30/11/2020

Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network

Yuxuan Wu, Hideki Nakayama

Keywords:

Abstract Paper Similar Papers

Abstract: Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules' structures, and its modularized architecture enables it to solve logical problems more reasonably. However, because of the non-differentiable procedure of module selection, NMN is hard to be trained end-to-end. To overcome this problem, existing work either included ground-truth program into training data or applied reinforcement learning to explore the program. However, both of these methods still have weaknesses. In consideration of this, we proposed a new learning framework for NMN. Graph-based Heuristic Search is the algorithm we proposed to discover the optimal program through a heuristic search on the data structure named Program Graph. Our experiments on FigureQA and CLEVR dataset show that our methods can realize the training of NMN without ground-truth programs and achieve superior program exploring efficiency compared to existing reinforcement learning methods.

The video of this talk cannot be embedded. You can watch it here:
https://accv2020.github.io/miniconf/poster_698.html
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at ACCV 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
 1:01 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 6:00 
 0
 0
 0
 0
 14:18 
 0
 0
 0
 0
 15:28 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 15:29 
 0
 0
 0
 0
 14:49 
 0
 0
 0
 0
 16:57 
 0
 0
 0
 0
 15:00 
 0
 0
 0
 0
 14:36 
 0
 0
 0
 0
 14:34 
 0
 0
 0
 0
 10:04 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 25:03 
 0
 0
 0
 0
 5:25 
 0
 0
 0
 0
 14:45 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 15:01 
 0
 0
 0
 0
 16:06 
 0
 0
 0
 0
 0:53 
 0
 0
 0
 0
 13:48 
 0
 0
 0
 0
 12:57 
 0
 0
 0
 0
 14:47 
 0
 0
 0
 0
 19:59 
 0
 0
 0
 0
 14:19 
 0
 0
 0
 0
 4:15 
 0
 0
 0
 0
 10:41 
 0
 0
 0
 0
 11:07 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 5:54 
 0
 0
 0
 0
 15:15 
 0
 0
 0
 0
 7:15 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 12:51 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 9:24 
 0
 0
 0
 0
 12:35 
 0
 0
 0
 0
 15:05