Robust Multi-modal Task-oriented Communications with Redundancy-aware Representations
Abstract
Semantic communications for multi-modal data can transmit task-relevant information efficiently over noisy and bandwidth-limited channels. However, a key challenge is to simultaneously compress inter-modal redundancy and improve semantic reliability under channel distortion. To address the challenge, we propose a robust and efficient multi-modal task-oriented communication framework that integrates a two-stage variational information bottleneck (VIB) with mutual information (MI) redundancy minimization. In the first stage, we apply uni-modal VIB to compress each modality separately, i.e., text, audio, and video, while preserving task-specific features. To enhance efficiency, an MI minimization module with adversarial training is then used to suppress cross-modal dependencies and to promote complementarity rather than redundancy. In the second stage, a multi-modal VIB is further used to compress the fused representation and to enhance robustness against channel distortion. Experimental results on multi-modal emotion recognition tasks demonstrate that the proposed framework significantly outperforms existing baselines in accuracy and reliability, particularly under low signal-to-noise ratio regimes. Our work provides a principled framework that jointly optimizes modality-specific compression, inter-modal redundancy, and communication reliability.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a robust framework for multi-modal task-oriented communication that effectively minimizes redundancy and enhances reliability under challenging conditions. The innovative integration of variational information bottleneck techniques with mutual information minimization represents a meaningful contribution to the field of machine learning and communication systems.
Methodology
The proposed methodology introduces a two-stage variational information bottleneck (VIB) framework that effectively addresses the challenges of multi-modal task-oriented communication. The first stage employs uni-modal VIB to compress each modality while preserving task-specific features, and the second stage utilizes multi-modal VIB to enhance robustness against channel distortion. The integration of mutual information (MI) redundancy minimization through adversarial training is a significant innovation that promotes complementarity among modalities, reducing redundancy. The use of gradient reversal layers (GRL) for adversarial training is a novel approach that enhances the stability of the optimization process. Overall, the methodology is well-structured and demonstrates a clear advancement in the field.
Results & evaluation
The experiments are comprehensive, utilizing two well-known multi-modal datasets (CMU-MOSI and CMU-MOSEI) for emotion recognition tasks. The results indicate that the proposed framework significantly outperforms existing baselines, particularly under low signal-to-noise ratio (SNR) conditions. The metrics used for evaluation (Top-2 accuracy, F1 score, MAE) are appropriate for the task, and the results are presented clearly. The ablation studies and analysis of the adversarial redundancy reduction provide additional insights into the effectiveness of the proposed approach.
Reproducibility
The paper provides a detailed description of the methodology, including the architecture of the neural networks, loss functions, and training procedures. However, there is no mention of code availability or a public repository, which limits reproducibility. The absence of a demo or project URL further hinders the ability to replicate the results.
Limitations
One limitation is the lack of a comprehensive comparison with a broader range of existing methods, which could provide a clearer context for the performance improvements. Additionally, while the paper addresses redundancy reduction effectively, it does not explore the trade-offs involved in the complexity of the model versus performance gains in detail. The reliance on specific datasets may also limit the generalizability of the findings.
Broader impact
The proposed framework has significant implications for applications in areas such as autonomous driving, remote healthcare, and any domain requiring efficient multi-modal communication under adverse conditions. By improving the robustness and efficiency of semantic communications, this work could enhance the performance of intelligent systems that rely on multi-modal data integration. The paper presents a robust framework for multi-modal task-oriented communication that effectively minimizes redundancy and enhances reliability under challenging conditions. The innovative integration of variational information bottleneck techniques with mutual information minimization represents a meaningful contribution to the field of machine learning and communication systems.
Author of this paper? Send a correction.