Papers › Graph-RISE: Graph-Regularized Image Semantic Embedding

Graph-RISE: Graph-Regularized Image Semantic Embedding

14 Feb 2019arXiv:1902.10814archive 2025-07-28

Da-Cheng Juan, Chun-Ta Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom Duerig, Andrew Tomkins, Sujith Ravi

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.

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Code

tensorflow/neural-structured-learning mentioned on GitHubtf report

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Tasks

Clustering General Classification Graph Learning Image Classification Image Retrieval Retrieval image-classification

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image Classification	ImageNet	Graph-RISE (40M)	Top 1 Accuracy	68.29%	#1034 of 1060	Archive leaderboard	report
Image Classification	iNaturalist	Graph-RISE (40M)	Top 1 Accuracy	31.12%	#17 of 19	Archive leaderboard	report
Image Classification	iNaturalist	Graph-RISE (40M)	Top 5 Accuracy	52.76%	#17 of 19	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

k-NN

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