One Last Attention for Your Vision-Language Model | Paper Citation Record

Observation 321dcd3b-9e0e-457d-a8cd-e4970c7b7d33 · outbound

This paper cites Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization.

One Last Attention for Your Vision-Language Model Align your prompts: Test-time prompting with distribution align- ment for zero-shot generalization

Reference 1

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Observation dbc2113f-d2af-4331-af92-f33e5587c5db · outbound

This paper cites Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.

One Last Attention for Your Vision-Language Model Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Reference 2

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Observation 66259e06-ce9c-44cf-aea3-cf82d209198f · outbound

This paper cites Food-101–mining discriminative components with random forests.

One Last Attention for Your Vision-Language Model Food-101–mining discriminative components with random forests

Reference 3

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Observation ec0bcd0c-b9ed-43de-a3be-9facfa120c7a · outbound

This paper cites Improved test-time adaptation for domain gen- eralization.

One Last Attention for Your Vision-Language Model Improved test-time adaptation for domain gen- eralization

Reference 4

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Observation 8dd90c77-46eb-418a-b889-cce7ab4a1de9 · outbound

This paper cites Domain generalization via rationale invariance.

One Last Attention for Your Vision-Language Model Domain generalization via rationale invariance

Reference 5

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Observation af2f4e97-fc3f-4daf-beb4-f14c7cc3646b · outbound

This paper cites Lfme: A simple framework for learning from multiple experts in domain generalization.NeurIPS, 2024.

One Last Attention for Your Vision-Language Model Lfme: A simple framework for learning from multiple experts in domain generalization.NeurIPS, 2024

Reference 6

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Observation 65a4ae63-dc37-4d90-b6eb-455391ffeda7 · outbound

This paper cites A causal inspired early-branching structure for domain generalization.IJCV, 132(9):4052–4072, 2024.

One Last Attention for Your Vision-Language Model A causal inspired early-branching structure for domain generalization.IJCV, 132(9):4052–4072, 2024

Reference 7

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Observation 6aab2e37-5c36-4390-b1a9-5944e6855af2 · outbound

This paper cites Vision transformer adapter for dense predictions.

One Last Attention for Your Vision-Language Model Vision transformer adapter for dense predictions

Reference 8

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Observation 253661df-edc8-4635-bca2-256884d2695e · outbound

This paper cites Describing textures in the wild.

One Last Attention for Your Vision-Language Model Describing textures in the wild

Reference 9

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Observation 1cc54bb4-8580-4eb3-8317-0bfbaed4e9aa · outbound

This paper cites John Wiley & Sons, 1999.

One Last Attention for Your Vision-Language Model John Wiley & Sons, 1999

Reference 10

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Observation c8913d0f-2b25-4327-885d-1f98ac450c8e · outbound

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

One Last Attention for Your Vision-Language Model Imagenet: A large-scale hierarchical image database

Reference 11

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Observation e19a8c1a-3472-4ffb-a77d-1483c2b7f8e2 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

One Last Attention for Your Vision-Language Model An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.904261Z

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Observation b77fccd7-91ff-4a65-b4f8-0ace6adb402b · outbound

This paper cites Magma–multimodal augmentation of generative models through adapter-based finetuning.

One Last Attention for Your Vision-Language Model Magma–multimodal augmentation of generative models through adapter-based finetuning

Reference 13

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Observation 63c7dc0a-99c8-4bbd-8d14-a5b0aae12bf4 · outbound

This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

One Last Attention for Your Vision-Language Model Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 14

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Observation 48dc7b24-8868-4700-93c9-f2e04161c3ad · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.IJCV, 132(2):581–595, 2024.

One Last Attention for Your Vision-Language Model Clip-adapter: Better vision-language models with feature adapters.IJCV, 132(2):581–595, 2024

Reference 15

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Observation bfeb9dd3-97c7-405c-9b5f-1a680497910f · outbound

This paper cites Finetune like you pretrain: Im- proved finetuning of zero-shot vision models.

One Last Attention for Your Vision-Language Model Finetune like you pretrain: Im- proved finetuning of zero-shot vision models

Reference 16

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Observation 30bef324-0875-4ee7-ba15-1c57cab3c218 · outbound

This paper cites Deep residual learning for image recognition.

One Last Attention for Your Vision-Language Model Deep residual learning for image recognition

Reference 17

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Observation e558ee89-7489-4ad1-a3ac-1ae16a029c01 · outbound

This paper cites an unresolved cited work.

One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 18

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Observation c9983d55-8e37-4b6c-b9e2-c95356ff1d3e · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.ICCV, 2021.

One Last Attention for Your Vision-Language Model The many faces of robustness: A critical analysis of out-of-distribution generalization.ICCV, 2021

Reference 19

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Observation 988166e3-e1f1-4edf-97e3-3ad96b945bfc · outbound

This paper cites Natural adversarial examples.

One Last Attention for Your Vision-Language Model Natural adversarial examples

Reference 20

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Observation 2786a37f-8214-4474-853d-a8637647470e · outbound

This paper cites Open- clip, 2021.

One Last Attention for Your Vision-Language Model Open- clip, 2021

Reference 21

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Observation a6a73f54-886b-4776-86a7-b67ebc3b9008 · outbound

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

One Last Attention for Your Vision-Language Model Scaling up visual and vision-language representation learning with noisy text supervision

Reference 22

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Observation 6bb61b29-3ceb-4454-bbde-9bf854be401b · outbound

This paper cites Vi- sual prompt tuning.

One Last Attention for Your Vision-Language Model Vi- sual prompt tuning

Reference 23

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Observation 15eb36b3-c394-4a95-bc76-f5b6466dfb38 · outbound

This paper cites Maple: Multi-modal prompt learning.

One Last Attention for Your Vision-Language Model Maple: Multi-modal prompt learning

Reference 24

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Observation ea2d5154-40d3-4ea3-8bbb-33e6ea943c4c · outbound

This paper cites 3d object representations for fine-grained categorization.

One Last Attention for Your Vision-Language Model 3d object representations for fine-grained categorization

Reference 25

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Observation 56fa0de1-2420-437c-8f1b-e48b6b17d613 · outbound

This paper cites Fine-tuning can distort pretrained fea- tures and underperform out-of-distribution.

One Last Attention for Your Vision-Language Model Fine-tuning can distort pretrained fea- tures and underperform out-of-distribution

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.823263Z

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Observation e25a7b87-2e78-4a2e-bbd8-28c9b150114f · outbound

This paper cites Su- pervision exists everywhere: A data efficient contrastive language-image pre-training paradigm.

One Last Attention for Your Vision-Language Model Su- pervision exists everywhere: A data efficient contrastive language-image pre-training paradigm

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.817023Z

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Observation 00737bd9-52cf-4d5f-9bb6-4760ecf175a5 · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive? InNeurIPS, 2021.

One Last Attention for Your Vision-Language Model Ttt++: When does self-supervised test-time training fail or thrive? InNeurIPS, 2021

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.809627Z

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Observation 053395b7-fb28-4de3-ba59-055f091220e1 · outbound

This paper cites Decoupled weight decay regularization.

One Last Attention for Your Vision-Language Model Decoupled weight decay regularization

Reference 29

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Observation a2ac80c5-e7ab-46a5-95d3-68808ae6df8e · outbound

This paper cites A Simple Long-Tailed Recognition Baseline via Vision-Language Model.

One Last Attention for Your Vision-Language Model A Simple Long-Tailed Recognition Baseline via Vision-Language Model

Reference 30

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Observation 36181347-35d0-41b1-9d1a-f95f1aa1c8d8 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

One Last Attention for Your Vision-Language Model Fine-Grained Visual Classification of Aircraft

Reference 31

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

One Last Attention for Your Vision-Language Model Automated flower classification over a large number of classes

Reference 32

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Observation c6adfb22-443e-4f5c-93f8-203da76a9710 · outbound

This paper cites Cats and dogs.

One Last Attention for Your Vision-Language Model Cats and dogs

Reference 33

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Observation 779e1ce5-b2f5-4364-ae70-0edef3638ac0 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

One Last Attention for Your Vision-Language Model Learn- ing transferable visual models from natural language super- vision

Reference 34

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Observation 2f9248d8-b7b1-47f7-9218-c9d206f1f8e6 · outbound

This paper cites Do imagenet classifiers generalize to im- agenet? InICML, 2019.

One Last Attention for Your Vision-Language Model Do imagenet classifiers generalize to im- agenet? InICML, 2019

Reference 35

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Observation 2619913b-af4b-4c99-a640-00c443a5023c · outbound

This paper cites Towards parameter-efficient integration of pre- trained language models in temporal video grounding.

One Last Attention for Your Vision-Language Model Towards parameter-efficient integration of pre- trained language models in temporal video grounding

Reference 36

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Observation fa70a0ce-9d68-4777-bb3e-bc0d626848ec · outbound

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

One Last Attention for Your Vision-Language Model Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 37

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Observation cad5eda9-24c2-4941-a8ea-f89d0c988cf1 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

One Last Attention for Your Vision-Language Model UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 38

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Observation d23df06a-72b1-4b50-a336-8ed954083cb1 · outbound

This paper cites Test-time training with self- supervision for generalization under distribution shifts.

One Last Attention for Your Vision-Language Model Test-time training with self- supervision for generalization under distribution shifts

Reference 39

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Observation a5c4ec97-e1bc-4078-8841-06758dd210df · outbound

This paper cites Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks.

One Last Attention for Your Vision-Language Model Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks

Reference 40

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Observation 2f8b4646-4474-4279-8f55-9ed25583b46c · outbound

This paper cites The information bottleneck method.

One Last Attention for Your Vision-Language Model The information bottleneck method

Reference 41

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This paper cites Visualizing data using t-sne.JMLR, 9(11), 2008.

One Last Attention for Your Vision-Language Model Visualizing data using t-sne.JMLR, 9(11), 2008

Reference 42

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Observation 31758730-5040-4e60-9897-046bdc88e252 · outbound

This paper cites Attention is all you need.

One Last Attention for Your Vision-Language Model Attention is all you need

Reference 43

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Observation b23944ac-2d87-4188-9460-edcc38dc012a · outbound

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

One Last Attention for Your Vision-Language Model Tent: Fully test-time adaptation by entropy minimization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.738958Z

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Observation 4e166775-ef4f-4b91-87ff-cbddc4eca21e · outbound

This paper cites Learning robust global representations by penalizing local predictive power.

One Last Attention for Your Vision-Language Model Learning robust global representations by penalizing local predictive power

Reference 45

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Observation 95dd0666-7e44-4b3e-bf21-8c59c4a10674 · outbound

This paper cites Robust fine-tuning of zero-shot models.

One Last Attention for Your Vision-Language Model Robust fine-tuning of zero-shot models

Reference 46

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Observation 22ddd1b0-7c36-4a93-bdb6-e26ff3c73e1e · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

One Last Attention for Your Vision-Language Model Sun database: Large-scale scene recognition from abbey to zoo

Reference 47

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Observation b49a82fa-951f-48ef-9666-241c2e2cf0a3 · outbound

This paper cites Explicit inductive bias for transfer learning with convolutional net- works.

One Last Attention for Your Vision-Language Model Explicit inductive bias for transfer learning with convolutional net- works

Reference 48

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Observation ba807956-e646-412e-875f-fdfec71eced4 · outbound

This paper cites Mma: Multi-modal adapter for vision-language models.

One Last Attention for Your Vision-Language Model Mma: Multi-modal adapter for vision-language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.710424Z

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Observation 83eff9e0-d053-41ee-86eb-3dab779de8c0 · outbound

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

One Last Attention for Your Vision-Language Model Visual- language prompt tuning with knowledge-guided context op- timization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.704017Z

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Observation 05fd11fb-97eb-45ba-9795-134c26d756d9 · outbound

This paper cites Filip: Fine-grained interactive language-image pre-training.

One Last Attention for Your Vision-Language Model Filip: Fine-grained interactive language-image pre-training

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.697968Z

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Observation e04ae707-b228-4540-b38a-0bd58c63ec1b · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

One Last Attention for Your Vision-Language Model Florence: A New Foundation Model for Computer Vision

Reference 52

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Observation 9ce889c6-3b7b-4414-9249-2591b49e9343 · outbound

This paper cites On the test-time zero- shot generalization of vision-language models: Do we really need prompt learning? InCVPR, 2024.

One Last Attention for Your Vision-Language Model On the test-time zero- shot generalization of vision-language models: Do we really need prompt learning? InCVPR, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.691779Z

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Observation 4e1bf780-8215-4fab-ba9e-e3142fd70d09 · outbound

This paper cites Unified Vision and Language Prompt Learning.

One Last Attention for Your Vision-Language Model Unified Vision and Language Prompt Learning

Reference 54

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Observation 3f0159fe-460e-41d4-951d-032cbe362786 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

One Last Attention for Your Vision-Language Model Lit: Zero-shot transfer with locked-image text tuning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.685112Z

Source-reported events for the cited work

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Observation f11ace67-f3e9-4537-9b16-8c6aa389621e · outbound

This paper cites Sigmoid loss for language image pre-training.

One Last Attention for Your Vision-Language Model Sigmoid loss for language image pre-training

Reference 56

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This paper cites Dept: Decoupled prompt tuning.

One Last Attention for Your Vision-Language Model Dept: Decoupled prompt tuning

Reference 57

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This paper cites Tip-adapter: Training-free clip-adapter for better vision- language modeling.

One Last Attention for Your Vision-Language Model Tip-adapter: Training-free clip-adapter for better vision- language modeling

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.665583Z

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This paper cites Contrastive learning of medical visual representations from paired images and text.

One Last Attention for Your Vision-Language Model Contrastive learning of medical visual representations from paired images and text

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.658055Z

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This paper cites Test-time adaptation with CLIP reward for zero-shot gener- alization in vision-language models.

One Last Attention for Your Vision-Language Model Test-time adaptation with CLIP reward for zero-shot gener- alization in vision-language models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.651821Z

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One Last Attention for Your Vision-Language Model Conditional prompt learning for vision-language models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.644663Z

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This paper cites Learning to prompt for vision-language models.IJCV, 130(9):2337–2348, 2022.

One Last Attention for Your Vision-Language Model Learning to prompt for vision-language models.IJCV, 130(9):2337–2348, 2022

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:42:16.637664Z

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This paper cites Prompt-aligned gradient for prompt tuning.

One Last Attention for Your Vision-Language Model Prompt-aligned gradient for prompt tuning

Reference 63

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One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 65

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One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 66

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One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 67

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One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 68

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One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 69

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One Last Attention for Your Vision-Language Model Unresolved cited work

Reference 70

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Reference 71

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Reference 72

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Reference 2023

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