SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality | Paper Citation Record

Observation 34ed44b5-4471-49d2-ae87-3d4a94cd94db · outbound

This paper cites Sequential modeling enables scalable learning for large vision models.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Sequential modeling enables scalable learning for large vision models

Reference 1

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Observation 20e2c845-7037-4c58-b29c-45b40b9db29e · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Beit: Bert pre-training of image transformers

Reference 2

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Observation bb5963a4-2dab-4b91-af83-d7bf7352569c · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality On the Opportunities and Risks of Foundation Models

Reference 3

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Observation c9e98013-8d77-4fa5-bd65-815532dbb8dd · outbound

This paper cites Multi-spectral sift for scene category recognition.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Multi-spectral sift for scene category recognition

Reference 4

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Observation 196e969f-6053-4630-8cc3-6c2273aad0a4 · outbound

This paper cites Language models are few-shot learners.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Language models are few-shot learners

Reference 5

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Observation 5608ce74-84a7-4f4f-af2e-35de469167f0 · outbound

This paper cites Pretrainable geometric graph neural network for antibody affinity maturation.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Pretrainable geometric graph neural network for antibody affinity maturation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.663163Z

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Observation 16c01c81-68b3-454b-8070-91fff15ebcfa · outbound

This paper cites SAD: Segment Any RGBD.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality SAD: Segment Any RGBD

Reference 7

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Observation af42e5b3-6fee-47b8-ad3f-d326585129a1 · outbound

This paper cites Hts-at: A hierarchical token-semantic audio transformer for sound classification and detection.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Hts-at: A hierarchical token-semantic audio transformer for sound classification and detection

Reference 8

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Observation 7771db4a-236a-438a-9878-2811fc1a62f9 · outbound

This paper cites Domain ad- aptation for semantic segmentation with maximum squares loss.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Domain ad- aptation for semantic segmentation with maximum squares loss

Reference 9

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Observation cc6bf476-9401-4b14-af7a-f53bee9d0c0c · outbound

This paper cites Adaptformer: Ad- apting vision transformers for scalable visual recognition.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Adaptformer: Ad- apting vision transformers for scalable visual recognition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.682832Z

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Observation 6379fb25-0387-4b33-b42b-fc7a63bb6b57 · outbound

This paper cites A simple framework for contrastive learn- ing of visual representations.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality A simple framework for contrastive learn- ing of visual representations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.687845Z

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Observation b7a0740e-7a2a-4eb7-86a5-7d84b5581169 · outbound

This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.692410Z

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Observation 00252cb6-46ce-479e-a8e6-b9e506700168 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Improved Baselines with Momentum Contrastive Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.697394Z

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Observation ff1ea2aa-2ee9-4dce-917f-a334af241bcc · outbound

This paper cites Imagenet: A large-scale hierarchical im- age database.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Imagenet: A large-scale hierarchical im- age database

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.702405Z

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Observation 928c5971-fa53-4acb-8471-ba12da6e487c · outbound

This paper cites Lift: Language- interfaced fine-tuning for non-language machine learning tasks.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Lift: Language- interfaced fine-tuning for non-language machine learning tasks

Reference 15

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Observation 875e70da-0c68-4c23-8bc3-e5aa735fb8e3 · outbound

This paper cites Hyperspectral image super-resolution via non-negative structured sparse repres- entation.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Hyperspectral image super-resolution via non-negative structured sparse repres- entation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.711124Z

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Observation bd480787-7f72-4abc-bfd0-cffbc8d25f69 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.715480Z

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Observation f0b7825d-2414-4013-85c3-3652625f2051 · outbound

This paper cites Multiscale vision transformers.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Multiscale vision transformers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.719637Z

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Observation 8c13dfe7-70b0-4a93-bfc1-3c129c603fde · outbound

This paper cites Event-based vision: A survey.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Event-based vision: A survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.724038Z

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Observation 3ebd8af7-2dc8-4963-9c6c-e7015a5dd3a2 · outbound

This paper cites Low-latency auto- motive vision with event cameras.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Low-latency auto- motive vision with event cameras

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.728339Z

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Observation aa4eda63-cb61-43dd-ab1a-2954d1001518 · outbound

This paper cites Imagebind: One embedding space to bind them all.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Imagebind: One embedding space to bind them all

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.733122Z

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Observation 0b1aa502-3d72-4f20-8bf4-22a2a8d23daa · outbound

This paper cites AST: Audio Spectrogram Transformer.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality AST: Audio Spectrogram Transformer

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.737620Z

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Observation f62be8ee-b251-4707-9c55-748b623a5dff · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Bootstrap your own latent-a new approach to self-supervised learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.742245Z

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Observation 513cca78-48d8-412c-8db2-0ebe4a256dc9 · outbound

This paper cites Pct: Point cloud transformer.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Pct: Point cloud transformer

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.746222Z

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Observation fb5e766a-4321-4d5e-815a-30dfe248b5c5 · outbound

This paper cites Learning rich features from rgb-d im- ages for object detection and segmentation.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Learning rich features from rgb-d im- ages for object detection and segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.750517Z

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Observation de1db3b7-f77d-4cac-97d7-79420518623a · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 26

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Observation 4cdcacbb-0e1e-4983-8dce-cf8e4c53a219 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Momentum contrast for unsupervised visual rep- resentation learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.760869Z

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Observation 7d0f6cc1-663c-40a0-a31b-ff87444dec00 · outbound

This paper cites Masked autoencoders are scal- able vision learners.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Masked autoencoders are scal- able vision learners

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.765839Z

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Observation 60bfe44c-850c-4425-bf79-d62a81ec2c09 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Parameter-efficient transfer learning for nlp

Reference 29

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Observation d07be425-4a83-4343-8586-231f47192a01 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Lora: Low- rank adaptation of large language models

Reference 30

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Observation e12e30d7-9be2-46c1-ae23-a30a9a0af1db · outbound

This paper cites Polarization structured light 3d depth image sensor for scenes with reflective sur- faces.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Polarization structured light 3d depth image sensor for scenes with reflective sur- faces

Reference 31

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Observation 546fd074-1fd8-486d-9478-130b67c5b4fb · outbound

This paper cites Glass segmentation with rgb-thermal image pairs.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Glass segmentation with rgb-thermal image pairs

Reference 32

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Observation f3635d6b-34bc-42c4-873c-bb9f7eded2a6 · outbound

This paper cites Perceiver: General perception with iterative attention.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Perceiver: General perception with iterative attention

Reference 33

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Observation 14d56bac-e093-44ad-ad57-cc3f9a47ff27 · outbound

This paper cites Perceiver io: A general architecture for structured inputs & outputs.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Perceiver io: A general architecture for structured inputs & outputs

Reference 34

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Observation 6fb28d69-b0d4-4eee-a5f0-02f02e3bbc74 · outbound

This paper cites SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.802397Z

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Observation 7c8b73c4-a382-4b95-902b-1030a05a2f36 · outbound

This paper cites Visual prompt tuning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Visual prompt tuning

Reference 36

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Observation 0795cdab-86d4-4dee-be71-6065a7f48257 · outbound

This paper cites A multi-modal pre-training transformer for univer- sal transfer learning in metal–organic frameworks.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality A multi-modal pre-training transformer for univer- sal transfer learning in metal–organic frameworks

Reference 37

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Observation b78f6f17-55fd-49c4-a3f6-9636edc4e4f9 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 38

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Observation 0c81c926-f572-471a-b139-222808e45c42 · outbound

This paper cites Segment anything.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Segment anything

Reference 39

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Observation 941c96fa-a584-48fa-a9af-edf8e2deebe1 · outbound

This paper cites Decou- plenet: Decoupled network for domain adaptive semantic segmentation.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Decou- plenet: Decoupled network for domain adaptive semantic segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.484572Z

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Observation 9dda10d9-39af-4c38-84c0-3d2e4233db57 · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Lisa: Reasoning segmentation via large language model

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.467397Z

Source-reported events for the cited work

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Observation f287ed60-7535-4bfa-af01-3a745882cdfd · outbound

This paper cites Polarized reflection re- moval with perfect alignment in the wild.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Polarized reflection re- moval with perfect alignment in the wild

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.449280Z

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Observation 4d708cc9-d7d1-4b37-9a77-615e376975d4 · outbound

This paper cites Shape from polarization for com- plex scenes in the wild.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Shape from polarization for com- plex scenes in the wild

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.433682Z

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Observation 25cd7fbd-0fd1-4ae4-8808-49a6b17f93bb · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality LLaVA-OneVision: Easy Visual Task Transfer

Reference 44

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Observation 4ad4e292-c23c-45ae-9f99-13789b6532fe · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.417416Z

Source-reported events for the cited work

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Observation e55e324e-3605-4a85-98e4-ff80721cc9f3 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.854002Z

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Observation 10eec4c9-99c6-40dd-8bb8-954998a9e43c · outbound

This paper cites Heterogen- eous domain adaptation: An unsupervised approach.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Heterogen- eous domain adaptation: An unsupervised approach

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.400239Z

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Observation 644e45ed-e840-40c7-a6ed-1f5780fa9686 · outbound

This paper cites Visual instruction tuning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Visual instruction tuning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.382106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4fa89c58-e36e-4b8c-bf18-fe7124be4f49 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Swin transformer: Hierarchical vision transformer using shifted windows

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.868821Z

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Observation 7dbbdce5-65bc-4bf2-bfce-02da01f68a64 · outbound

This paper cites Frozen pretrained transformers as universal compu- tation engines.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Frozen pretrained transformers as universal compu- tation engines

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.354759Z

Source-reported events for the cited work

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Observation d34170dc-33cc-40ab-aaa5-3b0eeb9349e6 · outbound

This paper cites Transferring knowledge fragments for learning dis- tance metric from a heterogeneous domain.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Transferring knowledge fragments for learning dis- tance metric from a heterogeneous domain

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.337493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9c2216cd-a132-41d5-93c7-02ccf00da84d · outbound

This paper cites Segment Anything in Medical Images.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Segment Anything in Medical Images

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.883286Z

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Observation 5666c991-91d2-4f8e-8c3b-c8bdbbefdc59 · outbound

This paper cites Segment anything in medical images.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Segment anything in medical images

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.889200Z

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Observation 46d10936-2142-4fbe-8605-1b83465b5f47 · outbound

This paper cites Mul- timodal tactile sensing fused with vision for dexterous ro- botic housekeeping.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Mul- timodal tactile sensing fused with vision for dexterous ro- botic housekeeping

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.309705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b55084c3-3729-411f-8ee3-9ba50ca06ed3 · outbound

This paper cites Glass segmentation using intensity and spectral polarization cues.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Glass segmentation using intensity and spectral polarization cues

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.291183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 53e53330-abf0-4386-b828-6d8a80fccbdb · outbound

This paper cites Scaling deep learning for materials discovery.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Scaling deep learning for materials discovery

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.275679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8043947-5ea6-4ad9-92ff-a2cb7c8b2809 · outbound

This paper cites 4m: Massively multimodal masked modeling.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality 4m: Massively multimodal masked modeling

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.260078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation accd9ac8-6cc7-4dd2-b6b9-81579f69a7e6 · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Indoor segmentation and support inference from rgbd images

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.244205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c95163c2-192d-4e88-99c3-61566478a627 · outbound

This paper cites Training lan- guage models to follow instructions with human feedback.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Training lan- guage models to follow instructions with human feedback

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.227679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 73d75103-60e8-4d46-beb7-ef5e3cfbf673 · outbound

This paper cites Foundation model for cancer imaging biomark- ers.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Foundation model for cancer imaging biomark- ers

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.209297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 068ef4fc-4dd2-4bf7-b946-93690bb7d9b9 · outbound

This paper cites Unsupervised intra-domain adaptation for semantic segmentation through self-supervision.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Unsupervised intra-domain adaptation for semantic segmentation through self-supervision

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.192584Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6f15a3b-7835-4e21-91e4-9f9a006069e7 · outbound

This paper cites Transfer learning for metal–organic frameworks.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Transfer learning for metal–organic frameworks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.177241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cff9635a-ef76-4153-b72a-072bfd67f0d2 · outbound

This paper cites A survey on transfer learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality A survey on transfer learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.160473Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86bad8f6-3810-4f19-acc8-0dc040de502f · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Learn- ing transferable visual models from natural language super- vision

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.145065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 06b5f924-987f-4566-a010-235b73cf2199 · outbound

This paper cites Transfer learning with kernel methods.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Transfer learning with kernel methods

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.127675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97891fc1-bfeb-41ba-ad2b-052fcd7258f3 · outbound

This paper cites High-resolution image synthesis with latent diffusion models, 2021.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality High-resolution image synthesis with latent diffusion models, 2021

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.111483Z

Source-reported events for the cited work

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Observation c1c4d721-e0e0-44bb-a6bb-5d9b63d3841c · outbound

This paper cites Cross-modal fine-tuning: Align then refine.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Cross-modal fine-tuning: Align then refine

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.094507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83d2ef80-e836-4ac3-953f-b6132494a1c8 · outbound

This paper cites Depth estimation from camera im- age and mmwave radar point cloud.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Depth estimation from camera im- age and mmwave radar point cloud

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.075398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e87b243-10b3-4917-860e-fd4e41809e8f · outbound

This paper cites Rna secondary structure prediction using an en- semble of two-dimensional deep neural networks and trans- fer learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Rna secondary structure prediction using an en- semble of two-dimensional deep neural networks and trans- fer learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.052479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c2e424d-cf75-40b9-bc24-64034af6a5ab · outbound

This paper cites Rtfnet: Rgb- thermal fusion network for semantic segmentation of urban scenes.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Rtfnet: Rgb- thermal fusion network for semantic segmentation of urban scenes

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.030411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b214f3e3-45fc-48a0-85c1-a2de8414bcdb · outbound

This paper cites Seeing far in the dark with patterned flash.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Seeing far in the dark with patterned flash

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:45.012532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4777f815-8e25-426e-bc29-61b3e24ab013 · outbound

This paper cites Dabs: A domain-agnostic benchmark for self-supervised learning.Advances in neural information processing systems, 2021.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Dabs: A domain-agnostic benchmark for self-supervised learning.Advances in neural information processing systems, 2021

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.995428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a79ed7bd-6687-42fe-aad5-1f16f47012a6 · outbound

This paper cites Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.990171Z

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Observation 565f0341-4757-42f8-8218-9e840c729478 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:43.995206Z

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Observation 3c729aae-4a88-4e9d-8863-f4d6003a21a7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.000620Z

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Unavailable: canonical work link unavailable.

Observation cad8bd9b-6993-4ec3-830f-0ad5ba3f59a9 · outbound

This paper cites Neural nano-optics for high-quality thin lens ima- ging.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Neural nano-optics for high-quality thin lens ima- ging

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.977736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ca7fac17-64c8-4745-9ace-07f5978b9f2d · outbound

This paper cites Attention is all you need.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Attention is all you need

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.010204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f7daf65-04ca-4676-b784-6acec83ac5b4 · outbound

This paper cites Audio Transformers.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Audio Transformers

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.014616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d822514d-043d-4ede-969f-f5a5ffdfbc44 · outbound

This paper cites Reprogramming Pretrained Language Models for Protein Sequence Representation Learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Reprogramming Pretrained Language Models for Protein Sequence Representation Learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.019553Z

Source-reported events for the cited work

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Observation 6823c460-b6af-4460-b198-39ec4edacad4 · outbound

This paper cites Advent: Adversarial entropy min- imization for domain adaptation in semantic segmentation.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Advent: Adversarial entropy min- imization for domain adaptation in semantic segmentation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.950380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f00e47c-49c2-4598-9746-21cd4bd3ebf2 · outbound

This paper cites Predicting fault slip via transfer learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Predicting fault slip via transfer learning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.934923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ad138850-2302-4609-b9e5-ccc696837950 · outbound

This paper cites Uncertainty-aware clustering for unsupervised domain adaptive object re- identification.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Uncertainty-aware clustering for unsupervised domain adaptive object re- identification

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.918456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 61fa1df1-6362-43c2-864f-0cec33c1367a · outbound

This paper cites Sub-surface thermal measurement in additive man- ufacturing via machine learning-enabled high-resolution fiber optic sensing.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Sub-surface thermal measurement in additive man- ufacturing via machine learning-enabled high-resolution fiber optic sensing

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.902779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4ecabf92-0f36-4334-a5b7-1c2a354b0b61 · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.885725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f127eddc-b36e-4979-895c-20aee68786c3 · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.869848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23c1628a-64b7-4d88-9e73-7b8b630f8893 · outbound

This paper cites Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.853975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10f59e66-5ad6-40ac-8963-0d29f393bb8f · outbound

This paper cites Randomized quantization: A generic aug- mentation for data agnostic self-supervised learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Randomized quantization: A generic aug- mentation for data agnostic self-supervised learning

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.837501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 066ba7b0-eb8c-4dc6-8b9d-8c6f27240a2a · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.060072Z

Source-reported events for the cited work

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Observation 7610d777-8719-496f-ac35-6cbbd246f965 · outbound

This paper cites Point transformer v2: Grouped vector attention and partition-based pooling.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Point transformer v2: Grouped vector attention and partition-based pooling

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.821699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation effa0f06-bd53-440b-ac5a-7ad065461ac3 · outbound

This paper cites Polarization- driven semantic segmentation via efficient attention- bridged fusion.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Polarization- driven semantic segmentation via efficient attention- bridged fusion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.804075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation afeee50c-7441-4767-bb11-0700f8bd5ea3 · outbound

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

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Simmim: A simple framework for masked image modeling

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.788279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81c6824e-a3df-404c-9586-bcd5347f2fc7 · outbound

This paper cites Video-rate hyperspectral camera based on a cmos-compatible random array of fabry–p ´erot filters.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Video-rate hyperspectral camera based on a cmos-compatible random array of fabry–p ´erot filters

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.771216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5dff3ee4-ed84-4a68-a5d6-c8bd09e63425 · outbound

This paper cites A vision chip with complement- ary pathways for open-world sensing.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality A vision chip with complement- ary pathways for open-world sensing

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.753707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 446d231f-7ffc-44c6-a56a-d94ac5de401b · outbound

This paper cites Superanimal pretrained pose estimation models for behavioral analysis.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Superanimal pretrained pose estimation models for behavioral analysis

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.737994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6fed5e57-f360-4e72-8d75-6b9b40c2d8db · outbound

This paper cites Taskonomy: Disentangling task transfer learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Taskonomy: Disentangling task transfer learning

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.720905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2350e08c-a058-4bf7-91aa-4b03f645b773 · outbound

This paper cites Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.704284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b4162d3-d1e2-4c53-8912-a03dd8574797 · outbound

This paper cites A generalist vision–language foundation model for diverse biomedical tasks.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality A generalist vision–language foundation model for diverse biomedical tasks

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.688419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 94ea45ef-deb7-475d-a6db-1711ba12dc73 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Adding conditional control to text-to-image diffusion models

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.105360Z

Source-reported events for the cited work

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Observation 46ade355-5cce-4fbb-bda0-a10bccdad22b · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Meta-Transformer: A Unified Framework for Multimodal Learning

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-12T11:11:44.110303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f3e07a2-5347-4f40-809f-d94f569c22d7 · outbound

This paper cites Point transformer.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Point transformer

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:11:44.660954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers