AI RESEARCH
iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models
arXiv CS.CV
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ArXi:2412.06263v2 Announce Type: replace Recent methods have made notable progress in accelerating Large Vision-Language Models (LVLMs) by exploiting the inherent redundancy in visual inputs. Most existing approaches, however, focus narrowly on reducing image tokens before or within the Large Language Model (LLM) stage to lower computational cost. This overlooks other major bottlenecks, particularly the image encoder, which itself requires substantial computation. As a result, these methods fall short of achieving true end-to-end acceleration.