56 citations · 69 across the 5 of their papers we have counts for
11 papers
Position-aware Location Regression Network for Temporal Video Grounding
Sunoh Kim, Kimin Yun, Jin Young Choi
The key to successful grounding for video surveillance is to understand a semantic phrase corresponding to important actors and objects. Conventional methods ignore comprehensive c…
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…
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks
Youngmin Ro, Jin Young Choi
Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single…
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
YoungJoon Yoo, Sangdoo Yun, Hyung Jin Chang +2
This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression method…
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…