This paper presents a generic modelling scheme to characterize the nonlinear structure of the manifold and to learn its multimodal distribution. Our approach ...

Apr 29, 2002 · This paper presents a generic modelling scheme to characterize the nonlinear structure of the manifold and to learn its multimodal distribution.

Mar 1, 2024 · Multimodal representation learning techniques reduce the heterogeneity gap between different modalities by processing raw heterogeneous data ...

Multimodal learning refers to the process of learning representations from different types of input modalities, such as image data, text or speech.

Sep 21, 2022 · In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data.

This study introduces a novel approach to enhance the performance of established multimodal transformer models without needing extra information.

Missing: Parameterized Local Structures.

Nov 22, 2023 · This paper addresses the issue of noisy pairs in vision-language pre-training by proposing a weighted contrastive loss that estimates the noisiness of data ...

The local MI opti- mization is usually an easier task given its lower dimension and more training samples to discover useful representations. The utility of our ...

Jun 6, 2024 · Typically, INR is parameterized by a multiplayer perceptron (MLP) which takes the coordinates as the inputs and generates corresponding ...