Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing

• URL: http://arxiv.org/abs/2508.01227v2
• Date: Wed, 06 Aug 2025 03:36:40 GMT
• Title: Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing
• Authors: Zihan Fang, Zhiyong Xu, Lan Du, Shide Du, Zhiling Cai, Shiping Wang,
• Abstract summary: We propose a multi-view open-set learning framework via ambiguity uncertainty calibration and view-wise debiasing.<n>Experiments on diverse multi-view benchmarks demonstrate that the proposed framework consistently enhances unknown-class recognition.
• Score: 10.975705043375212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multi-view learning models struggle in open-set scenarios due to their implicit assumption of class completeness. Moreover, static view-induced biases, which arise from spurious view-label associations formed during training, further degrade their ability to recognize unknown categories. In this paper, we propose a multi-view open-set learning framework via ambiguity uncertainty calibration and view-wise debiasing. To simulate ambiguous samples, we design O-Mix, a novel synthesis strategy to generate virtual samples with calibrated open-set ambiguity uncertainty. These samples are further processed by an auxiliary ambiguity perception network that captures atypical patterns for improved open-set adaptation. Furthermore, we incorporate an HSIC-based contrastive debiasing module that enforces independence between view-specific ambiguous and view-consistent representations, encouraging the model to learn generalizable features. Extensive experiments on diverse multi-view benchmarks demonstrate that the proposed framework consistently enhances unknown-class recognition while preserving strong closed-set performance.

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