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Growing Concerns Over AI Code Quality and Security

The feed has recently focused heavily on the advancements of AI agents and the new challenges they introduce to software development. There is a noticeable technical effort to ensure output reliability, seen in tools like 'depgraph' for detecting latent code debt from AI-generated code or the use of adaptive ensemble methods (COVE) for pre-validating LLM outputs. Furthermore, alongside predictions that AI agents will evolve to autonomously manage aspects of human life, there is a concurrent warning about escalating security risks and infrastructure vulnerabilities, indicating significant uncertainty.

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As of Nov 4, 2026, 11:36 PM UTC

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arXivSeptember 23, 2026

Estimating Accurate Hand Pose in Camera Space with Vision Transformer

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본 논문은 단안 RGB 기반 카메라 공간 손 자세 추정의 두 가지 근본적인 문제(깊이 모호성 및 국소/전역 자세 결합 효과)를 해결하는 새로운 프레임워크를 제안합니다. 변환 동형성 감독과 원근 정보 임베딩을 통합하여 정확도를 높였으며, CS-MJE에서 SOTA 대비 37.1%의 성능 향상을 달성했습니다.

hand pose estimation
vision transformer
depth ambiguity
single camera
computer vision

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Growing Concerns Over AI Code Quality and Security

Estimating Accurate Hand Pose in Camera Space with Vision Transformer

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