16 citations · 32 across the 4 of their papers we have counts for
4 papers
Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning
In-Ug Yoon, Tae-Min Choi, Sun-Kyung Lee +2
While many FSCIL studies have been undertaken, achieving satisfactory performance, especially during incremental sessions, has remained challenging. One prominent challenge is that…
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning
In-Ug Yoon, Tae-Min Choi, Young-Min Kim +1
Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions. To add…
Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach
Tae-Min Choi, Jong-Hwan Kim
In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous…
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data
Tae-Min Choi, Ji-Su Kang, Jong-Hwan Kim
Time-series data with missing values are commonly encountered in many fields, such as healthcare, meteorology, and robotics. The imputation aims to fill the missing values with val…