Semi-supervised Segmentation Based on Error-Correcting Supervision
By Robert Mendel, Luis Antonio De Souza, David Rauber, João Paulo Papa, Christoph Palm. 2020
125 citations recorded by the source corpus.
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
- Chen Change Loy
- Zeynep Akata
- Xiatian Zhu
- Jingdong Wang
- Bo Dai
- Trevor Darrell
- Yuhui Yuan
- Massimiliano Mancini
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Chen Change Loy
- Xiaogang Wang
- Dacheng Tao
- Yi Yang
- Ming-Hsuan Yang
- Jingdong Wang
- Trevor Darrell
Citations in the corpus
- ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes (2021, background): “limitations by jointly learning from both annotated and unannotated images [11, 22, 29, 40, 42, 45].”
- Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (2021, background): “A typical manner [31, 38] under this category is to train two or multiple learners simultaneously and exploit their agreement of predictions to measure the label reliability.”
- DenseCLIP: Extract Free Dense Labels from CLIP (2021, unknown): “Self-training has firstly become popular in the semi-supervised classification task [29, 33, 35, 43] and is also recently applied in the semi-supervised/zero-shot semantic segmentation settings [4, 10, 27, 28, 36, 37, 40, 56].”
- Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision (2021, methodology): “Recently it is applied for semi-supervised segmentation [6, 43, 42, 9, 13, 25, 14, 24].”
- Extract Free Dense Labels from CLIP (2021, methodology): “Self-training has firstly become popular in the semisupervised classification task [33,29,35,43] and is also recently applied in the semi-supervised/zero-shot semantic segmentation settings [27,37,4,28,40,36,56,8].”
- Semi-Supervised and Unsupervised Deep Visual Learning: A Survey (2022, methodology): “…or UL for image classification, and adapt techniques such as consistency regularization [31], [178], [179], [180], self-training [32], [168], [174], [181], [182], [183], [184], GAN frameworks [185], [186] in SSL, or contrastive learning [187], [188], [189], [190] in UL to learn from unlabeled…”