{"url":"/sota/zero-shot-learning-on-imagenet-cn","task":{"name":"Zero-Shot Learning","url":"/task/zero-shot-learning","note":null},"dataset":{"name":"ImageNet_CN","url":"/dataset/imagenet-cn"},"category":"Computer Vision","categories":["Computer Vision","Methodology"],"category_note":null,"description":"**Zero-shot learning (ZSL)** is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning. \r\n\r\nEarlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning. \r\n\r\nBenchmark datasets for zero-shot learning include [aPY](/dataset/apy), [AwA](/dataset/awa2-1), and [CUB](/dataset/cub-200-2011), among others. \r\n\r\n( Image credit: [Prototypical Networks for Few shot Learning in PyTorch\r\n](https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch) )\r\n\r\nFurther readings:  \r\n\r\n- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://paperswithcode.com/paper/zero-shot-learning-a-comprehensive-evaluation)\r\n- [Zero-Shot Learning in Modern NLP](https://joeddav.github.io/blog/2020/05/29/ZSL.html)\r\n- [Zero-Shot Learning for Text Classification](https://amitness.com/2020/05/zero-shot-text-classification/)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"$M^2$-Encoder","metrics":{"Accuracy":"80.7"},"uses_additional_data":true,"paper_date":"2024-01-29","paper":"/paper/boldsymbol-m-2-encoder-advancing-bilingual","paper_url":"https://arxiv.org/abs/2401.15896v2","paper_title":"M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining","code":"https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/M2_Encoder","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,795 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6795,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2785},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}