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Paper Citation Record · LEDGER

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption

As of 19 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 1 inbound Pith citation observation for arXiv:2505.12912.

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

pith.paper-citation-record.v1
2505.12912 v1

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:30:55.818301Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:17:08.998138Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T23:17:13.598642Z

Reference resolution

71 of 71 outbound references displayed

  • verified exact0
  • verified fuzzy52
  • unresolved19
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d92b7875-6bd3-45b3-899d-78efaec3161e · outbound

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

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Learning transferable visual models from natural language supervision,

Reference 1

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Source-reported events for the cited work

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Observation 1d4c1297-3f8e-4df5-9397-2c43292950ee · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 2

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Observation db56acde-00be-424e-9898-2b1d76c5cfa8 · outbound

This paper cites Improving zero-shot generalization and robustness of multi-modal models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Improving zero-shot generalization and robustness of multi-modal models,

Reference 3

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Observation 9a21336f-4388-4829-9477-84f0662b304d · outbound

This paper cites Improving zero- shot generalization for clip with synthesized prompts,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Improving zero- shot generalization for clip with synthesized prompts,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation cb1afc21-9f14-4334-aa90-595e68c11955 · outbound

This paper cites Effective con- ditioned and composed image retrieval combining clip-based features,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Effective con- ditioned and composed image retrieval combining clip-based features,

Reference 5

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Source-reported events for the cited work

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Observation d603bebf-4aad-4dee-8f2c-b110e1644d2d · outbound

This paper cites CLIP2Video: Mastering Video-Text Retrieval via Image CLIP.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption CLIP2Video: Mastering Video-Text Retrieval via Image CLIP

Reference 6

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Source-reported events for the cited work

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Observation a9f1ec32-1371-4125-82a3-b0b28e0eef49 · outbound

This paper cites Styleclip: Text-driven manipulation of stylegan imagery,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Styleclip: Text-driven manipulation of stylegan imagery,

Reference 7

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Source-reported events for the cited work

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Observation 2395769e-ef00-4bea-b515-f68f46878ba9 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 8

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Source-reported events for the cited work

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Observation 293fee36-ce59-4644-ac54-c30c4521bb4c · outbound

This paper cites OpenCLIP,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption OpenCLIP,

Reference 9

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Source-reported events for the cited work

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Observation 61c11760-bc1b-464c-b3a1-efa0f562e1ed · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Reproducible scaling laws for contrastive language-image learning,

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 378d05bc-a6a2-4daf-bc7a-5eeff5924663 · outbound

This paper cites LAION-5b: An open large-scale dataset for training next generation image-text models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption LAION-5b: An open large-scale dataset for training next generation image-text models,

Reference 11

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Source-reported events for the cited work

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Observation dfc9ab05-4f88-438f-af1b-6435fb770373 · outbound

This paper cites Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification,

Reference 12

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Source-reported events for the cited work

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Observation e4a43113-a3ac-4220-9d86-94e08a25f7de · outbound

This paper cites Lp++: A surprisingly strong linear probe for few-shot clip,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Lp++: A surprisingly strong linear probe for few-shot clip,

Reference 13

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Source-reported events for the cited work

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Observation 3234b87f-0373-4696-b762-7d6851762e02 · outbound

This paper cites PLOT: Prompt learning with optimal transport for vision-language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption PLOT: Prompt learning with optimal transport for vision-language models,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e5c8b994-a6d7-4a88-b2a0-f7d427e1bc55 · outbound

This paper cites Test-time prompt tuning for zero-shot generalization in vision-language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Test-time prompt tuning for zero-shot generalization in vision-language models,

Reference 15

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Source-reported events for the cited work

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Observation a57b2852-d6bf-4e0f-82cf-be10cd5edb35 · outbound

This paper cites Test-time distri- bution normalization for contrastively learned visual-language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Test-time distri- bution normalization for contrastively learned visual-language models,

Reference 16

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Source-reported events for the cited work

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Observation 1177ae5c-54da-44e3-b089-d33206bf4e4c · outbound

This paper cites Efficient Test-Time Adaptation of Vision-Language Models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Efficient Test-Time Adaptation of Vision-Language Models,

Reference 17

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Source-reported events for the cited work

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Observation b4e56081-214e-474f-aaf7-f0c9fdab4827 · outbound

This paper cites Dual memory networks: A versatile adaptation approach for vision-language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Dual memory networks: A versatile adaptation approach for vision-language models,

Reference 18

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Source-reported events for the cited work

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Observation 7687ec34-77fd-4e44-a703-b942307d73be · outbound

This paper cites On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b185e4dd-d544-47dc-aa6b-e7e9f9c9d1c3 · outbound

This paper cites A hard-to- beat baseline for training-free CLIP-based adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption A hard-to- beat baseline for training-free CLIP-based adaptation,

Reference 20

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Source-reported events for the cited work

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Observation 33bb79db-8e3d-4344-8f77-b83b81d5c8aa · outbound

This paper cites Online zero-shot classification with clip,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Online zero-shot classification with clip,

Reference 21

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Observation aa745f77-3259-4a5c-b353-1fa3f07797e6 · outbound

This paper cites Tent: Fully Test-Time Adaptation by Entropy Minimization,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Tent: Fully Test-Time Adaptation by Entropy Minimization,

Reference 22

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Source-reported events for the cited work

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Observation 249a99c5-87b4-4fea-b6af-ec5336e3791a · outbound

This paper cites Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adap- tation to Multiple Image Corruptions,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adap- tation to Multiple Image Corruptions,

Reference 23

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Source-reported events for the cited work

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Observation 2f438ead-b2ce-4efc-98a9-b46fdef7a99a · outbound

This paper cites A comprehensive survey on test-time adaptation under distribution shifts,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption A comprehensive survey on test-time adaptation under distribution shifts,

Reference 24

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Source-reported events for the cited work

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Observation ccb281d4-27b2-4bb5-82c1-2cd1e2900fde · outbound

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

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Learning to prompt for vision- language models,

Reference 25

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Source-reported events for the cited work

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Observation fe59cb33-ec01-4394-861c-45f02772c8a2 · outbound

This paper cites Conditional prompt learning for vision-language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Conditional prompt learning for vision-language models,

Reference 26

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Source-reported events for the cited work

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Observation 11f82d6a-af22-41c9-a811-c230916abfa8 · outbound

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

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning,

Reference 27

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Source-reported events for the cited work

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Observation 1ee07105-14fb-4272-92d4-aa1e0caca355 · outbound

This paper cites Maple: Multi-modal prompt learning,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Maple: Multi-modal prompt learning,

Reference 28

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 2e1ae621-dc28-456f-9ec2-475f2cd87a28 · outbound

This paper cites Intra-modal proxy learning for zero- shot visual categorization with clip,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Intra-modal proxy learning for zero- shot visual categorization with clip,

Reference 29

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Source-reported events for the cited work

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Observation 0d3464d8-7ed6-4110-9c00-894e0b688f34 · outbound

This paper cites Post- pre-training for Modality Alignment in Vision-Language Foundation Models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Post- pre-training for Modality Alignment in Vision-Language Foundation Models,

Reference 30

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Source-reported events for the cited work

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Observation 46bd7b49-8fe0-47aa-8ef1-6c0dd4bd2609 · outbound

This paper cites The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Reference 31

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Source-reported events for the cited work

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Observation f8c32f64-2e46-4784-9019-9749ed6b96a7 · outbound

This paper cites Do imagenet classifiers generalize to imagenet?.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Do imagenet classifiers generalize to imagenet?

Reference 32

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Source-reported events for the cited work

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Observation bf026203-260c-4c0b-9139-b5763521ce1e · outbound

This paper cites Natural Adversarial Examples,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Natural Adversarial Examples,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 0c3aeb36-fff9-4b73-9e6b-824c84507799 · outbound

This paper cites Learning Robust Global Representations by Penalizing Local Predictive Power,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Learning Robust Global Representations by Penalizing Local Predictive Power,

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.320848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.662094Z digest=sha256:89420108d7dd88a6d17e876271a9724e02e9dd7410430fee64f9a17da080a127

Observation cb00a1da-f9ae-4d09-bfec-e6c46573d3f8 · outbound

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

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Automated flower classification over a large number of classes,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.309491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.666704Z digest=sha256:22eabc3d40beecc66a0d29f996795814578d5d4f3120c0b09c33e7690c8752eb

Observation 59bae90b-7a5c-4687-a68c-e8334efff856 · outbound

This paper cites Cats and dogs,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Cats and dogs,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.298214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.670932Z digest=sha256:acd97186388884f93b059425f1320b42305fba3b764a89a48ad2d4d70bde801b

Observation 593e3724-62d3-4a4a-af16-7cccf23c4cff · outbound

This paper cites Benchmarking Neural Network Ro- bustness to Common Corruptions and Perturbations,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Benchmarking Neural Network Ro- bustness to Common Corruptions and Perturbations,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.287144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.675615Z digest=sha256:98c060aff3ffe54e185d03a7c33d5646c9482617b27c1d9639fc706a16d350d3

Observation ca6675d2-ebd8-430b-a3a2-fa8a609e495a · outbound

This paper cites Ar-tta: A simple method for real-world continual test-time adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Ar-tta: A simple method for real-world continual test-time adaptation,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.273298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.679587Z digest=sha256:46f77e5e85c6336f8104c0ffe4f2bcfef67c3b2d6468bf5572be5a4dad24f109

Observation 2556f4f0-7021-4ef9-92f4-464fd90115c2 · outbound

This paper cites Dark model adaptation: Semantic image segmentation from daytime to nighttime,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Dark model adaptation: Semantic image segmentation from daytime to nighttime,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.260543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.684055Z digest=sha256:c15f92aa6d9bbb8f2d8621da5d67d29ee5d51b04eb54fbfdfb6a2846826dc0d2

Observation 2f1dd4ff-96dd-440b-a1e8-20640793e70f · outbound

This paper cites Towards robust cnn-based object detection through augmentation with synthetic rain variations,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Towards robust cnn-based object detection through augmentation with synthetic rain variations,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.247814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.688741Z digest=sha256:884e0686966d0273ffb047cae3530edf1ba61763b0ce5fd99dba74f6a3964bc3

Observation 6ddb6e7d-c4e4-4e80-964e-f891797c2ffd · outbound

This paper cites Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.234751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.692605Z digest=sha256:d3ac54a9031bb9e00a4e09f610f82d626affc0bc231bc61556de22456de74f6c

Observation 8f676c0d-9352-41c2-b84d-6e3a2f1843e0 · outbound

This paper cites Test-time similar- ity modification for person re-identification toward temporal distribution shift,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Test-time similar- ity modification for person re-identification toward temporal distribution shift,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.220838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.699184Z digest=sha256:9d0f84e7ea9ffb07dc8f055d15b2a30fe6582c482edde45f63b21755f52cc112

Observation 13b15024-9476-47bd-ae07-8e9ed54c75ba · outbound

This paper cites Understanding and improving robustness of vision transform- ers through patch-based negative augmentation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Understanding and improving robustness of vision transform- ers through patch-based negative augmentation,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.208305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.703490Z digest=sha256:cc0bad8393ed2436404037aa894b4f4e7b8a81548dfd48655f40c8274c429311

Observation 677ed13c-eeae-43f2-bbd4-2fe497694aa3 · outbound

This paper cites On interaction between augmenta- tions and corruptions in natural corruption robustness,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption On interaction between augmenta- tions and corruptions in natural corruption robustness,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.194578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.707386Z digest=sha256:b537e94cbbfd928b25d2d657cc35139090f61e89231df032112c6f01378452d9

Observation 9d4fb63c-7c57-425c-850c-70b1705cf118 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.711604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.711604Z digest=sha256:8e5e11e07f71bdb8064e240b6fb24a2dc31d17fb9b30b1bfa2cc8f8b6753a5ee

Observation 13e9a982-5cf4-4e83-815f-e7ae0b859074 · outbound

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

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption LoRA: Low-rank adaptation of large language models,

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.715460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.715460Z digest=sha256:424e66c3a8182d2a88d2569c3311685c772eedeb80092ba2ffb17900578ff332

Observation 7bf91f1c-2e65-4e95-8739-ebb01a288581 · outbound

This paper cites Bayesian Adaptation for Covariate Shift,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Bayesian Adaptation for Covariate Shift,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.164543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.719603Z digest=sha256:d28d5765e28a6e5590d951365e92f341552c4cbb3b585e66b5e6a873e9957ddb

Observation 6f5034fa-4308-451f-9e36-93b4bf30f092 · outbound

This paper cites Ef- ficient test-time model adaptation without forgetting,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Ef- ficient test-time model adaptation without forgetting,

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.723555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.723555Z digest=sha256:17ea4f8c903786c357d82d91be4dfb8baf8001fddad10e9daf87c11e49445efc

Observation dfee3c25-953e-4b64-bdd4-56b5339d6e36 · outbound

This paper cites Memo: Test time robustness via adaptation and augmentation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Memo: Test time robustness via adaptation and augmentation,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.145489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.727971Z digest=sha256:080e67b48cb914ecfdc485b310eb45f8f4cde7b163f7322478d27bc597f54059

Observation 389e2db7-0839-4354-afdb-c89406c8be0d · outbound

This paper cites Test-time adaptation meets image enhancement: Improving accuracy via uncertainty-aware logit switching,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Test-time adaptation meets image enhancement: Improving accuracy via uncertainty-aware logit switching,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.133898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.732122Z digest=sha256:0fc8d676610aa4af19f69c5c32356e8ec4b98ff82f07677c68a6f8fd46135729

Observation 20232a8e-ef6a-4c6c-a033-d0e6df93b0e3 · outbound

This paper cites GPT-4o System Card.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption GPT-4o System Card

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.736156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.736156Z digest=sha256:1a026b1c33bd29c9152ff8efd355f61591f0c4386526ac5cdcf230047e9cbf3f

Observation 1d2923a5-2099-48de-81c6-b7c7fc7c53d3 · outbound

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

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Imagenet: A large-scale hierarchical image database,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.122650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.740353Z digest=sha256:8c7921d2bd4af5f8f2ce7641efcfb582629c59d1b520e4335631aa6e16c79b72

Observation c295d2cc-ca7e-4bdf-92e7-96af58828c4a · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Representation Learning with Contrastive Predictive Coding

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.744340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.744340Z digest=sha256:96a2bea1544d69b3fd241850cff4ed91ebfc4c13f0d1b53800750bf30979d88d

Observation bf4bc352-e437-491d-9a9d-5b1f869f6f7f · outbound

This paper cites Unsupervised Classifiers, Mutual Information and 'Phantom Targets,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Unsupervised Classifiers, Mutual Information and 'Phantom Targets,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.110372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.748374Z digest=sha256:ffee634ff1b25b894030fcf1149bc893049ec8645f1369376a67a6c34a6b4245

Observation 989ffab8-f264-4294-8f4d-9a273f5cf7a7 · outbound

This paper cites Discriminative Clustering by Regularized Information Maximization,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Discriminative Clustering by Regularized Information Maximization,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.097355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.752468Z digest=sha256:ce41ef384d5dc2d202361be3ec043ca50e5006ed4aac5112c83214252b9578f6

Observation b252f74d-7006-4534-93cb-08c704c4e7af · outbound

This paper cites Information-theoretical learning of discriminative clusters for unsupervised domain adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Information-theoretical learning of discriminative clusters for unsupervised domain adaptation,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.085267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.756112Z digest=sha256:2a9ed7f4286d36cb7a47e6abb77fa3262486cf18dbef867c6eee28874afd9661

Observation 5cc78612-3ee5-4b9f-8807-7b10a87bfce5 · outbound

This paper cites Learning discrete representations via information maximizing self-augmented training,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Learning discrete representations via information maximizing self-augmented training,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.073400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.759686Z digest=sha256:f2e546726fcc234cd549f4fc5842f63ddcc057e8b6a6c74c6833f3ad2f3c26d3

Observation a373f820-18cd-416c-8f91-7a91ef437548 · outbound

This paper cites Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.059971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.763282Z digest=sha256:71b61b5ff43adbe773c7e299f2ca46c4a2c83048edb5dffbaaf2aa3ec0305d80

Observation e51473ac-7f0a-43fe-b492-5857c1c7ee91 · outbound

This paper cites HVCLIP: High-dimensional vector in CLIP for unsupervised domain adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption HVCLIP: High-dimensional vector in CLIP for unsupervised domain adaptation,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.047715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.767272Z digest=sha256:48d3a927b88c832c06d477caca47e2b580df05e46ddb051d957a15b928655065

Observation 03e22135-8a92-45a4-9515-67ee2d1319d6 · outbound

This paper cites Low-rank few-shot adaptation of vision- language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Low-rank few-shot adaptation of vision- language models,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.035258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.770996Z digest=sha256:c646c98e1cd68b5d633d739f41c5471cf7bd416dd683da5dede8e327a00a8c9a

Observation d0915993-3a45-42e1-8eb1-30c43b8ff262 · outbound

This paper cites Continual test-time domain adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Continual test-time domain adaptation,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.023870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.774725Z digest=sha256:f054431f940a2319c99a62336fa7361bbf12d3631c1449a68a0c2a38fd694b76

Observation bc65c040-aed4-4183-b0b2-18ced119e24a · outbound

This paper cites Visual Prompt Tuning for Test-time Domain Adaptation.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Visual Prompt Tuning for Test-time Domain Adaptation

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.778521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.778521Z digest=sha256:fe2a00b71e1cc1282365c61eeeb3c447006f0cfed4dd1541d0797bde01cf3738

Observation 104def6c-e2d0-47c0-8de6-b64faa2a3e8c · outbound

This paper cites Robust mean teacher for continual and gradual test-time adaptation,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Robust mean teacher for continual and gradual test-time adaptation,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:56.011849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.782696Z digest=sha256:8536a93a677439ec0bea35f71cddbf852f145b981d1165cf70fb4fba4e4b00bd

Observation 43caa130-03e3-4e3b-b81f-1b47a672b534 · outbound

This paper cites Continual test-time domain adaptation via dynamic sample selection,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Continual test-time domain adaptation via dynamic sample selection,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:55.999674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.786538Z digest=sha256:04c10b77136df3984c8a140cc2b50950102fe5192774098c44791d5e6225e73c

Observation fd534095-825a-463e-bfa9-9902699081cf · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Datacomp: In search of the next generation of multimodal datasets,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:55.987995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.790511Z digest=sha256:3c42b61c369c2ad0ab858fbb548a30252c80259d8ca4104f02d1de6acaf077ee

Observation 7ddbe3dd-aefc-4e36-9992-80951e077e08 · outbound

This paper cites Decoupled weight decay regularization,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Decoupled weight decay regularization,

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.795024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.795024Z digest=sha256:dd0fe3776b6e1a7f196c74317672ba4d43dbb3e795a3382e8dbe9643f6f20515

Observation 0e10a115-4990-492e-a620-782c95aeb8a5 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.800187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.800187Z digest=sha256:2dedc04a4cf99d4ff7e5614048f634d44121bc392449f1d52fc448975a157af5

Observation a976656e-d0a1-4658-8887-69c11407de36 · outbound

This paper cites Frustratingly easy test-time adaptation of vision-language models,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Frustratingly easy test-time adaptation of vision-language models,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:55.960421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.805019Z digest=sha256:b84ebadfd87b0ba212cb6e4548df9c500c8bde3a09d4e922546a5c8dc33c3a4b

Observation 26b3c4d7-0aaf-4de3-a730-f0f69790823d · outbound

This paper cites Spherical principal component analysis,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Spherical principal component analysis,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:55.948876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.809779Z digest=sha256:328c2df0cced6b8c1f132f6b0f3f6756e63a78a4a765d42880aaceb8a3c58412

Observation 94cfabca-2b5b-4c71-bc11-654e00949814 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-15T20:30:55.814031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:30:55.814031Z digest=sha256:a18cab91618b4966a3fd1216bb401c61dc68a2a9515386d648eac183c4b419e0

Observation aa3f36e4-77da-4bba-bfa5-ad5177324ac2 · outbound

This paper cites C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion,.

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:30:55.937465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T20:30:55.818301Z digest=sha256:251c6584c2a5418ef6107397b50e23bec16e93567b807459ceea1928ffd47cae

Pith citing papers

Observation 1217c393-18e5-4a8a-9c27-f1dc89240e7b · inbound

Adapting Vision-Language Models Without Labels: A Comprehensive Survey cites this paper.

Adapting Vision-Language Models Without Labels: A Comprehensive Survey Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption

Reference 239

Resolution
verified exact
local_arxiv, observed 2026-08-05T23:17:13.605953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T23:17:08.998138Z digest=sha256:674da3c483a84692a757b5c3aa7ba3e577c888eb611706435a05840412c3578d