TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis
- URL: http://arxiv.org/abs/2404.04545v1
- Date: Sat, 6 Apr 2024 07:56:09 GMT
- Title: TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis
- Authors: Ming Zhou, Weize Quan, Ziqi Zhou, Kai Wang, Tong Wang, Dong-Ming Yan,
- Abstract summary: Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment by leveraging language, visual, and acoustic modalities.
Past research predominantly focused on improving representation learning techniques and feature fusion strategies.
We introduce a Text-oriented Cross-Attention Network (TCAN) emphasizing the predominant role of the text modality in MSA.
- Score: 34.28164104577455
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment by leveraging language, visual, and acoustic modalities. Despite the remarkable performance exhibited by previous MSA approaches, the presence of inherent multimodal heterogeneities poses a challenge, with the contribution of different modalities varying considerably. Past research predominantly focused on improving representation learning techniques and feature fusion strategies. However, many of these efforts overlooked the variation in semantic richness among different modalities, treating each modality uniformly. This approach may lead to underestimating the significance of strong modalities while overemphasizing the importance of weak ones. Motivated by these insights, we introduce a Text-oriented Cross-Attention Network (TCAN), emphasizing the predominant role of the text modality in MSA. Specifically, for each multimodal sample, by taking unaligned sequences of the three modalities as inputs, we initially allocate the extracted unimodal features into a visual-text and an acoustic-text pair. Subsequently, we implement self-attention on the text modality and apply text-queried cross-attention to the visual and acoustic modalities. To mitigate the influence of noise signals and redundant features, we incorporate a gated control mechanism into the framework. Additionally, we introduce unimodal joint learning to gain a deeper understanding of homogeneous emotional tendencies across diverse modalities through backpropagation. Experimental results demonstrate that TCAN consistently outperforms state-of-the-art MSA methods on two datasets (CMU-MOSI and CMU-MOSEI).
Related papers
- Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning [21.127950337002776]
Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities.
We propose a Hierarchical Representation Learning Framework (HRLF) for the task under uncertain missing modalities.
We show that HRLF significantly improves MSA performance under uncertain modality missing cases.
arXiv Detail & Related papers (2024-11-05T04:04:41Z) - Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach [10.376378437321437]
We propose a Multimedia Misinformation Detection framework for detecting misinformation from video content by leveraging cross-modal entity consistency.
Our results demonstrate that MultiMD outperforms state-of-the-art baseline models.
arXiv Detail & Related papers (2024-08-16T16:14:36Z) - Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations [19.731611716111566]
We propose a Multimodal fusion approach for learning modality-Exclusive and modality-Agnostic representations.
We introduce a predictive self-attention module to capture reliable context dynamics within modalities.
A hierarchical cross-modal attention module is designed to explore valuable element correlations among modalities.
A double-discriminator strategy is presented to ensure the production of distinct representations in an adversarial manner.
arXiv Detail & Related papers (2024-07-06T04:36:48Z) - WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual
World Knowledge [73.76722241704488]
We propose a plug-in framework named WisdoM to leverage the contextual world knowledge induced from the large vision-language models (LVLMs) for enhanced multimodal sentiment analysis.
We show that our approach has substantial improvements over several state-of-the-art methods.
arXiv Detail & Related papers (2024-01-12T16:08:07Z) - Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach [15.54426275761234]
Multimodal sentiment analysis aims to identify the emotions expressed by individuals through visual, language, and acoustic cues.
Most of the existing research efforts assume that all modalities are available during both training and testing, making their algorithms susceptible to the missing modality scenario.
We propose a novel knowledge-transfer network to translate between different modalities to reconstruct the missing audio modalities.
arXiv Detail & Related papers (2023-12-28T06:47:18Z) - A Novel Energy based Model Mechanism for Multi-modal Aspect-Based
Sentiment Analysis [85.77557381023617]
We propose a novel framework called DQPSA for multi-modal sentiment analysis.
PDQ module uses the prompt as both a visual query and a language query to extract prompt-aware visual information.
EPE module models the boundaries pairing of the analysis target from the perspective of an Energy-based Model.
arXiv Detail & Related papers (2023-12-13T12:00:46Z) - MIR-GAN: Refining Frame-Level Modality-Invariant Representations with
Adversarial Network for Audio-Visual Speech Recognition [23.042478625584653]
We propose an adversarial network to refine frame-level modality-invariant representations (MIR-GAN)
In particular, we propose an adversarial network to refine frame-level modality-invariant representations (MIR-GAN)
arXiv Detail & Related papers (2023-06-18T14:02:20Z) - Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical
Fusion for Multimodal Affect Recognition [69.32305810128994]
Incongruity between modalities poses a challenge for multimodal fusion, especially in affect recognition.
We propose the Hierarchical Crossmodal Transformer with Dynamic Modality Gating (HCT-DMG), a lightweight incongruity-aware model.
HCT-DMG: 1) outperforms previous multimodal models with a reduced size of approximately 0.8M parameters; 2) recognizes hard samples where incongruity makes affect recognition difficult; 3) mitigates the incongruity at the latent level in crossmodal attention.
arXiv Detail & Related papers (2023-05-23T01:24:15Z) - Group Gated Fusion on Attention-based Bidirectional Alignment for
Multimodal Emotion Recognition [63.07844685982738]
This paper presents a new model named as Gated Bidirectional Alignment Network (GBAN), which consists of an attention-based bidirectional alignment network over LSTM hidden states.
We empirically show that the attention-aligned representations outperform the last-hidden-states of LSTM significantly.
The proposed GBAN model outperforms existing state-of-the-art multimodal approaches on the IEMOCAP dataset.
arXiv Detail & Related papers (2022-01-17T09:46:59Z) - Learning Relation Alignment for Calibrated Cross-modal Retrieval [52.760541762871505]
We propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations.
We present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions mutually via inter-modal alignment.
arXiv Detail & Related papers (2021-05-28T14:25:49Z) - Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person
Re-Identification [208.1227090864602]
Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem.
Existing VI-ReID methods tend to learn global representations, which have limited discriminability and weak robustness to noisy images.
We propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.
arXiv Detail & Related papers (2020-07-18T03:08:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.