7 citations · 12 across the 6 of their papers we have counts for
6 papers
Scale Equalization for Multi-Level Feature Fusion
Bum Jun Kim, Sang Woo Kim
Deep neural networks have exhibited remarkable performance in a variety of computer vision fields, especially in semantic segmentation tasks. Their success is often attributed to m…
Analysis of NaN Divergence in Training Monocular Depth Estimation Model
Bum Jun Kim, Hyeonah Jang, Sang Woo Kim
The latest advances in deep learning have facilitated the development of highly accurate monocular depth estimation models. However, when training a monocular depth estimation netw…
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +1
DeepLab is a widely used deep neural network for semantic segmentation, whose success is attributed to its parallel architecture called atrous spatial pyramid pooling (ASPP). ASPP…
Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive Fields
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +1
Vision transformers (ViTs) that model an image as a sequence of partitioned patches have shown notable performance in diverse vision tasks. Because partitioning patches eliminates…
How to Use Dropout Correctly on Residual Networks with Batch Normalization
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +2
For the stable optimization of deep neural networks, regularization methods such as dropout and batch normalization have been used in various tasks. Nevertheless, the correct posit…
On the Ideal Number of Groups for Isometric Gradient Propagation
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +1
Recently, various normalization layers have been proposed to stabilize the training of deep neural networks. Among them, group normalization is a generalization of layer normalizat…