Generalized Few-shot Semantic Segmentation
By Zhuotao Tian, Xin Lai, Li Jiang, Michelle Shu, Hengshuang Zhao, Jiaya Jia. 2020
120 citations recorded by the source corpus.
Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few- Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Semantic Segmentation (GFS- Seg), to analyze the generalization ability of simultaneously segmenting the novel categories with very few examples and the base categories with sufficient examples. It is the first study showing that previous representative state-of-the-art FS-Seg methods fall short in GFS-Seg and the performance discrepancy mainly comes from the constrained setting of FS-Seg. To make GFS-Seg tractable, we set up a GFS-Seg baseline that achieves decent performance without structural change on the original model. Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image. Both two contributions are experimentally manifested for their substantial practical merit. Extensive experiments on Pascal-Voc and COCO also show that CAPL generalizes well to FS-Seg by achieving competitive performance. Code is available at https://github.com/dvlab-research/GFS-Seg.
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
- Pietro Zanuttigh
- Umberto Michieli
- Fabio Cermelli
- Arthur Douillard
- Matthieu Cord
- Barbara Caputo
- Brahim Chaib-draa
- Mathieu Pagé Fortin
In Computer Vision and Pattern Recognition
- Umberto Michieli
- Pietro Zanuttigh
- Fabio Cermelli
- Arthur Douillard
- Matthieu Cord
- Barbara Caputo
- Brahim Chaib-draa
- Mathieu Pagé Fortin
Citations in the corpus
- Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (2021, methodology): “Prototypes-based regularizing terms gained a great interest and, in particular, have been largely used in the literature of few-shot learning [15, 63, 60], to learn prototypical representations of each category, and domain adaptation, to enforce orthogonality [50, 65] or centroid matching [70, 12].”