Pith Number

pith:LHFLFPOV

pith:2026:LHFLFPOVLOCCYTTPKQLAKQT2U2

not attested not anchored not stored refs pending

RELO: Reinforcement Learning to Localize for Visual Object Tracking

Chuanyu Sun, Dong Wang, Houwen Peng, Huchuan Lu, Jiao Xu, Kede Ma, Xin Chen

RELO learns a reinforcement learning policy to localize targets in visual tracking by maximizing IoU and AUC rewards instead of relying on handcrafted spatial priors.

arxiv:2605.07379 v2 · 2026-05-08 · cs.CV · cs.AI

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\pithnumber{LHFLFPOVLOCCYTTPKQLAKQT2U2}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

RELO replaces handcrafted spatial priors with a localization policy learned over spatial positions via reinforcement learning, with rewards combining frame-level IoU and sequence-level AUC, attaining 57.5% AUC on LaSOText without template updates.

C2weakest assumption

That a policy trained with combined IoU and AUC rewards will generalize across diverse tracking scenarios and benchmarks without requiring template updates or extensive hyperparameter tuning.

C3one line summary

RELO replaces handcrafted spatial priors with a reinforcement learning policy for target localization in visual tracking and reports 57.5% AUC on LaSOText without template updates.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T01:05:15.747775Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

59cab2bdd55b842c4e6f541605427aa681fe387743014d38214f25662c9f923c

Aliases

arxiv: 2605.07379 · arxiv_version: 2605.07379v2 · doi: 10.48550/arxiv.2605.07379 · pith_short_12: LHFLFPOVLOCC · pith_short_16: LHFLFPOVLOCCYTTP · pith_short_8: LHFLFPOV

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LHFLFPOVLOCCYTTPKQLAKQT2U2 \
  | 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: 59cab2bdd55b842c4e6f541605427aa681fe387743014d38214f25662c9f923c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e242862be18eb9f789b5eb46325ddf33737f82ed5a93c88d1653deb0033fdb0d",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-08T07:34:29Z",
    "title_canon_sha256": "36e429072431a8e3c3669e4e2dec412c54b56d855cbf16207eb3c8c59ed4207c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.07379",
    "kind": "arxiv",
    "version": 2
  }
}