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Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences
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FrameONE revolutionizes echocardiographic keyframe detection by achieving state-of-the-art accuracy through a unified multi-view approach that overcomes traditional limitations.
Cleaner visual cues can boost multimodal reasoning performance by over 6 points, challenging the notion that simply extending reasoning traces is sufficient.
Imperceptible frequency-aware perturbations can effectively thwart model stealing in text-to-image generative models without sacrificing visual quality.
Forget complex per-sample loss calculations – this simple three-line code injection uses batch loss smoothing to prune 20-50% of training data without sacrificing performance.
