380 citations · 504 across the 16 of their papers we have counts for
17 papers
Domain Recentering and Confidence-Weighted Prior Calibration for Vision-Language Models
Youngeun Seol, Jimin Shin, Heeseo Yoon +1
Vision-language models such as CLIP achieve strong zero-shot classification, yet under distribution shift, visual embeddings drift from fixed text embeddings. Training-free calibra…
Enhancing Alignment for Unified Multimodal Models via Semantically-Grounded Supervision
Jiyeong Kim, Yerim So, Hyesong Choi +2
Unified Multimodal Models (UMMs) have emerged as a promising paradigm that integrates multimodal understanding and generation within a unified modeling framework. However, current…
CLIP-RD: Relational Distillation for Efficient CLIP Knowledge Distillation
Jeannie Chung, Hanna Jang, Ingyeong Yang +2
Contrastive Language-Image Pre-training (CLIP) demonstrates strong zero-shot generalization, but due to substantial computational and memory costs, distillation into lightweight mo…
SF(DA): Source-free Domain Adaptation Through the Lens of Data Augmentation
Uiwon Hwang, Jonghyun Lee, Juhyeon Shin +1
In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domain…
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
Jonghyun Lee, Dahuin Jung, Saehyung Lee +4
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online…
Improving Diffusion-Based Generative Models via Approximated Optimal Transport
Daegyu Kim, Jooyoung Choi, Chaehun Shin +2
We introduce the Approximated Optimal Transport (AOT) technique, a novel training scheme for diffusion-based generative models. Our approach aims to approximate and integrate optim…