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Multimodal large language models (MLLMs) have shown promising advancements in general visual and language understanding.
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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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
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Image retrieval on real-life images with pre-trained vision-and-language models
Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney, and Stephen Gould · 2021
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B Hall, Daniel Cer, and Yinfei Yang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Fashion iq: A new dataset towards retrieving images by natural language feedback
Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, and Rogerio Feris · 2021
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Promptbert: Improving bert sentence embeddings with prompts
Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, and Qi Zhang · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Zhenghao Liu, Chenyan Xiong, Yuanhuiyi Lv, Zhiyuan Liu, and Ge Yu · 2022
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Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Zou · 2022
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Sgpt: Gpt sentence embeddings for semantic search
Niklas Muennighoff · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Scaling sentence embeddings with large language models
Ting Jiang, Shaohan Huang, Zhongzhi Luan, Deqing Wang, and Fuzhen Zhuang · 2023
Cited alongside, same era.
Vision-by-language for training-free compositional image retrieval
Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini, and Zeynep Akata · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
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Language-only training of zero-shot composed image retrieval
Geonmo Gu, Sanghyuk Chun, Wonjae Kim, Yoohoon Kang, and Sangdoo Yun · 2024
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Language is not all you need: Aligning perception with language models
Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Barun Patra, et al · 2024
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Llava-next: Improved reasoning, ocr, and world knowledge, 2024
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee · 2024
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Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Cited alongside, same era.
Eva-clip: Improved training techniques for clip at scale
Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao · 2023
Cited alongside, same era.
Pic2word: Mapping pictures to words for zero-shot composed image retrieval
Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, and Tomas Pfister · 2023
Cited alongside, same era.
Uniir: Training and benchmarking universal multimodal information retrievers
Cong Wei, Yang Chen, Haonan Chen, Hexiang Hu, Ge Zhang, Jie Fu, Alan Ritter, and Wenhu Chen · 2023
Cited alongside, same era.
Improving text embeddings with large language models
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei · 2023
Cited alongside, same era.
A survey on multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen · 2023
Cited alongside, same era.
isearle: Improving textual inversion for zero-shot composed image retrieval
Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, and Alberto Del Bimbo · 2024
Cited alongside, same era.
Closest in time.
Llava-next: Stronger llms supercharge multimodal capabilities in the wild, May 2024
Bo Li, Kaichen Zhang, Hao Zhang, Dong Guo, Renrui Zhang, Feng Li, Yuanhan Zhang, Ziwei Liu, and Chunyuan Li · 2024
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Context-i2w: Mapping images to context-dependent words for accurate zero-shot composed image retrieval
Yuanmin Tang, Jing Yu, Keke Gai, Jiamin Zhuang, Gang Xiong, Yue Hu, and Qi Wu · 2024
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VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Junjie Zhou, Zheng Liu, Shitao Xiao, Bo Zhao, and Yongping Xiong · 2024
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Long-clip: Unlocking the long-text capability of clip
Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang · 2024
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