The paper introduces FairRSFM, a biome-aware benchmark for evaluating the ecological robustness of remote sensing foundation models (RSFMs). It highlights that aggregate metrics can mask performance disparities across different biomes and presents methods to mitigate these issues.
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This work addresses the need for more equitable evaluation of remote sensing models across different ecological regions, helping to identify and reduce performance disparities.
This survey explores post-training and alignment techniques in video generation models, highlighting challenges like temporal coherence and physical constraints. It categorizes methods into supervised fine-tuning, self-training, preference-based approaches, and inference-time strategies, while discussing datasets, evaluation practices, and open challenges.
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