InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective | Paper Citation Record

Observation 5c36f711-080a-406d-a188-17547f875cd7 · outbound

This paper cites write newline.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective write newline

Reference 1

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Observation 02dc8277-12ab-4522-95d2-b874665451ca · outbound

This paper cites X., Damianou, A., Lawrence, N.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective X., Damianou, A., Lawrence, N

Reference 2

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Observation ef60692b-03df-4e7e-96d0-3dbe1e775b42 · outbound

This paper cites B it F it: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective B it F it: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 3

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Observation 97a067f9-ebb1-47e4-80f9-02830be53cfd · outbound

This paper cites J., Fern \'a ndez-Esparrach, G., Gil, D., Rodr \' guez, C., and Vilari \ n o, F.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective J., Fern \'a ndez-Esparrach, G., Gil, D., Rodr \' guez, C., and Vilari \ n o, F

Reference 4

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Observation 9dd5c072-951e-4b23-bb16-068c6e664730 · outbound

This paper cites Infinitely divisible matrices.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Infinitely divisible matrices

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.832939Z

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Observation cb0bd8a6-838f-4126-9085-1b013de9e36d · outbound

This paper cites Convex optimization.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Convex optimization

Reference 6

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Observation 72bafcc3-ff0e-4b45-908c-af9b65076120 · outbound

This paper cites Pkd: General distillation framework for object detectors via pearson correlation coefficient.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Pkd: General distillation framework for object detectors via pearson correlation coefficient

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.482669Z

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Observation 1ceff2a6-4037-4034-9df2-f9726d9d16e2 · outbound

This paper cites All about structure: Adapting structural information across domains for boosting semantic segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective All about structure: Adapting structural information across domains for boosting semantic segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.281621Z

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Observation 6628f75b-7dc8-42b6-82ef-a3bdf122c3b6 · outbound

This paper cites Cross-layer distillation with semantic calibration.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Cross-layer distillation with semantic calibration

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.034343Z

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Observation 553a294b-5e63-4da2-88b9-46b5e74ef16f · outbound

This paper cites Distilling knowledge via knowledge review.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Distilling knowledge via knowledge review

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.889609Z

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Observation e7eb9969-e2b5-4d27-89b2-84740c10a13e · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 11

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Observation 5c2f106b-85a0-4bad-8e42-63819d5992e4 · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Sam-adapter: Adapting segment anything in underperformed scenes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.701345Z

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Observation 8461accf-b2a8-4ed6-a229-2d6b4dfa32d3 · outbound

This paper cites C., Gutman, D., Celebi, M.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective C., Gutman, D., Celebi, M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.526071Z

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Observation cbc5fb27-7706-4425-8317-94113456f849 · outbound

This paper cites An efficient segment anything model for the segmentation of medical images.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective An efficient segment anything model for the segmentation of medical images

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.292656Z

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Observation 3ac66ef4-c97d-478a-b620-d1e5e89100b7 · outbound

This paper cites Optimal randomized approximations for matrix-based r \'e nyi’s entropy.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Optimal randomized approximations for matrix-based r \'e nyi’s entropy

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.106794Z

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Observation 1e57c501-92e2-41fb-b0e7-7bb75d477c75 · outbound

This paper cites Camouflaged object detection.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Camouflaged object detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.911989Z

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Observation 296415be-9324-49ed-9095-72d1be71fa93 · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Pranet: Parallel reverse attention network for polyp segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.699345Z

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Observation 20a13996-6da4-40c3-b387-6853e5ad7e52 · outbound

This paper cites Computationally efficient approximations for matrix-based r \'e nyi's entropy.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Computationally efficient approximations for matrix-based r \'e nyi's entropy

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.502933Z

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Observation 44204db4-da0e-4fd6-ae13-ad0dd5ea61b7 · outbound

This paper cites J., and Tao, D.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective J., and Tao, D

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.345889Z

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Observation 0ad622ae-8f78-49ca-aada-dfbaea0ce161 · outbound

This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Cycada: Cycle-consistent adversarial domain adaptation

Reference 20

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Observation a4bf06c2-f4a9-4887-bdda-b207adbdace6 · outbound

This paper cites J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.049185Z

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Observation 58169081-2562-4e48-9a36-e45b42742a59 · outbound

This paper cites Multi-level adversarial network for domain adaptive semantic segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Multi-level adversarial network for domain adaptive semantic segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.896424Z

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Observation ac64dd48-8620-46dd-b796-439ee23f49f3 · outbound

This paper cites H., Riegler, M.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective H., Riegler, M

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.733755Z

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Observation 671803da-9cdc-417c-931d-e36d005d89de · outbound

This paper cites Segment anything is not always perfect: An investigation of sam on different real-world applications, 2024.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Segment anything is not always perfect: An investigation of sam on different real-world applications, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.571564Z

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Observation 97df1376-6dc3-4e28-9cc6-3be56e18c7a7 · outbound

This paper cites Segment anything in high quality.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Segment anything in high quality

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.370932Z

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Observation ecd16b16-7969-4b25-8e57-b870504bc57f · outbound

This paper cites Qr decomposition on gpus.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Qr decomposition on gpus

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.160098Z

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Observation c8c6b2b4-9869-4a75-ac46-efb2a0a0fa18 · outbound

This paper cites C., Lo, W.-Y., et al.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective C., Lo, W.-Y., et al

Reference 27

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Observation 1986c729-53f6-4cae-94b7-e95a373c5c5c · outbound

This paper cites Improving adversarial robustness via information bottleneck distillation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Improving adversarial robustness via information bottleneck distillation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.925823Z

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Observation f1471d50-2630-4a40-a90f-cdf31d356c0e · outbound

This paper cites V., Nie, Z., Tran, M.-T., and Sugimoto, A.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective V., Nie, Z., Tran, M.-T., and Sugimoto, A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.674364Z

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Observation 27645bcd-2369-41b1-98d6-ad8e93484018 · outbound

This paper cites Invariant information bottleneck for domain generalization.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Invariant information bottleneck for domain generalization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.472289Z

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Observation fb7908b2-ca73-4893-8c2d-dd244be6df05 · outbound

This paper cites A stepwise domain adaptive segmentation network with covariate shift alleviation for remote sensing imagery.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective A stepwise domain adaptive segmentation network with covariate shift alleviation for remote sensing imagery

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.278071Z

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Observation 55069a30-11db-42aa-a76e-311e0af55447 · outbound

This paper cites Decomposition-based unsupervised domain adaptation for remote sensing image semantic segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Decomposition-based unsupervised domain adaptation for remote sensing image semantic segmentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:32.057791Z

Source-reported events for the cited work

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Observation 076a0d6f-e793-4326-ad60-74f936079ad0 · outbound

This paper cites K., Manganelli, B., and Sa \`a -Garriga, A.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective K., Manganelli, B., and Sa \`a -Garriga, A

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.869782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation edb9fd26-6833-460c-8b81-5749a34a5cba · outbound

This paper cites Machine learning for aerial image labeling.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Machine learning for aerial image labeling

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.586201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 397a7a44-bfa3-4a50-92f4-f0d7b3fd87c5 · outbound

This paper cites Probabilistic knowledge transfer for lightweight deep representation learning.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Probabilistic knowledge transfer for lightweight deep representation learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.387589Z

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Observation 9536da5f-a7b6-4c4e-ac4d-b52ca2839a46 · outbound

This paper cites Learning to adapt sam for segmenting cross-domain point clouds.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Learning to adapt sam for segmenting cross-domain point clouds

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.215752Z

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Observation ad34329c-f1d1-4d87-92e7-b00ee9707e14 · outbound

This paper cites Parameter efficient fine-tuning via cross block orchestration for segment anything model.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Parameter efficient fine-tuning via cross block orchestration for segment anything model

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.004770Z

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Observation 3436b5b9-13da-4e15-93da-aba1eb2b4225 · outbound

This paper cites an unresolved cited work.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Unresolved cited work

Reference 38

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Observation f4c6d994-048a-4f26-b8bf-3f93f6cdb4a1 · outbound

This paper cites Sam 2: Segment anything in images and videos.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Sam 2: Segment anything in images and videos

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.664757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6147f86a-7f6a-4784-8d81-350f5abad755 · outbound

This paper cites Fastsam3d: An efficient segment anything model for 3d volumetric medical images.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Fastsam3d: An efficient segment anything model for 3d volumetric medical images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.469938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b580c7c-e118-4cd1-904d-4ed7a76c57e2 · outbound

This paper cites Tinysam: Pushing the envelope for efficient segment anything model.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Tinysam: Pushing the envelope for efficient segment anything model

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.296258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 42a0e7b1-9158-4f3e-aaa8-afd06cbb8ced · outbound

This paper cites Animal camouflage analysis: Chameleon database.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Animal camouflage analysis: Chameleon database

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.172683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c1200353-8478-4e67-92f4-faffa0db0a81 · outbound

This paper cites SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes

Reference 43

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Observation ca91978c-1206-47f7-a227-95695c2183d3 · outbound

This paper cites and Zaslavsky, N.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective and Zaslavsky, N

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cb95d32-6e5a-4a59-aeab-fe4ece7ef222 · outbound

This paper cites Samcl: Empowering sam to continually learn from dynamic domains.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Samcl: Empowering sam to continually learn from dynamic domains

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:26.470952Z

Source-reported events for the cited work

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Observation 33bee818-b6e5-4feb-9096-d51bc622624c · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Medical sam adapter: Adapting segment anything model for medical image segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.922288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 17e0f2a9-9c70-440b-92fc-e499b4f41969 · outbound

This paper cites Cat-sam: Conditional tuning for few-shot adaptation of segment anything model.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Cat-sam: Conditional tuning for few-shot adaptation of segment anything model

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.734746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 249e0e24-2f82-4f5d-aebd-9d0d71dae45c · outbound

This paper cites Dirl: Domain-invariant representation learning for generalizable semantic segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Dirl: Domain-invariant representation learning for generalizable semantic segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.616550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b516f58-c18f-4f25-a7d9-a59f742d2e7a · outbound

This paper cites an unresolved cited work.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:30:29.409367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81f43965-ed0f-49d7-aa31-04dba26f3694 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:27.445076Z

Source-reported events for the cited work

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Observation 33d94eb0-e15c-47f1-a909-6cc7bab4453a · outbound

This paper cites Blo-sam: Bi-level optimization based finetuning of the segment anything model for overfitting-preventing semantic segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Blo-sam: Bi-level optimization based finetuning of the segment anything model for overfitting-preventing semantic segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.173006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2082b952-5aa8-4128-88e4-e738813f8653 · outbound

This paper cites Distilling semantic priors from sam to efficient image restoration models.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Distilling semantic priors from sam to efficient image restoration models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.938367Z

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Observation 233c5e49-464a-4234-a7fd-8d4e88888de0 · outbound

This paper cites Learning shape-invariant representation for generalizable semantic segmentation.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Learning shape-invariant representation for generalizable semantic segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.845165Z

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Observation 1175ea38-f9f4-4d5f-9923-23cd7a06b67e · outbound

This paper cites Convolution meets lo RA : Parameter efficient finetuning for segment anything model.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Convolution meets lo RA : Parameter efficient finetuning for segment anything model

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.739790Z

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Observation 9feb730c-cfe0-4e72-b9dc-9ba4bd2efa04 · outbound

This paper cites Knowledge distillation by on-the-fly native ensemble.

InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective Knowledge distillation by on-the-fly native ensemble

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.580618Z

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