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"

7 / 7 papers shown

Title
VidTr: Video Transformer Without Convolutions Yanyi Zhang Xinyu Li Chunhui Liu Bing Shuai Yi Zhu Biagio Brattoli Hao Chen I. Marsic Joseph Tighe ViT 124 178 0 23 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 Yin Cui Boqing Gong ViT 229 573 0 22 Apr 2021
Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery Saba Dadsetan David Pichler David Wilson N. Hovakimyan Jennifer Hobbs 31 6 0 20 Apr 2021
Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius Heng Wang Lorenzo Torresani ViT 275 1,939 0 09 Feb 2021
Video Transformer Network Daniel Neimark Omri Bar Maya Zohar Dotan Asselmann ViT 191 375 0 01 Feb 2021
Superpixels: An Evaluation of the State-of-the-Art David Stutz Alexander Hermans Bastian Leibe SupR 57 468 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 231 1,801 0 25 Nov 2016