SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition

SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/scale-semantic-and-confidence-aware-conditional-variational-autoencoder-for-zero-shot-skeleton-based-action-recognition

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Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition

PDF: https://arxiv.org/pdf/2604.02222v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.241Z

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Watch and verify: SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition

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Watchwatch

Subject: SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition

Verdict

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Experiments on NTU-60 and NTU-120 datasets show that SCALE consistently improves over prior VAE- and alignment-based baselines
Implicationpartial
Directly stated in abstract with clear comparative language and reference to experimental results
Verificationpartial
partial
Evidencepartial
while remaining competitive with diffusion-based methods
Implicationpartial
Directly stated in abstract with comparative language, though 'competitive' is somewhat subjective
Verificationpartial
partial
Evidencepartial
Existing approaches frequently depend on explicit skeleton-text alignment, which can be brittle when action names underspecify fine-grained dynamics
Implicationpartial
Directly stated in abstract as a limitation of existing methods
Verificationpartial
partial
Evidencepartial
and when unseen classes are semantically confusable
Implicationpartial
Directly stated in abstract as a limitation of existing methods
Verificationpartial
partial
Evidencepartial
SCALE, a lightweight and deterministic Semantic- and Confidence-Aware Listwise Energy-based framework that formulates ZSAR as class-conditional energy ranking
Implicationpartial
Directly and explicitly stated in abstract with specific technical terminology
Verificationpartial
partial
Evidencepartial
SCALE builds a text-conditioned Conditional Variational Autoencoder where frozen text representations parameterize both the latent prior and the decoder
Implicationpartial
Directly and explicitly stated in abstract with specific technical details
Verificationpartial
partial
Evidencepartial
enabling likelihood-based evaluation for unseen classes without generating samples at test time
Implicationpartial
Directly stated in abstract as a capability of the method
Verificationpartial
partial
Evidencepartial
we introduce a semantic- and confidence-aware listwise energy loss that emphasizes semantically similar hard negatives
Implicationpartial
Directly and explicitly stated in abstract with specific technical details
Verificationpartial
partial

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SCALE: Semantic- and Confidence-Aware Conditional Variational Autoencoder for Zero-shot Skeleton-based Action Recognition

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