Search Results for author: Tae Soo Kim

Found 10 papers, 1 papers with code

Interactive Children’s Story Rewriting Through Parent-Children Interaction

no code implementations In2Writing (ACL) 2022 Yoonjoo Lee, Tae Soo Kim, Minsuk Chang, Juho Kim

Storytelling in early childhood provides significant benefits in language and literacy development, relationship building, and entertainment.

LMCanvas: Object-Oriented Interaction to Personalize Large Language Model-Powered Writing Environments

no code implementations27 Mar 2023 Tae Soo Kim, Arghya Sarkar, Yoonjoo Lee, Minsuk Chang, Juho Kim

However, these interfaces provide limited support for writers to create personal tools for their own unique tasks, and may not comprehensively fulfill a writer's needs -- requiring them to continuously switch between interfaces during writing.

Language Modelling

Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs

no code implementations30 Sep 2022 Tae Soo Kim, Geonwoon Jang, Sanghyup Lee, Thijs Kooi

As deep networks require large amounts of accurately labeled training data, a strategy to collect sufficiently large and accurate annotations is as important as innovations in recognition methods.

Video-based assessment of intraoperative surgical skill

no code implementations13 May 2022 Sanchit Hira, Digvijay Singh, Tae Soo Kim, Shobhit Gupta, Gregory Hager, Shameema Sikder, S. Swaroop Vedula

The neural network approach using attention mechanisms also showed high sensitivity and specificity.


SAFCAR: Structured Attention Fusion for Compositional Action Recognition

no code implementations3 Dec 2020 Tae Soo Kim, Gregory D. Hager

We present a general framework for compositional action recognition -- i. e. action recognition where the labels are composed out of simpler components such as subjects, atomic-actions and objects.

Action Recognition Time Series Analysis

DASZL: Dynamic Action Signatures for Zero-shot Learning

no code implementations8 Dec 2019 Tae Soo Kim, Jonathan D. Jones, Michael Peven, Zihao Xiao, Jin Bai, Yi Zhang, Weichao Qiu, Alan Yuille, Gregory D. Hager

There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large.

Action Detection Activity Detection +3

Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition

no code implementations22 Nov 2017 Jingxuan Hou, Tae Soo Kim, Austin Reiter

Based on the findings from the model interpretation analysis, we propose a targeted refinement technique, which can generalize to various other recognition models.

3D Action Recognition Fine-grained Action Recognition +1

Interpretable 3D Human Action Analysis with Temporal Convolutional Networks

1 code implementation14 Apr 2017 Tae Soo Kim, Austin Reiter

In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.

Action Analysis Multimodal Activity Recognition +1

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