Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:57:39.935785Z
Paper Citation Record · LEDGER
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation
As of 22 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2504.14231.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
53 of 53 outbound references displayed
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- verified fuzzy44
- unresolved8
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b49a7460-3b83-44a8-bef1-63936a31163e · outbound
This paper cites Beit: Bert pre-training of image transformers.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Beit: Bert pre-training of image transformers
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation 1f33d511-44e9-4487-b184-506199a7aac2 · outbound
This paper cites Cycle and seman- tic consistent adversarial domain adaptation for reducing simulation-to-real domain shift in lidar bird’s eye view.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Cycle and seman- tic consistent adversarial domain adaptation for reducing simulation-to-real domain shift in lidar bird’s eye view
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation d99b96db-2736-40e6-be7c-dc11a2b66f2e · outbound
This paper cites Se- mantickITTI: A dataset for semantic scene understanding of LiDAR sequences.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Se- mantickITTI: A dataset for semantic scene understanding of LiDAR sequences
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation 633bd2d3-a5e7-4650-8ce4-9da3c5e4f3dd · outbound
This paper cites CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes
Reference 4
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6294d9d6-fdf1-4a5a-a229-3f909f14e874 · outbound
This paper cites nuscenes: A mul- timodal dataset for autonomous driving.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation nuscenes: A mul- timodal dataset for autonomous driving
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation 27a1494f-4def-4dd8-afb4-1f3dac3b9353 · outbound
This paper cites Mopa: Multi-modal prior aided domain adaptation for 3d semantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Mopa: Multi-modal prior aided domain adaptation for 3d semantic segmentation
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5636d3dc-05f3-4f36-a678-7fbdd7ad5c91 · outbound
This paper cites Exploiting the complementarity of 2d and 3d networks to address domain-shift in 3d semantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Exploiting the complementarity of 2d and 3d networks to address domain-shift in 3d semantic segmentation
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation c154e098-20d9-4964-8989-4855e1222ba7 · outbound
This paper cites Emerg- ing properties in self-supervised vision transformers.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Emerg- ing properties in self-supervised vision transformers
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 68c672ad-7c54-4cad-9bf8-b93191c520b3 · outbound
This paper cites Self-training avoids using spurious features under domain shift.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Self-training avoids using spurious features under domain shift
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d1d7d7de-3368-44a8-b096-987fad6d5fd9 · outbound
This paper cites Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation b6d60685-5ac2-49b0-9e75-348c3ae3bdd0 · outbound
This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation An image is worth 16x16 words: Trans- formers for image recognition at scale
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 9bdae310-f2bf-48ed-892a-34bb4a2acb6f · outbound
This paper cites Virtual worlds as proxy for multi-object tracking anal- ysis.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Virtual worlds as proxy for multi-object tracking anal- ysis
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 1ec089f6-9e01-4ba2-a194-1ca178c55468 · outbound
This paper cites Domain-adversarial training of neural networks.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Domain-adversarial training of neural networks
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation f020ff30-6ea6-4e7e-8b7c-b2dfb0431630 · outbound
This paper cites A2D2: Audi Autonomous Driving Dataset.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation A2D2: Audi Autonomous Driving Dataset
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation 770274d9-133a-446b-8b40-b9e384d92f1e · outbound
This paper cites 3d semantic segmentation with submanifold sparse convolutional networks.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation 3d semantic segmentation with submanifold sparse convolutional networks
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation 3646c11f-acf5-4c91-89ac-a58ea158473c · outbound
This paper cites Deep residual learning for image recognition.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Deep residual learning for image recognition
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation 34a6f71c-04aa-4e2b-9754-a996ffc88a6e · outbound
This paper cites Cycada: Cycle-consistent adversarial domain adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Cycada: Cycle-consistent adversarial domain adaptation
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 9601391e-8a1f-4c8d-b60f-4c8a39774110 · outbound
This paper cites xMUDA: Cross-modal unsuper- vised domain adaptation for 3D semantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation xMUDA: Cross-modal unsuper- vised domain adaptation for 3D semantic segmentation
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation b1a47038-4e35-4bfa-a39d-23d5f76b0b10 · outbound
This paper cites Cross-modal learning for domain adaptation in 3D semantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Cross-modal learning for domain adaptation in 3D semantic segmentation
Reference 19
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c808be14-e56a-4a43-9f49-1c1cbe4cb1be · outbound
This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Scaling up visual and vision-language representation learning with noisy text supervision
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a7e66568-4dcb-445c-801f-faccf57fb0e8 · outbound
This paper cites Segment any- thing.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Segment any- thing
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation 99c764b7-9250-4e8d-82c0-d5068a2e5d95 · outbound
This paper cites Temporal ensembling for semi- supervised learning.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Temporal ensembling for semi- supervised learning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.554985Z
Source-reported events for the cited work
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Observation 6a85b042-d164-431d-89f5-3a936496d5d7 · outbound
This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 1c247baf-3733-410f-ad39-2ac76a800ede · outbound
This paper cites Mseg3d: Multi-modal 3d semantic segmentation for autonomous driv- ing.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Mseg3d: Multi-modal 3d semantic segmentation for autonomous driv- ing
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3b675444-d96d-48df-b0d0-4a04bd9c485f · outbound
This paper cites Adaptive batch normalization for practical do- main adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Adaptive batch normalization for practical do- main adaptation
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation 288f3db1-2798-4aa9-afef-bd7a2ee3cc8b · outbound
This paper cites Cycle self-training for domain adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Cycle self-training for domain adaptation
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 40982932-a089-487c-8102-3c53a5e59d17 · outbound
This paper cites Adversarial unsupervised domain adaptation for 3d semantic segmentation with multi-modal learning.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Adversarial unsupervised domain adaptation for 3d semantic segmentation with multi-modal learning
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation 0bbbb9cd-c236-4ab1-9756-ce257a7ad335 · outbound
This paper cites Segment any point cloud sequences by distilling vision foundation models.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Segment any point cloud sequences by distilling vision foundation models
Reference 28
- Resolution
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Source-reported events for the cited work
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Observation 18a9be52-f6b9-43ba-a02d-6aef0fbab971 · outbound
This paper cites A convnet for the 2020s.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation A convnet for the 2020s
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation e0950d15-34a3-406e-8baf-70b8ef01eb53 · outbound
This paper cites In- stance adaptive self-training for unsupervised domain adap- tation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation In- stance adaptive self-training for unsupervised domain adap- tation
Reference 30
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation b3635756-a666-478d-a2bd-8d65f6913a55 · outbound
This paper cites Saluda: Surface- based automotive lidar unsupervised domain adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Saluda: Surface- based automotive lidar unsupervised domain adaptation
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation e533cabd-edab-4d6e-b283-58101adcd0a3 · outbound
This paper cites The norm must go on: Dynamic unsuper- vised domain adaptation by normalization.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation The norm must go on: Dynamic unsuper- vised domain adaptation by normalization
Reference 32
- Resolution
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Source-reported events for the cited work
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Observation 6a0ec51d-4266-4ff4-ac95-aaa37ff93cbc · outbound
This paper cites Minimal-entropy correlation alignment for unsupervised deep domain adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Minimal-entropy correlation alignment for unsupervised deep domain adaptation
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation f47bcfba-6328-4d7c-a667-c5229edeadca · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation DINOv2: Learning Robust Visual Features without Supervision
Reference 34
- Resolution
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Source-reported events for the cited work
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Observation 7f936962-150c-4886-bc6c-95613816534b · outbound
This paper cites Sparse-to-dense feature matching: Intra and inter do- main cross-modal learning in domain adaptation for 3d se- mantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Sparse-to-dense feature matching: Intra and inter do- main cross-modal learning in domain adaptation for 3d se- mantic segmentation
Reference 35
- Resolution
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Source-reported events for the cited work
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Observation 46fbfc93-b5f1-453a-8189-636cb2b23121 · outbound
This paper cites Learning to adapt sam for segmenting cross-domain point clouds.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Learning to adapt sam for segmenting cross-domain point clouds
Reference 36
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 7cdc1878-1f9d-4dbd-9370-efb5db3c34f9 · outbound
This paper cites Three pillars improving vision foundation model distillation for lidar.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Three pillars improving vision foundation model distillation for lidar
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c7ad13b1-9405-43a3-9e5e-3042952dbddf · outbound
This paper cites Learn- ing transferable visual models from natural language super- vision.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Learn- ing transferable visual models from natural language super- vision
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation 12d047ac-053d-4ad5-924d-55381fda8a86 · outbound
This paper cites Am-radio: Agglomerative vision foundation model reduce all domains into one.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Am-radio: Agglomerative vision foundation model reduce all domains into one
Reference 39
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9c45cb3b-86d4-4c32-88a7-d6133c2e7065 · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation U-net: Convolutional networks for biomedical image segmentation
Reference 40
- Resolution
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Source-reported events for the cited work
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Observation abb34bee-e20e-44d4-b55b-36300e2f04ad · outbound
This paper cites Image-to-lidar self-supervised distillation for autonomous driving data.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Image-to-lidar self-supervised distillation for autonomous driving data
Reference 41
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 86d5109f-84c5-416e-990d-934fd8ee1f91 · outbound
This paper cites Deep coral: Correlation alignment for deep domain adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Deep coral: Correlation alignment for deep domain adaptation
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c7afe072-e402-46ec-b952-d08b95850408 · outbound
This paper cites Cross-modal unsu- pervised domain adaptation for 3d semantic segmentation via bidirectional fusion-then-distillation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Cross-modal unsu- pervised domain adaptation for 3d semantic segmentation via bidirectional fusion-then-distillation
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.161458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation cce93922-c4b9-4c04-86f3-4c6ac88519eb · outbound
This paper cites Unidseg: Unified cross-domain 3d semantic segmentation via visual foundation models prior.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Unidseg: Unified cross-domain 3d semantic segmentation via visual foundation models prior
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.149207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 91b1d294-3806-4346-b782-a28afec32ee8 · outbound
This paper cites Fusion-then-distillation: Toward cross-modal positive distillation for domain adaptive 3d semantic seg- mentation, 2024.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Fusion-then-distillation: Toward cross-modal positive distillation for domain adaptive 3d semantic seg- mentation, 2024
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.134893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2de46bec-4e9b-4a98-b36f-9d4b7e47d237 · outbound
This paper cites Cross-modal contrastive learning for domain adaptation in 3d semantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Cross-modal contrastive learning for domain adaptation in 3d semantic segmentation
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.118799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 69de93a8-50e4-4aed-9946-97a7357fdeea · outbound
This paper cites Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation, 2024.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation, 2024
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.100852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 16b824a2-09a1-45ed-b885-0d35228e4207 · outbound
This paper cites Complete & label: A domain adaptation approach to semantic segmen- tation of lidar point clouds.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Complete & label: A domain adaptation approach to semantic segmen- tation of lidar point clouds
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.086319Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 511408c9-b3fb-4473-9654-f28234bed04e · outbound
This paper cites Prototype-guided multitask adversarial network for cross-domain lidar point clouds semantic segmentation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Prototype-guided multitask adversarial network for cross-domain lidar point clouds semantic segmentation
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.071394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ecc17320-7e07-4cdd-a57f-18aeb067df82 · outbound
This paper cites Wide residual net- works, 2017.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Wide residual net- works, 2017
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.056645Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 482a17a8-69a6-40e5-be5f-b02cc4e8d046 · outbound
This paper cites Self-supervised ex- clusive learning for 3d segmentation with cross-modal unsu- pervised domain adaptation.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Self-supervised ex- clusive learning for 3d segmentation with cross-modal unsu- pervised domain adaptation
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.042989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 79badf17-9136-4109-b65f-a8994eb284ac · outbound
This paper cites Segment everything everywhere all at once.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Segment everything everywhere all at once
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:57:39.930892Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88aba1b9-42c5-4134-8213-c80126271a53 · outbound
This paper cites Confidence regularized self-training.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation Confidence regularized self-training
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:57:40.018686Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.