Fetching the paper…
Reading the bibliography…
This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark.
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
Earlier work this paper cites.
VQA: visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
Earlier work this paper cites.
OK-VQA: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. 2019 · 2019
Earlier work this paper cites.
Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020 · 2020
Earlier work this paper cites.
Oscar: Object-semantics aligned pre-training for vision-language tasks
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. 2020 · 2020
Earlier work this paper cites.
Unified vision-language pre-training for image captioning and VQA
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason J. Corso, and Jianfeng Gao. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Earlier work this paper cites.
Openflamingo: An open-source framework for training large autoregressive vision-language models
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, et al. 2023 · 2023
Earlier work this paper cites.
Holistic analysis of hallucination in gpt-4v (ision): Bias and interference challenges
Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Linjun Zhang, James Zou, and Huaxiu Yao. 2023 · 2023
Earlier work this paper cites.
Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, DONGXU LI, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. 2023 · 2023
Earlier work this paper cites.
Shortcut learning of large language models in natural language understanding
Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, and Xia Hu. 2023 · 2023
Earlier work this paper cites.
Llama-adapter v2: Parameter-efficient visual instruction model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al. 2023 · 2023
Earlier work this paper cites.
Otter: A multi-modal model with in-context instruction tuning
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Jingkang Yang, and Ziwei Liu. 2023 · 2023
Earlier work this paper cites.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2023b · 2023
Earlier work this paper cites.
MetaVL: Transferring in-context learning ability from language models to vision-language models
Masoud Monajatipoor, Liunian Harold Li, Mozhdeh Rouhsedaghat, Lin Yang, and Kai-Wei Chang. 2023 · 2023
Cited alongside, same era.
Gpt-4v(ision) system card
OpenAI. 2023 · 2023
Cited alongside, same era.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. 2023 · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models
Efficient multimodal large language models: A survey
Yizhang Jin, Jian Li, Yexin Liu, Tianjun Gu, Kai Wu, Zhengkai Jiang, Muyang He, Bo Zhao, Xin Tan, Zhenye Gan, et al. 2024 · 2024
Closest in time.
VisualWebArena: Evaluating multimodal agents on realistic visual web tasks
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, and Daniel Fried. 2024 · 2024
Closest in time.
Building and better understanding vision-language models: insights and future directions
Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon. 2024 · 2024
Closest in time.
Vila: On pre-training for visual language models
Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. 2024 · 2024
Closest in time.
Pixtral-12b. https://mistral.ai/news/pixtral-12b
Mistral. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Peng Xu, Wenqi Shao, Kaipeng Zhang, Peng Gao, Shuo Liu, Meng Lei, Fanqing Meng, Siyuan Huang, Yu Qiao, and Ping Luo. 2023 · 2023
Cited alongside, same era.
When and why vision-language models behave like bags-of-words, and what to do about it?
Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. 2023 · 2023
Cited alongside, same era.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. 2023 · 2023
Cited alongside, same era.
Llama-adapter: Efficient fine-tuning of language models with zero-init attention
Renrui Zhang, Jiaming Han, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, Hongsheng Li, Peng Gao, and Yu Qiao. 2023 · 2023
Cited alongside, same era.
Svit: Scaling up visual instruction tuning
Bo Zhao, Boya Wu, and Tiejun Huang. 2023 · 2023
Cited alongside, same era.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
Cited alongside, same era.
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al. 2024 · 2024
Cited alongside, same era.
Claude 3.5 sonnet. https://www.anthropic.com/news/claude-3-5-sonnet
Anthropic. 2024 · 2024
Cited alongside, same era.
Qwen2-vl: To see the world more clearly
Qwen. 2024 · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al. 2024 · 2024
Closest in time.
Mmlu-pro: A more robust and challenging multi-task language understanding benchmark
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al. 2024 · 2024
Closest in time.
From decoding to meta-generation: Inference-time algorithms for large language models
Sean Welleck, Amanda Bertsch, Matthew Finlayson, Hailey Schoelkopf, Alex Xie, Graham Neubig, Ilia Kulikov, and Zaid Harchaoui. 2024 · 2024
Closest in time.
V*: Guided visual search as a core mechanism in multimodal llms
Penghao Wu and Saining Xie. 2024 · 2024
Closest in time.
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al. 2024 · 2024
Closest in time.
Minicpm-v: A gpt-4v level mllm on your phone
Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. 2024 · 2024
Closest in time.
Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al. 2024 · 2024
Closest in time.
MM-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. 2024 · 2024
Closest in time.
Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. 2024 · 2024
Closest in time.
Mmicl: Empowering vision-language model with multi-modal in-context learning
Haozhe Zhao, Zefan Cai, Shuzheng Si, Xiaojian Ma, Kaikai An, Liang Chen, Zixuan Liu, Sheng Wang, Wenjuan Han, and Baobao Chang. 2024 · 2024
Closest in time.
Gpt-4v(ision) is a generalist web agent, if grounded
Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, and Yu Su. 2024 · 2024
Closest in time.