activity
20182022
most citedDecoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition

27 citations · 56 across the 7 of their papers we have counts for

collaborators

10 papers

cs.SE20223 cited

Leveraging Artificial Intelligence on Binary Code Comprehension

Yifan Zhang

Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary cod…

cs.CV20211 cited

Efficient Spatialtemporal Context Modeling for Action Recognition

Congqi Cao, Yue Lu, Yifan Zhang +2

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. How…

cs.CV2021

AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition

Lei Shi, Yifan Zhang, Jian Cheng +1

Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there…

cs.CV20212 cited

StablePose: Learning 6D Object Poses from Geometrically Stable Patches

Yifei Shi, Junwen Huang, Xin Xu +2

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from…

cs.CV202027 cited

Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition

Lei Shi, Yifan Zhang, Jian Cheng +1

Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and env…

cs.CV202010 cited

TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model

Bo Pang, Yizhuo Li, Yifan Zhang +2

Multi-object tracking is a fundamental vision problem that has been studied for a long time. As deep learning brings excellent performances to object detection algorithms, Tracking…