The paper creates a real-world corruption benchmark for promptable video object segmentation and proposes MoGA, which uses object-specific memory to improve robustness and temporal consistency under adverse conditions.
Layernorm: A key component in parameter-efficient fine-tuning
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GenD achieves state-of-the-art average cross-dataset AUROC in deepfake detection by parameter-efficient adaptation of a foundational vision encoder with hyperspherical manifold enforcement via L2 normalization and metric learning.
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Robust Promptable Video Object Segmentation
The paper creates a real-world corruption benchmark for promptable video object segmentation and proposes MoGA, which uses object-specific memory to improve robustness and temporal consistency under adverse conditions.
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Deepfake Detection that Generalizes Across Benchmarks
GenD achieves state-of-the-art average cross-dataset AUROC in deepfake detection by parameter-efficient adaptation of a foundational vision encoder with hyperspherical manifold enforcement via L2 normalization and metric learning.