R&D in China and the implications for industrial restructuring

The nation-wide introduction of foreign technology in China has been going on for over 20 years. This paper examines the R&D incentive of the Chinese innovators by analyzing the patent data for the period from 1985 to 1999. The following findings were obtained. First, individual innovators, as opposed to industrial enterprises and research institutes, have been supplying over 70% of all patent applications filed domestically. Second, innovators in China, including the industrial enterprises, have been devoting their R&D resources disproportionately to small innovations, rather than major ones. Third, the large and medium-sized enterprises are not yet the main force for innovation in China. The impacts of industrial structure on R&D incentive are emphasized. Regression analysis for 37 manufacturing industries in China shows that R&D output, measured by the number of patents per firm, is positively related to the eight-firm concentration ratio. I also analyze the microeconomic channels through which the vertical structure of an industry affects firm incentive to absorb imported technologies. “Excessive competition” and a low degree of vertical integration in Chinese industries are major factors leading to small-scale innovation, high propensity to purchase foreign technologies, and low propensity to absorb them. Establishing enterprise groups that are truly subject to market discipline can speed up the “imitation-first-and-then-innovate” process

Clothing Co-Parsing by Joint Image Segmentation and Labeling

This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (E-SVM) technique [23]. In the second phase (i.e. "region co-labeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.Comment: 8 pages, 5 figures, CVPR 201