Understanding Video Transformers via Universal Concept Discovery

Understanding Video Transformers via Universal Concept Discovery

19 January 2024

Adrien Gaidon

Konstantinos G. Derpanis

Papers citing "Understanding Video Transformers via Universal Concept Discovery"

10 / 10 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 70 0 0 28 Apr 2025
Representational Similarity via Interpretable Visual Concepts Neehar Kondapaneni Oisin Mac Aodha Pietro Perona DRL 65 0 0 19 Mar 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment Harrish Thasarathan Julian Forsyth Thomas Fel M. Kowal Konstantinos G. Derpanis 91 7 0 06 Feb 2025
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets A. Blattmann Tim Dockhorn Sumith Kulal Daniel Mendelevitch Maciej Kilian ... Zion English Vikram S. Voleti Adam Letts Varun Jampani Robin Rombach VGen 150 985 0 25 Nov 2023
What Do Self-Supervised Vision Transformers Learn? Namuk Park Wonjae Kim Byeongho Heo Taekyung Kim Sangdoo Yun SSL 65 76 1 01 May 2023
In-context Learning and Induction Heads Catherine Olsson Nelson Elhage Neel Nanda Nicholas Joseph Nova Dassarma ... Tom B. Brown Jack Clark Jared Kaplan Sam McCandlish C. Olah 237 453 0 24 Sep 2022
Toy Models of Superposition Nelson Elhage Tristan Hume Catherine Olsson Nicholas Schiefer T. Henighan ... Sam McCandlish Jared Kaplan Dario Amodei Martin Wattenberg C. Olah AAML MILM 117 314 0 21 Sep 2022
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation Yao Qin Chiyuan Zhang Ting Chen Balaji Lakshminarayanan Alex Beutel Xuezhi Wang ViT 39 41 0 15 Oct 2021
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 117 293 0 17 Oct 2019
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 226 438 0 01 Dec 2016