A Step from VQA to CQA: Adapting Visual QA Models for Community QA Tasks
Avikalp Srivastava (CMU), HsinWen Liu (Waseda Univ.), Sumio Fujita
Natural Language Processing Image Processing Information Retrieval Machine Learning
- In this paper, we study and develop methods to derive highlevel representations for image-text pairs in image-based community question answering (CQA) for performing tasks of practical significance on such a platform - automated question classification and finding experts for answering a question. Motivated by our aim to also utilize this work as a step towards basic question-answering on image-based CQA, and to utilize the advances in visual question answering models, we analyze the differences between visual QA & community QA datasets, understand the limitations of applying VQA models directly to CQA data and tasks, and make novel augmentations to VQA-inspired models to best exploit the multimodal data from Yahoo! Chiebukuro’s CQA dataset.
A Step from VQA to CQA: Adapting Visual QA Models for Community QA Tasks（External Site Link）