64 citations · 358 across the 46 of their papers we have counts for
8 papers · 1 filter
Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
Seongha Eom, Namgyu Ho, Jaehoon Oh +1
Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability, namely image classification using novel text labels. Existing works hav…
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…
CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
Sumyeong Ahn, Jongwoo Ko, Se-Young Yun
Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets.…
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Gihun Lee, Sangmook Kim, Joonkee Kim +1
Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However…
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
Taehyeon Kim, Shinhwan Kang, Hyeonjeong Shin +4
The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict super-resolution rain movies in various regions of Europe when low-resolution satellite contex…
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Jaehoon Oh, Sungnyun Kim, Namgyu Ho +3
Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Rece…
A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search
Stephen Cha, Taehyeon Kim, Hayeon Lee +1
This survey focuses on categorizing and evaluating the methods of supernet optimization in the field of Neural Architecture Search (NAS). Supernet optimization involves training a…