GraphVid: It Only Takes a Few Nodes to Understand a Video

4 July 2022

Papers citing "GraphVid: It Only Takes a Few Nodes to Understand a Video"

27 / 27 papers shown

Title
Video Swin Transformer Ze Liu Jia Ning Yue Cao Yixuan Wei Zheng Zhang Stephen Lin Han Hu ViT 100 1,482 0 24 Jun 2021
VidTr: Video Transformer Without Convolutions Yanyi Zhang Xinyu Li Chunhui Liu Bing Shuai Yi Zhu Biagio Brattoli Hao Chen I. Marsic Joseph Tighe ViT 188 196 0 23 Apr 2021
Multiscale Vision Transformers Haoqi Fan Bo Xiong K. Mangalam Yanghao Li Zhicheng Yan Jitendra Malik Christoph Feichtenhofer ViT 132 1,259 0 22 Apr 2021
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text Hassan Akbari Liangzhe Yuan Rui Qian Wei-Hong Chuang Shih-Fu Chang Huayu Chen Boqing Gong ViT 314 589 0 22 Apr 2021
ViViT: A Video Vision Transformer Anurag Arnab Mostafa Dehghani G. Heigold Chen Sun Mario Lucic Cordelia Schmid ViT 222 2,150 0 29 Mar 2021
Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius Heng Wang Lorenzo Torresani ViT 367 2,053 0 09 Feb 2021
Video Transformer Network Daniel Neimark Omri Bar Maya Zohar Dotan Asselmann ViT 264 432 0 01 Feb 2021
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators S. Abadal Akshay Jain Robert Guirado Jorge López-Alonso Eduard Alarcón GNN 109 228 0 30 Sep 2020
A Survey on Deep Learning Techniques for Video Anomaly Detection Jessie James P. Suarez P. Naval AI4TS 52 32 0 29 Sep 2020
Principal Neighbourhood Aggregation for Graph Nets Gabriele Corso Luca Cavalleri Dominique Beaini Pietro Lio Petar Velickovic GNN 111 668 0 12 Apr 2020
X3D: Expanding Architectures for Efficient Video Recognition Christoph Feichtenhofer 134 1,020 0 09 Apr 2020
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification Yu Rong Wenbing Huang Tingyang Xu Junzhou Huang 110 1,339 0 25 Jul 2019
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures Michael S. Ryoo A. Piergiovanni Mingxing Tan A. Angelova 52 102 0 30 May 2019
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond Yue Cao Jiarui Xu Stephen Lin Fangyun Wei Han Hu ISeg 80 1,571 0 25 Apr 2019
Fast Graph Representation Learning with PyTorch Geometric Matthias Fey J. E. Lenssen 3DH GNN 3DPC 226 4,341 0 06 Mar 2019
SlowFast Networks for Video Recognition Christoph Feichtenhofer Haoqi Fan Jitendra Malik Kaiming He 164 3,274 0 10 Dec 2018
Video Action Transformer Network Rohit Girdhar João Carreira Carl Doersch Andrew Zisserman ViT 126 709 0 06 Dec 2018
Videos as Space-Time Region Graphs Xinyu Wang Abhinav Gupta 104 756 0 05 Jun 2018
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification Saining Xie Chen Sun Jonathan Huang Zhuowen Tu Kevin Patrick Murphy 3DH 142 1,330 0 13 Dec 2017
Network Representation Learning: A Survey Daokun Zhang Jie Yin Xingquan Zhu Chengqi Zhang GNN AI4TS 95 622 0 04 Dec 2017
Modeling Relational Data with Graph Convolutional Networks Michael Schlichtkrull Thomas Kipf Peter Bloem Rianne van den Berg Ivan Titov Max Welling GNN 191 4,821 0 17 Mar 2017
Superpixels: An Evaluation of the State-of-the-Art David Stutz Alexander Hermans Bastian Leibe SupR 124 471 0 06 Dec 2016
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 412 1,823 0 25 Nov 2016
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding Gunnar Sigurdsson Gül Varol Xinyu Wang Ali Farhadi Ivan Laptev Abhinav Gupta VGen 104 1,245 0 06 Apr 2016
Beyond Short Snippets: Deep Networks for Video Classification Joe Yue-Hei Ng Matthew J. Hausknecht Sudheendra Vijayanarasimhan Oriol Vinyals R. Monga G. Toderici 145 2,337 0 31 Mar 2015
Unsupervised Learning of Video Representations using LSTMs Nitish Srivastava Elman Mansimov Ruslan Salakhutdinov SSL 132 2,591 0 16 Feb 2015
Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan Andrew Zisserman 244 7,535 0 09 Jun 2014