MuRAL: Multi-Scale Region-based Active Learning for Object Detection

Obtaining large-scale labeled object detection dataset can be costly and time-consuming, as it involves annotating images with bounding boxes and class labels. Thus, some specialized active learning methods have been proposed to reduce the cost by selecting either coarse-grained samples or fine-grained instances from unlabeled data for labeling. However, the former approaches suffer from redundant labeling, while the latter methods generally lead to training instability and sampling bias. To address these challenges, we propose a novel approach called Multi-scale Region-based Active Learning (MuRAL) for object detection. MuRAL identifies informative regions of various scales to reduce annotation costs for well-learned objects and improve training performance. The informative region score is designed to consider both the predicted confidence of instances and the distribution of each object category, enabling our method to focus more on difficult-to-detect classes. Moreover, MuRAL employs a scale-aware selection strategy that ensures diverse regions are selected from different scales for labeling and downstream finetuning, which enhances training stability. Our proposed method surpasses all existing coarse-grained and fine-grained baselines on Cityscapes and MS COCO datasets, and demonstrates significant improvement in difficult category performance

A HRNet-based Rehabilitation Monitoring System

The rehabilitation treatment helps to heal minor sports and occupational injuries. In a traditional rehabilitation process, a therapist will assign certain actions to a patient to perform in between hospital visits, and it will rely on the patient to remember actions correctly and the schedule to perform them. Unfortunately, many patients forget to perform actions or fail to recall actions in detail. As a consequence, the rehabilitation treatment is hampered or, in the worst case, the patient may suffer from additional injury caused by performing incorrect actions. To resolve these issues, we propose a HRNet-based rehabilitation monitoring system, which can remind a patient when to perform the actions and display the actions for the patient to follow via the patient's smartphone. In addition, it helps the therapist to monitor the progress of the rehabilitation for the patient. Our system consists of an iOS app and several components at the server side. The app is in charge of displaying and collecting action videos. The server computes the similarity score between the therapist's actions and the patient's in the videos to keep track of the number of repetitions of each action. Theses stats will be shown to both of the patient and therapist. The extensive experiments show that the F1-Score of the similarity calculation is as high as 0.9 and the soft accuracy of the number of repetitions is higher than 90%

D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation

In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations. Active learning, maximizing model performance with few informative labeled data, comes in handy for such a scenario. In this work, we present D2ADA, a general active domain adaptation framework for semantic segmentation. To adapt the model to the target domain with minimum queried labels, we propose acquiring labels of the samples with high probability density in the target domain yet with low probability density in the source domain, complementary to the existing source domain labeled data. To further facilitate labeling efficiency, we design a dynamic scheduling policy to adjust the labeling budgets between domain exploration and model uncertainty over time. Extensive experiments show that our method outperforms existing active learning and domain adaptation baselines on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than 5% target domain annotations, our method reaches comparable results with that of full supervision.Comment: 14 pages, 5 figure

A Sun Path Observation System Based on Augment Reality and Mobile Learning

This study uses the augmented reality technology and sensor functions of GPS, electronic compass, and three-axis accelerometer on mobile devices to develop a Sun path observation system for applications in astronomy education. The orientation and elevation of the Sun can be calculated by the system according to the user’s location and local time to simulate the Sun path. When holding the mobile device toward the sky, the screen will show the virtual Sun at the same position as that of the real Sun. The user can record the Sun path and the data of observation date, time, longitude, and latitude using the celestial hemisphere and the pole shadow on the system. By setting different observation times and locations, it can be seen that the Sun path changes with seasons and latitudes. The system provides contextual awareness of the Sun path concepts, and it can convert the observation data into organized and meaningful astronomical knowledge to enable combination of situated learning with spatial cognition. The system can solve the problem of being not able to record the Sun path due to a bad weather or topographical restrictions, and therefore it is helpful for elementary students when conducting observations. A teaching experiment has been conducted to analyze the learning achievement of students after using the system, and the results show that it is more effective than traditional teaching aids. The questionnaire results also reveal that the system is easy to operate and useful in recording the Sun path data. Therefore, it is an effective tool for astronomy education in elementary schools

Immunoprofiling of Equine Plasma against <i>Deinagkistrodon acutus</i> in Taiwan: Key to Understanding Differential Neutralization Potency in Immunized Horses

Snakebite envenoming is a public health issue linked to high mortality and morbidity rates worldwide. Although antivenom has been the mainstay treatment for envenomed victims receiving medical care, the diverse therapeutic efficacy of the produced antivenom is a major limitation. Deinagkistrodon acutus is a venomous snake that poses significant concern of risks to human life in Taiwan, and successful production of antivenom against D. acutus envenoming remains a considerable challenge. Among groups of horses subjected to immunization schedules, few or none subsequently meet the quality required for further scale-up harvesting. The determinants underlying the variable immune responses of horses to D. acutus venom are currently unknown. In this study, we assessed the immunoprofiles of high-potency and low-potency horse plasma against D. acutus venom and explored the conspicuous differences between these two groups. Based on the results of liquid chromatography with tandem mass spectrometry (LC-MS/MS), acutolysin A was identified as the major component of venom proteins that immunoreacted differentially with the two plasma samples. Our findings indicate underlying differences in antivenoms with variable neutralization efficacies, and may provide valuable insights for improvement of antivenom production in the future