Learning Clustering-based Prototypes for Compositional Zero-Shot Learning
Keywords: Compositional Zero-Shot Learning, Prototype Learning, Representation Disentanglement
TL;DR: We propose a clustering-based prototype mining framework for compositional zero-shot learning, which defines conceptual boundaries of primitives through a set of diversified prototypes, and automatically discovers these prototypes via clustering.
Abstract: Learning primitive (i.e., attribute and object) concepts from seen compositions is the primary challenge of Compositional Zero-Shot Learning (CZSL). Existing CZSL solutions typically rely on oversimplified data assumptions, e.g., modeling each primitive with a single centroid primitive presentation, ignoring the natural diversities of the attribute (resp. object) when coupled with different objects (resp. attribute). In this work, we develop ClusPro, a robust clustering-based prototype mining framework for CZSL that defines the conceptual boundaries of primitives through a set of diversified prototypes. Specifically, ClusPro conducts within-primitive clustering on the embedding space for automatically discovering and dynamically updating prototypes. To learn high-quality embeddings for discriminative prototype construction, ClusPro repaints a well-structured and independent primitive embedding space, ensuring intra-primitive separation and inter-primitive decorrelation through prototype-based contrastive learning and decorrelation learning. Moreover, ClusPro effectively performs prototype clustering in a non-parametric fashion without the introduction of additional learnable parameters or computational budget during testing. Experiments on three benchmarks demonstrate ClusPro outperforms various top-leading CZSL solutions under both closed-world and open-world settings. Code will be released.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 1003
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