Current state-of-the-art methods for salient object detection in optical remote sensing images (RSI-SOD) primarily relies on individual image context to detect salient objects. However, the potential of cross-image contexts remains largely unexplored in existing works, which can provide valuable auxiliary and complementary information for discriminating object representations in RSIs. In this paper, we investigate the utilization of cross-image contextual information for RSI-SOD. We propose a novel memory-based context propagation network (MCP-Net) to harness dataset-level contextual information. MCP-Net incorporates a cross-image dual memory module (CDM) to store dataset-level information and utilize it to generate contextual information for the current image. CDM effectively captures intra-scene variations by leveraging both foreground and background memory banks, resulting in improved object representations. Additionally, we enhance the representations by leveraging scale-aware context information within individual images. To preserve RSI details before memory modules, we introduce a shared attention-guided fusion module (SAF) to align the adjacent network-level features. Extensive evaluation results demonstrate the superior performance of our proposed method compared to state-of-the-art methods on three public benchmarks. These results affirm that the inclusion of cross-image contexts can significantly benefit salient object detection in remote sensing images.

Real-time detection of driver drowsiness is critical to reduce the risk of road accidents and fatalities. Current facial landmark-based methods usually use a two-stage paradigm, where faces and facial landmarks are localized separately. Additionally, most methods can be hindered by challenging conditions, such as night driving or eyes closed. To address these challenges, we present a refined YOLO network named YOLOFaceMark that can simultaneously detect faces and their facial landmarks. Furthermore, we introduce a drowsiness detection model based on facial landmarks. This model utilizes extracted eye and mouth information to identify drowsy states. We optimize the original YOLO components through structural re-parameterization, channel shuffling, and the design of a dual-branch detection head with an implicit module. These enhancements are designed to improve the accuracy while maintaining computational efficiency. We validate the real-time performance and accuracy of YOLOFaceMark on public datasets, including 300W and COFW. Additionally, we conduct further validation to demonstrate our ability to achieve effective and robust drowsiness detection solely based on the facial landmarks detected by YOLOFaceMark.

Accurately diagnosing crop diseases is crucial for agricultural productivity and food safety. This study addresses the challenge by developing an AI crop disease diagnosis platform, leveraging the strengths of Convolution Neural Networks (CNNs) and Vision Transformers. The proposed Convolution Self-Guided Transformer (CSGT) model integrates CNN’s local feature extraction with the Self-Guided Transformer’s(SGT) global information processing, enhancing the precision and efficiency of agricultural diagnostics.We selected two datasets with characteristics from Jiangsu and Guangdong provinces to validate our model, respectively representing controlled and uncontrolled agricultural environments. The CSGT model demonstrates exceptional performance in diagnosing crop diseases across diverse backgrounds, overcoming the limitations of current models, especially in complex settings. The CSGT model’s CNN layer, hybrid-scale, and self-guided attention mechanisms ensure accurate diagnoses, even in the presence of background clutter. CSGT has an accuracy of 96.9%(Apple),95.8%(Corn), 96.1%(Grape), and 96.5%(Tomato) when classifying crop diseases in a stable environment, and 95.8%(Rice) when classifying diseases in a complex environment. The research results are anticipated to enhance the effectiveness and stability of natural crop disease recognition applications.

Summary Two scoping reviews were conducted to review recommendations and guidelines for communication regarding general health risk, and to investigate communication strategies regarding risk of fracture. Healthcare professionals are invited to apply these recommendations to optimize a patient-centered approach to reducing risk of fracture. Introduction To conduct a scoping review of the medical literature regarding recommendations and tools for effective communication between healthcare professionals and patients regarding general health risk and risk of fracture. Methods The scoping review was divided into two parts to search for (1) studies presenting recommendations and guidelines for communication regarding general health risk; (2) studies investigating communication regarding risk of fracture for individuals at risk for fractures. Medline was searched in April 2020 to identify relevant studies. Results The scoping review included 43 studies on communication with regard to general health risk and 25 studies about communication regarding risk of fracture. Recommendations for effective communication with regard to risk are presented. Communication of numeric data on risk should be adapted to the literacy and numeracy levels of the individual patient. Patient understanding of numerical data can be enhanced with appropriate use of visual aids (e.g., pie charts, icon arrays, bar charts, pictograms). The FRAX® tool is the most recommended and most used tool for assessing risk of fracture. Communication sent as individualized letters to patients following DXA scans has been studied, although patient understanding of their risk of fracture is often reported as low using this technique. Use of visual aids may improve patient understanding. Conclusion Healthcare professionals are encouraged to apply recommendations presented in this scoping review in their clinical practice. Patient understanding of risk of fracture should be confirmed by making sure that patients feel free to ask questions and express their concerns. This will contribute to an optimal patient-centered approach. Developing online tools to convert the probability of fracture into patient-friendly visual presentations could facilitate communication between healthcare professionals and patients about risk of fracture.

Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure. We argue that the process of detecting actions in the video should be naturally one of observation and refinement: observe the current temporal window and refine the span of attended window to cover true action regions. In this paper, we propose an active action detection model that learns to search actions through continuously adjusting the bounds of temporal attended window in a self-adaptive way. The whole process can be deemed as an exploring procedure, where an agent is first placed at the beginning of the video and then traverses the whole video by adopting a sequence of transformations on the current attended window to discover actions according to a learned policy. We utilize reinforcement learning, especially the deep Q-learning algorithm to learn the agent’s decision policy. Actually, we construct an end-to-end trainable framework for the action detection task, which includes a proposal generation network based on deep Q-learning, and the classification and regression networks responsible for the action category prediction and the action location adjustment, respectively. In addition, we design a long short-term memory structure upon extracted convolutional neural network features of sparsely sampled frames to generate the effective feature representations for video sequences of various durations. We evaluate the action proposal performance of our approach on THUMOS’14 and assess the generalization ability for unseen action categories on ActivityNet . We also compare the action detection performance of ours with other state-of-the-art methods on both data sets. Experiment results validate the effectiveness of the proposed approach, which can achieve comparative or superior performance than other action detection methods via much fewer proposals.