pith:OB24PFTL
VMamba: Visual State Space Model
VMamba adapts Mamba's state-space model to vision by scanning 2D images along four fixed routes to reach linear time complexity.
arxiv:2401.10166 v4 · 2024-01-18 · cs.CV
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Claims
Extensive experiments demonstrate VMamba's promising performance across diverse visual perception tasks, highlighting its superior input scaling efficiency compared to existing benchmark models.
That scanning along exactly four fixed routes in the SS2D module collects sufficient contextual information from 2D data to match or exceed the modeling power of full 2D attention or convolution without missing important spatial relationships.
VMamba introduces a state-space vision backbone using 2D selective scanning across four routes to achieve linear complexity and strong performance on image tasks.
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| First computed | 2026-05-17T23:38:46.990659Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7075c7966b1a677bb7b288c3e98ab0c7e08b89cb4ba464e45e93771df872ee2a
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/OB24PFTLDJTXXN5SRDB6TCVQY7 \
| 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())"
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Canonical record JSON
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