Nano Banana 2
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MMAE: A Massive Multitask Audio Editing Benchmark
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GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration
arXiv:2605.31039v1 Announce Type: new Abstract: Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail to model real-world degradations, while real-world paired datasets are expensive and difficult to capture. As a result, IR models trained on these datasets show limited generalization in real-world scenarios.
GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration
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TECCI: Tricky Edits of Collected and Curated Images
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