activity
20152022
most citedSpatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

602 citations · 1.5k across the 18 of their papers we have counts for

collaborators

29 papers

cs.CV20212 cited

Learning Hierarchical Graph Neural Networks for Image Clustering

Yifan Xing, Tong He, Tianjun Xiao +6

We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated…

cs.CV202133 cited

Semi-TCL: Semi-Supervised Track Contrastive Representation Learning

Wei Li, Yuanjun Xiong, Shuo Yang +3

Online tracking of multiple objects in videos requires strong capacity of modeling and matching object appearances. Previous methods for learning appearance embedding mostly rely o…

cs.CV2021

Harnessing Unrecognizable Faces for Improving Face Recognition

Siqi Deng, Yuanjun Xiong, Meng Wang +2

The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector.…

cs.CV2021

Compatibility-aware Heterogeneous Visual Search

Rahul Duggal, Hao Zhou, Shuo Yang +4

We tackle the problem of visual search under resource constraints. Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery i…

cs.CL2021

Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates

Yuqing Xie, Yi-an Lai, Yuanjun Xiong +2

Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in…

cs.CV2021

SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation

Zhe Wang, Hao Chen, Xinyu Li +4

Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos…