1 citations · 2 across the 7 of their papers we have counts for
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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)…
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…
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…