Fine-grained Cross-media Representation Learning with Deep Quantization Attention Network

Liang, Meiyu; Du, Junping; Liu, Wu; Xue, Zhe; Geng, Yingsan; Yang, Congxian

doi:10.1145/3343031.3350892

Cited by 4 publications

(1 citation statement)

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“…We use the classic evaluation metric in cross-media retrieval [11,18,19], mean Average Precision (mAP), to evaluate the performance of algorithms in the two tasks: using text to retrieve image (txt2img) and using image to retrieve text (img2txt). To calculate mAP, we need to calculate the average precision of 𝑅 retrieved documents…”

Section: Evaluation Metricsmentioning

confidence: 99%

Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval

Li¹,

Du²,

Kou³

et al. 2022

Preprint

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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

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Section: Evaluation Metricsmentioning

confidence: 99%