Search Results for author: Guanhang Wu

Found 9 papers, 4 papers with code

Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts

no code implementations11 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.

Action Recognition

Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos

no code implementations1 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.

Action Recognition

Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

2 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.

Video Object Detection Video Understanding

Adversarial Multiple Source Domain Adaptation

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.

Classification Domain Adaptation +4

Multiple Source Domain Adaptation with Adversarial Learning

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.

Domain Adaptation Sentiment Analysis

Topology Adaptive Graph Convolutional Networks

no 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.

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

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.

Multiple Source Domain Adaptation with Adversarial Training of Neural Networks

3 code implementations26 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.

Domain Adaptation Sentiment Analysis

Understanding Traffic Density from Large-Scale Web Camera Data

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.

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