Masters Thesis Presentation - Aditya Aravind Chinchure

Name: Aditya Aravind Chinchure
Date: April 16
Time: 2 PM 
Location: ICCS 146
Zoom: https://ubc.zoom.us/j/64701175272?pwd=THNvMkNUaUl6dFZ3N2EySHhqNFJxZz09
Supervisor: Leonid Sigal, Co-supervisor: Renjie Liao

Title: Visual Question Answering with Contextualized Commonsense Knowledge
Abstract: There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus on questions that require commonsense reasoning. In contrast to previous methods which inject knowledge from static knowledge bases, we investigate the incorporation of contextualized knowledge using Commonsense Transformer (COMET), an existing knowledge model trained on human-curated knowledge bases.  We propose a method to generate, select, and encode external commonsense knowledge alongside visual and textual cues in a new pre-trained Vision-Language-Commonsense transformer model, VLC-BERT. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is capable of outperforming existing models that utilize static knowledge bases. Furthermore, through a detailed analysis, we explain which questions benefit, and which don’t, from contextualized commonsense knowledge from COMET.