Remote sensing image semantic segmentation is an essential aspect of the intelligent analysis of remote sensing, extensively applied in urban planning, economic assessment, and disaster monitoring. However, the expansive field of view and intricate backgrounds in remote sensing images cause numerous objects of varying sizes and categories to coexist. This results in incomplete object segmentation and poses challenges in restoring the spatial distribution of objects. In this paper, we present a Multilevel Object Aware Network (MOA-Net), which addresses the challenges of remote sensing image semantic segmentation. To be specific, this method is designed from three perspectives. Firstly, we establish a Progressive Multiscale Global-Local Decoder (PMGLD), integrating global-local context information of objects at varying scales through a progressive convolution strategy. Secondly, the Orientation-Aware Attention Mechanism (OAAM) provides orientation information and guides the restoration of interclass 2-D spatial relationships. Finally, obtaining fine-grained features through CNN Stem improves edge segmentation. Experimental outcomes on the Potsdam and Vaihingen datasets indicate that our method of performance and efficiency surpass those of existing methods.

Click-based interactive image segmentation intends to segment an object from the background under user click guidance. Recently, Vision Transformer has made significant strides in interactive image segmentation. However, the previous studies 1) overlook the importance of different clicks in terms of their contribution to the segmentation results; and 2) suffer from inconsistency across different feature scales in the multi-scale structure. In this paper, we propose a new interactive segmentation framework, named RCFormer, with two novel components: reconstruct click patch embedding (RCPE) for encoding the importance of clicks, and multi-scale adaptive fusion (MSAF) for the adaptive fusion of feature maps across different scales. RCPE enhances the effectiveness of click interactions by spatially distinguishing the importance of clicks. MSAF adaptively fuses useful spatial information and filters the redundant feature at multi-scales. The experiments on several benchmarks show that our proposed approach achieves state-of-the-art performance. Notably, our method achieves 2.31 NoC@90 on the Berkeley dataset, improving by 8.6% over the previous best results.

Most current Handwritten Mathematical Expression Recognition (HMER) methods employ an attention-based encoder-decoder framework, which generates LaTeX sequences from the given images, following the paradigm of predicting "one-by-one". However, this paradigm may have some challenges: 1) without considering the connectivity between characters, the prior information in the prediction process will be ignored inadvertently, especially implicit information, such as " " and " ˆ ". 2) Some characters of high similarities, such as "6/b" and "o/O", will have negative effects on prediction results. To solve these issues, we propose a simple but effective Character Relationship Refinement Network (CRRN), which consists of Joint Character Learning (JCL) and Character Refinement Mask (CRM). Specifically, JCL calculates the relationship probability between characters and uses them to improve prediction accuracy. CRM takes the character confidence coefficient in a coarse-to-fine way that can reassign the weights of all characters to improve model discriminability on easily confused characters. With the collaboration of both modules, our proposed CRRN can outperform the state-of-the-art on popular datasets.

Vehicle re-identification (ReID) is the task of identifying the same vehicle across numerous cameras. This is a complex classification task, and the fine-grained information and strong discrimination features have proven to be effective in handling the re-identification classification task. However, most existing methods focuses on extracting local area features in combination with global features, while exploring subtle distinguishing features, which is a difficult task, remains an open problem and unsolved. In this paper, we propose a multi-branch enhanced discriminative network (MED) to better extract subtle distinguishing features that have high discriminative power to improve the ReID performance. In the proposed MED method, each feature map obtained by convolutional neural network (CNN) is divided into 4 spatial sub-maps, on each of which, the vertical and the horizontal branches are used to extract the subtle distinguishing features intrinsically contained in sub-areas. The vertical and the horizontal branches are combined with the global branch to perform the ReID task. Moreover, our proposed method is capable of extracting rich fine-grained features without the need of extra manual annotation while maintaining a simple design structure. We conducted extensive experiments on the vehicle ReID datasets (VehicleID and VeRi-776), showing that the proposed MED method outperforms most existing methods. Further, we directly apply the MED method to the pedestrian ReID problem on the Market-1501, DUKEMTMC, and MSMT17 datasets, achieving the state-of-the-art (SOTA) performance as well. This demonstrates that the proposed method has good generality and can be flexibly applied to the ReID tasks.

Recently, transformers have shown great promising performance in various computer vision tasks. However, the current transformer based methods ignore the information exchanges between transformer blocks, and they have not been applied in the facial attribute recognition task. In this paper, we propose a multi-zone transformer based on self-distillation for FAR, termed MZTS, to predict the facial attributes. A multi-zone transformer encoder is firstly presented to achieve the interactions of the different transformer encoder blocks, thus avoiding forgetting the effective information between the transformer encoder block groups during the iteration process. Furthermore, we introduce a new self-distillation mechanism based on class tokens, which distills the class tokens obtained from the last transformer encoder block group to the other shallow groups by interacting with the significant information between the different transformer blocks through attention. Extensive experiments on the challenging CelebA and LFWA datasets have demonstrated the excellent performance of the proposed method for FAR.