Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T17:03:00.057845Z
Paper Citation Record · LEDGER
Quickly Tuning Foundation Models for Image Segmentation
As of 22 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2508.17283.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
25 of 25 outbound references displayed
- verified exact2
- verified fuzzy8
- unresolved14
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b20e20a4-94e4-43ae-95ac-29930bdc3f14 · outbound
This paper cites Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How.
Quickly Tuning Foundation Models for Image Segmentation Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T17:03:00.520780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 76c97cfb-d04c-4266-94b1-aa6c08055f6c · outbound
This paper cites E., and Gabbouj, M.
Quickly Tuning Foundation Models for Image Segmentation E., and Gabbouj, M
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:03.050686Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6951ee8c-1f68-4ed5-a251-411e0e8c66d5 · outbound
This paper cites J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.
Quickly Tuning Foundation Models for Image Segmentation J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:03.043622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7e841cac-05b0-4431-8aa1-c29ef1da1401 · outbound
This paper cites u cke, J., and Schmidt-Thieme, L. (2015). Beyond manual tuning of hyperparameters. KI-K \.
Quickly Tuning Foundation Models for Image Segmentation u cke, J., and Schmidt-Thieme, L. (2015). Beyond manual tuning of hyperparameters. KI-K \
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:03.035891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7ddfcdbe-782b-4f6e-8a69-7cc1e54c4044 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:03.028119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4a62d270-297f-4380-930b-b6527a48caad · outbound
This paper cites Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes.
Quickly Tuning Foundation Models for Image Segmentation Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T17:03:00.375276Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a986aeea-62d3-4641-b054-7ba246eef65e · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Quickly Tuning Foundation Models for Image Segmentation Adam: A Method for Stochastic Optimization
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:02:58.673519Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdadae13-b1a9-4b22-a18d-1d0e6fc82b3f · outbound
This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.
Quickly Tuning Foundation Models for Image Segmentation SGDR: Stochastic Gradient Descent with Warm Restarts
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:02:58.792981Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 00ce6188-1179-4b0b-96b7-44ae7fdc2336 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:02.948714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0caa2c68-23f8-4aca-8740-7ba72a9d67b7 · outbound
This paper cites O., Maskeli \=u nas, R., and Dama s evi c ius, R.
Quickly Tuning Foundation Models for Image Segmentation O., Maskeli \=u nas, R., and Dama s evi c ius, R
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:02.777187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f970e8d1-fbf5-422a-a957-95e8ff104a77 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:02.631567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c7df9863-5a26-440c-a719-f0585469c2c1 · outbound
This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.
Quickly Tuning Foundation Models for Image Segmentation PyTorch: An Imperative Style, High-Performance Deep Learning Library
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:02:59.165131Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 86880345-840f-456c-bd87-8953293a2bf9 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:02.285435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 15f9929e-8863-47ce-b1e5-9a95226a536f · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:02.014177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b6639c1b-2b35-426c-848d-268a941b3cd9 · outbound
This paper cites P., Kadra, A., Grabocka, J., and Hutter, F.
Quickly Tuning Foundation Models for Image Segmentation P., Kadra, A., Grabocka, J., and Hutter, F
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:01.757021Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 101c9e2a-83ca-46f3-ab0a-999af628f7ad · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
Quickly Tuning Foundation Models for Image Segmentation SAM 2: Segment Anything in Images and Videos
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:02:59.434568Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b967e103-6783-4a83-b143-5e450eb8a986 · outbound
This paper cites N., Hutter, F., and M \"u ller, A.
Quickly Tuning Foundation Models for Image Segmentation N., Hutter, F., and M \"u ller, A
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:01.463136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0dfcf138-846b-4d28-ad1e-f3b9ea68b441 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:01.325054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 09e4eff0-62f3-4b0a-9e3a-3a6f94f3681a · outbound
This paper cites K., Rapant, I., Ferreira, F., and Hutter, F.
Quickly Tuning Foundation Models for Image Segmentation K., Rapant, I., Ferreira, F., and Hutter, F
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:01.256939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d1eb3503-1705-482d-88b5-cdcd64597abf · outbound
This paper cites H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M.
Quickly Tuning Foundation Models for Image Segmentation H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T17:03:01.088454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2b94bdfe-3e1a-41cf-b19d-3054d3c0ae3a · outbound
This paper cites AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models.
Quickly Tuning Foundation Models for Image Segmentation AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:02:59.793399Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6119926f-9532-4a83-a849-5d0cc832c00b · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:00.982229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 83f5c277-1abd-4c41-86e9-61aef80212c7 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:00.831397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b495fcf8-c968-461c-a5d9-50dd3ef3657f · outbound
This paper cites MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation.
Quickly Tuning Foundation Models for Image Segmentation MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T17:03:00.233490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 87f89315-4fd7-48d2-b491-16f15c3b4599 · outbound
This paper cites an unresolved cited work.
Quickly Tuning Foundation Models for Image Segmentation Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T17:03:00.675070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.