With rapid calculation speed and relatively high accuracy, the AdaBoost-based detection framework has been successfully applied in some real applications of machine vision-based intelligent systems. The main shortcoming of the AdaBoost-based detection framework is that the off-line trained detector cannot be transfer retrained to adapt to unknown application scenes. In this paper, a new transfer learning structure based on two novel methods of supplemental boosting and cascaded ConvNet is proposed to address this shortcoming. The supplemental boosting method is proposed to supplementally retrain an AdaBoost-based detector for the purpose of transferring a detector to adapt to unknown application scenes. The cascaded ConvNet is designed and attached to the end of the AdaBoost-based detector for improving the detection rate and collecting supplemental training samples. With the added supplemental training samples provided by the cascaded ConvNet, the AdaBoost-based detector can be retrained with the supplemental boosting method. The detector combined with the retrained boosted detector and cascaded ConvNet detector can achieve high accuracy and a short detection time. As a representative object detection problem in intelligent transportation systems, the traffic sign detection problem is chosen to show our method. Through experiments with the public datasets from different countries, we show that the proposed framework can quickly detect objects in unknown application scenes.

Although clustering-based unsupervised domain adaptive person re-identification has achieved promising progress, negative influence of noisy pseudo labels is still unsolved. Training model with noisy labeled data will mislead feature representation and ultimately affect the improvement of model performance. To tackle the above problem, we propose a dual pseudo label refinement framework for unsupervised domain adaptive person re-identification. It has two pseudo label refinement modules, one learns cross consistency of corresponding features between two collaborative networks and the other explores mutual consistency between global and local feature spaces. By working complementarily and jointly, the two modules optimize the quality of training datatset. Specifically, a mean teacher-student framework with multiple branches is designed to extract global and local features. Cross consistency of corresponding features between mean teacher and student networks is learned to select candidate samples for training. Meanwhile, mutual consistency of global and local features in mean teacher network is also explored to select other candidates. Finally, the intersection of two selected candidate datasets is used to re-train the framework. Being trained with selected reliable samples iteratively, the framework becomes more and more effective and robust. Extensive results on benchmark datasets confirm that our dual pseudo label refinement brings performance improvement and outperforms the state-of-the-art clustering-based unsupervised domain adaptive re-identification approaches.

In this paper, we generated polarization-independent ring-Airy beams by designing metasurfaces that can realize modulations of both phase and amplitude. In numerical simulation, such metasurfaces are designed by placing subwavelength rectangular slits in Au film uniformly. Two orthogonal types of slits, with orientation angles of 45 and −45 degrees, are used to obtain the binary phase profile in the light transmitted from the metasurface under illumination with either right circular polarization (RCP) or left circular polarization (LCP). This satisfies the phase required for Airy beam generation. Meanwhile, the difference between the phase profile under RCP illumination and that under LCP illumination is right 2π, which can be regarded as the same. This makes the metasurface available to generate Airy beams regardless of incident polarization. We also analyzed the auto-focusing, self-healing, and frequency-response properties of the generated Airy beams with different parameters. This work opens up more opportunities for applications of Airy beams.

Although person re-identification has made great progress, unsupervised cross-domain adaptive person re-identification is still a challenging problem. With no labeled data in target domain, the performance may have a significant drop. In this paper, we propose an unsupervised cross-domain adaptive person re-identification framework based on horizontal pyramid similarity learning (UHPS). Firstly, horizontal pyramid features are extracted by dividing the deep feature maps into different number of partial feature bins. These feature bins with diverse scales can incorporate not only the global information but also local information in different spatial scales, making the framework more robust in complex environment. Then, horizontal pyramid similarity learning is proposed with the mechanism of fusing together the internal similarity of the target domain and the similarity between the source domain and target domain. Finally, the unsupervised clustering algorithm DBSCAN embeded with the horizontal pyramid similarity is employed to select training data in the target domain and estimate the pseudo labels in each training iteration, for the purpose of adapting the framework to the target domain. The results on Market1501 and DukeMTMC-reID confirm that the proposed framework can adapt to the target domain effectively and outperforms the state-of-the-art unsupervised cross domain person re-identification approaches.