In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. (ii) The negative impact of noise pseudo labels on training. Moreover, the labels used for 2D human pose estimation are relatively complex: keypoint category and keypoint position. To solve the problems mentioned above, we propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM). We introduce an additional auxiliary teacher and use the pseudo labels generated by the two teacher model in different periods to calculate the inconsistency score and remove outliers. Then, the two teacher models are updated through interactive training, and the student model is updated using the pseudo labels generated by two teachers. To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples. In addition, we also proposed a new indoor overhead fisheye human keypoint dataset WEPDTOF-Pose. Extensive experiments demonstrate that our method outperforms the previous best semi-supervised 2D human pose estimation method. We will release the code and dataset at https://github.com/hlz0606/SSPCM

The combined use of the multicarrier (MC) technique with the vertical Bell Labs layered space-time (V-BLAST) architecture, i.e. the MC V-BLAST system is well suited to the frequency-selective fading environment. A modified rotation invariance coding scheme and corresponding decoding algorithm without knowing channel state information (CSI) are proposed under this environment. Utilizing the time redundancy, this method has the advantage that the number of receive antennas is less than that of the transmit ones. Simulation results demonstrate the validity of the coding scheme and the performance of the direct decoding algorithm.