Long-tailed recognition dataset
Web9 de set. de 2024 · This survey focuses on the problems caused by long-tailed data distribution, sort out the representative long-tails visual recognition datasets and summarize some mainstream long-tail studies, and quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade. 16. PDF. View 1 excerpt, cites … Web19 de nov. de 2024 · In this work, we propose a framework to handle the long-tailed distribution problem existed in public face recognition datasets. This framework utilizes an encoder-decoder structure to transfer the data diversity from head identities to tail identities. It then uses a contrastive learning process to finetune the FR models.
Long-tailed recognition dataset
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Web17 de nov. de 2024 · Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition. Lie Ju, Zhen Yu, Lin Wang, Xin Zhao, Xin Wang, Paul Bonnington, Zongyuan Ge. In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have … Web27 de mai. de 2024 · A Survey on Long-Tailed Visual Recognition. Lu Yang, He Jiang, Qing Song, Jun Guo. The heavy reliance on data is one of the major reasons that …
WebImageNet Long-Tailed is a subset of /dataset/imagenet dataset consisting of 115.8K images from 1000 categories, with maximally 1280 images per class and minimally 5 images per class. The additional classes of images in ImageNet-2010 are used as the open set. Source: Large-Scale Long-Tailed Recognition in an Open World Web8 de ago. de 2024 · TLDR. This survey focuses on the problems caused by long-tailed data distribution, sort out the representative long-tails visual recognition datasets and …
Web12 de set. de 2024 · Abstract: Long-tailed distribution generally exists in large-scale face datasets, which poses challenges for learning discriminative feature in face recognition. … Webintroduce VideoLT, a large-scale long-tailed video recog-nition dataset, as a step toward real-world video recog-nition. VideoLT contains 256,218 untrimmed videos, an-notated …
Web13 de mai. de 2024 · We believe computational methods developed under open long-tailed recognition setting can ultimately satisfy the needs of natural-distributed datasets. In …
Web6 de mai. de 2024 · While long-tailed recognition has been extensively studied for image classification tasks, limited effort has been made for video domain. In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition. Our VideoLT contains 256,218 untrimmed videos, … prince charles pen leakingWebFCC: Feature Clusters Compression for Long-Tailed Visual Recognition Jian Li · Ziyao Meng · daqian Shi · Rui Song · Xiaolei Diao · Jingwen Wang · Hao Xu ... A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet Categories playwright c# azure functionWeb14 de abr. de 2024 · We comprehensively discuss the long-tailed time series classification learning and construct three corresponding long-tailed datasets. To the best of our knowledge, this is the first long-tailed time series classification work, which fills a gap in the field. To address the above Long-tailed TSC, we propose a novel Feature Space … playwright c# expectWeb11 de jan. de 2024 · Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks. 动机. to give a detailed experimental guideline of common tricks; to obtain the effective combinations of these tricks; propose a novel data augmentation approach; 论点. long-tailed datasets . poor accuray on the under … playwright celtic pub new york nyWeb21 linhas · Long-tail Learning. 66 papers with code • 20 benchmarks • 15 datasets. Long-tailed learning, one of the most challenging problems in visual recognition, aims to train … prince charles p companyWeb11 de abr. de 2024 · Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, ... prince charles personal staffWebExisting long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution. However, practical test class distributions often violate this assumption (e.g., being either long-tailed or even inversely long-tailed), which may lead existing methods … playwright c# docker