1 citations · 1 across the 5 of their papers we have counts for
5 papers
Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?
Eunki Kim, Na Min An, Wan Ju Kang +3
Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world…
When Tom Eats Kimchi: Evaluating Cultural Bias of Multimodal Large Language Models in Cultural Mixture Contexts
Jun Seong Kim, Kyaw Ye Thu, Javad Ismayilzada +6
In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should…
Diffusion Models Through a Global Lens: Are They Culturally Inclusive?
Zahra Bayramli, Ayhan Suleymanzade, Na Min An +5
Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent vario…
I0T: Embedding Standardization Method Towards Zero Modality Gap
Na Min An, Eunki Kim, James Thorne +1
Contrastive Language-Image Pretraining (CLIP) enables zero-shot inference in downstream tasks such as image-text retrieval and classification. However, recent works extending CLIP…
Stable Language Model Pre-training by Reducing Embedding Variability
Woojin Chung, Jiwoo Hong, Na Min An +2
Stable pre-training is essential for achieving better-performing language models. However, tracking pre-training stability by calculating gradient variance at every step is impract…