Author

Yanbing Chen

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Open access Aug 2026

Persona-centric Metamorphic Relation Guided Robustness Evaluation for Multi-turn Dialogue Modeling

Retrieval-based dialogue systems aim to select a proper response according to multi-turn conversational history. Persona-based conversation utilizes prior knowledge to maintain persona consistency, enhancing retrieval accuracy. However, reference-based evaluation relies on high-quality data annotation, which is costly and time-consuming. To address this, we discover persona-centric metamorphic relations to infer test samples from annotated data, without additional annotation cost. Benefiting from this, this work efficiently evaluates the robustness of personalized dialogue models regarding persona consistency. Specifically, we discover three types of metamorphic relations from three aspects: self-persona, partner-persona, and response, to automatically derive new test samples . Then the inherent inference relations between originals and derivatives allow for robustness evaluation. Using this evaluation methodology, our work assesses three widely used training paradigms: non-pretraining, fine-tuning after pre-training, and prompt learning, in personalized dialogue retrieval to observe whether these paradigms are more robust or exhibit the same flaws as the other two paradigms. Our experimental results, based on the three discovered metamorphic relations with consistent outputs reveal that prompt learning is more robust than training from scratch and fine-tuning. While traditional reference-based validation and natural language processing methods achieve competitively high retrieval accuracy (Hits@1 up to 87.4%), the persona consistency of dialogue retrieval systems is just 20.98% when persona descriptions are perturbed using various metamorphic relation-based transformations.

Lin Li, Xiaohua Wu, Yanbing Chen et al. · Cognitive Computation · 0 citations

Open access Jul 2026

DMFNet: exploring diverse mid-feature for visible-infrared person re-identification

Visible-infrared person re-identification remains a challenging task due to inherent modality discrepancies between RGB and infrared images. Existing methods often struggle to effectively capture both modality-specific and modality-invariant features simultaneously, limiting their cross-modal matching performance. This paper presents DMFNet (Diverse Mid-feature Network), a novel deep learning architecture that effectively harnesses intermediate shared features to bridge this cross-modal gap. DMFNet integrates two key modules: a Multi-layer Feature Cascade Module (MFCM) that aggregates discriminative features across different network stages, and a Dual Feature Generation Module (DFGM) that produces diverse intermediate representations through Instance-Batch Normalization variants. Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that DMFNet achieves state-of-the-art performance, with significant improvements in Rank-1 accuracy (up to 8.2% on SYSU-MM01 and 6.5% on RegDB) and mean Average Precision (mAP) over existing methods. Our approach not only enhances cross-modal matching capabilities but also provides interpretable feature visualizations, offering valuable insights into the network's decision-making process. These results pave the way for more robust person re-identification systems in real-world surveillance scenarios, particularly in low-light conditions where traditional visible-only systems often fail.

Ganqing Mo, Yanbing Chen, Hairong Ye · Frontiers of Computer Scienc... · 0 citations