# Graph-RISE: Graph-Regularized Image Semantic Embedding

arXiv: https://arxiv.org/abs/1902.10814 · HTML: https://mlsanity.com/paper/1902.10814/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 15 of 15)

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- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
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## Abstract (arXiv)

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to train embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking. We provide case studies to demonstrate that, qualitatively, image retrieval based on Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.