Adaptive Fusion Techniques for Multimodal Data
This work addresses the problem of effective multimodal data fusion for applications like translation and emotion recognition, presenting an incremental improvement over existing methods.
The paper tackles the challenge of fusing heterogeneous multimodal data by proposing adaptive fusion techniques that allow networks to decide how to combine features, resulting in lightweight networks that outperform existing methods on multimodal machine translation and emotion recognition tasks.
Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data. In this paper, we propose adaptive fusion techniques that aim to model context from different modalities effectively. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide "how" to combine a given set of multimodal features more effectively. We propose two networks: 1) Auto-Fusion, which learns to compress information from different modalities while preserving the context, and 2) GAN-Fusion, which regularizes the learned latent space given context from complementing modalities. A quantitative evaluation on the tasks of multimodal machine translation and emotion recognition suggests that our lightweight, adaptive networks can better model context from other modalities than existing methods, many of which employ massive transformer-based networks.