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System level power management must consider the uncertainty and variability that come from the environment, the application and the hardware. A robust power management technique must be able to learn the optimal decision from past events and improve itself as the environment changes. This article presents a novel on-line power management technique based on model-free constrained reinforcement learning (Q-learning). The proposed learning algorithm requires no prior information of the workload and dynamically adapts to the environment to achieve autonomous power management. We focus on the power management of the peripheral device and the microprocessor, two of the basic components of a computer. Due to their different operating behaviors and performance considerations, these two types of devices require different designs of Q-learning agent. The article discusses system modeling and cost function construction for both types of Q-learning agent. Enhancement techniques are also proposed to speed up the convergence and better maintain the required performance (or power) constraint in a dynamic system with large variations. Compared with the existing machine learning based power management techniques, the Q-learning based power management is more flexible in adapting to different workload and hardware and provides a wider range of power-performance tradeoff.

Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks.

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Face parsing has recently attracted increasing interest due to its numerous application potentials, such as facial make up and facial image generation. In this paper, we make contributions on face parsing task from two aspects. First, we develop a high-efficiency framework for pixel-level face parsing annotating and construct a new large-scale Landmark guided face Parsing dataset (LaPa). It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with an 11-category pixel-level label map and 106-point landmarks. The dataset is publicly accessible to the community for boosting the advance of face parsing.1 Second, a simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information. Extensive experiments on our LaPa benchmark and the public Helen dataset show the superiority of our proposed method.