P4Q: Co-designing Token Pruning and Quantization for Vision-Language Model Acceleration
cs.CV
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models
- SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
- Towards Joint Quantization and Token Pruning of Vision-Language Models
- CRISP: Pre-LLM Yet Text-Driven Visual Token Pruning for Efficient LVLM Inference
- Rethinking Practical and Efficient Quantization Calibration for Vision-Language Models
- QAPruner: Quantization-Aware Vision Token Pruning for Multimodal Large Language Models
- Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
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