A biologically inspired separable learning vision model for real-time traffic object perception in Dark | Paper Citation Record

Observation 7f60a154-49ff-433d-8a84-0555bf240c7c · outbound

This paper cites Instance segmentation in the dark[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Instance segmentation in the dark[J]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:02.262412Z

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Observation ce669f81-da79-40ab-9da7-8e14e858aaa4 · outbound

This paper cites A deep learning framework for neuroscience[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark A deep learning framework for neuroscience[J]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:02.108619Z

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Observation 3cd49cae-8745-4c3f-8b64-32fab89fbdc3 · outbound

This paper cites Learning task-state representations[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Learning task-state representations[J]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:01.950041Z

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Observation 041eca0c-df10-4b96-ac0d-d73af229935c · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 4

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Observation d20730af-4724-456b-814e-5e48e114095f · outbound

This paper cites Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:01.735684Z

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Observation 413ee626-e401-4d41-9c28-5eb782a8a08e · outbound

This paper cites You only look once: Unified, real -time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark You only look once: Unified, real -time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:01.567313Z

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Observation 1af79d4e-d610-4c20-bdf5-9b09cb5618d1 · outbound

This paper cites Yolact: Real -time instance segmentation[C]//Proceedings of the IEEE/CVF international conference on computer vision.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Yolact: Real -time instance segmentation[C]//Proceedings of the IEEE/CVF international conference on computer vision

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:01.373430Z

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Observation 20de58ce-a25b-49b4-8aa6-646348cb4430 · outbound

This paper cites Social learning in dogs[M]//The Social Dog.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Social learning in dogs[M]//The Social Dog

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:01.062829Z

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Observation bc2a12d1-0921-4350-9497-51ab7ef5a5c6 · outbound

This paper cites How dogs learn[M].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark How dogs learn[M]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:00.752939Z

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Observation 52e5674b-b037-4605-a0cb-d5dc47e97077 · outbound

This paper cites Getting to know low -light images with the exclusively dark dataset[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Getting to know low -light images with the exclusively dark dataset[J]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:00.430939Z

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Observation 3d592c4c-8098-429f-8a3e-dfeb41819320 · outbound

This paper cites LISU: Low -light indoor scene understanding with joint learning of reflectance restoration[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark LISU: Low -light indoor scene understanding with joint learning of reflectance restoration[J]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:46:00.149965Z

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Observation 00b18431-2de1-4e36-aec6-88b130340b8f · outbound

This paper cites Learning to see in the dark[C]//Procee dings of the IEEE conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Learning to see in the dark[C]//Procee dings of the IEEE conference on computer vision and pattern recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:59.868413Z

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Observation f3e5e07d-0d86-4204-86c5-39ebfc986e0d · outbound

This paper cites Optical flow in the dark[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Optical flow in the dark[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:59.652745Z

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Observation 44a94173-fb4a-4b70-9d1a-e298b6e901da · outbound

This paper cites Multi-scale retinex for color image enhancement[C]//Proceedings of 3rd IEEE international conference on image processing.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Multi-scale retinex for color image enhancement[C]//Proceedings of 3rd IEEE international conference on image processing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:59.340860Z

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Observation 8c496753-74dc-433a-b375-fa27ef9bbc7f · outbound

This paper cites An automated multi scale retinex with color restoration f or image enhancement[C]//2012 National Conference on Communications (NCC).

A biologically inspired separable learning vision model for real-time traffic object perception in Dark An automated multi scale retinex with color restoration f or image enhancement[C]//2012 National Conference on Communications (NCC)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:58.975005Z

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Observation f24d4699-a604-4db0-9d23-0a588fa01c63 · outbound

This paper cites Kindling the darkness: A practical low-light image enhancer[C]//Proceedings of the 27th ACM international conference on multimedia.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Kindling the darkness: A practical low-light image enhancer[C]//Proceedings of the 27th ACM international conference on multimedia

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:58.672118Z

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Observation eacd9121-5c4f-4716-8485-8155896b17d9 · outbound

This paper cites Zero -reference deep curve estimation for low -light image enhancement[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Zero -reference deep curve estimation for low -light image enhancement[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:58.394855Z

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Observation 23d62bde-b773-46db-b82b-110dbaf65c1a · outbound

This paper cites Fbnet: Hardware -aware efficient convnet design via differentiable neural architecture search[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Fbnet: Hardware -aware efficient convnet design via differentiable neural architecture search[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:58.220665Z

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Observation c583ad8b-adc7-4a3c-bd1a-160c66ea7b96 · outbound

This paper cites Rethinking the inception architect ure for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Rethinking the inception architect ure for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:57.994893Z

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Observation d3eba5e8-523a-406a-8007-93b78f7b78da · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European conference on computer vision (ECCV).

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]//Proceedings of the European conference on computer vision (ECCV)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:57.874716Z

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Observation 59156e7e-3cf3-42fa-ba42-9144936e1d7e · outbound

This paper cites Rethinking Features -Fused-Pyramid-Neck for Object Detection[C]//European Conference on Computer Vision.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Rethinking Features -Fused-Pyramid-Neck for Object Detection[C]//European Conference on Computer Vision

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:57.766943Z

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Observation 18b43dcd-c684-41c3-88cc-1eec665bde5f · outbound

This paper cites Enlightengan: Deep light enhancement without paired supervision[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Enlightengan: Deep light enhancement without paired supervision[J]

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:57.625586Z

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Observation 0899814c-7875-4765-8913-84a7e22fdc4d · outbound

This paper cites an unresolved cited work.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-05T05:45:57.386784Z

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Observation 6df4f5a1-1fd4-40b7-9bc5-6af852fe2c3d · outbound

This paper cites AI models collapse when trained on recursively generated data[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark AI models collapse when trained on recursively generated data[J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:57.250509Z

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Observation 9999bf2c-64a6-4dc1-b3a2-728fbd1aaab8 · outbound

This paper cites Adaptative machine vision with microsecond-level accurate perception beyond human retina[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Adaptative machine vision with microsecond-level accurate perception beyond human retina[J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:57.079776Z

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Observation 77b473e0-b266-4214-a730-77731f8d7839 · outbound

This paper cites How long is the coast of Britain? Statistical self -similarity and fractional dimension[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark How long is the coast of Britain? Statistical self -similarity and fractional dimension[J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:56.897816Z

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Observation bc1a9172-6bc7-45ef-90c3-23a54c67d340 · outbound

This paper cites Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:56.764945Z

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Observation ad2f37a6-4fab-43f6-beea-32ecb139abe1 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Very Deep Convolutional Networks for Large-Scale Image Recognition

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Resolution: unresolved; no resolver link, observed 2026-08-05T05:45:50.280898Z

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Observation 6b515261-d92c-41cb-a55d-ae05c2e7e186 · outbound

This paper cites CSPNet: A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark CSPNet: A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:56.589316Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c649db8f-cbe1-446e-a1fa-8de597345430 · outbound

This paper cites Object vision and spatial vision: two cortical pathways[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Object vision and spatial vision: two cortical pathways[J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:56.440063Z

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Observation 43d46c95-f912-4bd0-915f-4704b88967ea · outbound

This paper cites A dual-stream neural network explains the functional segregation of dorsal and ventral visual pathways in human brains [J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark A dual-stream neural network explains the functional segregation of dorsal and ventral visual pathways in human brains [J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:56.294886Z

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Observation 55babf5a-b040-4367-a680-5b7b8e492fcf · outbound

This paper cites Rethinking classification and localization for object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Rethinking classification and localization for object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:56.132827Z

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This paper cites YOLOX: Exceeding YOLO Series in 2021.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark YOLOX: Exceeding YOLO Series in 2021

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Resolution: unresolved; no resolver link, observed 2026-08-05T05:45:50.768178Z

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Observation 6696d590-9948-4064-84c9-c400ad4e6491 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite[C]//2012 IEEE conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Are we ready for autonomous driving? the kitti vision benchmark suite[C]//2012 IEEE conference on computer vision and pattern recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:55.975487Z

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Observation 65b42f06-822c-4006-a587-2520c8454550 · outbound

This paper cites Microsoft coco: Common objects in context[C]//Computer vision– ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Microsoft coco: Common objects in context[C]//Computer vision– ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:55.754372Z

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Observation 6832da12-ecfa-4d4c-8980-f632d4be9f0c · outbound

This paper cites an unresolved cited work.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-05T05:45:55.594193Z

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Observation 60ee9951-ef69-4483-b483-8386b7976d15 · outbound

This paper cites Slim-neck by GSConv: A lightweight-design for real-time detector architectures[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Slim-neck by GSConv: A lightweight-design for real-time detector architectures[J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:55.450896Z

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Observation e33df7c8-6594-4830-871b-53e3a45ba797 · outbound

This paper cites Computer software.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Computer software

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:55.282342Z

Source-reported events for the cited work

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Observation 0eba3f86-4775-4ec2-a111-cbf791f1b3e8 · outbound

This paper cites Yolov9: Learning what you want to learn using programmable gradient information[C]//European conference on computer vision.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Yolov9: Learning what you want to learn using programmable gradient information[C]//European conference on computer vision

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:55.161625Z

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Observation ea5fb1c6-6ed6-4a6f-b874-f20dcac27a49 · outbound

This paper cites Yolov10: Real -time end-to-end object detection[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Yolov10: Real -time end-to-end object detection[J]

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:55.029232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 71218298-3d68-4725-9036-de527d2d0dc0 · outbound

This paper cites YOLOv11: An Overview of the Key Architectural Enhancements.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark YOLOv11: An Overview of the Key Architectural Enhancements

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Resolution: unresolved; no resolver link, observed 2026-08-05T05:45:51.456152Z

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Observation b6b27f41-76a6-40d0-a3db-8a232fcaf100 · outbound

This paper cites YOLOv12: Attention-Centric Real-Time Object Detectors.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark YOLOv12: Attention-Centric Real-Time Object Detectors

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Resolution: unresolved; no resolver link, observed 2026-08-05T05:45:51.558935Z

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Observation 8123d6b9-2e88-4253-8867-f6687c3d2fd9 · outbound

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A biologically inspired separable learning vision model for real-time traffic object perception in Dark Detrs beat yolos on real -time object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:54.842606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dfd01f43-6ca5-4b9a-b730-d98f59d3b3fb · outbound

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A biologically inspired separable learning vision model for real-time traffic object perception in Dark Mask r -cnn[C]//Proceedings of the IEEE inter national conference on computer vision

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:54.712337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 47f0c4cb-a616-43f4-8576-7e468c27e4a5 · outbound

This paper cites A convnet for the 2020s[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark A convnet for the 2020s[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:54.493651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7fc09431-6d36-482f-ac25-84e6c723ea5f · outbound

This paper cites Swin transfo rmer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF international conference on computer vision.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Swin transfo rmer: Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF international conference on computer vision

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:54.320719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b37a63f2-cb42-459e-b723-f6db9b67fd86 · outbound

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A biologically inspired separable learning vision model for real-time traffic object perception in Dark Masked -attention mask transformer for universal image segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:54.192185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f0493a7d-cac1-482d-839f-2bc9045b6c02 · outbound

This paper cites Pointrend: Image segmentation as rendering[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Pointrend: Image segmentation as rendering[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:54.001822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f6a5022c-2799-4d1f-86a1-9e98439999f7 · outbound

This paper cites Restoring extremely dark images in real time[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Restoring extremely dark images in real time[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:53.848557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a01c81b-c68b-4421-92eb-84d89d7bbcdc · outbound

This paper cites Semantic instance se gmentation for autonomous driving[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Semantic instance se gmentation for autonomous driving[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:53.720453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7a2a697a-b12a-4d8a-b0b8-eb3b1248d3fd · outbound

This paper cites Gmflow: Learning optical flow via global matching[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Gmflow: Learning optical flow via global matching[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:53.536690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6cb63bc1-496c-45b5-b94f-98cd48a779df · outbound

This paper cites Vanillanet: the power of minimalism in deep learning[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Vanillanet: the power of minimalism in deep learning[J]

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:53.225624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77c2e749-0eb4-4dff-966f-23a589de09e1 · outbound

This paper cites NeuFlow v2: Push High-Efficiency Optical Flow To the Limit.

A biologically inspired separable learning vision model for real-time traffic object perception in Dark NeuFlow v2: Push High-Efficiency Optical Flow To the Limit

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T05:45:52.644051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1670a9b-5917-4210-aa5a-bad4eb837733 · outbound

This paper cites Accurate leukocyte detection based on deformable -DETR and multi - level feature fusion for aiding diagnosis of blood diseases[J].

A biologically inspired separable learning vision model for real-time traffic object perception in Dark Accurate leukocyte detection based on deformable -DETR and multi - level feature fusion for aiding diagnosis of blood diseases[J]

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:45:53.041815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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