6 accepted papers
Generating accurate dexterous grasps for objects with complex geometries remains a critical challenge in robotic manipulation. The paper proposes LLM-Diffu, a novel network architecture built on the principles of diffusion models. This architecture integrates a specialized basis point set (BPS) poin
Modern multi-view clustering (MVC) is dominated by two paradigms: multi-view fusion and pseudo-label-guided learning. Pseudo-labeling methods can suffer from confirmation bias; their reliance on a fixed-granularity supervision from an initial clustering can cause learned embeddings to drift from the
As a foundational clustering paradigm, Density Peak Clustering (DPC) partitions samples into clusters based on their density peaks, garnering widespread attention. However, traditional DPC methods usually focus on high-density regions, neglecting representative peaks in relatively low-density areas,…
Multi-view clustering aims to integrate complementary information from multiple views to improve clustering performance. However, existing ensemble-based methods suffer from information loss due to their reliance on single-granularity labels, limiting the discriminative capability of learned represe
Recently, Deep Unfolding Networks (DUNs) have achieved impressive reconstruction quality in the field of image Compressive Sensing (CS) by unfolding iterative optimization algorithms into neural networks. The reconstruction quality of DUNs depends on the learned prior knowledge, so introducing stron…
Object pose estimation is a crucial task for semantic robot manipulation involving the detection of suitable manipulation regions. Given the diversity of object shapes and scene complexities, object pose estimation remains an immense challenge. Accordingly, the letter presents a new approach for obj