295 citations · 588 across the 6 of their papers we have counts for
7 papers
Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB-D Camera
Hyomin Kim, Jungeon Kim, Hyeonseo Nam +2
This paper presents an effective method for generating a spatiotemporal (time-varying) texture map for a dynamic object using a single RGB-D camera. The input of our framework is a…
Reducing Domain Gap by Reducing Style Bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park +2
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies sug…
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
HyunJae Lee, Hyo-Eun Kim, Hyeonseob Nam
Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective. In this paper,…
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Hyeonseob Nam, Hyo-Eun Kim
Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc. Although these variations…
Dual Attention Networks for Multimodal Reasoning and Matching
Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim
We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend…
Modeling and Propagating CNNs in a Tree Structure for Visual Tracking
Hyeonseob Nam, Mooyeol Baek, Bohyung Han
We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure. The proposed algorithm employs Convolutional Neural Networks (CNNs…
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
Hyeonseob Nam, Bohyung Han
We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a CNN using a…