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2 articles
This article explains the challenges of evaluating object removal models in AI, focusing on why traditional metrics fail and how Xiaomi's PROVE benchmark introduces perception-aligned metrics to better reflect real-world performance.
This article explains how human disagreement in AI benchmarking can lead to unreliable performance metrics and why current practices need to evolve to account for annotation variability.
