Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation | Paper Citation Record

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This paper cites In 2005 IEEE computer society conference on com- puter vision and pattern recognition (CVPR’05), pages 763–.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation In 2005 IEEE computer society conference on com- puter vision and pattern recognition (CVPR’05), pages 763–

Reference 1

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This paper cites Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation

Reference 2

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This paper cites Weakly su- pervised learning of instance segmentation with inter-pixel relations.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Weakly su- pervised learning of instance segmentation with inter-pixel relations

Reference 3

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This paper cites Single-stage seman- tic segmentation from image labels.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Single-stage seman- tic segmentation from image labels

Reference 4

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This paper cites Convex optimiza- tion.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Convex optimiza- tion

Reference 5

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This paper cites Computing geodesics and minimal surfaces via graph cuts.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Computing geodesics and minimal surfaces via graph cuts

Reference 6

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This paper cites Interactive graph cuts for opti- mal boundary & region segmentation of objects in nd images.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Interactive graph cuts for opti- mal boundary & region segmentation of objects in nd images

Reference 7

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Observation 14c1a40a-bfa5-48d8-96eb-7bc09740213a · outbound

This paper cites Unsu- pervised classifiers, mutual information and’phantom targets.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Unsu- pervised classifiers, mutual information and’phantom targets

Reference 8

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This paper cites A first-order primal- dual algorithm for convex problems with applications to imag- ing.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation A first-order primal- dual algorithm for convex problems with applications to imag- ing

Reference 9

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Observation f9b1dc27-83a1-44b4-a6a5-3cc511b2b899 · outbound

This paper cites Seminar learning for click-level weakly supervised semantic segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Seminar learning for click-level weakly supervised semantic segmentation

Reference 10

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Observation f1181ea7-3134-46f6-82a6-3a316e20ac90 · outbound

This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 11

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

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 12

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

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 13

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Observation cf669066-93f5-45d1-b30b-4be01512e701 · outbound

This paper cites The cityscapes dataset for se- mantic urban scene understanding.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation The cityscapes dataset for se- mantic urban scene understanding

Reference 14

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Observation 8e2574a3-25d4-4ee4-ae18-3a63527dbc37 · outbound

This paper cites Power watershed: A unifying graph-based optimiza- tion framework.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Power watershed: A unifying graph-based optimiza- tion framework

Reference 15

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Observation 7b4d1043-e4a4-46f8-b600-432e4b7bb714 · outbound

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

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Imagenet: A large-scale hierarchical image database

Reference 16

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Observation da431c9b-2e03-4fe6-9ff7-1e403b6d8c7c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation The pascal visual object classes (voc) challenge

Reference 18

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Observation a87c3c38-a60b-4b7d-8ba7-215f7d17a9d9 · outbound

This paper cites Random walks for image segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Random walks for image segmentation

Reference 19

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Observation a8a39117-6f96-4355-be31-a35e8a45cdd6 · outbound

This paper cites Semi-supervised learn- ing by entropy minimization.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Semi-supervised learn- ing by entropy minimization

Reference 20

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This paper cites Deep residual learning for image recognition.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Deep residual learning for image recognition

Reference 21

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Observation 6be4f4cc-a15d-48c3-b4c8-66bf63fb51c7 · outbound

This paper cites Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning

Reference 22

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Observation 31f20a27-683b-4758-838b-a0799a6ecbf1 · outbound

This paper cites Seed, ex- pand and constrain: Three principles for weakly-supervised image segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Seed, ex- pand and constrain: Three principles for weakly-supervised image segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:15.299864Z

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This paper cites Efficient inference in fully connected CRFs with Gaussian edge potentials.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Efficient inference in fully connected CRFs with Gaussian edge potentials

Reference 24

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Observation a2a196aa-e290-4316-8abc-32dafa487f30 · outbound

This paper cites Kulharia, S.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Kulharia, S

Reference 25

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This paper cites Ficklenet: Weakly and semi-supervised se- mantic image segmentation using stochastic inference.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Ficklenet: Weakly and semi-supervised se- mantic image segmentation using stochastic inference

Reference 26

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This paper cites Tree energy loss: Towards sparsely annotated semantic segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Tree energy loss: Towards sparsely annotated semantic segmentation

Reference 27

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This paper cites Scribblesup: Scribble-supervised convolutional networks for semantic segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Scribblesup: Scribble-supervised convolutional networks for semantic segmentation

Reference 28

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Observation 28a6fcc5-62ad-4d78-a259-a2ad4d7b6e21 · outbound

This paper cites Robust trust region for weakly supervised segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Robust trust region for weakly supervised segmentation

Reference 29

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Observation ef0a0b9c-bee4-4f38-930a-6f2b5fbf4277 · outbound

This paper cites Beyond gradient descent for regularized segmen- tation losses.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Beyond gradient descent for regularized segmen- tation losses

Reference 30

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Observation b8b96ac2-e83e-4d73-a6c4-21f98f686bda · outbound

This paper cites When does label smoothing help? Advances in neural infor- mation processing systems, 32, 2019.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation When does label smoothing help? Advances in neural infor- mation processing systems, 32, 2019

Reference 31

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This paper cites Natural Scenes Dataset [NSD].

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Natural Scenes Dataset [NSD]

Reference 32

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This paper cites Scribble-supervised semantic segmentation by uncertainty reduction on neural representation and self- supervision on neural eigenspace.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Scribble-supervised semantic segmentation by uncertainty reduction on neural representation and self- supervision on neural eigenspace

Reference 33

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This paper cites A convex relaxation approach for comput- ing minimal partitions.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation A convex relaxation approach for comput- ing minimal partitions

Reference 34

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This paper cites Quadratic program- ming relaxations for metric labeling and Markov Random Field MAP estimation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Quadratic program- ming relaxations for metric labeling and Markov Random Field MAP estimation

Reference 35

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This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 36

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This paper cites Normalized cut loss for weakly-supervised cnn segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Normalized cut loss for weakly-supervised cnn segmentation

Reference 37

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This paper cites On reg- ularized losses for weakly-supervised cnn segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation On reg- ularized losses for weakly-supervised cnn segmentation

Reference 38

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This paper cites Efficient graph cut optimization for full crfs with quantized edges.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Efficient graph cut optimization for full crfs with quantized edges

Reference 39

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Observation e9a172bb-7e4a-411e-879e-9d695a4d6b53 · outbound

This paper cites Sparse non-local crf.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Sparse non-local crf

Reference 40

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This paper cites Boundary perception guidance: A scribble-supervised semantic segmentation ap- proach.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Boundary perception guidance: A scribble-supervised semantic segmentation ap- proach

Reference 41

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This paper cites Treating pseudo-labels generation as image matting for weakly supervised semantic segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Treating pseudo-labels generation as image matting for weakly supervised semantic segmentation

Reference 42

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This paper cites Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation

Reference 43

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This paper cites Scribble- supervised semantic segmentation inference.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Scribble- supervised semantic segmentation inference

Reference 44

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Observation 4dd097aa-ce05-49bc-a4bf-061e45b9dfe5 · outbound

This paper cites What is optimized in tight convex relaxations for multi-label prob- lems? In IEEE Conference on Computer Vision and Pattern Recognition, pages 1664–1671, 2012.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation What is optimized in tight convex relaxations for multi-label prob- lems? In IEEE Conference on Computer Vision and Pattern Recognition, pages 1664–1671, 2012

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:12.045416Z

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Observation a055efd8-e0af-4ae3-b24a-31614f3a762f · outbound

This paper cites Scene parsing through ade20k dataset.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Scene parsing through ade20k dataset

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:11.890745Z

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Observation 1d1f11cd-24ef-4740-8228-7ed3eb8f8be3 · outbound

This paper cites Learning from labeled and unlabeled data with label propagation.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation Learning from labeled and unlabeled data with label propagation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:11.792034Z

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Observation ba419207-7b44-4c13-a983-b2d9cbd2797d · outbound

This paper cites The backbone is ResNet101.

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation The backbone is ResNet101

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:53:11.583432Z

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