pith:UIB6QINC
ImageAttributionBench: How Far Are We from Generalizable Attribution?
Existing image attribution methods fail to generalize to unseen semantics and degraded images.
arxiv:2605.12967 v1 · 2026-05-13 · cs.CV
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Claims
current methods exhibit consistently poor performance, revealing significant limitations in their robustness and generalization to unseen semantic content.
That the two evaluation settings (training on balanced split then testing on degraded images, and training/testing on semantically disjoint splits) adequately simulate real-world attribution scenarios.
ImageAttributionBench is a benchmark dataset demonstrating that state-of-the-art image attribution methods lack robustness to image degradation and fail to generalize to semantically disjoint domains.
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Receipt and verification
| First computed | 2026-05-18T03:09:09.035699Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/UIB6QINCN5K65EPQMQT4R637SB \
| 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: a203e821a26f55ee91f06427c8fb7f9060b727fd5d48e359e6443e3784988e16
Canonical record JSON
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