Video Detection

ECCV2018

• Video Object Detection with an Aligned Spatial-Temporal Memory : Fanyi Xiao, Yong Jae Lee. ECCV(2018).

  • Paper: https://arxiv.org/pdf/1712.06317.pdf
  • Homepage: http://fanyix.cs.ucdavis.edu/project/stmn/project.html
  • Institution: University of California

• Spatial-Temporal Memory Networks for Video Object Detection :

  • Paper: https://arxiv.org/pdf/1712.06317.pdf
  • Homepage: http://fanyix.cs.ucdavis.edu/project/stmn/project.html
  • Institution: University of California

• Fully Motion-Aware Network for Video Object Detection : Shiyao Wang, Yucong Zhou, Junjie Yan, Zhidong Deng. ECCV (2018)

  • Paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/Shiyao_Wang_Fully_Motion-Aware_Network_ECCV_2018_paper.pdf
  • Code: https://github.com/wangshy31/MANet_for_Video_Object_Detection
  • Institution: SenseTime

• Object Detection in Video with Spatiotemporal Sampling Networks : Gedas Bertasius, Lorenzo Torresani, ianbo Shi. ECCV (2018).

  • Paper: https://arxiv.org/pdf/1803.05549.pdf
  • Institution: University of Pennsylvania

CVPR2018

• Towards High Performance Video Object Detection : Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei. CVPR (2018).
  • Paper: https://arxiv.org/abs/1711.11577
  • Institution: Microsoft Research Asia
• Optimizing Video Object Detection vis a Scale-Time Lattice : Kai Chen, Jiaqi Wang, Shuo Yang, Xingcheng Zhang, Yuanjun Xiong, Chen Chang Loy, Dahua Lin. CVPR (2018).
  • Paper: https://arxiv.org/pdf/1804.05472.pdf
  • Code: https://github.com/hellock/scale-time-lattice
  • Homepage: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
  • Institution: Chinese University of Hong Kong

ICCV2017

• Flow-Guided Feature Aggregation for Video Object Detection : Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei. ICCV (2017).
  • Paper: https://arxiv.org/abs/1703.10025
  • Code: https://github.com/msracver/Flow-Guided-Feature-Aggregation
  • Institution: Microsoft Research Asia
• Detect to Track and Track to Detect : Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman. ICCV (2017).
  • Paper: http://www.robots.ox.ac.uk/~vgg/publications/2017/Feichtenhofer17/feichtenhofer17.pdf
  • Homepage: http://www.robots.ox.ac.uk/~vgg/research/detect-track/
  • Institution: University of Oxford

CVPR2017

• Deep Feature Flow for Video Recognition : Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei. CVPR (2017).
  • Paper: https://arxiv.org/abs/1611.07715
  • Code: https://github.com/msracver/Deep-Feature-Flow
  • Institution: Microsoft Research Asia

Arxiv

• Impression Network for Video Object Detection : Congrui Hetang∗, Hongwei Qin, Shaohui Liu∗, Junjie Yan. (Arxiv Tech Report, 201712).

  • Paper: https://arxiv.org/pdf/1712.05896.pdf
  • Institution: SenseTime Research Institute

• Object Detection in Videos by High Quality Object Linking : Peng Tang∗, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wenjun Zeng, Jingdong Wang. (Arxiv Tech Report, 201806).

  • Paper: https://arxiv.org/pdf/1801.09823.pdf
  • Institution: Microsoft Research Asia