1 citations · 2 across the 6 of their papers we have counts for
6 papers
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation
Soojin Jang, Jungmin Yun, Junehyoung Kwon +2
Weakly supervised semantic segmentation (WSSS) approaches typically rely on class activation maps (CAMs) for initial seed generation, which often fail to capture global context due…
Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion
Juhwan Choi, YoungBin Kim
In the field of text data augmentation, rule-based methods are widely adopted for real-world applications owing to their cost-efficiency. However, conventional rule-based approache…
Colorful Cutout: Enhancing Image Data Augmentation with Curriculum Learning
Juhwan Choi, YoungBin Kim
Data augmentation is one of the regularization strategies for the training of deep learning models, which enhances generalizability and prevents overfitting, leading to performance…
Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks
Seunguk Yu, Juhwan Choi, Youngbin Kim
Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filte…
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Juhwan Choi, Kyohoon Jin, Junho Lee +2
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity. However, this method can potentially damage the original meaning of the text, ultimately hurti…
GPTs Are Multilingual Annotators for Sequence Generation Tasks
Juhwan Choi, Eunju Lee, Kyohoon Jin +1
Data annotation is an essential step for constructing new datasets. However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive…