Rethinking Query-based Transformer for Continual Image Segmentation | Paper Citation Record

Observation 40e208ad-a221-43de-b9ae-0d2be061eefb · outbound

This paper cites Decomposed knowledge distilla- tion for class-incremental semantic segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Decomposed knowledge distilla- tion for class-incremental semantic segmentation

Reference 2

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Observation ddb6d150-a12d-4f8b-8f55-c0aab543f571 · outbound

This paper cites Cascade r-cnn: Delv- ing into high quality object detection.

Rethinking Query-based Transformer for Continual Image Segmentation Cascade r-cnn: Delv- ing into high quality object detection

Reference 3

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Observation 33a35e18-5fd6-48af-815f-e39fa2758703 · outbound

This paper cites End-to- end object detection with transformers.

Rethinking Query-based Transformer for Continual Image Segmentation End-to- end object detection with transformers

Reference 4

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Observation 173ccf18-1b81-4ace-b830-f930340f071c · outbound

This paper cites End-to-end incre- mental learning.

Rethinking Query-based Transformer for Continual Image Segmentation End-to-end incre- mental learning

Reference 5

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Observation 636e035a-7e7a-4af2-8466-5a2b892637ae · outbound

This paper cites Modeling the background for incremental learning in semantic segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Modeling the background for incremental learning in semantic segmentation

Reference 6

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Observation d8806aef-810e-4d51-9413-6aeb1799c8bc · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Com- former: Continual learning in semantic and panoptic seg- mentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.918387Z

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Observation 75fd31cb-3903-4460-b625-3d25076a45f7 · outbound

This paper cites Ssul: Semantic segmentation with unknown label for exemplar- based class-incremental learning.

Rethinking Query-based Transformer for Continual Image Segmentation Ssul: Semantic segmentation with unknown label for exemplar- based class-incremental learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.922792Z

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Observation a521d222-cfaa-4b7c-be33-449e2a423571 · outbound

This paper cites Ssul: Semantic segmentation with unknown label for exemplar- based class-incremental learning.

Rethinking Query-based Transformer for Continual Image Segmentation Ssul: Semantic segmentation with unknown label for exemplar- based class-incremental learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.928252Z

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Observation fac3a53d-67f5-44e5-9c0f-6925dd5a33de · outbound

This paper cites Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence.

Rethinking Query-based Transformer for Continual Image Segmentation Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.932916Z

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Observation 152be923-460e-4cb8-a1a2-ba21bc2f59b1 · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Rethinking Query-based Transformer for Continual Image Segmentation Efficient Lifelong Learning with A-GEM

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.937533Z

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Observation 9a45ca4c-4832-416c-bf15-abec2351943b · outbound

This paper cites A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective.

Rethinking Query-based Transformer for Continual Image Segmentation A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.942852Z

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Observation a1f6419d-c9f6-4ab6-a765-302f38314f0f · outbound

This paper cites Strike a balance in continual panoptic segmentation, 2024.

Rethinking Query-based Transformer for Continual Image Segmentation Strike a balance in continual panoptic segmentation, 2024

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.947313Z

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Observation a43bb1f0-3eb2-4201-828b-1039508b34b9 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.951683Z

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Observation a8cf8d44-5dc5-41e6-b004-3e4c8e92748f · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.956257Z

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Observation bc78d4fb-7d7d-4af6-8d72-55be8525d694 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.960777Z

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Observation 50d64d28-dbd8-4a36-ba39-be1365dff3af · outbound

This paper cites Spgnet: Semantic prediction guidance for scene parsing.

Rethinking Query-based Transformer for Continual Image Segmentation Spgnet: Semantic prediction guidance for scene parsing

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.965161Z

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Observation bd60be66-b83e-4a72-a79f-133b6bc17231 · outbound

This paper cites Per- pixel classification is not all you need for semantic segmen- tation.

Rethinking Query-based Transformer for Continual Image Segmentation Per- pixel classification is not all you need for semantic segmen- tation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.969955Z

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Observation 77019db9-c662-4d20-ba1d-9e7e1a6311cc · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Masked-attention mask transformer for universal image segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:35.974831Z

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Observation 96468f65-08ac-4645-ad88-4e34b31fcf96 · outbound

This paper cites Curriculum point prompting for weakly-supervised referring image segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Curriculum point prompting for weakly-supervised referring image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.784934Z

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Observation 9f7da49b-c131-44fd-a1eb-f03288ef8c6d · outbound

This paper cites Learning without mem- orizing.

Rethinking Query-based Transformer for Continual Image Segmentation Learning without mem- orizing

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.768858Z

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Observation 1b9e0971-86ba-4505-b13a-bcd10f7171b0 · outbound

This paper cites Podnet: Pooled outputs dis- tillation for small-tasks incremental learning.

Rethinking Query-based Transformer for Continual Image Segmentation Podnet: Pooled outputs dis- tillation for small-tasks incremental learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.751267Z

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Observation 7c3c7f39-742a-44ed-bce6-878223160538 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Plop: Learning without forgetting for contin- ual semantic segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.734815Z

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Observation 8ecaebe8-66d6-4c26-ae1d-51c542a3dad4 · outbound

This paper cites Dytox: Transformers for continual learning with dynamic token expansion.

Rethinking Query-based Transformer for Continual Image Segmentation Dytox: Transformers for continual learning with dynamic token expansion

Reference 24

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Observation 1efe9c69-bc29-4ceb-8265-be4b693c62c8 · outbound

This paper cites BACS: Background Aware Continual Semantic Segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation BACS: Background Aware Continual Semantic Segmentation

Reference 25

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Observation 14105e74-6b7b-4645-87f0-885efea68cef · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation The pascal visual object classes (voc) challenge

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.698604Z

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Observation 62de6410-35b5-433d-bab9-1e4314218492 · outbound

This paper cites Catastrophic forgetting in connectionist networks.

Rethinking Query-based Transformer for Continual Image Segmentation Catastrophic forgetting in connectionist networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.680538Z

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Observation e7ee954e-b006-4e01-ad8a-52904098cb78 · outbound

This paper cites Multi-evidence filtering and fusion for multi-label classification, object de- tection and semantic segmentation based on weakly super- vised learning.

Rethinking Query-based Transformer for Continual Image Segmentation Multi-evidence filtering and fusion for multi-label classification, object de- tection and semantic segmentation based on weakly super- vised learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.659347Z

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Observation 9461a43e-4678-4537-bdac-2bf99de81294 · outbound

This paper cites Continual segmentation with disentangled objectness learn- ing and class recognition.

Rethinking Query-based Transformer for Continual Image Segmentation Continual segmentation with disentangled objectness learn- ing and class recognition

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.642657Z

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Observation 279f0f74-acc2-4225-8f8b-3654cb870747 · outbound

This paper cites Attribution-aware weight transfer: A warm- start initialization for class-incremental semantic segmenta- tion.

Rethinking Query-based Transformer for Continual Image Segmentation Attribution-aware weight transfer: A warm- start initialization for class-incremental semantic segmenta- tion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.625322Z

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Observation 0d2a24d5-f09e-465f-bd3d-3b47370e60c5 · outbound

This paper cites Simultaneous detection and segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Simultaneous detection and segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.603477Z

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Observation 50f4dc4e-0d99-482d-9045-db091c9325b1 · outbound

This paper cites Clustering algorithms.

Rethinking Query-based Transformer for Continual Image Segmentation Clustering algorithms

Reference 32

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Observation 98675c59-8718-4541-941f-83ad42b29ae5 · outbound

This paper cites Deep residual learning for image recognition.

Rethinking Query-based Transformer for Continual Image Segmentation Deep residual learning for image recognition

Reference 33

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Observation ff736a3f-0aa1-44f6-8179-f64a916d5a2f · outbound

This paper cites Mask r-cnn.

Rethinking Query-based Transformer for Continual Image Segmentation Mask r-cnn

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.558250Z

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Observation b57cffa0-1b11-4d18-bba1-b18cbde2657d · outbound

This paper cites Non-local context encoder: Robust biomedical image segmentation against adversarial attacks.

Rethinking Query-based Transformer for Continual Image Segmentation Non-local context encoder: Robust biomedical image segmentation against adversarial attacks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.065625Z

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Observation ab75a7f0-53d7-4da0-b210-252bddbdb82a · outbound

This paper cites Distilling the knowledge in a neural network.

Rethinking Query-based Transformer for Continual Image Segmentation Distilling the knowledge in a neural network

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.531425Z

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Observation 3d921752-defa-4451-ac12-c0c4ff90333b · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

Rethinking Query-based Transformer for Continual Image Segmentation Learning a unified classifier incrementally via rebalancing

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.075820Z

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Observation 9330a01c-fe3a-466e-adad-324d98755e06 · outbound

This paper cites Free-bloom: Zero-shot text-to-video gener- ator with llm director and ldm animator.

Rethinking Query-based Transformer for Continual Image Segmentation Free-bloom: Zero-shot text-to-video gener- ator with llm director and ldm animator

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.490948Z

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Observation 80d581a5-8ed9-420d-89ee-4eb1727a1bb3 · outbound

This paper cites MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow.

Rethinking Query-based Transformer for Continual Image Segmentation MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.086858Z

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Observation 2bdb4819-7d0d-4ad8-97f2-dde2a1ff07b6 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Ccnet: Criss-cross attention for semantic segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.474064Z

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Observation dea15fa6-0e64-4511-8768-e83736e2c6eb · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Oneformer: One transformer to rule universal image segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.456443Z

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Observation 445658d4-5312-494e-9e2c-9b85a0ebbae8 · outbound

This paper cites Vi- sual prompt tuning.

Rethinking Query-based Transformer for Continual Image Segmentation Vi- sual prompt tuning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.432790Z

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Observation dff27aee-911b-4e2c-bcd4-b91507090bf1 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Eclipse: Efficient continual learning in panoptic segmen- tation with visual prompt tuning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.416695Z

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Observation b3184ffe-6b53-409f-a0db-aa723b6ed022 · outbound

This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.399928Z

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Observation d3273682-b248-4f4c-a630-fe73764745bf · outbound

This paper cites Learning without forgetting.

Rethinking Query-based Transformer for Continual Image Segmentation Learning without forgetting

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.376759Z

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Observation 2a84e261-5387-42f1-8322-4b4bcd4a0404 · outbound

This paper cites Structured attention network for re- ferring image segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Structured attention network for re- ferring image segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.358958Z

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Observation 4447ae72-c72c-4295-9bf0-757dcf439056 · outbound

This paper cites Gradient episodic memory for continual learning.

Rethinking Query-based Transformer for Continual Image Segmentation Gradient episodic memory for continual learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.339378Z

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Observation d4a2e9d3-a1d2-4b96-856d-f156542049ce · outbound

This paper cites Packnet: Adding mul- tiple tasks to a single network by iterative pruning.

Rethinking Query-based Transformer for Continual Image Segmentation Packnet: Adding mul- tiple tasks to a single network by iterative pruning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.321835Z

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Observation 371b18fe-f275-40de-a01d-344a6320feac · outbound

This paper cites Piggy- back: Adapting a single network to multiple tasks by learn- ing to mask weights.

Rethinking Query-based Transformer for Continual Image Segmentation Piggy- back: Adapting a single network to multiple tasks by learn- ing to mask weights

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.304887Z

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Observation 66cce570-d1fe-481c-a927-5b344b871c41 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Incremental learn- ing techniques for semantic segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.286810Z

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Observation 9c081e59-ca9b-4d24-a42d-70667117e748 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Continual semantic segmentation via repulsion-attraction of sparse and disentan- gled latent representations

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.270399Z

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Observation ec3bace0-a0f5-4232-abd9-daadfc4137e3 · outbound

This paper cites Learning to remember: A synaptic plasticity driven framework for continual learning.

Rethinking Query-based Transformer for Continual Image Segmentation Learning to remember: A synaptic plasticity driven framework for continual learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.160873Z

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Observation 604b216c-1b0b-42ca-87f5-49282d5d67f0 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Class similarity weighted knowl- edge distillation for continual semantic segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.242244Z

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Observation a341a00d-76ee-4aca-903f-9888ae416d6c · outbound

This paper cites icarl: Incremental classifier and representation learning.

Rethinking Query-based Transformer for Continual Image Segmentation icarl: Incremental classifier and representation learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.225171Z

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Observation 9979b817-da93-4e8d-88fd-d109eecd34ca · outbound

This paper cites Catastrophic forgetting, rehearsal and pseudorehearsal.

Rethinking Query-based Transformer for Continual Image Segmentation Catastrophic forgetting, rehearsal and pseudorehearsal

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.206809Z

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Observation 1c2dea7e-0736-43d5-95bb-998fdd16ee61 · outbound

This paper cites Incremental learning for robust visual tracking.

Rethinking Query-based Transformer for Continual Image Segmentation Incremental learning for robust visual tracking

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.188981Z

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Observation f3db8ff0-f20f-4557-a421-4d4d88faf776 · outbound

This paper cites Learning representations by back-propagating er- rors.

Rethinking Query-based Transformer for Continual Image Segmentation Learning representations by back-propagating er- rors

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.171066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6cf738ba-b762-49c1-9a02-09e20d11c649 · outbound

This paper cites Progressive Neural Networks.

Rethinking Query-based Transformer for Continual Image Segmentation Progressive Neural Networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.196729Z

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Observation 260bee3d-7ef8-406d-a45d-370326e5ef49 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Incrementer: Transformer for class-incremental semantic segmentation with knowl- edge distillation focusing on old class

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.152700Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bcb838c1-579c-4638-80be-2429d33795b5 · outbound

This paper cites Edadet: Open-vocabulary object detection using early dense alignment.

Rethinking Query-based Transformer for Continual Image Segmentation Edadet: Open-vocabulary object detection using early dense alignment

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.206809Z

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Observation d9e00c9f-ab8f-4900-898f-4e33072ef505 · outbound

This paper cites Logoprompt: Synthetic text im- ages can be good visual prompts for vision-language models.

Rethinking Query-based Transformer for Continual Image Segmentation Logoprompt: Synthetic text im- ages can be good visual prompts for vision-language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.123403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd07def1-b17b-48a2-8eff-3e2d96bff023 · outbound

This paper cites The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models.

Rethinking Query-based Transformer for Continual Image Segmentation The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.215682Z

Source-reported events for the cited work

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Observation 7b4b3e25-c353-42cd-8c9f-b97a5d1bea47 · outbound

This paper cites Part2object: Hierarchical unsupervised 3d instance segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Part2object: Hierarchical unsupervised 3d instance segmentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.106741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44997287-b5d1-4563-83d4-d2d2c882cf9a · outbound

This paper cites Plain-det: A plain multi-dataset object detector.

Rethinking Query-based Transformer for Continual Image Segmentation Plain-det: A plain multi-dataset object detector

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.224797Z

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Unavailable: canonical work link unavailable.

Observation a3e2976f-2ca8-4aad-b8e7-6b22ec59a603 · outbound

This paper cites Continual learning with deep generative replay.

Rethinking Query-based Transformer for Continual Image Segmentation Continual learning with deep generative replay

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.079065Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b17c32c-9f39-4b49-823c-48bdd9eb92b2 · outbound

This paper cites Calibrating cnns for life- long learning.

Rethinking Query-based Transformer for Continual Image Segmentation Calibrating cnns for life- long learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.063146Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 041f39ef-3af6-493d-87f3-27572d914320 · outbound

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

Rethinking Query-based Transformer for Continual Image Segmentation Segmenter: Transformer for semantic segmenta- tion

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.047560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c25fc1f2-d6fc-4d0f-938d-74983743b0f8 · outbound

This paper cites Con- trastive grouping with transformer for referring image seg- mentation.

Rethinking Query-based Transformer for Continual Image Segmentation Con- trastive grouping with transformer for referring image seg- mentation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.242526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3a40bd4-2d8b-458e-94e4-9035e0e54520 · outbound

This paper cites Temporal collection and distribution for referring video object segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Temporal collection and distribution for referring video object segmentation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.248785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9211e1e-5f48-4fce-8926-b0ffa084a4d0 · outbound

This paper cites Lifelong learning algorithms.

Rethinking Query-based Transformer for Continual Image Segmentation Lifelong learning algorithms

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:37.009936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3473fa20-a580-45e8-bf8c-36a33596c190 · outbound

This paper cites Learning to prompt for continual learning.

Rethinking Query-based Transformer for Continual Image Segmentation Learning to prompt for continual learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.993623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b866e38c-80d3-4b41-a0b4-498375b8b2e4 · outbound

This paper cites Memory replay gans: Learning to generate new categories without forgetting.

Rethinking Query-based Transformer for Continual Image Segmentation Memory replay gans: Learning to generate new categories without forgetting

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.977877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 55fdc7d1-78af-4298-aaa2-17cfd12e47c4 · outbound

This paper cites Large scale incre- mental learning.

Rethinking Query-based Transformer for Continual Image Segmentation Large scale incre- mental learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.270021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a072444-52ff-46f8-afed-a062b6a426de · outbound

This paper cites Endpoints weight fusion for class incremental semantic segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Endpoints weight fusion for class incremental semantic segmentation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.950848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 736d4be6-76de-470e-8eec-be3942f935bb · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.

Rethinking Query-based Transformer for Continual Image Segmentation Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.280125Z

Source-reported events for the cited work

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Observation d2ede4b1-1f8c-4728-82ab-e3ca5c81bfcc · outbound

This paper cites Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:36:36.453997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 889afb32-c9d5-40f5-bde7-ad425c612389 · outbound

This paper cites Early preparation pays off: New classifier pre-tuning for class incremental semantic segmen- tation.

Rethinking Query-based Transformer for Continual Image Segmentation Early preparation pays off: New classifier pre-tuning for class incremental semantic segmen- tation

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.925339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d957fa93-0c7c-46e3-aa4b-c30215f080f8 · outbound

This paper cites Der: Dy- namically expandable representation for class incremental learning.

Rethinking Query-based Transformer for Continual Image Segmentation Der: Dy- namically expandable representation for class incremental learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.295124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43f32cb1-b068-40a3-bdd0-2f88cc4d089a · outbound

This paper cites Bottom-up shift and reasoning for referring im- age segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Bottom-up shift and reasoning for referring im- age segmentation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.898316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09982b3b-e781-4bac-b812-1d7195e5fb0c · outbound

This paper cites OCNet: Object Context Network for Scene Parsing.

Rethinking Query-based Transformer for Continual Image Segmentation OCNet: Object Context Network for Scene Parsing

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.304138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9ebdb2a-b2e8-431f-a849-613443c2380b · outbound

This paper cites Representation compensation networks for continual semantic segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Representation compensation networks for continual semantic segmentation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.882782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8d6f319-8ce4-4c9f-a252-ab8860952236 · outbound

This paper cites Slca: Slow learner with classifier align- ment for continual learning on a pre-trained model.

Rethinking Query-based Transformer for Continual Image Segmentation Slca: Slow learner with classifier align- ment for continual learning on a pre-trained model

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.867526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff301541-b06a-4c70-8f12-06c1d54c02aa · outbound

This paper cites Mining unseen classes via regional object- ness: A simple baseline for incremental segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Mining unseen classes via regional object- ness: A simple baseline for incremental segmentation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.851963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9e51bf89-670f-4efb-b364-2a6cd2f2668f · outbound

This paper cites Coinseg: Contrast inter-and intra-class representations for incremental segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation Coinseg: Contrast inter-and intra-class representations for incremental segmentation

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.836879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58410483-043d-4620-86e0-509aea21d5a8 · outbound

This paper cites Pyramid scene parsing network.

Rethinking Query-based Transformer for Continual Image Segmentation Pyramid scene parsing network

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.328134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3439cc88-5a09-48c6-8df8-a51de2fe7ca5 · outbound

This paper cites Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models.Advances in Neu- ral Information Processing Systems, 36:5168–5191, 2023.

Rethinking Query-based Transformer for Continual Image Segmentation Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models.Advances in Neu- ral Information Processing Systems, 36:5168–5191, 2023

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.809147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c35b9e9d-af14-438c-84d7-18dcfa842c49 · outbound

This paper cites Scene parsing through ade20k dataset.

Rethinking Query-based Transformer for Continual Image Segmentation Scene parsing through ade20k dataset

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:36.337996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d47f25a-bce9-4d96-b0d3-ab058c7b30ab · outbound

This paper cites Continual semantic segmentation with automatic memory sample selection.

Rethinking Query-based Transformer for Continual Image Segmentation Continual semantic segmentation with automatic memory sample selection

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.780055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c215e2c0-4098-4f05-a80c-13c6d10e4b91 · outbound

This paper cites an unresolved cited work.

Rethinking Query-based Transformer for Continual Image Segmentation Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:36:36.759718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0c9a31df-2380-4ae2-b65a-8a14f6596124 · outbound

This paper cites Following previous works [7, 29, 43], we use ADE20k [88] to train and evaluate our model for both continual panoptic segmentation and continual se- mantic segmentation tasks.

Rethinking Query-based Transformer for Continual Image Segmentation Following previous works [7, 29, 43], we use ADE20k [88] to train and evaluate our model for both continual panoptic segmentation and continual se- mantic segmentation tasks

Reference 91

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T18:36:36.742789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8182eaf0-420c-4a68-89c7-7bcef4ecb869 · outbound

This paper cites As shown in Tab.

Rethinking Query-based Transformer for Continual Image Segmentation As shown in Tab

Reference 92

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T18:36:36.724264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b440d09-edbd-4df1-ac84-6ab0b015212b · outbound

This paper cites As shown in the Tab.

Rethinking Query-based Transformer for Continual Image Segmentation As shown in the Tab

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.702831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6955f669-72eb-44c9-aeec-ff4fe405db1d · outbound

This paper cites 7, we additionally compare our SimCIS with BalConpas [13] in the 100-5 continual semantic seg- mentation task.

Rethinking Query-based Transformer for Continual Image Segmentation 7, we additionally compare our SimCIS with BalConpas [13] in the 100-5 continual semantic seg- mentation task

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.682909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9c94c54-6231-48ef-aa4d-6d2087abe71f · outbound

This paper cites In the multi-scale feature generated by the pixel decoder, we choose the fea- ture with the highest resolution for clustering.

Rethinking Query-based Transformer for Continual Image Segmentation In the multi-scale feature generated by the pixel decoder, we choose the fea- ture with the highest resolution for clustering

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.663961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0521e9cb-91e2-4277-94f7-f6f404d6a607 · outbound

This paper cites However, in our proposed Lazy Query Pre-alignment strategy, the query features have rich information.

Rethinking Query-based Transformer for Continual Image Segmentation However, in our proposed Lazy Query Pre-alignment strategy, the query features have rich information

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.647399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26737a08-5774-433b-aac4-7f47cf10636d · outbound

This paper cites To ensure a fair comparison, we adopt the same Mask2Former [19] as our meta-architecture for im- age segmentation.

Rethinking Query-based Transformer for Continual Image Segmentation To ensure a fair comparison, we adopt the same Mask2Former [19] as our meta-architecture for im- age segmentation

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:36:36.629435Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Reference resolution

Outbound references

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