3 accepted papers
Image retrieval targets to find images from a database that are visually similar to the query image. Two-stage methods following retrieve-and-rerank paradigm have achieved excellent performance, but their separate local and global modules are inefficient to real-world applications. To better trade-o…
To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is th…
"Learning from online data with noisy web labels is gaining more attention due to the increasing cost of fully annotated datasets in large-scale multi-label classification tasks. Partial (positive) annotated data, as a particular case of data with noisy labels, are economically accessible. And they…