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Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss
Li, Qianzhong1,2; Zhang, Yujia1; Sun, Shiying1; Zhao, Xiaoguang1; Li, Kang3; Tan, Min1
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2021-08-18
Volume449Pages:117-135
Corresponding AuthorLi, Qianzhong(liqianzhong2017@ia.ac.cn)
AbstractZero-shot object detection (ZSD) aims to locate and recognize novel objects without additional training samples. Most existing methods usually map visual features to semantic space, resulting in a hubness problem, and learning an effective feature mapping between the two modalities remains a considerable challenge. In this work, we propose a novel end-to-end framework, Semantic-Visual Auto-Encoder (SVAE) network, to tackle the above issues. Distinct from previous works that utilize fully-connected layers to learn the feature mapping, we implement a 1-dimensional convolution with various shared filters to con-struct the auto-encoder, which maps semantic features to visual space to alleviate the hubness problem. Specifically, we design a novel loss function, Softplus Margin Focal Loss (SMFL), for object classification channel to align the projected semantic features in visual space and address the class imbalance problem. The SMFL improves the discrimination of projections on positive and negative categories and maintains the property of focal loss. Besides, to promote the localization performance for novel objects, we also pro -vide semantic information for object localization channel and utilize a trainable matrix to align the semantic-visual mapping, considering noises in semantic representations. We conduct extensive exper-iments on four challenging benchmarks. The experimental results show the competitive performances compared with state-of-the-art approaches. Especially, we achieve 8.39%/6.58% mean average precision (mAP) improvements for ZSD/general-ZSD on Microsoft COCO benchmark. (c) 2021 Elsevier B.V. All rights reserved.
KeywordZero-shot object detection Softplus margin focal loss Semantic-visual alignment Auto-encoder architecture
DOI10.1016/j.neucom.2021.03.073
WOS KeywordATTRIBUTES
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Project of China[2019YFB1310601] ; National Key R&D Program of China[2017YFC082020303] ; National Natural Science Foundation of China[61673378]
Funding OrganizationNational Key Research and Development Project of China ; National Key R&D Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000652818400011
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45218
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorLi, Qianzhong
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligences, Beijing, Peoples R China
3.Information Sci Acad China Elect Technol Grp Corp, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Qianzhong,Zhang, Yujia,Sun, Shiying,et al. Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss[J]. NEUROCOMPUTING,2021,449:117-135.
APA Li, Qianzhong,Zhang, Yujia,Sun, Shiying,Zhao, Xiaoguang,Li, Kang,&Tan, Min.(2021).Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss.NEUROCOMPUTING,449,117-135.
MLA Li, Qianzhong,et al."Rethinking semantic-visual alignment in zero-shot object detection via a softplus margin focal loss".NEUROCOMPUTING 449(2021):117-135.
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