Evaluation Before Generation: A Paradigm for Robust Multimodal Sentiment Analysis with Missing Modalities

• URL: http://arxiv.org/abs/2604.05558v1
• Date: Tue, 07 Apr 2026 07:59:06 GMT
• Title: Evaluation Before Generation: A Paradigm for Robust Multimodal Sentiment Analysis with Missing Modalities
• Abstract summary: The missing modality problem poses a fundamental challenge in multimodal sentiment analysis.<n>Existing approaches primarily improve robustness through prompt learning and pre trained models.<n>A Prompt based Missing Modality Adaptation framework is proposed to address these issues.
• Score: 21.767502810187477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The missing modality problem poses a fundamental challenge in multimodal sentiment analysis, significantly degrading model accuracy and generalization in real world scenarios. Existing approaches primarily improve robustness through prompt learning and pre trained models. However, two limitations remain. First, the necessity of generating missing modalities lacks rigorous evaluation. Second, the structural dependencies among multimodal prompts and their global coherence are insufficiently explored. To address these issues, a Prompt based Missing Modality Adaptation framework is proposed. A Missing Modality Evaluator is introduced at the input stage to dynamically assess the importance of missing modalities using pretrained models and pseudo labels, thereby avoiding low quality data imputation. Building on this, a Modality invariant Prompt Disentanglement module decomposes shared prompts into modality specific private prompts to capture intrinsic local correlations and improve representation quality. In addition, a Dynamic Prompt Weighting module computes mutual information based weights from cross attention outputs to adaptively suppress interference from missing modalities. To enhance global consistency, a Multi level Prompt Dynamic Connection module integrates shared prompts with self attention outputs through residual connections, leveraging global prompt priors to strengthen key guidance features. Extensive experiments on three public benchmarks, including CMU MOSI, CMU MOSEI, and CH SIMS, demonstrate that the proposed framework achieves state of the art performance and stable results under diverse missing modality settings. The implementation is available at https://github.com/rongfei-chen/ProMMA

Related papers

• General Incomplete Multimodal Learning via Dynamic Quality Perception [5.340979083445884]
  We propose a unified framework that simultaneously handles both inter-modality missing and intra-modality degradation.<n>Specifically, GIML models heterogeneous missing patterns as continuous modality information degradation.<n>To achieve reliable quality perception, we introduce a Noise-aware Quality Estor that learns the mapping from corrupted features to noise intensity.
  arXiv  Detail & Related papers  (2026-07-08T03:13:43Z)
• Learning from Reliable Latent Prompts for Visual Recognition with Missing Modalities [0.0]
  Large-scale multimodal models (LMMs) have achieved superior performance in visual recognition by synergizing information across diverse, massive-scale paired modalities.<n>In real-world scenarios, however, missing-modality inputs are ubiquitous, causing models optimized for modality-complete data to exhibit precipitous performance degradation.<n>Existing research has introduced prompt learning to mitigate this issue, typically by generating dynamic prompts from instance-level features.<n>As higher missing rates increase the proportion of incomplete modalities, the resulting instability in prompt learning limits the model's performance.
  arXiv  Detail & Related papers  (2026-06-29T17:36:47Z)
• Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion [71.20734649881258]
  Real-world multimodal datasets often suffer from modality incompleteness due to sensor failures, annotation scarcity, or privacy constraints.<n>One effective solution is modality completion, which reconstructs missing features to provide modality-complete graphs for downstream tasks.<n>We propose GRE-MC, a Graph Retrieval-Enhanced Modality Completion framework, to overcome these limitations.
  arXiv  Detail & Related papers  (2026-05-01T13:50:52Z)
• LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations [53.20772659095155]
  This paper tackles the more challenging setting of IML under training-time incomplete observations.<n>We propose LIMSSR (LLM-Driven Incomplete Multimodal Sequence-to-Score Reasoning), a framework that reformulates this challenge as a conditional sequence reasoning task.
  arXiv  Detail & Related papers  (2026-05-01T06:11:42Z)
• Plug, Play, and Fortify: A Low-Cost Module for Robust Multimodal Image Understanding Models [6.350443894942629]
  Multimodal Weight Allocation Module (MWAM) is a plug-and-play component that dynamically re-balances the contribution of each branch during training.<n>MWAM delivers consistent performance gains across a wide range of tasks and modality combinations.
  arXiv  Detail & Related papers  (2026-02-26T05:51:41Z)
• I$^3$-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation [56.55935146424585]
  We introduce textbfI$3$-MRec, which learns with textbfInformation bottleneck principle for textbfIncomplete textbfModality textbfRecommendation.<n>By treating each modality as a distinct semantic environment, I$3$-MRec employs invariant risk minimization (IRM) to learn preference-oriented representations.<n>I$3$-MRec consistently outperforms existing state-of-the-art MRS methods across various modality-missing scenarios
  arXiv  Detail & Related papers  (2025-08-06T09:29:50Z)
• PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning [42.00851701431368]
  Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs.<n>A critical challenge remains: the issue of missing modalities during incremental learning phases.<n>We propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios.
  arXiv  Detail & Related papers  (2025-01-16T08:04:04Z)
• Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning [27.867369806400834]
  We propose RAGPT, a novel Retrieval-AuGmented dynamic Prompt Tuning framework.<n>RAGPT comprises three modules: (I) the multi-channel retriever, (II) the missing modality generator, and (III) the context-aware prompter.<n>Experiments conducted on three real-world datasets show that RAGPT consistently outperforms all competitive baselines in handling incomplete modality problems.
  arXiv  Detail & Related papers  (2025-01-02T07:39:48Z)
• Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues.<n>We propose a novel approach to address this issue at test time without requiring retraining.<n>MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast [23.936677199734213]
  In this paper, we introduce a prototype library into the FedAvg-based Federated Learning framework. The proposed method utilizes prototypes as masks representing missing modalities to formulate a task-calibrated training loss and a model-agnostic uni-modality inference strategy. Compared to the baselines, our method improved inference accuracy by 3.7% with 50% modality missing during training and by 23.8% during uni-modality inference.
  arXiv  Detail & Related papers  (2023-12-21T00:55:12Z)
• Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities [76.08541852988536]
  We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
  arXiv  Detail & Related papers  (2022-10-27T12:16:25Z)
• Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering [63.87200781247364]
  Correlation Information Bottleneck (CIB) seeks a tradeoff between compression and redundancy in representations. We derive a tight theoretical upper bound for the mutual information between multimodal inputs and representations.
  arXiv  Detail & Related papers  (2022-09-14T22:04:10Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

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.