- Zbigniew Wojna
- Vittorio Ferrari
- Sergio Guadarrama
- Nathan Silberman
- Liang-chieh Chen
- Alireza Fathi
- Jasper Uijlings
Abstract
Many machine vision applications require predictions for every pixel of the input image (for exam- ple semantic segmentation, boundary detection). Mod- els for such problems usually consist of encoders which decreases spatial resolution while learning a high-di- mensional representation, followed by decoders who re- cover the original input resolution and result in low- dimensional predictions. While encoders have been stud- ied rigorously, relatively few studies address the decoder side. Therefore this paper presents an extensive com- parison of a variety of decoders for a variety of pixel- wise tasks ranging from classification, regression to syn- thesis. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce new residual-like connections for decoders. (3) We in- troduce a novel decoder: bilinear additive upsampling. (4) We explore prediction artefacts.
Research Areas
Learn more about how we do research
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work