67 citations · 173 across the 8 of their papers we have counts for
8 papers
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition
Minki Jeong, Changick Kim
Real-world data often have a long-tailed distribution, where the number of samples per class is not equal over training classes. The imbalanced data form a biased feature space, wh…
Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos
Sangmin Woo, Jinyoung Park, Inyong Koo +3
Natural Language Video Grounding (NLVG) aims to localize time segments in an untrimmed video according to sentence queries. In this work, we present a new paradigm named Explore-An…
Improving Few-shot Learning with Weakly-supervised Object Localization
Inyong Koo, Minki Jeong, Changick Kim
Few-shot learning often involves metric learning-based classifiers, which predict the image label by comparing the distance between the extracted feature vector and class represent…
Few-shot Open-set Recognition by Transformation Consistency
Minki Jeong, Seokeon Choi, Changick Kim
In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt…
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Seokeon Choi, Taekyung Kim, Minki Jeong +2
Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, gene…
Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation
Jaehoon Choi, Minki Jeong, Taekyung Kim +1
To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation. However, the existence of false pseudo-la…