Exploring hard joints mining via hourglass-based generative adversarial network for human pose estimation

Human pose estimation has broad application prospects in the fields of human behavior recognition and human-computer interaction. Although the current human pose estimation methods have made tremendous progress, the partial occlusion of human bodies still remains a challenging problem. In this paper, we address the challenging joints in human bodies by the hard joints mining technique. The proposed hard joints mining method is based on the generative adversarial network, which consists of two stacked hourglasses with a similar architecture: the generator and the discriminator. During the training period, the discriminator distinguishes the generated heatmaps from the ground-truth heatmaps and introduces the adversarial loss to the generator through back-propagation to induce generator generates a more reasonable prediction. Moreover, the hard joints mining technique is used to focus the training attention on the difficult joint points in the generator. Finally, the experimental results demonstrate the effectiveness of the proposed approach for human pose estimation on Leeds Sports Pose (LSP) Dataset, LSP-extended datasets and MPII Human Pose Datasets

Pose-Guided Inflated 3D ConvNet for action recognition in videos

International audienceHuman action recognition in videos is still an important while challenging task. Existing methods based on RGB image or optical flow are easily affected by clutters and ambiguous backgrounds. In this paper, we propose a novel Pose-Guided Inflated 3D ConvNet framework (PI3D) to address this issue. First, we design a spatial–temporal pose module, which provides essential clues for the Inflated 3D ConvNet (I3D). The pose module consists of pose estimation and pose-based action recognition. Second, for multi-person estimation task, the introduced pose estimation network can determine the action most relevant to the action category. Third, we propose a hierarchical pose-based network to learn the spatial–temporal features of human pose. Moreover, the pose-based network and I3D network are fused at the last convolutional layer without loss of performance. Finally, the experimental results on four data sets (HMDB-51, SYSU 3D, JHMDB and Sub-JHMDB) demonstrate that the proposed PI3D framework outperforms the existing methods on human action recognition. This work also shows that posture cues significantly improve the performance of I3D

Effect of Tongxinluo on Podocyte Apoptosis via Inhibition of Oxidative Stress and P38 Pathway in Diabetic Rats

Diabetic nephropathy (DN) has been the leading cause of end-stage renal disease (ESRD). Podocyte apoptosis is a main mechanism of progression of DN. It has been demonstrated that activated P38 and caspase-3 induced by oxidative stress mainly account for increased podocyte apoptosis and proteinuria in DN. Meanwhile, Tongxinluo (TXL) can ameliorate renal structure disruption and dysfunction in DN patients in our clinical practice. However, the effect of TXL on podocyte apoptosis and P38 pathway remains unclear. To explore the effect of TXL on podocyte apoptosis and its molecular mechanism in DN, our in vivo and in vitro studies were performed. TXL attenuated oxidative stress in podocyte in DN in our in vivo and in vitro studies. Moreover, TXL inhibited the activation of P38 and caspase-3. Bcl-2 and Bax expression was partially restored by TXL treatment in our in vivo and in vitro studies. More importantly, TXL decreased podocyte apoptosis in diabetic rats and high glucose cultured podocyte. In conclusion, TXL protects podocyte from apoptosis in DN, partially through its antioxidant effect and inhibiting of the activation of P38 and caspase-3