Pith Number

pith:IPIIJVW4

pith:2026:IPIIJVW4CHHTS3GVX3X7QPE42U

not attested not anchored not stored refs resolved

Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection

Abdul Basit, Muhammad Shafique, Saim Rehman

Runtime controller switches among tiny-YOLO variants to cut on-device gesture energy by 4x while holding F1 at 0.8-0.9.

arxiv:2605.12506 v1 · 2026-03-16 · cs.CV · cs.AI · cs.HC · cs.RO · eess.IV

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\usepackage{pith}
\pithnumber{IPIIJVW4CHHTS3GVX3X7QPE42U}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

On a battery-powered laptop running gesture streams, our ACE controller reduces per-frame energy by 4x (from 6.9 mJ to 1.6 mJ) while maintaining high gesture-detection performance (event-level F1 = 0.8-0.9) and low mean latency (6 ms).

C2weakest assumption

That the device-calibrated ACE profiles and the motion-aware ROI gate will transfer to other hardware platforms and real-world lighting/pose variations without re-calibration or loss of the reported energy savings.

C3one line summary

Scale-Gest creates a runtime-selectable family of tiny-YOLO models with device-calibrated ACE profiles and an ROI gate that cuts per-frame energy by 4x while holding event-level F1 at 0.8-0.9 on a new driving-gesture dataset.

References

29 extracted · 29 resolved · 4 Pith anchors

[1] Applied Sciences 13, 20 (2023) 2023

[2] M., Pasricha, S., Maciejewski, A 2012

[3] In2024 IEEE/CVF Winter Conference on Applications of Computer Vision (W ACV)(Jan 2024

[4] Angell, L., Seaman, S., Payyanadan, R., Biever, W., Seppelt, B., Mehler, B., and Reimer, B.In the context of whole trips: New insights into driver management of attention and tasks. pp. 1–7

[5] YOLOv4: Optimal Speed and Accuracy of Object Detection 2004 · arXiv:2004.10934

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T03:10:03.114837Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

43d084d6dc11cf396cd5beeff83c9cd5245ed9e75224fbd400792ac6fe38d2b0

Aliases

arxiv: 2605.12506 · arxiv_version: 2605.12506v1 · doi: 10.48550/arxiv.2605.12506 · pith_short_12: IPIIJVW4CHHT · pith_short_16: IPIIJVW4CHHTS3GV · pith_short_8: IPIIJVW4

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IPIIJVW4CHHTS3GVX3X7QPE42U \
  | 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: 43d084d6dc11cf396cd5beeff83c9cd5245ed9e75224fbd400792ac6fe38d2b0

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2303a1424c86092e0ebda1ff26c74b26f43cf67f6f69042c8f7e4f0fca80fbf6",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.HC",
      "cs.RO",
      "eess.IV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-03-16T10:12:26Z",
    "title_canon_sha256": "af30fb908c2b8ac55cd3a25b81947433a3837b99ff08bdfb9cc7b7d71ba6a6ad"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12506",
    "kind": "arxiv",
    "version": 1
  }
}