Learning Representations for Clustering via Partial Information Discrimination and Cross-Level Interaction
This work addresses image clustering, a fundamental task in computer vision, with incremental improvements in performance.
The paper tackles the problem of deep image clustering by proposing PICI, a novel approach that combines partial information discrimination and cross-level interaction, achieving superior clustering performance over state-of-the-art methods on six real-world image datasets.
In this paper, we present a novel deep image clustering approach termed PICI, which enforces the partial information discrimination and the cross-level interaction in a joint learning framework. In particular, we leverage a Transformer encoder as the backbone, through which the masked image modeling with two paralleled augmented views is formulated. After deriving the class tokens from the masked images by the Transformer encoder, three partial information learning modules are further incorporated, including the PISD module for training the auto-encoder via masked image reconstruction, the PICD module for employing two levels of contrastive learning, and the CLI module for mutual interaction between the instance-level and cluster-level subspaces. Extensive experiments have been conducted on six real-world image datasets, which demononstrate the superior clustering performance of the proposed PICI approach over the state-of-the-art deep clustering approaches. The source code is available at https://github.com/Regan-Zhang/PICI.