Continual Learning with Vision-Language Models via Semantic-Geometry Preservation | Paper Citation Record

Observation f1b3937a-463f-430f-b1a4-7c5276b7ed79 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Learning transferable visual models from natural language supervision,

Reference 1

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Observation 6534e0bf-4070-47d1-b686-64aa1a041836 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 2

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Observation 1a1da1f0-c84b-4a0f-af2f-177b6d935cee · outbound

This paper cites Csta: Spatial-temporal causal adaptive learning for exemplar-free video class-incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Csta: Spatial-temporal causal adaptive learning for exemplar-free video class-incremental learning,

Reference 3

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Observation afac19ca-a651-4ba2-8aa3-0fe5fa30bc90 · outbound

This paper cites Achieving plasticity- stability trade-off in continual learning through adaptive orthogonal projection,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Achieving plasticity- stability trade-off in continual learning through adaptive orthogonal projection,

Reference 4

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Observation 616ef07a-5a65-46e2-ba07-47f627991fbf · outbound

This paper cites Joint memory optimiza- tion for continual learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Joint memory optimiza- tion for continual learning,

Reference 5

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Observation e33691af-2261-47b9-b33a-75a34f34e6ee · outbound

This paper cites Learning without forgetting for vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Learning without forgetting for vision-language models,

Reference 6

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Observation 8d5a4028-87c8-4f9e-9a12-a5eb137ed3ba · outbound

This paper cites Class-aware prompting for federated few-shot class- incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Class-aware prompting for federated few-shot class- incremental learning,

Reference 7

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Observation 0c91be2e-a8a7-433c-ab36-9cdca0a87602 · outbound

This paper cites Mingle: Mixture of null-space gated low-rank experts for test-time continual model merging,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Mingle: Mixture of null-space gated low-rank experts for test-time continual model merging,

Reference 8

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Observation 3534288a-0205-49cb-81ce-dd884a5f4b16 · outbound

This paper cites Null-space filtering for data-free continual model merging: Preserving stability, promoting plasticity,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Null-space filtering for data-free continual model merging: Preserving stability, promoting plasticity,

Reference 9

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Observation bf6ba89e-42c0-4cc8-9186-8671e19f574c · outbound

This paper cites New insights on relieving task-recency bias for online class incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation New insights on relieving task-recency bias for online class incremental learning,

Reference 10

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Observation 7e9ce8f6-4046-4e79-8152-b01d69c63e0b · outbound

This paper cites Continual learning of image classes with language guidance from a vision-language model,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Continual learning of image classes with language guidance from a vision-language model,

Reference 11

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Observation 3ee19e1a-d9e1-4d96-9ae3-f20d5fbef04d · outbound

This paper cites Ex- ternal knowledge injection for clip-based class-incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Ex- ternal knowledge injection for clip-based class-incremental learning,

Reference 12

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Observation 62c19383-1ace-47c7-8185-38b3fee824e6 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Class-incremental learning with clip: Adaptive representation adjustment and parameter fusion,

Reference 13

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Observation d71752d4-e468-48dc-9c92-b0788d539ea6 · outbound

This paper cites Overcoming generic knowledge loss with selective parameter update,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Overcoming generic knowledge loss with selective parameter update,

Reference 14

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Observation f218c161-b98c-40d7-8823-4c500fc9472c · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Slca: Slow learner with classifier alignment for continual learning on a pre-trained model,

Reference 15

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Observation 5ac4d007-3507-4536-88fb-03e6d61466ec · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Boosting continual learning of vision-language models via mixture-of-experts adapters,

Reference 16

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Observation f2dae506-7c13-48c7-8714-d22b8d55066c · outbound

This paper cites Desclip: Robust continual learning via general attribute descriptions for vlm-based visual recognition,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Desclip: Robust continual learning via general attribute descriptions for vlm-based visual recognition,

Reference 17

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Observation 6cdcd6c0-3e49-4415-b2ec-f2938d73e9e6 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Language guided concept bottleneck models for interpretable continual learning,

Reference 18

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Observation 37a2feea-e63b-4eb2-abe2-b25a2743e1d3 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Hierarchical semantic tree anchoring for clip-based class-incremental learning,

Reference 19

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Observation 9de14231-de6a-43c9-bd22-1f0536b13e19 · outbound

This paper cites Difference vector equalization for robust fine-tuning of vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Difference vector equalization for robust fine-tuning of vision-language models,

Reference 20

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Observation 990e050e-028b-4ef2-a283-3caf56947c96 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Mind the gap: Preserving and compensating for the modality gap in clip- based continual learning,

Reference 21

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Observation ebdafaa7-5317-4c43-80bf-110322ee35d6 · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Preventing zero-shot transfer degradation in continual learning of vision-language models,

Reference 22

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Observation 2adae78e-9b17-4465-bbc6-2cb037306e28 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,

Reference 23

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Observation 6bdc607d-0946-4ace-8881-eec92deda546 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Imagenet: A large-scale hierarchical image database,

Reference 24

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Observation 27beecf2-afea-4991-b3f7-0acd10bb225b · outbound

This paper cites Divergence measures based on the shannon entropy,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Divergence measures based on the shannon entropy,

Reference 25

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Observation 502390c1-8c0c-4a96-a6ee-7ec18c23d776 · outbound

This paper cites Text-guided attention is all you need for zero-shot robustness in vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Text-guided attention is all you need for zero-shot robustness in vision-language models,

Reference 26

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Observation 976f6b7b-51c7-465d-8d78-89a169e8fc7f · outbound

This paper cites Evaluating the adversarial robustness of vision-language models via internal feature perturbations,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Evaluating the adversarial robustness of vision-language models via internal feature perturbations,

Reference 27

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Observation 14639531-917b-4245-94d8-6caa7520ab97 · outbound

This paper cites Attention-guided hierarchical defense for multimodal attacks in vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Attention-guided hierarchical defense for multimodal attacks in vision-language models,

Reference 28

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Observation 513f44fd-e255-4ff1-852d-ea9c78a3e87a · outbound

This paper cites Synthetic data is an elegant gift for continual vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Synthetic data is an elegant gift for continual vision-language models,

Reference 29

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Observation 52b9b079-f92b-4922-9b4f-02a98f7aba5d · outbound

This paper cites Lora-loop: Closing the synthetic re- play cycle for continual vlm learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Lora-loop: Closing the synthetic re- play cycle for continual vlm learning,

Reference 30

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Observation e4bbfd83-03c5-41dc-bc97-87f42c80cfcb · outbound

This paper cites Dual-consistency model inversion for non-exemplar class incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Dual-consistency model inversion for non-exemplar class incremental learning,

Reference 31

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Observation 3ec0ec9f-4a4a-4788-bc90-05336bc118fd · outbound

This paper cites Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning,

Reference 32

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Observation 2e014c60-f125-4383-ba4f-f27cb5dd766d · outbound

This paper cites Clip-lora: Low-rank adaptation for clip,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Clip-lora: Low-rank adaptation for clip,

Reference 33

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Observation 4c82890c-a9e4-4104-a7f0-5b8a9dd2f291 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Learning to prompt for vision- language models,

Reference 34

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Observation 78d2f06c-e853-4a51-adf6-18c20748acca · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Conditional prompt learning for vision-language models,

Reference 35

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Observation 31c0bbea-713a-4379-b258-20dd9de29ec1 · outbound

This paper cites Graphadapter: Tuning vision-language models with dual knowledge graph,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Graphadapter: Tuning vision-language models with dual knowledge graph,

Reference 36

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Observation c46b2745-aea6-4db1-a413-f67258e604a3 · outbound

This paper cites Vmt-adapter: Parameter- efficient transfer learning for multi-task dense scene understanding,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Vmt-adapter: Parameter- efficient transfer learning for multi-task dense scene understanding,

Reference 37

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Observation 14e99aee-aed7-47a5-9ab1-34f4c20bd508 · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Lora: Low-rank adaptation of large language models

Reference 38

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Observation e5c99e5f-af01-4527-839b-233ed0e7eeea · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation A model or 603 exemplars: Towards memory-efficient class-incremental learning,

Reference 39

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Observation 873a2af0-9a3a-45e1-8b01-a315ee37d4cb · outbound

This paper cites Ceat: Continual expansion and absorption transformer for non-exemplar class- incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Ceat: Continual expansion and absorption transformer for non-exemplar class- incremental learning,

Reference 40

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Observation f317d5e4-ed3d-41c3-b6b9-ab4b0eacc158 · outbound

This paper cites Learning without forgetting,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Learning without forgetting,

Reference 41

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Observation 9b3c5ae1-a447-4599-87ae-06516d9aa035 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Overcoming catastrophic forgetting in neural networks,

Reference 42

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Observation cc7c76d2-9607-4a53-aca1-da00a0907c80 · outbound

This paper cites Learning to prompt for continual learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Learning to prompt for continual learning,

Reference 43

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Observation e6f704f9-3227-47ca-98ea-504ed4c615fb · outbound

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

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning,

Reference 44

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Observation acb7d825-768f-458a-91f6-9992a685ecc5 · outbound

This paper cites Expandable subspace ensemble for pre-trained model-based class-incremental learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Expandable subspace ensemble for pre-trained model-based class-incremental learning,

Reference 45

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Observation dca6ef5e-d04e-4a7d-8874-f6c1437cf385 · outbound

This paper cites Inflora: Interference-free low-rank adaptation for continual learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Inflora: Interference-free low-rank adaptation for continual learning,

Reference 46

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Observation 2a1f5bf0-efcd-40b2-b1f5-9b09600b6e34 · outbound

This paper cites Consistent prompting for rehearsal- free continual learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Consistent prompting for rehearsal- free continual learning,

Reference 47

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Observation 62faca89-1cad-4bda-beab-bdfb129c131b · outbound

This paper cites Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need,

Reference 48

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Observation f6747f81-a55e-4bc8-988a-9a306957da10 · outbound

This paper cites Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-02T18:22:13.979188Z

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Observation becaef37-7a0e-4215-8237-4624bd9e9830 · outbound

This paper cites Select and distill: Selective dual-teacher knowledge transfer for continual learning on vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Select and distill: Selective dual-teacher knowledge transfer for continual learning on vision-language models,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-02T18:22:14.090989Z

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Observation b2c02c29-7752-43bf-8a6f-9ed1f50c450c · outbound

This paper cites Adapt without forgetting: Distill proximity from dual teachers in vision-language models,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Adapt without forgetting: Distill proximity from dual teachers in vision-language models,

Reference 51

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Observation bfc94037-b27a-4232-baac-fe514c18f354 · outbound

This paper cites Explaining and harnessing adversarial examples,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Explaining and harnessing adversarial examples,

Reference 52

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Observation 3c7cce05-10a3-4999-bd7b-746e12788fd5 · outbound

This paper cites Adversarial examples in the physical world,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Adversarial examples in the physical world,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-02T18:22:14.466601Z

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Observation 589cf603-28bb-4cb7-8fc7-62119a4f3ea9 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Towards deep learning models resistant to adversarial attacks,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-02T18:22:14.551436Z

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Observation f9000dfe-5bb6-4dfb-9d38-6490af0f83cd · outbound

This paper cites BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

Reference 55

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Observation 945dd24f-4079-4d6e-a5df-2d2906a899b9 · outbound

This paper cites On information and sufficiency,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation On information and sufficiency,

Reference 56

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Observation 662c5c04-b557-4cc6-8858-fc2f9a0d3edd · outbound

This paper cites Dynamic multi-layer null space projection for vision-language continual learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Dynamic multi-layer null space projection for vision-language continual learning,

Reference 57

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Observation d564afcf-cbbd-4eee-b7ac-8139a6f8d361 · outbound

This paper cites Exemplar-free continual representation learning via learnable drift compensation,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Exemplar-free continual representation learning via learnable drift compensation,

Reference 58

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Observation 834f4bc0-5399-4d3e-9b9d-eb5b1c668af3 · outbound

This paper cites Resurrecting old classes with new data for exemplar-free continual learning,.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Resurrecting old classes with new data for exemplar-free continual learning,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-02T18:22:15.113748Z

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Observation a847d8b3-e4b8-4e78-9239-eb1b7e68e8fc · outbound

This paper cites CLIP model is an Efficient Continual Learner.

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation CLIP model is an Efficient Continual Learner

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Clap4clip: Continual learning with probabilistic finetuning for vision-language models,

Reference 61

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Learning multiple layers of features from tiny images,

Reference 62

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation The caltech-ucsd birds-200-2011 dataset,

Reference 63

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation The many faces of robustness: A critical analysis of out-of-distribution generalization,

Reference 64

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Ucf101: A dataset of 101 human actions classes from videos in the wild,

Reference 65

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Large-scale machine learning with stochastic gradient de- scent,

Reference 66

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation High- resolution image synthesis with latent diffusion models,

Reference 67

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Food-101–mining dis- criminative components with random forests,

Reference 68

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Cats and dogs,

Reference 69

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Ad- vancing cross-domain discriminability in continual learning of vision- language models,

Reference 70

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Continual Learning with Vision-Language Models via Semantic-Geometry Preservation Video segmentation and its applications

Reference 2005

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