Seil Na

Hello! I am a Research Scientist at Lunit Inc. Previously, I was a master’s student in Dept. of Computer Science and Engineering at Seoul National University, where I was advised by Prof. Gunhee Kim.

My research is on Computer Vision, Natural Language Processing and Machine Learning. I'm also interested in training neural networks on large-scale dataset.

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• [2020/07/03] Our paper "Learning Visual Context by Comparison" is accepted to ECCV 2020 as a spotlight presentation.
• [2019/03/04] I started working at Lunit.
• [2019/02/26] I received a master's degree in Dept. of Computer Science and Engineering at Seoul National University.
• [2018/12/21] Our paper "Discovery of Natural Language Concepts in Individual Units of CNNs" is accepted to ICLR 2019.

We propose Attent-and-Compare Module for chest X-ray abnomality detection task, whose key concept is capturing the difference between an object of interest and its corresponding context in image.

In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns.

We propose a deep neural network model, which consists of four components: the frame encoder, the classification layer, the label processing layer, and the loss function. We ranked 8th out of 655 teams in Kaggle - Google Cloud & YouTube-8M Video Understanding Challenge.

We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal movie story understanding.