MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking | Paper Citation Record

Observation 09f7746f-4bea-4fb8-a5ab-f19dc931492a · outbound

This paper cites Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network

Reference 1

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This paper cites Improved crop and weed detection with diverse data ensem- ble learning.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Improved crop and weed detection with diverse data ensem- ble learning

Reference 2

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This paper cites A comparative study of fourier trans- form and cyclegan as domain adaptation techniques for weed segmentation - code and data, 2023.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking A comparative study of fourier trans- form and cyclegan as domain adaptation techniques for weed segmentation - code and data, 2023

Reference 3

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This paper cites Geometry-aware masking strategies for crop row analysis.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Geometry-aware masking strategies for crop row analysis

Reference 4

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This paper cites 3d sketch-aware semantic scene comple- tion via semi-supervised structure prior.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking 3d sketch-aware semantic scene comple- tion via semi-supervised structure prior

Reference 5

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This paper cites Bi-directional cross-modality feature propagation with separation-and- aggregation gate for rgb-d semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Bi-directional cross-modality feature propagation with separation-and- aggregation gate for rgb-d semantic segmentation

Reference 6

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Observation 13b8871a-82c2-476a-b322-d1d2d4a6b955 · outbound

This paper cites Learning semantic segmentation from synthetic data: A geo- metrically guided input-output adaptation approach.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Learning semantic segmentation from synthetic data: A geo- metrically guided input-output adaptation approach

Reference 7

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This paper cites Scale-aware domain adap- tive faster r-cnn.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Scale-aware domain adap- tive faster r-cnn

Reference 8

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Observation 6c41edb1-ebec-4f35-83c8-fc883c07d231 · outbound

This paper cites Locality-sensitive deconvolution networks with gated fusion for rgb-d indoor semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Locality-sensitive deconvolution networks with gated fusion for rgb-d indoor semantic segmentation

Reference 9

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This paper cites Self- ensembling with gan-based data augmentation for domain adaptation in semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Self- ensembling with gan-based data augmentation for domain adaptation in semantic segmentation

Reference 10

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This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 11

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This paper cites PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 12

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

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This paper cites Seed vigour and crop establishment: extending performance be- yond adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Seed vigour and crop establishment: extending performance be- yond adaptation

Reference 14

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This paper cites Domain-adversarial train- ing of neural networks.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Domain-adversarial train- ing of neural networks

Reference 15

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This paper cites Imagenet-trained cnns are biased towards texture; increas- ing shape bias improves accuracy and robustness.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Imagenet-trained cnns are biased towards texture; increas- ing shape bias improves accuracy and robustness

Reference 16

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This paper cites Digging into self-supervised monocular depth estimation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Digging into self-supervised monocular depth estimation

Reference 17

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This paper cites Unsupervised domain adaptation for trans- ferring plant classification systems to new field environ- ments, crops, and robots.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unsupervised domain adaptation for trans- ferring plant classification systems to new field environ- ments, crops, and robots

Reference 18

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This paper cites Generative adversarial nets.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Generative adversarial nets

Reference 19

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This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Cycada: Cycle-consistent adversarial domain adaptation

Reference 20

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This paper cites Grid saliency for context expla- nations of semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Grid saliency for context expla- nations of semantic segmentation

Reference 21

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This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 22

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This paper cites Hrda: Context-aware high-resolution domain-adaptive semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Hrda: Context-aware high-resolution domain-adaptive semantic segmentation

Reference 23

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This paper cites Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation

Reference 24

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This paper cites MIC: Masked image consistency for context- enhanced domain adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking MIC: Masked image consistency for context- enhanced domain adaptation

Reference 25

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This paper cites Progressive domain adaptation for object detection.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Progressive domain adaptation for object detection

Reference 26

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This paper cites Squeeze-and-excitation net- works.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Squeeze-and-excitation net- works

Reference 27

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This paper cites Unsupervised domain adaptation for weed segmentation using greedy pseudo- labelling.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unsupervised domain adaptation for weed segmentation using greedy pseudo- labelling

Reference 28

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This paper cites Ilyas, J.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Ilyas, J

Reference 29

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

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization

Reference 30

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Observation 590de920-5d1e-47d2-b3a9-3429282e5099 · outbound

This paper cites Cascaded feature network for semantic seg- mentation of rgb-d images.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Cascaded feature network for semantic seg- mentation of rgb-d images

Reference 31

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This paper cites Learning selective self-mutual attention for rgb-d saliency detection.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Learning selective self-mutual attention for rgb-d saliency detection

Reference 32

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This paper cites Fully convolutional networks for semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Fully convolutional networks for semantic segmentation

Reference 33

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This paper cites Learning transferable features with deep adaptation networks.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Learning transferable features with deep adaptation networks

Reference 34

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This paper cites Decoupled Weight Decay Regularization.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Decoupled Weight Decay Regularization

Reference 35

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This paper cites In- stance adaptive self-training for unsupervised domain adap- tation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking In- stance adaptive self-training for unsupervised domain adap- tation

Reference 36

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This paper cites Weed density es- timation using semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Weed density es- timation using semantic segmentation

Reference 37

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Observation 6725c5de-8825-4a24-858a-e31e529d3d14 · outbound

This paper cites Transferrable prototypical networks for unsupervised domain adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Transferrable prototypical networks for unsupervised domain adaptation

Reference 38

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This paper cites Vi- sion transformers for dense prediction.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Vi- sion transformers for dense prediction

Reference 39

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This paper cites sch ¨afer, Nico M.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking sch ¨afer, Nico M

Reference 40

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This paper cites Correlation alignment for unsupervised domain adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Correlation alignment for unsupervised domain adaptation

Reference 41

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Observation 207f8bd3-7f50-4ecc-82e2-4b5cc7afc265 · outbound

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

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 42

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Observation 94200527-0b47-45a1-b3ce-b2a86f4d606e · outbound

This paper cites Dacs: Domain adaptation via cross-domain mixed sampling.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Dacs: Domain adaptation via cross-domain mixed sampling

Reference 43

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Observation 935525a7-c288-48ee-b9ed-38aa2715aa80 · outbound

This paper cites Learning to adapt structured output space for semantic seg- mentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Learning to adapt structured output space for semantic seg- mentation

Reference 44

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Observation 009fa1c1-3ac4-4758-880a-abf67f8f08dc · outbound

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MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unresolved cited work

Reference 45

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Observation bfa85be8-7a72-4a1d-aea1-6973c6881982 · outbound

This paper cites In- troduction to domain adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking In- troduction to domain adaptation

Reference 46

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Observation 98492a9e-3c55-47c7-b2a2-0a15392b288b · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Deep hashing network for unsupervised domain adaptation

Reference 47

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Observation 23b37296-d6a1-464d-aa2e-927d0f32a9a8 · outbound

This paper cites Dada: Depth-aware domain adap- tation in semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Dada: Depth-aware domain adap- tation in semantic segmentation

Reference 48

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Observation f92b30c0-1750-418e-9b37-1dd88559adfa · outbound

This paper cites Evo- lutionary generative adversarial networks.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Evo- lutionary generative adversarial networks

Reference 49

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Observation e1f7e1be-fe75-4a77-9647-529d0fbeff57 · outbound

This paper cites Classes matter: A fine-grained adversarial ap- proach to cross-domain semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Classes matter: A fine-grained adversarial ap- proach to cross-domain semantic segmentation

Reference 50

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Observation ff6d9385-82d2-4662-a04b-c6bd6320cafe · outbound

This paper cites Learning common and specific features for rgb- d semantic segmentation with deconvolutional networks.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Learning common and specific features for rgb- d semantic segmentation with deconvolutional networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:34.934149Z

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Observation c54a97e5-a271-4d04-893b-715f1e49f662 · outbound

This paper cites Understanding convolution for semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Understanding convolution for semantic segmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:34.700026Z

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Observation a39fb264-e9e2-4fe4-970e-e554148f0f5a · outbound

This paper cites Depth-aware cnn for rgb-d segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Depth-aware cnn for rgb-d segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:34.564494Z

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Observation af23927a-f9f0-4aa4-b0c3-92c78a7b9a90 · outbound

This paper cites Masked feature predic- tion for self-supervised visual pre-training.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Masked feature predic- tion for self-supervised visual pre-training

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:34.409297Z

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Observation 7008c255-e811-4534-9600-3ecff7224381 · outbound

This paper cites A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:34.258927Z

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Observation 12566836-80d1-49dd-9120-57eed65862f6 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:34.071405Z

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Observation d374f09e-4e29-42d0-bb3a-dd65578ed6cf · outbound

This paper cites Unifying flow, stereo and depth estimation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unifying flow, stereo and depth estimation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:33.929811Z

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Observation 27b2d920-6f87-435e-828c-c5be1823790e · outbound

This paper cites Micdrop: Masking image and depth features via complementary dropout for domain- adaptive semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Micdrop: Masking image and depth features via complementary dropout for domain- adaptive semantic segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:33.803892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation edaea2e0-8323-400d-93a7-5f31c5110ea9 · outbound

This paper cites an unresolved cited work.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:44:33.653013Z

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Observation 1e3ae5cb-71e5-41cb-b29f-5ac4f5b74746 · outbound

This paper cites Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers.IEEE Trans- actions on Intelligent Transportation Systems, 2023.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers.IEEE Trans- actions on Intelligent Transportation Systems, 2023

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:33.460875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a5999b4-7e77-410b-b3aa-eaf07e1d1836 · outbound

This paper cites Cat- egory anchor-guided unsupervised domain adaptation for se- mantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Cat- egory anchor-guided unsupervised domain adaptation for se- mantic segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:33.259619Z

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Observation 86f1e8a3-bb22-44ab-a165-15eca1d3ec82 · outbound

This paper cites Pattern-affinitive propagation across depth, surface normal and semantic segmentation.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Pattern-affinitive propagation across depth, surface normal and semantic segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:33.102020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 850f0102-f003-4dc7-bf63-4dd94365be45 · outbound

This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networks.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unpaired image-to-image translation using cycle- consistent adversarial networks

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:32.475186Z

Source-reported events for the cited work

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Observation b54a7f06-0ab6-40bb-9598-8598dc953afe · outbound

This paper cites Un- supervised domain adaptation for semantic segmentation via class-balanced self-training.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Un- supervised domain adaptation for semantic segmentation via class-balanced self-training

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:32.881799Z

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Observation f9cfc057-2739-4c98-8252-27662314abbf · outbound

This paper cites an unresolved cited work.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:44:36.105846Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Outbound references

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