In tasks such as intelligent surveillance and human–computer interaction, developing rapid and effective models for human action recognition is crucial. Currently, Graph Convolution Networks (GCNs) are widely used for skeleton-based action recognition. Still, they primarily face two issues: (1) The insufficient capture of global joint responses, making it difficult to utilize the correlations between all joints. (2) Existing models often tend to be over-parameterized. In this paper, we therefore propose an Efficient Hierarchical Co-occurrence Graph Convolution Network (EHC-GCN). By employing a simple and practical hierarchical co-occurrence framework to adjust the degree of feature aggregation on demand, we first use spatial graph convolution to learn the local features of joints and then aggregate the global features of all joints. Secondly, we introduce depth-wise separable convolution layers to reduce the model parameters. Additionally, we apply a two-stream branch and attention mechanism to further extract discriminative features. On two large-scale datasets, the proposed EHC-GCN achieves better or comparable performance on both 2D and 3D skeleton data to the state-of-the-art methods, with fewer parameters and lower computational complexity, which will be more beneficial for application on computing resource-limited robot platforms.

RGB–3D industrial anomaly detection seeks to jointly exploit texture and geometric cues for robust defect inspection. However, existing multimodal fusion methods still face two practical limitations: modality-specific anomaly evidence is often weakened after direct fusion, and image-level decisions remain unstable on difficult categories. To address these issues, this study develops a reliability-aware scoring enhancement on top of the released Hybrid Fusion/M3DM memory-bank pipeline. The method constructs a disagreement cue from RGB and point-cloud anomaly responses to enhance suspicious local regions and introduces a dual-branch image-level score calibration that combines a sensitive fusion branch with a robust statistical branch. Evaluated on MVTec 3D-AD under the official released-code full setting, the proposed method achieves 0.800 image-level ROCAUC, 0.980 pixel-level ROCAUC, and 0.926 AU-PRO, compared with 0.779, 0.975, and 0.915 for the corresponding released-code baseline in our environment. Additional evaluation on Eyecandies improves pixel-level ROCAUC and AU-PRO, while showing that image-level calibration remains dataset-sensitive. On a supplementary three-category Real-IAD D3 subset, the mean image-level ROCAUC, pixel-level ROCAUC, and AU-PRO improve from 0.963, 0.979, and 0.921 to 0.980, 0.988, and 0.941, respectively. These results indicate that explicit cross-modal disagreement modeling improves localization consistency, while image-level score calibration provides dataset-dependent gains rather than a uniform cross-dataset guarantee.