AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An improved boundary optimization framework based on the BPR paradigm, termed Enhanced Boundary Patch Refinement (E-BPR), is proposed to explicitly model boundary uncertainty through adaptive mask patch refinement and demonstrates that the proposed framework consistently improves boundary quality and instance segmentation accuracy over strong baselines.
Experimental results demonstrate that the method significantly narrows the performance gap in data-constrained scenarios, achieving a mean Intersection over Union increase of 4.6% in one-shot and 5.5% in five-shot settings over the baseline.
A Prior-Guided Part Aggregator (PGA) is designed, which leverages the foreground prior provided by foundation models to guide discriminative part discovery, enhancing target region responses while suppressing background interference, and a Topology-Informed Semantic Graph Convolutional Network (TIS-GCN) is designed to...
Experiments demonstrate that the proposed cross-layer semantic alignment mechanism outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.
A complementary prototype representation framework is proposed, employing three modules to collaboratively improve pseudo-label quality and improves the discriminative ability of confused categories by generating semantically similar sub-category negative samples.
Few-shot defect segmentation (FSDS) has emerged as an effective approach to address the challenge of limited annotated samples in industrial quality inspection. However, existing methods often rely on features extracted from a single semantic level, without effectively suppressing noise interference, which leads to sub...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.