Visual–Tactile Fused Graph Learning for Object Clustering
提出一种视觉-触觉融合图学习框架，通过最小化分歧策略对齐两种模态的表征，并施加拉普拉斯秩约束直接得到聚类标签，在五个公开数据集上验证了有效性。
highlights how to mitigate the differences between vision and touch, and further maximize the mutual information, which adopts a minimizing disagreement scheme to guide the modality-specific representations toward a unified affinity graph. To achieve ideal clustering performance, a Laplacian rank constraint is imposed to regularize the learned graph with ideal connected components, where noises that caused wrong connections are removed and clustering labels can be obtained directly. Finally, we propose an efficient alternating iterative minimization updating strategy, followed by a theoretical proof to prove framework convergence. Comprehensive experiments on five public datasets demonstrate the superiority of the proposed framework.