MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.

The optimal modeling method of specific tuberculosis peritonitis (experimental research)

The objective: to create a reproducible model of chronic tuberculosis peritonitis to study pathophysiological mechanisms of its progression and to develop pathogenetically based therapy.Subjects and Methods. The study was performed using 10 male rabbits of the Chinchilla breed. The animals were administered intraperitoneal culture of Mycobacterium tuberculosis, tuberculosis peritonitis modeling was performed according to the proposed method.Results. In the course of the experiment, it was proved that all animals developed tuberculous peritonitis with lesions of the large omentum and serous integuments of internal organs. Molecular genetic tests of fragments of the omentum and peritoneum detected DNA of Mycobacterium tuberculosis.