Author name cluster

Mingdie Jiang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2026 Conference Paper

Multi-Label Classification with Incremental and Decremental Features

Mingdie Jiang
Quanjiang Li
Tingjin Luo
Yiping Song
Chenping Hou

Feature dynamics have emerged as a critical topic about open-environment learning due to the instability of feature availability. While traditional feature evolution targets single-label tasks, multi-label learning is essential to accommodate the exploding annotation spaces. However, multi-label classification with incremental and decremental features is a crucial yet underexplored problem, which poses the challenge of preserving feature representations and label correlations from historical instances and simultaneously adapting to newly arriving streaming data. To address these issues, we propose a two-stage, one-pass learning approach termed MLID. It attempts to compress the informative content of vanished features into the domain of survived ones, facilitate the propagation of label dependencies via low-rank regularization of the classifier, and incorporate augmented features to construct an adaptive classification mechanism. Besides, we design optimization strategies for each stage and provide theoretical guarantees of convergence. Moreover, we establish the generalization error bound of MLID and demonstrate that the compactness of the trace norm and the reuse of models based on effective features can enhance the generalization performance. Finally, we extend it to multi-shot case and extensive experimental results validate the superiority of our MLID.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Semi-Supervised Multi-View Multi-Label Learning with View-Specific Transformer and Enhanced Pseudo-Label

Quanjiang Li
Tingjin Luo
Mingdie Jiang
Zhangqi Jiang
Chenping Hou
Feijiang Li

Multi-view multi-label learning has become a research focus for describing objects with rich expressions and annotations. However, real-world data often contains numerous unlabeled instances, due to the high cost and technical limitations of manual labeling. This crucial problem involves three main challenges: i) How to extract advanced semantics from available views? ii) How to build a refined classification framework with limited labeled space? iii) How to provide more high-quality supervisory information? To address these problems, we propose a Semi-Supervised Multi-View Multi-Label Learning Method with View-Specific Transformer and Enhanced Pseudo-Label named SMVTEP. Specifically, Generative Adversarial Networks are employed to extract informative shared and specific representations and their consistency and distinctiveness are ensured through the adversarial mechanism and information theory based contrastive learning. Then we build specific classifiers for each extracted feature and apply instance-level manifold constraints to reduce bias across classifiers. Moreover, we design a transformer-style fusion approach that simultaneously captures the imbalance of expressive power among views, mapping effects on specific labels, and label dependencies by incorporating confidence scores and category semantics into the self-attention mechanism. Furthermore, after using Mixup for data augmentation, category-enhanced pseudo-labels are leveraged to improve the reliability of additional annotations by aligning the label distribution of unlabeled samples with the true distribution. Finally, extensive experimental results validate the effectiveness of SMVTEP against state-of-the-art methods.

PDF Details DOI