As a periodic movement, gait contains informative biometric traits formed by individual body structures, motion patterns, and behavioral habits. Previous gait recognition methods mainly focus on mining the appearance cues from gait sequences, while neglecting the dynamic motion characteristics. Motion cues are important complementary information for generating high-quality gait representations that can help models accurately recognize individuals. In this article, we propose a novel gait recognition framework named GaitDS to model dynamic motion information and construct salient gait representations. Specifically, we develop a motion information perception module that can directly represent dynamic regions during walking and extract fine-grained motion features based on the appearance of body parts over time. In addition, since some frames in gait sequences share partial similarities, we present saliency identity representation learning to focus on key frames along the temporal dimension, and integrate salient identity features to enhance sequence-level representations. Furthermore, a channel enhanced module is designed to generate more discriminative gait representations, where motion and temporal salient features can be complemented with global representations. Compared with existing state-of-the-art methods, our model achieves superior average rank-1 recognition accuracy on three benchmark datasets, i.e., 93.7% on CASIA-B, 92.4% on OU-MVLP, and 50.7% on Gait3D.

Label distribution learning (LDL) is a novel machine learning paradigm for solving ambiguous tasks, where the degree to which each label describing the instance is ambiguous. However, obtaining the label distribution is high cost and the description degree is difficult to quantify. Most existing research works focus on designing an objective function to obtain the whole description degrees at once but seldom care about the sequentiality in the process of recovering the label distribution. In this article, we formulate the label distribution recovering task as a sequential decision process called sequential label enhancement (Seq_LE), which is more consistent with the process of annotating the label distribution in human brains. Specifically, the discrete label and its description degree are serially mapped by the reinforcement learning (RL) agent. Besides, we carefully design a joint reward function to drive the agent to fully learn the optimal decision policy. Extensive experiments on 16 LDL datasets are conducted under various evaluation metrics. The experimental results demonstrate convincingly that the proposed sequential label enhancement (LE) leads to better performance over the state-of-the-art methods.

Label Distribution Learning (LDL) has attracted increasing research attentions due to its potential to address the label ambiguity problem in machine learning and success in many real-world applications. In LDL, it is usually expensive to obtain the ground-truth label distributions of data, but it is relatively easy to obtain the logical labels of data. How to use training instances only with logical labels to learn an effective LDL model is a challenging problem. In this paper, we propose a two-step framework to address this problem. Specifically, we firstly design an efficient recovery model to recover the latent label distributions of training instances, named Fast Label Enhancement (FLE). Our idea is to use non-negative matrix factorization (NMF) to mine the label distribution information from the feature space. Moreover, we take the instance-class similarities into consideration to discover the importance of each label to training instances, which is useful for learning precise label distributions. Then, we train a predictive model for testing instances based on generated label distributions of training instances and an existing LDL method (e.g., SA-BFGS). Experimental results on fifteen benchmark datasets show the effectiveness of the proposed two-step framework and verify the superiority of FLE over several state-of-the-art approaches.

Label Distribution Learning (LDL) is a general learning paradigm in machine learning, which includes both single-label learning (SLL) and multi-label learning (MLL) as its special cases. Recently, many LDL algorithms have been proposed to handle different application tasks such as facial age estimation, head pose estimation and visual sentiment distributions prediction. However, the training time complexity of most existing LDL algorithms is too high, which makes them unapplicable to large-scale LDL. In this paper, we propose a novel LDL method to address this issue, termed Discrete Binary Coding based Label Distribution Learning (DBC-LDL). Specifically, we design an efficiently discrete coding framework to learn binary codes for instances. Furthermore, both the pair-wise semantic similarities and the original label distributions are integrated into this framework to learn highly discriminative binary codes. In addition, a fast approximate nearest neighbor (ANN) search strategy is utilized to predict label distributions for testing instances. Experimental results on five real-world datasets demonstrate its superior performance over several state-of-the-art LDL methods with the lower time cost.

Label Distribution Learning (LDL) is a novel learning paradigm in machine learning, which assumes that an instance is labeled by a distribution over all labels, rather than labeled by a logic label or some logic labels. Thus, LDL can model the description degree of all possible labels to an instance. Although many LDL methods have been put forward to deal with different application tasks, most existing methods suffer from the scalability issue. In this paper, a scalable LDL framework named Binary Coding based Label Distribution Learning (BC-LDL) is proposed for large-scale LDL. The proposed framework includes two parts, i.e., binary coding and label distribution generation. In the binary coding part, the learning objective is to generate the optimal binary codes for the instances. We integrate the label distribution information of the instances into a binary coding procedure, leading to high-quality binary codes. In the label distribution generation part, given an instance, the k nearest training instances in the Hamming space are searched and the mean of the label distributions of all the neighboring instances is calculated as the predicted label distribution. Experiments on five benchmark datasets validate the superiority of BC-LDL over several state-of-the-art LDL methods.

The target of cross-modal hashing is to embed heterogeneous multimedia data into a common low-dimensional Hamming space, which plays a pivotal part in multimedia retrieval due to the emergence of big multimodal data. Recently, matrix factorization has achieved great success in cross-modal hashing. However, how to effectively use label information and local geometric structure is still a challenging problem for these approaches. To address this issue, we propose a cross-modal hashing method based on collective matrix factorization, which considers both the label consistency across different modalities and the local geometric consistency in each modality. These two elements are formulated as a graph Laplacian term in the objective function, leading to a substantial improvement on the discriminative power of latent semantic features obtained by collective matrix factorization. Moreover, the proposed method learns unified hash codes for different modalities of an instance to facilitate cross-modal search, and the objective function is solved using an iterative strategy. The experimental results on two benchmark data sets show the effectiveness of the proposed method and its superiority over state-of-the-art cross-modal hashing methods.