92 citations · 106 across the 5 of their papers we have counts for
5 papers
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
Long Lian, Zhirong Wu, Stella X. Yu
We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move…
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance
Zhihang Zhong, Xiao Sun, Zhirong Wu +3
We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without consi…
Debiased Learning from Naturally Imbalanced Pseudo-Labels
Xudong Wang, Zhirong Wu, Long Lian +1
Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e…
Deep Markov Random Field for Image Modeling
Zhirong Wu, Dahua Lin, Xiaoou Tang
Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact…
3D ShapeNets: A Deep Representation for Volumetric Shapes
Zhirong Wu, Shuran Song, Aditya Khosla +4
3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability…