Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation | Paper Citation Record

Observation 1853c321-2521-47ad-87b7-10f709a5ad4a · outbound

This paper cites Style blind domain generalized semantic segmentation via covariance alignment and semantic consis- tence contrastive learning.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Style blind domain generalized semantic segmentation via covariance alignment and semantic consis- tence contrastive learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.570172Z

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Observation 026622a1-fbc7-419c-a6a1-43c0c0bffd61 · outbound

This paper cites Metareg: Towards domain generalization using meta- regularization.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Metareg: Towards domain generalization using meta- regularization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.561395Z

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Observation 8205e890-1503-472b-84f3-d233ab09ae34 · outbound

This paper cites Lara: Latents and rays for multi-camera bird’s-eye-view semantic segmen- tation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Lara: Latents and rays for multi-camera bird’s-eye-view semantic segmen- tation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.553474Z

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Observation bc8cd3e2-bb79-483f-99a7-8c64a4b6d2f2 · outbound

This paper cites Collaborating foundation models for domain generalized semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Collaborating foundation models for domain generalized semantic segmentation

Reference 4

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Observation 696bfd34-200f-4211-a913-70b172768fc4 · outbound

This paper cites Learning content- enhanced mask transformer for domain generalized urban- scene segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Learning content- enhanced mask transformer for domain generalized urban- scene segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.535817Z

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Observation 6dee16c6-51a0-4409-9d65-4b424574b9cf · outbound

This paper cites Unirestore: Unified perceptual and task-oriented image restoration model using diffusion prior.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unirestore: Unified perceptual and task-oriented image restoration model using diffusion prior

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.526963Z

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Observation 2fa1d4a3-4365-4672-b794-b1dc5168020a · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 7

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Observation ad4c5377-a368-4ece-8482-e5ed267226ec · outbound

This paper cites Rvsl: Robust vehicle similarity learning in real hazy scenes based on semi-supervised learning.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Rvsl: Robust vehicle similarity learning in real hazy scenes based on semi-supervised learning

Reference 8

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Observation baff2005-87d4-4025-9b02-81365e1f1647 · outbound

This paper cites Sjdl-vehicle: Semi-supervised joint defogging learning for foggy vehicle re-identification.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Sjdl-vehicle: Semi-supervised joint defogging learning for foggy vehicle re-identification

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.509209Z

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Observation de8fefd7-c17b-4576-b718-715cf04ad088 · outbound

This paper cites Schwing, Alexan- der Kirillov, and Rohit Girdhar.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Schwing, Alexan- der Kirillov, and Rohit Girdhar

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.500289Z

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Observation e7d50ea0-a185-4789-a07f-d48ea4c4affb · outbound

This paper cites Robustnet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Robustnet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.491551Z

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Observation 995086ac-7ef4-4565-9055-a3e83948b264 · outbound

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

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 12

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Observation 0b03fec0-c69e-4f5a-81bb-7934234ebc2d · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Imagenet: A large-scale hierarchical image database

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.477606Z

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Observation a1bd0096-a461-4b17-babb-94eafb8bbdaf · outbound

This paper cites Diffusion models beat gans on image synthesis.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Diffusion models beat gans on image synthesis

Reference 14

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Observation 66bac002-184f-41ff-8f71-c03c51e41ae1 · outbound

This paper cites Hgformer: Hierarchical grouping transformer for domain generalized semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Hgformer: Hierarchical grouping transformer for domain generalized semantic segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.463616Z

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Observation 148d77af-2b83-4209-9996-580036946d8d · outbound

This paper cites Domain generalization via model-agnostic learning of semantic features.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Domain generalization via model-agnostic learning of semantic features

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.455894Z

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Observation 77726f8c-7bb3-4bf1-8bd3-d87529c3bcd7 · outbound

This paper cites an unresolved cited work.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unresolved cited work

Reference 17

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Observation a12ba44f-34c8-45f9-9460-6ce619b98ae8 · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unsupervised domain adaptation by backpropagation

Reference 18

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Observation a12e57d2-e9d0-49dc-a2b1-408bb2b89f20 · outbound

This paper cites Kleijn, Mengjie Zhang, and David Balduzzi.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Kleijn, Mengjie Zhang, and David Balduzzi

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.434291Z

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Observation 3c822dac-8f4c-47e9-a65b-9a5872fb6bc3 · outbound

This paper cites Prompting Diffusion Representations for Cross-Domain Semantic Segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Prompting Diffusion Representations for Cross-Domain Semantic Segmentation

Reference 20

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Observation c4389561-6aef-4286-89cc-77da7c009bc3 · outbound

This paper cites Zhang, Shaoqing Ren, and Jian Sun.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Zhang, Shaoqing Ren, and Jian Sun

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.427276Z

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Observation f4959427-83b7-4c89-b180-72ac2f0bf6d7 · outbound

This paper cites an unresolved cited work.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unresolved cited work

Reference 22

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Observation 7643948c-ce5f-47cd-85e9-c6007cb7412f · outbound

This paper cites Planning-oriented autonomous driving.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Planning-oriented autonomous driving

Reference 23

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Observation a67f1dc8-e3d3-4610-a7a0-37f3657ad328 · outbound

This paper cites Fsdr: Frequency space domain randomization for domain generalization.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Fsdr: Frequency space domain randomization for domain generalization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.402227Z

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Observation 20819b53-706c-4840-ac8d-4a868fad8428 · outbound

This paper cites Itera- tive normalization: Beyond standardization towards efficient whitening.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Itera- tive normalization: Beyond standardization towards efficient whitening

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.393785Z

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Observation ff2f8b35-171b-4eea-9f72-d7e8320bca97 · outbound

This paper cites Style projected clustering for domain generalized semantic seg- mentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Style projected clustering for domain generalized semantic seg- mentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.385476Z

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Observation 873f0125-9abc-4461-b300-f4c15dd18e9e · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.377998Z

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Observation 13b550d0-e9b4-4e27-984f-e830050f5fa3 · outbound

This paper cites Diffusion Features to Bridge Domain Gap for Semantic Segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Diffusion Features to Bridge Domain Gap for Semantic Segmentation

Reference 28

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Observation 06ae482b-a3e0-4c30-8476-307359e0797a · outbound

This paper cites Dgin- style: Domain-generalizable semantic segmentation with image diffusion models and stylized semantic control.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Dgin- style: Domain-generalizable semantic segmentation with image diffusion models and stylized semantic control

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.370181Z

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Observation 66193430-fb3e-4730-bf2d-99c339f004f0 · outbound

This paper cites Scedit: Efficient and controllable image diffusion generation via skip connection editing.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Scedit: Efficient and controllable image diffusion generation via skip connection editing

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.362390Z

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Observation 392dac4d-c765-40d9-8c6b-7ae65bc2d13e · outbound

This paper cites Kingma and Jimmy Ba.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Kingma and Jimmy Ba

Reference 31

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Observation 425e8a0c-c093-49e1-b25e-2cfec5091978 · outbound

This paper cites Kingma and Max Welling.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Kingma and Max Welling

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.347010Z

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Observation 14ed586d-3a0c-4e78-81aa-6e568cb4e555 · outbound

This paper cites Variational Diffusion Models.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Variational Diffusion Models

Reference 33

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Observation 7c3297fb-b9ad-421a-85dd-a23148863577 · outbound

This paper cites Wildnet: Learning domain generalized semantic seg- mentation from the wild.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Wildnet: Learning domain generalized semantic seg- mentation from the wild

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.338766Z

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Observation 91c97e1c-7ed0-4b2b-8c9c-a05ec5e75522 · outbound

This paper cites Hospedales.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Hospedales

Reference 35

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Observation c3063ede-d039-4179-8f0b-27dcb55fa2c6 · outbound

This paper cites Domain generalization with ad- versarial feature learning.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Domain generalization with ad- versarial feature learning

Reference 36

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Observation 9426fc25-bf35-492c-925a-2fc0acdc77d2 · outbound

This paper cites Deep domain generaliza- tion via conditional invariant adversarial networks.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Deep domain generaliza- tion via conditional invariant adversarial networks

Reference 37

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Observation 056ff619-97a1-411a-8717-3d2f1da158c5 · outbound

This paper cites Cdformer:when degradation prediction embraces diffusion model for blind image super-resolution.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Cdformer:when degradation prediction embraces diffusion model for blind image super-resolution

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.307461Z

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Observation 4eaa2667-9a48-4393-89fa-3ad2c05b4ca8 · outbound

This paper cites Unbiased faster r-cnn for single- source domain generalized object detection.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unbiased faster r-cnn for single- source domain generalized object detection

Reference 39

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Observation b0c636b0-97bb-45b2-b256-31427c3cd138 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 40

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Observation b15e0865-de36-492a-851c-be3a7ecb0d44 · outbound

This paper cites Adjeroh, and Gi- anfranco Doretto.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Adjeroh, and Gi- anfranco Doretto

Reference 41

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Observation a9fc0e92-445b-484c-818a-5a6b1233bbe6 · outbound

This paper cites The mapillary vistas dataset for semantic understanding of street scenes.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation The mapillary vistas dataset for semantic understanding of street scenes

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.279945Z

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Observation 4dbeba87-79cb-400c-bfc8-ae2272d5e694 · outbound

This paper cites Embodied visual active learning for semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Embodied visual active learning for semantic segmentation

Reference 43

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Observation 881659a8-45f9-4be2-8c6b-2f7369314f6c · outbound

This paper cites Au- tonomous mobile robot navigation independent of road boundary using driving recommendation map.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Au- tonomous mobile robot navigation independent of road boundary using driving recommendation map

Reference 44

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This paper cites Two at once: Enhancing learning and generalization capacities via ibn-net.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Two at once: Enhancing learning and generalization capacities via ibn-net

Reference 45

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Observation 04b17a74-8c8e-434f-9b11-824909d15fd1 · outbound

This paper cites Switchable whitening for deep representation learning.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Switchable whitening for deep representation learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.251435Z

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Observation bc40c922-ab6f-4226-a922-aafc3864531c · outbound

This paper cites Global and local texture randomization for synthetic-to-real semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Global and local texture randomization for synthetic-to-real semantic segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.244288Z

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Observation 0461f8ae-8f02-476c-becd-d298739c50e5 · outbound

This paper cites Semantic-aware domain generalized segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Semantic-aware domain generalized segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.237015Z

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Observation 207d72f7-4fa4-4a55-be54-03d9045c5226 · outbound

This paper cites Semantic-aware domain generalized segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Semantic-aware domain generalized segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.229658Z

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Observation 22079b12-2811-4406-88b9-c9cb05e76bd7 · outbound

This paper cites Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

Reference 50

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Observation d7935f45-0a2f-4a7a-9a9b-0e65f7acb5a2 · outbound

This paper cites Lead: Learn- ing decomposition for source-free universal domain adapta- tion.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Lead: Learn- ing decomposition for source-free universal domain adapta- tion

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.222324Z

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Observation 102f8c27-92c7-4e15-8369-e1bfaca11ebe · outbound

This paper cites Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.214920Z

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Observation a83ce77e-e1ec-46f2-a214-58ac06724d5a · outbound

This paper cites Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer

Reference 53

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9ada530-5333-4d0a-877b-01f52a7d8b7a · outbound

This paper cites an unresolved cited work.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:57:00.200575Z

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Observation e132d4fb-2113-404c-bca3-2d1b0de8cdd4 · outbound

This paper cites Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.193262Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 437e93c7-8892-4853-8199-9472efdcbd22 · outbound

This paper cites Learning to optimize domain specific normalization for domain generalization.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Learning to optimize domain specific normalization for domain generalization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.185769Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4af74b30-7164-4857-8e5a-efde1ffed514 · outbound

This paper cites Denoising Diffusion Implicit Models.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Denoising Diffusion Implicit Models

Reference 57

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Observation f1748537-0e78-43c1-9612-68bf66d588c4 · outbound

This paper cites Your classifier can secretly suffice multi-source domain adaptation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Your classifier can secretly suffice multi-source domain adaptation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.178411Z

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Observation c4dd9dad-8c9d-410c-8ac7-87c9af32539c · outbound

This paper cites Exploiting diffusion prior for real-world image super-resolution.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Exploiting diffusion prior for real-world image super-resolution

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.171086Z

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Observation 75f72a9c-2b26-48f2-9df4-8a8474b97285 · outbound

This paper cites Recovering realistic texture in image super-resolution by deep spatial feature transform.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Recovering realistic texture in image super-resolution by deep spatial feature transform

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.163732Z

Source-reported events for the cited work

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Observation 7c7b94d6-11e1-42e2-9635-6c17290309a3 · outbound

This paper cites Domain Generalization Guided by Large-Scale Pre-Trained Priors.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Domain Generalization Guided by Large-Scale Pre-Trained Priors

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:56:59.978068Z

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Observation 41ca0db8-877a-430b-a2de-d8c0c717f014 · outbound

This paper cites Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.156493Z

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Observation 5a7953d4-cf22-44f5-900a-0193bd63f83e · outbound

This paper cites Datasetdm: Synthesizing data with perception anno- tations using diffusion models.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Datasetdm: Synthesizing data with perception anno- tations using diffusion models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.148048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe1bb35e-a6b7-47c2-bf58-014ed51ceffe · outbound

This paper cites Diffir: Efficient diffusion model for image restoration.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Diffir: Efficient diffusion model for image restoration

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.140756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9254a145-6fe8-4b59-b70e-801197828add · outbound

This paper cites Dirl: Domain-invariant representation learning for gen- eralizable semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Dirl: Domain-invariant representation learning for gen- eralizable semantic segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.132491Z

Source-reported events for the cited work

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Observation 35ab0839-892f-40fd-a4d9-40827addab86 · outbound

This paper cites Generalized seman- tic segmentation by self-supervised source domain projec- tion and multi-level contrastive learning.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Generalized seman- tic segmentation by self-supervised source domain projec- tion and multi-level contrastive learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.123585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f79a00d5-8358-4597-9bb2-e0a69197df92 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.115422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e43a94aa-5654-41f4-8705-70105df05bce · outbound

This paper cites Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.107513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 757ed4fc-1d43-4dd8-be25-a10c4f8cc114 · outbound

This paper cites Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.099489Z

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Observation bddc296c-e9da-44d8-952e-f7495995ab0c · outbound

This paper cites C3net: Compound conditioned controlnet for multi- modal content generation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation C3net: Compound conditioned controlnet for multi- modal content generation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.091638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e60e0c11-6149-45ca-846f-01dfb1e5c342 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Adding conditional control to text-to-image diffusion models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T12:56:59.933912Z

Source-reported events for the cited work

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Observation 002c0c48-a8e2-4e73-9a4a-f77490159023 · outbound

This paper cites Mamba as a bridge: Where vision foundation models meet vision language models for domain-generalized semantic segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Mamba as a bridge: Where vision foundation models meet vision language models for domain-generalized semantic segmentation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.077856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f75dbfd-879e-4243-a383-4030d3d5cefc · outbound

This paper cites Fishertune: Fisher- guided robust tuning of vision foundation models for domain generalized segmentation.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Fishertune: Fisher- guided robust tuning of vision foundation models for domain generalized segmentation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.069297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 012e7206-01c4-433f-9ad5-0ccdd94072e9 · outbound

This paper cites Uni-controlnet: All-in-one control to text-to-image diffusion models.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Uni-controlnet: All-in-one control to text-to-image diffusion models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.060758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f6aa5cc-eca5-4d69-b08f-504a45fc5f21 · outbound

This paper cites Sebe, and Gim Hee Lee.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Sebe, and Gim Hee Lee

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:57:00.052548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c6f1c9e-5c11-4a0f-bf21-f35538e19744 · outbound

This paper cites an unresolved cited work.

Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentation Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:57:00.043722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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