Mostly Text, Smart Visuals: Asymmetric Text-Visual Pruning for Large Vision-Language Models
Network pruning is an effective technique for enabling lightweight Large Vision-Language Models (LVLMs), which primarily incorporates both weights and activations into the importance metric. However, existing efforts typically process calibration data from different modalities in a unified manner, overlooking modality-specific behaviors. This raises a critical challenge: how to address the divergent behaviors of textual and visual tokens for accurate pruning of LVLMs. To this end, we systematically investigate the sensitivity of visual and textual tokens to the pruning operation by decoupling their corresponding weights, revealing that: (i) the textual pathway should be calibrated via tex...
| Authors | Sijie Li, Biao Qian, Jungong Han |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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