E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental Learning

2026-01-23

AI Summary

This work addresses the dual challenges of arbitrary view missingness and continuous class expansion in real-world multi-view multi-label classification by formally introducing, for the first time, the task of Incomplete Multi-View Multi-Label Class-Incremental Learning. To tackle this setting, the authors propose the E2PL framework, which jointly models class incrementality and arbitrary missing-view patterns through task-specific and missingness-aware prompting mechanisms. A linear-complexity prototype tensorization strategy based on atomic tensor decomposition is devised to circumvent exponential parameter growth, while dynamic contrastive learning is incorporated to capture dependencies among diverse missingness patterns. Extensive experiments on three benchmark datasets demonstrate that E2PL consistently outperforms existing methods in both classification accuracy and computational efficiency.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are rife with missing views and continuously emerging classes, which pose significant obstacles to robust learning. Prevailing methods are ill-equipped for this reality, as they either lack adaptability to new classes or incur exponential parameter growth when handling all possible missing-view patterns, severely limiting their scalability in web environments. To systematically address this gap, we formally introduce a novel task, termed \emph{incomplete multi-view multi-label class incremental learning} (IMvMLCIL), which requires models to simultaneously address heterogeneous missing views and dynamic class expansion. To tackle this task, we propose \textsf{E2PL}, an Effective and Efficient Prompt Learning framework for IMvMLCIL. \textsf{E2PL} unifies two novel prompt designs: \emph{task-tailored prompts} for class-incremental adaptation and \emph{missing-aware prompts} for the flexible integration of arbitrary view-missing scenarios. To fundamentally address the exponential parameter explosion inherent in missing-aware prompts, we devise an \emph{efficient prototype tensorization} module, which leverages atomic tensor decomposition to elegantly reduce the prompt parameter complexity from exponential to linear w.r.t. the number of views. We further incorporate a \emph{dynamic contrastive learning} strategy explicitly model the complex dependencies among diverse missing-view patterns, thus enhancing the model's robustness. Extensive experiments on three benchmarks demonstrate that \textsf{E2PL} consistently outperforms state-of-the-art methods in both effectiveness and efficiency. The codes and datasets are available at https://anonymous.4open.science/r/code-for-E2PL.

Problem

• incomplete multi-view
• multi-label classification
• class incremental learning
• missing views
• dynamic class expansion

Innovation

• Prompt Learning
• Class Incremental Learning
• Multi-view Multi-label Classification
• Tensor Decomposition
• Contrastive Learning