Long-tailed recognition
WebOur work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme … WebFigure 1. Long-tailed recognition (LTR) requires training on long-tailed class distributed data (black curve in (a)). (a) Networks naively trained on such data are …
Long-tailed recognition
Did you know?
Web24 de jun. de 2024 · Targeted Supervised Contrastive Learning for Long-Tailed Recognition. Abstract: Real-world data often exhibits long tail distributions with heavy … WebMain 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 …
http://boqinggong.info/publications.html WebOpen Long-Tailed Recognition In A Dynamic World. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024 Z Liu=, Z Miao=, X Zhan, J Wang, B Gong, and S Yu. [ ArXiv] CVIU. 2.5 D visual relationship detection.
Web28 de set. de 2024 · In this paper, we discover that networks trained on long-tailed datasets are more prone to miscalibrated and over-confident. The two-stage models suffer the same issue as well. We design two novel methods to improve calibration and performance in such scenarios. Motivated by the predicted probability distributions of classes are highly … Web“Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” In International Conference on Machine Learning (ICML), 2024. Long presentation Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, and Zhangyang Wang. “Removing Batch Normalization Boosts Adversarial ...
Web20 de jul. de 2024 · Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier. Long-tail recognition tackles the natural non-uniformly distributed data in real …
Web20 de jul. de 2024 · Existing 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 ... sharon baldacci booksWebDeep long-tailed learning is a formidable challenge in practical visual recognition tasks. The goal of long-tailed learning is to train effective models from a vast number of images, but most involving categories contain only a mini-mal number of samples. Such a long-tailed data distribution is prevalent in various real-world applications ... sharon bakery edgwareWeb22 de mar. de 2024 · Attentive Feature Augmentation for Long-Tailed Visual Recognition Abstract: Deep neural networks have achieved great success on many visual … population of saint vincent and grenadinesWebHá 5 horas · If indeed the black-tailed wrasses were showing signs of self-recognition—and not just in a laboratory tank, but while swimming freely in their … sharon bamberWeb5 de out. de 2024 · Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, Stella X. Yu. Natural data … population of salem village in 1692WebThe long-tailed problem in face recognition is reminis-cent of the conventional class imbalance problem that has been comprehensively studied in classical machine learn-ing [2,10], but significantly differs from conventional class imbalanceproblemintwoaspects: first,thelong-taileddata in face recognition is large-scale, with millions of identi- sharon bamberryWeb6 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 … sharon ball properties