Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances | Paper Citation Record

Observation c8d7f45c-e2b3-48e1-a38e-c18c13f0607a · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances The cityscapes dataset for semantic urban scene understanding

Reference 1

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Observation a6054310-7e58-4b3a-8bce-f110dadfc8bc · outbound

This paper cites On exploring weakly supervised domain adaptation strategies for seman- tic segmentation using synthetic data.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances On exploring weakly supervised domain adaptation strategies for seman- tic segmentation using synthetic data

Reference 2

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Observation a3c32f5e-2575-4045-857f-6007de47e833 · outbound

This paper cites SanMiguel, Marcos Escudero-Vi˜nolo, and Pablo Caballeira.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances SanMiguel, Marcos Escudero-Vi˜nolo, and Pablo Caballeira

Reference 3

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Observation d82c8a32-4ee5-4d92-8a6d-445c16594a14 · outbound

This paper cites Exploiting semantic segmentation to boost reinforcement learning in video game environments.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Exploiting semantic segmentation to boost reinforcement learning in video game environments

Reference 4

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Observation 504195d7-8238-4baa-b3cd-92dfb9754a8c · outbound

This paper cites Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks

Reference 5

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Observation b3fbbc5b-4252-489c-ac7b-c3c039676024 · outbound

This paper cites Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks

Reference 6

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Observation 9e1d9c8d-2760-489d-a466-2011340d24c6 · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances DAFormer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 7

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Observation 10966d62-ae76-4cd3-bf26-029892ca2e9e · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances HRDA: Context-aware high-resolution domain-adaptive semantic segmentation

Reference 8

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Observation 938877dd-f070-46e8-a943-2ec6291dcc1e · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances MIC: Masked image consistency for context- enhanced domain adaptation

Reference 9

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Observation 16e2d28d-648c-4347-948f-384b8a0799b6 · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Dacs: Domain adaptation via cross- domain mixed sampling

Reference 10

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Observation e0dbc6cc-c2a9-40c5-9fbd-d254b0ddd1e9 · outbound

This paper cites Adversarial style discrepancy minimization for unsu- pervised domain adaptation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Adversarial style discrepancy minimization for unsu- pervised domain adaptation

Reference 11

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Observation 714c6e0d-a640-416f-8615-4391e1d182d0 · outbound

This paper cites Unsupervised pixel-level domain adaptation with generative adversarial networks.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Unsupervised pixel-level domain adaptation with generative adversarial networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:30:05.098128Z

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Observation 75f3d34c-7d24-440c-8ee2-be939875bf93 · outbound

This paper cites Stage-aware feature alignment network for real-time seman- tic segmentation of street scenes.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Stage-aware feature alignment network for real-time seman- tic segmentation of street scenes

Reference 13

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This paper cites Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation

Reference 14

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Observation 762153a8-ff58-414e-a42a-6963163c98b1 · outbound

This paper cites SanMiguel, and Jose M.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances SanMiguel, and Jose M

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:30:05.047411Z

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Observation 448d8810-93d8-4885-b9ae-bf29f664a8e5 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmenta- tion.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Encoder-decoder with atrous separable convolution for semantic image segmenta- tion

Reference 16

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Observation 94effa36-f59d-4067-b50a-abda4e05410c · outbound

This paper cites Hierarchical Multi-Scale Attention for Semantic Segmentation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Hierarchical Multi-Scale Attention for Semantic Segmentation

Reference 17

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Observation e2779881-d36c-432f-bdcf-10c2f4143e81 · outbound

This paper cites Training deep networks with synthetic data: Bridging the reality gap by domain randomization.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Training deep networks with synthetic data: Bridging the reality gap by domain randomization

Reference 18

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Observation e792fbd3-168e-4142-86ea-ebc17676d9c7 · outbound

This paper cites Stochastic classifiers for unsuper- vised domain adaptation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Stochastic classifiers for unsuper- vised domain adaptation

Reference 19

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Observation bf5902b0-1b48-40e9-9228-6b4fa72fd11d · outbound

This paper cites Domain adaptive and generalizable network architectures and train- ing strategies for semantic image segmentation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Domain adaptive and generalizable network architectures and train- ing strategies for semantic image segmentation

Reference 20

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Observation 32795a1b-480b-4768-b0d9-8f140ab29944 · outbound

This paper cites Sakaridis, D.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Sakaridis, D

Reference 21

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Observation 5896e5bb-92ae-471b-8fa3-c35876a58bf4 · outbound

This paper cites Map- guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence,.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Map- guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence,

Reference 22

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Observation 45e9e1f8-ea4d-47ba-a881-7e453ef49f9c · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun

Reference 23

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Observation 06ec593f-cb50-49e9-9fec-f361650307ab · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances The mapillary vistas dataset for semantic understanding of street scenes

Reference 24

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This paper cites an unresolved cited work.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Unresolved cited work

Reference 25

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Observation 81523098-0e92-4f78-af21-7fd598ead694 · outbound

This paper cites SanMiguel, and Marcos Escudero-Vi˜nolo.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances SanMiguel, and Marcos Escudero-Vi˜nolo

Reference 26

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Observation 3b4b624a-d914-44cc-982f-a51fade5e8f5 · outbound

This paper cites The robust semantic segmen- tation uncv2023 challenge results.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances The robust semantic segmen- tation uncv2023 challenge results

Reference 27

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Observation d6a8f7c6-94e4-4438-b7ec-722260a98fc0 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Denoising dif- fusion probabilistic models

Reference 28

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Observation 9a75ef2c-3c89-4ece-971c-ff6f9d1e0311 · outbound

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances High-resolution image synthesis with latent diffusion models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:30:04.817262Z

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Observation 051b8959-69ab-4290-8ebb-59d5e8286064 · outbound

This paper cites What the DAAM: Interpreting Stable Diffusion Using Cross Attention.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances What the DAAM: Interpreting Stable Diffusion Using Cross Attention

Reference 30

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Observation 7d9dd9ef-4a86-4d27-90e4-45019bf9dcf3 · outbound

This paper cites SanMiguel, and Jos ´e M.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances SanMiguel, and Jos ´e M

Reference 31

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Observation 6d4f7d58-2bf3-412d-986c-ea63f7036546 · outbound

This paper cites SPIN: Spacecraft Imagery for Navigation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances SPIN: Spacecraft Imagery for Navigation

Reference 32

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Observation cf6a8dbd-448b-4adf-be76-d8af3a387fc6 · outbound

This paper cites Self-supervised monocular depth esti- mation on unseen synthetic cameras.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Self-supervised monocular depth esti- mation on unseen synthetic cameras

Reference 33

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Observation 3f9a865e-e248-4e4b-bbbd-3147897d94d5 · outbound

This paper cites Lgsvl simulator: A high fidelity simulator for autonomous driving.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Lgsvl simulator: A high fidelity simulator for autonomous driving

Reference 35

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Observation 42e78948-8c22-4c0d-a42f-ee9ff320d2c7 · outbound

This paper cites sch ¨afer, Nico M.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances sch ¨afer, Nico M

Reference 36

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This paper cites Nguyen, Trinh V.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Nguyen, Trinh V

Reference 37

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This paper cites Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation

Reference 38

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This paper cites Weighted and class-specific maxi- mum mean discrepancy for unsupervised domain adaptation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Weighted and class-specific maxi- mum mean discrepancy for unsupervised domain adaptation

Reference 39

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This paper cites an unresolved cited work.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Unresolved cited work

Reference 40

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Observation 5ee211ae-dd0c-4a73-9271-48f18103f8c0 · outbound

This paper cites Visual domain adaptation through lo- cality information.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Visual domain adaptation through lo- cality information

Reference 41

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

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Learning to adapt structured output space for semantic seg- mentation

Reference 42

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This paper cites Road: Reality ori- ented adaptation for semantic segmentation of urban scenes.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Road: Reality ori- ented adaptation for semantic segmentation of urban scenes

Reference 43

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Observation a0eba0bc-54d0-45e8-a7b1-3627c1b1d098 · outbound

This paper cites Borgwardt, Malte J.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Borgwardt, Malte J

Reference 44

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This paper cites Domain condi- tioned adaptation network.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Domain condi- tioned adaptation network

Reference 45

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Observation 7ecf394d-9e2c-4a33-bb83-48a17afb2e62 · outbound

This paper cites Enhanced Online Test-time Adaptation with Feature-Weight Cosine Alignment.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Enhanced Online Test-time Adaptation with Feature-Weight Cosine Alignment

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:30:04.370277Z

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Observation efe97e87-0fb1-4081-ba34-9d6e95a08b17 · outbound

This paper cites Self-training domain adaptation via weight trans- mission between generators.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Self-training domain adaptation via weight trans- mission between generators

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:30:04.545942Z

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Observation 977d86d8-3d36-4d60-a313-80c56cef0c92 · outbound

This paper cites Pseudo-label assisted optimization of multi-branch net- work for cross-domain person re-identification.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Pseudo-label assisted optimization of multi-branch net- work for cross-domain person re-identification

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:30:04.526058Z

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Observation 3432b8e8-701f-4a55-8210-157284b10766 · outbound

This paper cites CARLA: An open urban driving simulator.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances CARLA: An open urban driving simulator

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T10:30:04.305013Z

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Observation 3d237c3c-7c1a-4f70-81ad-d9d8f03ca343 · outbound

This paper cites The pascal visual object classes (voc) challenge.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances The pascal visual object classes (voc) challenge

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:30:04.508545Z

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Observation b66b413b-ca43-49bd-99e2-71a6486131f8 · outbound

This paper cites Style-hallucinated dual consistency learning for domain generalized semantic segmentation.

Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances Style-hallucinated dual consistency learning for domain generalized semantic segmentation

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