# Ada2I: Enhancing Modality Balance for Multimodal Conversational Emotion Recognition

arXiv: https://arxiv.org/abs/2408.12895 · HTML: https://mlsanity.com/paper/2408.12895/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2024.

## Built on by (most cited first, 8 of 8)

- [Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.16144/) (2026)
- [MCE: Towards a General Framework for Handling Missing Modalities under Imbalanced Missing Rates](https://mlsanity.com/paper/2510.10534/) (2025)
- [Effective Context Modeling Framework for Emotion Recognition in Conversations](https://mlsanity.com/paper/2412.16444/) (2024)
- [MissBench: Benchmarking Multimodal Affective Analysis under Imbalanced Missing Modalities](https://mlsanity.com/paper/2603.09874/) (2026)
- [BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under Imbalanced Missing Rates](https://mlsanity.com/paper/2603.19718/) (2026)
- [Cross-Space Synergy: A Unified Framework for Multimodal Emotion Recognition in Conversation](https://mlsanity.com/paper/2512.03521/) (2025)
- [Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment](https://mlsanity.com/paper/2604.19547/) (2026)
- [Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition](https://mlsanity.com/paper/2605.21565/) (2026)

## Abstract (arXiv)

Multimodal Emotion Recognition in Conversations (ERC) is a typical multimodal learning task in exploiting various data modalities concurrently. Prior studies on effective multimodal ERC encounter challenges in addressing modality imbalances and optimizing learning across modalities. Dealing with these problems, we present a novel framework named Ada2I, which consists of two inseparable modules namely Adaptive Feature Weighting (AFW) and Adaptive Modality Weighting (AMW) for feature-level and modality-level balancing respectively via leveraging both Inter- and Intra-modal interactions. Additionally, we introduce a refined disparity ratio as part of our training optimization strategy, a simple yet effective measure to assess the overall discrepancy of the model's learning process when handling multiple modalities simultaneously. Experimental results validate the effectiveness of Ada2I with state-of-the-art performance compared to baselines on three benchmark datasets, particularly in addressing modality imbalances.