Brain encoding models based on multimodal transformers can transfer across language and vision

20 May 2023

Papers citing "Brain encoding models based on multimodal transformers can transfer across language and vision"

9 / 9 papers shown

Title
Do Large Language Models know who did what to whom? Joseph M. Denning Xiaohan Bryor Snefjella Idan A. Blank 222 1 0 23 Apr 2025
The Wisdom of a Crowd of Brains: A Universal Brain Encoder Roman Beliy Navve Wasserman Amit Zalcher Michal Irani 75 2 0 18 Jun 2024
Multimodal foundation models are better simulators of the human brain Haoyu Lu Qiongyi Zhou Nanyi Fei Zhiwu Lu Mingyu Ding ... Changde Du Xin Zhao Haoran Sun Huiguang He J. Wen AI4CE 72 13 0 17 Aug 2022
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision Wonjae Kim Bokyung Son Ildoo Kim VLM CLIP 128 1,749 0 05 Feb 2021
Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers Lisa Anne Hendricks John F. J. Mellor R. Schneider Jean-Baptiste Alayrac Aida Nematzadeh 125 116 0 31 Jan 2021
LXMERT: Learning Cross-Modality Encoder Representations from Transformers Hao Hao Tan Joey Tianyi Zhou VLM MLLM 247 2,488 0 20 Aug 2019
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks Jiasen Lu Dhruv Batra Devi Parikh Stefan Lee SSL VLM 231 3,693 0 06 Aug 2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy M. Lewis Luke Zettlemoyer Veselin Stoyanov AIMat 668 24,528 0 26 Jul 2019
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain) Mariya Toneva Leila Wehbe MILM AI4CE 75 229 0 28 May 2019