If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions

25 March 2024

Papers citing "If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions"

6 / 6 papers shown

Title
What do we learn from inverting CLIP models? Hamid Kazemi Atoosa Malemir Chegini Jonas Geiping S. Feizi Tom Goldstein 21 3 0 05 Mar 2024
Linearly Mapping from Image to Text Space Jack Merullo Louis Castricato Carsten Eickhoff Ellie Pavlick VLM 159 104 0 30 Sep 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022
Does Vision-and-Language Pretraining Improve Lexical Grounding? Tian Yun Chen Sun Ellie Pavlick VLM CoGe 32 30 0 21 Sep 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Chao Jia Yinfei Yang Ye Xia Yi-Ting Chen Zarana Parekh Hieu H. Pham Quoc V. Le Yun-hsuan Sung Zhen Li Tom Duerig VLM CLIP 293 3,683 0 11 Feb 2021
Language Models as Knowledge Bases? Fabio Petroni Tim Rocktaschel Patrick Lewis A. Bakhtin Yuxiang Wu Alexander H. Miller Sebastian Riedel KELM AI4MH 393 2,576 0 03 Sep 2019