When Text Matters: Design Principles for Visual Token Pruning in Vision-Language Model
cs.CV, cs.LG
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/kmc3661/DeFT
Terminology
Sources
- LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
- Qwen3-VL Technical Report
- Token Merging: Your ViT But Faster
- Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference
- The Llama 3 Herd of Models
- Distilling the Knowledge in a Neural Network
- LLaVA-OneVision: Easy Visual Task Transfer
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
- PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction
- SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
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