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SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training.

, , , , and . ICCV, page 2459-2469. IEEE, (2023)

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Learning Transferable Adversarial Examples via Ghost Networks., , , , , and . CoRR, (2018)Intriguing Properties of Adversarial Training at Scale., and . ICLR, OpenReview.net, (2020)Single-Shot Object Detection with Enriched Semantics., , , , , and . CoRR, (2017)Masked Autoencoders Enable Efficient Knowledge Distillers., , , , , , , and . CoRR, (2022)Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images., , , , , , and . ICCV, page 14890-14899. IEEE, (2021)SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training., , , , and . ICCV, page 2459-2469. IEEE, (2023)One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks., , , and . CoRR, (2022)Neural Architecture Search for Lightweight Non-Local Networks., , , , , , , , , and . CVPR, page 10294-10303. Computer Vision Foundation / IEEE, (2020)Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks., , , , , and . NeurIPS, (2022)ViP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers., , and . ECCV (25), volume 13685 of Lecture Notes in Computer Science, page 573-587. Springer, (2022)