Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable.
Ranked #7 on 3D Part Segmentation on ShapeNet-Part
Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.
Ranked #14 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
Ranked #8 on Multi-Label Classification on NUS-WIDE
Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation.