274 citations · 649 across the 21 of their papers we have counts for
4 papers · 1 filter
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
Sangwoo Mo, Minkyu Kim, Kyungmin Lee +1
Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle…
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma +4
Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised represen…
Diffusion Probabilistic Models for Structured Node Classification
Hyosoon Jang, Seonghyun Park, Sangwoo Mo +1
This paper studies structured node classification on graphs, where the predictions should consider dependencies between the node labels. In particular, we focus on solving the prob…
Discovering and Mitigating Visual Biases through Keyword Explanation
Younghyun Kim, Sangwoo Mo, Minkyu Kim +3
Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often iden…