We explore 3D mesh saliency detection as a nonlinear regression problem, leveraging geometric features derived from spiral patches. The saliency prediction involves a multi-scale analysis, utilizing roughness, geometric, and spectral saliencies, which are calculated through spiral patches at three levels of mesh decimation. These features are processed using a multi-layer perceptron to predict vertex-level saliency. Evaluation on the Schelling dataset demonstrates the approach’s efficacy, achieving competitive results compared to state-of-the-art methods, particularly in saliency prediction and extraction of keypoints. The method emphasizes the integration of advanced geometric descriptors with machine learning for enhanced 3D content analysis.
The widespread use of 3D objects in many fields poses new challenges for their secure transmission, storage and visualization. While selective encryption methods allow format compliance and enable adjustable security by selectively encrypting 3D geometry, they produce visible noise and disrupt geometric coherence which reduces compression efficiency. In this paper, we propose a reversible deformation method for 3D object protection that breaks with state-of-the-art 3D security approaches by offering more natural and visually coherent protection. The deformation is applied directly to the geometry, controlled by a secret key, and can be adjusted to achieve the desired level of visual security. Experimental results presented on a large database demonstrate that our method offers protection comparable to selective encryption, while providing significantly greater resilience to reconstruction attacks such as Laplacian smoothing, particularly at low security levels where selective encryption is more vulnerable. This work introduces a new direction for format-compliant 3D object protection, designed for secure visualization in untrusted environments.
The total variation models are popular in various image processing tasks such as smoothing, decomposition and depth estimation. However, solving these models are challenging. They can be solved by traditional iterative algorithms that require a large number of iterations to converge or deep neural networks that have a large number of trainable parameters. In this paper, we propose a novel Anderson accelerated residual solver (AARS) for these models. Although our method is iterative, it requires much less iteration numbers than the traditional iterative methods, thanks to the Anderson acceleration. Mean-while, it can theoretically guarantee to converge to the global optimal solution. This is theoretically proved and numerically confirmed. Several numerical experiments are conducted to show the effectiveness and efficiency of the proposed solver. It can be applied in various image processing tasks where solving the total variation models is necessary.
Egocentric continual action recognition faces severe challenges such as sudden viewpoint changes, occlusions, and complex backgrounds. In such scenarios, relying solely on visual modalities is susceptible to interference and lacks sufficient recognition robustness. To overcome the limitations of unimodal approaches, multimodal fusion methods are widely adopted, significantly enhancing recognition performance. However, existing multimodal schemes generally suffer from insufficient exploration of cross-modal complementarity and the vulnerability of modal independence. To address this, this paper proposes a Dual-path Decoupling-Distillation NetWork (D3Net), aiming to achieve more effective dynamic fusion of modal information and knowledge transfer.D3Net first explicitly separates the shared and private features of modalities through a dual-path decoupling module, combined with a dynamic gating mechanism to adaptively adjust the modal fusion weights. Secondly, it designs a complementary distillation module, leveraging cross-modal contrastive learning to effectively mitigate the issues of poor unimodal robustness and vulnerability to interference. Finally, through a cross-task distillation mechanism, it efficiently extracts knowledge from old tasks, alleviating the catastrophic forgetting problem during learning. Experimental results demonstrate that D3Net achieves an average accuracy of 83.97% under the 8×4 task configuration on the UESTC MMEA CL dataset, surpassing baseline method by 5.17%.
Anomaly detection in industrial manufacturing is vital for ensuring product quality and operational efficiency. However, supervised deep learning approaches often struggle due to the scarcity of defective samples and class imbalance in real-world datasets. In this work, we propose a generative defect synthesis framework that aims to enhance industrial anomaly detection by producing realistic and diverse defective samples. Our approach leverages generative models to synthesize high-fidelity anomalies while preserving the underlying texture and structural patterns of normal samples. We evaluate the proposed method on the MVTec AD dataset, a benchmark for unsupervised industrial anomaly detection, and investigate how the realism of synthetic data affects detection performance. Experimental results demonstrate that augmenting training with generated defects significantly improves model robustness, particularly in low-data regimes. Furthermore, we explore model compression techniques, including quantization and pruning, showing that 8-bit quantization and moderate pruning yield a 3-4x reduction in model size with minimal performance degradation. A detailed case study across various object and texture categories highlights substantial gains in both image-level and pixel-level AUC scores, and explores the optimal trade-off between synthetic and real training data for efficient deployment.
