Exploring bottom-up and top-down cues with attentive learning for webly supervised object detection

• Main Authors: Wu, Zhonghua, Tao, Qingyi, Lin, Guosheng, Cai, Jianfei
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Object Detection; Detectors
• Online Access: https://hdl.handle.net/10356/144343
• Institution: Nanyang Technological University

Fully supervised object detection has achieved great success in recent years. However, abundant bounding boxes annotations are needed for training a detector for novel classes. To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes...