1 citations · 2 across the 7 of their papers we have counts for
8 papers
P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models
Geunyoung Jung, Soohong Kim, Kyungwoo Song +1
With the rise of pre-trained models in the 3D point cloud domain for a wide range of real-world applications, adapting them to downstream tasks has become increasingly important. H…
Generating Accurate and Detailed Captions for High-Resolution Images
Hankyeol Lee, Gawon Seo, Kyounggyu Lee +3
Vision-language models (VLMs) often struggle to generate accurate and detailed captions for high-resolution images since they are typically pre-trained on low-resolution inputs (e.…
Enhancing Visual Classification using Comparative Descriptors
Hankyeol Lee, Gawon Seo, Wonseok Choi +3
The performance of vision-language models (VLMs), such as CLIP, in visual classification tasks, has been enhanced by leveraging semantic knowledge from large language models (LLMs)…
DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
Changdae Oh, Yixuan Li, Kyungwoo Song +2
Adapting a pre-trained foundation model on downstream tasks should ensure robustness against distribution shifts without the need to retrain the whole model. Although existing weig…
Robust Adaptation of Foundation Models with Black-Box Visual Prompting
Changdae Oh, Gyeongdeok Seo, Geunyoung Jung +4
With a surge of large-scale pre-trained models, parameter-efficient transfer learning (PETL) of large models has garnered significant attention. While promising, they commonly rely…
Flat Posterior Does Matter For Bayesian Model Averaging
Sungjun Lim, Jeyoon Yeom, Sooyon Kim +5
Bayesian neural networks (BNNs) estimate the posterior distribution of model parameters and utilize posterior samples for Bayesian Model Averaging (BMA) in prediction. However, des…
Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer
Haeji Jung, Changdae Oh, Jooeon Kang +4
Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. While there are effor…
Towards Calibrated Robust Fine-Tuning of Vision-Language Models
Changdae Oh, Hyesu Lim, Mijoo Kim +6
Improving out-of-distribution (OOD) generalization during in-distribution (ID) adaptation is a primary goal of robust fine-tuning of zero-shot models beyond naive fine-tuning. Howe…