arXiv subjects

Qi Mo

Publications and source records attributed to Qi Mo.

3 recordsLinked to original sources

arXiv2026

CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation

Recently Mamba-based methods have shown promise in abdominal organ segmentation. However, existing approaches neglect cross-channel anatomical semantic collaboration and lack explicit boundary-aware feature fusion mechanisms. To address these limitations, we propose CS-MUNet with two purpose-built modules. The Boundary-Aware State Mamba module employs a Bayesian-attention framework to generate pixel-level boundary posterior maps, injected directly into Mamba's core scan parameters to embed boundary awareness into the SSM state transition mechanism, while dual-branch weight allocation enables complementary modulation between global and local structural representations. The Channel Mamba State Aggregation module redefines the channel dimension as the SSM sequence dimension to explicitly model cross-channel anatomical semantic collaboration in a data-driven manner. Experiments on two public benchmarks demonstrate that CS-MUNet consistently outperforms state-of-the-art methods across multiple metrics, establishing a new SSM modeling paradigm that jointly addresses channel semantic collaboration and boundary-aware feature fusion for abdominal multi-organ segmentation.

cs.CV↗

arXiv2025

BIDO: An Out-Of-Distribution Resistant Image-based Malware Detector

While image-based detectors have shown promise in Android malware detection, they often struggle to maintain their performance and interpretability when encountering out-of-distribution (OOD) samples. Specifically, OOD samples generated by code obfuscation and concept drift exhibit distributions that significantly deviate from the detector's training data. Such shifts not only severely undermine the generalisation of detectors to OOD samples but also compromise the reliability of their associated interpretations. To address these challenges, we propose BIDO, a novel generative classifier that reformulates malware detection as a likelihood estimation task. Unlike conventional discriminative methods, BIDO jointly produces classification results and interpretations by explicitly modeling class-conditional distributions, thereby resolving the long-standing separation between detection and explanation. Empirical results demonstrate that BIDO substantially enhances robustness against extreme obfuscation and concept drift while achieving reliable interpretation without sacrificing performance. The source code is available at https://github.com/whatishope/BIDO/.

cs.CR↗

arXiv2014

Tracing Analytic Ray Curves for Light and Sound Propagation in Non-linear Media

The physical world consists of spatially varying media, such as the atmosphere and the ocean, in which light and sound propagates along non-linear trajectories. This presents a challenge to existing ray-tracing based methods, which are widely adopted to simulate propagation due to their efficiency and flexibility, but assume linear rays. We present a novel algorithm that traces analytic ray curves computed from local media gradients, and utilizes the closed-form solutions of both the intersections of the ray curves with planar surfaces, and the travel distance. By constructing an adaptive unstructured mesh, our algorithm is able to model general media profiles that vary in three dimensions with complex boundaries consisting of terrains and other scene objects such as buildings. We trace the analytic ray curves using the adaptive unstructured mesh, which considerably improves the efficiency over prior methods. We highlight the algorithm's application on simulation of sound and visual propagation in outdoor scenes.

cs.GR↗