492066Z

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 e014b3f4-ab86-4d21-a94a-70b49e61f43d · outbound

This paper cites Null-text inversion for editing real images using guided diffusion models,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Null-text inversion for editing real images using guided diffusion models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.407379Z

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 1fc53ccd-915b-4454-a004-5dd4cf124ae4 · outbound

This paper cites Dense text-to-image generation with attention modulation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Dense text-to-image generation with attention modulation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.309849Z

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Observation 1c1b04c2-6edb-4028-8d22-641cc08ca36b · outbound

This paper cites Comparing images using the hausdorff distance,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Comparing images using the hausdorff distance,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.206772Z

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 9ca5e093-535e-4f25-84dd-629fdbe8eae9 · outbound

This paper cites The pascal visual object classes challenge: A retrospective,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models The pascal visual object classes challenge: A retrospective,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:50.078091Z

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 484fdbc4-e89d-4102-8d49-092f6fca2196 · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models The role of context for object detection and semantic segmentation in the wild,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:49.986843Z

Source-reported events for the cited work

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Observation 6621bb58-1289-4b3f-a863-8fe2132c566c · outbound

This paper cites Microsoft coco: Common objects in context,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Microsoft coco: Common objects in context,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:49.832453Z

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 9677ca6b-986a-4473-8368-b8e94e903b41 · outbound

This paper cites Open-world semantic segmentation via contrasting and clustering vision-language embedding,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Open-world semantic segmentation via contrasting and clustering vision-language embedding,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:49.715499Z

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

Observation 912cc573-a85b-4327-b3ad-e14ce5232dc9 · outbound

This paper cites Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:46:58.749008Z

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Observation 42bc25ba-3779-45d8-8b7b-4cea5066d7da · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Groupvit: Semantic segmentation emerges from text supervision,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:49.593478Z

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 3c5e0857-bb13-4cfa-8410-dee5aff32334 · outbound

This paper cites ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency

Reference 32

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Observation 2f165137-ca4d-4140-8946-f807de31712e · outbound

This paper cites SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation

Reference 33

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Source-reported events for the cited work

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Observation bb14e0de-f58e-4dfb-9a45-c74d35640633 · outbound

This paper cites Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:46:58.668790Z

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

Observation bfb35a8e-8a47-43dd-a8d9-ffc6a04e049c · outbound

This paper cites Extract free dense labels from clip,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Extract free dense labels from clip,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.788893Z

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 09dbf5f1-110e-488e-9ac2-1ae488ea66a6 · outbound

This paper cites SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

Reference 36

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Observation ae86d0b0-8b4e-4c59-ba1e-4361d44990a7 · outbound

This paper cites Weaktr: Exploring plain vision transformer for weakly-supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Weaktr: Exploring plain vision transformer for weakly-supervised semantic segmentation,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:46:58.276588Z

Source-reported events for the cited work

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Observation 1e8c6996-c47b-4a16-9a6b-0437414a81a0 · outbound

This paper cites Weakly supervised learn- ing of instance segmentation with inter-pixel relations,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Weakly supervised learn- ing of instance segmentation with inter-pixel relations,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.702209Z

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 dbafcf11-2bce-4d49-87d3-590a1280a4fc · outbound

This paper cites Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.634154Z

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 de963183-7550-4514-bf24-322419ca2802 · outbound

This paper cites Multi-class token transformer for weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Multi-class token transformer for weakly supervised semantic segmentation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.566631Z

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 6c57d5eb-80b9-448d-b1f1-97a1039e5553 · outbound

This paper cites Max pooling with vision transformers reconciles class and shape in weakly supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Max pooling with vision transformers reconciles class and shape in weakly supervised semantic segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.457322Z

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 a0e7b82e-3537-4314-8522-a784289e2568 · outbound

This paper cites Token contrast for weakly-supervised semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Token contrast for weakly-supervised semantic segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.377449Z

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 c1505729-7576-4fc7-a7c8-aaa4b15ce5c9 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.305605Z

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 70467f7f-d56c-4b56-84ee-a34392914cac · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Laion-5b: An open large-scale dataset for training next generation image-text models,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.217941Z

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 2cfe7161-6c5f-4675-8e81-a1894239a7bc · outbound

This paper cites Slice segmentation propagator: Propagating the single slice annotation to 3d volume,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Slice segmentation propagator: Propagating the single slice annotation to 3d volume,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.147515Z

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 4de2debc-307c-4ece-b9c2-5503322b47c5 · outbound

This paper cites Tracking the rareness of diseases: Improving long-tail medical detection with a calibrated diffusion model,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Tracking the rareness of diseases: Improving long-tail medical detection with a calibrated diffusion model,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:47:00.065586Z

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 823322e1-a4c4-4f59-9b67-7f891de209ba · outbound

This paper cites Open-vocabulary attention maps with token optimization for semantic segmentation in diffusion models,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Open-vocabulary attention maps with token optimization for semantic segmentation in diffusion models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.980702Z

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 37ae6b42-c663-44f7-b671-4e838166cfdc · outbound

This paper cites Zero-shot semantic segmentation with decoupled one-pass network,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Zero-shot semantic segmentation with decoupled one-pass network,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.897050Z

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 cd6e72f9-95cc-46fd-bf7a-f49f304dc5ef · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.815948Z

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 e54e9949-b559-4f59-a407-db9061bc7dba · outbound

This paper cites Masked-attention mask transformer for universal image segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Masked-attention mask transformer for universal image segmentation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.718100Z

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Observation dfff3a67-4acd-48f3-a95f-1bba08159466 · outbound

This paper cites Disentangle then parse: Night-time semantic segmentation with illumination disentanglement,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Disentangle then parse: Night-time semantic segmentation with illumination disentanglement,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.649056Z

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Observation dd7aed04-157e-4339-9574-a4df91ce6a40 · outbound

This paper cites Night-time scene parsing with a large real dataset,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Night-time scene parsing with a large real dataset,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.557323Z

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Observation d24e2870-e57a-4bf1-bfc2-8e91c634e9bb · outbound

This paper cites Adding conditional control to text-to-image diffusion models,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Adding conditional control to text-to-image diffusion models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.487501Z

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Observation 48875b1d-1401-4d62-a6a5-43d6e6b79427 · outbound

This paper cites Cas- cadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Cas- cadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.416915Z

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Observation 042a948f-55f3-4030-8633-6b4ff26418af · outbound

This paper cites Seg controlnet,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Seg controlnet,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.338187Z

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Observation d8f2c3a1-53ce-4076-b21e-b2016241c333 · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Unleashing text-to-image diffusion models for visual perception,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.259979Z

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Observation 89dde0bc-2cc7-47c7-ad8e-776264b8e92e · outbound

This paper cites Diffusion Models for Open-Vocabulary Segmentation.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Diffusion Models for Open-Vocabulary Segmentation

Reference 57

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Observation 36451c3a-912b-4a29-a086-e53412ecb53a · outbound

This paper cites Training-free open-vocabulary segmentation with offline diffusion-augmented prototype generation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Training-free open-vocabulary segmentation with offline diffusion-augmented prototype generation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.179570Z

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Observation 63a8c968-8e21-4cee-af2b-496e28731dc0 · outbound

This paper cites Emerging properties in self- supervised vision transformers,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Emerging properties in self- supervised vision transformers,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.112048Z

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Observation 803e4f73-6f44-412f-adde-9d92b8006a09 · outbound

This paper cites Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation,.

G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:59.033193Z

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Pith citing papers

Observation 4a1323c7-e37a-4752-986f-cc94580c628e · inbound

ConText: Driving In-context Learning for Text Removal and Segmentation cites this paper.

ConText: Driving In-context Learning for Text Removal and Segmentation G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

Reference 93

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MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition cites this paper.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

Reference 85

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Decouple before Align: Visual Disentanglement Enhances Prompt Tuning cites this paper.

Decouple before Align: Visual Disentanglement Enhances Prompt Tuning G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

Reference 31

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