# Learning a Deep ConvNet for Multi-label Classification with Partial Labels

arXiv: https://arxiv.org/abs/1902.09720 · HTML: https://mlsanity.com/paper/1902.09720/

## Key facts

- Cited by 124 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 30 of 124)

- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations](https://mlsanity.com/paper/2206.09541/) (2022)
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- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [ML-Decoder: Scalable and Versatile Classification Head](https://mlsanity.com/paper/2111.12933/) (2021)
- [Detecting and Grounding Multi-Modal Media Manipulation and Beyond](https://mlsanity.com/paper/2309.14203/) (2023)
- [Texts as Images in Prompt Tuning for Multi-Label Image Recognition](https://mlsanity.com/paper/2211.12739/) (2022)
- [Structured Semantic Transfer for Multi-Label Recognition with Partial Labels](https://mlsanity.com/paper/2112.10941/) (2021)
- [KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2205.06784/) (2022)
- [CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification](https://mlsanity.com/paper/2307.16634/) (2023)
- [Large Loss Matters in Weakly Supervised Multi-Label Classification](https://mlsanity.com/paper/2206.03740/) (2022)
- [Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution](https://mlsanity.com/paper/2109.04712/) (2021)
- [Modeling Multi-Label Action Dependencies for Temporal Action Localization](https://mlsanity.com/paper/2103.03027/) (2021)
- [Simple and Robust Loss Design for Multi-Label Learning with Missing Labels](https://mlsanity.com/paper/2112.07368/) (2021)
- [Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2203.02172/) (2022)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [Multi-label Classification with Partial Annotations using Class-aware Selective Loss](https://mlsanity.com/paper/2110.10955/) (2021)
- [Integrative Few-Shot Learning for Classification and Segmentation](https://mlsanity.com/paper/2203.15712/) (2022)
- [Semantic Diversity Learning for Zero-Shot Multi-label Classification](https://mlsanity.com/paper/2105.05926/) (2021)
- [Acknowledging the Unknown for Multi-label Learning with Single Positive Labels](https://mlsanity.com/paper/2203.16219/) (2022)
- [Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels](https://mlsanity.com/paper/2303.13223/) (2023)
- [Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation](https://mlsanity.com/paper/2307.03407/) (2023)
- [One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement](https://mlsanity.com/paper/2206.00517/) (2022)
- [DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotations](https://mlsanity.com/paper/2308.01890/) (2023)
- [MlTr: Multi-label Classification with Transformer](https://mlsanity.com/paper/2106.06195/) (2021)
- [Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation](https://mlsanity.com/paper/2111.13517/) (2021)
- [Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention](https://mlsanity.com/paper/2203.03483/) (2022)
- [Generative Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2101.11606/) (2021)

## Abstract (arXiv)

Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multi-label. Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex. Furthermore, collecting clean multi-label annotations is more difficult to scale-up than single-label annotations. To reduce the annotation cost, we propose to train a model with partial labels i.e. only some labels are known per image. We first empirically compare different labeling strategies to show the potential for using partial labels on multi-label datasets. Then to learn with partial labels, we introduce a new classification loss that exploits the proportion of known labels per example. Our approach allows the use of the same training settings as when learning with all the annotations. We further explore several curriculum learning based strategies to predict missing labels. Experiments are performed on three large-scale multi-label datasets: MS COCO, NUS-WIDE and Open Images.