# DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification

arXiv: https://arxiv.org/abs/2303.08358 · HTML: https://mlsanity.com/paper/2303.08358/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition](https://mlsanity.com/paper/1908.07325/) (2019)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition](https://mlsanity.com/paper/2203.13504/) (2022)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering](https://mlsanity.com/paper/2011.10331/) (2020)
- [Spectral Perturbation Meets Incomplete Multi-view Data](https://mlsanity.com/paper/1906.00098/) (2019)
- [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)
- [Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers](https://mlsanity.com/paper/2303.07180/) (2023)
- [Adaptively-weighted Integral Space for Fast Multiview Clustering](https://mlsanity.com/paper/2208.12808/) (2022)

## Built on by (most cited first, 13 of 13)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [Information Recovery-Driven Deep Incomplete Multiview Clustering Network](https://mlsanity.com/paper/2304.00429/) (2023)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (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)
- [Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2409.04473/) (2024)
- [Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification](https://mlsanity.com/paper/2409.07931/) (2024)
- [Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2501.06524/) (2025)
- [Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework](https://mlsanity.com/paper/2411.18267/) (2024)
- [E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental Learning](https://mlsanity.com/paper/2601.17076/) (2026)
- [Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation](https://mlsanity.com/paper/2604.04170/) (2026)
- [When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning](https://mlsanity.com/paper/2609.07525/) (2026)

## Abstract (arXiv)

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means that not only multi-view features are often missing, and label completeness is also difficult to be satisfied. To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet. Different from conventional methods, our DICNet focuses on leveraging deep neural network to exploit the high-level semantic representations of samples rather than shallow-level features. First, we utilize the stacked autoencoders to build an end-to-end multi-view feature extraction framework to learn the view-specific representations of samples. Furthermore, in order to improve the consensus representation ability, we introduce an incomplete instance-level contrastive learning scheme to guide the encoders to better extract the consensus information of multiple views and use a multi-view weighted fusion module to enhance the discrimination of semantic features. Overall, our DICNet is adept in capturing consistent discriminative representations of multi-view multi-label data and avoiding the negative effects of missing views and missing labels. Extensive experiments performed on five datasets validate that our method outperforms other state-of-the-art methods.