5 citations · 5 across the 2 of their papers we have counts for
4 papers
Codebook-Guided Cross-Modal Knowledge Distillation for Structurally Heterogeneous Features
Dae Ung Jo, Jongin Lim, YoungJoon Yoo +1
Cross-modal knowledge distillation transfers knowledge from a teacher modality to a student modality. Existing feature-level alignment methods typically assume that teacher and stu…
Influence-Balanced Loss for Imbalanced Visual Classification
Seulki Park, Jongin Lim, Younghan Jeon +1
In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that a…
Class-Attentive Diffusion Network for Semi-Supervised Classification
Jongin Lim, Daeho Um, Hyung Jin Chang +2
Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal…
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Youngmin Ro, Jongwon Choi, Dae Ung Jo +3
In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a la…