Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods.

Partial Multi-label Learning (PML) addresses the scenario where each instance is assigned with multiple candidate labels, while only a subset of the labels are relevant. This task is very challenging because the training procedure can be misguided by the noisy (irrelevant) labels. Exploiting label correlations is useful for partial multi-label learning. However, the existing PML methods often ignore to explicitly and sufficiently leverage the label correlation information for handling the noisy labels. To this end, in this paper, we propose a novel Global-Local Label Correlation (GLC) approach for partial multi-label learning . On one hand, we introduce a label coefficient matrix to explicitly exploit the global structure information of labels from multiple subspaces. On the other hand, we present a new label manifold regularizer to capture the local label correlations to further improve the performance of our method. By jointly taking advantage of the global and local label correlations, our proposed approach achieves superior performance on both the synthetic and real-world data sets from diverse domains.

Over the past decades, researches on facial expression recognition have been restricted within six basic expressions (anger, fear, disgust, happiness, sadness and surprise). However, these six words can not fully describe the richness and diversity of human beings’ emotions. To enhance the recognitive capabilities for computers, in this paper, we focus on fine-grained facial expression recognition in the wild and build a brand new benchmark FG-Emotions to push the research frontiers on this topic, which extends the original six classes to more elaborate thirty-three classes. Our FG-Emotions contains 10,371 images and 1,491 video clips annotated with corresponding fine-grained facial expression categories and landmarks. FG-Emotions also provides several features (e.g., LBP features and dense trajectories features) to facilitate related research. Moreover, on top of FG-Emotions, we propose a new end-to-end Multi-Scale Action Unit (AU)-based Network (MSAU-Net) for facial expression recognition with image which learns a more powerful facial representation by directly focusing on locating facial action units and utilizing “zoom in” operation to aggregate distinctive local features. As for recognition with video, we further extend the MSAU-Net to a two-stream model (TMSAU-Net ) by adding a module with attention mechanism and a temporal stream branch to jointly learn spatial and temporal features. (T)MSAU-Net consistently outperforms existing state-of-the-art solutions on our FG-Emotions and several other datasets, and serves as a strong baseline to drive the future research towards fine-grained facial expression recognition in the wild.

Tensorial Multi-view Clustering (TMC), a prominent approach in multi-view clustering, leverages low-rank tensor learning to capture high-order correlation among views for consistent clustering structure identification. Despite its promising performance, the TMC algorithms face three key challenges: 1). The severe computational burden makes it difficult for TMC methods to handle large-scale datasets. 2). Estimation bias problem caused by the convex surrogate of the tensor rank. 3). Lack of explicit balance of consistency and complementarity. Being aware of these, we propose a basic framework Efficient and Scalable Tensorial Multi-View Subspace Clustering (ESTMC) for large-scale multi-view clustering. ESTMC integrates anchor representation learning and non-convex function-based low-rank tensor learning with a Generalized Non-convex Tensor Rank (GNTR) into a unified objective function, which enhances the efficiency of the existing subspace-based TMC framework. Furthermore, a novel model ESTMC-C$^{2}$ with the proposed Enhanced Tensor Rank (ETR), Consistent Geometric Regularization (CGR), and Tensorial Exclusive Regularization (TER) is extended to balance the learning of consistency and complementarity among views, delivering divisible representations for the clustering task. Efficient iterative optimization algorithms are designed to solve the proposed ESTMC and ESTMC-C$^{2}$, which enjoy time-economical complexity and exhibit theoretical convergence. Extensive experimental results on various datasets demonstrate the superiority of the proposed algorithms as compared to state-of-the-art methods.

Graph matching aims at establishing correspondences between graph elements, and is widely used in many computer vision tasks. Among recently proposed graph matching algorithms, those utilizing the path following strategy have attracted special research attentions due to their exhibition of state-of-the-art performances. However, the paths computed in these algorithms often contain singular points, which could hurt the matching performance if not dealt properly. To deal with this issue, we propose a novel path following strategy, named branching path following (BPF), to improve graph matching accuracy. In particular, we first propose a singular point detector by solving a KKT system, and then design a branch switching method to seek for better paths at singular points. Moreover, to reduce the computational burden of the BPF strategy, an adaptive path estimation (APE) strategy is integrated into BPF to accelerate the convergence of searching along each path. A new graph matching algorithm named ABPF-G is developed by applying APE and BPF to a recently proposed path following algorithm named GNCCP (Liu & Qiao 2014). Experimental results reveal how our approach consistently outperforms state-of-the-art algorithms for graph matching on five public benchmark datasets.

Attention mechanisms have been proven highly effective for salient object detection. Most previous works utilize attention as a self-gated module to reweigh the feature maps at different levels independently. However, they are limited to certain-level guidance and could not satisfy the need of both accurately detecting intact objects and maintaining their detailed boundaries. In this paper, we build dense attention upon features from multiple levels simultaneously and propose a novel Dense Attentive Feature Enhancement (DAFE) module for efficient feature enhancement in saliency detection. DAFE stacks several attentional units and densely connects attentive feature output from current unit to its all subsequent units. This allows feature maps at deep units to absorb attentive information from shallow units, thus more discriminative information can be efficiently selected at the final output. Note that DAFE is plug and play, which can be effortlessly inserted into any saliency or video saliency models for their performance improvements. We further instantiate a highly effective Dense Attentive Feature Enhancement Network (DAFE-Net) for accurate salient object detection. DAFE-Net constructs DAFE over the aggregation feature that contains both semantics and saliency details, the entire salient objects and their boundaries can be well retained through dense attentions. Extensive experiments demonstrate that the proposed DAFE module is highly effective, and the DAFE-Net performs favorably compared with state-of-the-art approaches.