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VLMs can ace the ranking but bomb the scoring, revealing a critical flaw in how we evaluate multimodal systems.
Decomposing uncertainty into aleatoric and epistemic types lets robots recover from errors 21% more effectively than treating all uncertainty the same.
Transformer-based visual trackers can slash compute by up to 12% without sacrificing accuracy, simply by dynamically adjusting their depth based on uncertainty.
