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This article addresses the problem of the building an out-of-the-box deep detector, motivated by the need to perform anomaly detection across multiple hyperspectral images (HSIs) without repeated training. To solve this challenging task, we propose a unified detector [anomaly detection network (AUD-Net)] inspired by few-shot learning. The crucial issues solved by AUD-Net include: how to improve the generalization of the model on various HSIs that contain different categories of land cover; and how to unify the different spectral sizes between HSIs. To achieve this, we first build a series of subtasks to classify the relations between the center and its surroundings in the dual window. Through relation learning, AUD-Net can be more easily generalized to unseen HSIs, as the relations of the pixel pairs are shared among different HSIs. Secondly, to handle different HSIs with various spectral sizes, we propose a pooling layer based on the vector of local aggregated descriptors, which maps the variable-sized features to the same space and acquires the fixed-sized relation embeddings. To determine whether the center of the dual window is an anomaly, we build a memory model by the transformer, which integrates the contextual relation embeddings in the dual window and estimates the relation embeddings of the center. By computing the feature difference between the estimated relation embeddings of the centers and the corresponding real ones, the centers with large differences will be detected as anomalies, as they are more difficult to be estimated by the corresponding surroundings. Extensive experiments on both the simulation dataset and 13 real HSIs demonstrate that this proposed AUD-Net has strong generalization for various HSIs and achieves significant advantages over the specific-trained detectors for each HSI.

The facial expression recognition is very important for human-computer interaction. Therefore, a large number of researchers are focusing on this topic research and have acquired many valuable research achievements. However, there still exist many problems that need to be solved for practical applications, such as the impact of identity and appearance differences, posture change etc. In this work, a dual-branch residual disentangled adversarial learning network is proposed to learn more accurate expression features by disentangling the non-expression features from basic features through a novel combinatorial loss function. In the proposed method, dual-branch network structure is designed, one branch with a D-Net module is utilized to explore non-expression features and another branch just uses subtraction operation to obtain expression features. Based on the above network structure, a novel loss function is constructed to guide the two branches to learn different type features, which contains expression recognition loss, adversarial loss and cosine similarity loss. The main highlight of this work is that the proposed method could achieve the disentanglement of expression features and non-expression features just based on a low-complexity network and expression datasets without other auxiliary data. Finally, abundant experimental results on multiple expression datasets have confirmed the proposed method could obtain better expression recognition results than other state-of-the-art methods.

This article focuses on end-to-end image matching through joint key-point detection and descriptor extraction. To find repeatable and high discrimination key points, we improve the deep matching network from the perspectives of network structure and network optimization. First, we propose a concurrent multiscale detector (CS-det) network, which consists of several parallel convolutional networks to extract multiscale features and multilevel discriminative information for key-point detection. Moreover, we introduce an attention module to fuse the response maps of various features adaptively. Importantly, we propose two novel rank consistent losses (RC-losses) for network optimization, significantly improving image matching performances. On the one hand, we propose a score rank consistent loss (RC-S-loss) to ensure that the key points have high repeatability. Different from the score difference loss merely focusing on the absolute score of an individual key point, our proposed RC-S-loss pays more attention to the relative score of key points in the image. On the other hand, we propose a score-discrimination RC-loss to ensure that the key point has high discrimination, which can reduce the confusion from other key points in subsequent matching and then further enhance the accuracy of image matching. Extensive experimental results demonstrate that the proposed CS-det improves the mean matching result of deep detector by 1.4%–2.1%, and the proposed RC-losses can boost the matching performances by 2.7%–3.4% than score difference loss. Our source codes are available at https://github.com/iquandou/CS-Net.