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.

Tactile sensing offers rich and complementary information to vision and language, enabling robots to perceive fine-grained object properties. However, existing tactile sensors lack standardization, leading to redundant features that hinder cross-sensor generalization. Moreover, existing methods fail to fully integrate the intermediate communication among tactile, language, and vision modalities. To address this, we propose TLV-CoRe, a CLIP-based Tactile-Language-Vision Collaborative Representation learning method. TLV-CoRe introduces a Sensor-Aware Modulator to unify tactile features across different sensors and employs tactile-irrelevant decoupled learning to disentangle irrelevant tactile features. Additionally, a Unified Bridging Adapter is introduced to enhance tri-modal interaction within the shared representation space. To fairly evaluate the effectiveness of tactile models, we further propose the RSS evaluation framework, focusing on Robustness, Synergy, and Stability across different methods. Experimental results demonstrate that TLV-CoRe significantly improves sensor-agnostic representation learning and cross-modal alignment, offering a new direction for multimodal tactile representation.

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.

Incomplete features and label noise in multi-view multi-label data significantly undermine the reliability and performance, motivating researchers to explore the mechanism of representation and information recovery. However, learning for such dual deficiencies is crucial but rarely studied. In this paper, we propose a theory-inspired Deep Multi-View Multi-Label Learning method with Incomplete Views and Noisy Labels named DMMIvNL to address these problems. Specifically, to promote the synthesis of task-relevant shared information and preserve the distinctiveness of individual features from limited views, we have developed a feature extraction modular based on the information bottleneck theory, and formulated its theoretical upper bound into its objective. Meanwhile, we theoretically prove that minimizing the volume of the transition matrix ensures the statistical consistency with classifier training. Besides, a cycle-consistency estimation principle is proposed in the volume minimization network to improve the recognition stability of multi-label noise. Moreover, leveraging inherent real semantics information and label correlations are employed as model regularization to reduce the risk of excessive noise fitting. Finally, extensive experimental results validate the effectiveness and robustness of our DMMIvNL.

Multi-view multi-label learning typically suffers from dual data incompleteness due to limitations in feature storage and annotation costs. The interplay of hetero geneous features, numerous labels, and missing information significantly degrades model performance. To tackle the complex yet highly practical challenges, we propose a Theory-Driven Label-Specific Representation (TDLSR) framework. Through constructing the view-specific sample topology and prototype association graph, we develop the proximity-aware imputation mechanism, while deriving class representatives that capture the label correlation semantics. To obtain semantically distinct view representations, we introduce principles of information shift, inter action and orthogonality, which promotes the disentanglement of representation information, and mitigates message distortion and redundancy. Besides, label semantic-guided feature learning is employed to identify the discriminative shared and specific representations and refine the label preference across views. Moreover, we theoretically investigate the characteristics of representation learning and the generalization performance. Finally, extensive experiments on public datasets and real-world applications validate the effectiveness of TDLSR.

Due to the explosive growth in data sources and label categories, multi-view multi-label learning has garnered widespread attention. However, multi-view multi-label data often exhibits incomplete features and a huge number of unlabeled instances, due to the technical limitations and high cost of manual labeling in practice. Learning for such simultaneous missing of view features and labels is crucial but rarely studied, particularly when the labeled samples are limited. In this paper, we tackle this problem by proposing a novel Deep Incomplete Multi-View Semi-Supervised Multi-Label Learning method (DIMvSML). Specifically, to improve high-level representations of missing features, deep graph network is firstly employed to recover the feature information with structural similarity relations. Meanwhile, we design the structure-specific deep feature extractors to obtain discriminative information and preserve the cross-view consistency for the recovered data with instance-level contrastive loss. Furthermore, to eliminate the bias of the estimate of the risk that the semi-supervised multi-label methods minimise, we design a safe estimate framework with an unbiased loss and improve its empirical performance by using pseudo-labels of unlabeled data. Besides, we provide both the theoretical proof of better estimate variance and the intuitive explanation of our debiased framework. Finally, extensive experimental results on public datasets validate the superiority of DIMvSML compared with state-of-the-art methods.