Few-Shot Semantic Segmentation with Democratic Attention Networks

By Haochen Wang, Xudong Zhang, Yutao Hu, Yandan Yang, Xianbin Cao, Xiantong Zhen. 2020

234 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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. Yi Yang
2. Trevor Darrell
3. Yunchao Wei
4. Jiaya Jia
5. Jiashi Feng
6. Luc Van Gool
7. Serge J. Belongie
8. X. Wang

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Yi Yang
5. Trevor Darrell
6. Jitendra Malik
7. Xiaogang Wang
8. Alan L. Yuille

In Meta & Continual Learning

1. René Ranftl
2. Vladlen Koltun
3. Gengwei Zhang
4. Qixiang Ye
5. Jiaya Jia
6. Juhong Min
7. Dahyun Kang
8. Jianbin Jiao

Citations in the corpus

• Language-driven Semantic Segmentation (2022, background): “…(Shaban et al., 2017; Rakelly et al., 2018; Siam et al., 2019; Wang et al., 2019; Zhang et al., 2019; Nguyen & Todorovic, 2019; Liu et al., 2020b; Wang et al., 2020; Tian et al., 2020; Boudiaf et al., 2021; Min et al., 2021) offer ways to learn to segment novel classes based on only a few…”
• GroupViT: Semantic Segmentation Emerges from Text Supervision (2022, methodology): “For example, few-shot learning [21, 43, 50, 55, 70, 77, 84] and active learning [8, 62, 66, 67, 83] are proposed to perform segmentation with as few pixel-wise labels as possible.”
• Few-shot Semantic Image Synthesis Using StyleGAN Prior (2021, unknown): “An alternative approach might be to use few-shot semantic segmentation [9, 35, 20, 33, 34, 39] to annotate unlabeled images to train image-to-image translation models.”
• Learning What Not to Segment: A New Perspective on Few-Shot Segmentation (2022, methodology): “Fueled by the success of few-shot classification, most existing FSS approaches strive to achieve generalization through meta-learning frameworks [23, 30–34, 36–38, 47, 55, 56, 58, 61, 62, 64–67].”
• Hypercorrelation Squeeze for Few-Shot Segmenation (2021, methodology): “We evaluate the proposed model on PASCAL-5, COCO20, and FSS-1000 and compare the results with recent methods [4, 37, 46, 54, 61, 63, 70, 74, 75, 86].”
• Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? (2020, methodology): “Graph CNNs have also been employed to establish more robust correspondences between the support and query images, enhancing the learned prototypes [36].”
• Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation (2021, methodology): “We follow the same scheme for training and evaluation as on the Pascal-5i.”
• Generalized Few-shot Semantic Segmentation (2020, background): “Few-Shot Segmentation (FSSeg) places semantic segmentation in the few-shot scenario [2,8,11,15,18,20,21,24,27,28,33,34,38,41,48,51,52,57, 61, 62, 66, 67], where dense pixel labeling is performed on new classes with only a few support samples.”
• Few-Shot Segmentation via Cycle-Consistent Transformer (2021, methodology): “Rather than using prototypes to abstract the support information, [37, 30] (Figure 1(c)) propose to employ the attention mechanism to extract information from support foreground pixels for segmenting query.”