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Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis
Huisheng Mao, Baozheng Zhang, Hua Xu, Ziqi Yuan, Yihe Liu
TL;DR
Real-world MSA data can contain visual occlusion, noisy audio, and ASR errors, raising the question of how modality noise affects model robustness. Robust-MSA is an interactive platform for simulating and visualizing noise and trying simple defenses; its demonstration shows that dropping visual input changes a prediction from Positive to Neutral.
Problem
Real-world MSA samples may contain visual occlusion, environmental audio noise, and ASR errors, making robustness to modality noise a key challenge.
Source: Introduction · p. 1Method
Robust-MSA is an interactive platform that simulates modality noise, visualizes effects on features and predictions, and offers simple noise defenses.
Source: Introduction · p. 1, Demonstrating Robust-MSA · p. 1, Noise Generation · p. 1Results
Dropping the visual modality changes the demonstration video’s prediction from Positive to Neutral.
Source: Engaging the Audience · p. 2Takeaways & Limitations
The authors hope the demonstration will raise concern about model robustness when applying MSA models in real-world applications.
Source: Engaging the Audience · p. 2
Abstract
from arXiv · showhide
Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.
Introduction
Real-world MSA inputs can contain visual, audio, and transcript noise unlike the carefully selected, often manually revised samples common in popular datasets. Robust-MSA presents an interactive platform for examining how modality noise affects MSA models.
Introduction
- Popular MSA datasets often contain clear, frontal videos and manually revised transcripts, while real-world samples may include occlusion, environmental audio noise, and ASR errors.These differences make model robustness to modality noise a key challenge for MSA.Source: Introduction · p. 1
- Robust-MSA is an interactive visualization platform for understanding how modality noise influences MSA models.The paper frames understanding noise effects as essential to developing robust models.Source: Introduction · p. 1
Demonstrating Robust-MSA
Robust-MSA lets researchers inject modality noise into videos, inspect its effects on features and predictions, and try simple defenses. Its interface combines word-level video, audio, and text perturbations with aligned views and an end-to-end MSA pipeline.
Demonstrating Robust-MSA
- The platform aligns user videos with transcripts, supports word-level noise customization, and visualizes how noise changes features and can lead to wrong predictions.Users first revise the automatically generated transcript to obtain a “perfect” instance.Source: Demonstrating Robust-MSA · p. 1
Noise Generation
- Six word-level simulators cover blank-screen or blurred video, muted or background-noise audio, and replaced or removed text.Injected noise can be applied to a word and marked with modality-specific background colors.Source: Noise Generation · p. 1
Noise Defence Methods
- Audio denoising, video motion-compensated interpolation, and feature interpolation are offered as simple noise defenses.The methods operate on raw audio, missing video frames, and missing features, respectively.Source: Noise Defence Methods · p. 2
End-to-End MSA Pipeline
- The pipeline uses eGeMapsv02 audio, facial-landmark and Action Unit visual, and BERT text features; it averages model outputs into three sentiment classes.Wav2vec2 generates timestamps for video/audio-to-text alignment, and the supported models are trained on MOSEI.Source: End-to-End MSA Pipeline · p. 2
Noise Influence Demonstration
- The platform aligns original, noise-injected, and defended video and audio with transcripts, and plots feature versions against transcript words.Figure 1 contrasts noise injection with feature and prediction comparison.Source: Noise Influence Demonstration · p. 2, Figure 1 · p. 2
Engaging the Audience
The demonstration illustrates that a small modality disturbance can change an MSA prediction, even for models designed to handle such noise. Dropping the visual modality changes the example’s classification from Positive to Neutral.
Engaging the Audience
- Dropping the visual modality changes the example’s prediction from Positive to Neutral, despite the demonstration’s focus on inconspicuous noise such as brief facial occlusion.The feature view identifies the “wink” action unit as a crucial visual sentiment cue.Source: Engaging the Audience · p. 2
- The authors hope the demonstration will raise concern about robustness for applying MSA models in real-world settings.Source: Engaging the Audience · p. 2