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Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on both single-and cross-modality Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

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Background: N-Acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) is a short peptide with an anti-silicosis effect. However, the short biological half-life and low plasma concentration of Ac-SDKP hamper discovery of specific targets in organisms and reduce the anti-silicosis effect. A novel peptide, Ac-SDK (biotin) proline, termed "Ac-B", with anti-fibrotic properties was synthesized. Methods: Ac-B was detected quantitatively by high-performance liquid chromatography. Phagocytosis of Ac-B by the alveolar epithelial cell line A549 was investigated by confocal laser scanning microscopy and flow cytometry. To further elucidate the cellular-uptake mechanism of Ac-B, chemical inhibitors of specific uptake pathways were used. After stimulation with transforming growth factor-β1, the effects of Ac-B on expression of the myofibroblast marker vimentin and accumulation of collagen type I in A549 cells were analyzed by Western blotting. Sirius Red staining and immunohistochemical analyses of the effect of Ac-B on expression of α-smooth muscle actin (SMA) in a rat model of silicosis were undertaken. Results: Ac-B had good traceability during the uptake, entry, and distribution in cells. Ac-B treatment prevented an increase in α-SMA expression in vivo and in vitro and was superior to that of Ac-SDKP. Caveolae-mediated uptake of Ac-B by A549 cells led to achieving antiepithelial-mesenchymal transformation (EMT) effects. Conclusion: Ac-B had an anti-fibrotic effect and could be a promising agent for the fibrosis observed in silicosis in the future.

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