Image classification techniques have succeeded greatly on various large-scale visual datasets using deep convolution neural networks. However, previous models usually suffer severe performance degradation in highly skewed datasets, which restricts their practical application. In this paper, we propose a novel Hierarchical Rebalancing Dual-Classifier model for long-tailed recognition. To better ...

While self-training achieves state-of-the-art results in semi-supervised object detection (SSOD), it severely suffers from foreground-background and foreground-foreground imbalances SSOD. In this paper, we propose an Adaptive Class-Rebalancing Self-Training (ACRST) with a novel memory module called CropBank to alleviate these generate unbiased pseudo-labels. Besides, observe that both data-reba...

Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the fleet management charging scheduling of a shared mobility-on-demand system, whereby electric vehicle fleets are operated centralized platform to provide customers with service. We comprehensive review system operation based on operational objec...