87 citations · 240 across the 13 of their papers we have counts for
25 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…
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…
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…
FreeSOLO: Learning to Segment Objects without Annotations
Xinlong Wang, Zhiding Yu, Shalini De Mello +4
Instance segmentation is a fundamental vision task that aims to recognize and segment each object in an image. However, it requires costly annotations such as bounding boxes and se…
When to Prune? A Policy towards Early Structural Pruning
Maying Shen, Pavlo Molchanov, Hongxu Yin +1
Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooki…