This paper deals with the challenging problem of building visual reasoning models for answering questions related to artworks in artistic domains. The nature of abstract styles and cultural contexts within an artistic image makes the corresponding learning tasks extremely difficult. We propose a novel framework termed as Hierarchical-Knowledge Embedded Meta-Learning to address the critical issues of visual reasoning in artistic domains. In particular, we firstly present a deep relational model to capture and memorize the relations among different samples. Then, we provide the hierarchical-knowledge embedding that mines the implicit relationship between question-answer pairs for knowledge representation as the guidance of our meta-learner. This is a case of "knowledge is power" in the sense that the hierarchical knowledge representation is incorporated into our meta-learning based model. The final classification is derived from our model by learning to compare the features of samples. Experimental results show that our approach achieves significantly higher performance compared with other state-of-the-arts.

Person search often requires a query photo of the target person. However, in many practical scenarios, there is no guarantee that such a photo is always available. In this paper, we define the problem of sketch based person search, which uses a sketch instead of a photo as the probe for retrieving. We tackle this problem in a weak supervision setting and propose a clustering and feature attention based weakly supervised learning framework, which contains two stages of pedestrian detection and sketch based person re-identification. Specially, we introduce multiple detectors, followed by fuzzy c-means clustering to achieve weakly supervised pedestrian detection. Moreover, we design an attention module to learn discriminative features in subsequent re-identification network. Extensive experiments show the superiority of our method.

Adversarial images generated by generative adversarial networks are not close to any existing benign images, and contain nonrobust features that have been identified as critical to the robustness of a machine learning model. Since adversarial images have an underlying distribution that differs from normal images, these kinds of images can offer valuable features for training a robust model. To deal with these special features, we focus on a novel machine learning task of adversarial images classification, where adversarial images can be used to investigate the problem of classifying adversarial images themselves. In the setting of this novel task, adversarial images are the ONLY kind of data used in training and testing, rather than not just a set of testing images as usual. To this end, we propose a novel spatial–frequency ensemble relation network with generative adversarial learning. First, we present a spatial–frequency ensemble representation learning to extract the feature of training images. Second, we design a meta-learning-based relation model to gain the relationship between images. Third, to achieve a robust model, we utilize generative adversarial learning and transform the relationship into a Jacobian matrix. Finally, we design a discriminator model that determines whether an adversarial image is from the matching category or not. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts.

The goal of diagnosing the coronavirus disease 2019 (COVID-19) from suspected pneumonia cases, that is, recognizing COVID-19 from chest X-ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep-learning-based methods. (2) Many public COVID-19 data sets contain only a few images without fine-grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID-19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID-19. To address these issues, we propose a novel framework called Unsupervised Meta-Learning with Self-Knowledge Distillation to address the problem of differentiating COVID-19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self-knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods. Moreover, we construct a new COVID-19 pneumonia data set based on text mining, consisting of 2696 COVID-19 images (347 X-ray + 2349 CT), 10,155 images (9661 X-ray + 494 CT) about other types of pneumonia, and the fine-grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

While existing person search methods have achieved good performance, they require the images used for training contain labels about the identity and bounding box location of each person. However, it is expensive and difficult to manually annotate these labels in the large scale scenario. To overcome this issue, we consider weakly supervised person search. The weakly supervised setting means during training we only know which identities appear in the image set and how many individuals present in each image, without any identity or location information on the image. Facing this challenge, we propose a clustering and patch based weakly supervised learning (CPBWSL) framework, which separately addresses two sub-tasks including pedestrian detection and person re-identification. Particularly, we introduce multiple detectors to provide more detection results as well as fuzzy c-means clustering algorithm to cluster these results and remove low membership ones. Moreover, a patch based learning network is designed to generate different patches and learn discriminative patch features. Extensive experiments on two benchmarks indicate that the proposed weakly supervised setting is feasible and our method can achieve performance comparable to some fully supervised person search methods.

As the size and source of network traffic increase, so does the challenge of monitoring and analyzing network traffic. The challenging problems of classifying encrypted traffic are the imbalanced property of network data, the generalization on an unseen dataset, and overly dependent on data size. In this paper, we propose an application of a meta-learning approach to address these problems in encrypted traffic classification, named Flow-Based Relation Network (RBRN). The RBRN is an end-to-end classification model that learns representative features from the raw flows and then classifies them in a unified framework. Moreover, we design “hallucinator” to produce additional training samples for the imbalanced classification, and then focus on meta-learning to classify unseen categories from few labeled samples. We validate the effectiveness of the RBRN on the real-world network traffic dataset, and the experimental results demonstrate that the RBRN can achieve an excellent classification performance and outperform the state-of-the-art methods on encrypted traffic classification. What is more interesting, our model trained on the real-world dataset can generalize very well to unseen datasets, outperforming multiple state-of-art methods.