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 [BibTeX] [Marc21]
Learning to Refine Object Segments
Type of publication: Conference paper
Citation: Pinheiro_ECCV_2016
Publication status: Published
Booktitle: Computer Vision - ECCV 2016
Series: Lecture Notes in Computer Science
Volume: 9905
Year: 2016
Month: September
Pages: 75-91
Publisher: Springer
Location: Amsterdam
ISSN: 0302-9743
ISBN: 978-3-319-46448-0
URL: http://link.springer.com/chapt...
DOI: 10.1007/978-3-319-46448-0_5
Abstract: Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowledge but are invariant to factors such as pose and appearance. In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach. The resulting bottom-up/top-down architecture is capable of efficiently generating high-fidelity object masks. Similarly to skip connections, our approach leverages features at all layers of the net. Unlike skip connections, our approach does not attempt to output independent predictions at each layer. Instead, we first output a coarse ‘mask encoding’ in a feedforward pass, then refine this mask encoding in a top-down pass utilizing features at successively lower layers. The approach is simple, fast, and effective. Building on the recent DeepMask network for generating object proposals, we show accuracy improvements of 10–20% in average recall for various setups. Additionally, by optimizing the overall network architecture, our approach, which we call SharpMask, is 50 % faster than the original DeepMask network (under .8 s per image).
Projects Idiap
Authors Pinheiro, Pedro H. O.
Lin, Tsung-Yi
Collobert, Ronan
Dollar, Piotr
Added by: [UNK]
Total mark: 0
  • Pinheiro_ECCV_2016.pdf