no code implementations • 21 Jul 2023 • Daria Reshetova, Guanhang Wu, Marcel Puyat, Chunhui Gu, Huizhong Chen
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.
Generative Adversarial Network Image-to-Image Translation +4
no code implementations • 11 May 2022 • Shuzhi Yu, Guanhang Wu, Chunhui Gu, Mohammed E. Fathy
However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive and hard to obtain.
no code implementations • CVPR 2022 • Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang
We introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
no code implementations • 11 Jan 2021 • Kunpeng Li, Zizhao Zhang, Guanhang Wu, Xuehan Xiong, Chen-Yu Lee, Zhichao Lu, Yun Fu, Tomas Pfister
To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.
no code implementations • 1 Jan 2021 • Kunpeng Li, Zizhao Zhang, Guanhang Wu, Xuehan Xiong, Chen-Yu Lee, Yun Fu, Tomas Pfister
To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.
3 code implementations • CVPR 2020 • Sara Beery, Guanhang Wu, Vivek Rathod, Ronny Votel, Jonathan Huang
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
no code implementations • NeurIPS 2018 • Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira, Geoffrey J. Gordon
In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation.
Ranked #3 on Domain Adaptation on GTA5+Synscapes to Cityscapes
no code implementations • ICLR 2018 • Han Zhao, Shanghang Zhang, Guanhang Wu, Jo\~{a}o P. Costeira, Jos\'{e} M. F. Moura, Geoffrey J. Gordon
We propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.
2 code implementations • ICLR 2018 • Jian Du, Shanghang Zhang, Guanhang Wu, Jose M. F. Moura, Soummya Kar
Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss.
1 code implementation • ICCV 2017 • Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura
To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion.
4 code implementations • 26 May 2017 • Han Zhao, Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura, Geoffrey J. Gordon
As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.
1 code implementation • CVPR 2017 • Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura
Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective.