Search Results for author:
Found 6 papers, 3 papers with code
UGG: Unified Generative Grasping
Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information.
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable.
Jigsaw: Learning to Assemble Multiple Fractured Objects
Our framework consists of four components: (1) front-end point feature extractor with attention layers, (2) surface segmentation to separate fracture and original parts, (3) multi-parts matching to find correspondences among fracture surface points, and (4) robust global alignment to recover the global poses of the pieces.
Learning Universe Model for Partial Matching Networks over Multiple Graphs
We consider the general setting for partial matching of two or multiple graphs, in the sense that not necessarily all the nodes in one graph can find their correspondences in another graph and vice versa.
Understanding and Predicting the Memorability of Outdoor Natural Scenes
In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores.
What Makes Natural Scene Memorable?
Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable.
