Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks

3 November 2022

Papers citing "Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks"

6 / 6 papers shown

Title
Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video Sonia Joseph Praneet Suresh Lorenz Hufe Edward Stevinson Robert Graham Yash Vadi Danilo Bzdok Sebastian Lapuschkin Lee Sharkey Blake A. Richards 72 0 0 28 Apr 2025
Understanding Video Transformers via Universal Concept Discovery M. Kowal Achal Dave Rares Ambrus Adrien Gaidon Konstantinos G. Derpanis P. Tokmakov ViT 27 8 0 19 Jan 2024
Deeply Explain CNN via Hierarchical Decomposition Mingg-Ming Cheng Peng-Tao Jiang Linghao Han Liang Wang Philip H. S. Torr FAtt 48 15 0 23 Jan 2022
Interpretable Deep Feature Propagation for Early Action Recognition He Zhao Richard P. Wildes FAtt 21 8 0 11 Jul 2021
Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius Heng Wang Lorenzo Torresani ViT 278 1,939 0 09 Feb 2021
Motion-Attentive Transition for Zero-Shot Video Object Segmentation Tianfei Zhou Shunzhou Wang Yi Zhou Yazhou Yao Jianwu Li Ling Shao VOS 122 189 0 09 Mar 2020