Papers › AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation

AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation

11 May 2022arXiv:2205.05277archive 2025-07-28

Xu Cao, Xiaoye Li, Liya Ma, Yi Huang, Xuan Feng, Zening Chen, Hongwu Zeng, Jianguo Cao

Movement and pose assessment of newborns lets experienced pediatricians predict neurodevelopmental disorders, allowing early intervention for related diseases. However, most of the newest AI approaches for human pose estimation methods focus on adults, lacking publicly benchmark for infant pose estimation. In this paper, we fill this gap by proposing infant pose dataset and Deep Aggregation Vision Transformer for human pose estimation, which introduces a fast trained full transformer framework without using convolution operations to extract features in the early stages. It generalizes Transformer + MLP to high-resolution deep layer aggregation within feature maps, thus enabling information fusion between different vision levels. We pre-train AggPose on COCO pose dataset and apply it on our newly released large-scale infant pose estimation dataset. The results show that AggPose could effectively learn the multi-scale features among different resolutions and significantly improve the performance of infant pose estimation. We show that AggPose outperforms hybrid model HRFormer and TokenPose in the infant pose estimation dataset. Moreover, our AggPose outperforms HRFormer by 0.8 AP on COCO val pose estimation on average. Our code is available at github.com/SZAR-LAB/AggPose.

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Attention SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository ran · metamorphic tier: deterministic AGPL-3.0 (copyleft) · pointer only · 58193b289e41db55 · report
DWConv SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository ran · metamorphic tier: deterministic AGPL-3.0 (copyleft) · pointer only · e68b276a44902fe9 · report
Mlp SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository ran · metamorphic tier: deterministic AGPL-3.0 (copyleft) · pointer only · 150e9a8b4f87d226 · report
MlpUpsample SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository ran · metamorphic tier: deterministic AGPL-3.0 (copyleft) · pointer only · 48c4de8068ed4f7a · report
OverlapPatchEmbed SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository ran · metamorphic tier: deterministic fingerprinted AGPL-3.0 (copyleft) · pointer only · c319db933db9b552 · report
AggPose SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository unverified AGPL-3.0 (copyleft) · pointer only · 599ead8c797aad19 · report
Block SZAR-LAB/AggPose/lib/models/pose_aggpose.py official repository unverified AGPL-3.0 (copyleft) · pointer only · f5274e90d3e2d72b · report

Tasks

Keypoint DetectionPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Keypoint Detection COCO (Common Objects in Context) AggPose(256x192) Test AP 75.7 #7 of 24 Archive leaderboard report
Keypoint Detection COCO (Common Objects in Context) AggPose(256x192) Validation AP 76.4 #7 of 24 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEConvolutionDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformerVision Transformer

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