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.
CDPruner: Be- yond attention or similarity: Maximizing conditional diversity for token pruning in MLLMs
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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.