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The Mixture-of-Experts (MoE) has gained increasing attention in studying Large Vision-Language Models (LVLMs).
Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Automatically constructing a corpus of sentential paraphrases
Bill Dolan and Chris Brockett · 2005
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The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo · 2009
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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
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A diagram is worth a dozen images
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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Vizwiz grand challenge: Answering visual questions from blind people
Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
N Reimers · 2019
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Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
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Neural network acceptability judgments
A Warstadt · 2019
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Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
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Gradients as features for deep representation learning
Fangzhou Mu, Yingyu Liang, and Yin Li · 2020
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Gradient estimation with stochastic softmax tricks
Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, and Chris J Maddison · 2020
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Clue: A chinese language understanding evaluation benchmark
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, et al · 2020
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Learning with noisy class labels for instance segmentation
Longrong Yang, Fanman Meng, Hongliang Li, Qingbo Wu, and Qishang Cheng · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Beyond distillation: Task-level mixture-of-experts for efficient inference
Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, and Orhan Firat · 2021
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Docvqa: A dataset for vqa on document images
Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar · 2021
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Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Moefication: Transformer feed-forward layers are mixtures of experts
Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei · 2022
Cited alongside, same era.
Gradients without backpropagation
Atılım Güneş Baydin, Barak A Pearlmutter, Don Syme, Frank Wood, and Philip Torr · 2022
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
Cited alongside, same era.
Sparse upcycling: Training mixture-of-experts from dense checkpoints
Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, and Neil Houlsby · 2022
Phi-2: The surprising power of small language models
Microsoft · 2023
Later among the works it cites.
From sparse to soft mixtures of experts
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, and Neil Houlsby · 2023
Later among the works it cites.
Zihan Qiu, Zeyu Huang, and Jie Fu · 2023
Later among the works it cites.
Estimator meets equilibrium perspective: A rectified straight through estimator for binary neural networks training
Xiao-Ming Wu, Dian Zheng, Zuhao Liu, and Wei-Shi Zheng · 2023
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Bridging cross-task protocol inconsistency for distillation in dense object detection
Longrong Yang, Xianpan Zhou, Xuewei Li, Liang Qiao, Zheyang Li, Ziwei Yang, Gaoang Wang, and Xi Li · 2023
Later among the works it cites.
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Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
Cited alongside, same era.
Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Victor Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y Zou · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan · 2022
Cited alongside, same era.
Chartqa: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
Cited alongside, same era.
Rome: Role-aware mixture-of-expert transformer for text-to-video retrieval
Burak Satar, Hongyuan Zhu, Hanwang Zhang, and Joo Hwee Lim · 2022
Cited alongside, same era.
Gradient estimation with discrete stein operators
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis Titsias, and Lester Mackey · 2022
Cited alongside, same era.
Image as a foreign language: Beit pretraining for all vision and vision-language tasks
Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al · 2022
Cited alongside, same era.
mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
Later among the works it cites.
Pushing mixture of experts to the limit: Extremely parameter efficient moe for instruction tuning
Ted Zadouri, Ahmet Üstün, Arash Ahmadian, Beyza Ermiş, Acyr Locatelli, and Sara Hooker · 2023
Later among the works it cites.
Svit: Scaling up visual instruction tuning
Bo Zhao, Boya Wu, and Tiejun Huang · 2023
Later among the works it cites.
Scaling physics-informed hard constraints with mixture-of-experts
Nithin Chalapathi, Yiheng Du, and Aditi Krishnapriyan · 2024
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Shaoxiang Chen, Zequn Jie, and Lin Ma · 2024
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Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models
Damai Dai, Chengqi Deng, Chenggang Zhao, RX Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y Wu, et al · 2024
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Dynamic mixture of experts: An auto-tuning approach for efficient transformer models
Yongxin Guo, Zhenglin Cheng, Xiaoying Tang, and Tao Lin · 2024
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Mixture of nested experts: Adaptive processing of visual tokens
Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, and Sujoy Paul · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Moai: Mixture of all intelligence for large language and vision models
Byung-Kwan Lee, Beomchan Park, Chae Won Kim, and Yong Man Ro · 2024
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Moe-llava: Mixture of experts for large vision-language models
Bin Lin, Zhenyu Tang, Yang Ye, Jiaxi Cui, Bin Zhu, Peng Jin, Junwu Zhang, Munan Ning, and Li Yuan · 2024
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Open-llava-next: An open-source implementation of llava-next series for facilitating the large multi-modal model community
Chen Lin and Xing Long · 2024
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Adamole: Fine-tuning large language models with adaptive mixture of low-rank adaptation experts
Zefang Liu and Jiahua Luo · 2024
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Mome: Mixture of multimodal experts for generalist multimodal large language models
Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie · 2024
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Xun Wu, Shaohan Huang, and Furu Wei · 2024
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Rcs-prompt: Learning prompt to rearrange class space for prompt-based continual learning
Longrong Yang, Hanbin Zhao, Yunlong Yu, Xiaodong Zeng, and Xi Li · 2024
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Moextend: Tuning new experts for modality and task extension
Shanshan Zhong, Shanghua Gao, Zhongzhan Huang, Wushao Wen, Marinka Zitnik, and Pan Zhou · 2024
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Exploring training on heterogeneous data with mixture of low-rank adapters
Yuhang Zhou, Zihua Zhao, Haolin Li, Siyuan Du, Jiangchao Yao, Ya Zhang, and Yanfeng Wang · 2024
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Gcstg: Generating class-confusion-aware samples with a tree-structure graph for few-shot object detection
Longrong Yang, Hanbin Zhao, Hongliang Li, Liang Qiao, Ziwei Yang, and Xi Li · 2025
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