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Paper Citation Record · LEDGER

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP

As of 5 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2509.26036.

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

pith.paper-citation-record.v1
2509.26036 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T12:52:04.328951Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:09:22.797680Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

  • verified exact2
  • verified fuzzy23
  • unresolved1
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b2c76070-4143-4a95-8249-107e9835657b · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Food-101--mining discriminative components with random forests

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.352361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:99cfd906e38a365b1919ab6391ad57c0dbb254ebc2a239a3bcdf3e7912f9ba0c

Observation 308161ce-7ff7-48c2-beec-a2ac32ddd985 · outbound

This paper cites Describing textures in the wild.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Describing textures in the wild

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.335138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:40f0a484383d49638b8c349002be57595eab7376a3b20831a36e333926341d54

Observation 10a72315-076d-4bf4-89da-734e25e60d13 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Imagenet: A large-scale hierarchical image database

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.356419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:40ffb1338292e93d512d6b8e8f67a3f71abd100a47d296754205e563b713bb16

Observation ffc51911-5b11-4696-b3c6-39ddb5306644 · outbound

This paper cites The clip model is secretly an image-to-prompt converter.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP The clip model is secretly an image-to-prompt converter

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.348090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:863216e823c3037337e9ba2b758d53eb86ef802ccecacb4914ba72308e00d525

Observation 12f7af96-a3b7-451b-ae44-c37ab2471f23 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.343383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:b9d313331c61eee4e012e862d52cafaed7ecef0ec9916296ec6f3a1607cfeee0

Observation a7b4d8b2-b7ce-4316-952b-50dfdb5cc1ae · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.431126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:758efc2e9bf45bcc0da40445c28ce27b0bf483335b7d6fe39d9f7eaa57906630

Observation 8cd888e9-01c9-4bed-905f-afdfb828c5d5 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP 3d object representations for fine-grained categorization

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.364771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:15b5204e156487533c07cc091250e10ec1fa653317579e0340d1c4fde8217ebe

Observation 316ca301-9802-4a05-bfe4-490b97e9bcde · outbound

This paper cites Logits deconfusion with clip for few-shot learning.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Logits deconfusion with clip for few-shot learning

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.392078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:cd139ffa4dc9af6481992d7c3f478e09d8f2982ff9c3c6d6d9c99c8e48fded8d

Observation 51fc7f96-7c2c-481d-9dec-3ab75454d289 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.371639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:c6195c5e018b624b010d249b837050d4d531ed34ea0ad6688c98757cf41b2d0d

Observation bd2be4e0-14fa-43b1-a15f-97046d3383c5 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Fine-Grained Visual Classification of Aircraft

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-05-18T12:52:37.313897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:be12840678eafc149479f24abdbadce6235a8ad9adcb3a61b896820f390a40f6

Observation 34aa3084-e12f-4cd2-9f13-f744da8f436b · outbound

This paper cites Bagdanov.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Bagdanov

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.387267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:3c0c81e1d63b1dce7f45268fb5f0224999fcd6ff3427ec9f2034b5dc8dd91c55

Observation 090609f3-b61a-402c-97c0-daa9ad1d7dcf · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Automated flower classification over a large number of classes

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.434421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:2e0eeafccd5cc1439c38d4f63bc621cc10bd57312ec9fcdde4812c3853a225bf

Observation 00ad20ca-f598-478e-acc9-24c8d1c00c63 · outbound

This paper cites Cats and dogs.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Cats and dogs

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.418530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:97c0c6095cfe3df28e9b728e74369bbe8fde3d2b9f550fc82b8c0a3e79a81889

Observation 6d782232-6b62-418b-ba38-30bc367b833e · outbound

This paper cites A generalized inverse for matrices.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP A generalized inverse for matrices

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.409235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:33ac502bcb280ed25fc667191888634b881bd11782bfc2cdd301a33ca2826267

Observation 1070743e-93d8-4ff6-b73a-83527659b623 · outbound

This paper cites What does a platypus look like? generating customized prompts for zero-shot image classification.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP What does a platypus look like? generating customized prompts for zero-shot image classification

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.412384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:e35b45b796fbd90a21f01a255a1083f76579892227ce89806aaaf458c1d43483

Observation 34633539-4fe6-4f54-9a31-b1a8ae3ed174 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Learning transferable visual models from natural language supervision

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.361028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:274966aeec7a6c8219b12aec41d7679fe66159f58a84ae9f6f4d207d2b838f9d

Observation 7f6b8aeb-f54e-46f9-a652-68860a91f746 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.428050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:36c8dc07fbda3f7df6e47191410cc0be1d4cf247d2796a303f84d79d0611a1d4

Observation 98f4c2a8-3ca4-42a2-8f65-840604f290ab · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP High-resolution image synthesis with latent diffusion models

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.406118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:caf01b873c0a4ef26e21fcb6619f7425164917a19c1f9682267916089d61dd2c

Observation 3169d21c-5b83-434d-9e0e-99748eb0efcb · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-05-18T12:52:37.294861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:61e2925636b5e709caf024f6647199a3d9ccac4d8e3ca8e5ffaf4ffb907c1264

Observation 86c424b2-02f9-47a6-b32d-7ce1042f4bdc · outbound

This paper cites Sus-x: Training-free name-only transfer of vision-language models.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Sus-x: Training-free name-only transfer of vision-language models

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.399337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:b84a66fadd78efc36cc466802b959578eaa18d65663bba4d088b060127d0af6c

Observation 32baf8e6-3954-4531-baab-c51bfceb5bf0 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Sun database: Large-scale scene recognition from abbey to zoo

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.415735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:07bba58c579b21f39bb323979d598b8e537c875a9b4318319fc1799c0e05b8d2

Observation ac064180-90fa-49bf-83e2-51ed74ce3074 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T12:52:37.300859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:1a47c20d2a5863af22d62498a68e294d9dfd007b872eac089dbda79b8c1ba36e

Observation 04dbcd6f-9e2b-4165-ab56-2aa44876ac49 · outbound

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

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Learning to prompt for vision-language models

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.421272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:83a81e2a095b55bfda264a8e1d747419356c5a0538faa3c11dae0d380c35e4f7

Observation 12e86c55-545f-4608-99bf-f3c870e0398c · outbound

This paper cites Not all features matter: Enhancing few-shot clip with adaptive prior refinement.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Not all features matter: Enhancing few-shot clip with adaptive prior refinement

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.395764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:a7ca8b778170f0e7024bab5787380e372bbba2ee8fcac075887e9a601931775c

Observation edd9363c-62bc-463c-86c1-a93f902b1af8 · outbound

This paper cites write newline.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP write newline

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.379588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:8e1c25908b6e5a8822118e10a71e01817e261713c1c8fd76c7841e72a87881f7

Observation 18f47e08-c702-4a85-b28c-21f37a9c5935 · outbound

This paper cites @esa (Ref.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP @esa (Ref

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-18T12:52:37.375659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:1127f428f6ad4630a226c70415cf440451ba1d76282ad69ea05801066b16bb49

Observation 3e196367-3bcb-4e8b-b691-c5556702f2d1 · outbound

This paper cites an unresolved cited work.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-05-18T12:52:37.424620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:9877c591076580d226ab97ea219d776876f18bb54939f920a9a46da85bceedf6

Observation 6721613f-ff70-46a9-b82e-cf3973ee6a55 · outbound

This paper cites jǷk㻧mtG^ 3*6 YQ [Bz2!Lp i ;j WG UF􆫣 m JW > ή񷳫WU|ھf w _Box J8 k [> S ' XfZ#]O fomƝ5\ / KyƧ5< Y ۗ`#JW L ` 0Qdwe#_ ,̋ >7E /Vky(¶Zny\ϵ9 cٻ O WG yێ 3j*.

SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP jǷk㻧mtG^ 3*6 YQ [Bz2!Lp i ;j WG UF􆫣 m JW > ή񷳫WU|ھf w _Box J8 k [> S ' XfZ#]O fomƝ5\ / KyƧ5< Y ۗ`#JW L ` 0Qdwe#_ ,̋ >7E /Vky(¶Zny\ϵ9 cٻ O WG yێ 3j*

Reference 28

Resolution
malformed identifier
arxiv_id, observed 2026-05-18T12:52:37.308130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-18T12:52:04.328951Z digest=sha256:1c80489cd228a473a67fab7fe813a596f3e69348bc23df1ceef678ed07cd5c2c

Pith citing papers

Observation ffbeefb3-4ae3-414a-8382-5cbca355f2c5 · inbound

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion cites this paper.

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-01T02:09:22.797680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T02:09:22.797680Z digest=sha256:6da1de56dad1e9411da82341ac9a47c47fdbdc6a37e8745ac2e97e5db0ee6b4f