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One does not fit all! On the Complementarity of Vision Encoders for
  Vision and Language Tasks

One does not fit all! On the Complementarity of Vision Encoders for Vision and Language Tasks

12 October 2022
Gregor Geigle
Chen Cecilia Liu
Jonas Pfeiffer
Iryna Gurevych
    VLM
ArXivPDFHTML

Papers citing "One does not fit all! On the Complementarity of Vision Encoders for Vision and Language Tasks"

5 / 5 papers shown
Title
BLIP: Bootstrapping Language-Image Pre-training for Unified
  Vision-Language Understanding and Generation
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Junnan Li
Dongxu Li
Caiming Xiong
S. Hoi
MLLM
BDL
VLM
CLIP
388
4,010
0
28 Jan 2022
How Much Can CLIP Benefit Vision-and-Language Tasks?
How Much Can CLIP Benefit Vision-and-Language Tasks?
Sheng Shen
Liunian Harold Li
Hao Tan
Mohit Bansal
Anna Rohrbach
Kai-Wei Chang
Z. Yao
Kurt Keutzer
CLIP
VLM
MLLM
185
403
0
13 Jul 2021
Unifying Vision-and-Language Tasks via Text Generation
Unifying Vision-and-Language Tasks via Text Generation
Jaemin Cho
Jie Lei
Hao Tan
Mohit Bansal
MLLM
249
518
0
04 Feb 2021
Decoupling the Role of Data, Attention, and Losses in Multimodal
  Transformers
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
75
110
0
31 Jan 2021
How Good is Your Tokenizer? On the Monolingual Performance of
  Multilingual Language Models
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
Phillip Rust
Jonas Pfeiffer
Ivan Vulić
Sebastian Ruder
Iryna Gurevych
69
235
0
31 Dec 2020
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