(SP)²Net for Generalized Zero-Label Semantic Segmentation
METADATA ONLY
Loading...
Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Generalized zero-label semantic segmentation aims to make pixel-level predictions for both seen and unseen classes in an image. Prior works approach this task by leveraging semantic word embeddings to learn a semantic projection layer or generate features of unseen classes. However, those methods rely on standard segmentation networks that may not generalize well to unseen classes. To address this issue, we propose to leverage a class-agnostic segmentation prior provided by superpixels and introduce a superpixel pooling (SP-pooling) module as an intermediate layer of a segmentation network. Also, while prior works ignore the pixels of unseen classes that appear in training images, we propose to minimize the log probability of seen classes alleviating biased predictions in those ignore regions. We show that our (SP)2Net significantly outperforms the state-of-the-art on different data splits of PASCAL VOC 2012 and PASCAL-Context benchmarks.
Permanent link
Publication status
published
External links
Book title
Pattern Recognition. DAGM GCPR 2021
Journal / series
Volume
13024
Pages / Article No.
235 - 249
Publisher
Springer
Event
43rd DAGM German Conference on Pattern Recognition (DAGM GCPR 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Scene understanding; Zero-shot semantic segmentation
Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
Notes
Conference lecture held on September 30, 2021.