217 citations · 345 across the 12 of their papers we have counts for
15 papers
2.5D Visual Relationship Detection
Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen +8
Visual 2.5D perception involves understanding the semantics and geometry of a scene through reasoning about object relationships with respect to the viewer in an environment. Howev…
MoViNets: Mobile Video Networks for Efficient Video Recognition
Dan Kondratyuk, Liangzhe Yuan, Yandong Li +4
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference. 3D convolutional…
FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling
Christopher Xie, Keunhong Park, Ricardo Martin-Brualla +1
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able…
GeLaTO: Generative Latent Textured Objects
Ricardo Martin-Brualla, Rohit Pandey, Sofien Bouaziz +2
Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem. Inspired by billboards and geometric proxies used in c…
When Ensembling Smaller Models is More Efficient than Single Large Models
Dan Kondratyuk, Mingxing Tan, Matthew Brown +1
Ensembling is a simple and popular technique for boosting evaluation performance by training multiple models (e.g., with different initializations) and aggregating their prediction…
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang +2
Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our e…