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...
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。
Feature and label collaborationConfidence estimationSmoothness assumptionLow-rankPartial 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 ...
应该是后者。根据字面，根本就没有“偏多”的意思。
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...
Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as identify...
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...
A general form of the partially annotated multi-label classification loss can be defined as follows, (1) where , and are the loss terms of the positive, negative and un-annotated labels for sample , respectively. Given a set of labeled samples , our goal is to train a neural-network ...
input4 --> label1,label2 — 1% data input5 --> label1,label3 — 1% data input6 --> label2,label3 — 1% data input7 --> label1,label2,label3 — 1% data 解决思路: 《Learning a Deep ConvNet for Multi-label Classification with Partial Labels》 ...
Multi-label Iterated Learning for Image Classification with Label Ambiguity pres 26 -- 4:55 App CVPR 2022 Degradation-agnostic Correspondence from Resolution-asymmetric Stereo 21 -- 1:13 App CVPR 2022 Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximati 26 -- 58:09 App NeuroEvo...