81 citations · 414 across the 42 of their papers we have counts for
6 papers · 1 filter
Joint Global and Local Hierarchical Priors for Learned Image Compression
Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee
Recently, learned image compression methods have outperformed traditional hand-crafted ones including BPG. One of the keys to this success is learned entropy models that estimate t…
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
Seulki Park, Youngkyu Hong, Byeongho Heo +2
The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes. In this paper, we propose…
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun +5
Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object…
Rethinking Spatial Dimensions of Vision Transformers
Byeongho Heo, Sangdoo Yun, Dongyoon Han +3
Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision tasks as being an alternative architecture against the existing c…
Show, Attend and Distill:Knowledge Distillation via Attention-based Feature Matching
Mingi Ji, Byeongho Heo, Sungrae Park
Knowledge distillation extracts general knowledge from a pre-trained teacher network and provides guidance to a target student network. Most studies manually tie intermediate featu…
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
Sangdoo Yun, Seong Joon Oh, Byeongho Heo +3
ImageNet has been arguably the most popular image classification benchmark, but it is also the one with a significant level of label noise. Recent studies have shown that many samp…