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The contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts, enabling effective zero-shot image recognition.
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Mask r-cnn
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Decoupled weight decay regularization
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Human centric spatio-temporal action localization
Jianwen Jiang, Yu Cao, Lin Song, Shiwei Zhang, Yunkai Li, Ziyao Xu, Qian Wu, Chuang Gan, Chi Zhang, and Gang Yu · 2018
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Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Meta-learning in neural networks: A survey
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Dynamic grained encoder for vision transformers
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End-to-end object detection with fully convolutional network
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Workshop on autonomous driving at cvpr 2021: Technical report for streaming perception challenge
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Learning robust global representations by penalizing local predictive power
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Lvis: A dataset for large vocabulary instance segmentation
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Glnet: Global local network for weakly supervised action localization
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An image is worth 16x16 words: Transformers for image recognition at scale
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Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Aligning pretraining for detection via object-level contrastive learning
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
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Coca: Contrastive captioners are image-text foundation models
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Robust fine-tuning of zero-shot models
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Class-aware visual prompt tuning for vision-language pre-trained model
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Dbq-ssd: Dynamic ball query for efficient 3d object detection
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Not all features matter: Enhancing few-shot clip with adaptive prior refinement
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Gmm: Delving into gradient aware and model perceive depth mining for monocular 3d detection
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