This paper presents a novel approach to Single-Positive Multi-label Learning.
In general multi-label learning, a model learns to predict multiple labels or
categories for a single input image. This is in contrast with standard
multi-class image classification, where the task is predicting a single label
from many possible labels for an image. Single-Positive Multi-label Learning
(SPML) specifically considers learning to predict multiple labels when there is
only a single annotation per image in the training data. Multi-label learning
is in many ways a more realistic task than single-label learning as real-world
data often involves instances belonging to multiple categories simultaneously;
however, most common computer vision datasets predominantly contain single
labels due to the inherent complexity and cost of collecting multiple high
quality annotations for each instance. We propose a novel approach called
Vision-Language Pseudo-Labeling (VLPL), which uses a vision-language model to
suggest strong positive and negative pseudo-labels, and outperforms the current
SOTA methods by 5.5% on Pascal VOC, 18.4% on MS-COCO, 15.2% on NUS-WIDE, and
8.4% on CUB-Birds. Our code and data are available at
this https URL

提出了一种称为 Vision-Language Pseudo-Labeling 的新方法，通过使用视觉语言模型来建议强正负伪标签，在 Pascal VOC 上提高 5.5％，在 MS-COCO 上提高 18.4％，在 NUS-WIDE 上提高 15.2％，在 CUB-Birds 上提高 8.4％，优于目前的 SOTA 方法。

单正多标语义学习中的视觉语言伪标签

Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

Single-positive multi-label learning (SPMLL) is a typical weakly supervised
multi-label learning problem, where each training example is annotated with
only one positive label. Existing SPMLL methods typically assign pseudo-labels
to unannotated labels with the assumption that prior probabilities of all
classes are identical. However, the class-prior of each category may differ
significantly in real-world scenarios, which makes the predictive model not
perform as well as expected due to the unrealistic assumption on real-world
application. To alleviate this issue, a novel framework named {\proposed},
i.e., Class-pRiors Induced Single-Positive multi-label learning, is proposed.
Specifically, a class-priors estimator is introduced, which could estimate the
class-priors that are theoretically guaranteed to converge to the ground-truth
class-priors. In addition, based on the estimated class-priors, an unbiased
risk estimator for classification is derived, and the corresponding risk
minimizer could be guaranteed to approximately converge to the optimal risk
minimizer on fully supervised data. Experimental results on ten MLL benchmark
datasets demonstrate the effectiveness and superiority of our method over
existing SPMLL approaches.

提出了一种名为 proposed 的新框架，即类别先验引导的单正多标签学习，通过引入类别先验估计器并基于该估计值推导出一种无偏风险估计器，实验证明该方法在十个多标签学习基准数据集上比现有方法更加有效和优越。