Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement | Paper Citation Record

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This paper cites Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation

Reference 1

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This paper cites Rainbow memory: Continual learning with a memory of diverse samples.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Rainbow memory: Continual learning with a memory of diverse samples

Reference 2

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This paper cites Modeling the back- ground for incremental learning in semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Modeling the back- ground for incremental learning in semantic segmentation

Reference 3

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This paper cites Incremental learning in semantic segmentation from image labels.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Incremental learning in semantic segmentation from image labels

Reference 4

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This paper cites Com- former: Continual learning in semantic and panoptic seg- mentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Com- former: Continual learning in semantic and panoptic seg- mentation

Reference 5

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This paper cites an unresolved cited work.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 6

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

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Schwing, Alexan- der Kirillov, and Rohit Girdhar

Reference 7

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Observation b56e55bc-e90e-4209-8e55-4222f6f9a3e3 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Reproducible scaling laws for contrastive language-image learning

Reference 8

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Observation 610ddc77-136c-4542-b847-bb039e3b8a38 · outbound

This paper cites MTA- CLIP: language-guided semantic segmentation with mask- text alignment.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement MTA- CLIP: language-guided semantic segmentation with mask- text alignment

Reference 9

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Observation d0dfbb19-fac8-4351-9867-5cffdb6176a7 · outbound

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

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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This paper cites Plop: Learning without forgetting for contin- ual semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Plop: Learning without forgetting for contin- ual semantic segmentation

Reference 11

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Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 12

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This paper cites kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies

Reference 13

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

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Zhang, Shaoqing Ren, and Jian Sun

Reference 14

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This paper cites POP: Prompt Of Prompts for Continual Learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement POP: Prompt Of Prompts for Continual Learning

Reference 15

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This paper cites Visual Prompt Tuning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Visual Prompt Tuning

Reference 16

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This paper cites ECLIPSE: efficient continual learning in panoptic segmen- tation with visual prompt tuning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement ECLIPSE: efficient continual learning in panoptic segmen- tation with visual prompt tuning

Reference 17

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Observation ab9228d8-27c3-4bd8-b790-c72d00c05088 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Overcoming catastrophic forgetting in neu- ral networks

Reference 18

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This paper cites Clearclip: Decom- posing CLIP representations for dense vision-language in- ference.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Clearclip: Decom- posing CLIP representations for dense vision-language in- ference

Reference 19

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This paper cites Continual pro- totype evolution: Learning online from non-stationary data streams.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Continual pro- totype evolution: Learning online from non-stationary data streams

Reference 20

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This paper cites Continual learning with extended kronecker- factored approximate curvature.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Continual learning with extended kronecker- factored approximate curvature

Reference 21

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This paper cites Learn to grow: A continual structure learn- ing framework for overcoming catastrophic forgetting.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Learn to grow: A continual structure learn- ing framework for overcoming catastrophic forgetting

Reference 22

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This paper cites A closer look at the explainability of con- trastive language-image pre-training.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement A closer look at the explainability of con- trastive language-image pre-training

Reference 23

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This paper cites Continual semantic segmentation via structure preserving and projected feature alignment.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Continual semantic segmentation via structure preserving and projected feature alignment

Reference 24

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This paper cites Learning from the web: Language drives weakly- supervised incremental learning for semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Learning from the web: Language drives weakly- supervised incremental learning for semantic segmentation

Reference 25

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This paper cites Dynamic extension nets for few-shot se- mantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Dynamic extension nets for few-shot se- mantic segmentation

Reference 26

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This paper cites A new generative replay approach for incremental class learning of medical image for semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement A new generative replay approach for incremental class learning of medical image for semantic segmentation

Reference 27

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This paper cites Decoupled weight de- cay regularization.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Decoupled weight de- cay regularization

Reference 28

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This paper cites Recall: Replay-based continual learning in semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Recall: Replay-based continual learning in semantic segmentation

Reference 29

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This paper cites Incremental learning techniques for semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Incremental learning techniques for semantic segmentation

Reference 30

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This paper cites Incremental learn- ing techniques for semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Incremental learn- ing techniques for semantic segmentation

Reference 31

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This paper cites Continual semantic segmentation via repulsion-attraction of sparse and disentan- gled latent representations.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Continual semantic segmentation via repulsion-attraction of sparse and disentan- gled latent representations

Reference 32

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This paper cites Mitigating background shift in class- incremental semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Mitigating background shift in class- incremental semantic segmentation

Reference 33

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This paper cites Re- lational knowledge distillation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Re- lational knowledge distillation

Reference 34

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This paper cites Class similarity weighted knowl- edge distillation for continual semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Class similarity weighted knowl- edge distillation for continual semantic segmentation

Reference 35

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This paper cites Class similarity weighted knowledge distillation for continual semantic seg- mentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Class similarity weighted knowledge distillation for continual semantic seg- mentation

Reference 36

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This paper cites SATS: Self-Attention Transfer for Continual Semantic Segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement SATS: Self-Attention Transfer for Continual Semantic Segmentation

Reference 37

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

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Learning transferable visual models from natural language supervision

Reference 38

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This paper cites Denseclip: Language-guided dense prediction with context- aware prompting.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Denseclip: Language-guided dense prediction with context- aware prompting

Reference 39

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This paper cites Micro: Modeling cross-image semantic relationship dependencies for class-incremental semantic segmentation in remote sens- ing images.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Micro: Modeling cross-image semantic relationship dependencies for class-incremental semantic segmentation in remote sens- ing images

Reference 40

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This paper cites Rasp: Relation-aware semantic prior for weakly su- pervised incremental segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Rasp: Relation-aware semantic prior for weakly su- pervised incremental segmentation

Reference 41

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This paper cites Incrementer: Transformer for class-incremental semantic segmentation with knowl- edge distillation focusing on old class.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Incrementer: Transformer for class-incremental semantic segmentation with knowl- edge distillation focusing on old class

Reference 42

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This paper cites Segmenter: Transformer for semantic segmenta- tion.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Segmenter: Transformer for semantic segmenta- tion

Reference 43

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This paper cites FOSTER: feature boosting and compression for class- incremental learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement FOSTER: feature boosting and compression for class- incremental learning

Reference 44

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This paper cites Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning

Reference 45

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This paper cites Dy, and Tomas Pfister.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Dy, and Tomas Pfister

Reference 46

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This paper cites Reinforced continual learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Reinforced continual learning

Reference 47

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This paper cites DER: dynam- ically expandable representation for class incremental learn- ing.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement DER: dynam- ically expandable representation for class incremental learn- ing

Reference 48

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This paper cites Der: Dynam- ically expandable representation for class incremental learn- ing.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Der: Dynam- ically expandable representation for class incremental learn- ing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:26.923837Z

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This paper cites Deep Model Reassembly.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Deep Model Reassembly

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:37:21.652739Z

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This paper cites Adaptive deep models for incremental learning: Considering capacity scalability and sustainability.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Adaptive deep models for incremental learning: Considering capacity scalability and sustainability

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:26.806972Z

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This paper cites Cost-effective incremental deep model: Matching model capacity with the least sampling.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Cost-effective incremental deep model: Matching model capacity with the least sampling

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:26.620629Z

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Observation 46580a07-2f1f-4eee-99e1-3215348977fb · outbound

This paper cites Learning with Recoverable Forgetting.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Learning with Recoverable Forgetting

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:37:21.447013Z

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Observation 4ba96b43-9225-4a79-9011-d4ded8fc0c5a · outbound

This paper cites Lifelong learning with dynamically expandable net- works.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Lifelong learning with dynamically expandable net- works

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:26.492769Z

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This paper cites Foundation model drives weakly incremental learning for semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Foundation model drives weakly incremental learning for semantic segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:26.262651Z

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This paper cites Tikp: Text-to-image knowledge preservation for continual seman- tic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Tikp: Text-to-image knowledge preservation for continual seman- tic segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:26.018865Z

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This paper cites A survey on continual seman- tic segmentation: Theory, challenge, method and applica- tion.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement A survey on continual seman- tic segmentation: Theory, challenge, method and applica- tion

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:25.779709Z

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This paper cites Frozen CLIP: A strong backbone for weakly supervised semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Frozen CLIP: A strong backbone for weakly supervised semantic segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:25.531182Z

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This paper cites Representation compensation networks for continual semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Representation compensation networks for continual semantic segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:25.292682Z

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This paper cites Memory-efficient class-incremental learning for image clas- sification.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Memory-efficient class-incremental learning for image clas- sification

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:25.098882Z

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This paper cites RBC: rectifying the biased context in continual semantic segmen- tation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement RBC: rectifying the biased context in continual semantic segmen- tation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:24.933295Z

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This paper cites From pose to part: Weakly-supervised pose evolution for human part segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement From pose to part: Weakly-supervised pose evolution for human part segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:24.750793Z

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This paper cites Scene parsing through ade20k dataset.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Scene parsing through ade20k dataset

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:24.573385Z

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Observation 53160fd5-ce07-4a69-9a5a-74815778ac67 · outbound

This paper cites Extract free dense labels from CLIP.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Extract free dense labels from CLIP

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:24.376779Z

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Observation 9f725dd6-04d1-4919-83c1-8063b7654e2a · outbound

This paper cites A model or 603 exemplars: Towards memory-efficient class-incremental learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement A model or 603 exemplars: Towards memory-efficient class-incremental learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:24.174765Z

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Observation 12762e9b-bce5-455e-83d0-97a1f10c10d1 · outbound

This paper cites Learning to prompt for vision-language models.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Learning to prompt for vision-language models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:23.967746Z

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Observation 916577f6-94d3-45fa-a402-1f4029101b89 · outbound

This paper cites Zegclip: Towards adapting CLIP for zero-shot semantic segmentation.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Zegclip: Towards adapting CLIP for zero-shot semantic segmentation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:23.746876Z

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Observation c6e79013-266c-4c30-a1d2-54288a96549d · outbound

This paper cites Prototype augmentation and self-supervision for incremental learning.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Prototype augmentation and self-supervision for incremental learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:23.513082Z

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This paper cites Visual Encoder Since the original version of CLIP [8, 38] was trained on classification tasks at the image level, it cannot be directly applied to segmentation tasks.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Visual Encoder Since the original version of CLIP [8, 38] was trained on classification tasks at the image level, it cannot be directly applied to segmentation tasks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:23.396989Z

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Observation 7bb11f43-bb1d-4579-9370-b044d5f6ad97 · outbound

This paper cites an unresolved cited work.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:37:23.209568Z

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Observation f5bab5cc-f96e-479f-9204-b6fe6b29c60d · outbound

This paper cites Additionally, we replaced the attention mechanism in the final layer with v-v attention.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Additionally, we replaced the attention mechanism in the final layer with v-v attention

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:23.035284Z

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Observation cbb2f903-b2c5-4841-a95d-7fcda5cd62cb · outbound

This paper cites This feature was then used as input to the decoder.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement This feature was then used as input to the decoder

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:37:22.879598Z

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Observation 38dde378-5f23-47de-9047-a6a8e3b52831 · outbound

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Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:37:22.682978Z

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Observation bfe6b368-0a3e-4a19-bf95-34c258cc81c4 · outbound

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Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:37:22.514546Z

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Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:37:22.336427Z

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Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Unresolved cited work

Reference 9007

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