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This work proposes POMP, a prompt pre-training method for vision-language models.
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Unsupervised feature learning via non-parametric instance discrimination
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Learning representations by maximizing mutual information across views
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Denseclip: Language-guided dense prediction with context-aware prompting
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A simple baseline for zero-shot semantic segmentation with pre-trained vision-language model
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Filip: Fine-grained interactive language-image pre-training
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Probabilistic two-stage detection
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Eqco: Equivalent rules for self-supervised contrastive learning
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Spt: Semi-parametric prompt tuning for multitask prompted learning
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Variational prompt tuning improves generalization of vision-language models
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Learning to prompt for open-vocabulary object detection with vision-language model
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Promptdet: Towards open-vocabulary detection using uncurated images
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Maple: Multi-modal prompt learning
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Language-driven semantic segmentation
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Prompt distribution learning
Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, and Xinmei Tian · 2022
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Expanding language-image pretrained models for general video recognition
Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models
Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao · 2022
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Unified vision and language prompt learning
Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
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Detecting twenty-thousand classes using image-level supervision
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Delving into the openness of CLIP
Shuhuai Ren, Lei Li, Xuancheng Ren, Guangxiang Zhao, and Xu Sun · 2023
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