Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation | Paper Citation Record

Observation e0377f81-dc07-4fc7-bdd7-ecbd2c27546a · outbound

This paper cites Threshold-adaptive unsu- pervised focal loss for domain adaptation of semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Threshold-adaptive unsu- pervised focal loss for domain adaptation of semantic segmentation,

Reference 1

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Observation 77b00084-200a-4663-a03b-62b1bdb08f12 · outbound

This paper cites Sfnet-n: An improved sfnet algorithm for semantic segmentation of low-light autonomous driving road scenes,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Sfnet-n: An improved sfnet algorithm for semantic segmentation of low-light autonomous driving road scenes,

Reference 2

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Observation 72051996-9a10-404e-b7a5-515dc6474a18 · outbound

This paper cites Multiple relational learning network for joint referring expression comprehension and segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Multiple relational learning network for joint referring expression comprehension and segmentation,

Reference 3

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Observation 82f6f54c-b8d0-4979-909f-f72921c099fd · outbound

This paper cites Contrastive tokens and label acti- vation for remote sensing weakly supervised semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Contrastive tokens and label acti- vation for remote sensing weakly supervised semantic segmentation,

Reference 4

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Observation 731eefca-f296-4fa2-9086-aa5f8d70d6b0 · outbound

This paper cites Improving robustness of single image super-resolution models with monte carlo method,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Improving robustness of single image super-resolution models with monte carlo method,

Reference 5

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Observation 773eda0c-20f7-4e8b-98ba-cdf7ffedca10 · outbound

This paper cites Token contrast for weakly- supervised semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Token contrast for weakly- supervised semantic segmentation,

Reference 6

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Observation 62409aaf-5191-4301-8aff-f363f0004cd7 · outbound

This paper cites Exploring more concentrated and consistent activation regions for cross-domain semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Exploring more concentrated and consistent activation regions for cross-domain semantic segmentation,

Reference 7

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Observation 27ba8b51-0a37-4d03-96a6-18335e649649 · outbound

This paper cites Transfer beyond the field of view: Dense panoramic semantic segmentation via unsupervised domain adaptation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Transfer beyond the field of view: Dense panoramic semantic segmentation via unsupervised domain adaptation,

Reference 8

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Observation 40b74346-30a9-47a4-9da0-2b355d293b1a · outbound

This paper cites Dual geometric perception for cross-domain road segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Dual geometric perception for cross-domain road segmentation,

Reference 9

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Observation 48519e9b-5774-44d7-af63-1c04cefb4f4a · outbound

This paper cites Fda: Fourier domain adaptation for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Fda: Fourier domain adaptation for semantic segmentation,

Reference 10

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Observation c49c988f-0c83-455f-ab2f-db089fdb56be · outbound

This paper cites Feature-based style randomization for domain generalization,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Feature-based style randomization for domain generalization,

Reference 11

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Observation b890293a-2b40-4ffd-87ad-09db926901a0 · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Generalizing to unseen domains: A survey on domain generalization,

Reference 12

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Observation bc3dae32-c30b-47de-88f8-f5a666255e47 · outbound

This paper cites Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data,

Reference 13

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Observation 7f883354-1589-463c-9190-59c4f2fce5ce · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Fsdr: Frequency space domain randomization for domain generalization,

Reference 14

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Observation a0f3f244-c202-4cd8-9a55-95e5959ccf17 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 15

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Observation 545ee539-250c-47cc-ac73-90f40ee0ed77 · outbound

This paper cites Switchable whitening for deep representation learning,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Switchable whitening for deep representation learning,

Reference 16

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Observation 70d23e57-a0b0-4413-8a93-697f3210d9d2 · outbound

This paper cites Bapa-net: Boundary adaptation and prototype alignment for cross-domain semantic segmen- tation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Bapa-net: Boundary adaptation and prototype alignment for cross-domain semantic segmen- tation,

Reference 17

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Observation 7b7d4a61-0ca8-4932-97bc-f8df4d834658 · outbound

This paper cites Category anchor-guided unsupervised domain adaptation for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Category anchor-guided unsupervised domain adaptation for semantic segmentation,

Reference 18

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Observation 2106cefc-0535-4cf7-a6fc-a5e307cf42e5 · outbound

This paper cites Proto- typical contrast adaptation for domain adaptive semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Proto- typical contrast adaptation for domain adaptive semantic segmentation,

Reference 19

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Observation 378179e1-a149-43a9-846f-dfb5e790df1e · outbound

This paper cites Bi-directional contrastive learning for domain adaptive semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Bi-directional contrastive learning for domain adaptive semantic segmentation,

Reference 20

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Observation ddbe7a33-2e4d-458b-8ce6-c818f044796a · outbound

This paper cites Image style transfer using convolutional neural networks,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Image style transfer using convolutional neural networks,

Reference 21

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Observation 1b775fc8-2631-4900-8d94-1e46c500ce94 · outbound

This paper cites Fully convolutional adaptation networks for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Fully convolutional adaptation networks for semantic segmentation,

Reference 22

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Observation e7889716-13ec-489d-a966-4d0301ffedab · outbound

This paper cites Contextual-relation consis- tent domain adaptation for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Contextual-relation consis- tent domain adaptation for semantic segmentation,

Reference 23

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Observation 46bd895a-715f-421e-936e-3ed552bb4bfc · outbound

This paper cites Scale variance minimization for unsupervised domain adaptation in image segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Scale variance minimization for unsupervised domain adaptation in image segmentation,

Reference 24

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Observation 906d536f-8684-4d36-8242-b281170d2461 · outbound

This paper cites Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training,

Reference 25

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Observation 56d6b4e3-c709-4616-be2b-e1b97bca231b · outbound

This paper cites Characterizing and avoiding negative transfer,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Characterizing and avoiding negative transfer,

Reference 26

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Observation 92b84642-f11f-43b4-88d2-83baf9c75362 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Learning transferable visual models from natural language supervision,

Reference 27

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Observation 57bccc50-9edc-482a-90a6-b75d0e67a0fc · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Curriculum domain adaptation for semantic segmentation of urban scenes,

Reference 28

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Observation dc8ea4f4-c3d9-4229-aba1-db295e41a756 · outbound

This paper cites Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation,

Reference 29

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Observation e29b5100-1ba0-487e-8f0e-831514642e48 · outbound

This paper cites Adversarial domain adaptation with domain mixup,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Adversarial domain adaptation with domain mixup,

Reference 30

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Observation c28b8111-48cb-42fd-87c0-3c4b06d34c0f · outbound

This paper cites Dual mixup regularized learning for adversarial domain adaptation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Dual mixup regularized learning for adversarial domain adaptation,

Reference 31

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Observation 4f9e56a1-2663-462e-85f2-04d9370dac9d · outbound

This paper cites A hybrid domain learning framework for unsupervised semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation A hybrid domain learning framework for unsupervised semantic segmentation,

Reference 32

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Observation 1c49a14d-a847-4f30-9aef-3a4d17946625 · outbound

This paper cites Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes,

Reference 33

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Observation 6547dae3-9682-415c-b32b-92fc43690efb · outbound

This paper cites Delivering arbitrary-modal semantic segmenta- tion,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Delivering arbitrary-modal semantic segmenta- tion,

Reference 34

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Observation 3c9760b2-6698-40dd-bacd-fa93414f981b · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Fully convolutional networks for semantic segmentation,

Reference 35

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Observation 3f16cd62-21bb-40f3-b8dd-d3c9b5815b23 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Segnet: A deep convolutional encoder-decoder architecture for image segmentation,

Reference 36

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Observation d06069d7-5521-42b2-ae63-e52f7fe9d953 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 37

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Observation 973da502-ec8b-4f9b-bfd4-91d7661cc248 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 38

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Observation f0b9a866-b84e-401b-8235-5ab4e774e2f6 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic im- age segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Encoder-decoder with atrous separable convolution for semantic im- age segmentation,

Reference 39

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Observation 7b9da585-d350-4486-9034-85e5e58570f8 · outbound

This paper cites Densely connected convolutional networks,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Densely connected convolutional networks,

Reference 40

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Observation 2b7aef29-5d9c-4397-8514-02fdb1ce7c71 · outbound

This paper cites Deep high-resolution represen- tation learning for human pose estimation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Deep high-resolution represen- tation learning for human pose estimation,

Reference 41

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Observation 4c7fafd2-f7ea-472a-980c-30ae75c46a82 · outbound

This paper cites Lite-hrnet: A lightweight high-resolution network,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Lite-hrnet: A lightweight high-resolution network,

Reference 42

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Observation 92191281-f22a-4285-a656-4538b69d5d27 · outbound

This paper cites Segnext: Rethinking convolutional attention design for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Segnext: Rethinking convolutional attention design for semantic segmentation,

Reference 43

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Observation 0e6db587-3113-429f-bb5f-426c39d047a1 · outbound

This paper cites Segmenter: Trans- former for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Segmenter: Trans- former for semantic segmentation,

Reference 44

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Observation a3b520c8-026b-4c31-b9a7-bdd68f7f8a61 · outbound

This paper cites Multi-scale high-resolution vision transformer for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Multi-scale high-resolution vision transformer for semantic segmentation,

Reference 45

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Observation a412e995-9bed-4d37-b097-90bca1c77b03 · outbound

This paper cites Gcnet: Non-local networks meet squeeze-excitation networks and beyond,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Gcnet: Non-local networks meet squeeze-excitation networks and beyond,

Reference 46

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Observation e77032cd-77ee-405b-84fb-116d55cb0885 · outbound

This paper cites Ccnet: Criss-cross attention for semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Ccnet: Criss-cross attention for semantic segmentation,

Reference 47

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Observation 8e1c2f54-6b38-4a67-aa11-b2b7dc5e949a · outbound

This paper cites Pidnet: A real-time semantic segmentation network inspired by pid controllers,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Pidnet: A real-time semantic segmentation network inspired by pid controllers,

Reference 48

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Observation 37f5aef6-02df-43d2-9146-a532f4f409a1 · outbound

This paper cites Erfnet: Effi- cient residual factorized convnet for real-time semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Erfnet: Effi- cient residual factorized convnet for real-time semantic segmentation,

Reference 49

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Observation d74509a9-38f1-45bf-9011-8c0c6865deb1 · outbound

This paper cites Mscfnet: a lightweight network with multi-scale context fusion for real-time semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Mscfnet: a lightweight network with multi-scale context fusion for real-time semantic segmentation,

Reference 50

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Observation 57f0ddc6-18af-4283-94b8-5f89daad83ef · outbound

This paper cites A multi-phase camera-lidar fusion network for 3d semantic segmentation with weak supervision,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation A multi-phase camera-lidar fusion network for 3d semantic segmentation with weak supervision,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.210955Z

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Observation 0b11a6f9-c2a5-4c2d-b898-049ec0597909 · outbound

This paper cites Rgb-d semantic segmentation and label-oriented voxelgrid fusion for accurate 3d semantic mapping,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Rgb-d semantic segmentation and label-oriented voxelgrid fusion for accurate 3d semantic mapping,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.193358Z

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Observation 405f2185-ad8b-484b-8963-078fd0522754 · outbound

This paper cites Confidence-and-refinement adaptation model for cross-domain seman- tic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Confidence-and-refinement adaptation model for cross-domain seman- tic segmentation,

Reference 53

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Observation 9fb4fa61-20d7-4b0b-98e2-fb9c7b62a4c8 · outbound

This paper cites Learning texture invariant representation for domain adaptation of semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Learning texture invariant representation for domain adaptation of semantic segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.151643Z

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Observation 4f25c7d2-6d74-484e-9712-b16cc827359b · outbound

This paper cites Affinity space adaptation for semantic segmentation across domains,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Affinity space adaptation for semantic segmentation across domains,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.131715Z

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Observation 8dd5a659-c4cf-4040-9779-ffed52549cd0 · outbound

This paper cites Confidence regularized self-training,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Confidence regularized self-training,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.114647Z

Source-reported events for the cited work

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Observation 6eae430a-0989-4745-afda-0452bcd2c00e · outbound

This paper cites Rectifying pseudo label learning via uncer- tainty estimation for domain adaptive semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Rectifying pseudo label learning via uncer- tainty estimation for domain adaptive semantic segmentation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.096139Z

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Observation a57b6ef8-ade3-4c6e-98d6-cfebd1626c1b · outbound

This paper cites Towards robust semantic segmentation of accident scenes via multi- source mixed sampling and meta-learning,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Towards robust semantic segmentation of accident scenes via multi- source mixed sampling and meta-learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.080380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dddf3cab-f747-4909-b74d-c2ce3a0cc601 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Dacs: Domain adaptation via cross-domain mixed sampling,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.063475Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bd58a12c-75ca-484c-a8bb-d7eeacb3a5cf · outbound

This paper cites Context-aware mixup for domain adaptive semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Context-aware mixup for domain adaptive semantic segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.043957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e54fafad-91f5-4c89-be3d-3d604d2e7557 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Daformer: Improving network architectures and training strategies for domain-adaptive semantic seg- mentation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.025762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7d6ad64d-4ca5-424b-9910-490e80174aa3 · outbound

This paper cites Domain- invariant information aggregation for domain generalization semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Domain- invariant information aggregation for domain generalization semantic segmentation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.008594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7819b9ca-54ce-4c14-8a13-b35098e6c4d3 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Global and local texture randomization for synthetic-to-real semantic segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.991871Z

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Observation 2bb74a8f-5f08-46d5-b224-90e281ef2ef0 · outbound

This paper cites Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:01:59.055202Z

Source-reported events for the cited work

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Observation 8342595b-f3a3-4f15-886c-00ee2e23da21 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Two at once: Enhancing learning and generalization capacities via ibn-net,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.974124Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76524e07-9b98-4529-a1b5-61816ee377ec · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.958083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f92e7e1c-8eb3-4a9c-bcdf-b11856e119e9 · outbound

This paper cites Semantic-aware domain generalized segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Semantic-aware domain generalized segmentation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.941523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9eff0d3-7db6-42bc-8d0a-fccb5a1edfa5 · outbound

This paper cites Generalizable model-agnostic se- mantic segmentation via target-specific normalization,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Generalizable model-agnostic se- mantic segmentation via target-specific normalization,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.923971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96bac6ac-3174-4b4f-b8b8-9a9e953dcd08 · outbound

This paper cites Pin the memory: Learning to generalize semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Pin the memory: Learning to generalize semantic segmentation,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.908897Z

Source-reported events for the cited work

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Observation 6c019888-c287-41bd-9c20-37b595d5e56d · outbound

This paper cites Fine- grained self-supervision for generalizable semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Fine- grained self-supervision for generalizable semantic segmentation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.832547Z

Source-reported events for the cited work

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Observation 278388d0-ea39-49e1-b52b-f2fae8d0cd03 · outbound

This paper cites Class-balanced sampling and discriminative stylization for domain generalization se- mantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Class-balanced sampling and discriminative stylization for domain generalization se- mantic segmentation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.637196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c869747-6e2d-4f14-943b-008447489bbd · outbound

This paper cites Calibration- based multi-prototype contrastive learning for domain generalization semantic segmentation in traffic scenes,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Calibration- based multi-prototype contrastive learning for domain generalization semantic segmentation in traffic scenes,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.501965Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8eb73aa1-97ae-4610-8f6a-b0cab83bda1b · outbound

This paper cites Cris: Clip-driven referring image segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Cris: Clip-driven referring image segmentation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.431959Z

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Observation 1ff28e1f-edf3-498f-bf6a-d76a0693918b · outbound

This paper cites Referring image segmentation using text supervision,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Referring image segmentation using text supervision,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.373359Z

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Observation d4a8c134-490d-4a52-88f0-98e037ffb6d1 · outbound

This paper cites Unsupervised domain adaptation for referring semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Unsupervised domain adaptation for referring semantic segmentation,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.065532Z

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Observation 81329301-0237-4cbe-97b0-89f677d95f5c · outbound

This paper cites A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.978678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a2541fb-573f-42a1-9652-461060f6c699 · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Groupvit: Semantic segmentation emerges from text supervision,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.902482Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2404deba-2a7a-4b21-9719-2bd8b47f5f92 · outbound

This paper cites Open-world semantic segmentation via contrasting and clustering vision-language JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 16 embedding,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Open-world semantic segmentation via contrasting and clustering vision-language JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 16 embedding,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.752639Z

Source-reported events for the cited work

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Observation bf9f9489-f473-4c78-baa6-9c099b7bec44 · outbound

This paper cites Decouplenet: Decoupled network for domain adaptive semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Decouplenet: Decoupled network for domain adaptive semantic segmentation,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.633890Z

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Observation 98b3b7a4-7c22-4186-8396-c9432e551645 · outbound

This paper cites Subsidiary prototype alignment for universal domain adaptation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Subsidiary prototype alignment for universal domain adaptation,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.475494Z

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Observation da52d4be-455e-42a0-89ce-f040291333a4 · outbound

This paper cites Adaptive refining-aggregation-separation framework for unsupervised domain adaptation semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Adaptive refining-aggregation-separation framework for unsupervised domain adaptation semantic segmentation,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.281263Z

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Observation 61dc2f96-1d2f-498c-b5f8-1a7ea1b00a1c · outbound

This paper cites Performance evaluation of texture measures with classification based on kullback discrimination of distributions,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Performance evaluation of texture measures with classification based on kullback discrimination of distributions,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:04.078360Z

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Observation 8f973cac-6616-41bb-b797-8c43e6eb56a4 · outbound

This paper cites Pietik ¨ainen, A.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Pietik ¨ainen, A

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:03.841632Z

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Observation df7b26d6-162d-4b54-8db8-d2c1ed383d74 · outbound

This paper cites A global reweighting approach for cross-domain semantic segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation A global reweighting approach for cross-domain semantic segmentation,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:03.606887Z

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Observation 717910c0-a0a6-4058-9756-7b8918c70a6f · outbound

This paper cites A theory of learning from different domains,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation A theory of learning from different domains,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:03.394821Z

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Observation 29f2970d-f96b-4148-9748-b0b04a4f6905 · outbound

This paper cites Generalizing to unseen domains via distribution matching.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Generalizing to unseen domains via distribution matching

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:56.481709Z

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Observation f1df5c99-8237-45b2-af17-f70aa8432b39 · outbound

This paper cites Aadg: automatic augmentation for domain generalization on retinal image segmentation,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Aadg: automatic augmentation for domain generalization on retinal image segmentation,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:03.127021Z

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Observation ea4bb37e-922e-4109-8f85-4278aee53eb5 · outbound

This paper cites Learning shape-invariant representation for generalizable semantic segmenta- tion,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Learning shape-invariant representation for generalizable semantic segmenta- tion,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:02.716657Z

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Observation 2f79bfa8-4d01-409f-b51d-6aefc557e3a5 · outbound

This paper cites Video generalized semantic segmentation via non-salient feature rea- soning and consistency,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Video generalized semantic segmentation via non-salient feature rea- soning and consistency,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:02.579732Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 814d1f01-ce5a-4bb2-b6d4-0ea04187cf86 · outbound

This paper cites Towards robust object detection invariant to real-world domain shifts,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Towards robust object detection invariant to real-world domain shifts,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:02.490586Z

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Observation 2f95eeb5-9bf6-4938-bf5c-5cfdfbacd6f8 · outbound

This paper cites Progres- sive random convolutions for single domain generalization,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Progres- sive random convolutions for single domain generalization,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:02.368321Z

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Observation ec34b16e-109d-4060-bab8-2cebe2801752 · outbound

This paper cites An information-theoretic method to automatic shortcut avoidance and domain generalization for dense prediction tasks,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation An information-theoretic method to automatic shortcut avoidance and domain generalization for dense prediction tasks,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:02.254793Z

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Observation 5db93660-f9f7-423f-b780-b02391bd3732 · outbound

This paper cites Order-preserving consistency regularization for domain adaptation and generalization,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Order-preserving consistency regularization for domain adaptation and generalization,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:02.142513Z

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Observation 4f4a0575-3286-4a41-8dcb-5032954baf11 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation The cityscapes dataset for semantic urban scene understanding,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:01.998730Z

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Observation be66b6f4-d1d4-4bd2-8107-55c937a1cc38 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Bdd100k: A diverse driving dataset for heterogeneous multitask learning,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:01.867415Z

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Observation c093d1f1-8a70-490f-a778-4cb14fe42142 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation The mapillary vistas dataset for semantic understanding of street scenes,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:01.737045Z

Source-reported events for the cited work

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Observation 3dda165c-3f54-498c-b353-1e64c66fa0a8 · outbound

This paper cites Idd: A dataset for exploring problems of autonomous navigation in unconstrained environments,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Idd: A dataset for exploring problems of autonomous navigation in unconstrained environments,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:01.606568Z

Source-reported events for the cited work

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Observation ebabd0f6-ec90-4ddb-8e0c-52cc544a8985 · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:01.444466Z

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Observation 53bf48a8-359c-4e9f-bee9-3eef4ce9596e · outbound

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

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Playing for data: Ground truth from computer games,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:01.274256Z

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Observation 604bd70b-193d-47e8-a59b-707557518001 · outbound

This paper cites Deep residual learning for image recognition,.

Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation Deep residual learning for image recognition,

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-06T17:01:57.729474Z

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