8 citations · 14 across the 6 of their papers we have counts for
7 papers
Self-supervised AutoFlow
Hsin-Ping Huang, Charles Herrmann, Junhwa Hur +5
Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Obse…
Omnimatte: Associating Objects and Their Effects in Video
Erika Lu, Forrester Cole, Tali Dekel +3
Computer vision is increasingly effective at segmenting objects in images and videos; however, scene effects related to the objects -- shadows, reflections, generated smoke, etc --…
Self-supervised Video Object Segmentation by Motion Grouping
Charig Yang, Hala Lamdouar, Erika Lu +2
Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a comput…
On the Origin of Species of Self-Supervised Learning
Samuel Albanie, Erika Lu, Joao F. Henriques
In the quiet backwaters of cs.CV, cs.LG and stat.ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for extern…
Layered Neural Rendering for Retiming People in Video
Erika Lu, Forrester Cole, Tali Dekel +5
We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur. We can temp…
MAST: A Memory-Augmented Self-supervised Tracker
Zihang Lai, Erika Lu, Weidi Xie
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained o…
Class-Agnostic Counting
Erika Lu, Weidi Xie, Andrew Zisserman
Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this…