Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network
arXiv:2009.14759
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 efficiency over existing reinforcement learning methods in program exploration.
arXiv commentin Neural Module Network[C]//Proceedings of the Asian Conference on Computer Vision. 2020
References in corpus (6)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- VQA: Visual Question Answering
- Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
- Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
- FigureQA: An Annotated Figure Dataset for Visual Reasoning