This paper is not motivated to seek innovation within the attention mechanism. Instead it
focuses on overcoming the existing trade-offs between accuracy and efficiency within the …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

The use of pretrained backbones with fine-tuning has been successful for 2D vision and
natural language processing tasks, showing advantages over task-specific networks. In this …

Semantic instance and panoptic segmentation of 3D point clouds have been addressed
using task-specific models of distinct design. Thereby the similarity of all segmentation tasks …

To advance the state of the art in the creation of 3D foundation models, this paper introduces
the ConDense framework for 3D pre-training utilizing existing pre-trained 2D networks and …

Three-dimensional point cloud data are widely used in many fields, as they can be easily
obtained and contain rich semantic information. Recently, weakly supervised segmentation …

Numerous prior studies predominantly emphasize constructing relation vectors for individual
neighborhood points and generating dynamic kernels for each vector and embedding these …

The lack of timely progress monitoring and quality control contributes to cost-escalation,
lowering of productivity, and broadly poor project performance. This paper addressed the …

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to
blindness. It is diagnosed via color fundus injections, but the manual analysis is …

Recently, 3D point cloud classification has made significant progress with the help of many
datasets. However, these datasets do not reflect the incomplete nature of real-world point …