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Paper Citation Record · LEDGER

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

As of 7 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 2026-08-07T06:34:17.273281+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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Source: cited_works

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

Reference 21

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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

Reference 26

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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

Reference 28

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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

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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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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:52d03b047f6e2cc448bd55b0b1511da16239fd933195a390d48b8189eb678496

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:af0c5a34200ca94af9e1a05ebf96f3abf74a31e36e4ab6c9e252fab6411233e6

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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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:62e94e0930b7f1f660bbc62a5072024c65fb7608dbc5d801298af4ed53d7c298

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=sha256:36617dff088c0957be5a2ff91ac4def5a6338518e5626136f997d4360097e23a

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:aadde2d9f5d4caa12c0f3ecbf93516e71cdb8c095d25f544199e9c0f79661d60

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=sha256:18f3d4bd1b940d0ef883d70714dd1e2481a5f8b1332f0a7ee442e9de4218fb7c

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:d0b3197168d1cc313cb5c1fc70b49eced2d4ebb66b847958724f445a90963994

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:efc8d71a4c5ec21370809e2e03707f9f911984e1eb75bc4e854e56431b05dfe7

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:f8bd0306d210e477ba1d5b3dd6914dda1de441e2617ade23618f449082991e8b

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=sha256:3eef5a214f091c5807d51f73235eba100d60b41707b8c5163f8cf69dd9bf1573

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:b458d555ea904958a93d1115701df93efb69002e8e0f9e399a9325302fae3f0a

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=sha256:5cfa5ba979781c847577a4a042e7fc6a1bf7d5e9990fcaa222a905b7afdbf0ad

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=sha256:3810d780f6d27f1eb42b28985e6ee755bbc709179568463919481003a61185f2

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=sha256:20fef65a93f59d70c7283c0f9533b4435857f9b19101d9a8ba6b469247faebdb

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

Reference 52

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:4ec8320cb87bb52dcd70a3c290a49df43b58c449c58c659755138799550d7aa2

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:c6a0110b45b458fe7a367e9f1a53002b5fde0918dc750d6f8f81c936d73db68f

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:d5ac40cb095890f84165d4a92a6be178e4f43944afbea4d2e914ed2d3c60999a

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

Reference 55

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:ba8d2831e52fcfa90e7bc272960423d7410dffc42bacaca8faf960e54e88a59d

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:c4971b52732a4d83391662955bcbe42220820499806e90206ba37ae2ee343ba5

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=sha256:71db70bab0b0e55cbbea5b6f6fee46de58d7a8919cb4b6f0063eec647c5caff1

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

Reference 58

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:b9dc3b4dd054e94dfec90573424160b0b9610e0375002a4ae0b239e07a422527

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

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:b3983130dcc80fba17f18de03c4b80db60378c4ef048107cc4746cc64484b7e0

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=sha256:1a4199752dca1467a9ee893e3d2c3fafe0a34ad642f30d1901ad7a3660116f04

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:ee1bc0304438f2ae3c038dba84a79a1dda066c096e5ef1c85da44ce1fca79c98

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:f604575b0475551d654a6c04dfc712e875a6a7a712c50d817a51d8e0af1a2ec4

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=sha256:5c5416746e43f944ff01528864faf1e721ed60ed7900768d91b45f55da1167de

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

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:2a4acb4beb84a8e5ccbb77f3bbe8bb1a955b796c644501ed5e8a7779cd205652

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=sha256:19bc88c7ce40836f94eb9056c3baa1ec67739ea1575cd959e13926d8293c5c9b

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=sha256:74be7fd536bf245a8e758c4dfb2d576798a00eaeb422674d4da6e516319e76ea

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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