Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation | Paper Citation Record

Observation 7314fdf8-2b03-4231-9779-1ca6bc38c7b7 · outbound

This paper cites Beit: Bert pre-training of image transformers.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Beit: Bert pre-training of image transformers

Reference 1

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Observation 69778c02-3b3d-4913-849c-c3e2feae3273 · outbound

This paper cites In search for a general- izable method for source free domain adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation In search for a general- izable method for source free domain adaptation

Reference 2

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Observation 3b70f7cf-b8c9-41a4-b988-3efe3f4db234 · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Large scale GAN training for high fidelity natural image synthesis

Reference 3

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Observation 6ff77939-944f-4f92-8f25-5ee6a6ac5522 · outbound

This paper cites Angular visual hardness.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Angular visual hardness

Reference 4

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Observation e8f7889d-659c-4b68-8439-bebedb0074c6 · outbound

This paper cites Contrastive test-time adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Contrastive test-time adaptation

Reference 5

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

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Robustnet: Improving domain generalization in urban-scene segmentation via in- stance selective whitening

Reference 6

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Observation 71100894-d8ba-4fbc-9e00-f48d89a78765 · outbound

This paper cites Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes

Reference 7

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This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.https : / / github.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.https : / / github

Reference 8

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Observation 9d9d801d-36dd-48c7-859e-1b5dd82499fe · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Qlora: Efficient finetuning of quantized llms

Reference 9

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Observation d4fc8bbb-b18b-4351-b3a1-d66bb8ad066f · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:42.530561Z

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Observation de7beb9b-131e-434f-b062-7a63b657c4e9 · outbound

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

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Improved Regularization of Convolutional Neural Networks with Cutout

Reference 11

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Observation 98382b21-f245-4e88-83b3-baabe67801c1 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 12

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Observation fcae5b00-c866-4cfb-b495-89a1410bad5d · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with sim- ple and efficient sparsity.JMLR, 23(120):1–39, 2022.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Switch transformers: Scaling to trillion parameter models with sim- ple and efficient sparsity.JMLR, 23(120):1–39, 2022

Reference 13

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Observation 562a36ad-881b-48ce-ba32-908cc6ecca23 · outbound

This paper cites Decorate the newcomers: Visual domain prompt for continual test time adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Decorate the newcomers: Visual domain prompt for continual test time adaptation

Reference 14

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Observation 14b63dbf-3508-4d37-bf47-881504cab7ed · outbound

This paper cites Test-time training with masked autoencoders.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Test-time training with masked autoencoders

Reference 15

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Observation 0ee614ab-a680-4a90-9205-8d0e65e45534 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.IJCV, 132(2):581–595, 2024.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Clip-adapter: Better vision-language models with feature adapters.IJCV, 132(2):581–595, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.756188Z

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Observation 28ef495a-fe61-4987-af8d-3eec37f378a7 · outbound

This paper cites Mimic before reconstruct: Enhancing masked autoencoders with feature mimicking.IJCV, 132(5):1546–1556, 2024.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Mimic before reconstruct: Enhancing masked autoencoders with feature mimicking.IJCV, 132(5):1546–1556, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.740125Z

Source-reported events for the cited work

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Observation 8640a283-c6f8-4c43-8685-941cc2a03fb8 · outbound

This paper cites Visual Prompt Tuning for Test-time Domain Adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Visual Prompt Tuning for Test-time Domain Adaptation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:42.575789Z

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Observation 0b1b3080-08dd-468d-98c7-da13aaa1dd4d · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Towards a unified view of parameter-efficient transfer learning

Reference 19

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Observation a092ecef-3264-483e-ad17-391036767650 · outbound

This paper cites Deep residual learning for image recognition.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Deep residual learning for image recognition

Reference 20

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Observation c450fdd9-fcd8-4485-9dbb-d6975994098c · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Momentum contrast for unsupervised visual rep- resentation learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.697618Z

Source-reported events for the cited work

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Observation b97c6023-55a9-43f9-bf3e-0732747c6077 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Masked autoencoders are scalable vision learners

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.681845Z

Source-reported events for the cited work

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Observation 61a78edb-2d3b-4680-a5c8-cb821c9979e3 · outbound

This paper cites MILAN: Masked Image Pretraining on Language Assisted Representation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation MILAN: Masked Image Pretraining on Language Assisted Representation

Reference 23

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Source-reported events for the cited work

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Observation 89bf63c6-6ec3-4cfb-9104-c717bc3e5b57 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Parameter-efficient transfer learning for nlp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.665493Z

Source-reported events for the cited work

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Observation d2119ad4-2418-4e5b-9b9d-134491218ab4 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation LoRA: Low-rank adaptation of large language models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.648741Z

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Observation 1ad075d5-bc6f-47ae-9768-6503adfdcdbd · outbound

This paper cites Densely connected convolutional net- works.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Densely connected convolutional net- works

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.633272Z

Source-reported events for the cited work

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Observation d990dbb5-1597-4b25-b6ff-98b129ff2adf · outbound

This paper cites Test-time classifier ad- justment module for model-agnostic domain generalization.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Test-time classifier ad- justment module for model-agnostic domain generalization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.615930Z

Source-reported events for the cited work

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Observation 9694fe5c-d29c-462c-a0ab-d3c7e906010d · outbound

This paper cites Vi- sual prompt tuning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Vi- sual prompt tuning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.598544Z

Source-reported events for the cited work

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Observation da85aace-a349-4697-9f80-3f732de3a7f0 · outbound

This paper cites TSIT: A simple and versatile framework for image-to-image translation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation TSIT: A simple and versatile framework for image-to-image translation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.582097Z

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Observation 916a97dc-95b7-48f6-b349-bd0f78d4f046 · outbound

This paper cites Novel dataset for fine-grained image categorization: Stanford dogs.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Novel dataset for fine-grained image categorization: Stanford dogs

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.565763Z

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Observation 19770476-5b0d-47d6-8664-e47866c77413 · outbound

This paper cites Kingma and J.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Kingma and J

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.550749Z

Source-reported events for the cited work

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Observation a16face4-4164-41c6-94e2-1145f5a0f662 · outbound

This paper cites Learning multiple layers of features from tiny images.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Learning multiple layers of features from tiny images

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.535698Z

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Observation d451731c-91b5-4e69-b831-404413f56485 · outbound

This paper cites Universal source-free domain adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Universal source-free domain adaptation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.518664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c640949-77fc-46c0-a958-e5f2a3a21045 · outbound

This paper cites Becotta: Input-dependent online blending of experts for continual test-time adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Becotta: Input-dependent online blending of experts for continual test-time adaptation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.503357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3319bb58-31af-4f0b-86d2-7c24ff4bc77c · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 35

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Source-reported events for the cited work

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Observation a69faf26-985f-4dcf-9040-10de7ab0b23d · outbound

This paper cites Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.488133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88a7dca7-bc25-4915-816c-ed6ffb3d4ef1 · outbound

This paper cites Expansion and shrinkage of localization for weakly- supervised semantic segmentation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Expansion and shrinkage of localization for weakly- supervised semantic segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.471739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b7f8bea9-e9b5-4af3-b092-901b6b811b29 · outbound

This paper cites Weakly supervised semantic segmentation via pro- gressive patch learning.IEEE Transactions on multimedia, 25:1686–1699, 2022.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Weakly supervised semantic segmentation via pro- gressive patch learning.IEEE Transactions on multimedia, 25:1686–1699, 2022

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.454522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3e1e2115-918b-4230-8931-2d2caf488ee8 · outbound

This paper cites Weakly supervised semantic segmentation via self-supervised destruction learning.Neurocomputing, 561: 126821, 2023.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Weakly supervised semantic segmentation via self-supervised destruction learning.Neurocomputing, 561: 126821, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.438605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c1fed57-e1fe-4b75-ad54-340c398a6cd7 · outbound

This paper cites Cross-modal and uncertainty-aware agglomeration for open- vocabulary 3d scene understanding.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Cross-modal and uncertainty-aware agglomeration for open- vocabulary 3d scene understanding

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.418782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5d2eb44e-ff88-448d-b853-ee46d80d8179 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 41

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Source-reported events for the cited work

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Observation 02068c04-3f21-4ac6-8288-7175c3ae3136 · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for un- supervised domain adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Do we really need to access the source data? source hypothesis transfer for un- supervised domain adaptation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.401302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 93b21a02-cf29-4d4c-a60c-09ba6242b46a · outbound

This paper cites Vida: Homeostatic visual domain adapter for continual test time adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Vida: Homeostatic visual domain adapter for continual test time adaptation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.382029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c87f2ec1-4da0-4e78-8e85-e47e0cca1d05 · outbound

This paper cites Continual-mae: Adaptive distribution masked autoencoders for continual test-time adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Continual-mae: Adaptive distribution masked autoencoders for continual test-time adaptation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.366145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59bc20e3-22b0-4e13-91a9-d7341980d2d5 · outbound

This paper cites Deep hyperspherical learning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Deep hyperspherical learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.350465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59866ec1-e25f-4c3e-8179-73950575d492 · outbound

This paper cites Learning towards minimum hy- perspherical energy.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Learning towards minimum hy- perspherical energy

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.333774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b201dc0c-10e6-4c49-bbc1-897319b0cdad · outbound

This paper cites Decoupled net- works.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Decoupled net- works

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.318071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ca4c085-10aa-4096-ab58-b33f02185500 · outbound

This paper cites Rehg, Liam Paull, Li Xiong, Le Song, and Adrian Weller.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Rehg, Liam Paull, Li Xiong, Le Song, and Adrian Weller

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.299441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b324c0d5-26b9-47cc-8cc0-0f4cb63fd730 · outbound

This paper cites Less: Label-efficient and single-stage referring 3d instance segmentation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Less: Label-efficient and single-stage referring 3d instance segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.284024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 86d2ce9a-110c-46a9-bc24-1a51cc0b17ce · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive? InNeurIPS, pages 21808–21820, 2021.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Ttt++: When does self-supervised test-time training fail or thrive? InNeurIPS, pages 21808–21820, 2021

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.268087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 91537022-638f-4a13-afc9-de818b49192d · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.398570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a7b98be-7a8c-4d5f-9eeb-8cb6745ab109 · outbound

This paper cites Unsupervised domain adaptation with residual trans- fer networks.NeurIPS, 29, 2016.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Unsupervised domain adaptation with residual trans- fer networks.NeurIPS, 29, 2016

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.240645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd3909d8-c02f-4c94-b6c0-70db8a72078c · outbound

This paper cites MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.408642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b619cb1d-79aa-42ec-b472-b776d3ca3068 · outbound

This paper cites Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.460075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 631a6444-0d31-4702-9804-10943267446e · outbound

This paper cites Efficient test- time model adaptation without forgetting.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Efficient test- time model adaptation without forgetting

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.221789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3ae212bb-0a39-4749-af2b-f869c2ac0d33 · outbound

This paper cites Towards stable test-time adaptation in dynamic wild world.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Towards stable test-time adaptation in dynamic wild world

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.202633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fae8349a-d410-4962-8e98-7a61fb9a2c93 · outbound

This paper cites One-Step Image Translation with Text-to-Image Models.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation One-Step Image Translation with Text-to-Image Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.587072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ed847b4-594b-4a4b-9002-cda4dbb2b20e · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Pytorch: An imperative style, high-performance deep learning library

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.592036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 256688ad-d2b7-44b7-a267-2f45c65e6d1a · outbound

This paper cites Adapters: A unified library for parameter-efficient and modular trans- fer learning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Adapters: A unified library for parameter-efficient and modular trans- fer learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.165453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6b4ee12e-b4b3-43dd-9009-6e5a9e6dfef1 · outbound

This paper cites Controlling text-to-image diffusion by orthogo- nal finetuning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Controlling text-to-image diffusion by orthogo- nal finetuning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.138226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1a147aa9-e7cd-4755-9937-d9cbac93e10b · outbound

This paper cites Outra- geously large neural networks: The sparsely-gated mixture- of-experts layer.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Outra- geously large neural networks: The sparsely-gated mixture- of-experts layer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.114851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e205f01b-d8fb-4c96-8f5f-6815a471604d · outbound

This paper cites Mm-tta: multi-modal test-time adaptation for 3d se- mantic segmentation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Mm-tta: multi-modal test-time adaptation for 3d se- mantic segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.096281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 522df3bf-de83-43eb-8f7f-a006d39a3722 · outbound

This paper cites Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.693087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 671f8ffe-8acc-4aba-ba21-909a0ed23472 · outbound

This paper cites Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.074972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4cff51ec-600a-4b6c-afbe-5f3c93f99706 · outbound

This paper cites Shift: a synthetic driving dataset for continuous multi-task domain adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Shift: a synthetic driving dataset for continuous multi-task domain adaptation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.052895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b260c738-8489-4e45-8e9e-8d6d845fb9fe · outbound

This paper cites On orthogonality and learning recurrent networks with long term dependencies.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation On orthogonality and learning recurrent networks with long term dependencies

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.029849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 80c91d1b-5e4f-4141-b981-606a44e4b78a · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Tent: Fully test-time adaptation by entropy minimization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:45.002405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 18fc914f-9cba-4416-bf32-3841524157be · outbound

This paper cites Con- tinual test-time domain adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Con- tinual test-time domain adaptation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.975213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7910b8d9-62bf-47d5-99fa-e0d1d4e821c1 · outbound

This paper cites Segformer: Simple and ef- ficient design for semantic segmentation with transformers.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Segformer: Simple and ef- ficient design for semantic segmentation with transformers

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.953044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 02158468-b66a-488e-be1b-b25bc1278718 · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Simmim: A simple framework for masked image modeling

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.936346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1080993-b871-473b-b288-7286ed9cfd5b · outbound

This paper cites 3d weakly supervised semantic segmentation with 2d vision-language guidance.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation 3d weakly supervised semantic segmentation with 2d vision-language guidance

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.909376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e3bcfe1-2af4-4646-8823-6f5851881e38 · outbound

This paper cites Generalized source-free domain adaptation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Generalized source-free domain adaptation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.879672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 618a3366-f822-492e-9428-44b6884fe391 · outbound

This paper cites Exploring sparse visual prompt for domain adaptive dense prediction.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Exploring sparse visual prompt for domain adaptive dense prediction

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.857369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0734716-a488-493f-8d93-7f31f59f31cc · outbound

This paper cites Robust test-time adaptation in dynamic scenarios.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Robust test-time adaptation in dynamic scenarios

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.836422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 668d8d0b-c222-4eee-916f-7ab17b57e6fc · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.812783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dfd0b74b-73d8-406c-948f-4e8be5ccadb5 · outbound

This paper cites Generalized source- free domain-adaptive segmentation via reliable knowledge propagation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Generalized source- free domain-adaptive segmentation via reliable knowledge propagation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.791966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 68d90005-937b-4bb4-885f-fb0dd3dee2f8 · outbound

This paper cites Boosting novel category dis- covery over domains with soft contrastive learning and all in one classifier.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Boosting novel category dis- covery over domains with soft contrastive learning and all in one classifier

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.768296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2139aac7-f89d-4aab-850e-e622618e2299 · outbound

This paper cites S2 transformer for image captioning.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation S2 transformer for image captioning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.749487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5eef3e0a-6f5c-47d6-9fb1-c0d30640eb3f · outbound

This paper cites mixup: Beyond empirical risk minimiza- tion.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation mixup: Beyond empirical risk minimiza- tion

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.730211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0f8b8ced-6e01-473b-b0ea-ec3079400a6c · outbound

This paper cites Mpt: Multi-grained prompt tuning for text-video retrieval.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Mpt: Multi-grained prompt tuning for text-video retrieval

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.706423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28fc86cd-3134-40eb-a3aa-0addd8bb2891 · outbound

This paper cites Tip-adapter: Training-free clip-adapter for better vision- language modeling.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Tip-adapter: Training-free clip-adapter for better vision- language modeling

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.681152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 67c64fd5-9975-46b8-90b6-0284ea9d7abc · outbound

This paper cites Auxadapt: Stable and efficient test-time adaptation for temporally consistent video semantic segmentation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Auxadapt: Stable and efficient test-time adaptation for temporally consistent video semantic segmentation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.664280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15e4c08e-d011-4d7a-9b35-a73942fa55bc · outbound

This paper cites Fishertune: Fisher- guided robust tuning of vision foundation models for domain generalized segmentation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Fishertune: Fisher- guided robust tuning of vision foundation models for domain generalized segmentation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.648515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 446fd346-8a12-472e-ad1c-2d622ae040d9 · outbound

This paper cites Random erasing data augmentation.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Random erasing data augmentation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.632163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a8b4d82a-8da6-4d42-ba48-29c7ac74bfcf · outbound

This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Taming Sparsely Activated Transformer with Stochastic Experts

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.912081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74336fe3-c688-45a7-8c4c-94c1026cc371 · outbound

This paper cites First, based on previous studies [4, 45, 47], the angles of weights in neural networks capture most informative char- acteristics.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation First, based on previous studies [4, 45, 47], the angles of weights in neural networks capture most informative char- acteristics

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.608853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c3f0dc6c-6860-42bf-b119-1d0c00586fe2 · outbound

This paper cites an unresolved cited work.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:44:44.587418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 25bb3d28-e4da-4bc8-b0a3-812d6ef76664 · outbound

This paper cites As shown in Tab.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation As shown in Tab

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.562028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5acf217c-be5b-4ddc-a180-1378fcf7a9cd · outbound

This paper cites an unresolved cited work.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:44:44.542251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a201ccf-f187-4b1a-ba3e-f961cece00a2 · outbound

This paper cites In our main paper, we ablate the experimental results with differ- entgrid sizesand masking ratioα.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation In our main paper, we ablate the experimental results with differ- entgrid sizesand masking ratioα

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.422411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8388cfe3-a843-457a-abc6-4439a5ed0bb7 · outbound

This paper cites We obtain 59.6% mIoU and 59.7% mIoU for reimple-BECoTTAM and BECoTTAM w masking, respectively.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation We obtain 59.6% mIoU and 59.7% mIoU for reimple-BECoTTAM and BECoTTAM w masking, respectively

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.302987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d4e58b25-7997-4c2a-872b-1452623d092e · outbound

This paper cites We adopt the same comparative environment with one single Nvidia A6000 48GB.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation We adopt the same comparative environment with one single Nvidia A6000 48GB

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.265566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a57c74c2-7d4b-4cd8-81cf-998cc04c234a · outbound

This paper cites Our implemen- tal hyper-parameters are shown in Tab: 9.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Our implemen- tal hyper-parameters are shown in Tab: 9

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.247258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3dbe20e8-45c3-4087-b987-350f5fbf4004 · outbound

This paper cites However, it requires detailed hyperparameters choices, in- cluding the rankrin OPS, the maskinggrid size sand masking ratioαin IMS, and loss weight tuningλin Lorth.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation However, it requires detailed hyperparameters choices, in- cluding the rankrin OPS, the maskinggrid size sand masking ratioαin IMS, and loss weight tuningλin Lorth

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:44:44.231067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers