面向科技信息的语义对抗与媒体对抗跨媒体检索
Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval
AI总结：
针对现有跨媒体检索方法忽略语义一致性与语义内媒体判别性的问题，提出面向科技信息的SMCR方法，通过双损失优化与双映射网络提升检索性能，实验效果优于现有最优方法。
摘要：
科技信息跨媒体检索是跨媒体研究中的重要任务之一。跨媒体科技信息检索从海量多源异构科技资源中获取目标信息，有助于设计满足用户需求的应用，包括科技信息推荐、个性化科技信息检索等。跨媒体检索的核心是学习一个公共子空间，使不同媒体的数据映射到该子空间后可直接相互比较。在子空间学习中，现有方法通常关注建模媒体内数据的判别性和媒体间数据映射后的不变性，但忽略了媒体间数据映射前后的语义一致性以及语义内数据的媒体判别性，这限制了跨媒体检索的效果。鉴于此，我们提出一种面向科技信息的语义对抗与媒体对抗跨媒体检索方法（Semantics-adversarial and Media-adversarial Cross-media Retrieval，SMCR）以寻找有效的公共子空间。具体而言，SMCR除了建模媒体内语义判别性外，还最小化媒体间语义一致性损失，以保留映射前后的语义相似性。此外，SMCR构建基础特征映射网络和精细化特征映射网络，联合最小化语义内的媒体判别损失，从而增强特征映射网络混淆媒体判别网络的能力。在两个数据集上的实验结果表明，所提出的SMCR在跨媒体检索任务中优于现有最优方法。
英文摘要：
Cross-media retrieval of scientific and technological information is one of the important tasks in the cross-media study. Cross-media scientific and technological information retrieval obtain target information from massive multi-source and heterogeneous scientific and technological resources, which helps to design applications that meet users' needs, including scientific and technological information recommendation, personalized scientific and technological information retrieval, etc. The core of cross-media retrieval is to learn a common subspace, so that data from different media can be directly compared with each other after being mapped into this subspace. In subspace learning, existing methods often focus on modeling the discrimination of intra-media data and the invariance of inter-media data after mapping; however, they ignore the semantic consistency of inter-media data before and after mapping and media discrimination of intra-semantics data, which limit the result of cross-media retrieval. In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace. Specifically, SMCR minimizes the loss of inter-media semantic consistency in addition to modeling intra-media semantic discrimination, to preserve semantic similarity before and after mapping. Furthermore, SMCR constructs a basic feature mapping network and a refined feature mapping network to jointly minimize the media discriminative loss within semantics, so as to enhance the feature mapping network's ability to confuse the media discriminant network. Experimental results on two datasets demonstrate that the proposed SMCR outperforms state-of-the-art methods in cross-media retrieval.
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