YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation | Paper Citation Record

Observation 21ce4e99-9f1b-4add-b70d-eef7c82d0e68 · outbound

This paper cites Analysis of representations for domain adaptation.Advances in neural information processing systems, 19, 2006.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Analysis of representations for domain adaptation.Advances in neural information processing systems, 19, 2006

Reference 1

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Observation 33c66903-00d5-4479-be48-cffe4380814b · outbound

This paper cites A theory of learning from different domains.Machine learning, 79(1): 151–175, 2010.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation A theory of learning from different domains.Machine learning, 79(1): 151–175, 2010

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.954292Z

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Observation 9d6530a4-a88f-4ee1-8030-ac86c4e13313 · outbound

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

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 3

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Observation fd3e7776-c5a8-4577-b08f-b1c11df33060 · outbound

This paper cites Visdrone-det2021: The vision meets drone object detection challenge results.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Visdrone-det2021: The vision meets drone object detection challenge results

Reference 4

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Observation 9a39e241-27a6-42c7-a199-cdb0789a0b8c · outbound

This paper cites End-to-end object detection with transformers.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation End-to-end object detection with transformers

Reference 5

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Observation 4ab53255-4650-4149-a9f2-955fc9ae7c6d · outbound

This paper cites Domain adaptive faster r-cnn for object detection in the wild.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Domain adaptive faster r-cnn for object detection in the wild

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.847732Z

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Observation bd5b7744-06ed-4408-8f78-5dd906aabe35 · outbound

This paper cites Yolo-ms: Rethinking multi-scale representation learning for real-time object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Yolo-ms: Rethinking multi-scale representation learning for real-time object detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.740162Z

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Observation 07f5418a-cbb1-47bf-b161-4d83d36348a2 · outbound

This paper cites Domain adaptation in regression.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Domain adaptation in regression

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.560746Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb7281fd-e8db-4e0f-9fa4-d11c6ef073d6 · outbound

This paper cites Autoaugment: Learning augmentation strategies from data.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Autoaugment: Learning augmentation strategies from data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:13.280025Z

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Observation 7474d2a2-3909-44ef-9edf-87766e3d2370 · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Randaugment: Practical automated data augmentation with a reduced search space

Reference 10

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Observation 64cddc19-762f-41ad-a807-888bec875840 · outbound

This paper cites Deformable convolutional networks.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Deformable convolutional networks

Reference 11

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Observation e6a498aa-1b9b-48ed-bebd-aa778f561bc6 · outbound

This paper cites Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.388516Z

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Observation cfc1c31b-5125-4b02-94cc-bf413c844d32 · outbound

This paper cites Object detection in aerial images: A large-scale benchmark and challenges.IEEE transactions on pattern analysis and machine intelligence, 44 (11):7778–7796, 2021.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Object detection in aerial images: A large-scale benchmark and challenges.IEEE transactions on pattern analysis and machine intelligence, 44 (11):7778–7796, 2021

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.262848Z

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Observation bc1f3725-2973-424e-b474-612d4c80f250 · outbound

This paper cites Cswin transformer: A general vision transformer backbone with cross-shaped windows.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Cswin transformer: A general vision transformer backbone with cross-shaped windows

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.146030Z

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Observation 471a0b6f-4733-483f-87ba-9adf02bc8aa0 · outbound

This paper cites Domain-adversarial training of neural networks.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Domain-adversarial training of neural networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:13.711121Z

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Observation b887534d-6e2e-42eb-9578-e9e0012a7a54 · outbound

This paper cites Ota: Optimal transport assignment for object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Ota: Optimal transport assignment for object detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.968976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f997c27-4fc5-4b10-a05f-fe4260697d59 · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation YOLOX: Exceeding YOLO Series in 2021

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:13.937355Z

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Observation ce40e628-9bd6-43e0-8f25-30839a48e03d · outbound

This paper cites SIoU Loss: More Powerful Learning for Bounding Box Regression.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation SIoU Loss: More Powerful Learning for Bounding Box Regression

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:13.986457Z

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Observation 3ab47597-83c2-4d19-9659-6202f9db5555 · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Arbitrary style transfer in real-time with adaptive instance normalization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.101670Z

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Observation a54730c4-a6cd-4e5f-a669-17b2d9ad2fe0 · outbound

This paper cites Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.222931Z

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Observation dc044972-fcee-407c-afe1-e1a9c0995a87 · outbound

This paper cites Analyzing and improving the image quality of stylegan.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Analyzing and improving the image quality of stylegan

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.257761Z

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Observation a542cab0-b0f8-46d2-848b-ba4d63265626 · outbound

This paper cites Omnidet: Surround view cameras based multi-task visual perception network for autonomous driving.IEEE Robotics and Automation Letters, 6 (2):2830–2837, 2021.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Omnidet: Surround view cameras based multi-task visual perception network for autonomous driving.IEEE Robotics and Automation Letters, 6 (2):2830–2837, 2021

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.858450Z

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Observation 2d122435-d647-4ce7-a534-3a00f22197bd · outbound

This paper cites YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.340453Z

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Observation a82a09a0-ccb9-430a-8940-e110e68994fe · outbound

This paper cites YOLOv6 v3.0: A Full-Scale Reloading.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation YOLOv6 v3.0: A Full-Scale Reloading

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.375718Z

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Observation 28d7c896-a920-4ad5-b510-2c2e1bdab925 · outbound

This paper cites Deep domain adaptive object detection: A survey.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Deep domain adaptive object detection: A survey

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.756888Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08d235ff-e41b-4089-8d82-ce79a773597e · outbound

This paper cites Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection.Advances in neural information processing systems, 33:21002–21012, 2020.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection.Advances in neural information processing systems, 33:21002–21012, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.617146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbf2498b-1ccd-4397-b992-196f9340cb66 · outbound

This paper cites Microsoft coco: Common objects in context.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Microsoft coco: Common objects in context

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.507722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f88559d-a65d-4e79-890a-33dcdff9d18b · outbound

This paper cites Feature pyramid networks for object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Feature pyramid networks for object detection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.569191Z

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Observation fc112f4c-ff62-43ff-8a64-ff42a4328d24 · outbound

This paper cites SSD: Single Shot MultiBox Detector.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation SSD: Single Shot MultiBox Detector

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.635810Z

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Observation 9b28359c-aa5b-4d79-b63d-8e5da0b11fb4 · outbound

This paper cites Conditional adversarial domain adaptation.Advances in neural information processing systems, 31, 2018.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Conditional adversarial domain adaptation.Advances in neural information processing systems, 31, 2018

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.526860Z

Source-reported events for the cited work

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Observation baf26d7a-8921-4aa1-ba32-c006a7de3ccc · outbound

This paper cites RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.711496Z

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Observation c99a2ecb-1c2b-42dc-9dcf-b629ae709e9e · outbound

This paper cites Domain Adaptation: Learning Bounds and Algorithms.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Domain Adaptation: Learning Bounds and Algorithms

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.750205Z

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Observation 4339212d-74e8-4268-ac6b-589ebc04c6fd · outbound

This paper cites MIT press, 2018.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation MIT press, 2018

Reference 33

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Observation 183af223-dcd3-4545-b2f4-d3ec1feb943a · outbound

This paper cites Trivialaugment: Tuning-free yet state-of-the-art data augmentation.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Trivialaugment: Tuning-free yet state-of-the-art data augmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.403814Z

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Observation f7be9d47-9282-4085-95ea-941bbd8720dc · outbound

This paper cites YOLOv3: An Incremental Improvement.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation YOLOv3: An Incremental Improvement

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.864926Z

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Observation d48524db-66fc-45fb-8400-573c313dfdf1 · outbound

This paper cites You only look once: Unified, real-time object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation You only look once: Unified, real-time object detection

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:14.887647Z

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Observation 1e22869e-67a2-4b3f-8892-e03d070d7892 · outbound

This paper cites Generalized intersection over union: A metric and a loss for bounding box regression.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Generalized intersection over union: A metric and a loss for bounding box regression

Reference 37

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Observation ca338b07-eeac-4fa1-9ade-447889aff07e · outbound

This paper cites Maximum classifier discrepancy for unsupervised domain adaptation.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Maximum classifier discrepancy for unsupervised domain adaptation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.296373Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c261e80a-36ed-4ec3-bc3e-2e3b155851bc · outbound

This paper cites Efficientdet: Scalable and efficient object detec- tion.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Efficientdet: Scalable and efficient object detec- tion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.178082Z

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Observation ded14473-23f1-42d8-aefe-c3dc695d84b4 · outbound

This paper cites Yolov12: Attention-centric real-time object detectors.Advances in neural information processing systems, 38:78433–78457, 2026.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Yolov12: Attention-centric real-time object detectors.Advances in neural information processing systems, 38:78433–78457, 2026

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.080119Z

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Observation 950bbef8-9834-47ea-b1c1-ccb66ba3ea26 · outbound

This paper cites Fcos: Fully convolutional one-stage object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Fcos: Fully convolutional one-stage object detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.979313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 586a6108-8a29-44bc-9777-53a9dede1e3d · outbound

This paper cites Yolov10: Real-time end-to-end object detection.Advances in neural information processing systems, 37:107984–108011, 2024.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Yolov10: Real-time end-to-end object detection.Advances in neural information processing systems, 37:107984–108011, 2024

Reference 42

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Observation 1c61ec63-991f-46e1-ad1e-02e9448f23f9 · outbound

This paper cites Gold-yolo: Efficient object detector via gather-and-distribute mechanism.Advances in neural information processing systems, 36:51094–51112, 2023.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Gold-yolo: Efficient object detector via gather-and-distribute mechanism.Advances in neural information processing systems, 36:51094–51112, 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.922368Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6baa36ab-a1c1-4a8c-958c-f09bbec210c7 · outbound

This paper cites Yolov7: Trainable bag-of- freebies sets new state-of-the-art for real-time object detectors.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Yolov7: Trainable bag-of- freebies sets new state-of-the-art for real-time object detectors

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.876684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6aef4a89-692f-460f-93a7-267026a1623b · outbound

This paper cites Yolov9: Learning what you want to learn using programmable gradient information.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Yolov9: Learning what you want to learn using programmable gradient information

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.814447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1760188-dee7-4a1c-97da-f47e61b030ba · outbound

This paper cites Exploring dcn-like architecture for fast image generation with arbitrary resolution.Advances in Neural Information Processing Systems, 37:87959–87977, 2024.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Exploring dcn-like architecture for fast image generation with arbitrary resolution.Advances in Neural Information Processing Systems, 37:87959–87977, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.691745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c993bf1-bcf6-4e48-9f6a-ab32eaa7195e · outbound

This paper cites Vision transformer with deformable attention.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Vision transformer with deformable attention

Reference 47

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Observation 98f7f632-0eed-4df8-8128-58d7628cb89e · outbound

This paper cites Recognizing scene viewpoint using panoramic place representation.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Recognizing scene viewpoint using panoramic place representation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.534290Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10e047f2-1ac1-4c10-8ce3-87a35744e0e3 · outbound

This paper cites PP-YOLOE: An evolved version of YOLO.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation PP-YOLOE: An evolved version of YOLO

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.401849Z

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Observation 55c7080e-f253-410c-8bec-b513645bbc2b · outbound

This paper cites Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.436063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40a80ca3-b00a-4da2-bcf5-16ff9c9182cd · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.476046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddc54e9d-334d-4ae7-8e47-2aab97f62d3b · outbound

This paper cites Dino: Detr with improved denoising anchor boxes for end-to-end object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Dino: Detr with improved denoising anchor boxes for end-to-end object detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.267947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd9bc56c-6f2f-474f-bf8a-5578cf901f44 · outbound

This paper cites Varifocalnet: An iou-aware dense object detector.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Varifocalnet: An iou-aware dense object detector

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.892242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6eea0a96-4fdb-4e7d-b295-340515ad1946 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation mixup: Beyond Empirical Risk Minimization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.605940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eee1e46-ff44-4c69-bb0a-13954a4f59f9 · outbound

This paper cites Remote sensing object detection meets deep learning: A metareview of challenges and advances.IEEE Geoscience and Remote Sensing Magazine, 11(4):8–44, 2023.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Remote sensing object detection meets deep learning: A metareview of challenges and advances.IEEE Geoscience and Remote Sensing Magazine, 11(4):8–44, 2023

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.725093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2b7429b-9768-434e-8024-0145feb358f5 · outbound

This paper cites On learning invariant representations for domain adaptation.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation On learning invariant representations for domain adaptation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.807557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b729537d-4e7f-458b-96db-6c5347c1dca0 · outbound

This paper cites Detrs beat yolos on real-time object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Detrs beat yolos on real-time object detection

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.836707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2007a21-f90a-4008-aa2f-352fc5033d31 · outbound

This paper cites Distance-iou loss: Faster and better learning for bounding box regression.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Distance-iou loss: Faster and better learning for bounding box regression

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.580525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ededd6f-c583-406b-938a-c8882fd21bb4 · outbound

This paper cites Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.460327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c76136fc-84d3-4d60-9e00-3147a52ef917 · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:16.029291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6300f7ca-992d-40ef-bd24-326d780ec797 · outbound

This paper cites Deformable convnets v2: More deformable, better results.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Deformable convnets v2: More deformable, better results

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.311901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4efea4db-3bb9-4467-a2e2-d1e4fe9ae8fc · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:16.218750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1418bb8-b1d7-4d1c-b13d-a87aaf5550ca · outbound

This paper cites Learning data augmentation strategies for object detection.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Learning data augmentation strategies for object detection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.163005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd0fa00c-91e0-43cc-8b2e-de9bb15b4535 · outbound

This paper cites The abstract and Section 1 list five contributions; each is substantiated in Sections 3 and 4 with quantitative evidence.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation The abstract and Section 1 list five contributions; each is substantiated in Sections 3 and 4 with quantitative evidence

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.091857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07ca49bb-bb72-46f7-a768-1fd98f5e4f8d · outbound

This paper cites See Section 5 (Conclusion) for a dedicated Limitations paragraph.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation See Section 5 (Conclusion) for a dedicated Limitations paragraph

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.950828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e199e2af-3652-47ec-9b5b-9ae56804dc74 · outbound

This paper cites Propositions 1–2 and Theorem 1 state all assumptions; proof sketches appear in Section 3.8, full proofs in Appendix B.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Propositions 1–2 and Theorem 1 state all assumptions; proof sketches appear in Section 3.8, full proofs in Appendix B

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.786391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d6029a7-d6d8-4f6f-ab6c-44c953d32142 · outbound

This paper cites Training hyperparameters (Table 8), architecture details (Appendix A.2), and evaluation protocols (Section 4.5) are specified.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Training hyperparameters (Table 8), architecture details (Appendix A.2), and evaluation protocols (Section 4.5) are specified

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.601219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79c1466a-0148-4837-aea1-653d75ac33b2 · outbound

This paper cites [TODO: Provide anonymous GitHub repo link for review.].

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation [TODO: Provide anonymous GitHub repo link for review.]

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.409961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d01e5b30-4ea9-4a6f-90fa-64b3918bc217 · outbound

This paper cites See Section 4.1 (Implementation Details) and Table 8.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation See Section 4.1 (Implementation Details) and Table 8

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.299478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d78e552-f427-4122-b8a8-63a8de6a90c8 · outbound

This paper cites Table 1 and Table 2 report±standard deviation over 3 runs.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Table 1 and Table 2 report±standard deviation over 3 runs

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.152926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b8630e0-308f-4b1e-ac97-c168e3d963ff · outbound

This paper cites Section 4.1 specifies 4×A100 GPUs for training, single T4 GPU for inference.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation Section 4.1 specifies 4×A100 GPUs for training, single T4 GPU for inference

Reference 71

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T18:10:18.023690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1025fbb1-51d9-4288-9d7c-8ec8d9468622 · outbound

This paper cites COCO [27] and all prior YOLO works are cited.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation COCO [27] and all prior YOLO works are cited

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:17.879688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3f2c6f3-dfcf-4535-adb8-33c8f92d6aab · outbound

This paper cites No human subjects were involved.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation No human subjects were involved

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:17.725731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9764e4c7-cffc-4315-a9c0-735df2a2f015 · outbound

This paper cites No human subjects were involved.

YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation No human subjects were involved

Reference 74

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T18:10:17.572149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Reference resolution

Outbound references

Pith citing papers