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Large-scale vision language (VL) models use Transformers to perform cross-modal interactions between the input text and image.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Sparsifying Transformer Models with Differentiable Representation Pooling
Michał Pietruszka, Łukasz Borchmann, and Filip Graliński. 2020 · 2009
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MiniVLM: A Smaller and Faster Vision-Language Model
Jianfeng Wang, Xiaowei Hu, Pengchuan Zhang, Xiujun Li, Lijuan Wang, Lei Zhang, Jianfeng Gao, and Zicheng Liu. 2020 · 2012
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014 · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. 2015 · 2015
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Making the v in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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A Corpus for Reasoning about Natural Language Grounded in Photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi. 2019 · 2019
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Visual Entailment Task for Visually-Grounded Language Learning
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. 2019 · 2019
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2020 · 2020
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PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan Chakaravarthy, Yogish Sabharwal, and Ashish Verma. 2020 · 2020
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FastBERT: a Self-distilling BERT with Adaptive Inference Time
Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, and Qi Ju. 2020 · 2020
Cited alongside, same era.
The Right Tool for the Job: Matching Model and Instance Complexities
Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Early Exiting BERT for Efficient Document Ranking
Ji Xin, Rodrigo Nogueira, Yaoliang Yu, and Jimmy Lin. 2020 · 2020
Cited alongside, same era.
BERT loses patience: Fast and robust inference with early exit
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. 2020 · 2020
Cited alongside, same era.
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. 2021 · 2021
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A Unified Pruning Framework for Vision Transformers
Hao Yu and Jianxin Wu. 2021 · 2021
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Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Yan Zeng, Xinsong Zhang, and Hang Li. 2021 · 2021
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VinVL: Revisiting Visual Representations in Vision-Language Models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. 2021 · 2021
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Deepspeed
2022 · 2022
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Compressing Visual-Linguistic Model via Knowledge Distillation
Zhiyuan Fang, Jianfeng Wang, Xiaowei Hu, Lijuan Wang, Yezhou Yang, and Zicheng Liu. 2021 · 2021
Cited alongside, same era.
LeViT: A Vision Transformer in ConvNet’s Clothing for Faster Inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. 2021 · 2021
Cited alongside, same era.
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021 · 2021
Cited alongside, same era.
Block Pruning For Faster Transformers
François Lagunas, Ella Charlaix, Victor Sanh, and Alexander Rush. 2021 · 2021
Cited alongside, same era.
Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021 · 2021
Cited alongside, same era.
EViT: Expediting Vision Transformers via Token Reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie. 2021 · 2021
Cited alongside, same era.
Token Pooling in Vision Transformers
Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan, Anish Prabhu, Mohammad Rastegari, and Oncel Tuzel. 2021 · 2021
Cited alongside, same era.
huggingface/accelerate
2022 · 2022
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Token Merging: Your ViT But Faster
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman. 2022 · 2022
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MobiVQA: Efficient On-Device Visual Question Answering
Qingqing Cao, Prerna Khanna, Nicholas D. Lane, and Aruna Balasubramanian. 2022 · 2022
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An empirical study of training end-to-end vision-and-language transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. 2022 · 2022
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SPViT: Enabling Faster Vision Transformers via Soft Token Pruning
Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Xuan Shen, Geng Yuan, Bin Ren, Minghai Qin, Hao Tang, and Yanzhi Wang. 2022 · 2022
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Efficient Transformers with Dynamic Token Pooling
Piotr Nawrot, Jan Chorowski, Adrian Łańcucki, and Edoardo M. Ponti. 2022 · 2022
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Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
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GroupViT: Semantic Segmentation Emerges From Text Supervision
Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. 2022 · 2022
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A-vit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M. Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov. 2022 · 2022
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Width & depth pruning for vision transformers
Fang Yu, Kun Huang, Meng Wang, Yuan Cheng, Wei Chu, and Li Cui. 2022 · 2022
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