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pith:EJ2FVMYC

pith:2026:EJ2FVMYCVWCH5QA3JDIXJLXIGQ

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A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction

Meng'en Qin, Quanling Zhao, Xiaodong Yang, Xiaohui Yang, Yingtao Che, Yu Song

A3-FPN augments feature pyramids with an asymptotically disentangled column network and content-aware attention to capture more discriminative multi-scale features.

arxiv:2604.10210 v1 · 2026-04-11 · cs.CV · cs.AI · cs.LG

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Claims

C1strongest claim

A3-FPN can be easily integrated into state-of-the-art CNN and Transformer-based architectures, yielding remarkable performance gains. Notably, when paired with OneFormer and Swin-L backbone, A3-FPN achieves 49.6 mask AP on MS COCO and 85.6 mIoU on Cityscapes.

C2weakest assumption

That the asymptotically disentangled column network and content-aware attention modules provide genuine improvements in discriminative feature capture rather than dataset-specific fitting or unmeasured overhead that would not generalize beyond the three evaluated benchmarks.

C3one line summary

A3-FPN augments multi-scale representations with asymptotic global interaction and content-aware resampling, delivering gains such as 49.6 mask AP on MS COCO when paired with OneFormer and Swin-L.

References

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[1] M. Chen, L. Zhang, R. Feng, X. Xue, J. Feng, Rethinking local and global feature repre- sentation for dense prediction, Pattern Recognition 135 (2023) 109168 2023

[2] G. Zhang, Z. Li, C. Tang, J. Li, X. Hu, Cednet: A cascade encoder–decoder network for dense prediction, Pattern Recognition 158 (2025) 111072 2025

[3] Y . Chen, Z. Zhang, Y . Cao, L. Wang, S. Lin, H. Hu, Reppoints v2: Verification meets regression for object detection, in: Advances in Neural Information Processing Systems, V ol. 33, 2020, pp. 5621–5 2020

[4] X. Ding, R. Zhang, Q. Liu, Y . Yang, Real-time small object detection using adaptive weighted fusion of efficient positional features, Pattern Recognition 167 (2025) 111717 2025

[5] K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask r-cnn, in: Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2961–2969 2017

Cited by

1 paper in Pith

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Receipt and verification

First computed	2026-05-20T00:01:41.330785Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

22745ab302ad847ec01b48d174aee8341ba762f53cd90138330d26423170423e

Aliases

arxiv: 2604.10210 · arxiv_version: 2604.10210v1 · doi: 10.48550/arxiv.2604.10210 · pith_short_12: EJ2FVMYCVWCH · pith_short_16: EJ2FVMYCVWCH5QA3 · pith_short_8: EJ2FVMYC

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/EJ2FVMYCVWCH5QA3JDIXJLXIGQ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 22745ab302ad847ec01b48d174aee8341ba762f53cd90138330d26423170423e

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2026-04-11T13:38:40Z",
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