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Paper Citation Record · LEDGER

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes

As of 22 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2607.09008.

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

pith.paper-citation-record.v1
2607.09008 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T01:03:27.721212Z

measured 67 of 67 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

67 of 67 outbound references displayed

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Outbound references

Observation d8a38870-3797-493b-bbeb-162983dcff0f · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Flamingo: a visual language model for few-shot learning

Reference 1

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Observation d13e7d89-cd13-4c3b-bb41-bca2523dc9c0 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

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Observation 2322de0a-0624-4ed2-838f-5b6fc8801c8d · outbound

This paper cites Khan, and Fahad Shahbaz Khan.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Khan, and Fahad Shahbaz Khan

Reference 3

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Observation 5110977b-83bc-4238-9c92-3f7e7430fce6 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 4

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Observation a61b1cb2-2eb2-4eb0-ba16-f983abe7b986 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Emerging properties in self-supervised vision transformers

Reference 5

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Observation 996b68e7-2615-42e5-8229-79f898e06de2 · outbound

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

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 6

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Observation 9c8b6d85-92ae-4bf1-900a-4cfc330958c2 · outbound

This paper cites PaLI: A jointly-scaled multilingual language- image model.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes PaLI: A jointly-scaled multilingual language- image model

Reference 7

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Observation d00a79d0-b91f-4043-8b89-01476a61dbdf · outbound

This paper cites YOLO-World: Real- time open-vocabulary object detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes YOLO-World: Real- time open-vocabulary object detection

Reference 8

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Observation 784c6c7e-7790-47dc-98ee-dd8095ce60d9 · outbound

This paper cites Open-Vocabulary Object Detection using Pseudo Caption Labels.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Open-Vocabulary Object Detection using Pseudo Caption Labels

Reference 9

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Observation 9d367007-6b9b-4635-a381-d4270cff1f22 · outbound

This paper cites The cityscapes dataset for semantic urban scene understand- ing.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes The cityscapes dataset for semantic urban scene understand- ing

Reference 10

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Observation bae68df1-83b8-433e-87d2-0c1a49b639b4 · outbound

This paper cites Class-balanced loss based on effective number of samples.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Class-balanced loss based on effective number of samples

Reference 11

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Observation 06b14241-3d0b-4955-831b-df198936504c · outbound

This paper cites BERT: Pre-training of deep bidi- rectional transformers for language understanding.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes BERT: Pre-training of deep bidi- rectional transformers for language understanding

Reference 12

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Observation 9b1f51df-28a1-470e-a511-1f895a8ddcbd · outbound

This paper cites Learning to prompt for open- vocabulary object detection with vision-language model.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Learning to prompt for open- vocabulary object detection with vision-language model

Reference 13

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Observation a755bfc6-df12-4df7-8deb-9b416a64843f · outbound

This paper cites Cut, paste and learn: Surprisingly easy synthesis for instance detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Cut, paste and learn: Surprisingly easy synthesis for instance detection

Reference 14

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Observation a2d1b15a-d7bd-406a-bbf4-60eda22cc28c · outbound

This paper cites PromptDet: Towards open-vocabulary detection using uncurated images.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes PromptDet: Towards open-vocabulary detection using uncurated images

Reference 15

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Observation 3c7b3fd9-f071-437f-a555-8acc41454441 · outbound

This paper cites Simple copy-paste is a strong data augmentation method for instance segmentation.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Simple copy-paste is a strong data augmentation method for instance segmentation

Reference 16

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Observation 852ab463-4088-4f6d-9462-baa7a61985b2 · outbound

This paper cites Open-vocabulary object detection via vision and lan- 9 guage knowledge distillation.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Open-vocabulary object detection via vision and lan- 9 guage knowledge distillation

Reference 17

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Observation 1b6abb73-c4b2-4f1e-a8d1-acaf8900f662 · outbound

This paper cites LVIS: A dataset for large vocabulary instance segmentation.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes LVIS: A dataset for large vocabulary instance segmentation

Reference 18

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Observation 78eac4c5-17bd-4fc6-96de-ff3604acb886 · outbound

This paper cites Masked autoencoders are scalable vision learners.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Masked autoencoders are scalable vision learners

Reference 19

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Observation afac1053-d8ab-4431-8f51-768c82b5299a · outbound

This paper cites Disen- tangling label distribution for long-tailed visual recog- nition.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Disen- tangling label distribution for long-tailed visual recog- nition

Reference 20

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Observation 4877e3ab-2079-4b1a-8a80-ea349dde6ede · outbound

This paper cites Unsupervised Prompt Learning for Vision-Language Models.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Unsupervised Prompt Learning for Vision-Language Models

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Observation 13b3d113-b329-4486-9eca-a6ce819f0c01 · outbound

This paper cites MDETR – modulated detection for end-to-end multi-modal under- standing.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes MDETR – modulated detection for end-to-end multi-modal under- standing

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Observation 39cb5d50-cbc4-4339-812a-b20d370a71dd · outbound

This paper cites MaPLe: Multi-modal prompt learning.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes MaPLe: Multi-modal prompt learning

Reference 23

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Observation 51e1d214-9737-4752-bec8-a138ea705bfc · outbound

This paper cites Grounded language-image pre- training.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Grounded language-image pre- training

Reference 24

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Observation edcddd80-8352-47ed-af94-1fb7f35c4408 · outbound

This paper cites A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts

Reference 25

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Observation eca558fc-169c-42d8-b903-9a3f0f5bafd2 · outbound

This paper cites Lawrence Zitnick.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Lawrence Zitnick

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Observation dd9b96fb-cb14-4686-974a-2a608fd81d6f · outbound

This paper cites Focal loss for dense object detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Focal loss for dense object detection

Reference 27

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Observation bf20bdb2-b07a-485b-aed1-2b6755ebafc5 · outbound

This paper cites Visual instruction tuning.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Visual instruction tuning

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Observation 420e6937-a05a-41d0-9022-e989a5b74643 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 29

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Observation 45caed43-a91c-4a46-9f7e-cef67e63673d · outbound

This paper cites Long-tail learning via logit ad- justment.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Long-tail learning via logit ad- justment

Reference 30

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Observation 4bc5d39e-c307-4b8a-ae9e-438f0652649d · outbound

This paper cites Simple open- vocabulary object detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Simple open- vocabulary object detection

Reference 31

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Observation 24bc332d-16ba-4a59-a2e7-8844ba187985 · outbound

This paper cites Scaling open-vocabulary object detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Scaling open-vocabulary object detection

Reference 32

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Observation b1fcdddc-ca28-4614-a1a9-66d558aa5f2e · outbound

This paper cites Imbalance problems in object detection: A re- view.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, pages 1–1, 2020.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Imbalance problems in object detection: A re- view.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, pages 1–1, 2020

Reference 33

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Observation 98c1e5a8-f933-4098-b424-195aa0265c0b · outbound

This paper cites GPT-4 technical report.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes GPT-4 technical report

Reference 34

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Observation 39793ec3-826d-4a21-ad58-a94ce4be7726 · outbound

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

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Learning transferable visual models from natural language supervision

Reference 35

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Observation 7dbd3952-5111-4cbe-8bd1-e94153b1d361 · outbound

This paper cites Aligning and prompting everything all at once for universal visual perception.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Aligning and prompting everything all at once for universal visual perception

Reference 36

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Observation 3cc1253e-990c-4caa-a3ef-36f34ce6a0be · outbound

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

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Test-time prompt tuning for zero-shot generalization in vision-language models

Reference 37

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Observation 983d7793-9601-4f0e-b8d2-2a6b3c4eb4ab · outbound

This paper cites Equal- ization loss for long-tailed object recognition.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Equal- ization loss for long-tailed object recognition

Reference 38

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Observation 22b51d12-11ae-4a4e-9f70-09bf6db4206f · outbound

This paper cites Equalization loss v2: A new gradient bal- ance approach for long-tailed object detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Equalization loss v2: A new gradient bal- ance approach for long-tailed object detection

Reference 39

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:cd0303f6b00af5a193b55293dac88329dade0533559b4c52b586dc21fe46b9ae

Observation a1950c97-a0a4-4955-a771-ac22b754fb06 · outbound

This paper cites Moondream: A small vision language model.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Moondream: A small vision language model

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Observation 48b95d46-a9dc-4858-8741-24f423ec97dd · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Tent: Fully test-time adaptation by entropy minimization

Reference 41

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:966fece9f27115177819c4bdc26512675265fae2421702b2fc2da81e67a4925a

Observation 721e0868-4bd8-4aa3-802a-d8e3645ba53f · outbound

This paper cites Seesaw loss for long-tailed instance segmentation.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Seesaw loss for long-tailed instance segmentation

Reference 42

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:6a24373ab7064cf9823cd0c39e949418163232c55ec97aee6bdf87663a7ee4a5

Observation 08072b10-2a2a-4447-a8a0-e2132c57e693 · outbound

This paper cites GroupViT: Semantic segmentation emerges from text su- pervision.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes GroupViT: Semantic segmentation emerges from text su- pervision

Reference 43

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:1166d613e4ca1912c59f8e13e654328e3942d9fb40a727c9eec25c1341169477

Observation b6da45c8-35a1-4885-8de2-73e8687bc2eb · outbound

This paper cites End-to-end semi-supervised object detection with soft teacher.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes End-to-end semi-supervised object detection with soft teacher

Reference 44

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:4dc796dcb28e1f5d799cdaff1993df95017e76183d70e9c9447155f5300cfb8b

Observation 39efb0a4-b80e-4f99-8375-9ad5ddf7e143 · outbound

This paper cites Improving pseudo labels for open-vocabulary object detection.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Improving pseudo labels for open-vocabulary object detection

Reference 45

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:3dc08c6ba59b8460308b1d2a69b01170be09ef7282b39420b51e0d72969221a3

Observation 66f5c8a1-031c-4b6d-8abd-f0c393ac2644 · outbound

This paper cites UniTab: Uni- fying tabular learning at scale.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes UniTab: Uni- fying tabular learning at scale

Reference 46

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:1a8c9e1452535429797b2a73b86ef01ce2d83a40ca22373939a3854a44777dd8

Observation 26ff197a-012a-4acd-92bd-b4d8a6880300 · outbound

This paper cites Visual- language prompt tuning with knowledge-guided context optimization.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Visual- language prompt tuning with knowledge-guided context optimization

Reference 47

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:9d529eea015b7895007fd52f32f6e8027f77b0fceae9863fd2ad33534fc13d65

Observation 2ce8b913-0bfd-41b4-a936-3805228cf8df · outbound

This paper cites CutMix: Regularization strategy to train strong classi- fiers with localizable features.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes CutMix: Regularization strategy to train strong classi- fiers with localizable features

Reference 48

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:e1fcb6caf2f11c507efcc2de78351f3f7b66c9e45d816a56e7ba6905f3ec0e3d

Observation 3e0f31da-da57-4de7-ac15-d60d471b9346 · outbound

This paper cites Open-vocabulary DETR with condi- tional matching.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Open-vocabulary DETR with condi- tional matching

Reference 49

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:43d0352116a137066a459d668ca1d258ab1f65926815c7256297bafacab122f1

Observation b6ab8ff8-cd44-49a3-838d-280f7150b7ac · outbound

This paper cites Unified Vision and Language Prompt Learning.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Unified Vision and Language Prompt Learning

Reference 50

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:17357605e3764a97eb56723552e1283b51dc017944d1d86861fc8026f67b2fba

Observation a2b59379-07e9-4489-9002-69f0db897f57 · outbound

This paper cites Open-vocabulary object detection using captions.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Open-vocabulary object detection using captions

Reference 51

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:8c8e40f48918f87c5483deba8e46551d18ab2efa3b991879aa42fa4bb84eacbb

Observation e53bb192-3b6a-4386-99d4-7dfef59dc38e · outbound

This paper cites Dauphin, and David Lopez-Paz.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Dauphin, and David Lopez-Paz

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:02029b0ad0b358b5e9eb20b1489002c88e9df77c1ba584affed19c655c1e443f

Observation 18886383-2b5d-4abb-a251-1e25754983f7 · outbound

This paper cites GLIPv2: Unifying localization and vision-language un- derstanding.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes GLIPv2: Unifying localization and vision-language un- derstanding

Reference 53

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:c2a9e0346e663e5c030b560bc1111aa6345fd518bb1708d376f618e066779be6

Observation 1c5efbe0-6bac-4900-ae2b-967ca6bd989a · outbound

This paper cites Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head

Reference 54

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:782701524cbfa9f20f0a0a2abb17f09725d3ddeeab30231a9c73ce513b34c27d

Observation a84fd865-03b7-47d1-aeea-89fd4789f3da · outbound

This paper cites Re- gionCLIP: Region-based language-image pretraining.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Re- gionCLIP: Region-based language-image pretraining

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:d0da4128c7bbe9d79f7419c5eae3b1a151a4dabda945c6d1f25c1cab3b087a3a

Observation 2a963a03-883f-4982-b7be-eaeec8698097 · outbound

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

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Conditional prompt learning for vision- language models

Reference 56

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:c83b4ea52d126a80e388382d785e6a581a0a6c43edd883a3a215b6eddc70f670

Observation d2657d6f-e39d-4fb1-94ea-bb949bb84597 · outbound

This paper cites Learning to prompt for vision-language mod- els.International Journal of Computer Vision, 130: 2337–2348, 2022.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Learning to prompt for vision-language mod- els.International Journal of Computer Vision, 130: 2337–2348, 2022

Reference 57

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:4ccd7a415801c91cb3963d76d63f9af64da4b37687250590bab0cb4c87fa73e7

Observation 42efb28c-514c-400d-924a-d51c04ad94cf · outbound

This paper cites Detecting twenty- thousand classes using image-level supervision.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Detecting twenty- thousand classes using image-level supervision

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:49b5d09d0c4cf0c4d887f951fcb2ff5409d9bef38256e7b94b4d12891dee5b74

Observation 556e3719-ae7a-4acb-a5d4-7e33b07f74e9 · outbound

This paper cites MiniGPT-4: Enhancing vision- language understanding with advanced large language models.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes MiniGPT-4: Enhancing vision- language understanding with advanced large language models

Reference 59

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:fea1826bba5680e0739c7bee9bebe275bbfe1550facafcf4baf25e8e57133647

Observation 21aa748b-e3b9-4057-94dd-21aba2636fee · outbound

This paper cites Data augmentation remedies include copy-paste [16], Cut- Paste [14], MixUp [52], CutMix [48], and mosaic tiling [4, 8].

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Data augmentation remedies include copy-paste [16], Cut- Paste [14], MixUp [52], CutMix [48], and mosaic tiling [4, 8]

Reference 60

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:f31f124aba932f2c114a22e96f32eefc622953dceb4e22fdfcf51a23974a5b9b

Observation a994cda8-e940-4d52-8dc5-edafcfcf7e4b · outbound

This paper cites Per-class instance counts in each validation split.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Per-class instance counts in each validation split

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:50f0ad3b9e415f3d81e34076b13c9538e64a5b20e6d120410337f2bb0120f2e9

Observation 9d3694a6-70d3-4e77-8f54-386af1c7e605 · outbound

This paper cites Detector Inference Settings All detector weights are frozen throughout and no fine- tuning is performed at any stage.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Detector Inference Settings All detector weights are frozen throughout and no fine- tuning is performed at any stage

Reference 62

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:3b362d0db385f0eb185b2034c3220ddfff2f4a23222b50fda9a4c1f534c75508

Observation 49b62e83-7c75-41dd-a01e-a5702685c0a8 · outbound

This paper cites an unresolved cited work.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Unresolved cited work

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:a9f39bc8b322d92439d6bec04c0dddb74de7d7228b26e463fc00138f8edd8569

Observation 284de3bd-8397-4603-823a-0749dd230a35 · outbound

This paper cites We additionally ran CGAP with LLaV A (7B) as an ex- ploratory Phase II refiner for all completed backbone– dataset configurations.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes We additionally ran CGAP with LLaV A (7B) as an ex- ploratory Phase II refiner for all completed backbone– dataset configurations

Reference 64

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:05dcc48d1f724efac965e337e34935fb6e632510ea4acb3196dfe75167c70533

Observation 0a06acf6-d83c-42cc-a248-0c042377e4c3 · outbound

This paper cites moondream (K=15,T 0=CC, strati- fied, three runs).

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes moondream (K=15,T 0=CC, strati- fied, three runs)

Reference 65

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:4a95fea85b5263fc3fe04337cb85de9f53055aa18b6c0fbacf6d908048c3f6da

Observation 3d8846d6-8ea0-416b-9570-da464608a8c7 · outbound

This paper cites Detection Quality Figure 12 plots refined caption length (words) against minority-class AP@0.5 across all 1,104 CGAP trials, colored by bucket assignment.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Detection Quality Figure 12 plots refined caption length (words) against minority-class AP@0.5 across all 1,104 CGAP trials, colored by bucket assignment

Reference 66

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:e238dfc256e2855dadaf0093a5082207f96107627f455f156c22edd3a7ca4f59

Observation 01377427-9604-490a-891d-354dbd5c6985 · outbound

This paper cites The primary metric is AP@0.5 for the designated mi- nority class (bus/COCO, truck/Cityscapes, Bike/Chula Vista).

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes The primary metric is AP@0.5 for the designated mi- nority class (bus/COCO, truck/Cityscapes, Bike/Chula Vista)

Reference 67

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