Human pose estimation in crowded scenes has always been a challenging task in bottom-up multi-person pose estimation. To improve the accuracy of pose estimation in dense crowds, we propose an improved bottom-up human pose estimation model called H-DEKR, which is based on Disentangled Keypoint Regression for Bottom-Up Human Pose Estimation (DEKR). The model first enhances the coarse/fine-grained feature extraction abilities of the backbone (HRNet) by introducing different structures of Polarized Self-attention (PSA). Then, Pyramid Convolution (PyConv) is introduced to extract multi-scale information, alleviating the problem of uneven human scales. Results show that our model based on HRNet-W32 achieves accuracy of 67.1% on the CrowdPose dataset, which is 1.4% higher than the DEKR, respectively. Therefore, the proposed model in this paper is able to improve the accuracy of human pose estimation in dense crowds.
Quantum Langevin noise makes the observation of quantum-optical parity-time (PT) symmetry in an open system with both gain and loss elusive. Here, we challenge this problem by exploiting twin beams produced from a nonlinear parametric process, one undergoing phase-sensitive linear quantum amplification (PSA) and the other engaging balanced loss merely. Unlike previous studies involving phase-insensitive linear quantum amplification (PIA), our PSA-loss scheme enables only one pair of quadratures to evolve PT-symmetrically with variances transiting from
periodic oscillations to exponential growths when crossing an exceptional point (EP), while tailors the conjugate pair with PT-adjusted quadrature squeezing. We further investigate such asymmetric PT-quadrature squeezing for quantum sensing by evaluating the quantum Cramer–Rao bound with distinct features beyond existing protocols. The proposed quadrature PT sheds new light on continuous-variable based quantum information and technology.
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