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Paper Citation Record · LEDGER

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

As of 6 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2604.22823.

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

pith.paper-citation-record.v1
2604.22823 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T19:35:19.592168Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-29T00:22:50.689041Z

Reference resolution

49 of 49 outbound references displayed

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  • verified fuzzy0
  • unresolved48
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  • malformed identifier1
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Outbound references

Observation d6ec6856-665b-4018-84c1-d3f44e2e687f · outbound

This paper cites A brief survey on semantic segmentation with deep learning,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging A brief survey on semantic segmentation with deep learning,

Reference 1

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Observation a377cf98-9022-4ec6-bd84-069994df82a7 · outbound

This paper cites Review the state-of-the-art technologies of se- mantic segmentation based on deep learning,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Review the state-of-the-art technologies of se- mantic segmentation based on deep learning,

Reference 2

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Observation 0652146c-d87c-4862-be43-a0f600638447 · outbound

This paper cites Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Vision-based semantic segmentation in scene understanding for autonomous driving: Recent achievements, challenges, and outlooks,

Reference 3

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Observation 8ce1c4e3-c871-41bb-89ad-8b74bcdac29a · outbound

This paper cites Semantic segmentation for self-driving cars us- ing deep learning: a survey,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semantic segmentation for self-driving cars us- ing deep learning: a survey,

Reference 4

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Observation 0e651d4b-16ac-458c-8035-67ec3f86f57d · outbound

This paper cites Real-time semantic image segmen- tation with deep learning for autonomous driving: A survey,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Real-time semantic image segmen- tation with deep learning for autonomous driving: A survey,

Reference 5

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Observation fd131328-e261-4c92-bfb9-9a845a5acec0 · outbound

This paper cites View-coherent correlation consistency for semi-supervised se- mantic segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging View-coherent correlation consistency for semi-supervised se- mantic segmentation,

Reference 6

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Observation 16e08bf8-7414-479b-97ad-30574922f5b0 · outbound

This paper cites Perturbed and strict mean teachers for semi-supervised semantic segmenta- tion,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Perturbed and strict mean teachers for semi-supervised semantic segmenta- tion,

Reference 7

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Observation ff7cae82-8e74-4250-b18f-9239a07dd274 · outbound

This paper cites Semi- supervised semantic segmentation using cross- consistency training for pavement crack detec- tion,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi- supervised semantic segmentation using cross- consistency training for pavement crack detec- tion,

Reference 8

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Observation 8d5d62fa-318c-45d7-908b-182fdf116924 · outbound

This paper cites Semi- supervised semantic segmentation with cross- consistency training,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi- supervised semantic segmentation with cross- consistency training,

Reference 9

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Observation b20ea112-ef19-4586-9bf7-4ad3039ff280 · outbound

This paper cites Semi-supervised semantic segmentation using unreliable pseudo- labels,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmentation using unreliable pseudo- labels,

Reference 10

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Observation 51502568-65c9-4fb6-8266-0196d8abfe0c · outbound

This paper cites Learn- ing pseudo labels for semi-and-weakly super- vised semantic segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Learn- ing pseudo labels for semi-and-weakly super- vised semantic segmentation,

Reference 11

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Observation 38428c2e-098a-4a06-a4e6-178fe4c1340d · outbound

This paper cites Semi-supervised semantic segmentation via en- tropy minimization,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmentation via en- tropy minimization,

Reference 12

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Observation 59981cb9-a332-4a17-8060-f0805442a026 · outbound

This paper cites Semantic segmentation with genera- tive models: Semi-supervised learning and strong out-of-domain generalization,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semantic segmentation with genera- tive models: Semi-supervised learning and strong out-of-domain generalization,

Reference 13

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Observation 00249789-d866-4d39-8d5f-e7958979e87c · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning re- sults,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning re- sults,

Reference 14

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Observation 70a4c292-6149-4237-bcd5-c764f79a34d7 · outbound

This paper cites Semantic segmentation in autonomous driving—an example of fcn,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semantic segmentation in autonomous driving—an example of fcn,

Reference 15

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Observation 82f8943b-89ad-4f6d-b8d3-c421451c54d8 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Segnet: A deep convolutional encoder-decoder architecture for image segmentation,

Reference 16

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Observation 91f9f0bc-1ef9-4d3c-8387-9c5923ec4fc2 · outbound

This paper cites Image segmentation for self-driving car,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Image segmentation for self-driving car,

Reference 17

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Observation f8e987ed-c907-4a7b-ab0b-f11fcd337182 · outbound

This paper cites Two-stage framework with improved u-net based on self-supervised contrastive learning for pavement crack segmen- tation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Two-stage framework with improved u-net based on self-supervised contrastive learning for pavement crack segmen- tation,

Reference 18

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Observation 2347c346-81e8-4447-b4e2-584eb1e8eae0 · outbound

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

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 19

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Observation 82dd7cfb-7623-4ffc-8e98-e29914f03396 · outbound

This paper cites Sats: Self-attention transfer for continual semantic segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Sats: Self-attention transfer for continual semantic segmentation,

Reference 20

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Observation d1c78840-8e11-447b-8087-f5a5f92d8f6b · outbound

This paper cites Scribblenet: Efficient interactive anno- tation of urban city scenes for semantic segmen- tation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Scribblenet: Efficient interactive anno- tation of urban city scenes for semantic segmen- tation,

Reference 21

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Observation e77fdb1d-3771-4207-ba3e-5111ce839a0f · outbound

This paper cites Simpler is better: Few-shot semantic segmentation with classifier weight transformer,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Simpler is better: Few-shot semantic segmentation with classifier weight transformer,

Reference 22

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Observation 23a75819-5db6-40e3-b676-a53fea6ee50f · outbound

This paper cites Beyond low- dimensional features: Enhancing semi-supervised medical image semantic segmentation with ad- vanced consistency learning techniques,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Beyond low- dimensional features: Enhancing semi-supervised medical image semantic segmentation with ad- vanced consistency learning techniques,

Reference 23

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Observation 1e4fd3bb-80b7-4d4f-ab17-c082abe27ed0 · outbound

This paper cites Semi-supervised semantic segmentation with prototype-based consistency regularization,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmentation with prototype-based consistency regularization,

Reference 24

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Observation 356373bb-4e1c-4d52-8df5-da9189307563 · outbound

This paper cites Enhancing semi-supervised semantic segmen- tation of remote sensing images via fea- ture perturbation-based consistency regulariza- tion methods,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Enhancing semi-supervised semantic segmen- tation of remote sensing images via fea- ture perturbation-based consistency regulariza- tion methods,

Reference 25

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Observation 6aef27a3-5a9b-4b6e-8774-6a5fc292d5fb · outbound

This paper cites Improving semi-supervised and domain-adaptive semantic segmentation with self-supervised depth estimation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Improving semi-supervised and domain-adaptive semantic segmentation with self-supervised depth estimation,

Reference 26

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Observation fbbe91a4-0bfd-479e-ba63-82a2b4814e5d · outbound

This paper cites Learning from pixel-level label noise: A new perspective for semi-supervised semantic seg- mentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Learning from pixel-level label noise: A new perspective for semi-supervised semantic seg- mentation,

Reference 27

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Observation 3c8bf0b7-7dfa-44c8-a9d5-8074d14c523f · outbound

This paper cites Semi-supervised remote sensing image semantic segmentation via consistency regular- ization and average update of pseudo-label,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised remote sensing image semantic segmentation via consistency regular- ization and average update of pseudo-label,

Reference 28

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Observation 5ec6877b-55b9-498f-8f5f-2c4dd0701bf6 · outbound

This paper cites Ambiguity-selective con- sistency regularization for mean-teacher semi- supervised medical image segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Ambiguity-selective con- sistency regularization for mean-teacher semi- supervised medical image segmentation,

Reference 29

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Observation 42777416-8600-493b-b060-45ae1a12925a · outbound

This paper cites Dual attention based uncertainty-aware mean teacher model for semi-supervised cardiac image segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Dual attention based uncertainty-aware mean teacher model for semi-supervised cardiac image segmentation,

Reference 30

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Observation 6b5d7a9a-848d-4aae-90a9-784104dd26ca · outbound

This paper cites Semi-supervised brain lesion segmentation with an adapted mean teacher model,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised brain lesion segmentation with an adapted mean teacher model,

Reference 31

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Observation 71a3411f-ae2b-450c-8a7f-9a8007778a92 · outbound

This paper cites Semi-supervised semantic segmen- tation with cross teacher training,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmen- tation with cross teacher training,

Reference 32

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Observation 5de8a950-cf5e-4665-aa9d-251b0742b62d · outbound

This paper cites Semi-supervised semantic segmentation via gentle teaching assis- tant,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmentation via gentle teaching assis- tant,

Reference 33

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Observation eee7d296-c075-4a04-a706-cbc90a48e1a9 · outbound

This paper cites Automated evaluation of semantic segmentation robustness for autonomous driving,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Automated evaluation of semantic segmentation robustness for autonomous driving,

Reference 34

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Observation dac15e81-4be5-4333-b661-168baaa8e202 · outbound

This paper cites Deep cluster- ing for weakly-supervised semantic segmentation in autonomous driving scenes,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Deep cluster- ing for weakly-supervised semantic segmentation in autonomous driving scenes,

Reference 35

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Observation 147627bc-6430-4fe8-8b91-74b08dcf6491 · outbound

This paper cites Acdc: The adverse conditions dataset with correspon- dences for semantic driving scene understand- ing,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Acdc: The adverse conditions dataset with correspon- dences for semantic driving scene understand- ing,

Reference 36

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Rainy wcity: A real rainfall dataset with diverse conditions for semantic driv- ing scene understanding

Reference 37

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This paper cites Revisiting weak-to-strong consistency in semi- supervised semantic segmentation,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Revisiting weak-to-strong consistency in semi- supervised semantic segmentation,

Reference 38

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This paper cites Semi-supervised semantic segmen- tation via adaptive equalization learning,.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmen- tation via adaptive equalization learning,

Reference 39

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmentation with cross pseudo supervision,

Reference 40

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging En- hanced soft label for semi-supervised semantic segmentation,

Reference 41

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging The cityscapes dataset for se- mantic urban scene understanding,

Reference 42

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging The pascal vi- sual object classes (voc) challenge,

Reference 43

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Unresolved cited work

Reference 44

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised seman- tic segmentation needs strong, high-dimensional perturbations,

Reference 45

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised semantic segmentation using unreliable pseudo- labels,

Reference 46

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi-supervised seman- tic segmentation with error localization network,

Reference 47

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PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Augmentation matters: A simple-yet- effective approach to semi-supervised semantic segmentation,

Reference 48

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Observation 614de962-9793-4a5d-9d28-4db3b0490934 · outbound

This paper cites His research interests include intelli- gent agents, decision making, social net- works, and computer games.

PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging His research interests include intelli- gent agents, decision making, social net- works, and computer games

Reference 49

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Pith citing papers

Observation 16eea924-674c-4e63-945e-8710ea9910ae · inbound

EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization cites this paper.

EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging

Reference 10

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