April 25, 2022, 1:11 a.m. | Yu-Jung Heo, Eun-Sol Kim, Woo Suk Choi, Byoung-Tak Zhang

cs.LG updates on arXiv.org arxiv.org

Knowledge-based visual question answering (QA) aims to answer a question
which requires visually-grounded external knowledge beyond image content
itself. Answering complex questions that require multi-hop reasoning under weak
supervision is considered as a challenging problem since i) no supervision is
given to the reasoning process and ii) high-order semantics of multi-hop
knowledge facts need to be captured. In this paper, we introduce a concept of
hypergraph to encode high-level semantics of a question and a knowledge base,
and to learn …

arxiv cv hypergraph knowledge question answering reasoning transformer weakly-supervised

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Lead Data Engineer

@ JPMorgan Chase & Co. | Jersey City, NJ, United States

Senior Machine Learning Engineer

@ TELUS | Vancouver, BC, CA

CT Technologist - Ambulatory Imaging - PRN

@ Duke University | Morriville, NC, US, 27560

BH Data Analyst

@ City of Philadelphia | Philadelphia, PA, United States