# Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers

arXiv: https://arxiv.org/abs/2303.07180 · HTML: https://mlsanity.com/paper/2303.07180/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Multi-Label Learning with Global and Local Label Correlation](https://mlsanity.com/paper/1704.01415/) (2017)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning](https://mlsanity.com/paper/2005.00976/) (2020)
- [Localized Sparse Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.02998/) (2022)
- [Adaptively-weighted Integral Space for Fast Multiview Clustering](https://mlsanity.com/paper/2208.12808/) (2022)

## Built on by (most cited first, 17 of 17)

- [You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos](https://mlsanity.com/paper/2303.07863/) (2023)
- [DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2303.08358/) (2023)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (2023)
- [Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning](https://mlsanity.com/paper/2308.04142/) (2023)
- [Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective](https://mlsanity.com/paper/2605.26441/) (2026)
- [Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning](https://mlsanity.com/paper/2404.17340/) (2024)
- [A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness](https://mlsanity.com/paper/2403.12211/) (2024)
- [Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language](https://mlsanity.com/paper/2605.29793/) (2026)
- [TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining](https://mlsanity.com/paper/2310.05210/) (2023)
- [Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation](https://mlsanity.com/paper/2411.03713/) (2024)
- [Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2501.06524/) (2025)
- [Adaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2601.05785/) (2026)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)
- [Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework](https://mlsanity.com/paper/2411.18267/) (2024)
- [Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets](https://mlsanity.com/paper/2506.09745/) (2025)
- [Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation](https://mlsanity.com/paper/2604.04170/) (2026)
- [FeatureHospital: A Skill-Driven Multi-Agent Framework for Automated Algorithm Customization in Multi-View Multi-Label Feature Selection](https://mlsanity.com/paper/2608.16148/) (2026)

## Abstract (arXiv)

As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across all views? ii) How to exploit and utilize category correlations of multi-label to guide inference? iii) How to avoid the negative impact resulting from the incompleteness of views or labels? To cope with these problems, we propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper. First, we design two transformer-style based modules for cross-view features aggregation and multi-label classification, respectively. The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance. Second, considering the imbalance of expressive power among views, an adaptively weighted view fusion module is proposed to obtain view-consistent embedding features. Third, we impose a label manifold constraint in sample-level representation learning to maximize the utilization of supervised information. Last but not least, all the modules are designed under the premise of incomplete views and labels, which makes our method adaptable to arbitrary multi-view and multi-label data. Extensive experiments on five datasets confirm that our method has clear advantages over other state-of-the-art methods.