# Learning Structured Inference Neural Networks with Label Relations

arXiv: https://arxiv.org/abs/1511.05616 · HTML: https://mlsanity.com/paper/1511.05616/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2015.

## Built on by (most cited first, 13 of 13)

- [When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes](https://mlsanity.com/paper/2104.02215/) (2021)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation](https://mlsanity.com/paper/2203.04187/) (2022)
- [Improving Scene Graph Classification by Exploiting Knowledge from Texts](https://mlsanity.com/paper/2102.04760/) (2021)
- [Joining datasets via data augmentation in the label space for neural networks](https://mlsanity.com/paper/2106.09260/) (2021)
- [Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI](https://mlsanity.com/paper/2211.12817/) (2022)
- [Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network](https://mlsanity.com/paper/2104.01032/) (2021)
- [A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases](https://mlsanity.com/paper/2104.06991/) (2021)
- [Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels](https://mlsanity.com/paper/2410.04256/) (2024)
- [A Hierarchical Approach to Remote Sensing Scene Classification](https://mlsanity.com/paper/2103.15463/) (2021)
- [Cleaning and Structuring the Label Space of the iMet Collection 2020](https://mlsanity.com/paper/2106.00815/) (2021)
- [Context-driven Visual Object Recognition based on Knowledge Graphs](https://mlsanity.com/paper/2210.11233/) (2022)
- [What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits](https://mlsanity.com/paper/2409.02335/) (2024)

## Abstract (arXiv)

Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with fine-grained labels that describe major components, coarse-grained labels that depict high level abstraction or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.