To maintain sustainable economic growth, China has created a national innovation system (NIS) and strengthened the central status of firms. Our data show that the effect of turnover growth in small and medium-sized enterprises (SMEs) on China’s aggregate Gross Domestic Product (GDP)growth is significant, but the status of SMEs in the NIS and related policies is not significant. To determine whether there is a correspondence between the sustainability of innovation in SMEs and the support of China’s NIS, we developed a framework for China’s innovation policy under the NIS framework, taking into account its transition characteristics, to examine the texts of SME innovation policies and reveal the sustainability of SMEs’ innovation. The relevant national government policy texts were collected from the yearbooks of Chinese SMEs between 1999 and 2017 and government notices between 1994 and 2017. On this basis, we also compared with some other countries’ innovation systems. The findings indicate that China’s NIS pays little attention to the sustainability of SMEs’ innovation activities for two reasons. First, the scope of the NIS is very narrowly defined. Second, the top-down, government-oriented Research and Development (R&D) system that focuses on large state-owned firms leaves little room for innovation policies in SMEs.

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A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the capability to characterize the prediction error, generate feedback information from the test sample, and correct the prediction error on the fly for each individual test sample, which results in degraded performance in generalization. In this work, we introduce a self-correctable and adaptable inference (SCAI) method to address the generalization challenge of network prediction and use human pose estimation as an example to demonstrate its effectiveness and performance. We learn a correction network to correct the prediction result conditioned by a fitness feedback error. This feedback error is generated by a learned fitness feedback network which maps the prediction result to the original input domain and compares it against the original input. Interestingly, we find that this self-referential feedback error is highly correlated with the actual prediction error. This strong correlation suggests that we can use this error as feedback to guide the correction process. It can be also used as a loss function to quickly adapt and optimize the correction network during the inference process. Our extensive experimental results on human pose estimation demonstrate that the proposed SCAI method is able to significantly improve the generalization capability and performance of human pose estimation.

We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This group-wise structural correlation can be explored to improve the accuracy and robustness of human pose estimation. In this work, we develop a self-constrained prediction-verification network to characterize and learn the structural correlation between keypoints during training. During the inference stage, the feedback information from the verification network allows us to perform further optimization of pose prediction, which significantly improves the performance of human pose estimation. Specifically, we partition the keypoints into groups according to the biological structure of human body. Within each group, the keypoints are further partitioned into two subsets, high-confidence base keypoints and low-confidence terminal keypoints. We develop a self-constrained prediction-verification network to perform forward and backward predictions between these keypoint subsets. One fundamental challenge in pose estimation, as well as in generic prediction tasks, is that there is no mechanism for us to verify if the obtained pose estimation or prediction results are accurate or not, since the ground truth is not available. Once successfully learned, the verification network serves as an accuracy verification module for the forward pose prediction. During the inference stage, it can be used to guide the local optimization of the pose estimation results of lowconfidence keypoints with the self-constrained loss on high-confidence keypoints as the objective function. Our extensive experimental results on benchmark MS COCO and CrowdPose datasets demonstrate that the proposed method can significantly improve the pose estimation results.