2 days ago · Multimodal tasks require a model to process multiple data modalities (text, image, audio, video) to solve a particular problem.

6 days ago · We propose canonical similarity analysis (CSA), which uses two unimodal encoders to replicate multimodal encoders using limited data. \namemaps unimodal ...

Sep 25, 2024 · Multimodal embedding is the process of generating a vector representation of an image that captures its features and characteristics.

Oct 3, 2024 · Illustration of learning similarity between multiple modalities. Each modality has an encoder and the representations extracted by different encoders are ...

2 days ago · A similarity metric is used to assess the degree of similarity between two images connected via a spatial transformation. Hence, it motivates us to design a new ...

Oct 6, 2024 · To address this task, we propose an inter- modality similarity learning (IMSL) framework with four modules that interact iteratively in a mutually beneficial.

Oct 8, 2024 · Leveraging cosine similarity, we pinpoint the class exhibiting the highest similarity score. We use typical classification metrics to evaluate the model:.

Sep 19, 2024 · The purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi- ...

2 days ago · We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation ...

Sep 27, 2024 · Multimodal Named Entity Recognition (MNER) leverages semantic information from multiple modalities to enhance the identification and classification of name.