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Representing contextual features at multiple scales is important for RGB-D SOD. Recently, due to advances in backbone convolutional neural networks (CNNs) revealing stronger multi-scale representation ability, many methods achieved comprising performance. However, most of them represent multi-scale features in a layer-wise manner, which ignores the fine-grained global contextual cues in a single layer. In this paper, we propose a novel global contextual exploration network (GCENet) to explore the performance gain of multi-scale contextual features in a fine-grained manner. Concretely, a cross-modal contextual feature module (CCFM) is proposed to represent the multi-scale contextual features at a single fine-grained level, which can enlarge the range of receptive fields for each network layer. Furthermore, we design a multi-scale feature decoder (MFD) that integrates fused features from CCFM in a top-down way. Extensive experiments on five benchmark datasets demonstrate that the proposed GCENet outperforms the other state-of-the-art (SOTA) RGB-D SOD methods. • A global contextual exploration network (GCENet) is proposed to exploit the role of multi-scale features at a single fine-grained level for RGB-D SOD, where a cross-modal contextual feature module (CCFM) is designed to extract and fuse cross-modal and multi-scale features at a single micro level. • To integrate the multi-scale from multiple blocks of the backbone, we design a multi-scale feature decoder (MFD) to fuse these multi-scale features in a top-down layer-wise strategy, where the top features are embedded into all the features of the lower layer by a fully transferred strategy. • Extend experiments on five benchmark datasets demonstrate that the proposed GCENet outperforms other 18 well-known RGB-D SOD methods and achieves state-of-the-art performance in terms of five widely used evaluation metrics. [ABSTRACT FROM AUTHOR]

Named entity recognition is a basic task in natural language processing, and there is a large number of nested structures in named entities. Nested named entities become the basis for solving many tasks in NLP. A nested named entity recognition model based on dual-flow features complementary is proposed for obtaining efficient feature information after text coding. Firstly, sentences are embedded at both the word level and the character level of the words, then sentence context information is obtained separately via the neural network Bi-LSTM; Afterward, two vectors perform low-level feature complementary to reinforce low-level semantic information; Sentence-local information is captured with the multi-head attention mechanism, then the feature vector is sent to the high-level feature complementary module to obtain deep semantic information; Finally, the entity word recognition module and the fine-grained division module are entered to obtain the internal entity. The experimental results show that the model has a great improvement in feature extraction compared to the classical model. [ABSTRACT FROM AUTHOR]

Although the salient object detection (SOD) methods based on fully convolutional networks have made extraordinary achievements, it is still a challenge to accurately detect salient objects with complicated structure from cluttered real-world scenes due to their rarely considering the effectiveness and correlation of the captured different scale context and how to efficient interaction of complementary information. Motivate by this, in this paper, a novel Dense Multi-scale Inference Network (DMINet) is proposed for the accurate SOD task, which mainly consists of a dual-stream multi-receptive field module and a residual multi-mode interaction strategy. The former uses the well-designed different receptive field convolution operations and dense guidance connections to efficiently capture and utilize multi-scale contextual features for better salient objects inferring, while the latter adopts diverse interaction manners to adequately interact complementary information from multi-level features, generating powerful feature representations for predicting high-quality saliency maps. Quantitative and qualitative comparison results on five SOD datasets convincingly demonstrate that our DMINet performs favorably compared with 17 state-of-the-art SOD methods under different evaluation metrics. [ABSTRACT FROM AUTHOR]