7 citations · 18 across the 10 of their papers we have counts for
6 papers · 1 filter
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers
Bum Jun Kim, Sang Woo Kim
Vision transformers (ViTs) have demonstrated remarkable performance in a variety of vision tasks. Despite their promising capabilities, training a ViT requires a large amount of di…
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…
Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +3
L2 regularization for weights in neural networks is widely used as a standard training trick. However, L2 regularization for gamma, a trainable parameter of batch normalization, re…