Paper Citation Record · LEDGER

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

As of 9 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 5 inbound Pith citation observations for arXiv:2502.05091.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05091 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:22:31.669704Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:47:07.624830Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T09:04:04.564701Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy18
unresolved31
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f4627d5-021d-47b5-8bd9-72a7715a51af · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 1

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Source-reported events for the cited work

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Observation 1feaf703-fe71-4e2f-99f5-2765fc3d83d9 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-08T20:22:32.591018Z

Source-reported events for the cited work

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Observation 14ef91bd-1594-403a-8895-1f98f7f51335 · outbound

This paper cites & Suk, H.-I.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Suk, H.-I

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T20:22:31.419274Z

Source-reported events for the cited work

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Observation 31706781-f351-48fb-85f7-11fb519f20b5 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-08T20:22:32.564402Z

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Observation 6be1ff46-a2ae-413d-af9a-a68518bed240 · outbound

This paper cites Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.548552Z

Source-reported events for the cited work

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Observation 53548f38-f838-4499-b33b-48fdd1cbc3e9 · outbound

This paper cites Handwritten digit recognition with a back-propagation network.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Handwritten digit recognition with a back-propagation network

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.532372Z

Source-reported events for the cited work

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Observation 2e6f5687-69aa-4eb4-808a-d7a830eba79b · outbound

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

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Source-reported events for the cited work

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Observation 304a5fb3-e63c-4f0b-9c7b-6178feb7b8bb · outbound

This paper cites & Brox, T.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Brox, T

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.516096Z

Source-reported events for the cited work

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Observation f868bb9a-2623-45c6-bff4-457e1550adb6 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T20:22:31.452346Z

Source-reported events for the cited work

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Observation ce171980-96d0-4a05-acc7-57be20612808 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 10

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Source-reported events for the cited work

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Observation 4380bf20-ed78-4264-9b72-d34ec2332413 · outbound

This paper cites & Zaiane, O.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Zaiane, O

Reference 11

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Source-reported events for the cited work

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Observation 1e31ac2f-3edd-46bf-a91a-096a4f259204 · outbound

This paper cites C., Mohan, P.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions C., Mohan, P

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.473304Z

Source-reported events for the cited work

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Observation 13ad220b-f439-478c-a5e2-4e95508a3fc4 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-08T20:22:32.456815Z

Source-reported events for the cited work

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Observation 395254c3-9abd-4421-8f27-cf924a819e81 · outbound

This paper cites Self-supervised pre-training of swin transformers for 3d medical image analysis.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Self-supervised pre-training of swin transformers for 3d medical image analysis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.440825Z

Source-reported events for the cited work

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Observation 569108c9-6e4a-45d8-a8a9-56def5f73632 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 15

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Observation 02ea5657-52e5-42f3-a4d5-36fff5c89543 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 16

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Source-reported events for the cited work

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Observation 909fa245-6964-4731-b4c6-218fd3429bae · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 8f2c9c74-bc4c-4997-be80-b08a9a8ef0c7 · outbound

This paper cites CLIP model is an Efficient Continual Learner.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions CLIP model is an Efficient Continual Learner

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T20:22:31.499009Z

Source-reported events for the cited work

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Observation 6751b79c-3263-4126-b3d8-2842c852eb6b · outbound

This paper cites MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T20:22:31.505068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42e85e4c-ce6a-4237-86d7-453289cf4db8 · outbound

This paper cites Y .et al.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Y .et al

Reference 20

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Observation c8e76da4-480b-4797-8fc6-3ca95400a2aa · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-08T20:22:32.357921Z

Source-reported events for the cited work

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Observation 0a3f2d49-a26c-469a-bc35-08f5f8609e42 · outbound

This paper cites an unresolved cited work.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 22

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Source-reported events for the cited work

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Observation 5e24ec39-b4a6-4c49-a3a2-14e014b6302d · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Hoogs, A

Reference 23

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Source-reported events for the cited work

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Observation 8fa4aa6e-0681-4c8c-a186-f0558f62751b · outbound

This paper cites & De Melo, G.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & De Melo, G

Reference 24

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Observation c3205b3e-dbd5-4b65-9824-b403ff05941f · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 25

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Observation cb36bbc2-3e4a-4666-9743-aff58dab8f09 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 26

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Observation 3c979471-f5a3-44f9-a60d-5834cba0ae03 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 27

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Observation 6b85243e-c964-4a72-878f-9d46a58c7e81 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 28

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Observation 0ae100d5-f1a4-4960-ac6b-45e1afb422a0 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Xception: Deep learning with depthwise separable convolutions

Reference 29

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Observation 4e95742f-f58d-426e-ad80-cd20d752fecd · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 30

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 31

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Metaformer is actually what you need for vision

Reference 32

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions C., Gong, K

Reference 33

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Beyer, L

Reference 34

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Observation ceeca7d5-7905-45d9-b5ff-4e32cc4d4559 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Lee, Y

Reference 35

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Observation 868142d8-6833-4294-82c7-fbaf7a5cc2b1 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 36

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Observation fbaa89d4-4d1e-4cac-99e7-9241454e2fca · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Metaformer baselines for vision

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.128761Z

Source-reported events for the cited work

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Observation 6237c074-21bc-4fa4-8b8a-c2dd17e63eb4 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Sun, J

Reference 38

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T20:22:31.616684Z

Source-reported events for the cited work

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Layer Normalization

Reference 40

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Source-reported events for the cited work

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Hinton, G

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.101451Z

Source-reported events for the cited work

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Observation eceab749-53a3-4e53-8c93-a832be0ab0b1 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Gaussian Error Linear Units (GELUs)

Reference 42

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Wang, X

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.083878Z

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Observation c716ac88-8919-49db-98cd-0117a1942ed3 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions & Ding, G

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.067358Z

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Observation 213f2d4d-e857-46e8-ad5f-4654d99043b7 · outbound

This paper cites More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity.

DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

Reference 45

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Source-reported events for the cited work

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Observation 2520711c-7e0c-4d7c-825e-609bb9647939 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Unresolved cited work

Reference 46

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Observation f842f6f1-f9fa-4f1a-8af1-44042e2f24ac · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Internimage: Exploring large-scale vision foundation models with deformable convolutions

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:22:32.034306Z

Source-reported events for the cited work

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Observation c1dcebba-0de5-4e14-84c7-4af712e75db0 · outbound

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Reference 48

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Observation 9c4e5950-47e2-44ae-9fd2-e99c3b731341 · outbound

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DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions Adam: A Method for Stochastic Optimization

Reference 49

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Source-reported events for the cited work

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Pith citing papers

Observation 656efc58-2110-4998-a288-a64b06889b1a · inbound

HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding cites this paper.

HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

Reference 2

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Observation 41515c9d-78eb-4bda-9f62-a0d8bd3239c4 · inbound

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training cites this paper.

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

Reference 37

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Observation b641ae26-628b-4a9d-82e2-480f236dfd1f · inbound

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation cites this paper.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

Reference 9

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Source-reported events for the cited work

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Observation d8cad099-a01a-49af-b433-1072ea394c17 · inbound

UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation cites this paper.

UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:58:24.707647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 60510827-52e7-417c-baba-c88185d2e735 · inbound

UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation cites this paper.

UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-21T09:04:04.566460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.