230 citations · 296 across the 8 of their papers we have counts for
8 papers
ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection
Daria Reshetova, Guanhang Wu, Marcel Puyat +2
Object detection is the key technique to a number of Computer Vision applications, but it often requires large amounts of annotated data to achieve decent results. Moreover, for pe…
TDT: Teaching Detectors to Track without Fully Annotated Videos
Shuzhi Yu, Guanhang Wu, Chunhui Gu +1
Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art r…
Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts
Kunpeng Li, Zizhao Zhang, Guanhang Wu +5
Learning visual knowledge from massive weakly-labeled web videos has attracted growing research interests thanks to the large corpus of easily accessible video data on the Internet…
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Sara Beery, Guanhang Wu, Vivek Rathod +2
In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavi…
Topology Adaptive Graph Convolutional Networks
Jian Du, Shanghang Zhang, Guanhang Wu +2
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss. This paper pr…
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
Shanghang Zhang, Guanhang Wu, João P. Costeira +1
In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low r…