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Found 6 papers, 1 papers with code
Adaptive Annotation Distribution for Weakly Supervised Point Cloud Semantic Segmentation
Based on our analysis, we propose a label-aware point cloud downsampling strategy to increase the proportion of annotations involved in the training stage.
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering
Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies.
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace
Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations.
Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace
Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts.
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds
Instance segmentation in point clouds is one of the most fine-grained ways to understand the 3D scene.
Super Diffusion for Salient Object Detection
Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection.
