We focus on the one-shot learning for video-based person re-Identification (re-ID). Unlabeled tracklets for the person re-ID tasks can be easily obtained by preprocessing, such as pedestrian detection and tracking. In this paper, we propose an approach to exploiting unlabeled tracklets by gradually but steadily improving the discriminative capability of the Convolutional Neural Network (CNN) feature representation via stepwise learning. We first initialize a CNN model using one labeled tracklet for each identity. Then we update the CNN model by the following two steps iteratively: 1. sample a few candidates with most reliable pseudo labels from unlabeled tracklets; 2. update the CNN model according to the selected data. Instead of the static sampling strategy applied in existing works, we propose a progressive sampling method to increase the number of the selected pseudo-labeled candidates step by step. We systematically investigate the way how we should select pseudo-labeled tracklets into the training set to make the best use of them. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 21.46 points (absolute, i.e., 62.67% vs. 41.21%) on the MARS dataset, and 16.53 points on the DukeMTMC-VideoReID dataset 1 .

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The radiative forcing of black carbon aerosol (BC) is one of the largest sources of uncertainty in climate change assessments. Contrasting results of BC absorption enhancement ( E) after aging are estimated by field measurements and modeling studies, causing ambiguous parametrizations of BC solar absorption in climate models. Here we quantify E using a theoretical model parametrized by the complex particle morphology of BC in different aging scales. We show that E continuously increases with aging and stabilizes with a maximum of ∼3.5, suggesting that previous seemingly contrast results of E can be explicitly described by BC aging with corresponding particle morphology. We also report that current climate models using Mie Core-Shell model may overestimate E at a certain aging stage with a rapid rise of E, which is commonly observed in the ambient. A correction coefficient for this overestimation is suggested to improve model predictions of BC climate impact.