Ordering Matters: Rank-Aware Selective Fusion for Blended Emotion Recognition

Problem
This paper addresses the challenge of blended emotion recognition, where emotions are expressed through overlapping multimodal cues rather than distinct signals. The existing literature lacks effective methods for selectively fusing information from multiple modalities (video and audio) to enhance recognition accuracy. This work is presented as a preprint, indicating it has not yet undergone peer review.

Method
The authors propose a rank-aware multi-encoder framework that integrates features from diverse pre-extracted video and audio encoders. The architecture consists of the following key components:

• Feature Projection: Heterogeneous encoder outputs are projected into a shared latent space to facilitate effective fusion.
• Attention-based Gating Module: This module estimates the importance of each encoder’s output on a sample-wise basis, allowing the model to selectively fuse only the top-n most informative encoders.
• Decoupled Prediction Heads: The model employs two separate heads for predicting the presence and salience of emotions, which are aligned through probability-level fusion.
• Unsupervised Domain Adaptation: The framework incorporates feature-level unsupervised domain adaptation without the use of pseudo-labeling, enhancing robustness against distribution shifts.

The training process and compute resources utilized are not explicitly disclosed in the abstract.

Results
The proposed framework was evaluated on the BlEmoRE challenge, where it achieved a second-place ranking, demonstrating its effectiveness in the context of blended emotion recognition. The authors report that their method outperformed strong individual encoders and naïve multi-encoder fusion baselines, although specific numerical results (e.g., accuracy percentages or F1 scores) are not provided in the abstract.

Limitations
The authors acknowledge that their approach may be limited by the reliance on pre-extracted features, which could constrain the model’s ability to learn optimal representations directly from raw data. Additionally, the performance metrics and comparisons to baselines are not detailed in the abstract, which may hinder a comprehensive evaluation of the method’s effectiveness. The lack of peer review also raises questions about the robustness of the findings.

Why it matters
This work has significant implications for the field of emotion recognition, particularly in applications requiring nuanced understanding of blended emotions, such as human-computer interaction, affective computing, and social robotics. The rank-aware selective fusion approach could inspire further research into adaptive fusion techniques that leverage the strengths of multiple modalities while mitigating the noise from less informative signals. Additionally, the incorporation of unsupervised domain adaptation techniques may pave the way for more resilient models capable of generalizing across diverse datasets and real-world scenarios.

Authors: Junghyun Lee, Hyunseo Kim, Hanna Jang, Junhyug Noh
Source: arXiv:2605.21417
URL: https://arxiv.org/abs/2605.21417v1