Search Results for author: Xuehan Xiong

Found 12 papers, 2 papers with code

Multiview Transformers for Video Recognition

1 code implementation12 Jan 2022 Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, Cordelia Schmid

Video understanding requires reasoning at multiple spatiotemporal resolutions -- from short fine-grained motions to events taking place over longer durations.

 Ranked #1 on Action Classification on Kinetics-400 (using extra training data)

Action Classification Action Recognition +1

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

Spatial-Temporal Alignment Network for Action Recognition and Detection

no code implementations4 Dec 2020 Junwei Liang, Liangliang Cao, Xuehan Xiong, Ting Yu, Alexander Hauptmann

The experimental results show that the STAN model can consistently improve the state of the arts in both action detection and action recognition tasks.

Action Detection Action Recognition

PERF-Net: Pose Empowered RGB-Flow Net

no code implementations28 Sep 2020 Yinxiao Li, Zhichao Lu, Xuehan Xiong, Jonathan Huang

In recent years, many works in the video action recognition literature have shown that two stream models (combining spatial and temporal input streams) are necessary for achieving state of the art performance.

Action Classification Action Recognition +1

AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification

no code implementations ECCV 2020 Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, Wei Hua

The discovered attention cells can be seamlessly inserted into existing backbone networks, e. g., I3D or S3D, and improve video classification accuracy by more than 2% on both Kinetics-600 and MiT datasets.

General Classification Video Classification

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

no code implementations3 Jun 2018 Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang

In this paper, we study how to design a genetic programming approach for optimizing the structure of a CNN for a given task under limited computational resources yet without imposing strong restrictions on the search space.

Global Supervised Descent Method

no code implementations CVPR 2015 Xuehan Xiong, Fernando de la Torre

It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function.

Camera Calibration

Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision

no code implementations3 May 2014 Xuehan Xiong, Fernando de la Torre

Using generic descent maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems.

3D Pose Estimation Camera Calibration

Supervised Descent Method and Its Applications to Face Alignment

no code implementations CVPR 2013 Xuehan Xiong, Fernando de la Torre

It is generally accepted that 2 nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function.

Camera Calibration Face Alignment

Bayesian Optimal Active Search and Surveying

no code implementations27 Jun 2012 Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff Schneider, Richard Mann

In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class.

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