Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation | Paper Citation Record

Observation d3ccbc5f-258c-4780-be06-162b4d552448 · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 1

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This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Label-Efficient Semantic Segmentation with Diffusion Models

Reference 2

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Observation bd66a8a1-211c-4726-bcc0-34d4a674e027 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 3

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Observation 5a6cc54d-e79f-43b3-949f-86fddcf0f1f0 · outbound

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

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 4

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Observation 1a7786b1-3233-43e4-bc88-3f53929ef03f · outbound

This paper cites Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Boxsup: Exploit- ing bounding boxes to supervise convolutional networks for semantic segmentation

Reference 5

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Observation c432d832-e56f-48af-85ab-ec2b3b1a791b · outbound

This paper cites Mevis: A large-scale benchmark for video segmentation with motion expressions.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Mevis: A large-scale benchmark for video segmentation with motion expressions

Reference 6

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Observation 63e20378-f632-4a33-b724-8bf2705a8c48 · outbound

This paper cites Mose: A new dataset for video object segmentation in complex scenes.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Mose: A new dataset for video object segmentation in complex scenes

Reference 7

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Observation b74ebd60-980c-48b9-86d7-3b91a3bbc463 · outbound

This paper cites Semi-supervised semantic segmentation needs strong, varied perturbations.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Semi-supervised semantic segmentation needs strong, varied perturbations

Reference 8

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This paper cites Deep residual learning for image recognition.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Deep residual learning for image recognition

Reference 9

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Observation c55a3c54-8203-48b8-96ff-11eee7758a70 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Masked autoencoders are scalable vision learners

Reference 10

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Observation df666694-7ac9-48a3-9abc-bc79d03e1faf · outbound

This paper cites Denoising diffu- sion probabilistic models.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Denoising diffu- sion probabilistic models

Reference 11

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Observation 1f2524b2-7c95-4f1b-b869-21cbeb4f04ac · outbound

This paper cites FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Reference 12

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Observation 36ee4b00-4a52-405c-9b67-8ee6abbbc793 · outbound

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

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Cycada: Cycle-consistent adversarial domain adaptation

Reference 13

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Observation 62dc7916-4c1e-452d-94f9-25af2afa50ed · outbound

This paper cites Three ways to improve semantic segmentation with self-supervised depth estimation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Three ways to improve semantic segmentation with self-supervised depth estimation

Reference 14

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Observation 0148e73f-f6ed-4618-a46d-8fadd65ca6f9 · outbound

This paper cites DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

Reference 15

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Observation c01452e6-3c02-4809-b272-885c74a53150 · outbound

This paper cites HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

Reference 16

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Observation 24e85b19-b0a8-499c-b380-b9585151cc73 · outbound

This paper cites Semi-supervised semantic seg- mentation with directional context-aware consistency.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Semi-supervised semantic seg- mentation with directional context-aware consistency

Reference 17

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Observation 32942575-0113-46f8-873d-290046ec9325 · outbound

This paper cites Handwritten digit recognition with a back-propagation net- work.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Handwritten digit recognition with a back-propagation net- work

Reference 18

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Observation a70c3b2d-e86a-46d1-a352-402fec136587 · outbound

This paper cites Bidirectional learning for domain adaptation of semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Bidirectional learning for domain adaptation of semantic segmentation

Reference 19

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Observation 96b5bde0-7f65-4464-b5a8-58bace16d798 · outbound

This paper cites Bapa-net: Boundary adaptation and prototype align- ment for cross-domain semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Bapa-net: Boundary adaptation and prototype align- ment for cross-domain semantic segmentation

Reference 20

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Observation 249296f1-b02e-48bc-848f-c39ece087a25 · outbound

This paper cites Learn- ing deconvolution network for semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Learn- ing deconvolution network for semantic segmentation

Reference 21

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Observation ffdc8c62-e47d-47fa-8e93-002882d90fc3 · outbound

This paper cites Classmix: Segmentation-based data aug- mentation for semi-supervised learning.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Classmix: Segmentation-based data aug- mentation for semi-supervised learning

Reference 22

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Observation a3de4053-a7ef-4fc1-bb74-95fd2575fe65 · outbound

This paper cites Unsupervised domain adap- tation via domain-adaptive diffusion.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Unsupervised domain adap- tation via domain-adaptive diffusion

Reference 23

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Observation 6771c745-947b-4226-add2-32e61548c169 · outbound

This paper cites Learning target-domain-specific classifier for partial domain adaptation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Learning target-domain-specific classifier for partial domain adaptation

Reference 24

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Observation 4be6438a-7af8-47b3-a34e-daf162f77efb · outbound

This paper cites Playing for data: Ground truth from computer games.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Playing for data: Ground truth from computer games

Reference 25

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Observation 8855fa9f-c9c2-4ba6-83b3-00608c037633 · outbound

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

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation High-resolution image synthesis with latent diffusion models

Reference 26

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Observation 4ef3fcbd-980c-486a-8bf5-dabd010a2d64 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 27

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Observation b736e5ee-3af1-4c0a-840f-3b0a5bc52c8a · outbound

This paper cites The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes

Reference 28

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Observation 95a17523-0305-4a2d-a461-a4f5dd17c7ca · outbound

This paper cites Adversarial learning approach for open set domain adaptation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Adversarial learning approach for open set domain adaptation

Reference 29

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Observation dca76c39-d237-4a3e-80a8-fa9adafcb6c1 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 30

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Observation 01bbe9df-0e42-4104-bdf5-291217917c34 · outbound

This paper cites Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation

Reference 31

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Observation 6bb52238-f204-4870-9838-60aa1189c2b7 · outbound

This paper cites Denoising Diffusion Implicit Models.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Denoising Diffusion Implicit Models

Reference 32

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Observation cef92843-99fa-4dbb-a151-2f2aa7ed091c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 33

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Observation 497c0ad2-91ec-4c06-ab7f-834901c78bc1 · outbound

This paper cites Semi supervised semantic segmentation using generative adver- sarial network.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Semi supervised semantic segmentation using generative adver- sarial network

Reference 34

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Observation 4388aa89-b062-49d6-b1be-7948cbb232c1 · outbound

This paper cites Segmenter: Transformer for semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Segmenter: Transformer for semantic segmentation

Reference 35

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Observation dd1fd154-4215-482d-b9c2-ecc5717bb3aa · outbound

This paper cites Semantic diffusion network for semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Semantic diffusion network for semantic segmentation

Reference 36

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Observation c0f34717-af94-472f-96f5-7601472da8e4 · outbound

This paper cites Un- supervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Un- supervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings

Reference 37

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Observation 2b117cd3-1ccf-404f-9bcf-b5db4489920c · outbound

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

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Dacs: Domain adaptation via cross- domain mixed sampling

Reference 38

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Observation 208ab54f-a750-406d-ba95-0bc0d903524a · outbound

This paper cites Learn- ing to adapt structured output space for semantic segmenta- tion.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Learn- ing to adapt structured output space for semantic segmenta- tion

Reference 39

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Observation 7382ae2e-bc33-4353-905f-862614ccd6e1 · outbound

This paper cites CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation

Reference 40

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Observation ec3b06b6-3b2a-44e7-83c2-c442eff289bb · outbound

This paper cites Reseg: A recurrent neural network-based model for semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Reseg: A recurrent neural network-based model for semantic segmentation

Reference 41

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Observation 414bff64-6853-4fef-8b61-b459ce543f06 · outbound

This paper cites Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

Reference 42

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Observation 82245b45-9e6f-4696-833e-27e8ecdcf046 · outbound

This paper cites LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Reference 43

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Observation 756d61d9-ac02-4049-ab69-3455f48a31f8 · outbound

This paper cites Domain adaptive semantic segmentation with self- supervised depth estimation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Domain adaptive semantic segmentation with self- supervised depth estimation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:56.149890Z

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Observation e5811150-216f-4c83-8c81-005a81a88d70 · outbound

This paper cites Cross-modality lge-cmr segmen- tation using image-to-image translation based data augmen- tation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Cross-modality lge-cmr segmen- tation using image-to-image translation based data augmen- tation

Reference 45

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Observation 07a95548-875a-4ae4-9bec-bca60d48ec02 · outbound

This paper cites Inet: convolutional networks for biomedical image segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Inet: convolutional networks for biomedical image segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:56.127701Z

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Observation c3c8aa50-ee85-448f-9d38-8815cde0b217 · outbound

This paper cites Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffu- sion models.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffu- sion models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:56.116191Z

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Observation ac0d1e34-bc28-441c-a4d0-1b19280eab8e · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:56.105600Z

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Observation 9844d23c-d214-4777-a8a9-dc9c97e00487 · outbound

This paper cites Multi-source domain adaptation for unsupervised road defect segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Multi-source domain adaptation for unsupervised road defect segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:56.094675Z

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Observation cf18f1cf-1397-4220-ae9e-4814ad774867 · outbound

This paper cites Adversar- ial denoising diffusion model for unsupervised anomaly de- tection.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Adversar- ial denoising diffusion model for unsupervised anomaly de- tection

Reference 50

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Observation ec58830b-0517-4a13-b450-8aa3c9c814a8 · outbound

This paper cites Prototypical pseudo label denoising and target 10 structure learning for domain adaptive semantic segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Prototypical pseudo label denoising and target 10 structure learning for domain adaptive semantic segmentation

Reference 51

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Observation b37846f2-8ede-4c8d-9c05-e12b78c823e7 · outbound

This paper cites Curriculum domain adaptation for semantic segmentation of urban scenes.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Curriculum domain adaptation for semantic segmentation of urban scenes

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:20:56.061863Z

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Observation ba7cedd9-8822-44fc-94b3-51bb0e8d7b01 · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation

Reference 53

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

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Un- supervised domain adaptation for semantic segmentation via class-balanced self-training

Reference 54

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Observation 4243eb39-5afc-4364-b580-73db13269e8f · outbound

This paper cites PseudoSeg: Designing Pseudo Labels for Semantic Segmentation.

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Reference 55

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