Certain coverage-based visual token pruning strategies lower Expected Calibration Error compared to unpruned models while keeping accuracy similar on POPE, and pruning reduces ECE on ScienceQA-IMG.
Investigat- ing calibration and corruption robustness of post-hoc pruned perception CNNs: An image classification benchmark study
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Does Visual Token Pruning Improve Calibration? An Empirical Study on Confidence in MLLMs
Certain coverage-based visual token pruning strategies lower Expected Calibration Error compared to unpruned models while keeping accuracy similar on POPE, and pruning reduces ECE on ScienceQA-IMG.