activity
20172022
most citedDifferentiable Linearized ADMM

26 citations · 193 across the 30 of their papers we have counts for

collaborators
Showing cs.CVShow all

20 papers · 1 filter

cs.CV2021

Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration

Qigong Sun, Xiufang Li, Fanhua Shang +4

The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage. Thus, DNNs cannot be efficiently applied to mobile phones and…

cs.CV2021

Graph Contrastive Clustering

Huasong Zhong, Jianlong Wu, Chong Chen +5

Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, thes…

cs.CV20218 cited

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Xiangtai Li, Hao He, Xia Li +6

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two…

cs.CV20218 cited

Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search

Yibo Yang, Shan You, Hongyang Li +3

Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for evaluation. There exists a significant gap betw…

cs.CV2020

Towards Efficient Scene Understanding via Squeeze Reasoning

Xiangtai Li, Xia Li, Ansheng You +5

Graph-based convolutional model such as non-local block has shown to be effective for strengthening the context modeling ability in convolutional neural networks (CNNs). However, i…

cs.CV2020

ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding

Yibo Yang, Hongyang Li, Shan You +3

Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods…