To improve the performance of video compression for machine vision analysis tasks, a video coding for machines (VCM) standard working group was established to promote standardization procedures. In this paper, recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases, requirements, evaluation frameworks and corresponding metrics of the VCM standard are given. Then the existing methods are presented, introducing the existing proposals by category and the research progress of the latest VCM conference. Finally, we give conclusions.

Federated learning (FL) is a distributed machine learning approach that could provide secure 6G communications to preserve user privacy. In 6G communications, unmanned aerial vehicles (UAVs) are widely used as FL parameter servers to collect and broadcast related parameters due to the advantages of easy deployment and high flexibility. However, the challenge of limited energy restricts the popularization of UAV-enabled FL applications. An air-ground integrated low-energy federated learning framework is proposed, which minimizes the overall energy consumption of application communication while maintaining the quality of the FL model. Specifically, a hierarchical FL framework is proposed, where base stations (BSs) aggregate model parameters updated from their surrounding users separately and send the aggregated model parameters to the server, thereby reducing the energy consumption of communication. In addition, we optimize the deployment of UAVs through a deep Q-network approach to minimize their energy consumption for transmission as well as movement, thus improving the energy efficiency of the air-ground integrated system. The evaluation results show that our proposed method can reduce the system energy consumption while maintaining the accuracy of the FL model.

Transfer learning aims to transfer source models to a target domain. Leveraging the feature matching can alleviate the domain shift effectively, but this process ignores the relationship of the marginal distribution matching and the conditional distribution matching. Simultaneously, the discriminative information of both domains is also neglected, which is important for improving the performance on the target domain. In this paper, we propose a novel method called Balanced Discriminative Transfer Feature Learning for Visual Domain Adaptation (BDTFL). The proposed method can adaptively balance the relationship of both distribution matchings and capture the category discriminative information of both domains. Therefore, balanced feature matching can achieve more accurate feature matching and adaptively adjust itself to different scenes. At the same time, discriminative information is exploited to alleviate category confusion during feature matching. And with assistance of the category discriminative information captured from both domains, the source classifier can be transferred to the target domain more accurately and boost the performance of target classification. Extensive experiments show the superiority of BDTFL on popular visual cross-domain benchmarks.