Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T13:44:09.539689Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2512.23273.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T13:44:09.539689Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-15T14:15:00.859119Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T17:08:01.069935Z
45 of 45 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation d5d3cade-dd7e-4b52-b682-befe3349a2ab · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection End-to-end object detection with trans- formers.European Conference on Computer Vision, pages 213–229, 2020
Reference 1
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Observation 776e9714-6093-487a-9f4f-157ac9971471 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Modality-agnostic mixed-expert training for vision- language models
Reference 2
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Observation b7868b74-e219-48d7-a6b6-e46f36adc60d · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Cswin transformer: A general vision transformer backbone with cross-shaped windows
Reference 3
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Observation 05ef61a3-b09a-4d79-a1b8-aebc0fe3e3ae · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection An image is worth 16x16 words: Transformers for image recognition at scale
Reference 4
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Observation 9f1f8daf-e8d2-4211-86fa-1b5c846f6ad8 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Visdrone-det2019: The vision meets drone object detection in image chal- lenge results
Reference 5
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Observation 49a399c5-8fbd-4d21-87a0-889b658b3716 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Unresolved cited work
Reference 6
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Observation 2efb5c2f-0aa5-4d8e-a732-9340d644745f · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022
Reference 7
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Observation 91688981-eeb9-4667-844b-411050bb2f56 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Are we ready for autonomous driving? the KITTI vision benchmark suite
Reference 8
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Observation e847ea29-cdaf-413a-9e50-51d959927369 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Precise detection in densely packed scenes
Reference 9
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Unavailable: canonical work link unavailable.
Observation 1030701a-1397-4730-97f0-aca88504849a · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Coordinate attention for efficient mobile network design.Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13713–13722, 2021
Reference 10
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Observation c0e7b353-ac63-4f15-b548-45d9f57aee5b · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Reference 11
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Observation 00bffe4f-ca8f-4796-a0ca-61fc05baaecf · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Gather-excite: Exploiting feature context in convolutional neural net- works
Reference 12
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Observation 602aadb2-6ae4-4e19-8882-621d58456388 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Squeeze-and- excitation networks.Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, pages 7132–7141, 2018
Reference 13
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Observation ae4dbeaa-5dd4-4d8f-b06b-b8953bc1df72 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Adaptive mixtures of local experts.Neural Computation, 3(1):79–87, 1991
Reference 14
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Observation 29093b82-0391-459d-b333-68d494df9a35 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Ultralytics yolov5, 2020
Reference 15
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Observation d3b3bd9b-747b-4886-97c2-ebf3fdd5297a · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Ultralytics yolo11, 2024
Reference 16
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Unavailable: canonical work link unavailable.
Observation 6a3ced89-97b4-465f-bf83-c5bf77443bd7 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Ultra- lytics yolov8, 2023
Reference 17
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Unavailable: canonical work link unavailable.
Observation 3448fa59-41fb-4358-aae2-887e16ec27cb · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
Reference 18
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Observation b7e82894-f907-4a5e-826a-1c693d9a4767 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Gshard: Scaling giant models with conditional computation and automatic sharding
Reference 19
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Observation 3e4dd246-784d-43f5-b717-7922809edfdd · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Gshard: Scaling giant models with conditional computation and automatic sharding
Reference 20
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Unavailable: canonical work link unavailable.
Observation 0f0f7a03-bb60-4dff-9c86-68117566cb16 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Base layers: Simplifying training of large, sparse models
Reference 21
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Unavailable: canonical work link unavailable.
Observation 782a03e2-5a6e-443f-ad5c-62e8ec67895a · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Reference 22
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Observation 092071fe-aeab-45f7-83ee-a6ac3a14c476 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Network in network
Reference 23
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Observation a674b688-cece-4d4c-a4e4-1bd2ec334d06 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Lawrence Zitnick
Reference 24
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Unavailable: canonical work link unavailable.
Observation 357d888a-d356-421d-9027-72b40669a429 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Feature Pyramid Networks for Object Detection
Reference 25
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Unavailable: canonical work link unavailable.
Observation db3f5341-8a2e-4b27-b07b-4cdcd761286a · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Swin trans- former: Hierarchical vision transformer using shifted windows
Reference 26
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Unavailable: canonical work link unavailable.
Observation 5e2b2609-799b-4db4-a7dc-18d32917fce1 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Small object detection: A com- prehensive survey on challenges, techniques and real- world applications.Array, 25:100421, 2025
Reference 27
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Unavailable: canonical work link unavailable.
Observation 23aeabba-c92b-42fd-8cea-c75f7bc7bcaf · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Chained-tracker: Chaining paired attentive regression results for end- to-end joint multiple-object detection and tracking
Reference 28
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Unavailable: canonical work link unavailable.
Observation 19c0d25c-bfd3-4f25-8c3d-a220e5435a37 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection From Sparse to Soft Mixtures of Experts
Reference 29
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Unavailable: canonical work link unavailable.
Observation 92bbc505-d455-466a-be5f-775dc8fa2e09 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection YOLOv3: An Incremental Improvement
Reference 30
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Observation 36d8e07a-7e2d-4ce9-b8bc-65c617634afc · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection You Only Look Once: Unified, Real-Time Object Detection
Reference 31
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Observation 2c961f25-8489-4d86-872e-5d745078a863 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection You only look once: Unified, real-time object detection.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 779–788, 2016
Reference 32
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Observation b5e878ff-6348-4568-906a-c02457ce96b6 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Scaling vision with sparse mixture of experts.Advances in Neural Information Processing Systems, 34:8583–8595, 2021
Reference 33
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Observation 98768c89-4dae-4b8c-9f40-e8ae59d2d344 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Yolo advances to its genesis: a decadal and com- prehensive review of the you only look once (yolo) series.Artificial Intelligence Review, 2025
Reference 34
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Observation 3d729348-547f-4314-8d27-482b843b300c · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Reference 35
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Observation 1fe207dc-95d9-4b46-969c-c03595193ff8 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection YOLOv12: Attention-Centric Real-Time Object Detectors
Reference 36
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Observation dca5a0fc-254c-4039-8cb9-2f53334b7089 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Attention is all you need
Reference 37
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Observation b321c696-9d7b-47f9-be6a-f6dac530199e · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection YOLOv10: Real-Time End-to-End Object Detection
Reference 38
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Observation 72e6d02c-06d7-4276-a7ff-deb4b34083f6 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Mamba-yolo- world: marrying yolo-world with mamba for open- vocabulary detection
Reference 39
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Observation 80ab7270-c85e-4e7f-b126-261366b56987 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Residual Mixture of Experts
Reference 40
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Observation 52a39bf5-543b-4f96-80cd-f52650efbf9d · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Eca- net: Efficient channel attention for deep convolutional neural networks
Reference 41
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Observation af795e51-430e-4a45-9b83-a3fd8b24d330 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Cbam: Convolutional block attention module.Proceedings of the European Conference on Computer Vision (ECCV), pages 3–19, 2018
Reference 42
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Observation 1c11262f-5f31-424f-aae9-b53cc6719acc · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Detrs beat yolos on real-time object detection
Reference 43
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Observation 390a00bc-b6d5-4bb9-8ad5-ccccc5e066d1 · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Distance-iou loss: Faster and better learning for bounding box regression
Reference 44
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Observation 0de4c8a4-a6f6-4c2a-9c6a-4c810cef391a · outbound
YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection Mixture- of-experts with expert choice routing
Reference 45
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Unavailable: canonical work link unavailable.
Observation 9590ff11-3fb8-444f-b920-b3e4ad2b5fe2 · inbound
CollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detection YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection
Reference 26
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Unavailable: canonical work link unavailable.
Observation 5edc5ce3-5e71-4234-a053-9e6ea5e657b2 · inbound
SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Robust SAR Ship Detection YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection
Reference 19
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.