With the goal of addressing the challenges of small, densely packed targets in remote sensing images, we propose a high-resolution instance segmentation model named QuadTransPointRend Net (QTPR-Net). This model significantly enhances instance segmentation performance in remote sensing images. The model consists of two main modules: preliminary edge feature extraction (PEFE) and edge point feature refinement (EPFR). We also created a specific approach and strategy named TransQTA for edge uncertainty point selection and feature processing in high-resolution remote sensing images. Multi-scale feature fusion and transformer technologies are used in QTPR-Net to refine rough masks and fine-grained features for selected edge uncertainty points while balancing model size and accuracy. Based on experiments performed on three public datasets: NWPU VHR-10, SSDD, and iSAID, we demonstrate the superiority of QTPR-Net over existing approaches.

Hyperspectral image (HSI) and LiDAR data fusion is valuable for land-cover classification in complex surface scenes. Existing methods typically extract features from each modality independently and then consider how to fuse them, ignoring the semantic consistency between features of different modalities and across different hierarchical levels. Moreover, fully mining and exploiting the complementary information between multimodal remote sensing data remains a critical issue. To address these challenges, this paper proposes a hierarchical semantic consistency constraint (HSCC) framework for HSI and LiDAR data joint classification. The framework is co-constructed by a progressive interactive fusion network (PIFNet) and a semantic consistency constraint (SCC) strategy. Specifically, PIFNet progressively calibrates the semantic representations of multimodal features at different abstraction levels through Cross-Modal Shared Attention and Symmetric Cross-Attention mechanisms, promoting information parity in deep interactions. The SCC strategy establishes multi-level semantic associations and employs a semantic consistency constraint loss to guide the network to autonomously maintain the consistency of the same land-cover object across heterogeneous feature representations, thereby further enhancing the discriminative power of the fused features. Experiments on three public datasets, MUUFL, Houston2013, and Augsburg, demonstrate that HSCC outperforms current state-of-the-art methods, validating its effectiveness in multi-source remote sensing data fusion classification tasks.

Tables, as an efficient form of structured data representation, are widely applied across domains. However, traditional manual processing methods are inadequate in the big data era, and existing table recognition models, such as SLANet, still face performance limitations. To address these issues, this paper proposes an improved SLANet framework. First, the original H-Swish activation is replaced with the Mish function to enhance feature representation. Second, an end-of-sequence (EOS) termination mechanism is introduced to reduce computational redundancy during inference. Third, a three-phase training strategy is designed to achieve progressive performance improvements. Experimental evaluation on the PubTabNet benchmark demonstrates that the improved SLANet achieves 77.25% accuracy with an average inference time of 774 ms, outperforming the baseline and most mainstream algorithms while retaining lightweight efficiency. The proposed algorithm achieves a TEDS score of 96.67%, significantly surpassing SLANet-based and other state-of-the-art methods. The code will be released upon acceptance.