Hierachical Delta-Attention Method for Multimodal Fusion

In vision and linguistics; the main input modalities are facial expressions, speech patterns, and the words uttered. The issue with analysis of any one mode of expression (Visual, Verbal or Vocal) is that lot of contextual information can get lost. This asks researchers to inspect multiple modalities to get a thorough understanding of the cross-modal dependencies and temporal context of the situation to analyze the expression. This work attempts at preserving the long-range dependencies within and across different modalities, which would be bottle-necked by the use of recurrent networks and adds the concept of delta-attention to focus on local differences per modality to capture the idiosyncrasy of different people. We explore a cross-attention fusion technique to get the global view of the emotion expressed through these delta-self-attended modalities, in order to fuse all the local nuances and global context together. The addition of attention is new to the multi-modal fusion field and currently being scrutinized for on what stage the attention mechanism should be used, this work achieves competitive accuracy for overall and per-class classification which is close to the current state-of-the-art with almost half number of parameters

Text-oriented Modality Reinforcement Network for Multimodal Sentiment Analysis from Unaligned Multimodal Sequences

Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from text, visual, and acoustic modalities. Previous works have focused on representation learning and feature fusion strategies. However, most of these efforts ignored the disparity in the semantic richness of different modalities and treated each modality in the same manner. That may lead to strong modalities being neglected and weak modalities being overvalued. Motivated by these observations, we propose a Text-oriented Modality Reinforcement Network (TMRN), which focuses on the dominance of the text modality in MSA. More specifically, we design a Text-Centered Cross-modal Attention (TCCA) module to make full interaction for text/acoustic and text/visual pairs, and a Text-Gated Self-Attention (TGSA) module to guide the self-reinforcement of the other two modalities. Furthermore, we present an adaptive fusion mechanism to decide the proportion of different modalities involved in the fusion process. Finally, we combine the feature matrices into vectors to get the final representation for the downstream tasks. Experimental results show that our TMRN outperforms the state-of-the-art methods on two MSA benchmarks.Comment: Accepted by CICAI 2023 (Finalist of Best Student Paper Award

Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses the data sharing problem, enabling privacy-preserved collaborative training to provide sufficient precious data. Great potential, therefore, arises with the confluence of them, known as multimodal federated learning. However, limitation lies in the predominant approaches as they often assume that each local dataset records samples from all modalities. In this paper, we aim to bridge this gap by proposing an Unimodal Training - Multimodal Prediction (UTMP) framework under the context of multimodal federated learning. We design HA-Fedformer, a novel transformer-based model that empowers unimodal training with only a unimodal dataset at the client and multimodal testing by aggregating multiple clients' knowledge for better accuracy. The key advantages are twofold. Firstly, to alleviate the impact of data non-IID, we develop an uncertainty-aware aggregation method for the local encoders with layer-wise Markov Chain Monte Carlo sampling. Secondly, to overcome the challenge of unaligned language sequence, we implement a cross-modal decoder aggregation to capture the hidden signal correlation between decoders trained by data from different modalities. Our experiments on popular sentiment analysis benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that HA-Fedformer significantly outperforms state-of-the-art multimodal models under the UTMP federated learning frameworks, with 15%-20% improvement on most attributes.Comment: 10 pages,5 figure

Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities

One of the main challenges of multimodal learning is the need to combine heterogeneous modalities (e.g., video, audio, text). For example, video and audio are obtained at much higher rates than text and are roughly aligned in time. They are often not synchronized with text, which comes as a global context, e.g., a title, or a description. Furthermore, video and audio inputs are of much larger volumes, and grow as the video length increases, which naturally requires more compute dedicated to these modalities and makes modeling of long-range dependencies harder. We here decouple the multimodal modeling, dividing it into separate, focused autoregressive models, processing the inputs according to the characteristics of the modalities. We propose a multimodal model, called Mirasol3B, consisting of an autoregressive component for the time-synchronized modalities (audio and video), and an autoregressive component for the context modalities which are not necessarily aligned in time but are still sequential. To address the long-sequences of the video-audio inputs, we propose to further partition the video and audio sequences in consecutive snippets and autoregressively process their representations. To that end, we propose a Combiner mechanism, which models the audio-video information jointly within a timeframe. The Combiner learns to extract audio and video features from raw spatio-temporal signals, and then learns to fuse these features producing compact but expressive representations per snippet. Our approach achieves the state-of-the-art on well established multimodal benchmarks, outperforming much larger models. It effectively addresses the high computational demand of media inputs by both learning compact representations, controlling the sequence length of the audio-video feature representations, and modeling their dependencies in time