# Text-Region Matching for Multi-Label Image Recognition with Missing Labels

arXiv: https://arxiv.org/abs/2407.18520 · HTML: https://mlsanity.com/paper/2407.18520/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 52 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 30 of 34)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [Learning a Deep ConvNet for Multi-label Classification with Partial Labels](https://mlsanity.com/paper/1902.09720/) (2019)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition](https://mlsanity.com/paper/1908.07325/) (2019)
- [DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations](https://mlsanity.com/paper/2206.09541/) (2022)
- [Multi-label Image Recognition by Recurrently Discovering Attentional Regions](https://mlsanity.com/paper/1711.02816/) (2017)
- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition](https://mlsanity.com/paper/2012.02994/) (2020)
- [Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification](https://mlsanity.com/paper/1912.07872/) (2019)
- [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)
- [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)
- [Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels](https://mlsanity.com/paper/2303.13223/) (2023)
- [Probabilistic Prompt Learning for Dense Prediction](https://mlsanity.com/paper/2304.00779/) (2023)

## Built on by (most cited first, 9 of 9)

- [Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport](https://mlsanity.com/paper/2503.15337/) (2025)
- [Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning](https://mlsanity.com/paper/2504.09990/) (2025)
- [Curiosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning](https://mlsanity.com/paper/2510.17520/) (2025)
- [SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models](https://mlsanity.com/paper/2502.16911/) (2025)
- [Domain Adaptive Lung Nodule Detection in X-ray Image](https://mlsanity.com/paper/2407.19397/) (2024)
- [Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2507.17281/) (2025)
- [Incomplete Multi-Label Image Recognition by Co-learning Semantic-Aware Features and Label Recovery](https://mlsanity.com/paper/2510.10055/) (2025)
- [FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models](https://mlsanity.com/paper/2605.28347/) (2026)
- [Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2608.25653/) (2026)

## Abstract (arXiv)

Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing labels, leveraging VLP prompt-tuning technology. However, they usually cannot match text and vision features well, due to complicated semantics gaps and missing labels in a multi-label image. To tackle this challenge, we propose Text-Region Matching for optimizing Multi-Label prompt tuning, namely TRM-ML, a novel method for enhancing meaningful cross-modal matching. Compared to existing methods, we advocate exploring the information of category-aware regions rather than the entire image or pixels, which contributes to bridging the semantic gap between textual and visual representations in a one-to-one matching manner. Concurrently, we further introduce multimodal contrastive learning to narrow the semantic gap between textual and visual modalities and establish intra-class and inter-class relationships. Additionally, to deal with missing labels, we propose a multimodal category prototype that leverages intra- and inter-category semantic relationships to estimate unknown labels, facilitating pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC, Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art methods by a significant margin. Our code is available here: https://github.com/yu-gi-oh-leilei/TRM-ML.