ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network

20 July 2022

Papers citing "ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network"

8 / 8 papers shown

Title
An Integrated Framework for Multi-Granular Explanation of Video Summarization K. Tsigos Evlampios Apostolidis Vasileios Mezaris 14 0 0 16 May 2024
Masked Feature Modelling: Feature Masking for the Unsupervised Pre-training of a Graph Attention Network Block for Bottom-up Video Event Recognition Dimitrios Daskalakis Nikolaos Gkalelis Vasileios Mezaris 34 0 0 24 Aug 2023
Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism Nikolaos Gkalelis Dimitrios Daskalakis Vasileios Mezaris 13 4 0 18 Jan 2023
VidTr: Video Transformer Without Convolutions Yanyi Zhang Xinyu Li Chunhui Liu Bing Shuai Yi Zhu Biagio Brattoli Hao Chen I. Marsic Joseph Tighe ViT 136 193 0 23 Apr 2021
Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius Heng Wang Lorenzo Torresani ViT 280 1,981 0 09 Feb 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 167 592 0 31 Dec 2020
AdaFrame: Adaptive Frame Selection for Fast Video Recognition Zuxuan Wu Caiming Xiong Chih-Yao Ma R. Socher L. Davis 118 194 0 29 Nov 2018
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,194 0 01 Sep 2014