Segmentation Propagation in ImageNet

By Daniel Küttel, Matthieu Guillaumin, Vittorio Ferrari. 2012

616 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Trevor Darrell
2. Li Fei-Fei
3. Mohit Bansal
4. Abhinav Kumar Gupta
5. Bernt Schiele
6. Quoc V. Le
7. Alan L. Yuille
8. Luc Van Gool

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Ming-Hsuan Yang
4. Dacheng Tao
5. Trevor Darrell
6. Jitendra Malik
7. Bernt Schiele
8. Andrew Zisserman

Citations in the corpus

• ImageNet Large Scale Visual Recognition Challenge (2014, background): “Additionally, several influential lines of research have emerged, such as large-scale weakly supervised localization work of (Kuettel et al., 2012) which was awarded the best paper award in ECCV 2012 and large-scale zero-shot learning, e.g., (Frome et al., 2013).”
• Feedforward semantic segmentation with zoom-out features (2014, methodology): “There has been some work on loss design for learning segmentation [37], but the simple weighted loss we describe below has to our knowledge been missed in segmentation literature, with the exception of [20] and [22], where it was used for binary segmentation.”
• We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification (2016, background): “An alternative is transfer learning, where learning a model for a new class is helped by labeled examples of related classes [2, 17, 21, 29, 31].”