  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.
