{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/zero-shot-recognition-via-semantic-embeddings","title":"Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs","arxiv_id":"1803.08035","date":"2018-03-21","proceeding":"CVPR 2018 6","authors":["Xiaolong Wang","Yufei Ye","Abhinav Gupta"],"abstract":"We consider the problem of zero-shot recognition: learning a visual\nclassifier for a category with zero training examples, just using the word\nembedding of the category and its relationship to other categories, which\nvisual data are provided. The key to dealing with the unfamiliar or novel\ncategory is to transfer knowledge obtained from familiar classes to describe\nthe unfamiliar class. In this paper, we build upon the recently introduced\nGraph Convolutional Network (GCN) and propose an approach that uses both\nsemantic embeddings and the categorical relationships to predict the\nclassifiers. Given a learned knowledge graph (KG), our approach takes as input\nsemantic embeddings for each node (representing visual category). After a\nseries of graph convolutions, we predict the visual classifier for each\ncategory. During training, the visual classifiers for a few categories are\ngiven to learn the GCN parameters. At test time, these filters are used to\npredict the visual classifiers of unseen categories. We show that our approach\nis robust to noise in the KG. More importantly, our approach provides\nsignificant improvement in performance compared to the current state-of-the-art\nresults (from 2 ~ 3% on some metrics to whopping 20% on a few).","url_abs":"http://arxiv.org/abs/1803.08035v2","url_pdf":"http://arxiv.org/pdf/1803.08035v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"zero-shot-recognition-via-semantic-embeddings","repo_url":"https://github.com/JudyYe/zero-shot-gcn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"zero-shot-recognition-via-semantic-embeddings","repo_url":"https://github.com/MARMOTatZJU/ZSLPR-TIANCHI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"zero-shot-recognition-via-semantic-embeddings","repo_url":"https://github.com/ruotianluo/zsl-gcn-pth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"}],"methods":[{"method_slug":"gcn","method_name":"GCN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.08035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08035"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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