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Recent studies have shown that Vision Language Large Models (VLLMs) may output content not relevant to the input images.
K-adapter: Infusing knowledge into pre-trained models with adapters
Wang, R.; Tang, D.; Duan, N.; Wei, Z.; Huang, X.; Cao, G.; Jiang, D.; Zhou, M.; et al. 2020 · 2002
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y.; Khot, T.; Summers-Stay, D.; Batra, D.; and Parikh, D. 2017 · 2017
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Vizwiz grand challenge: Answering visual questions from blind people
Gurari, D.; Li, Q.; Stangl, A. J.; Guo, A.; Lin, C.; Grauman, K.; Luo, J.; and Bigham, J. P. 2018 · 2018
Earlier work this paper cites.
An overview of multi-task learning
Zhang, Y.; and Yang, Q. 2018 · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Hudson, D. A.; and Manning, C. D. 2019 · 2019
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Towards vqa models that can read
Singh, A.; Natarajan, V.; Shah, M.; Jiang, Y.; Chen, X.; Batra, D.; Parikh, D.; and Rohrbach, M. 2019 · 2019
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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Rethinking pre-training and self-training
Zoph, B.; Ghiasi, G.; Lin, T.-Y.; Cui, Y.; Liu, H.; Cubuk, E. D.; and Le, Q. 2020 · 2020
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Simcse: Simple contrastive learning of sentence embeddings
Gao, T.; Yao, X.; and Chen, D. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Earlier work this paper cites.
Image-text retrieval: A survey on recent research and development
Cao, M.; Li, S.; Li, J.; Nie, L.; and Zhang, M. 2022 · 2022
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Visual prompt tuning
Jia, M.; Tang, L.; Chen, B.-C.; Cardie, C.; Belongie, S.; Hariharan, B.; and Lim, S.-N. 2022 · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Li, J.; Li, D.; Xiong, C.; and Hoi, S. 2022 · 2022
Cited alongside, same era.
P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Liu, X.; Ji, K.; Fu, Y.; Tam, W.; Du, Z.; Yang, Z.; and Tang, J. 2022 · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P.; Mishra, S.; Xia, T.; Qiu, L.; Chang, K.-W.; Zhu, S.-C.; Tafjord, O.; Clark, P.; and Kalyan, A. 2022 · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; et al. 2022 · 2022
Cited alongside, same era.
From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models
Guo, J.; Li, J.; Li, D.; Tiong, A. M. H.; Li, B.; Tao, D.; and Hoi, S. 2023 · 2023
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Ciem: Contrastive instruction evaluation method for better instruction tuning
Hu, H.; Zhang, J.; Zhao, M.; and Sun, Z. 2023 · 2023
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Jiang, A. Q.; Sablayrolles, A.; Mensch, A.; Bamford, C.; Chaplot, D. S.; Casas, D. d. l.; Bressand, F.; Lengyel, G.; Lample, G.; Saulnier, L.; et al. 2023 · 2023
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REVO-LION: Evaluating and Refining Vision-Language Instruction Tuning Datasets
Liao, N.; Zhang, S.; Xia, R.; Zhang, B.; Cao, M.; Qiao, Y.; and Yan, J. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Zhao, M.; Zhang, L.; Xu, Y.; Ding, J.; Guan, J.; and Zhou, S. 2022 · 2022
Cited alongside, same era.
Deep learning-based question answering: a survey
Abdel-Nabi, H.; Awajan, A.; and Ali, M. Z. 2023 · 2023
Cited alongside, same era.
Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023
Cited alongside, same era.
Qwen-vl: A frontier large vision-language model with versatile abilities
Bai, J.; Bai, S.; Yang, S.; Wang, S.; Tan, S.; Wang, P.; Lin, J.; Zhou, C.; and Zhou, J. 2023 · 2023
Cited alongside, same era.
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Dai, W.; Li, J.; Li, D.; Tiong, A.; Zhao, J.; Wang, W.; Li, B.; Fung, P.; and Hoi, S. 2023 · 2023
Cited alongside, same era.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N.; Qin, Y.; Yang, G.; Wei, F.; Yang, Z.; Su, Y.; Hu, S.; Chen, Y.; Chan, C.-M.; Chen, W.; et al. 2023 · 2023
Cited alongside, same era.
Eva: Exploring the limits of masked visual representation learning at scale
Fang, Y.; Wang, W.; Xie, B.; Sun, Q.; Wu, L.; Wang, X.; Huang, T.; Wang, X.; and Cao, Y. 2023 · 2023
Cited alongside, same era.
Peng, B.; Li, C.; He, P.; Galley, M.; and Gao, J. 2023 · 2023
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Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
Shum, K.; Diao, S.; and Zhang, T. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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V*: Guided visual search as a core mechanism in multimodal llms
Wu, P.; and Xie, S. 2023 · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Ye, Q.; Xu, H.; Xu, G.; Ye, J.; Yan, M.; Zhou, Y.; Wang, J.; Hu, A.; Shi, P.; Shi, Y.; et al. 2023 · 2023
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Analyzing and mitigating object hallucination in large vision-language models
Zhou, Y.; Cui, C.; Yoon, J.; Zhang, L.; Deng, Z.; Finn, C.; Bansal, M.; and Yao, H. 2023 · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D.; Chen, J.; Shen, X.; Li, X.; and Elhoseiny, M. 2023 · 2023
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Allava: Harnessing gpt4v-synthesized data for a lite vision-language model
Chen, G. H.; Chen, S.; Zhang, R.; Chen, J.; Wu, X.; Zhang, Z.; Chen, Z.; Li, J.; Wan, X.; and Wang, B. 2024 · 2024
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Lamm: Language-assisted multi-modal instruction-tuning dataset, framework, and benchmark
Yin, Z.; Wang, J.; Cao, J.; Shi, Z.; Liu, D.; Li, M.; Huang, X.; Wang, Z.; Sheng, L.; Bai, L.; et al. 2024 · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L.; Chiang, W.-L.; Sheng, Y.; Zhuang, S.; Wu, Z.; Zhuang, Y.; Lin, Z.; Li, Z.; Li, D.; Xing, E.; et al. 2024 · 2024
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