Partial multi-label learningFew-shot learningMeta-learningWeakly-supervised learningNoisy labelsLabel correlationsPartial multi-label learning (PML) models the scenario where each training sample is annotated with a candidate label set, among which only a subset corresponds to the ground-truth labels. ...
Title: 《PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification》 ECCV 2022w Highlight 提出了一个新的partial-label的multi-label setting,只有一部分数据有部分标签,另一部分数据没有标签。 引入了momentum更新pseudo label的方法,类似于EMA,还把课程学习那一套的概念引入了进来。
Partial multi-label learning (PML) aims to learn from the training data where each training example is annotated with a candidate label set, among which only a subset is relevant. Despite the success of existing PML approaches, a major drawback of them lies in lacking of robustness to noisy...
为了解决这样的问题，我们提出了偏多标记学习框架(Partial Multi-label Learning, PML)。首先来看一个现实...
Partial Multi-label Learning (PML) tackles the problem where each training instance is associated with a set of candidate labels that include both the relevant ground-truth labels and irrelevant false positive labels. Most of the existing PML methods try to iteratively update the confidence of each...
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML problem is practical in real-world scenarios, as it is difficult and even impossible to obtain precisely labeled sa...
https://www.youtube.com/watch?v=x0sXkJ5G2Hs转自:https://www.youtube.com/watch?v=x0sXkJ5G2HsPLM: Partial Label Masking for Imbalanced Multi-label Classification by Kevin Duarte, 视频播放量 12、弹幕量 0、点赞数 0、投硬币枚数 0、收藏人数 0、转发人数 0, 视
Zhang, B. Gao, J. Wu, and J. Cai, "Can partial strong labels boost multi-label object recognition?" CoRR, 2015.Hao Yang, Joey Tianyi Zhou, Yu Zhang, Bin-Bin Gao, Jianxin Wu, and Jianfei Cai. 2015. Can Partial Strong Labels Boost Multi-label Object Recognition? arXiv.org (2015)....
Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a ...
2022 4 SARB 77.9 Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels 2022 5 HST 77.9 Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels 2022 6 SST 76.7 Structured Semantic Transfer for Multi-Label Recognition with Partial Labels 2021Con...