Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/shape-of-you-fused-gromov-wasserstein-optimal-transport-for-semantic-correspondence-in-the-wild

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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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Canonical ID shape-of-you-fused-gromov-wasserstein-optimal-transport-for-semantic-correspondence-in-the-wild | Route /signal-canvas/shape-of-you-fused-gromov-wasserstein-optimal-transport-for-semantic-correspondence-in-the-wild

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/shape-of-you-fused-gromov-wasserstein-optimal-transport-for-semantic-correspondence-in-the-wild

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: Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

PDF: https://arxiv.org/pdf/2603.11618v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Watch and verify: Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

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Watchwatch

Subject: Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

Verdict

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
In this work, we address this by reformulating pseudo-label generation as a Fused Gromov-Wasserstein (FGW) problem
Implicationpartial
Directly and explicitly stated in the abstract as the core methodological contribution.
Verificationpartial
partial
Evidencepartial
which jointly optimizes inter-feature similarity and intra-structural consistency.
Implicationpartial
Directly stated in the abstract, clearly defining the two key terms optimized by the proposed method.
Verificationpartial
partial
Evidencepartial
Our framework, Shape-of-You (SoY), leverages a 3D foundation model to define this intra-structure in the geometric space, resolving abovementioned ambiguity.
Implicationpartial
Directly stated in the abstract as a key component of the method and its purpose.
Verificationpartial
partial
Evidencepartial
However, since FGW is a computationally prohibitive quadratic problem, we approximate it through anchor-based linearization.
Implicationpartial
Directly stated in the abstract as a challenge and the specific solution implemented.
Verificationpartial
partial
Evidencepartial
Thus, we introduce a soft-target loss dynamically blending guidance from this plan with network predictions to build a learning framework robust to this noise.
Implicationpartial
Directly stated in the abstract as a specific technical contribution to address a stated problem.
Verificationpartial
partial
Evidencepartial
SoY achieves state-of-the-art performance on SPair-71k and AP-10k datasets
Implicationpartial
Explicitly and directly stated as a key result in the abstract.
Verificationpartial
partial
Evidencepartial
establishing a new benchmark in semantic correspondence without explicit geometric annotations.
Implicationpartial
Directly stated in the abstract as a summary of the achievement.
Verificationpartial
partial
Evidencepartial
it operates locally, ignoring structural relationships, and consequently its reliance on 2D appearance fails to resolve geometric ambiguities arising from symmetries or repetitive features.
Implicationpartial
Directly stated in the abstract as the problem motivation, though framed as a limitation of prior work.
Verificationpartial
partial

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Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

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