A variational Bayesian framework exploits reciprocity between referents and context plus semantic reproduction to improve referring expression grounding over pairwise methods in supervised and unsupervised settings.
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cs.CV 3years
2019 3verdicts
UNVERDICTED 3representative citing papers
The MIA model with GC, RGA, and BFM modules achieves state-of-the-art performance on the CUHK-PEDES dataset for description-based person re-identification.
M3D-GAN introduces modality subnets, a shared computing body, and a universal attention module to enable translation across text, image, and speech modalities and their internal domains.
citing papers explorer
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Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions
A variational Bayesian framework exploits reciprocity between referents and context plus semantic reproduction to improve referring expression grounding over pairwise methods in supervised and unsupervised settings.
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Improving Description-based Person Re-identification by Multi-granularity Image-text Alignments
The MIA model with GC, RGA, and BFM modules achieves state-of-the-art performance on the CUHK-PEDES dataset for description-based person re-identification.
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M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention
M3D-GAN introduces modality subnets, a shared computing body, and a universal attention module to enable translation across text, image, and speech modalities and their internal domains.