GazeMoE: Perception of Gaze Target with Mixture-of-Experts

GazeMoE: Perception of Gaze Target with Mixture-of-Experts | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/gazemoe-perception-of-gaze-target-with-mixture-of-experts

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 gazemoe-perception-of-gaze-target-with-mixture-of-experts | Route /signal-canvas/gazemoe-perception-of-gaze-target-with-mixture-of-experts

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/gazemoe-perception-of-gaze-target-with-mixture-of-experts

MCP example

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source_context

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  "query": "GazeMoE: Perception of Gaze Target with Mixture-of-Experts",
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  "paper_ref": "gazemoe-perception-of-gaze-target-with-mixture-of-experts",
  "topic_slug": null,
  "benchmark_ref": null,
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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: GazeMoE: Perception of Gaze Target with Mixture-of-Experts

PDF: https://arxiv.org/pdf/2603.06256v1

Source count: Pending verification

Coverage: 17%

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

Signal Canvas receipt window

Watch and verify: GazeMoE: Perception of Gaze Target with Mixture-of-Experts

/buildability/gazemoe-perception-of-gaze-target-with-mixture-of-experts

Watchwatch

Subject: GazeMoE: Perception of Gaze Target with Mixture-of-Experts

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we propose GazeMoE, a novel end-to-end framework that selectively leverages gaze-target-related cues from a frozen foundation model through MoE modules.
Implicationpartial
This is a core statement of the proposed method, directly from the abstract.
Verificationpartial
partial
Evidencepartial
GazeMoE incorporates a class-balancing auxiliary loss alongside strategic data augmentations, including region-specific cropping and photometric transformations.
Implicationpartial
This describes specific techniques used in the proposed method, directly from the abstract.
Verificationpartial
partial
Evidencepartial
Extensive experiments on benchmark datasets demonstrate that our GazeMoE achieves state-of-the-art performance, outperforming existing methods on challenging gaze estimation tasks.
Implicationpartial
The abstract explicitly states this achievement, supported by experimental results.
Verificationpartial
partial
Evidencepartial
The model requires fine-tuning and may not be as effective with low-quality input data.
Implicationpartial
This is mentioned as a caveat in the analysis, implying a necessary step for effective use.
Verificationpartial
partial
Evidencepartial
The model requires fine-tuning and may not be as effective with low-quality input data.
Implicationpartial
This is explicitly stated as a caveat in the analysis.
Verificationpartial
partial
Evidencepartial
The paper proposes GazeMoE, a model using Mixture-of-Experts layers to dynamically route and analyze visual cues such as eye landmarks, head poses, gestures and scene context to accurately estimate gaze direction from images, using DINOv2 as a frozen foundation model for feature extraction.
Implicationpartial
The analysis section clearly states the foundation model used.
Verificationpartial
partial
Evidencepartial
The market is substantial, involving sectors like robotics, automotive (for driver monitoring), retail (consumer analytics), and healthcare (autism research), where accurate gaze tracking is crucial.
Implicationpartial
The 'product_opportunity' section details the market size and relevant sectors.
Verificationpartial
partial
Evidencepartial
The model was tested on several benchmark datasets, showing superior performance in terms of prediction accuracy and robustness in diverse and out-of-distribution visual environments compared to existing methods.
Implicationpartial
The 'method_eval' section provides a summary of the experimental findings.
Verificationpartial
partial

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GazeMoE: Perception of Gaze Target with Mixture-of-Experts

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