Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation | Paper Citation Record

Observation 173a057b-8594-4983-986c-a3d277c84e27 · outbound

This paper cites Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg

Reference 1

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Observation a2175c4f-29a7-4e8a-8282-c15b2b7e4134 · outbound

This paper cites Gaussian kernel smoothing.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Gaussian kernel smoothing

Reference 9

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Observation 2a918c38-b296-4e5d-9aad-84aa91e2f4ca · outbound

This paper cites Instance-sensitive fully convolutional networks.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Instance-sensitive fully convolutional networks

Reference 10

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Observation 346ecdfb-e739-4898-9fd5-6d2b9e37a661 · outbound

This paper cites Bottom-up human pose estimation via disentangled keypoint reg.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Bottom-up human pose estimation via disentangled keypoint reg

Reference 13

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Observation c51b746f-ee50-468e-ae2c-da6694be69e9 · outbound

This paper cites Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model

Reference 14

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Observation 878f1e3e-1419-480c-9824-7144b949b8a6 · outbound

This paper cites Human pose estima- tion for real-world crowded scenarios.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Human pose estima- tion for real-world crowded scenarios

Reference 15

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Observation 1b0a41e6-59f1-4540-b410-0c233b637606 · outbound

This paper cites Pi-net: Pose interacting network for multi-person monocular 3d pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Pi-net: Pose interacting network for multi-person monocular 3d pose estimation

Reference 16

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Observation cb37fa23-8f0e-4fb3-a9af-3836ecaee666 · outbound

This paper cites Occluded human pose estimation based on limb joint augmentation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Occluded human pose estimation based on limb joint augmentation

Reference 17

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Observation ca67da9c-89e5-48bd-bbbc-dd907b33b9bd · outbound

This paper cites Deep residual learning for image recognition.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Deep residual learning for image recognition

Reference 18

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Observation 6bb296b1-8749-4f76-b86e-f976be702155 · outbound

This paper cites Mask r-cnn.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Mask r-cnn

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.290668Z

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Observation 4fa05ee1-3768-4af3-9175-73e986d9abdb · outbound

This paper cites A coarse-fine network for keypoint local- ization.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation A coarse-fine network for keypoint local- ization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.275619Z

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Observation a3588970-d6e7-421d-ba0a-c4e5f8914d3f · outbound

This paper cites Deepercut: A deeper, stronger, and faster multi-person pose estimation model.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Deepercut: A deeper, stronger, and faster multi-person pose estimation model

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.260146Z

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Observation 56865a86-f855-4098-b1b3-cdd104d55fce · outbound

This paper cites Posetrans: A simple yet effective pose transformation augmentation for human pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Posetrans: A simple yet effective pose transformation augmentation for human pose estimation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.245256Z

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Observation 889fee2f-0466-4633-b7f9-e38e20864356 · outbound

This paper cites Differ- entiable hierarchical graph grouping for multi-person pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Differ- entiable hierarchical graph grouping for multi-person pose estimation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.230117Z

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Observation 6ffe1572-018c-41e3-8e59-66d741c0879d · outbound

This paper cites Multi instance pose nets: Rethinking topdown pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Multi instance pose nets: Rethinking topdown pose estimation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.214936Z

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Observation f7611839-7768-46b8-b56b-35b0b9f3e74a · outbound

This paper cites Multiposenet: Fast multi-person pose estimation using pose residual network.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Multiposenet: Fast multi-person pose estimation using pose residual network

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.199639Z

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Observation e9b67c6e-621d-4b5f-8918-b3d3b103f06a · outbound

This paper cites Pifpaf: Composite fields for human pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Pifpaf: Composite fields for human pose estimation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.185110Z

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Observation 732f3d1c-b65b-4a82-8a4a-499213495c3b · outbound

This paper cites Crowdpose: Efficient crowded scenes pose estimation and a new benchmark.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Crowdpose: Efficient crowded scenes pose estimation and a new benchmark

Reference 27

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Observation 61eb6705-4f3f-4e45-8c7b-b0b485afa5e3 · outbound

This paper cites Mi- crosoft coco: Common objects in context.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Mi- crosoft coco: Common objects in context

Reference 28

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Observation dc0d9ba7-d788-4e09-8f98-ae15cf3f6bc0 · outbound

This paper cites Path aggregation network for instance seg- mentation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Path aggregation network for instance seg- mentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.127085Z

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Observation d686848b-860b-44c2-9e6a-47bb94eac532 · outbound

This paper cites Group pose: A simple baseline for end-to-end multi-person pose estima- tion.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Group pose: A simple baseline for end-to-end multi-person pose estima- tion

Reference 31

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Observation d33e7c19-d817-44e0-97b6-49a212cc30c6 · outbound

This paper cites Fcpose: Fully convolutional multi-person pose estimation with dynamic instance-aware convolu- tions.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Fcpose: Fully convolutional multi-person pose estimation with dynamic instance-aware convolu- tions

Reference 35

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Observation 6b6c5157-8d03-4463-aeae-f1fce100c375 · outbound

This paper cites Single-stage multi-person pose machines.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Single-stage multi-person pose machines

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.037370Z

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Observation 90871b7a-4a3a-4004-a842-e41810e62a17 · outbound

This paper cites Towards accurate multi- person pose estimation in the wild.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Towards accurate multi- person pose estimation in the wild

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.021885Z

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Observation c72997f3-ff14-4b72-9bd3-1b9ad94a3da3 · outbound

This paper cites Deepcut: Joint subset partition and labeling for multi person pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Deepcut: Joint subset partition and labeling for multi person pose estimation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.006656Z

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Observation 48cc5013-00f9-49fb-a73a-a7b965be7ed2 · outbound

This paper cites an unresolved cited work.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:45:35.990842Z

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Observation b8142743-8708-4f4c-a5c2-dcbf68c90cfd · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal nets.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Faster r-cnn: Towards real-time object detection with region proposal nets

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.975834Z

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Observation 5e45a4a6-1488-473a-9345-d2f74fb3ba4c · outbound

This paper cites End-to-end multi-person pose estima- tion with transformers.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation End-to-end multi-person pose estima- tion with transformers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.961040Z

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Observation f2066969-a234-4d8b-8521-4f63dc41fc57 · outbound

This paper cites Multi-person pose estimation with enhanced channel-wise and spatial information.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Multi-person pose estimation with enhanced channel-wise and spatial information

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.945619Z

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Observation b1e1b2d4-5378-402c-81c8-36ab98651ca0 · outbound

This paper cites Integral human pose regression.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Integral human pose regression

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.929521Z

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Observation b34ee7bf-59d4-42f4-b97e-e12ed24e9eef · outbound

This paper cites DirectPose: Direct End-to-End Multi-Person Pose Estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation DirectPose: Direct End-to-End Multi-Person Pose Estimation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:35.663034Z

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Observation 2c2b791b-dd74-492c-a471-757b4c854759 · outbound

This paper cites Contextual instance decoupling for robust multi-person pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Contextual instance decoupling for robust multi-person pose estimation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.913917Z

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Observation 7ac2ea5c-c827-44fe-8071-b4ede5d245a5 · outbound

This paper cites Decenternet: Bottom-up human pose estimation via decentralized pose representation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Decenternet: Bottom-up human pose estimation via decentralized pose representation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.898167Z

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Observation 6aa597ea-6c8f-4552-b26c-86a0751f1d87 · outbound

This paper cites Convolutional pose machines.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Convolutional pose machines

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.881011Z

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Observation 488abc45-0d2c-4621-ae40-4b7c902c5aed · outbound

This paper cites Simple baselines for human pose estimation and tracking.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Simple baselines for human pose estimation and tracking

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.862876Z

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Observation 96542a25-b9e4-408c-af52-bbf97fc8c212 · outbound

This paper cites Learning local-global contextual adapta- tion for multi-person pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Learning local-global contextual adapta- tion for multi-person pose estimation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.845079Z

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Observation fb431b20-ec33-4e96-aa54-bb18fd7022e8 · outbound

This paper cites Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:35.691924Z

Source-reported events for the cited work

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Observation 211fcec4-f88f-431e-80ef-edc10116e8cb · outbound

This paper cites Pose2seg: Detection free human instance segmentation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Pose2seg: Detection free human instance segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.827760Z

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Observation f3255ca0-e1f9-4cf9-a8fd-338e0ba7f720 · outbound

This paper cites Simple: Single- network with mimicking and point learning for bottom-up human pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Simple: Single- network with mimicking and point learning for bottom-up human pose estimation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.811906Z

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Observation 3ebf2363-fdd6-499a-8ea2-1f1fff07927a · outbound

This paper cites Rethinking pose es- timation in crowds: overcoming the detection information bottleneck and ambiguity.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Rethinking pose es- timation in crowds: overcoming the detection information bottleneck and ambiguity

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:35.795011Z

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Observation 4f3e0b7a-2db2-467f-8521-8a5dce432506 · outbound

This paper cites Alphapose: Whole-body regional multi-person pose estimation and tracking in real-time.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Alphapose: Whole-body regional multi-person pose estimation and tracking in real-time

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.394241Z

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Observation e531d4f7-9583-4b4b-85f9-7a57101b9a07 · outbound

This paper cites Fea- ture pyramid nets for object detection.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Fea- ture pyramid nets for object detection

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.141857Z

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Observation 6b7aef3f-9ca8-4a97-8c1b-37fe17b5a0da · outbound

This paper cites Rtmo: Towards high- performance one-stage real-time multi-person pose esti- mation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Rtmo: Towards high- performance one-stage real-time multi-person pose esti- mation

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.082761Z

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Observation 8d462cbf-58a5-471e-ad15-81dac026978e · outbound

This paper cites Human pose estimation using body parts dependent joint regressors.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Human pose estimation using body parts dependent joint regressors

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.410310Z

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Observation 9469810b-9b34-4a11-99cb-d5a36786c8b6 · outbound

This paper cites Cas- caded pyramid network for multi-person pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Cas- caded pyramid network for multi-person pose estimation

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.453058Z

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Observation d8fca2b7-eaa8-4157-b8d0-8216f4225331 · outbound

This paper cites Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.438817Z

Source-reported events for the cited work

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Observation e40c917c-8a06-4d5c-91ca-24698017cc34 · outbound

This paper cites The center of attention: Center-keypoint grouping via attention for multi-person pose estimation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation The center of attention: Center-keypoint grouping via attention for multi-person pose estimation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.496802Z

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Observation 95dd1ca1-b5bb-431a-99ce-64a5c2e6bea8 · outbound

This paper cites Realtime multi-person 2d pose estimation using part affinity fields.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Realtime multi-person 2d pose estimation using part affinity fields

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.467878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d9ae04de-cb7e-45d2-a9ba-6863183af6fd · outbound

This paper cites Learning delicate local representations for multi-person pose esti- mation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Learning delicate local representations for multi-person pose esti- mation

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.482185Z

Source-reported events for the cited work

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Observation adda0d83-d528-4edb-a82d-349b98a6ccbb · outbound

This paper cites Visualcent: Visual human analysis us- ing dynamic centroid representation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Visualcent: Visual human analysis us- ing dynamic centroid representation

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.526103Z

Source-reported events for the cited work

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Observation d05281d5-6f2c-4a7b-a385-1b9b9f930fe3 · outbound

This paper cites Fully convolutional networks for seman- tic segmentation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Fully convolutional networks for seman- tic segmentation

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.097872Z

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Observation 4db3db6c-4d17-426d-b199-d432471b1a42 · outbound

This paper cites Rethink- ing the heatmap regression for bottom-up human pose es- timation.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Rethink- ing the heatmap regression for bottom-up human pose es- timation

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.067474Z

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Observation bf0497b4-f19d-4384-89c2-4b46d43a78ba · outbound

This paper cites Yolact: Real-time instance segmenta- tion.

Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Yolact: Real-time instance segmenta- tion

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:36.511476Z

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