no code implementations • ICCV 2023 • Jungbeom Lee, Sungjin Lee, Jinseok Nam, Seunghak Yu, Jaeyoung Do, Tara Taghavi
Referring image segmentation (RIS) aims to localize the object in an image referred by a natural language expression.
no code implementations • NAACL (ACL) 2022 • Mohammad Kachuee, Jinseok Nam, Sarthak Ahuja, Jin-Myung Won, Sungjin Lee
Skill routing is an important component in large-scale conversational systems.
no code implementations • 4 Mar 2021 • Han Li, Sunghyun Park, Aswarth Dara, Jinseok Nam, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya
Ensuring model robustness or resilience in the skill routing component is an important problem since skills may dynamically change their subscription in the ontology after the skill routing model has been deployed to production.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • NeurIPS 2017 • Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz
Multi-label classification is the task of predicting a set of labels for a given input instance.
no code implementations • COLING 2016 • Fabian Hirschmann, Jinseok Nam, Johannes F{\"u}rnkranz
Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems.
no code implementations • LREC 2016 • Eneldo Loza Menc{\'\i}a, Gerard de Melo, Jinseok Nam
In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words.
no code implementations • 22 Dec 2014 • Jinseok Nam, Johannes Fürnkranz
We present a novel method to learn vector representations of a label space given a hierarchy of labels and label co-occurrence patterns.
no code implementations • 19 Dec 2013 • Jinseok Nam, Jungi Kim, Eneldo Loza Mencía, Iryna Gurevych, Johannes Fürnkranz
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer.