Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection | |
Liu, Yongcheng4; Sheng, Lu1; Shao, Jing2; Yan, Junjie2; Xiang, Shiming4; Pan, Chunhong4 | |
2018 | |
会议日期 | 2018-10-22 |
会议地点 | Soeul, Korea |
英文摘要 | Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/38551] |
专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
作者单位 | 1.CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2.SenseTime Research 3.School of Artificial Intelligence, University of Chinese Academy of Sciences 4.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Liu, Yongcheng,Sheng, Lu,Shao, Jing,et al. Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection[C]. 见:. Soeul, Korea. 2018-10-22. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论