3 citations · 5 across the 7 of their papers we have counts for
8 papers
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity
Sumin Seo, JaeWoong Shin, Jaewoo Kang +2
Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents wid…
Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs
Tae Soo Kim, Geonwoon Jang, Sanghyup Lee +1
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 reco…
Video-based assessment of intraoperative surgical skill
Sanchit Hira, Digvijay Singh, Tae Soo Kim +4
Purpose: The objective of this investigation is to provide a comprehensive analysis of state-of-the-art methods for video-based assessment of surgical skill in the operating room.…
Motion Guided Attention Fusion to Recognize Interactions from Videos
Tae Soo Kim, Jonathan Jones, Gregory D. Hager
We present a dual-pathway approach for recognizing fine-grained interactions from videos. We build on the success of prior dual-stream approaches, but make a distinction between th…
SAFCAR: Structured Attention Fusion for Compositional Action Recognition
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-acti…
DASZL: Dynamic Action Signatures for Zero-shot Learning
Tae Soo Kim, Jonathan D. Jones, Michael Peven +6
There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training o…
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition
Jingxuan Hou, Tae Soo Kim, Austin Reiter
Despite the growing discriminative capabilities of modern deep learning methods for recognition tasks, the inner workings of the state-of-art models still remain mostly black-boxes…
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
Tae Soo Kim, Austin Reiter
The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent. In conjunction with the recent resurgence of 3D human action repr…