5 citations · 8 across the 4 of their papers we have counts for
6 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…
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples
Dae Ung Jo, Kyuewang Lee, JaeHo Chung +1
Securing a sufficient amount of paired data is important to train an image-text retrieval (ITR) model, but collecting paired data is very expensive. To address this issue, in this…
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels
Seulki Park, Hwanjun Song, Daeho Um +3
Deep neural network can easily overfit to even noisy labels due to its high capacity, which degrades the generalization performance of a model. To overcome this issue, we propose 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…
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
Dae Ung Jo, ByeongJu Lee, Jongwon Choi +2
In this paper, we propose a novel structure for a cross-modal data association, which is inspired by the recent research on the associative learning structure of the brain. We form…
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…