6 accepted papers
WiFi-based human activity recognition (HAR) holds significant application potential across various fields. To handle dynamic environments where new activities are continuously introduced, WiFi-based HAR systems must adapt by learning new concepts without forgetting previously learned ones. Furthermo…
This paper aims at generating anomalous images and their segmentation labels to address the lack of real-world anomaly samples and privacy issues. Departing from conventional approaches that use masks solely to guide the generation of anomaly images, we propose a dual-branch training strategy for th
Neural Architecture Search (NAS) is a core component of automated machine learning, enabling the automatic discovery of task-specific architectures. However, directly employing raw data for NAS faces challenges in terms of large data volume, low search efficiency, and potential data privacy concerns…
Driver attention recognition in driving scenarios is a popular direction in traffic scene perception technology. It aims to understand human driver attention to focus on specific targets/objects in the driving scene. However, traffic scenes contain not only a large amount of visual information but a…
Intelligent vehicles need to provide rational driving strategies for assisted driving systems based on driving scenarios. Since pedestrians and vehicles are the main players in these scenarios, accurate detection and localization of pedestrians and vehicles are crucial for intelligent driving system…
Multi-label text classification (MLTC) is an important task in the field of natural language processing. Most existing models rely on high-quality text representations provided by pre-trained language models (PLMs). They hence face the challenge of input length limitation caused by PLMs, when dealin…