Monocular depth estimation is a critical component in understanding spatial relationships for various computer vision applications, including autonomous driving and augmented reality. However, accurate depth prediction remains challenging due to two primary factors: (1) the low pixel density of objects in distant regions and (2) the loss of essential features during the resolution reduction process in traditional encoder architectures. To address these challenges, this work introduces an innovative encoder-decoder architecture that incorporates uncertainty maps to improve feature extraction, particularly in long-distance regions. The proposed model utilizes auxiliary uncertainty networks to identify areas with high prediction difficulty, enabling the generation of more robust feature representations through hierarchical feature combinations. Additionally, the decoder architecture is designed to emphasize structural details by introducing an uncertainty edge weighting mask (UEWM) generation module, which further enhances depth prediction performance in challenging regions. Experimental results demonstrate that the proposed method significantly improves depth estimation accuracy in long-range scenarios, as evaluated on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI, Republic of Korea) and dense depth for autonomous driving (DDAD) datasets. These findings highlight the potential of this uncertainty-aware monocular depth estimation approach for practical applications, including autonomous driving and robotic perception.

Automatic depression recognition is essential to depression diagnosis. In this paper, we investigate the problem of depression recognition from facial images, each of which is labeled with one Beck Depression Inventory (BDI-II) score. Because of the ambiguity between one facial image and the depression score, the annotators may not present the accurate score but tend to give those around the ground-truth one. To solve the problem, this paper adopts label distribution to annotate each image, in which each (score) label has a relevance degree. First, we apply the Gaussian distribution to generate the depression score distributions, in which the ground-truth score attains the highest degree, while the neighborhood scores also have degrees to some extent. Thus, each image can contribute to not only its ground-truth score but also neighborhood scores. Second, we generate the depression severity level distributions from the score distributions according to the mapping relationship between BDI-II score and severity level. Finally, we propose a novel method to learn joinT depression scoRE And level distribuTion, termed as TREAT. In the experiments, we compare TREAT with several state-of-the-art methods on three publicly released datasets AVEC 2013, AVEC 2014, and AVEC 2019, and the experimental results justified that TREAT achieves the best performance.

The rapid growth of digital healthcare applications has led to an increasing demand for efficient and reliable task scheduling and resource management in edge computing environments. However, the limited resources of edge servers and the need to process delay-sensitive healthcare tasks pose significant challenges. Existing solutions often need help to balance the trade-off between system cost and quality of service, particularly in resource-constrained scenarios. To address these challenges, we propose a novel cooperative task scheduling and resource management framework for digital healthcare applications in edge intelligence systems. Our approach leverages a two-step optimization strategy that combines the Multi-armed Combinatorial Selection Problem (MCSP) for task scheduling and the Sequential Markov Decision Process (SMDP) with alternative reward estimation for computation offloading. The MCSP-based scheduling algorithm efficiently explores the combinatorial task scheduling space to minimize healthcare task completion time and costs. The SMDP-based offloading strategy incorporates alternative reward estimation to improve robustness against dynamic variations in the system environment. Extensive simulations using real-world healthcare data demonstrate the superior performance of our proposed framework compared to state-of-the-art baselines, achieving significant improvements in cost, task success rate, and fairness. The proposed approach enables reliable and efficient digital healthcare services in resource-constrained edge computing environments.