Knowledge Commons of Institute of Automation,CAS
Task-aware adaptive attention learning for few-shot semantic segmentation | |
Mao, Binjie1,2; Wang, Lingfeng1,2; Xiang, Shiming1,2; Pan, Chunhong1 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2022-07-14 | |
卷号 | 494页码:104-115 |
摘要 | Few-shot semantic segmentation is a newly developing and challenging computer vision task which aims to predict pixel-wise segmentation on the novel categories where only a few annotated samples are supplied. Because of the scarcity of the annotated novel class samples, the main obstacle of this issue is the diversity of objects in the support set and query set. This paper proposes a novel network aiming to bridge the gap by exploring the correlation between the support feature and the query feature. Specifically, a task-aware adaptive attention module(TAAM) is introduced to extract the task-specific information from the current input and integrates it into the feature representations both in channel dimension and spatial dimension for adaptive reinforcement. Besides, an additional prediction refinement module(RPM) is attached to further optimize the predictions to present more details of objects. Furthermore, through a non-parameter aggregation operation, the proposed network is easy to generalize to k-shot segmentation without developing specific architectures. Extensive experiments on three benchmarks demonstrate that our method exceeds previous state-of-the-arts with a sizable margin, verifying the effectiveness of the proposed method. (C) 2022 Elsevier B.V. All rights reserved. |
关键词 | Few-shot semantic segmentation Adaptive feature learning Attention mechanism Task-aware |
DOI | 10.1016/j.neucom.2022.04.089 |
关键词[WOS] | NETWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018-AAA0100400] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[62071466] ; National Natural Science Foundation of China[61976208] ; National Natural Science Foundation of China[62076242] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000830184400010 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 小样本高噪声数据学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49756 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Wang, Lingfeng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Dept Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Mao, Binjie,Wang, Lingfeng,Xiang, Shiming,et al. Task-aware adaptive attention learning for few-shot semantic segmentation[J]. NEUROCOMPUTING,2022,494:104-115. |
APA | Mao, Binjie,Wang, Lingfeng,Xiang, Shiming,&Pan, Chunhong.(2022).Task-aware adaptive attention learning for few-shot semantic segmentation.NEUROCOMPUTING,494,104-115. |
MLA | Mao, Binjie,et al."Task-aware adaptive attention learning for few-shot semantic segmentation".NEUROCOMPUTING 494(2022):104-115. |
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