# Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations

arXiv: https://arxiv.org/abs/1608.01441 · HTML: https://mlsanity.com/paper/1608.01441/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2016.

## Built on by (most cited first, 14 of 14)

- [Learning to Predict Visual Attributes in the Wild](https://mlsanity.com/paper/2106.09707/) (2021)
- [Large Loss Matters in Weakly Supervised Multi-Label Classification](https://mlsanity.com/paper/2206.03740/) (2022)
- [Multi-label Classification with Partial Annotations using Class-aware Selective Loss](https://mlsanity.com/paper/2110.10955/) (2021)
- [Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation](https://mlsanity.com/paper/2111.13517/) (2021)
- [PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification](https://mlsanity.com/paper/2307.09066/) (2023)
- [Multi-Label Image Classification with Contrastive Learning](https://mlsanity.com/paper/2107.11626/) (2021)
- [G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification](https://mlsanity.com/paper/2210.11469/) (2022)
- [PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification](https://mlsanity.com/paper/2208.09999/) (2022)
- [An Effective Approach for Multi-label Classification with Missing Labels](https://mlsanity.com/paper/2210.13651/) (2022)
- [Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition](https://mlsanity.com/paper/2307.07680/) (2023)
- [Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification](https://mlsanity.com/paper/2310.00665/) (2023)
- [Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels](https://mlsanity.com/paper/2401.16991/) (2024)
- [Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels](https://mlsanity.com/paper/2211.07846/) (2022)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)

## Abstract (arXiv)

Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Associating multiple labels to a complex image is very difficult, not only due to the intricacy of describing the image, but also because of the incompleteness nature of the observed labels. Existing works on the problem either ignore the label-label and instance-instance correlations or just assume these correlations are linear and unstructured. Considering that semantic correlations between images are actually structured, in this paper we propose to incorporate structured semantic correlations to solve the missing label problem of multi-label learning. Specifically, we project images to the semantic space with an effective semantic descriptor. A semantic graph is then constructed on these images to capture the structured correlations between them. We utilize the semantic graph Laplacian as a smooth term in the multi-label learning formulation to incorporate the structured semantic correlations. Experimental results demonstrate the effectiveness of the proposed semantic descriptor and the usefulness of incorporating the structured semantic correlations. We achieve better results than state-of-the-art multi-label learning methods on four benchmark datasets.