Posts by Zhenhua Liu
Introducing AMD EVLM: Efficient Vision-Language Models with Parameter-Space Visual Conditioning
- 22 August 2025
This blog introduces a novel and computationally efficient paradigm for Vision-Language Models (VLMs), which diverges from the conventional method of prepending visual tokens to textual input. Instead of elongating the input sequence, this approach injects visual information directly into the Large Language Model’s (LLM) parameters. It achieves this by using a vision encoder to extract image features and then employing a perceptual weight generator to transform these features into dynamic, low-rank adapter weights. These weights are temporarily integrated with the LLM’s parameters, effectively conditioning the model on the image without increasing the input length. This mechanism allows the model to achieve performance comparable to traditional VLMs on standard benchmarks while significantly reducing computational costs during inference.