Zishan Xu

Objects in remotesensing imagery are often unevenly distributed and densely clustered, making foreground selection particularly challenging. In two-stage detectors, the Region Proposal Network (RPN) is pivotal, yet proposals from conventional RPNs can be imprecise and redundant, hurting recall and inflating computation.We introduce GaussFocus, a density-guided two-stage detector that builds a unified Gaussian foreground prior from rotated annotations and injects it into proposal generation, feature sampling, and RoI score calibration. Specifically, we derive an image-level Gaussian foreground density map and train a lightweight density branch inside the RPN, where MDeformConv uses regression-guided offsets and density-aware gating to produce features that better align with object geometry and foreground regions. A Gauss Guided Selector further leverages the predicted density map to retain proposals concentrated in foreground-dense areas, reducing background clutter and second-stage computation. In addition, an inference-only Gauss-Guided RoI logit shift calibrates RoI classification scores using the same density prior without retraining, leading to more reliable foreground–background separation and ranking. Across four challenging datasets (including DOTA-v1.0, DOTA-v2.0, and HRSC2016), GaussFocus consistently improves proposal quality and detection accuracy over strong baselines while reducing redundant RoIs and computation.

Remote sensing (RS) imagery contains vast backgrounds and densely packed, rotated small objects. One-stage rotated detectors therefore allocate most convolutional samples to background because they lack an explicit foreground discovery step. We close this gap with a lightweight discover→focus pipeline composed of two modules. First, a Foreground Attention Loss (FAL) supervises the classifier’s foreground heatmap by matching it to a rotated target–density map rendered from OBB annotations, giving the detector an explicit notion of “where the objects are.” Second, AGConv computes closed-form semantic–geometric dual offsets by fusing the learned foreground attention with anchor geometry, thereby concentrating sampling in salient regions while suppressing clutter without an extra offset-conv branch. This preserves one-stage efficiency while approximating the proposal-guided foreground extraction of two-stage detectors. Under identical ResNet–50+FPN settings, our single-stage detector attains 76.48% mAP on DOTA-v1.0, 68.65% on DOTA-v1.5, 67.2% on DIOR–R, and 97.28% (VOC12) / 90.82% (VOC07) on HRSC2016, with negligible parameter and latency overhead—demonstrating a strong accuracy–efficiency trade-off for complex RS scenes with dense, rotated targets.

In the context of the digital age, utilizing cutting-edge technology for the digitization and creative expansion of ancient murals is crucial, aimed at preserving and passing on cultural heritage. Existing image outpainting techniques suffer from a lack of semantic guidance. This paper introduces MuralAgent, a multimodal model based on Retrieval-Augmented Generation (RAG) technology. It precisely extracts key information from mural images and integrates it with a constructed ancient texts knowledge base to ensure the cultural and semantic consistency of the expanded images. Moreover, fine-tuning the Stable Diffusion model ensures the fidelity of the generated image styles. Specifically, this study involves constructing an ancient texts knowledge base for accurate matching, designing specific prompts for GPT-4V(ision) to extract key information, and innovatively expanding artworks through Stable Diffusion, providing a novel way for the public to reinterpret ancient murals.

The explosive growth of remote-sensing images generated by emerging space-air–ground integrated IoT networks makes centralized detector training infeasible due to limited bandwidth and strict data privacy constraints. While lightweight single-stage object detectors offer efficiency, they suffer significant accuracy degradation for small, dense, and arbitrarily oriented targets. Furthermore, existing federated object detection frameworks typically neglect client heterogeneity. To overcome these limitations, we propose a two-stage personalized federated detection framework. In Stage 1, we independently train a conventional single-stage rotated object detector on each client and aggregate model updates using an adaptive similarity momentum aggregation (ASMA) strategy, effectively pooling knowledge across non-IID client datasets to improve global generalization. In Stage 2, each client is equipped with a private selective depthwise attention convolution (SDAConv) module, leveraging Stage-1 priors to reconstruct fine-grained, client-specific features without additional communication overhead, thus tailoring predictions to local data distributions. Experiments conducted on five non-IID splits derived from DOTA-1.0, along with DIOR and VisDrone datasets, demonstrate improvements of up to +3.5 mAP compared to federated learning baselines under the same communication budget, simultaneously maintaining global robustness and enhancing local detection accuracy.

Low-light image enhancement is a crucial visual task, and many unsupervised methods overlook the degradation of visible information in low-light scenes, adversely affecting the fusion of complementary information and hindering the generation of satisfactory results. To address this, we introduce Wakeup-Darkness, a multimodal enhancement framework that innovatively enriches user interaction through voice and textual commands. This approach signifies a technical leap and represents a paradigm shift in user engagement. We introduce a Cross-Modal Feature Fusion (CMFF) that synergizes semantic and depth context with low-light enhancement operations. Moreover, we propose a Gated Residual Block (GRB) and a channel-aware Look-Up Table (LUT) to adjust the intensity distribution of each channel. Crucially, the proposed Wakeup-Darkness scheme demonstrates remarkable generalization in unsupervised scenarios. The source code can be accessed from https://github.com/zhangbaijin/Wakeup-Dakness .

Segment Anything Model (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we proposed ShadClips, which consists of SAM-optimizer and SONet. It has dramatically enhanced SAM’s ability to segment shadow images, differentiating between the background and both soft and hard shadows adeptly. Due to its dependence on pixel point inputs, the SAM-Optimizer interface could do better. This method presents challenges, especially when dealing with long, extended shadows. To make the user experience more intuitive and effective, we incorporated the capabilities of CLIPs. Therefore, simple text descriptions like “A photo of a shadow” can be used to guide the SAM-Optimizer, allowing it to select the most relevant shadow mask from SAM’s comprehensive category list. Meanwhile, we introduce SONet to shadow removal. A large number of experiments on ISTD/SRD prove that the proposed method is effective and satisfactory. The source code of the ShadClips can be accessed from https://github.com/zhangbaijin/SAM-helps-Shadow.

This paper focuses on the crack detection and digital restoration of ancient mural cultural heritage, proposing a comprehensive method that combines the Unet network structure and diffusion model. Firstly, the Unet network structure is used for efficient crack detection in murals by constructing an ancient mural image dataset for training and validation, achieving accurate identification of mural cracks. Next, an edge-guided optimized masking strategy is adopted for mural restoration, effectively preserving the information of the murals and reducing the damage to the original murals during the restoration process. Lastly, a diffusion model is employed for digital restoration of murals, improving the restoration performance by adjusting parameters to achieve natural repair of mural cracks. Experimental results show that comprehensive method based on the Unet network and diffusion model has significant advantages in the tasks of crack detection and digital restoration of murals, providing a novel and effective approach for the protection and restoration of ancient murals. In addition, this research has significant implications for the technological development in the field of mural restoration and cultural heritage preservation, contributing to the advancement and technological innovation in related fields.