DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models | Paper Citation Record

Observation 1cafc980-708b-41bf-8121-ed2bdc89b58f · outbound

This paper cites Continual lifelong learning with neural networks: A review.Neural networks.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Continual lifelong learning with neural networks: A review.Neural networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.098984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 21c9d530-b06d-4460-82ba-15ef7e0d2f07 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.IEEE transactions on pattern analysis and machine intelligence (TPAMI).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models A continual learning survey: Defying forgetting in classification tasks.IEEE transactions on pattern analysis and machine intelligence (TPAMI)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.136781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation a16c2ca6-afb9-48ab-8994-9d1fc993519a · outbound

This paper cites Dark experience for general continual learning: a strong, simple baseline.Advances in neural information processing systems (NeurIPS).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Dark experience for general continual learning: a strong, simple baseline.Advances in neural information processing systems (NeurIPS)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.176226Z

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No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 3191bdb8-6da4-4a0f-9ef9-75af0aac8412 · outbound

This paper cites Continual learning through synaptic intelligence.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Continual learning through synaptic intelligence

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.159788Z

Source-reported events for the cited work

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Observation 08bdbc64-3c99-40fa-a4f0-8c8042caedbd · outbound

This paper cites Continual learning with deep generative replay.Advances in neural information processing systems (NeurIPS).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Continual learning with deep generative replay.Advances in neural information processing systems (NeurIPS)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.179645Z

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No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 5883f897-a0df-4a94-a575-faf99f95d496 · outbound

This paper cites Gcr: Gradient coreset based replay buffer selection for continual learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Gcr: Gradient coreset based replay buffer selection for continual learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.097205Z

Source-reported events for the cited work

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Observation 1fcbc588-f791-4a01-adeb-be9ac333b65b · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learning transferable visual models from natural language supervision

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.100885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 75f08d90-855c-4422-a3ac-36652c942f83 · outbound

This paper cites Learning to prompt for vision-language models.International Journal of Computer Vision (IJCV).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learning to prompt for vision-language models.International Journal of Computer Vision (IJCV)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.161619Z

Source-reported events for the cited work

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Observation c09ac0d8-ae98-4d71-89df-c3553ef52065 · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.163466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation c50fdca3-3292-43fb-b67c-fff84952fb0c · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.182881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 6c189136-43c2-4da2-9cbf-17f31eafd83b · outbound

This paper cites Self-expansion of pre-trained models with mixture of adapters for continual learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Self-expansion of pre-trained models with mixture of adapters for continual learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.172215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation cda491af-325f-4f17-82a2-855367e8f7e7 · outbound

This paper cites Incremental embedding learning via zero-shot translation.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Incremental embedding learning via zero-shot translation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.188144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation f4fe0a46-3996-4d95-85b5-ab8cb4350a16 · outbound

This paper cites Progressive Neural Networks.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Progressive Neural Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T16:15:26.375551Z

Source-reported events for the cited work

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Observation c45f85f9-2673-4c0d-b238-0722fcacfc28 · outbound

This paper cites Packnet: Adding multiple tasks to a single network by iterative pruning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Packnet: Adding multiple tasks to a single network by iterative pruning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.158015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 1a82eb60-0bf7-4dec-8d74-fb71af0e08d7 · outbound

This paper cites Compress to one point: neural collapse for pre-trained model-based class-incremental learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Compress to one point: neural collapse for pre-trained model-based class-incremental learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.122035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 7bee35c7-70f5-482b-b458-a261f00bc4c9 · outbound

This paper cites Gradient episodic memory for continual learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Gradient episodic memory for continual learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.151063Z

Source-reported events for the cited work

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Observation dbc76f24-8bf4-4414-a73f-e60162a48b80 · outbound

This paper cites icarl: Incremental classifier and representation learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models icarl: Incremental classifier and representation learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.152598Z

Source-reported events for the cited work

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Observation d706a03c-6399-46c7-ad34-ca4ae8aad184 · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation.Advances in neural information processing systems (NeurIPS).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Generalizing to unseen domains via adversarial data augmentation.Advances in neural information processing systems (NeurIPS)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.154576Z

Source-reported events for the cited work

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Observation 46ab104b-c6b9-46a9-a990-7fae9c1356dd · outbound

This paper cites Visual prompt tuning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Visual prompt tuning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.129174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation f5c3b368-1bc8-44d9-af58-519368bdb94b · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM computing surveys.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM computing surveys

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.173921Z

Source-reported events for the cited work

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Observation 0d48a5d8-0e74-4b59-b35e-7c5534fbfb15 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Parameter-efficient transfer learning for nlp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.145513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 8e1d212a-b1b0-4a07-9a66-4ffad9cf90a5 · outbound

This paper cites K-adapter: Infusing knowledge into pre-trained models with adapters.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models K-adapter: Infusing knowledge into pre-trained models with adapters

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.142056Z

Source-reported events for the cited work

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Observation d5fda168-6c8d-4c13-8451-57f6f67aac74 · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Lora: Low-rank adaptation of large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.147594Z

Source-reported events for the cited work

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Observation 574e8cc3-c1ea-4a51-b24b-876f0178802e · outbound

This paper cites Clap4clip: Continual learning with probabilistic finetuning for vision-language models.Advances in neural information processing systems (NeurIPS).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Clap4clip: Continual learning with probabilistic finetuning for vision-language models.Advances in neural information processing systems (NeurIPS)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.120286Z

Source-reported events for the cited work

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Observation 17150afa-392e-4b7a-ba48-912a04cb987e · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.132841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 29875bd3-c590-4b76-aed2-cc25b4442e1c · outbound

This paper cites Adaptive mixtures of local experts.Neural computation.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Adaptive mixtures of local experts.Neural computation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.123759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation bda77ec5-d8ce-4822-81c9-55fca96930e9 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-11T03:46:00.404688Z

Source-reported events for the cited work

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Observation 29f2f1bf-d838-4771-803d-7d337d7328ab · outbound

This paper cites Expert gate: Lifelong learning with a network of experts.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Expert gate: Lifelong learning with a network of experts

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.143907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 47001cd3-e2da-4839-961e-950e297956a9 · outbound

This paper cites Lifelong language pretraining with distribution-specialized experts.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Lifelong language pretraining with distribution-specialized experts

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.131052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 8b7d8c1a-ef2d-458e-9460-03a5d4aeeae2 · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.International Journal of Computer Vision (IJCV).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Clip-adapter: Better vision-language models with feature adapters.International Journal of Computer Vision (IJCV)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.127192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 12b8af30-d41d-4614-81d9-aa5ace08b61f · outbound

This paper cites Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations (ICLR), 2021a.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations (ICLR), 2021a

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:46:00.389345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation f9ee268f-0477-4023-af47-c049edc0d323 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-11T03:46:00.444419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 228a681a-b90f-4951-9863-6c2d0fc6566f · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.170568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 7b32de07-6c3a-4006-8166-8a329b34d9b4 · outbound

This paper cites Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence (TPAMI).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence (TPAMI)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.156382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 4f7a16ca-a37d-405d-b2ec-68a61a14f0ab · outbound

This paper cites Robust fine-tuning of zero-shot models.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Robust fine-tuning of zero-shot models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.138645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 9a462409-9e7c-49d7-8671-5c7c22f74948 · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Synthetic data is an elegant gift for continual vision-language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.125440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation d85359ee-6ae5-4d09-ae97-f1336fe1e8aa · outbound

This paper cites Learn and ensemble bridge adapters for multi-domain task incremental learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learn and ensemble bridge adapters for multi-domain task incremental learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.118406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation c7b76166-513f-433c-9ba4-0bb298482285 · outbound

This paper cites Don't Stop Learning: Towards Continual Learning for the CLIP Model.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Don't Stop Learning: Towards Continual Learning for the CLIP Model

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:46:00.432528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 412a57d4-2422-473e-9bea-88532e0e668c · outbound

This paper cites Dytox: Trans- formers for continual learning with dynamic token expansion.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Dytox: Trans- formers for continual learning with dynamic token expansion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.177957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation a646c4d6-89d3-47ea-a15d-c77f10445d1f · outbound

This paper cites Decoupled Weight Decay Regularization.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Decoupled Weight Decay Regularization

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-11T03:46:00.423936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation a6a6f610-1348-4116-94dd-1013e203bfc4 · outbound

This paper cites When does label smoothing help? Advances in neural information processing systems (NeurIPS).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models When does label smoothing help? Advances in neural information processing systems (NeurIPS)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.116360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 9423fd53-8d36-48a5-955d-2e12642907be · outbound

This paper cites Visualizing and understanding convolutional networks.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Visualizing and understanding convolutional networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.168883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 52ff63fb-144c-4b65-9c30-6a2bb55fb541 · outbound

This paper cites How transferable are features in deep neural networks?Advances in neural information processing systems (NeurIPS), 27.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models How transferable are features in deep neural networks?Advances in neural information processing systems (NeurIPS), 27

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.134676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation cb51f3a0-3309-4bbf-9a7c-f8c1b1912fa2 · outbound

This paper cites Transfusion: Understanding transfer learning for medical imaging.Advances in neural information processing systems (NeurIPS).

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Transfusion: Understanding transfer learning for medical imaging.Advances in neural information processing systems (NeurIPS)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.140377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation e909d35c-c210-4da6-a4de-eaff4dcbfd53 · outbound

This paper cites Acceleration of stochastic approximation by averaging.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Acceleration of stochastic approximation by averaging

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.184714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 69595d0d-72cf-4810-969a-684132ace309 · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learning multiple layers of features from tiny images

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.112063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation d2cd5194-1319-4fd7-b0d3-e6d29f8dd05e · outbound

This paper cites Der: Dynamically expandable representation for class incremental learning.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Der: Dynamically expandable representation for class incremental learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.113797Z

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No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation cf1f2c04-717b-402e-87ef-b570d37e21e2 · outbound

This paper cites Learning without memorizing.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learning without memorizing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.149442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 977bd44a-de85-422e-b107-67f79a72048c · outbound

This paper cites Moment matching for multi-source domain adaptation.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Moment matching for multi-source domain adaptation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.108389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation a6a3773b-1324-4db7-812e-1c414ddf0d7e · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:07.065951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 4c9bd47c-c324-404e-8e00-ad1e605dc38a · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.181279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation d022a97b-2c86-474b-b104-016c8712d724 · outbound

This paper cites Describing textures in the wild.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Describing textures in the wild

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.106433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 81ca353c-7f3d-423e-85bc-587aa19a01c2 · outbound

This paper cites an unresolved cited work.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Unresolved cited work

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.186493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation ecdfb266-1a6a-42e3-8da7-9477bfca8675 · outbound

This paper cites Automated flower classification over a large number of classes.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Automated flower classification over a large number of classes

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.110371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 6fe0890d-48eb-43b8-b320-877495604b56 · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Food-101–mining discriminative components with random forests

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.190676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 1cb09acf-56ed-48a7-ac53-2787fd616258 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].IEEE signal processing magazine.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models The mnist database of handwritten digit images for machine learning research [best of the web].IEEE signal processing magazine

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.165489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 4f270a84-0a63-4267-8f3c-7d1b817eac2c · outbound

This paper cites Cats and dogs.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Cats and dogs

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.104534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation 0c0e28b8-e0cf-4ecb-bc8e-1c8f2c99c6aa · outbound

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

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models 3d object representations for fine- grained categorization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T13:30:55.167277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Observation ce3a65c5-04cf-4686-917c-b072356eb9ce · outbound

This paper cites Avg.”) and the final accuracy after learning all tasks (“Last.

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models Avg.”) and the final accuracy after learning all tasks (“Last

Reference 59

Resolution: malformed identifier; raw_fallback, observed 2026-05-14T13:30:55.102700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-24T06:31:00.690269+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers