Title: CAMDrop: Gradient-Guided Dynamic Feature Dropping for Multimodal Balanced Learning
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Multimodal Learning, Modality Imbalance, Gradient-Guided Dropping

Abstract: Multimodal learning seeks to integrate complementary information from diverse modalities to enhance model performance. However, modality imbalance—where dominant modalities overshadow weaker ones—often hinders effective representation learning and generalization. Existing solutions are either gradient-based, which lack fine-grained control, or feature-based, which suffer from randomness and low interpretability. To address these challenges, we propose CAMDrop, a lightweight and plug-and-play strategy that suppresses dominant modality regions guided by class activation maps (CAMs). By leveraging GradCAM, CAMDrop identifies class-relevant spatial features and adaptively masks them based on instance-level importance, enabling semantically meaningful and dynamically adjusted suppression without altering model architecture or training objectives. Extensive experiments on three benchmarks, demonstrate that CAMDrop consistently improves accuracy and robustness, effectively mitigating modality imbalance while enhancing model interpretability. Beyond quantitative gains, CAMDrop provides qualitative insights into modality contributions, offering a transparent mechanism for balanced learning. We believe our method can serve as a practical component for future multimodal systems in applications such as emotion recognition, event localization, and human–computer interaction.