Urban scene parsing is the core of the intelligent transportation system, and RGB–thermal urban scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing approaches fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, an edge-aware guidance fusion network (EGFNet) was developed in this study for RGB–thermal urban scene parsing. First, a prior edge map generated using the RGB and thermal images were introduced to capture detailed information in the prediction map and then embed the prior edge cues into the feature maps. To fuse the RGB and thermal information effectively, a multimodal fusion module was designed that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, global and semantic information modules were proposed to extract rich semantic information from the high-level features. For decoding, simple elementwise addition was utilized for cascaded feature fusion. Finally, to improve the parsing accuracy, multitask deep supervision was applied to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with the state-of-the-art methods.

RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed information in the prediction map and then embed the prior edge information in the feature maps. To effectively fuse the RGB and thermal information, we propose a multimodal fusion module that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features. For decoding, we use simple elementwise addition for cascaded feature fusion. Finally, to improve the parsing accuracy, we apply multitask deep supervision to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with state-of-the-art methods. The code and results can be found at https://github.com/ShaohuaDong2021/EGFNet.

Clinical named entity recognition (CNER) identifies entities from unstructured medical records and classifies them into predefined categories. It is of great significance for follow‐up clinical studies. Most of the existing CNER methods fail to give enough thought to Chinese radical‐level characteristics and the specialty of the Chinese field. This paper proposes the Ra‐RC model, which combines radical features and a deep learning structure to fix this problem. A bidirectional encoder representation of transformer (RoBERTa) is utilized to learn medical features thoroughly. Simultaneously, we use the bidirectional long short‐term memory (BiLSTM) network to extract radical‐level information to capture the internal relevance of characteristics and stitch the eigenvectors generated by RoBERTa. In addition, the relationship between labels is considered to obtain the optimal tag sequence by applying conditional random field (CRF). The experimental results demonstrate that the proposed Ra‐RC model achieves F1 score 93.26% and 82.87% on the CCKS2017 and CCKS2019 datasets, respectively.

We propose a dialogic system based on a relevance feedback strategy that allows for the semiautomatic synthesis of a facial image that only exists in a user's mind. The user is presented with several facial images and judges whether each one resembles the face that he or she is imagining. Based on the feedback from the user, a set of sample facial images are used to train an Optimum-Path Forest classifying the relevance of facial images. An interpolation method is then employed to synthesize new facial images that closely resemble the imagined face. A series of experiments are conducted to evaluate and verify the effectiveness and efficiency of the proposed technique.