X-GACMN: An X-Shaped Generative Adversarial Cross-Modal Network with Hypersphere Embedding | |
Weikuo Guo1; Jian Liang2,3; Xiangwei Kong1; Lingxiao Song2; Ran He2,3 | |
2018 | |
会议名称 | Asian Conference on Computer Vision (ACCV 2018) |
会议日期 | 2018 |
会议地点 | Australia |
摘要 | How to bridge heterogeneous gap between different modalities is one of the main challenges in cross-modal retrieval task. Most existing methods try to tackle this problem by projecting data from different modalities into a common space. In this paper, we introduce a novel X-Shaped Generative Adversarial Cross-Modal Network (X-GACMN) to learn a better common space between different modalities. Specifically, the proposed architecture combines the process of synthetic data generation and distribution adapting into a unified framework to make sure the heterogeneous modality distributions similar to each other in the learned common subspace. To promote the discriminative ability, a new loss function that combines intra-modality angular softmax loss and cross-modality pair-wise consistent loss is further imposed on the common space, hence the learned features can well preserve both intermodality structure and intra-modality structure on a hypersphere manifold. Extensive experiments on three benchmark datasets show the effectiveness of the proposed approach. |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23807 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Xiangwei Kong |
作者单位 | 1.Dalian University of Technology 2.University of Chinese Academy of Science(UCAS) 3.CRIPAC and NLPR, CASIA |
推荐引用方式 GB/T 7714 | Weikuo Guo,Jian Liang,Xiangwei Kong,et al. X-GACMN: An X-Shaped Generative Adversarial Cross-Modal Network with Hypersphere Embedding[C],2018. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
accv18.pdf(1088KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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