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Contrastive learning (CL) is a prevalent technique for training embedding models, which pulls semantically similar examples (positives) closer in the representation space while pushing dissimilar ones (negatives) further apart.
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
Earlier work this paper cites.
An empirical comparison of the summarization power of graph clustering methods
Yike Liu, Neil Shah, and Danai Koutra · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
A theoretical analysis of contrastive unsupervised representation learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, and Hrishikesh Khandeparkar · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick SH Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Earlier work this paper cites.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Earlier work this paper cites.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
Earlier work this paper cites.
Ngame: Negative mining-aware mini-batching for extreme classification
Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, et al · 2023
Cited alongside, same era.
Global selection of contrastive batches via optimization on sample permutations
Vin Sachidananda, Ziyi Yang, and Chenguang Zhu · 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.
Batchsampler: Sampling mini-batches for contrastive learning in vision, language, and graphs
Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, and Jie Tang · 2023
Cited alongside, same era.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
Cited alongside, same era.
Eva-clip-18b: Scaling clip to 18 billion parameters
Quan Sun, Jinsheng Wang, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, and Xinlong Wang · 2024
Later among the works it cites.
Sumcse: Summary as a transformation for contrastive learning
Raghuveer Thirukovalluru, Xiaolan Wang, Jun Chen, Shuyang Li, Jie Lei, Rong Jin, and Bhuwan Dhingra · 2024
Later among the works it cites.
Fastvlm: Efficient vision encoding for vision language models
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, et al · 2024
Later among the works it cites.
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 · 2024
Later among the works it cites.
Dreamlip: Language-image pre-training with long captions
Kecheng Zheng, Yifei Zhang, Wei Wu, Fan Lu, Shuailei Ma, Xin Jin, Wei Chen, and Yujun Shen · 2024
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Sfr-embedding-2: Advanced text embedding with multi-stage training, 2024
Rui Meng*, Ye Liu*, Shafiq Rayhan Joty, Yingbo Zhou Caiming Xiong, and Semih Yavuz · 2024
Cited alongside, same era.
Nv-retriever: Improving text embedding models with effective hard-negative mining
Gabriel de Souza P Moreira, Radek Osmulski, Mengyao Xu, Ronay Ak, Benedikt Schifferer, and Even Oldridge · 2024
Cited alongside, same era.
Contextual document embeddings
John X Morris and Alexander M Rush · 2024
Cited alongside, same era.
Generative representational instruction tuning
Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela · 2024
Cited alongside, same era.
E5-v: Universal embeddings with multimodal large language models
Ting Jiang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang, Deqing Wang, and Fuzhen Zhuang
Cited in the paper.
Vlm2vec: Training vision-language models for massive multimodal embedding tasks
Ziyan Jiang, Rui Meng, Xinyi Yang, Semih Yavuz, Yingbo Zhou, and Wenhu Chen
Cited in the paper.
Nv-embed: Improved techniques for training llms as generalist embedding models
Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping
Cited in the paper.
Later among the works it cites.
Megapairs: Massive data synthesis for universal multimodal retrieval
Junjie Zhou, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian, and Yongping Xiong · 2024
Later among the works it cites.
mme5: Improving multimodal multilingual embeddings via high-quality synthetic data
Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, and Zhicheng Dou · 2025
Closest in time.
Breaking the modality barrier: Universal embedding learning with multimodal llms
Tiancheng Gu, Kaicheng Yang, Ziyong Feng, Xingjun Wang, Yanzhao Zhang, Dingkun Long, Yingda Chen, Weidong Cai, and Jiankang Deng · 2025
Closest in time.
Llave: Large language and vision embedding models with hardness-weighted contrastive learning
Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, and Jinsong Su · 2025
Closest in time.