Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning | Paper Citation Record

Observation 41f509eb-ecf7-4172-ab41-b852f34c9409 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Memory aware synapses: Learning what (not) to forget

Reference 1

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Observation dd7e24cd-3478-4795-8cb6-0938a6466554 · outbound

This paper cites Qwen-vl: A versatile vision-language model for understanding, localization.Text Reading, and Beyond, 2(1):1.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Qwen-vl: A versatile vision-language model for understanding, localization.Text Reading, and Beyond, 2(1):1

Reference 2

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Observation d94c56ea-a4ed-4406-bf47-07c6bb0be52f · outbound

This paper cites Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.Advances in neural information processing systems, 32.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.Advances in neural information processing systems, 32

Reference 3

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Observation d7f56ae7-cdd2-4a04-9c46-a267a7aa93d3 · outbound

This paper cites Food-101–mining discriminative components with random forests.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Food-101–mining discriminative components with random forests

Reference 4

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Observation 1681365e-bf30-4a9c-99f5-0395a7afe12f · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Efficient Lifelong Learning with A-GEM

Reference 5

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Observation e08dec3e-c261-4363-a83a-e26a9215d908 · outbound

This paper cites PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:12:50.607661Z

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Observation fc11c6db-9a42-4543-9e1d-f88bd3def351 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information processing systems, 26.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information processing systems, 26

Reference 7

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Observation d52246ec-1b7f-4986-8b72-628e7297d9e6 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning A continual learning survey: Defying forgetting in classification tasks

Reference 8

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Observation f25f70ad-6427-44c1-9f5e-cefa1c7f6ac0 · outbound

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

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:12:50.594133Z

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Observation 6935b1bd-3313-4497-946f-e4f55f9702a8 · outbound

This paper cites Catastrophic forgetting in connectionist networks.Trends in cognitive sciences, 3(4): 128–135.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Catastrophic forgetting in connectionist networks.Trends in cognitive sciences, 3(4): 128–135

Reference 10

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Observation 20408b24-855f-480c-9467-28acca8ec82a · outbound

This paper cites Adapter merging with centroid prototype mapping for scalable class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Adapter merging with centroid prototype mapping for scalable class-incremental learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.380391Z

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Observation 3ad9e9f4-864a-432f-9dad-32d960f325ab · outbound

This paper cites The geometry of optimal transportation.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning The geometry of optimal transportation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.592686Z

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Observation c55ad5ce-676f-48e9-9b88-8a4e67534e64 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.International journal of computer vision, 132(2):581–595.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Clip-adapter: Better vision-language models with feature adapters.International journal of computer vision, 132(2):581–595

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.349444Z

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Observation 369a1809-ab01-4b49-8838-aa39f6d7ee22 · outbound

This paper cites R-dfcil: Relation-guided representation learning for data-free class incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning R-dfcil: Relation-guided representation learning for data-free class incremental learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.533491Z

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Observation eaffc3d9-a323-45a8-9488-c24d5229db2d · outbound

This paper cites Deep residual learning for image recognition.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Deep residual learning for image recognition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.482150Z

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Observation 0e316020-6328-4aa1-90d1-b7b20b9ca880 · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.430774Z

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Observation e268c917-44e1-4044-961e-cf1872150696 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Distilling the Knowledge in a Neural Network

Reference 17

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Observation 3f7ada23-6d07-49e9-b7bf-a4f236ce58d0 · outbound

This paper cites Hierarchical semantic tree anchoring for clip-based class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Hierarchical semantic tree anchoring for clip-based class-incremental learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:12:50.587758Z

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Observation cdce86d4-9ced-431b-a6da-c3af359863aa · outbound

This paper cites Class-incremental learning with clip: Adaptive representation adjustment and parameter fusion.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Class-incremental learning with clip: Adaptive representation adjustment and parameter fusion

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.375719Z

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Observation cfebab37-44de-4d08-9b96-cbed22040427 · outbound

This paper cites Mind the gap: Preserving and compensating for the modality gap in clip-based continual learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Mind the gap: Preserving and compensating for the modality gap in clip-based continual learning

Reference 20

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Observation 8811e0e1-6a17-466f-8544-15520fe16efa · outbound

This paper cites Openclip.Zenodo.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Openclip.Zenodo

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.544443Z

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Observation 6904787f-e9ed-4b99-9d45-b890945aa746 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Scaling up visual and vision-language representation learning with noisy text supervision

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.557844Z

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Observation e70b3b8b-0887-4757-acc4-fcfe12d40aad · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526

Reference 23

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Observation cee309c6-034e-413b-82f5-4d0fea7e6ad4 · outbound

This paper cites 3d object representations for fine-grained categorization.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning 3d object representations for fine-grained categorization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.562250Z

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Observation df02bf71-c548-48f7-9680-4a721796d41b · outbound

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

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning multiple layers of features from tiny images

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.584036Z

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Observation f247a8e3-151f-489f-b08e-ad87b88620a6 · outbound

This paper cites Gallop: Learning global and local prompts for vision-language models.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Gallop: Learning global and local prompts for vision-language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.537274Z

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Observation 4ce0708f-21e5-4927-bbd9-78debdbd8f30 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 27

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Observation 855007c4-bb32-48a7-bd67-8e7ea523849e · outbound

This paper cites Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:12:50.580789Z

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Observation fd9d42f0-9560-483c-95e4-fadbda47330b · outbound

This paper cites Bofa: Bridge-layer orthogo- nal low-rank fusion for clip-based class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Bofa: Bridge-layer orthogo- nal low-rank fusion for clip-based class-incremental learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.575102Z

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Observation d0cfad44-b820-4be6-abe8-a32987e97b6b · outbound

This paper cites Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.486807Z

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Observation da43a134-6cfe-4d14-b982-9af2f498f1e9 · outbound

This paper cites Adaptive aggregation networks for class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Adaptive aggregation networks for class-incremental learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.340194Z

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Observation 552b7861-5e73-4d00-9ddf-b8b61b89cc70 · outbound

This paper cites Deep learning face attributes in the wild.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Deep learning face attributes in the wild

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.498806Z

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Observation 93f5e532-54b7-4894-9d7b-d25d569fda47 · outbound

This paper cites Class-incremental exemplar compression for class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Class-incremental exemplar compression for class-incremental learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.579535Z

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Observation 5c17c0ca-9369-4df1-b08d-36e614ff6578 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Fine-Grained Visual Classification of Aircraft

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:12:50.574235Z

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Observation 3b59b3ae-d48e-41a0-8ca6-3aed76483fd9 · outbound

This paper cites Class-incremental learning: survey and performance evaluation on image classification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5513–5533.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Class-incremental learning: survey and performance evaluation on image classification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5513–5533

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.426732Z

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Observation c77aec00-62e3-46cf-abf2-24e53cbc4524 · outbound

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

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning to remember: A synaptic plasticity driven framework for continual learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.434969Z

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Observation a6bf21c9-67d6-435c-a4e1-06e1a64c78a6 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.358528Z

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Observation a44e1170-fd8a-407b-bc1d-ad81872aa312 · outbound

This paper cites Adaptive adapter routing for long-tailed class-incremental learning.Machine Learning, 114(3):68.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Adaptive adapter routing for long-tailed class-incremental learning.Machine Learning, 114(3):68

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.353779Z

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Observation d5f957c6-b963-40a2-abf6-b2e7af74e667 · outbound

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

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning transferable visual models from natural language supervision

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.491259Z

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Observation 0e8d7151-8e0d-42b8-89f5-8abe303ec24e · outbound

This paper cites icarl: Incremental classifier and representation learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning icarl: Incremental classifier and representation learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.344805Z

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Observation 4d7be05c-692d-4c89-a0a4-8bb445e5c9bc · outbound

This paper cites Overcoming catastrophic forgetting with hard attention to the task.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Overcoming catastrophic forgetting with hard attention to the task

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.588524Z

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Observation 95cf3cf1-3865-4b64-937b-935812af68a1 · outbound

This paper cites Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima.Advances in neural information processing systems, 34:6747–6761.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima.Advances in neural information processing systems, 34:6747–6761

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.571158Z

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Observation 1814fff4-0d39-4cfa-a721-4a44c9f4b462 · outbound

This paper cites OpenAI GPT-5 System Card.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning OpenAI GPT-5 System Card

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:12:50.619180Z

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Observation 9ba9165a-e021-425b-b853-e6fa0ad73e35 · outbound

This paper cites Coda-prompt: Continual decomposed attention- based prompting for rehearsal-free continual learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Coda-prompt: Continual decomposed attention- based prompting for rehearsal-free continual learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.362940Z

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Observation 36d94246-c0bd-4bfa-871e-92807e38bf7e · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:12:50.631447Z

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Observation 86b6494b-de77-4cc8-8cf5-1dcd2e9f2fe7 · outbound

This paper cites C3box: A clip-based class-incremental learning toolbox.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning C3box: A clip-based class-incremental learning toolbox

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:12:50.568132Z

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Observation e2793d14-ce61-4192-81f6-d4c730b1a091 · outbound

This paper cites Semantically-shifted incremental adapter-tuning is a continual vitransformer.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Semantically-shifted incremental adapter-tuning is a continual vitransformer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.502687Z

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Observation c5b5bb90-aeda-4ef2-af5c-a8fd1ca52f84 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning The caltech-ucsd birds-200-2011 dataset

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.507465Z

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Observation ccc56333-bd46-4feb-8a39-b13e122f06b5 · outbound

This paper cites Beef: Bi- compatible class-incremental learning via energy-based expansion and fusion.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Beef: Bi- compatible class-incremental learning via energy-based expansion and fusion

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.529584Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c1ef8b82-b1b5-4c3b-b3ba-a0133404f991 · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.Advances in Neural Information Processing Systems, 35: 5682–5695.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning.Advances in Neural Information Processing Systems, 35: 5682–5695

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.566641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23f16eef-a2cc-4493-a0e4-62ad3d07c862 · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.439636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e10a7fb0-fb0c-404a-a706-09839f30ce5b · outbound

This paper cites Learning to prompt for continual learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning to prompt for continual learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.402647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 16b161ec-e486-4ec0-80f0-cc2717c5d335 · outbound

This paper cites Llm2clip: Powerful language model unlock richer visual representation.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Llm2clip: Powerful language model unlock richer visual representation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.449156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bbb92c4f-0aee-41e1-a1f0-b0d37efa7374 · outbound

This paper cites Controlmllm: Training-free visual prompt learning for multimodal large language models.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Controlmllm: Training-free visual prompt learning for multimodal large language models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.417062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 17604f5b-cebb-40f9-bcff-8ee00ff9d6ae · outbound

This paper cites Incremental learning using conditional adversarial networks.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Incremental learning using conditional adversarial networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.495047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46871804-10ba-415e-b4a6-531946df47ec · outbound

This paper cites Sun database: Large- scale scene recognition from abbey to zoo.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Sun database: Large- scale scene recognition from abbey to zoo

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.469950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee9c6e90-cc18-4b3e-99ff-cd3c4d8f3c53 · outbound

This paper cites Reinforced continual learning.Advances in neural information processing systems, 31.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Reinforced continual learning.Advances in neural information processing systems, 31

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.473877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29d16e6d-4275-4971-8ec1-d58793de7498 · outbound

This paper cites Pevl: Position-enhanced pre-training and prompt tuning for vision-language models.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Pevl: Position-enhanced pre-training and prompt tuning for vision-language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.389949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 365df992-ffae-436c-80f1-287d1c1f5641 · outbound

This paper cites Lifelong Learning with Dynamically Expandable Networks.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Lifelong Learning with Dynamically Expandable Networks

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:12:50.613927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c3fb6fc1-7c9c-48c9-931a-12e7d948ac12 · outbound

This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.512277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5b820465-6f2f-4a35-9dd9-d3f8da986c31 · outbound

This paper cites Language guided concept bottleneck models for interpretable continual learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Language guided concept bottleneck models for interpretable continual learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.516217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c866308c-0d4a-420a-96fe-b140b193cace · outbound

This paper cites Continual learning through synaptic intelligence.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Continual learning through synaptic intelligence

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.457536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 666f2f66-9f50-4e2c-af23-9a5f3054791b · outbound

This paper cites Maintaining discrimination and fairness in class incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Maintaining discrimination and fairness in class incremental learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.461714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d8abacef-53cc-43e2-84e4-25449b2be3fa · outbound

This paper cites Task-agnostic guided feature expansion for class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Task-agnostic guided feature expansion for class-incremental learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.385072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f1e8896-4053-4593-821a-8ea0ba90a14e · outbound

This paper cites Continual learning with pre-trained models: a survey.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Continual learning with pre-trained models: a survey

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.394250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d8827aa-6f38-41ff-988b-a5a09f7fdd40 · outbound

This paper cites Class-incremental learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9851–9873.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Class-incremental learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9851–9873

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.407255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9247f3e-23ff-4d39-8548-86939309365f · outbound

This paper cites Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need.International Journal of Computer Vision, 133(3):1012–1032.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need.International Journal of Computer Vision, 133(3):1012–1032

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.367011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e0c76a5-8dc7-44fc-9d9c-f1ce8dfab296 · outbound

This paper cites External know- ledge injection for clip-based class-incremental learning.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning External know- ledge injection for clip-based class-incremental learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.465904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca80390d-5fd0-46bf-96ad-0c4c091af3f8 · outbound

This paper cites Learning without forgetting for vision-language models.IEEE Transactions on Pattern Analysis and Machine Intelligence.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning without forgetting for vision-language models.IEEE Transactions on Pattern Analysis and Machine Intelligence

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.548796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9dbf52eb-52a4-4b38-8bc8-5c735aa4f3ad · outbound

This paper cites Conditional prompt learning for vision- language models.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Conditional prompt learning for vision- language models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.335819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58e5ddf0-795f-4c55-a630-9837875eb6b7 · outbound

This paper cites Learning to prompt for vision-language models.International journal of computer vision, 130(9):2337–2348.

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning Learning to prompt for vision-language models.International journal of computer vision, 130(9):2337–2348

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T20:01:34.371499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4894e838-3a94-4d19-a25f-7041e0491a05 · outbound

This paper cites a photo of a [CLASS].

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning a photo of a [CLASS]

Reference 72

Resolution: malformed identifier; raw_fallback, observed 2026-05-15T20:01:34.524963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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