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

• المؤلفون الرئيسيون: Zhou, Chengju, Wu, Meiqing, Lam, Siew-Kei
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2021
• الموضوعات: Engineering::Computer science and engineering; Multi-Task Learning; Pedestrian Detection; Semantic Segmentation; Feature Aggregation
• الوصول للمادة أونلاين: 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...