Cross-Modal Deep Variational Hashing In this paper, we propose a cross-modal deep variational hashing (CMDVH) method for cross-modality multimedia retrieval. Unlike existing cross-modal hashing methods which learn a single pair of projections to map each example as a binary... VE Liong,J Lu,...
In this paper, we propose a variational deep representation learning (VDRL) approach for cross-modal retrieval. Numerous existing methods map the image and text to the point representations, which is challenging to model the semantic multiplicity of the sample. To address this issue, our VDRL ...
multimodalone-for-allcross-domain-recommendationimage-recommendationllmmulti-modal-recommendationfoundation-recommendation-modeltext-recommendationmultimodal-recommenddationtransfer-learning-recommendationtransferable-recommendationpre-training-datasetmulti-domain-recommendationllm-recommendationcross-platform-recommendationpre-train...
(ACT🎬). The pretrained Transformers are transferred as cross-modal 3D teachers using discrete variational autoencoding self-supervision, during which the Transformers are frozen with prompt tuning for better knowledge inheritance. The latent features encoded by the 3D teachers are used as the target...
Cross-modal retrieval has become a highlighted research topic, to provide flexible retrieval experience across multimedia data such as image, video, text a
Matrix factorization is one of the techniques that leverages deep learning models’ methods to make recommendations across different domains [66]. 3.3.4. Generative artificial intelligence models Generative AI models such as generative adversarial networks (GANs) and variational autoencoders (VAEs), ...
This paper presents a novel cross-modal retrieval framework, called deep dual variational hashing (DDVH), by exploring dual variational mappings between modalities to bridge the inherent modality gap. Specifically, DDVH consists of two sub-modules, which are visual variational mapping (VVM) and ...
A deep subdomain associate adaptation network for cross-session and cross-subject EEG emotion recognition. Biomed. Signal Process. Control 2022, 78, 103873. [Google Scholar] [CrossRef] Wang, Y.; Qiu, S.; Li, D.; Du, C.; Lu, B.L.; He, H. Multi-modal domain adaptation variational ...