3 papers
cs.CL2022
Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
Jihyun Lee, Chaebin Lee, Yunsu Kim +1
In dialogue state tracking (DST), labeling the dataset involves considerable human labor. We propose a new self-training framework for few-shot generative DST that utilize unlabele…
cs.CV2020
CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks
Youngmin Baek, Daehyun Nam, Sungrae Park +5
Despite the recent success of text detection and recognition methods, existing evaluation metrics fail to provide a fair and reliable comparison among those methods. In addition, t…
cs.CV2019
TedEval: A Fair Evaluation Metric for Scene Text Detectors
Chae Young Lee, Youngmin Baek, Hwalsuk Lee
Despite the recent success of scene text detection methods, common evaluation metrics fail to provide a fair and reliable comparison among detectors. They have obvious drawbacks in…