Prevailing incomplete multi-view clustering (IMVC) approaches typically fail to account for the interference of view-exclusive artifacts when learning view-consensus representations, which could compromise the fidelity of the resulting similarity measure. Moreover, inconsistencies in anchor order across views may distort the graph structure, impairing the clustering performance. The reliance on carefully-tuned regularization hyper-parameters also usually undermines the model's practical utility. To alleviate these issues, we propose a plug-and-play IMVC framework named PJFTH that incorporates Janus-faced affinity learning with topology harmonization. It explicitly models the exclusive-to-consensus interplay, derives a view-private graph from each view, and adaptively integrates them into a global consensus affinity according to the respective view's intrinsic characteristics. Furthermore, a permutation transformation with unary encoding constraints is applied to anchor matrix, realigning anchor topology while preserving the values. This process synchronizes anchor order prior to similarity integration and maintains original anchor properties. Notably, all components are coupled seamlessly and optimized in a joint manner. Also, the provable overall linear complexity further enlarges its scalability and practicality. Experimental results confirm that PJFTH receives competitive performance compared to several leading methods.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
In real-world applications, multi-view data often suffer from missing situations due to privacy protection and sensor failures. Such incomplete scenarios not only reduce information availability but also cause significant imbalance among views: certain "strong views" dominate the fusion process, while "weak views" contribute marginally, undermining cross-view collaboration. Existing incomplete multi-view clustering methods mainly focus on handling missing data, but overlook the view contribution imbalance induced by incompleteness and its impact on representation learning and clustering performance. To address this issue, we first analyze the data imbalance caused by missing views and the resulting disparities in view learning quality. Then, we propose an Imbalanced Contribution Evaluation and Refinement framework (ICER) for imbalanced incomplete multi-view clustering. Specifically, we employ Shapley values to quantify each view's marginal contribution, and incorporate unbalanced optimal transport to characterize distributional deviations across views. On this basis, we construct the view contribution imbalance metric to comprehensively evaluate cross-view collaboration and fusion quality, and design a collaboration enhancement module to explicitly reinforce inter-view cooperative optimization. Extensive experiments on multiple datasets demonstrate that the proposed method outperforms existing incomplete multi-view clustering approaches, validating the effectiveness and necessity of explicitly modeling and mitigating view imbalance in imbalanced incomplete scenarios. Code is released at https://github.com/Evelyn-zhou24/ICER.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表