When applying multimodal machine learning in downstream inference, both joint and coordinated multimodal representations rely on the complete presence of modalities as in training. However, modal-incomplete data, where certain modalities are missing, greatly reduces performance in Multimodal Sentiment Analysis (MSA) due to varying input forms and semantic information deficiencies. This limits the applicability of the predominant MSA methods in the real world, where the completeness of multimodal data is uncertain and variable. The generation-based methods attempt to generate the missing modality, yet they require complex hierarchical architecture with huge computational costs and struggle with the representation gaps across different modalities. Diversely, we propose a novel representation learning approach named MissModal, devoting to increasing robustness to missing modality in a classification approach. Specifically, we adopt constraints with geometric contrastive loss, distribution distance loss, and sentiment semantic loss to align the representations of modal-missing and modal-complete data, without impacting the sentiment inference for the complete modalities. Furthermore, we do not demand any changes in the multimodal fusion stage, highlighting the generality of our method in other multimodal learning systems. Extensive experiments demonstrate that the proposed method achieves superior performance with minimal computational costs in various missing modalities scenarios (flexibility), including severely missing modality (efficiency) on two public MSA datasets.

In the field of Multimodal Sentiment Analysis (MSA), the prevailing methods are devoted to developing intricate network architectures to capture the intra- and inter-modal dynamics, which necessitates numerous parameters and poses more difficulties in terms of interpretability in multimodal modeling. Besides, the heterogeneous nature of multiple modalities (text, audio, and vision) introduces significant modality gaps, thereby making multimodal representation learning an ongoing challenge. To address the aforementioned issues, by considering the learning process of modalities as multiple subtasks, we propose a novel approach named Multi-Task Momentum Distillation (MTMD) which succeeds in reducing the gap among different modalities. Specifically, according to the abundance of semantic information, we treat the subtasks of textual and multimodal representations as the teacher networks while the subtasks of acoustic and visual representations as the student ones to present knowledge distillation, which transfers the sentiment-related knowledge guided by the regression and classification subtasks. Additionally, we adopt unimodal momentum models to explore modality-specific knowledge deeply and employ adaptive momentum fusion factors to learn a robust multimodal representation. Furthermore, we provide a theoretical perspective of mutual information maximization by interpreting MTMD as generating sentiment-related views in various ways. Extensive experiments illustrate the superiority of our approach compared with the state-of-the-art methods in MSA.

Aiming to match the person identity between daytime VISible (VIS) and nighttime Near-InfraRed (NIR) images, VIS-NIR re-identification (Re-ID) has attracted increasing attention due to its wide applications in low-light scenes. However, dramatic modality discrepancies between VIS and NIR images lead to a considerable intra-class gap in the feature space, which impacts identity matching. To bridge the modality gap, we propose a Tri-level Modality-information Disentanglement (TMD) to disentangle modality information at the levels of raw image, features distribution and instance features. Our model consists of three key modules, including Style-Aligned Converter (SAC), Two-Steps Wasserstein Loss (TSWL) and Self-supervised Orthogonal Disentanglement (SOD) to handle the modality information at the three levels. Firstly, aiming at reducing modality discrepancy at image-level, the SAC is introduced to generate style-aligned images by the designed style converter and $\mathcal {A}$ -distance learning approach. The SAC can effectively alleviate the style discrepancy between VIS and NIR images with a negligible increase in model complexity. Secondly, considering the heterogeneity of VIS and NIR feature distribution caused by the structure- and style-misaligned raw images, we propose the TSWL to decrease the VIS-NIR gap at distribution-level by two distribution alignment steps. Specifically, after generating style-consistent images, we eliminate modality-related discrepancy by aligning the distribution between structure-aligned original and generated VIS/NIR images and bridge the modality-unrelated gap by aligning the style-consistent generated VIS-NIR images. Thirdly, focusing on further reducing the modality discrepancy at instance-level, the SOD is presented to construct orthogonal constraints between the extracted modality- and identity-related features. Since the modality-related factors are disentangled from the instance features, the proposed TMD efficiently learns the modality-unrelated and identity-discriminative representations, which are productive to conduct person Re-ID task on the VIS-NIR images. Comprehensive experiments are carried out on two cross-modality pedestrian Re-ID datasets to demonstrate the effectiveness of TMD.

In the field of multimodal machine learning, multimodal sentiment analysis task has been an active area of research. The predominant approaches focus on learning efficient multimodal representations containing intra- and inter-modality information. However, the heterogeneous nature of different modalities brings great challenges to multimodal representation learning. In this paper, we propose a multi-stage fusion framework to dynamically fine-tune multimodal representations via a hybrid-modal attention mechanism. Previous methods mostly only fine-tune the textual representation due to the success of large corpus pre-trained models and neglect the inconsistency problem of different modality spaces. Thus, we design a module called the Multimodal Shifting Gate (MSG) to fine-tune the three modalities by modeling inter-modality dynamics and shifting representations. We also adopt a module named Masked Bimodal Adjustment (MBA) on the textual modality to improve the inconsistency of parameter spaces and reduce the modality gap. In addition, we utilize syntactic-level and semantic-level textual features output from different layers of the Transformer model to sufficiently capture the intra-modality dynamics. Moreover, we construct a Shifting HuberLoss to robustly introduce the variation of the shifting value into the training process. Extensive experiments on the public datasets, including CMU-MOSI and CMU-MOSEI, demonstrate the efficacy of our approach.

Striving to match the person identities between visible (VIS) and near-infrared (NIR) images, VIS-NIR reidentification (Re-ID) has attracted increasing attention due to its wide applications in low-light scenes. However, owing to the modality and pose discrepancies exhibited in heterogeneous images, the extracted representations inevitably comprise various modality and posture factors, impacting the matching of cross-modality person identity. To solve the problem, we propose a disentangling modality and posture factors (DMPFs) model to disentangle modality and posture factors by fusing the information of features memory and pedestrian skeleton. Specifically, the DMPF comprises three modules: three-stream features extraction network (TFENet), modality factor disentanglement (MFD), and posture factor disentanglement (PFD). First, aiming to provide memory and skeleton information for modality and posture factors disentanglement, the TFENet is designed as a three-stream network to extract VIS-NIR image features and skeleton features. Second, to eliminate modality discrepancy across different batches, we maintain memory queues of previous batch features through the momentum updating mechanism and propose MFD to integrate features in the whole training set by memory-attention layers. These layers explore intramodality and intermodality relationships between features from the current batch and memory queues under the optimization of the optimal transport (OT) method, which encourages the heterogeneous features with the same identity to present higher similarity. Third, to decouple the posture factors from representations, we introduce the PFD module to learn posture-unrelated features with the assistance of the skeleton features. Besides, we perform subspace orthogonal decomposition on both image and skeleton features to separate the posture-related and identity-related information. The posture-related features are adopted to disentangle the posture factors from representations by a designed posture-features consistency (PfC) loss, while the identity-related features are concatenated to obtain more discriminative identity representations. The effectiveness of DMPF is validated through comprehensive experiments on two VIS-NIR pedestrian Re-ID datasets.