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Bridging multimedia heterogeneity gap via Graph Representation Learning for cross-modal retrieval
Neural Networks, 2021Information retrieval among different modalities becomes a significant issue with many promising applications. However, inconsistent feature representation of various multimedia data causes the "heterogeneity gap" among various modalities, which is a challenge in cross-modal retrieval.
Qingrong, Cheng, Xiaodong, Gu
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OTCMR: Bridging Heterogeneity Gap with Optimal Transport for Cross-modal Retrieval
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021Cross-modal retrieval is a classic task in the multimedia community, which aims to search for semantically similar results from different modalities. The core of cross-modal retrieval is to learn the most correlated features in a common feature space for the multi-modal data so that the similarity can be directly measured.
Mingyang Li, Shao-Lun Huang, Lin Zhang
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Bridging the Cross-Modality Semantic Gap in Visual Question Answering
IEEE Transactions on Neural Networks and Learning SystemsThe objective of visual question answering (VQA) is to adequately comprehend a question and identify relevant contents in an image that can provide an answer. Existing approaches in VQA often combine visual and question features directly to create a unified cross-modality representation for answer inference.
Boyue Wang +5 more
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Mind the Gap: Learning Modality-Agnostic Representations With a Cross-Modality UNet
IEEE Transactions on Image ProcessingCross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap include reducing the distributional differences of representations of different modalities, learning indistinguishable representations or explicit modality transfer.
Xin Niu +5 more
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