Author

Linxian Zhu

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Aug 2026

Graph-driven contextual synergy network for robust 3D object detection.

3D object detection stands as a pivotal task in scene understanding. However, two primary bottlenecks constrain current methodologies: semantic ambiguity arising from spatial misalignment during cross-modal fusion, and the inadequate contextual representation of individual candidate points within complex scenes. To address these challenges, this paper presents a Graph-driven Contextual Synergy Network (GCS3D), which is designed to systematically enhance point representations across both semantic and geometric dimensions. Specifically, the proposed method incorporates a Semantic Representation Rectification (G-SRR) module for cross-modal representation enhancement. By performing region-level semantic aggregation based on 3D neighborhoods to mitigate projection bias, this module achieves robust cross-modal fusion through a Spatial-aware Gating Mechanism (SGM) that adaptively regulates visual feature injection. Regarding contextual correlation modeling, the framework introduces a Graph-guided Geometric Consistency Interaction (G-GCI) module. By constructing a local topology graph among anchors and executing position-aware feature interaction, this module facilitates the aggregation of complementary neighborhood information, thereby bolstering the feature consistency and discriminability of anchor representations. Furthermore, a Spatial-Scale Aware Assigner (SSA-Assigner) is utilized to dynamically allocate supervision signals based on prediction quality, fully exploiting the performance potential inherent in the enhanced anchor representations. Extensive experiments on the SUN RGB-D and ScanNet V2 datasets demonstrate that GCS3D achieves superior results with mAP@0.25 scores of 70.39 and 73.86 respectively, validating the effectiveness and robustness of the proposed strategy in complex indoor scenes.

Miaohui Zhang, Chengyi Zhang, Linxian Zhu et al. · Neural Networks · 0 citations

Preprint Jul 2026

One Anchor for All: Unified Multilingual and Multimodal Safety Alignment for LVLMs

As large vision-language models (LVLMs) are deployed globally, the combination of multilingual instructions and visual information makes malicious attacks more covert and sophisticated than ever before. However, existing methods isolate language and modality defenses, which, coupled with the scarcity of safety data and high fine-tuning costs, makes it difficult for models to defend against compound attacks. To address this severe challenge, we propose a neuron-level cross-dimensional safety alignment framework driven by modality- and language-shared safety neurons (MLS-Neurons). First, we identify monolingual and unimodal safety neurons by comparing responses to harmful and benign samples, quantifying functional saliency through activation strength and downstream impact. Then, by intersecting these unimodal neurons within each language, we extract modality-shared safety neurons (MS-Neurons) responsive to both visual and textual risks, bridging the safety representation gap between modalities. Furthermore, using English as a semantic anchor, we intersect MS-Neurons across languages to identify modality- and language-shared safety neurons (MLS-Neurons), serving as key defenses against compound attacks. Finally, we update only this minimal subset of shared neurons (~0.03% of parameters), transferring English-only safety supervision to multilingual and multimodal scenarios. Extensive experiments show that our method significantly outperforms state-of-the-art approaches across diverse multilingual and multimodal safety benchmarks while preserving general utility.

Enyi Shi, Fei Shen, Chuancheng Shi et al. · 0 citations