.png){kind=icon}
返回
Multi-Label Image Classification by Feature Attention Network
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2929512.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset.
Keyword:
Deep neural network
multi-label recognition
label correlation
attention
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Efficient monte carlo methods for multi-dimensional learning with classifier chains.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏General memristor with applications in multilayer neural networks通用忆阻器及其在多层神经网络中的应用NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现Indoor AirOAAI
Indoor AirIF0
OAAI分享收藏
查看更多内容