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Paper Citation Record · LEDGER

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation

As of 18 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 1 inbound Pith citation observation for arXiv:2506.21233.

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

pith.paper-citation-record.v1
2506.21233 v2

Coverage vector

measured 88 of 88 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:36:50.080336Z

measured 89 of 89 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T02:20:34.568987Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:21:16.444115Z

Reference resolution

88 of 88 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cf7ffdbe-619a-4043-8f38-01740027583d · outbound

This paper cites GPT-4 Technical Report.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation GPT-4 Technical Report

Reference 1

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Observation cd914686-f234-451d-9949-7d09c783182c · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Flamingo: a visual language model for few-shot learning

Reference 2

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Observation 74c5eecd-f12d-4c99-98b5-ff0aba3e3442 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 3

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Observation 0baa2d50-c5a6-41f9-952a-5795148417ec · outbound

This paper cites Qwen2.5-VL Technical Report.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Qwen2.5-VL Technical Report

Reference 4

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Observation 46710b7c-4fe9-49d9-82b9-dba06738be3b · outbound

This paper cites Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval

Reference 5

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Observation 333a0029-aa3a-4a3a-88b9-80e6799c5484 · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Training-free open- vocabulary segmentation with offline diffusion-augmented prototype generation

Reference 6

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Observation d76d690d-d53b-4205-a2e2-7e76aedd0ed8 · outbound

This paper cites Grounding everything: Emerging localiza- tion properties in vision-language transformers.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Grounding everything: Emerging localiza- tion properties in vision-language transformers

Reference 7

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Observation 9a8c96ff-c8a7-4241-b597-68eba597dd03 · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Coco- stuff: Thing and stuff classes in context

Reference 8

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Observation 94387481-f722-4ff7-b6d5-4a4d5bacff81 · outbound

This paper cites Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness

Reference 9

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Observation ca4bd275-f430-4cc3-976a-6f1df19623e8 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Emerg- ing properties in self-supervised vision transformers

Reference 10

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Observation 11b8a85c-faab-4680-9636-0f94aa33aa3f · outbound

This paper cites Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

Reference 11

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Observation 67565dd0-a3ce-4640-8fa7-5b9562b3ecc1 · outbound

This paper cites Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only

Reference 12

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Observation adb1df3d-4eeb-44cc-af5b-64b414e4d944 · outbound

This paper cites Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation

Reference 13

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Observation f7b5927d-492a-4cb0-93ea-2e1a17daf067 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 14

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Observation 36ef914b-a060-49d1-a8ec-59918348b329 · outbound

This paper cites A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series

Reference 15

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Observation 2cd16033-0233-4c96-a8bf-3ba19040934a · outbound

This paper cites De- coupling zero-shot semantic segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation De- coupling zero-shot semantic segmentation

Reference 16

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Observation 6c908253-b44e-4cbb-ab13-92d2447b06f6 · outbound

This paper cites The pascal visual object classes (voc) challenge.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation The pascal visual object classes (voc) challenge

Reference 17

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Observation 3892d368-a669-48c5-8cf8-0a505b637656 · outbound

This paper cites Scaling laws of synthetic images for model training.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Scaling laws of synthetic images for model training

Reference 18

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Observation 311b996a-43aa-43ef-a8a4-b2df7c035b97 · outbound

This paper cites Data filtering networks.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Data filtering networks

Reference 19

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Observation 954082e9-66ad-49e9-b105-1853337e8dfb · outbound

This paper cites Efficient graph-based image segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Efficient graph-based image segmentation

Reference 20

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Observation d803ab9f-f081-4f38-9ce5-14389720d6f7 · outbound

This paper cites Dat- acomp: In search of the next generation of multimodal datasets.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Dat- acomp: In search of the next generation of multimodal datasets

Reference 21

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Observation 4ed156c9-1ccd-4c66-85a5-d6259b2ed1a4 · outbound

This paper cites Scal- ing open-vocabulary image segmentation with image-level labels.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Scal- ing open-vocabulary image segmentation with image-level labels

Reference 22

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Observation b4d3db01-e840-4138-8b42-31e5b166fbfd · outbound

This paper cites kNN-CLIP: Retrieval enables training-free segmentation on continually expanding large vocabularies.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation kNN-CLIP: Retrieval enables training-free segmentation on continually expanding large vocabularies

Reference 23

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Observation 52935343-e4da-4be0-84d9-3be0fb9dea9c · outbound

This paper cites Robustifying token attention for vision transformers.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Robustifying token attention for vision transformers

Reference 24

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Observation eb7229ce-55a5-423c-acd2-5a7a34c4a8ca · outbound

This paper cites Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation

Reference 25

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Observation 9a0960c0-ee4f-45ec-9e1f-9e051843e8e3 · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 26

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Observation 8a30cfe1-fe1a-4f29-a0aa-658120423536 · outbound

This paper cites Diffusion models for open-vocabulary segmen- tation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Diffusion models for open-vocabulary segmen- tation

Reference 27

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Observation f8a112ce-fa41-4054-8fa9-ca0fdae66fbe · outbound

This paper cites Segment any- thing.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Segment any- thing

Reference 28

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Observation 3b4bf006-a2cb-4476-9dc6-33e3c00841fa · outbound

This paper cites Efficient inference in fully connected crfs with gaussian edge potentials.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Efficient inference in fully connected crfs with gaussian edge potentials

Reference 29

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Observation 81b4d717-c583-40c0-92f1-222e9f679150 · outbound

This paper cites LISA: Reasoning segmentation via large language model.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation LISA: Reasoning segmentation via large language model

Reference 30

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Observation dfe96d18-2965-425a-adf8-a6fc94efafe6 · outbound

This paper cites Veclip: Improving clip training via visual-enriched captions.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Veclip: Improving clip training via visual-enriched captions

Reference 31

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Observation 4c8af45d-efa0-401f-93b7-9205e54e89c1 · outbound

This paper cites Proxyclip: Proxy at- tention improves clip for open-vocabulary segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Proxyclip: Proxy at- tention improves clip for open-vocabulary segmentation

Reference 32

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Observation 331b762f-f057-49cd-a03f-10158e0fe608 · outbound

This paper cites TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation

Reference 33

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Observation 180ecf60-04bc-4158-8db5-28f171710624 · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 34

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Source-reported events for the cited work

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Observation 8beec294-3051-4213-8b55-992b160f942d · outbound

This paper cites A Closer Look at the Explainability of Contrastive Language-Image Pre-training.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 35

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Observation 16dbde27-7b52-4aa6-9434-a11135ffa506 · outbound

This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

Reference 36

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Observation 3a351e8b-0260-4233-8d10-03e5542bf1ab · outbound

This paper cites Microsoft coco: Common objects in context.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Microsoft coco: Common objects in context

Reference 37

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Observation fba273c8-4571-4c9f-ba49-e2c19b85bc7f · outbound

This paper cites Visual instruction tuning.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Visual instruction tuning

Reference 38

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Observation 5125b102-0d7b-419c-8686-4989c1d9110f · outbound

This paper cites Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation

Reference 39

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Observation 3b06323d-273a-437f-9e1a-03618052a566 · outbound

This paper cites Emergent open-vocabulary semantic segmenta- tion from off-the-shelf vision-language models.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Emergent open-vocabulary semantic segmenta- tion from off-the-shelf vision-language models

Reference 40

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Observation 6002662a-966c-4168-9b96-2978718d2b6e · outbound

This paper cites Sieve: Multimodal dataset pruning using image captioning models.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Sieve: Multimodal dataset pruning using image captioning models

Reference 41

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Observation c9af95e9-08f0-48e3-9e2e-054a974cf749 · outbound

This paper cites Towards interactive 3d surgical scene reconstruction: An incremental training and monitoring framework.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Towards interactive 3d surgical scene reconstruction: An incremental training and monitoring framework

Reference 42

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Observation 8a6d29d4-e7ac-489b-9ecb-07effd638e79 · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation The role of context for object detection and semantic segmentation in the wild

Reference 43

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Observation 7bd015a8-43b4-4111-8c61-7c5e69ec2db0 · outbound

This paper cites Open vocabulary semantic segmentation with patch aligned con- trastive learning.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Open vocabulary semantic segmentation with patch aligned con- trastive learning

Reference 44

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Observation 1244d5a0-8689-405c-8415-f8912b86acd7 · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation

Reference 45

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Observation 66d335a9-8cfd-46e8-94e4-e5ba3e893868 · outbound

This paper cites Improving multimodal datasets with image captioning.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Improving multimodal datasets with image captioning

Reference 46

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Observation 9e61ecd3-404d-4af5-83fe-7fb003037fd8 · outbound

This paper cites an unresolved cited work.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Unresolved cited work

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 93041323-ebb8-40c9-bbec-e1bfeb8885e8 · outbound

This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 48

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Observation abd72d5f-9ef4-41b9-a29a-cad84a9053a0 · outbound

This paper cites and et al.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation and et al

Reference 49

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Observation 603d34ca-b464-4b79-affb-fcec8c418b06 · outbound

This paper cites Filtering, distil- lation, and hard negatives for vision-language pre-training.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Filtering, distil- lation, and hard negatives for vision-language pre-training

Reference 50

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Observation c5de6fb1-fd74-4917-a404-c71029f69adb · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Learning transferable visual models from natural language supervi- sion

Reference 51

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Observation 54d4afd6-97ee-4556-b348-9bb92f1b3abe · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation SAM 2: Segment Anything in Images and Videos

Reference 52

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Unavailable: canonical work link unavailable.

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Observation bcdc1e21-0cc8-4230-8e0d-5325b9b5e63a · outbound

This paper cites Zero- guidance segmentation using zero segment labels.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Zero- guidance segmentation using zero segment labels

Reference 53

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Observation 37ebec69-623b-43d2-8dd3-50d2be1383ee · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation High-resolution image synthesis with latent diffusion models

Reference 54

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Observation e32bfd97-c36c-496f-ad7a-5c05f34c0968 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 55

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Unavailable: canonical work link unavailable.

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Observation 700574c9-37fb-49ba-8225-ed76d2ca434e · outbound

This paper cites LAION-5b: An open large-scale dataset for train- ing next generation image-text models.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation LAION-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 56

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Source-reported events for the cited work

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Observation 1d84a963-fb5e-4ca1-9e78-8e53df002a54 · outbound

This paper cites Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

Reference 57

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Observation c4f2a0fe-8c59-4474-ba13-cc2f43d14b0d · outbound

This paper cites Reco: Re- trieve and co-segment for zero-shot transfer.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Reco: Re- trieve and co-segment for zero-shot transfer

Reference 58

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Source-reported events for the cited work

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Observation e0a010d1-d229-44bd-8efa-cee0425f0b95 · outbound

This paper cites Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Is synthetic data all we need? benchmarking the robustness of models trained with synthetic images

Reference 59

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Source-reported events for the cited work

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Observation 83d77fd3-a0b2-479b-8ae2-36eb0da908ac · outbound

This paper cites Clip as rnn: Segment countless visual concepts without training endeavor.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Clip as rnn: Segment countless visual concepts without training endeavor

Reference 60

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Source-reported events for the cited work

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Observation 02be3e0b-e9bd-4d43-8b88-6728f84301b6 · outbound

This paper cites Attention is all you need.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Attention is all you need

Reference 61

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Observation 5f87a123-372f-44e3-9c41-ef8a0c82ad77 · outbound

This paper cites Sclip: Rethink- ing self-attention for dense vision-language inference.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Sclip: Rethink- ing self-attention for dense vision-language inference

Reference 62

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Source-reported events for the cited work

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Observation 41427905-8526-4e77-8d91-442d53537a6f · outbound

This paper cites Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding

Reference 63

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Source-reported events for the cited work

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Observation 309c8087-76f6-409b-b9a7-1eeabbd38cb1 · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Reference 64

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Source-reported events for the cited work

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Observation f5ff4a74-34cd-4f60-822d-7ff79976c443 · outbound

This paper cites Use: Universal segment embeddings for open-vocabulary image segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Use: Universal segment embeddings for open-vocabulary image segmentation

Reference 65

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a4876ccb-0095-4a07-9b26-ce67a5934714 · outbound

This paper cites Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation

Reference 66

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 7c51c9f8-092b-4b3f-8468-bb066e5cb98f · outbound

This paper cites Probabilistic pixel-adaptive refinement networks.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Probabilistic pixel-adaptive refinement networks

Reference 67

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8eed80cd-4bf3-467a-b25c-edc5a6ed56c9 · outbound

This paper cites Image-text co- decomposition for text-supervised semantic segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Image-text co- decomposition for text-supervised semantic segmentation

Reference 68

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a08aac2d-72b3-4835-91a3-5794fd848797 · outbound

This paper cites Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free

Reference 69

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b55d44bf-32a9-4afd-8a9c-0882d73331d5 · outbound

This paper cites Florence-2: Advancing a unified representation for a variety of vision tasks.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Florence-2: Advancing a unified representation for a variety of vision tasks

Reference 70

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ab6c9639-e480-4eba-acce-1449f601e5fc · outbound

This paper cites Rewrite caption semantics: Bridging seman- tic gaps for language-supervised semantic segmentation.Ad- vances in Neural Information Processing Systems, 36, 2024.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Rewrite caption semantics: Bridging seman- tic gaps for language-supervised semantic segmentation.Ad- vances in Neural Information Processing Systems, 36, 2024

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4c496ad9-1609-4cde-9375-aa13bb9887fc · outbound

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

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Groupvit: Semantic segmentation emerges from text supervision

Reference 72

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 66b577f4-10ea-46e7-b8aa-7ebf025f3ec4 · outbound

This paper cites Learning open-vocabulary semantic segmentation models from natural language supervision.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Learning open-vocabulary semantic segmentation models from natural language supervision

Reference 73

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fc8ff5d2-783f-4225-87bb-08d2cd59cd79 · outbound

This paper cites A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model

Reference 74

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation be625883-898b-4e83-af6d-619f375df21b · outbound

This paper cites Side adapter network for open-vocabulary semantic segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Side adapter network for open-vocabulary semantic segmentation

Reference 75

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ea1d4c6b-1f80-4025-be9e-86883b3755c0 · outbound

This paper cites V AC-CNN: A visual analytics system for compar- ative studies of deep convolutional neural networks.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation V AC-CNN: A visual analytics system for compar- ative studies of deep convolutional neural networks

Reference 76

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1ec1d09f-238b-4f3f-a1b0-cbdf222634b8 · outbound

This paper cites Suny: A visual interpretation framework for convolutional neural networks from a necessary and suf- ficient perspective.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Suny: A visual interpretation framework for convolutional neural networks from a necessary and suf- ficient perspective

Reference 77

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 181d2d2a-36bf-4b86-81b6-42412eec4de8 · outbound

This paper cites SLIM: Spuriousness mitigation with minimal human annotations.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation SLIM: Spuriousness mitigation with minimal human annotations

Reference 78

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 83e490e4-ce6c-4795-8c24-9696f3cb06e4 · outbound

This paper cites AttributionScanner: A visual analytics system for model validation with metadata-free slice finding.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation AttributionScanner: A visual analytics system for model validation with metadata-free slice finding

Reference 79

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9e3cefe5-8395-4cb9-ae90-f0df2e3b1b36 · outbound

This paper cites VISTA: A visual analytics framework to enhance foundation model-generated data labels.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation VISTA: A visual analytics framework to enhance foundation model-generated data labels

Reference 80

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3c3e2c41-bf7c-44ba-beb5-881c22edc89f · outbound

This paper cites Vislix: An xai framework for val- idating vision models with slice discovery and analysis.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Vislix: An xai framework for val- idating vision models with slice discovery and analysis

Reference 81

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d5d65c32-dd3d-42a1-86a3-778889d3e525 · outbound

This paper cites A simple framework for text- supervised semantic segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation A simple framework for text- supervised semantic segmentation

Reference 82

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 5cba2281-be1c-4c55-9237-96172f817b4a · outbound

This paper cites CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation

Reference 83

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4fab9d7d-1604-42b9-98f8-480652ec23e8 · outbound

This paper cites Tip- adapter: Training-free adaption of clip for few-shot classi- fication.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Tip- adapter: Training-free adaption of clip for few-shot classi- fication

Reference 84

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6500ca74-6770-42a8-b284-04cce307a393 · outbound

This paper cites Labelvizier: Interactive validation and relabeling for technical text annotations.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Labelvizier: Interactive validation and relabeling for technical text annotations

Reference 85

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cb7908d7-b580-4ca3-8f1a-43ecba8a208a · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Semantic under- standing of scenes through the ade20k dataset

Reference 86

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3d7417e8-12e5-42aa-8179-965fb5ba9ca4 · outbound

This paper cites Extract free dense labels from clip.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation Extract free dense labels from clip

Reference 87

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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 2026-08-18T06:34:40.430872+00:00.

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Observation 5afc66ba-1db5-4322-8cac-109c8ec26260 · outbound

This paper cites ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions.

ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions

Reference 88

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

Observation 3514aca8-2c36-42e1-b4ba-e32671ab8a27 · inbound

Investigating Anisotropy in Visual Grounding under Controlled Counterfactual Perturbations cites this paper.

Investigating Anisotropy in Visual Grounding under Controlled Counterfactual Perturbations ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation

Reference 38

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arxiv_id, observed 2026-05-12T02:21:16.446524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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