One of the main difficulties in facial age estimation is that the learning algorithms cannot expect sufficient and complete training data. Fortunately, the faces at close ages look quite similar since aging is a slow and smooth process. Inspired by this observation, instead of considering each face image as an instance with one label (age), this paper regards each face image as an instance associated with a label distribution. The label distribution covers a certain number of class labels, representing the degree that each label describes the instance. Through this way, one face image can contribute to not only the learning of its chronological age, but also the learning of its adjacent ages. Two algorithms, named IIS-LLD and CPNN, are proposed to learn from such label distributions. Experimental results on two aging face databases show remarkable advantages of the proposed label distribution learning algorithms over the compared single-label learning algorithms, either specially designed for age estimation or for general purpose.

Label distribution is more general than both single-label annotation and multi-label annotation. It covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. To solve this problem, one way is to recover the label distributions from the logical labels in the training set via leveraging the topological information of the feature space and the correlation among the labels. Such process of recovering label distributions from logical labels is defined as label enhancement (LE), which reinforces the supervision information in the training sets. This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE). Experimental results on one artificial dataset and fourteen real-world LDL datasets show clear advantages of GLLE over several existing LE algorithms. Furthermore, experimental results on eleven multi-label learning datasets validate the advantage of GLLE over the state-of-the-art multi-label learning approaches.

Multi-label classification aims to recognize multiple objects or attributes from images. The key to solving this issue relies on effectively characterizing the inter-label correlations or dependencies, which bring the prevailing graph neural network. However, current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model’s generalization ability. This article proposes a Graph Attention Transformer Network, a general framework for multi-label image classification by mining rich and effective label correlation. First, we use the cosine similarity value of the pre-trained label word embedding as the initial correlation matrix, which can represent richer semantic information than the co-occurrence one. Subsequently, we propose the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve highly competitive performance on three datasets.

Label correlation is helpful to alleviate the overwhelming output space of label distribution learning (LDL). However, existing studies either only consider one of global and local label correlations or exploit label correlation by some prior knowledge (e.g., low-rank assumption, which may not hold sometimes). To efficiently exploit both global and local label correlations in a data-driven way, we propose in this article a new LDL method called label distribution learning by exploiting label distribution manifold (LDL-LDM). Our basic idea is that the underlying manifold structure of label distribution may encode the correlations among labels. LDL-LDM works as follows. First, to exploit global label correlation, we learn the label distribution manifold and encourage the outputs of our model to lie in the same manifold. Second, we learn the label distribution manifold of different clusters of samples to consider local label correlations. Third, to handle incomplete label distribution learning (incomplete LDL), we jointly learn label distribution and label distribution manifold. Theoretical analysis demonstrates the generalization of our method. Finally, experimental results validate the effectiveness of LDL-LDM in both full and incomplete LDL cases.

In this paper, we tackle the problem of crowd counting in indoor videos, where people often stay almost static for a long time. The label distribution, which covers a certain number of crowd counting labels, representing the degree to which each label describes the video frame, is previously adopted to model the label ambiguity of the crowd number. However, since the label ambiguity is significantly affected by the crowd number of the scene, we initialize the label distribution of each frame by the discretized Gaussian distribution with adaptive variance instead of the original single static Gaussian distribution. Moreover, considering the gradual change of crowd numbers in the adjacent frames, a mixture of Gaussian models is proposed to generate the final label distribution representation for each frame. The weights of the Gaussian models rely on the frame and feature distances between the current frame and the adjacent frames. The mixed l _2,1 -norm is adopted to restrict the weights of predicting the adjacent crowd numbers to be locally correlated. We collect three new indoor video datasets with frame number annotation for further research. The proposed approach achieves state-of-the-art performance on seven challenging indoor videos and cross-scene experiments.