**HRank** is a filter pruning method that explores the High Rank of the feature map in each layer (HRank). The proposed HRank  is inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive. Based on HRank, the authors develop a method that is mathematically formulated to prune filters with low-rank feature maps.

**FFB6D** is a full flow bidirectional fusion network for 6D pose estimation of known objects from a single RGBD image. Unlike previous works that extract the RGB and point cloud features independently and fuse them in the final stage, FFB6D builds bidirectional fusion modules as communication bridges in the full flow of the two networks. In this way, the two networks can obtain complementary information from the other and learn representations containing rich appearance and geometry information of the scene.

FFB6D

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

HRank

HRank: Filter Pruning using High-Rank Feature Map

A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear and log-concave synapse function of excitatory and inhibitory probabilities of channels.

Source	HRank: Filter Pruning using High-Rank Feature Map
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com