Facial expression recognition is of significant importance in criminal investigation and digital entertainment. Under unconstrained conditions, existing expression datasets are highly class-imbalanced, and the similarity between expressions is high. Previous methods tend to improve the performance of facial expression recognition through deeper or wider network structures, resulting in increased storage and computing costs. In this paper, we propose a new adaptive supervised objective named AdaReg loss, re-weighting category importance coefficients to address this class imbalance and increasing the discrimination power of expression representations. Inspired by human beings’ cognitive mode, an innovative coarse-fine (C-F) labels strategy is designed to guide the model from easy to difficult to classify highly similar representations. On this basis, we propose a novel training framework named the emotional education mechanism (EEM) to transfer knowledge, composed of a knowledgeable teacher network (KTN) and a self-taught student network (STSN). Specifically, KTN integrates the outputs of coarse and fine streams, learning expression representations from easy to difficult. Under the supervision of the pre-trained KTN and existing learning experience, STSN can maximize the potential performance and compress the original KTN. Extensive experiments on public benchmarks demonstrate that the proposed method achieves superior performance compared to current state-of-the-art frameworks with 88.07% on RAF-DB, 63.97% on AffectNet and 90.49% on FERPlus.

Automatic ship detection in high-resolution remote sensing images has attracted increasing research interest due to its numerous practical applications. However, there still exist challenges when directly applying state-of-the-art object detection methods to real ship detection, which greatly limits the detection accuracy. In this article, we propose a novel cascade rotated anchor-aided detection network to achieve high-precision performance for detecting arbitrary-oriented ships. First, we develop a data preprocessing embedded cascade structure to reduce large amounts of false positives generated on blank areas in large-size remote sensing images. Second, to achieve accurate arbitrary-oriented ship detection, we design a rotated anchor-aided detection module. This detection module adopts a coarse-to-fine architecture with a cascade refinement module (CRM) to refine the rotated boxes progressively. Meanwhile, it utilizes an anchor-aided strategy similar to anchor-free, thus breaking through the bottlenecks of anchor-based methods and leading to a more flexible detection manner. Besides, a rotated align convolution layer is introduced in CRM to extract features from rotated regions accurately. Experimental results on the challenging DOTA and HRSC2016 data sets show that the proposed method achieves 84.12% and 90.79% AP, respectively, outperforming other state-of-the-art methods.

With the development of synthetic aperture radar (SAR) imaging and deep learning, SAR ship detection based on convolutional neural networks (CNNs) has been extensively applied in the last few years. Nevertheless, there are two main obstacles in SAR ship detection: 1) the SAR images have too much noise, such as the interference from land area, making it difficult to distinguish ship objects from the surrounding background, and 2) due to the multiscale characteristics of ship objects, there are numerous false negatives in the detection results, especially for small objects. To alleviate the above problems, we propose a one-stage ship detector with strong robustness against scale changes and various interferences. First, to mitigate the disturbance from complex background, a coordinate attention module (CoAM) is introduced for obtaining more representative semantic features to accurately locate and distinguish ship objects. Second, a receptive field increased module (RFIM) is devised to capture multiscale contextual information to improve the detection performance for ships with diverse scales. Finally, we verify the robustness of our method on several public SAR datasets, i.e., SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results demonstrate that the proposed method has a competitive performance, exceeding other state-of-the-art methods by at least 2.6% AP ₅₀ on HRSID.

Person re-identification (Re-ID) aims to retrieve images of the same person across disjoint camera views. Most Re-ID studies focus on pedestrian images captured by visible cameras, without considering the infrared images obtained in the dark scenarios. Person retrieval between visible and infrared modalities is of great significance to public security. Current methods usually train a model to extract global feature descriptors and obtain discriminative representations for visible infrared person Re-ID (VI-REID). Nevertheless, they ignore the detailed information of heterogeneous pedestrian images, which affects the performance of Re-ID. In this article, we propose a flexible body partition (FBP) model-based adversarial learning method (FBP-AL) for VI-REID. To learn more fine-grained information, FBP model is exploited to automatically distinguish part representations according to the feature maps of pedestrian images. Specially, we design a modality classifier and introduce adversarial learning which attempts to discriminate features between visible and infrared modality. Adaptive weighting-based representation learning and threefold triplet loss-based metric learning compete with modality classification to obtain more effective modality-sharable features, thus shrinking the cross-modality gap and enhancing the feature discriminability. Extensive experimental results on two cross-modality person Re-ID data sets, i.e., SYSU-MM01 and RegDB, exhibit the superiority of the proposed method compared with the state-of-the-art solutions.