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Hefei University of Technology, Nanjing University of Posts and Telecommunications
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FlexDepth achieves state-of-the-art monocular depth estimation in complex driving scenarios with minimal computational requirements, making it a game-changer for real-time automotive perception.
Escaping the curse of noisy data in semi-supervised learning: S$^2$MAM adaptively selects features and tunes similarity metrics, leading to more robust and interpretable models.
Even with only 0.3% data poisoning, BadCLIP++ achieves near-perfect attack success rates on multimodal models and maintains effectiveness against a wide range of defenses, highlighting a significant vulnerability.
