5 papers
Model Stock: All we need is just a few fine-tuned models
Dong-Hwan Jang, Sangdoo Yun, Dongyoon Han
This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away fr…
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…
Masking meets Supervision: A Strong Learning Alliance
Byeongho Heo, Taekyung Kim, Sangdoo Yun +1
Pre-training with random masked inputs has emerged as a novel trend in self-supervised training. However, supervised learning still faces a challenge in adopting masking augmentati…
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models
Seulki Park, Daeho Um, Hajung Yoon +3
With the extensive use of vision-language models in various downstream tasks, evaluating their robustness is crucial. In this paper, we propose a benchmark for assessing the robust…
Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images
Jiwon Kim, Byeongho Heo, Sangdoo Yun +2
Semantic correspondence methods have advanced to obtaining high-quality correspondences employing complicated networks, aiming to maximize the model capacity. However, despite the…