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New capsule architecture enhances gaze estimation accuracy and speed

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CapStARE, a novel capsule-based architecture for gaze estimation. This system utilizes a frozen ConvNeXt backbone for efficient feature extraction and capsule formation with attention-based routing for structured facial reasoning. It employs dual GRU decoders for lightweight sequential modeling, achieving real-time inference speeds and strong performance on benchmark datasets like ETH-XGaze and MPIIFaceGaze. AI

IMPACT This new architecture offers a practical and robust framework for real-time gaze estimation, potentially improving human-computer interaction and robotics applications.

RANK_REASON The cluster contains a new academic paper detailing a novel architecture for gaze estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New capsule architecture enhances gaze estimation accuracy and speed

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The cluster contains a new academic paper detailing a novel architecture for gaze estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Miren Samaniego, Igor Rodriguez, Elena Lazkano ·

    CapStARE: Capsule-based Sequential Architecture for Robust and Efficient Gaze Estimation

    arXiv:2509.19936v2 Announce Type: replace Abstract: Human gaze estimation is essential for applications such as human-computer interaction, social robotics, and assistive systems. However, achieving accurate, interpretable, and real-time performance in unconstrained environments …

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