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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation

February 1, 2023

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Authors

Pinzhuo Tian

Nanjing University


Zhangkai Wu

Nanjing University


Lei Qi

Nanjing University


Lei Wang

University of Wollongong


Yinghuan Shi

Nanjing University


Yang Gao

Nanjing University


DOI:

10.1609/aaai.v34i07.6887


Abstract:

To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm. However, most existing methods only focus on the traditional 1-way segmentation setting (i.e., one image only contains a single object). This is far away from practical semantic segmentation tasks where the K-way setting (K > 1) is usually required by performing the accurate multi-object segmentation. To deal with this issue, we formulate the few-shot semantic segmentation task as a learning-based pixel classification problem, and propose a novel framework called MetaSegNet based on meta-learning. In MetaSegNet, an architecture of embedding module consisting of the global and local feature branches is developed to extract the appropriate meta-knowledge for the few-shot segmentation. Moreover, we incorporate a linear model into MetaSegNet as a base learner to directly predict the label of each pixel for the multi-object segmentation. Furthermore, our MetaSegNet can be trained by the episodic training mechanism in an end-to-end manner from scratch. Experiments on two popular semantic segmentation datasets, i.e., PASCAL VOC and COCO, reveal the effectiveness of the proposed MetaSegNet in the K-way few-shot semantic segmentation task.

Topics: AAAI

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Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation AAAI 2020, 12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao (2020). Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao. Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao. 2020. Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao. (2020) "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.12087-12094

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao, "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation", AAAI, p.12087-12094, 2020.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao. "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao. "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 12087-12094.

Pinzhuo Tian||Zhangkai Wu||Lei Qi||Lei Wang||Yinghuan Shi||Yang Gao. Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation. AAAI[Internet]. 2020[cited 2023]; 12087-12094.


ISSN: 2374-3468


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