KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image
为解决单深度图像3D手姿态估计中的拓扑依赖性和特征空间干扰问题，提出KAD-Net，通过手指拓扑约束模块和任务解耦框架提高估计准确性。
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Existing text-to-3D generation methods often suffer from macroscopic topological errors—such as the Janus effect—and microscopic geometric discontinuities, primarily due to view-dependent bias and gradient noise under high classifier-free guidance (CFG) scales. To address these issues, this work proposes MOC-3D, which introduces two key innovations within the ScaleDreamer framework: a viewpoint sequencing constraint grounded in CLIP semantic priors to enforce global structural consistency, and a feature continuity mechanism based on Riemannian metrics over symmetric positive-definite manifolds to ensure smooth local geometry. These components operate synergistically to enable high-quality 3D generation with multi-scale consistency, significantly mitigating topological artifacts while enhancing both structural and textural continuity across multiple viewpoints.
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KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image
为解决单深度图像3D手姿态估计中的拓扑依赖性和特征空间干扰问题，提出KAD-Net，通过手指拓扑约束模块和任务解耦框架提高估计准确性。
0 citationsRead paper
Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning
Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this sample-level kernel construction requires O(n^2) quantum circuit executions for n data points, creating a major bottleneck under near-term quantum resource constraints. Prior solutions fail to resolve this efficiency-accuracy dilemma: classical granular-ball clustering reduces sample complexity but relies on Euclidean metrics that cannot capture quantum correlations, while existing quantum compression schemes prioritize efficiency over structural preservation, degrading performance on non-convex or noisy data. Here we propose Granular-Ball Quantum Clustering (GBQC), a framework that tightly couples granular-ball structural abstraction with quantum feature learning. GBQC first compresses raw data into compact, representative granular balls via a PCA-guided splitting strategy, reducing kernel evaluations by 80% compared to full-sample methods. A quantum cohesion mechanism then filters noisy granules in Hilbert space to improve clustering robustness. Extensive experiments on synthetic, noisy, overlapping, and real-world datasets demonstrate that GBQC consistently achieves superior clustering accuracy and robustness compared with representative classical and quantum clustering methods. Meanwhile, the proposed granular-ball compression significantly reduces quantum kernel evaluations and computational overhead, enabling quantum clustering experiments on larger datasets within parameterized quantum learning frameworks. These results suggest that granular-ball representations serve not only as a compression mechanism to reduce quantum computational costs but also as an effective structural abstraction mechanism that improves clustering quality by eliminating redundant and structurally ambiguous learning units.
0 citationsRead paper
Sparse and robust geometric twin support vector machine via asymmetric RoBoSS loss function
This work addresses the limitations of traditional support vector machines in real-world scenarios, where redundant features, label noise, and feature noise often lead to poor efficiency, lack of feature selection capability, and high sensitivity to outliers. To overcome these issues, we propose an asymmetric robust sparse geometric twin support vector machine (aRSGTSVM) that integrates ℓ₁-norm regularization with a novel asymmetric robust bounded sparse smooth (aR) loss function. This unified framework simultaneously enables effective feature selection and strong robustness by design: the aR loss explicitly handles both label noise and zero-mean feature noise near decision boundaries. We further introduce influence function analysis to theoretically characterize its statistical robustness—a first in this context. An efficient proximal gradient descent algorithm is developed for optimization. Extensive experiments demonstrate that aRSGTSVM outperforms state-of-the-art methods on synthetic and UCI datasets and achieves promising performance in Chinese stock index tracking tasks.
0 citationsRead paper
MOC-3D: Manifold-Order Consistency for Text-to-3D Generation
Existing text-to-3D generation methods often suffer from macroscopic topological errors—such as the Janus effect—and microscopic geometric discontinuities, primarily due to view-dependent bias and gradient noise under high classifier-free guidance (CFG) scales. To address these issues, this work proposes MOC-3D, which introduces two key innovations within the ScaleDreamer framework: a viewpoint sequencing constraint grounded in CLIP semantic priors to enforce global structural consistency, and a feature continuity mechanism based on Riemannian metrics over symmetric positive-definite manifolds to ensure smooth local geometry. These components operate synergistically to enable high-quality 3D generation with multi-scale consistency, significantly mitigating topological artifacts while enhancing both structural and textural continuity across multiple viewpoints.
0 citationsRead paper
Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs
This work addresses the challenge that traditional recommender systems struggle to effectively model the interplay between explicit individual interests and implicit group interests, thereby limiting a comprehensive understanding of user preferences. To bridge this gap, the paper proposes an Iterative Semantic Reasoning Framework (ISRF), which introduces, for the first time in generative recommendation, a bidirectional iterative reasoning mechanism between individual and group interests. ISRF leverages large language models to perform multi-step attribute reasoning, constructs semantic interaction graphs, models similar-user graphs, and employs an iterative batch optimization strategy to dynamically integrate and mutually enhance both types of interests. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that the proposed method significantly outperforms current state-of-the-art approaches, confirming its effectiveness and strong generalization capability.
0 citationsRead paper