Secure transmission of private information over public channels without arousing suspicion remains a fundamental challenge in stega-nography. Traditional methods modify pixel-level or frequency-domain features, making them vulnerable to detection and degradation. Recent synthesis-based approaches leverage generative models to embed data but often suffer from limited capacity or visual artifacts. In this work, we propose a pose conditioned generative steganographic framework that decouples message representation from image content. Binary messages are first mapped to human poses using a geometry-aware codebook derived from real-world data. These poses then serve as structural conditions to guide diffusion-based image generation, producing semantically coherent and visually natural stego images. By encoding multiple human poses in a single image, our framework increases message capacity while preserving visual coherence. To enhance robustness, we introduce a randomized linear expansion scheme to stabilize pose-code mapping under occlusion and detection noise. We evaluate the method under various perturbations and assess detectability using state-of-the-art steganalysis models. Experimental results show strong imperceptibility, decoding accuracy, and semantic flexibility, highlighting the effectiveness of our framework in enabling secure and coverless generative steganography. The code for PCGS is made available at https://github.com/Neo-0-Gu/PCGS .
Current multi-dataset 3D object detection pipelines directly train the detectors from scratch, which achieves suboptimal performance owing to intrinsic discrepancies between various scenarios. In this paper, we observe that there are underlying shared spatial and semantic characteristics that can be excavated in a self-supervised and fully-supervised manner. Motivated by this, we propose a novel pre-training method (UniPT) with geometry-aware and semantic-aware supervision to obtain a universal representation of multiple datasets, thus reducing the vast differences. Concretely, we firstly perform the reconstruction of point cloud and point density estimation. Then, the occupancy prediction and classification guidance are provided in semantic-aware supervision. Moreover, we devise a cross-dataset integration scheme to minimize the differences of dataset-level features between domains. Extensive experiments on Waymo Open Dataset (WOD), nuScenes and KITTI with consolidation settings illustrate the effectiveness of our method. It is notable that UniPT surpasses the previous work Uni3D by 2.29 mAPH (L1) on WOD, 1.45 mAP (BEV) on nuScenes and 2.08 mAP (3D) on KITTI. Our code will be available at https://github.com/microjie372/UniPT .
Existing online hashing methods generally employ the Hamming distance for similarity evaluation, which leads to the information loss of data location. Candidate images may have the same Hamming distance but different similarity from the query, which reduces the retrieval accuracy. Especially for non-stationary data environments, concept drift problems are prevalent. The location information loss makes it more difficult for capturing the distribution changes in data environments. To alleviate above concerns, Incremental Hashing with Asymmetric Distance (ICHAD) is proposed in this paper for image retrieval in non-stationary environments. In ICHAD, the online asymmetric distance based on learned hash codes is employed for the similarity evaluation. It preserves the location information of data more accurately and is computed efficiently without accessing the old data. Experimental results show that ICHAD outperforms existing hashing methods in various non-stationary data scenarios with concept drift.
GUI automation testing is a mainstream approach to ensure the software quality of mobile applications. To reduce manual testing costs, a large number of automated test cases are typically executed for regression and compatibility testing whenever there are requirement changes or version updates. Although extensive research has applied LLMs and MLLMs to GUI automation, most of these works conduct testing in stable, interference-free environments. In contrast, real-world business scenarios often involve numerous dynamic interference factors and strong business-specific contexts, leading to lower success rates for these methods. To address these challenges, we propose a novel UI automation testing technology (UI-Most) based on a multi-agent architecture. This method is designed to enhance the robustness of UI automation testing by assigning specialized roles to independent agents and enabling their collaboration. At the same time, it leverages the business knowledge from AppGraph for one-shot learning, thereby improving the recognition of UI elements in new scenarios. The effectiveness of our approach has been validated on real test case sets. Furthermore, this method has already been applied to automated regression testing, significantly reducing both manual testing costs and maintenance overhead of test cases.
With the continuous development of 3D laser scanning technology, point clouds can quickly and intuitively obtain real-world information. Point cloud registration can solve the limitation that a single data source cannot fully reflect objective things. However, the partial overlap and the difficulty in identifying matchable and repeatable corresponding points lead to a large number of outliers and low registration accuracy. To address these issues, we propose a relative geometric-driven registration network with virtual correspondences (VRGNet). First, robust features between scenes are extracted through self-attention and cross-attention mechanisms. The virtual point generation (VPG) module is used to optimize point cloud position information and improve the point matching probability in overlapping areas. Then, combine the original and generated point clouds to learn rotation-invariant geometric features through relative geometric embedding (RGE). Finally, the coarse-to-fine point matching method is used to obtain reliable correspondences and high-precision transformation matrices. We conduct extensive testing on the large-scale outdoor KITTI dataset. The experimental results demonstrate that our method achieves higher efficiency and registration accuracy.
