DCP: Dual-Cue Pruning for Efficient Large Vision-Language Models

Large Vision-Language Models (LVLMs) achieve remarkable performance in multimodal tasks but suffer from high computational costs due to the large number of visual tokens. Existing pruning methods either apply after visual tokens enter the LLM or perform pre-pruning based solely on visual attention. Both fail to balance efficiency and semantic alignment, as post-pruning incurs redundant computation, while visual-only pre-pruning overlooks multimodal relevance. To address this limitation, we propose Dual-Cue Pruning (DCP), a novel cross-modal pruning framework that jointly considers textual semantics and visual selfattention. DCP consists of a text-aware computation module, which employs a gradientweighted attention mechanism to enhance textvisual alignment, and an image-aware computation module, which utilizes deep-layer selfattention distributions to retain essential structural information. By integrating both cues, DCP adaptively selects the most informative visual tokens, achieving efficient inference acceleration while maintaining strong task performance. Experimental results show that DCP can retain only 25% of the visual tokens, with a minimal performance degradation of 0.063% on LLaVA-1.5-13B, demonstrating its effectiveness in balancing efficiency and accuracy.