36 citations · 82 across the 10 of their papers we have counts for
13 papers · 1 filter
Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding
Sunoh Kim, Jungchan Cho, Joonsang Yu +2
In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by…
Learning Features with Parameter-Free Layers
Dongyoon Han, YoungJoon Yoo, Beomyoung Kim +1
Trainable layers such as convolutional building blocks are the standard network design choices by learning parameters to capture the global context through successive spatial opera…
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +2
Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realisti…
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
Sanghyuk Chun, Seong Joon Oh, Sangdoo Yun +3
Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans. They easily change predictions when small corruptions su…
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +3
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…
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…