1 citations · 2 across the 18 of their papers we have counts for
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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…
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks
Juhwan Choi, Junehyoung Kwon, JungMin Yun +2
Domain generalizability is a crucial aspect of a deep learning model since it determines the capability of the model to perform well on data from unseen domains. However, research…
Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification
Jungmin Yun, Mihyeon Kim, Youngbin Kim
Transformer-based models have achieved dominant performance in numerous NLP tasks. Despite their remarkable successes, pre-trained transformers such as BERT suffer from a computati…
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Juhwan Choi, Yeonghwa Kim, Seunguk Yu +2
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited…
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
Juhwan Choi, Jungmin Yun, Kyohoon Jin +1
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included…