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Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating urban traffic pressure. However, it is highly challenging due to the complexity of the urban geospatial structure and the highly nonlinear temporal and spatial dependence on human mobility. Most existing works proposed to rely on strict periods (e.g. daily and weekly) and separate the extraction of temporal and spatial features. Besides, most Recurrent Neural Network (RNN)-based models either fail to capture variations of spatial–temporal features in adjacent timestamps or ignore details of closeness. In this paper, we propose a M ulti-attention based H ybrid-convolution S patial-temporal R ecurrent N etwork (MHSRN) for region-based traffic flow prediction. In MHSRN, we leverage a hybrid-convolution module to capture both shifting features and rich information at the nearest timestamps, and we apply the downsampling procedure to reduce the computation of RNN-based model. Furthermore, we propose to adopt a space-aware multi-attention module to re-perceive global and local spatial–temporal features. We conduct extensive experiments based on three real-world datasets. The results show that the MHSRN outperforms other challenging baselines by approximately 0.2–8.1% in mean absolute error on all datasets. On datasets other than TaxiBJ, the MHSRN reduces the root mean square error by at least 2.8% compared with the RNN-based model. [ABSTRACT FROM AUTHOR]

Deep learning has achieved remarkable success in the semantic segmentation of remote sensing images (RSIs).In the domain of semantic segmentation, where classification and localization tasks need to be performed simultaneously, it is crucial to consider both global and local spatial relationships in RSIs. This is especially important for the recognition of ground objects that have a slim and elongated appearance. However, existing methods for land use semantic segmentation lack an effective mechanism to coordinate and address these two aspects, resulting in limitations on the recognition of slim targets and the continuity of land object identification. Here, a specific attention-based network called PaANet is developed for semantic segmentation. Our proposed framework builds upon the Swin transformer by incorporating two key modules: the position-aware attention (PaA) module and the pyramid pooling expectation-maximization (PPEM) module. These modules provide significant improvements in recognition accuracy and the continuity of ground object recognition while preserving structural classification details. Furthermore, we propose a multiresolution data augmentation method that utilizes scale-related information to guide the encoder. This approach leads to improved performance and generalization ability for the model. In experiments, the mIoU of our approach for the BLU and GID datasets is 2.37% and 3.94% higher than that of the baseline model, respectively. Our results also show significant superior to those of other methods regarding the continuity of ground object recognition. [ABSTRACT FROM AUTHOR]

Fine-grained primate behavior recognition is crucial for conservation efforts and longitudinal research, but a lack of suitable datasets and complex network structures has hindered its progress. To address these challenges, we propose a novel approach that employs a region-focused deep convolutional neural network and a labeled fine-grained recognition dataset. Our model leverages a progressive attention training strategy that emphasizes discriminative region attention and encourages complementarity with multiple leading levels. Additionally, we incorporate a region-focused image generator that identifies informative local regions and provides a priori knowledge for the subsequent layer. Our experiments demonstrate that our model surpasses state-of-the-art performance on fine-grained primate behavior recognition benchmark datasets, even outperforming some models by leveraging auxiliary information. Our approach produces the automated fine-grained action recognition of primate behavior, making it a significant step for exploiting large datasets in ethology and conservation. This approach can be readily applied to a more comprehensive range of species and scenarios. • We propose a deep learning model for identifying primate behaviors from still images. • We have developed a new fine-grained primate behavior detection dataset. • Experimental results demonstrate the effectiveness of our model. [ABSTRACT FROM AUTHOR]