Learning Modality-Specific and -Agnostic Representations for Asynchronous Multimodal Language Sequences

Understanding human behaviors and intents from videos is a challenging task. Video flows usually involve time-series data from different modalities, such as natural language, facial gestures, and acoustic information. Due to the variable receiving frequency for sequences from each modality, the collected multimodal streams are usually unaligned. For multimodal fusion of asynchronous sequences, the existing methods focus on projecting multiple modalities into a common latent space and learning the hybrid representations, which neglects the diversity of each modality and the commonality across different modalities. Motivated by this observation, we propose a Multimodal Fusion approach for learning modality-Specific and modality-Agnostic representations (MFSA) to refine multimodal representations and leverage the complementarity across different modalities. Specifically, a predictive self-attention module is used to capture reliable contextual dependencies and enhance the unique features over the modality-specific spaces. Meanwhile, we propose a hierarchical cross-modal attention module to explore the correlations between cross-modal elements over the modality-agnostic space. In this case, a double-discriminator strategy is presented to ensure the production of distinct representations in an adversarial manner. Eventually, the modality-specific and -agnostic multimodal representations are used together for downstream tasks. Comprehensive experiments on three multimodal datasets clearly demonstrate the superiority of our approach.