Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries
By Yuke Zhu, Ce Zhang, Christopher R'e, Li Fei-Fei. 2015
63 citations recorded by the source corpus.
Subfields: Multimodal Learning, Question Answering & Reading Comprehension
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Multimodal Learning
- Anton van den Hengel
- Qi Wu
- Peng Wang
- Chunhua Shen
- Anthony R. Dick
- Damien Teney
- Ali Farhadi
- Devi Parikh
In Question Answering & Reading Comprehension
- Anton van den Hengel
- Qi Wu
- Chunhua Shen
- Peng Wang
- Anthony R. Dick
- Ali Farhadi
- Yong Jae Lee
- Abhinav Kumar Gupta
Citations in the corpus
- Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “The VQA approach which is closest to KB-QA (and to our approach) is that of [Zhu et al., 2015] as they use a KB and RDBMS to answer image-based questions.”
- Symbolic Graph Reasoning Meets Convolutions (2018, unknown): “In this paper, we explore how to incorporate rich commonsense human knowledge [33, 53] into intermediate feature representation learning beyond local convolutions, and further achieve global semantic coherency.”
- DOCK: Detecting Objects by Transferring Common-Sense Knowledge (2018, methodology): “Such external knowledge bases have been used in several vision tasks including image classification [25,7], VQA [47,41], visual relationship detection [24,30], and modeling object affordances [46].”
- Iterative Visual Reasoning Beyond Convolutions (2018, background): “Our framework, while being more general, is along the line of research that applies visual knowledge base to end tasks, such as affordances [56], image classification [32], or question answering [49].”
- Visual question answering: A survey of methods and datasets (2016, unknown): “Multimodal KB [101] X generation ZeilerNet [96]”