33 citations · 100 across the 12 of their papers we have counts for
5 papers · 1 filter
Learning from the Tangram to Solve Mini Visual Tasks
Yizhou Zhao, Liang Qiu, Pan Lu +3
Current pre-training methods in computer vision focus on natural images in the daily-life context. However, abstract diagrams such as icons and symbols are common and important in…
GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis
Yizhou Zhao, Wensi Ai, Liang Qiu +4
With the recent success of deep learning algorithms, many researchers have focused on generative models for human motion animation. However, the research community lacks a platform…
Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis
Feng Shi, Chonghan Lee, Mohammad Khairul Bashar +3
CNF-based SAT and MaxSAT solvers are central to logic synthesis and verification systems. The increasing popularity of these constraint problems in electronic design automation enc…
STAR: Sparse Transformer-based Action Recognition
Feng Shi, Chonghan Lee, Liang Qiu +6
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in rec…
VersaGNN: a Versatile accelerator for Graph neural networks
Feng Shi, Ahren Yiqiao Jin, Song-Chun Zhu
\textit{Graph Neural Network} (GNN) is a promising approach for analyzing graph-structured data that tactfully captures their dependency information via node-level message passing.…