A unified multi-task learning architecture for fast and accurate pedestrian detection

• Main Authors: Zhou, Chengju, Wu, Meiqing, Lam, Siew-Kei
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2021
• 主題: Engineering::Computer science and engineering; Multi-Task Learning; Pedestrian Detection; Semantic Segmentation; Feature Aggregation
• 在線閱讀: https://hdl.handle.net/10356/147488

We present a unified multi-task learning architecture for fast and accurate pedestrian detection. Different from existing methods which often focus on either a new loss function or architecture, we propose an improved multi-task convolutional neural network learning architecture to effectively and...