# Order-Free RNN with Visual Attention for Multi-Label Classification

arXiv: https://arxiv.org/abs/1707.05495 · HTML: https://mlsanity.com/paper/1707.05495/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 26 of 26)

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- [Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks](https://mlsanity.com/paper/2209.09883/) (2022)
- [Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification](https://mlsanity.com/paper/2408.08125/) (2024)
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- [SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition](https://mlsanity.com/paper/2407.20920/) (2024)
- [Learning Disentangled Label Representations for Multi-label Classification](https://mlsanity.com/paper/2212.01461/) (2022)
- [Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation](https://mlsanity.com/paper/2309.12075/) (2023)
- [Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks](https://mlsanity.com/paper/2107.05941/) (2021)
- [Visual Transformers with Primal Object Queries for Multi-Label Image Classification](https://mlsanity.com/paper/2112.05485/) (2021)
- [Modeling Multi-modal Cross-interaction for Multi-label Few-shot Image Classification Based on Local Feature Selection](https://mlsanity.com/paper/2412.13732/) (2024)
- [Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook](https://mlsanity.com/paper/2607.00839/) (2026)

## Abstract (arXiv)

In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.