{"url":"/dataset/imbalanced-minikinetics200","name":"Imbalanced-MiniKinetics200","full_name":"Imbalanced-MiniKinetics200","description_markdown":"**Imbalanced-MiniKinetics200** was proposed by \"Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition\" to evaluate varying scenarios of video long-tailed recognition. Similar to CIFAR-10/100-LT, it utilizes an imbalance factor to construct long-tailed variants of the MiniKinetics200 dataset. **Imbalanced-MiniKinetics200** is a subset of Mini-Kinetics-200 consisting of 200 categories which is also a subset of Kinetics400.\r\nBoth the raw frames and extracted features with ResNet50/101 are provided.","description_withheld":null,"homepage":"https://github.com/wjun0830/MOVE","introduced_date":"2022-11-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/minority-oriented-vicinity-expansion-with","title":"Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition","first_author":"WonJun Moon","url":null},"license":null,"modalities":[],"tasks":[{"name":"Long-tail Learning","url":"/task/long-tail-learning","datasets_with_task":"/datasets/task/long-tail-learning"},{"name":"Video Recognition","url":"/task/video-recognition","datasets_with_task":"/datasets/task/video-recognition"},{"name":"imbalanced classification","url":"/task/imbalanced-classification","datasets_with_task":"/datasets/task/imbalanced-classification"}],"languages":[],"variants":["Imbalanced-MiniKinetics200"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}