913 citations · 2.7k across the 51 of their papers we have counts for
9 papers · 1 filter
Soft Masking for Cost-Constrained Channel Pruning
Ryan Humble, Maying Shen, Jorge Albericio Latorre +2
Structured channel pruning has been shown to significantly accelerate inference time for convolution neural networks (CNNs) on modern hardware, with a relatively minor loss of netw…
Structural Pruning via Latency-Saliency Knapsack
Maying Shen, Hongxu Yin, Pavlo Molchanov +3
Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global re…
Optimizing Data Collection for Machine Learning
Rafid Mahmood, James Lucas, Jose M. Alvarez +2
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting dat…
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
Jiayu Yang, Jose M. Alvarez, Miaomiao Liu
Recent cost volume pyramid based deep neural networks have unlocked the potential of efficiently leveraging high-resolution images for depth inference from multi-view stereo. In ge…
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
Shuxuan Guo, Yinlin Hu, Jose M. Alvarez +1
Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely…
MBEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
Enze Xie, Zhiding Yu, Daquan Zhou +5
In this paper, we propose MBEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inp…