Long-tailed recognition dataset
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 … 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 …
Long-tailed recognition dataset
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Web5 de jul. de 2024 · In this paper, we propose an effective data augmentation method, referred to as bilateral mixup augmentation, which can improve the performance of long-tailed visual recognition. The bilateral mixup augmentation combines two samples generated by a uniform sampler and a re-balanced sampler and augments the training … 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.
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, … Web10 de abr. de 2024 · Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, …
WebExisting 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 … Web11 de ago. de 2024 · In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes will dominate the training results and achieve a very low accuracy on the rare …
Web25 de mai. de 2024 · MS1M-LT is a face recognition dataset, a long-tailed version of MS1M-ArcFace dataset Guo et al. ; Deng et al. . In MS1M-LT, each identity is sampled …
Web9 de ago. de 2024 · Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous … pope heretic letterWebThe classification folder supports long-tailed classification on ImageNet-LT, Long-Tailed CIFAR-10/CIFAR-100 datasets. The lvis_old folder (deprecated) supports long-tailed … sharepoint watermarkingWeb11 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 … sharepoint was ist möglichWeb[44] indicates that, a model trained on the whole long-tailed dataset will perform worse than that trained on a spe-cific proportion of the whole dataset (cutting 50% tail in their work). The phenomenon indicates that, it would be sub-optimal to train on the whole face dataset without con-sidering characteristics of the data. The tail ... pope high school girls lacrosseWebintroduce 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 … sharepoint wat is datWeb11 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, ... pope high school counselingWebMain challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. While techniques have been proposed to achieve a … pope high school fencing