Recent advances in weakly multi-label learning (MLL) have demonstrated impressive potential in multi-label classification tasks. Unfortunately, collecting multi-labels for each instance proves to be time-consuming and labor-intensive, since these MLL methods requires the assessment of all the candidate labels. To alleviate this challenge, a novel labeling setting termed Determined Multi-Label Learning is proposed, aiming to effectively reduce the cost for browsing labels in multi-label tasks. In this setting, each training instance is associated with a determined multi-label, which indicates whether the instance contains the provided class label. Besides, each instance only need to be determined once, which significantly reduce the annotation cost of the labeling task for multi-label datasets. In this paper, we theoretically derive an risk-consistent estimator to learn from these determined-labeled training data. Additionally, we introduce a similarity-based prompt learning method, which minimizes the risk-consistent loss of large-scale pre-trained models to learn a supplemental prompt with richer semantic information. Extensive experimental validation underscores the efficacy of our approach. Our code is available at the link: https://github.com/WilsonMqz/DMLL

Multi-label learning (MLL) learns from samples associated with multiple labels, where it is expensive and time consuming to provide detailed annotation for each sample in real-world datasets. To deal with this challenge, single positive multi-label learning (SPML) has been studied in recent years. In SPML, each sample is annotated with only one positive label, which is much easier and less costly. However, in many real-world scenarios, single positive labels may have global unannotated categories (GUCs) in annotation process, which exist in the label space but do not serve as single positive label for any samples. Unfortunately, previous SPML approaches are less applicable to classify GUCs due to the absence of supervised information. To solve this problem, we propose a novel prompt expanding framework that leverages a large-scale pretrained vision and language model called the Recognize Anything Model (RAM) to offer supervision signals for GUCs. Specifically, we first provide a simple but effective strategy to generate reliable pseudo-labels for GUCs by utilizing zero-shot predictions of RAM. Subsequently, we introduce additional prompts from a large common category list and fuse them by learnable weighting factors, which expends the semantic representation of GUCs. Experiments show that our method achieves state-of-the-art results on all four benchmarks. The code to reproduce the experiments is at: https://github.com/yingpenga/VLSPE

Annotating data for sensitive labels (e.g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios. To cope with this problem, we propose a novel setting to protect privacy of each instance, namely learning from concealed labels for multi-class classification. Concealed labels prevent sensitive labels from appearing in the label set during the label collection stage, which specifies none and some random sampled insensitive labels as concealed labels set to annotate sensitive data. In this paper, an unbiased estimator can be established from concealed data under mild assumptions, and the learned multi-class classifier can not only classify the instance from insensitive labels accurately but also recognize the instance from the sensitive labels. Moreover, we bound the estimation error and show that the multi-class classifier achieves the optimal parametric convergence rate. Experiments demonstrate the significance and effectiveness of the proposed method for concealed labels in synthetic and real-world datasets. Source code is available at https://github.com/WilsonMqz/CLF

Local feature extraction is one of the key characteristics of convolutional neural networks (CNNs). This study proposes an adaptive local feature enhancement (ALFE) model with a low‐frequency general appearance‐enhancement operator and a high‐frequency local detail enhancement operator to improve local features of CNNs. Through supervised training, the model could adaptively adjust enhancement parameters and achieve a global‐local enhancement of training images and CNNs. The performance of ALFE was first preliminarily evaluated with a self‐built CNN on CIFAR‐10 data set in different conditions of image augmentation and feature pooling. CNNs with ALFE could increase the top‐1 accuracy compared against CNNs in the same conditions, and nearly reach the same level of performance for different pooling approaches. With only two extra adjustable parameters, this model could effectively avoid overfitting, without affecting the convergence speed of CNN. In addition, the extra burden of network complexity could be neglected. Further, experiments of three existing popular CNNs (AlexNet, VGGNet and ResNet) with ALFE were carried out on dogs versus cats, Tiny ImageNet, and SVHN data sets, respectively. The results show that ALFE is feasible for the existing popular CNN models, improving their top‐1 accuracies without changing their convergence speeds.