Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as the region classifier to supervise the training of a detector. The key element that leads to the success of this model is the proper prompt, which requires careful words tuning and ingenious design. To avoid laborious prompt engineering, there are some prompt representation learning methods being proposed for the image classification task, which however can only be sub-optimal solutions when applied to the detection task. In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model. Different from the previous classification-oriented methods, DetPro has two highlights: 1) a background interpretation scheme to include the proposals in image background into the prompt training; 2) a context grading scheme to separate proposals in image foreground for tailored prompt training. We assemble DetPro with ViLD, a recent state-of-the-art openworld object detector, and conduct experiments on the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 datasets. Experimental results show that our DetPro outperforms the baseline ViLD [7] in all settings, e.g., +3.4 APboxand +3.0 APmaskimprovements on the novel classes of LVIS. Code and models are available at https://github.com/dyabel/detpro.

The Internet of Things (IoT) technology has recently drawn more attention due to its ability to achieve the interconnections of massive physic devices. However, how to provide a reliable power supply to energy-constrained devices and improve the energy efficiency in the wireless powered IoT (WP-IoT) is a twofold challenge. In this paper, we develop a novel wireless power transmission (WPT) system, where an unmanned aerial vehicle (UAV) equipped with radio frequency energy transmitter charges the IoT devices. A machine learning framework of echo state networks together with an improved k -means clustering algorithm is used to predict the energy consumption and cluster all the sensor nodes at the next period, thus automatically determining the charging strategy. The energy obtained from the UAV by WPT supports the IoT devices to communicate with each other. In order to improve the energy efficiency of the WP-IoT system, the interference mitigation problem is modeled as a mean field game, where an optimal power control policy is presented to adapt and analyze the large number of sensor nodes randomly deployed in WP-IoT. The numerical results verify that our proposed dynamic charging policy effectively reduces the data packet loss rate, and that the optimal power control policy greatly mitigates the interference, and improve the energy efficiency of the whole network.

In emergency situations such as earthquakes, the cellular infrastructure cannot support communication services because of equipment damage. The use of the large number of unmanned aerial vehicles (UAVs) has been drawn significant attentions as an important solution for providing air-to-ground communication services in such situations. In this paper, we research the flight direction policy (velocity vector) of the UAVs where every UAV acts as the base station to serve the multi-users communications. As the trajectory of UAVs have a huge impact on the performance of communication, we investigate an adaptive coverage problem, that all the UAVs can adjust their velocities to increase the number of served users. However, such behavior may cause larger flight energy consumption. We propose a discrete-time mean-field game (MFG) framework that each UAV adjusts its velocity in order to minimize the flight energy consumption. In this framework, each UAV evolves according to the dynamic equation and seeks to minimize its flight energy consumption containing the average distribution of all UAVs. We investigate a deterministic function φ to approximate the average distribution of all UAVs as the number of UAVs tends to infinity. Furthermore, the optimal velocity vectors generate a certain asymptotic Nash equilibrium as time tends to infinity, which implies that the flight energy consumption of each UAV can reach its minimal value as the number of UAVs increases to infinity. The simulation results show the optimal trajectory and optimal flight tendency of the UAVs. Moreover, we show that as users move, the amount of the users served is maintained at a relatively stable range, which represents met the demand of user's adaptive coverage.