A Simple Hierarchical Pooling Data Structure for Loop Closure
We propose a data structure obtained by hierarchically averaging bag-of-word descriptors during a sequence of views that achieves average speedups in large-scale loop closure applications ranging from 4 to 20 times on benchmark datasets. Although simple, the method works as well as sophisticated agglomerative schemes at a fraction of the cost with minimal loss of performance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hierarchical Graph Pooling with Structure Learning
Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing…
Graph ClassificationGraph Neural NetworkRepresentation LearningSPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling
We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling. The framework incorporates…
EEGElectroencephalogram (EEG)CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classifica…
Graph ClassificationGraph Representation LearningRepresentation LearningStructural Entropy Guided Graph Hierarchical Pooling
Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of the fixed compression quota and stepwise p…
Classificationglobal-optimizationGraph ClassificationNode ClassificationUnderstanding Image Structure via Hierarchical Shape Parsing
Exploring image structure is a long-standing yet important research subject in the computer vision community. In this paper, we focus on understanding image structure inspired by the "simple-to-complex" biological eviden…