Incomplete multi-modal emotion recognition (IMER) aims at understanding human intentions and sentiments by comprehensively exploring the partially-observed multi-source data. Although the multi-modal data is expected to provide more abundant information, the performance gap and modality under-optimization problem hinder effective multi-modal learning in practice, and are exacerbated in the confrontation of the missing data. To address this issue, we devise a novel Cross-modal Prompting (ComP) method, which emphasizes coherent information by enhancing modality-specific features and improves the overall recognition accuracy by boosting each modality's performance. Specifically, a progressive prompt generation module with a dynamic gradient modulator is proposed to produce concise and consistent modality semantic cues. Meanwhile, cross-modal knowledge propagation selectively amplifies the consistent information in modality features with the delivered prompts to enhance the discrimination of the modality-specific output. Additionally, a coordinator is employed to dynamically re-weight the modality outputs as a complement to the balance strategy to improve the model's efficacy. Extensive experiments on 4 datasets with 7 SOTA methods under different missing rates validate the effectiveness of our proposed method.

Efficiently learning informative yet compact representations from heterogeneous data remains challenging in incomplete multi-view clustering (IMC). The prevalent resource-efficient IMC models excel in constructing small-size anchors for fast similarity learning and data partition. However, existing anchor-based methods still suffer from shared deficiencies: 1) unstable and less informative anchor generation by random anchor selection or clueless learning and 2) imbalanced coherence and versatility capabilities of the learned anchors among different views. To mitigate these issues, we propose a novel dual-correlation-guided anchor learning (DCGA) method for scalable IMC, which learns informative anchor spaces to simultaneously incorporate both intra-view and inter-view correlations. Specifically, the intra-view anchor space is constructed and stabilized by compressing the view-specific data under the guidance of the conceived anchors as a bottleneck (A3B) strategy, with a strict theoretic analysis. Importantly, we, for the first time, build an unsupervised anchor learning scheme for incomplete multi-view data under the guidance of the bottleneck of information flow with the well-defined IB principle. As such, our model can simultaneously eliminate information redundancy and preserve the versatile knowledge derived from each view. Moreover, to endow the coherence of the learned anchors, an informative anchor constraint (IAC) is imposed to align the anchor spaces across different views. Extensive experiments on seven datasets against 11 state-of-the-art IMC methods validate the effectiveness and efficiency of our method. Code is available at https://github.com/DarrenZZhang/TNNLS25-DCGA.

Graph-based multi-view clustering methods construct affinity graphs to depict the potential cluster structure in given data and further partition them into respective groups without the supervision of labels. However, affinity graphs constructed by most of the existing methods lack the ability to precisely reflect the cluster structure of the original data, and fail to recover similarity information when there are missing instances involved. Additionally, current methods mainly employ pairwise relationships between instances to build the affinity graphs but ignore topological relationships, which causes insufficient use of underlying information and results in inferior results. To overcome these shortcomings, in this paper, we propose a novel graph learning method, which is enabled to solve a more general multi-view clustering (MVC) problem, i.e., MVC with probable missing instances. Specifically, a rank-constraint affinity learning method, which is capable to deal with both fully observed and partially observed data, is put forward to preserve similarity between existing instances and infer the similarity related to the missing instances. Moreover, a topological constraint is introduced on the learned affinity matrix, so that more comprehensive information, rather than limited pairwise relationships only, is embraced in each entry of the affinity matrix. Importantly, this is the first work using topological structure to conduct both complete and incomplete multi-view clustering in one unified learning framework. Extensive experiments under both complete and incomplete situations validate the effectiveness of our proposed method when compared to other state-of-the-art multi-view clustering algorithms. Our code has been released at https://github.com/WenjueHE/ATGL_GMVC.