In this paper, we propose a multi-loss network (PFME) based on progressive fusion and mixtures of experts for multimodal sentiment analysis.

Abstract—In this paper, we propose a multi-loss network. (PFME) based on progressive fusion and mixtures of experts for multimodal sentiment analysis.

May 28, 2024 · In this work, we propose the targeted aspect-based multimodal sentiment analysis (TABMSA) for the first time. Furthermore, an attention capsule ...

Dec 13, 2023 · The core of multimodal sentiment analysis is to find effective encoding and fusion methods to make accurate predictions.

Missing: Network Mixture Experts

A deep dense fusion network with multimodal residual (DFMR) to integrate multi-aspect information including language, acoustic speeches, and visual images ...

A multi-loss network based on progressive fusion and mixtures of experts for multimodal sentiment analysis that achieves state-of-the-art performance on the ...

Following the classical Mixture-of-Experts (MoE) [27] framework, we design a set of expert networks as follows. Each expert specializes in a subset of all M ...

Multimodal fusion networks have a clear advantage over their unimodal counterparts in various applications, such as sentiment analysis [21,38,50], action ...

We test Progressive Fusion on tasks including affective sentiment detection, multimedia analysis, and time series fusion with different models, demonstrating ...