13 citations · 27 across the 6 of their papers we have counts for
6 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…
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Xudong Wang, Rohit Girdhar, Stella X. Yu +1
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'disc…
Open Long-Tailed Recognition in a Dynamic World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (ta…
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…
Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Jian Shi, Yue Dong, Hao Su +1
We consider the non-Lambertian object intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. We build a large-scale obje…
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling
Michael Maire, Stella X. Yu, Pietro Perona
We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code repr…