Our paper “Structural Pruning of Large Vision-Language Models: Pruning Dynamics, Recovery, and Data Efficiency” has been accepted to the International Journal of Computer Vision (IJCV) 2026.
This is an extended journal version of our GCPR 2025 oral, joint with Lukas Thede, Massimiliano Mancini, Wei Xu, and Zeynep Akata.
The paper studies layerwise and widthwise structural pruning in open-source vision-language models. Our key finding: supervised fine-tuning combined with hidden-state distillation can retain more than 95% of original performance using only 5% of the recovery data — making post-pruning recovery realistic on academic compute.