19/10/2020

Deep adaptive feature aggregation in multi-task convolutional neural networks

Zhen Shen, Chaoran Cui, Jin Huang, Jian Zong, Meng Chen, Yilong Yin

Keywords: convolutional neural networks, multi-task learning, adaptive feature aggregation

Abstract Paper Similar Papers

Abstract: Convolutional Neural Network (CNN) based multi-task learning methods have been widely used in a variety of applications of computer vision. Towards effective multi-task CNN architectures, recent studies automatically learn the optimal combinations of task-specific features at single network layers. However, they generally construct an unchanged operation of feature aggregation after training, regardless of the characteristics of input features. In this paper, we propose a novel Adaptive Feature Aggregation (AFA) layer for multi-task CNNs, in which a dynamic aggregation mechanism is designed to allow each task to adaptively determine the degree to which the feature aggregation of different tasks is needed according to the feature dependencies. On both pixel-level and image-level tasks, we demonstrate that our approach significantly outperforms the previous state-of-the-art methods of multi-task CNNs.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3340531.3412132#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at CIKM 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 0:53 
 0
 0
 0
 0
 11:18 
 0
 0
 0
 0
 6:41 
 0
 0
 0
 0
 4:55 
 0
 0
 0
 0
 4:36 
 0
 0
 0
 0
 5:10 
 0
 0
 0
 0
 4:28 
 0
 0
 0
 0
 0:58 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 10:42 
 0
 0
 0
 0
 5:48 
 0
 0
 0
 0
 19:59 
 0
 0
 0
 0
 7:15 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 5:13 
 0
 0
 0
 0
 5:18 
 0
 0
 0
 0
 5:10 
 0
 0
 0
 0
 13:14 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 11:46 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 16:15 
 0
 0
 0
 0
 5:02 
 0
 0
 0
 0
 5:27 
 1
 0
 0
 1
 5:27 
 0
 0
 0
 0
 3:13 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 2:45 
 0
 0
 0
 0
 14:06 
 0
 0
 0
 0
 5:02 
 0
 0
 0
 0
 8:50 
 0
 0
 0
 0
 7:36 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 3:15 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 3:27 
 0
 0
 0
 0
 15:08