# Multi-Label Image Classification with Regional Latent Semantic Dependencies

arXiv: https://arxiv.org/abs/1612.01082 · HTML: https://mlsanity.com/paper/1612.01082/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2016.

## Built on by (most cited first, 23 of 23)

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- [FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network](https://mlsanity.com/paper/2301.08160/) (2023)
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- [Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark](https://mlsanity.com/paper/2103.10895/) (2021)
- [Learning Robust Visual-semantic Mapping for Zero-shot Learning](https://mlsanity.com/paper/2104.05668/) (2021)
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- [End-to-End Supervised Multilabel Contrastive Learning](https://mlsanity.com/paper/2307.03967/) (2023)
- [Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning](https://mlsanity.com/paper/2403.01209/) (2024)
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- [Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision](https://mlsanity.com/paper/2211.12716/) (2022)
- [HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification](https://mlsanity.com/paper/2407.16244/) (2024)
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- [Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance](https://mlsanity.com/paper/2305.05228/) (2023)
- [ShuffleMix: Improving Representations via Channel-Wise Shuffle of Interpolated Hidden States](https://mlsanity.com/paper/2305.18684/) (2023)
- [Semantic-Aware Representation Learning via Conditional Transport for Multi-Label Image Classification](https://mlsanity.com/paper/2507.14918/) (2025)
- [Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook](https://mlsanity.com/paper/2607.00839/) (2026)

## Abstract (arXiv)

Deep convolution neural networks (CNN) have demonstrated advanced performance on single-label image classification, and various progress also have been made to apply CNN methods on multi-label image classification, which requires to annotate objects, attributes, scene categories etc. in a single shot. Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at global level, largely improving the labeling capacity. However, predicting small objects and visual concepts is still challenging due to the limited discrimination of the global visual features. In this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to address this problem. The utilized model includes a fully convolutional localization architecture to localize the regions that may contain multiple highly-dependent labels. The localized regions are further sent to the recurrent neural networks (RNN) to characterize the latent semantic dependencies at the regional level. Experimental results on several benchmark datasets show that our proposed model achieves the best performance compared to the state-of-the-art models, especially for predicting small objects occurred in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using bounding box coordinates during training, the experimental results also show that our RLSD can approach the upper bound without using the bounding-box annotations, which is more realistic in the real world.