Human Instance Segmentation from Video using Detector-based Conditional Random Fields

By Vibhav Vineet, Jonathan H. Warrell, Lubor Ladicky, Philip H. S. Torr. 2011

43 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Philip H. S. Torr
3. Ivan Laptev
4. Bernt Schiele
5. Andrew Zisserman
6. Cordelia Schmid
7. Trevor Darrell
8. Li Fei-Fei

Citations in the corpus

• Recurrent Instance Segmentation (2015, background): “The solution to this problem is useful in its own right, for example to segment and to count people in images [6], but we consider that this is also the first step towards general semantic learning systems that can segment and classify different kinds of instances.”
• Instance-Level Video Segmentation from Object Tracks (2016, methodology): “Semantic object segmentation in images and videos is a challenging computer vision task [24, 29, 31, 39, 45].”