DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization | Paper Citation Record

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This paper cites Lightweight dual stream network with knowledge distillation for rgb-d scene parsing,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Lightweight dual stream network with knowledge distillation for rgb-d scene parsing,

Reference 1

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Observation 754279a3-e3e0-469c-ad13-46dbe5dd075d · outbound

This paper cites SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection,

Reference 2

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Observation 1c13d608-b8fe-4985-bf2e-b6a009a726f2 · outbound

This paper cites S 3M-Net: Joint learning of semantic segmentation and stereo matching for autonomous driving,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization S 3M-Net: Joint learning of semantic segmentation and stereo matching for autonomous driving,

Reference 3

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This paper cites SNE-RoadSegV2: Advancing heterogeneous feature fusion and fallibility awareness for freespace detection,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization SNE-RoadSegV2: Advancing heterogeneous feature fusion and fallibility awareness for freespace detection,

Reference 4

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This paper cites Road damage detection based on unsupervised disparity map segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Road damage detection based on unsupervised disparity map segmentation,

Reference 5

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This paper cites Fully Convolutional Networks for Semantic Segmenta- tion,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Fully Convolutional Networks for Semantic Segmenta- tion,

Reference 6

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This paper cites Pothole detection based on disparity transformation and road surface modeling,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Pothole detection based on disparity transformation and road surface modeling,

Reference 7

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Observation 29dc038b-f190-4601-96a5-df8500b66fda · outbound

This paper cites RoadFormer+: Delivering RGB-X scene parsing through scale-aware information decoupling and advanced heteroge- neous feature fusion,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization RoadFormer+: Delivering RGB-X scene parsing through scale-aware information decoupling and advanced heteroge- neous feature fusion,

Reference 8

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Observation ca739441-6892-4b64-8e61-b66c2923899a · outbound

This paper cites MFFENet: Multiscale feature fusion and enhancement network for rgb–thermal urban road scene parsing,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization MFFENet: Multiscale feature fusion and enhancement network for rgb–thermal urban road scene parsing,

Reference 9

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This paper cites MDNet: Mamba-effective diffusion-distillation network for rgb-thermal urban dense prediction,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization MDNet: Mamba-effective diffusion-distillation network for rgb-thermal urban dense prediction,

Reference 10

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Observation f7ed54e7-a460-4867-92c8-6b7dc96ef52b · outbound

This paper cites FuseNet: Incorporating Depth into Semantic Seg- mentation via Fusion-Based CNN Architecture,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization FuseNet: Incorporating Depth into Semantic Seg- mentation via Fusion-Based CNN Architecture,

Reference 11

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Observation cbd05918-d2de-4990-ab2a-d8555d844293 · outbound

This paper cites RoadFormer: Duplex Transformer for RGB-Normal Se- mantic Road Scene Parsing,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization RoadFormer: Duplex Transformer for RGB-Normal Se- mantic Road Scene Parsing,

Reference 12

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Observation 4e74b6fc-6280-4516-a533-9be7d3afd704 · outbound

This paper cites Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning

Reference 13

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This paper cites Depth-assisted semi-supervised rgb-d rail surface defect inspection,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Depth-assisted semi-supervised rgb-d rail surface defect inspection,

Reference 14

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Observation 155274cc-f452-450f-af64-dfc87a81c337 · outbound

This paper cites UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation

Reference 15

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Observation a67015d5-1c13-4934-aab4-ec10eb4bdba8 · outbound

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

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Semi-supervised semantic segmentation needs strong, high-dimensional perturbations,

Reference 16

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Observation 16126050-494c-4881-a46d-90c402dfc289 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Improved Regularization of Convolutional Neural Networks with Cutout

Reference 17

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Observation 6c1bc66f-b498-4b14-a4df-56591b32faf2 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 18

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Observation a7e8c86a-606a-49ed-9a99-d1471556e5b3 · outbound

This paper cites Revisiting weak-to-strong consistency in semi- supervised semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Revisiting weak-to-strong consistency in semi- supervised semantic segmentation,

Reference 19

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This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Fixmatch: Simplifying semi-supervised learning with consistency and confidence,

Reference 20

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This paper cites Dinov2: Learning robust visual features without supervision,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Dinov2: Learning robust visual features without supervision,

Reference 21

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Observation 8fd6c249-5673-48f8-b13e-3dcb71bebc37 · outbound

This paper cites Vision transformers for dense prediction,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Vision transformers for dense prediction,

Reference 22

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Observation f3bc548c-e0fa-451e-a7a1-ba4fac7e0947 · outbound

This paper cites Self-enhanced feature fusion for rgb-d semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Self-enhanced feature fusion for rgb-d semantic segmentation,

Reference 23

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Observation 6caed587-579d-45e8-9958-6c389627e23e · outbound

This paper cites DFormer: Rethinking RGBD representation learning for semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization DFormer: Rethinking RGBD representation learning for semantic segmentation,

Reference 24

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Observation 6c985786-d438-49fc-aa45-dc07815391de · outbound

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

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 25

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Observation e22e948d-36f2-473c-9591-49afc8fa1432 · outbound

This paper cites Frnet: Feature reconstruction network for rgb-d indoor scene parsing,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Frnet: Feature reconstruction network for rgb-d indoor scene parsing,

Reference 26

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Observation 778c6bfb-d801-4078-b914-f7a8c686ab4b · outbound

This paper cites Feature contrast difference and enhanced network for RGB-D indoor scene classification in internet of things,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Feature contrast difference and enhanced network for RGB-D indoor scene classification in internet of things,

Reference 27

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This paper cites Continuous pseudo-label rectified domain adaptive semantic segmentation with implicit neural representations,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Continuous pseudo-label rectified domain adaptive semantic segmentation with implicit neural representations,

Reference 28

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This paper cites Complementary random masking for rgb-thermal seman- tic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Complementary random masking for rgb-thermal seman- tic segmentation,

Reference 29

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Observation f3d8c6cf-b583-40ca-ab48-2dc39da94976 · outbound

This paper cites Self-supervised model adaptation for multimodal semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Self-supervised model adaptation for multimodal semantic segmentation,

Reference 30

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Observation 33d640e7-e37b-4e8e-a2c7-1c3b40fabbc5 · outbound

This paper cites Active boundary loss for semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Active boundary loss for semantic segmentation,

Reference 31

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Observation 06f0c81b-3c40-455e-b56f-8042eba7a096 · outbound

This paper cites Conditional boundary loss for semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Conditional boundary loss for semantic segmentation,

Reference 32

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Observation ed44b668-998b-4c86-8063-458d0e24a3e6 · outbound

This paper cites Guided contrastive boundary learning for semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Guided contrastive boundary learning for semantic segmentation,

Reference 33

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Observation 2f6fa6eb-9ef7-4bfb-94b9-3dda2982ffb3 · outbound

This paper cites Effective whole-body pose estimation with two-stages distillation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Effective whole-body pose estimation with two-stages distillation,

Reference 34

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This paper cites Augmented reality meets computer vision: Efficient data generation for urban driving scenes,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Augmented reality meets computer vision: Efficient data generation for urban driving scenes,

Reference 35

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This paper cites Playing to Vision Foundation Model’s Strengths in Stereo Matching,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Playing to Vision Foundation Model’s Strengths in Stereo Matching,

Reference 36

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This paper cites Decoupled Weight Decay Regularization,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Decoupled Weight Decay Regularization,

Reference 37

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Observation 811d938e-19a6-4252-99d9-8755ed8fc6c2 · outbound

This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,

Reference 38

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Observation 5d98b2a6-e041-4859-8e41-76d2f36a09a4 · outbound

This paper cites Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation,

Reference 39

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Observation 48e34cfc-a73e-4ae5-ab2f-f38684171833 · outbound

This paper cites CMX: Cross-modal fusion for RGB-X semantic segmentation with Transformers,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization CMX: Cross-modal fusion for RGB-X semantic segmentation with Transformers,

Reference 40

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Observation 8a714b12-82fd-445c-b1b3-0d4f5267d774 · outbound

This paper cites Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation

Reference 41

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This paper cites Geminifusion: Efficient pixel-wise multimodal fusion for vision transformer,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Geminifusion: Efficient pixel-wise multimodal fusion for vision transformer,

Reference 42

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Observation df0a1120-b92a-4851-8243-290782af4559 · outbound

This paper cites Sun RGB-D: A RGB-D scene understanding benchmark suite,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Sun RGB-D: A RGB-D scene understanding benchmark suite,

Reference 43

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Observation f3246946-3ce4-4c26-8e6c-c2737076fef9 · outbound

This paper cites Improving semantic segmentation via video propagation and label relaxation,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Improving semantic segmentation via video propagation and label relaxation,

Reference 44

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Observation 68082ad9-50ab-4429-aa9c-169a2d1bac4d · outbound

This paper cites Multi-target pan-class intrinsic relevance driven model for improving semantic segmentation in autonomous driving,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Multi-target pan-class intrinsic relevance driven model for improving semantic segmentation in autonomous driving,

Reference 45

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Observation 8f207aa7-6539-4309-a369-ac6de3bced6e · outbound

This paper cites Warp-refine propagation: Semi-supervised auto- labeling via cycle-consistency,.

DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization Warp-refine propagation: Semi-supervised auto- labeling via cycle-consistency,

Reference 46

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