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pith:LBBHR5BX

pith:2026:LBBHR5BXK4S6BCMEDLF2DCSCWS
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DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition

Can Wang, Jiaying Lin, Jinfu Liu, Mengyuan Liu, Shiman Wu

DAP-Net aligns mmWave radar point clouds across devices by treating Doppler patterns as invariant anchors and adding text-based semantic guidance.

arxiv:2605.09604 v2 · 2026-05-10 · cs.CV

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3 Author claim open · sign in to claim
4 Citations open
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Claims

C1strongest claim

DAP-Net significantly outperforms existing methods under heterogeneous radar settings, achieving state-of-the-art accuracy and strong cross-source robustness.

C2weakest assumption

That action-consistent spatio-temporal Doppler patterns provide reliable anchors for cross-source alignment and that the pretrained textual space supplies stable semantic guidance without introducing domain mismatch.

C3one line summary

Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net architecture with Doppler reparameterization and text alignment for cross-source robustness.

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Receipt and verification
First computed 2026-07-03T01:17:21.292735Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

584278f4375725e089841acba18a42b4a263eaa022a8c556db35275c346149f2

Aliases

arxiv: 2605.09604 · arxiv_version: 2605.09604v2 · doi: 10.48550/arxiv.2605.09604 · pith_short_12: LBBHR5BXK4S6 · pith_short_16: LBBHR5BXK4S6BCME · pith_short_8: LBBHR5BX
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LBBHR5BXK4S6BCMEDLF2DCSCWS \
  | 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: 584278f4375725e089841acba18a42b4a263eaa022a8c556db35275c346149f2
Canonical record JSON
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-10T15:34:53Z",
    "title_canon_sha256": "840a32f7d5846098e02becd489b0b84ed869c91148af9258a11d436144005033"
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