【深度学习】Video-Detection 论文集合

Video-Detection

video detection papers based deep learning

Arxiv

• Looking Fast and Slow: Mason Liu, Menglong Zhu, Marie White, Yinxiao Li, Dmitry Kalenichenko.
  “Looking Fast and Slow: Memory-Guided Mobile Video Object Detection” Arxiv(2019). [
  paper]
• Towards High Performance for Mobiles: Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan.
  “Towards High Performance Video Object Detection for Mobiles” Arxiv(2018). [
  paper]
• Learning Motion Priors: Zhengkai Jiang, Yu Liu, Ceyuan Yang, Jihao Liu, Qian Zhang, Shiming Xiang, Chunhong Pan.
  “Learning Motion Priors for Efficient Video Object Detection” Arxiv(2019). [
  paper]

ICCV2019

• Sequence Level Semantics Aggregation: Haiping Wu, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang.
  “Sequence Level Semantics Aggregation for Video Object Detection” ICCV(2019). [
  paper]

• Average Delay: Huizi Mao, Xiaodong Yang, William J. Dally.
  “A Delay Metric for Video Object Detection: What Average Precision Fails to Tell.” ICCV (2019). [
  paper]

• Fully Motion-Aware Networks: Jiajun Deng, Yingwei Pan, Ting Yao, Wengang Zhou, Houqiang Li, Tao Mei.
  “Relation Distillation Networks for Video Object Detection.” ICCV (2019). [
  paper]

AAAI2019

• Locally-Weighted Deformable Neighboors: Zhengkai Jiang, Peng Gao, Chaoxu Guo, Qian Zhang, Shiming Xiang, Chunhong Pan.
  “Video Object Detection with Locally-Weighted Deformable Neighbors” AAAI(2019). [
  paper]

ECCV2018

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

• SpatioTemporal Sampling Network: Gedas Bertasius, Lorenzo Torresani, ianbo Shi.
  “Object Detection in Video with Spatiotemporal Sampling Networks.” ECCV (2018). [
  paper]

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

CVPR2018

• Towards High Performance: Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei.
  “Towards High Performance Video Object Detection.” CVPR (2018). [
  paper]
• Scale-Time Lattice: Kai Chen, Jiaqi Wang, Shuo Yang, Xingcheng Zhang, Yuanjun Xiong, Chen Chang Loy, Dahua Lin.
  “Optimizing Video Object Detection vis a Scale-Time Lattice.” CVPR (2018). [
  paper] [project]
• Mobile Video Object Detection: Mason Liu, Menglong Zhu.
  “Mobile Video Object Detection with Temporally-Aware Feature Maps.” CVPR (2018). [
  paper]

ICCV2017

• FGFA: Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei.
  “Flow-Guided Feature Aggregation for Video Object Detection.” ICCV (2017). [
  paper]. [code]
• D_T: Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman.
  “Detect to Track and Track to Detect.” ICCV (2017). [
  paper]. [project]

CVPR2017

• DFF: Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei.
  “Deep Feature Flow for Video Recognition.” CVPR (2017). [paper] [code]

Object-Detection

object detection papers based deep learning

Arxiv

• Light-Head R-CNN: Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun.
  “Light-Head R-CNN: In Defense of Two-Stage Object Detector.”[paper]
• YOLOv3: Joseph Redmon, Ali Farhadi.
  “YOLOv3: An Incremental Improvement.” [paper]

ECCV2018

• DetNet: Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun.
  “DetNet: A Backbone network for Object Detection.” [paper]
• IOU-Net: Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang.
  “Acquisition of Localization Confidence for Accurate Object Detection.” ECCV(2018). [
  paper] [code]

CVPR2018

• SNIP: Bharat Singh, Larry S. Davis.
  “An Analysis of Scale Invariance in Object Detection - SNIP.” [paper] [code]
• Cascade-RCNN: Zhaowei Cai, Nuno Vasconcelos.
  “Cascade R-CNN: Delving into High Quality Object Detectio.” [paper] [code]
• Relation-Networks: Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, Yichen Wei.
  “Relation Networks for Object Detection.” [paper] [code]

ICCV2017

• RetinaNet: Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollar.
  “Focal Loss for Dense Object Detection.” [paper]
• Mask R-CNN: Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick.
  “Mask R-CNN.” [paper] [caffe2_code][pytorch_code]

CVPR2017

• YOLO9000: Joseph Redmon, Ali Farhadi.
  “YOLO9000: Better, Faster, Stronger.” [paper] [project]
• FPN: Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie.
  “Feature Pyramid Networks for Object Detection.” [paper]

NIPS2016

• R-FCN: Jifeng Dai, Yi Li, Kaiming He, Jian Sun.
  “R-FCN: Object Detection via Region-based Fully Convolutional Networks.” [paper]

ECCV2016

• SSD: Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C.Berg.
  “SSD: Single Shot MultiBox Detector.” [paper] [code]

CVPR2016

• YOLO: Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi.
  “You Only Look Once: Unified, Real-Time Object Detection.” [paper] [project]

NIPS2015

• Faster R-CNN: Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
  “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” [paper] [code]

ICCV2015

• Fast R-CNN: Ross Girshick.
  “Fast R-CNN.” [paper]

CVPR2014

• R-CNN: Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
  “Rich feature hierarchies for accurate object detection and semantic segmentation.” [paper]