64 citations · 176 across the 23 of their papers we have counts for
4 papers · 1 filter
VACoDe: Visual Augmented Contrastive Decoding
Sihyeon Kim, Boryeong Cho, Sangmin Bae +2
Despite the astonishing performance of recent Large Vision-Language Models (LVLMs), these models often generate inaccurate responses. To address this issue, previous studies have f…
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
Sungnyun Kim, Sangmin Bae, Se-Young Yun
Deep learning in general domains has constantly been extended to domain-specific tasks requiring the recognition of fine-grained characteristics. However, real-world applications f…
Re-thinking Federated Active Learning based on Inter-class Diversity
SangMook Kim, Sangmin Bae, Hwanjun Song +1
Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may…
MixCo: Mix-up Contrastive Learning for Visual Representation
Sungnyun Kim, Gihun Lee, Sangmin Bae +1
Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation. By learning to contrast positive pairs' representation from the co…