Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T19:35:19.592168Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T19:35:19.592168Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-29T00:22:50.689041Z
49 of 49 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation d6ec6856-665b-4018-84c1-d3f44e2e687f · outbound
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
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
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
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
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
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
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
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
PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Semi- supervised semantic segmentation with cross- consistency training,
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Observation b20ea112-ef19-4586-9bf7-4ad3039ff280 · outbound
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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Observation 02765730-ffde-41c0-b593-b2b5e794f867 · outbound
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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Observation b057c5aa-3a23-4bb9-a45b-6e08152acbb3 · outbound
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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Observation 6f757aad-f54e-425c-9dd4-912a608581b4 · outbound
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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Observation b8b03bbd-7b2e-4898-a145-c5056af14a4e · outbound
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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Observation ac0da051-71b2-4ecb-b73d-924f5b2e959d · outbound
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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Observation 2d74ddbb-efe3-453d-8f64-453bda610068 · outbound
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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Observation a3415aa9-28d7-46ac-9b64-bcbb665cab26 · outbound
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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Observation 8321de08-b2cf-4054-aa8d-129b85c6008f · outbound
PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging Unresolved cited work
Reference 44
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Observation 15e58c2f-2f0c-4ef7-9623-7262f9e63b83 · outbound
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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Observation eb1d719d-ca26-4365-8824-6b6c802718b3 · outbound
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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Observation 6fe17ad7-4b5f-43b5-851b-baf43aed822d · outbound
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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Observation 5d380791-26a5-4a71-96a4-6788febc1535 · outbound
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
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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Observation 16eea924-674c-4e63-945e-8710ea9910ae · inbound
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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