61 citations · 147 across the 13 of their papers we have counts for
13 papers
FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
Joo Chan Lee, Daniel Rho, Jong Hwan Ko +1
Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of…
Masked Wavelet Representation for Compact Neural Radiance Fields
Daniel Rho, Byeonghyeon Lee, Seungtae Nam +3
Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. Howev…
Streamable Neural Fields
Junwoo Cho, Seungtae Nam, Daniel Rho +2
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network p…
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation
Wencan Cheng, Jong Hwan Ko
Scene flow estimation, which extracts point-wise motion between scenes, is becoming a crucial task in many computer vision tasks. However, all of the existing estimation methods ut…
NAS-VAD: Neural Architecture Search for Voice Activity Detection
Daniel Rho, Jinhyeok Park, Jong Hwan Ko
Various neural network-based approaches have been proposed for more robust and accurate voice activity detection (VAD). Manual design of such neural architectures is an error-prone…
Neural Residual Flow Fields for Efficient Video Representations
Daniel Rho, Junwoo Cho, Jong Hwan Ko +1
Neural fields have emerged as a powerful paradigm for representing various signals, including videos. However, research on improving the parameter efficiency of neural fields is st…