The methodology section of Qwen-VL focuses on model architecture design and input/output interface definition. The model adopts a three-component architecture comprising the Qwen-7B large language model as its backbone, a ViT-bigG visual encoder initialized from OpenCLIP, and the core innovation—a position-aware vision-language adapter.
This adapter compresses the image feature sequence to a fixed length of 256 via a single-layer cross-attention mechanism, and incorporates 2D absolute position encodings to preserve spatial information, bringing the total parameter count to 9.6B (visual encoder 1.9B + adapter 0.08B + LLM 7.7B).
On the input/output side, image features are bounded by special tokens that mark their start and end, while bounding box coordinates are normalized to the [0,1000) range and formatted as (x1,y1),(x2,y2) strings. The <ref> and <box> tokens enable fine-grained association between text descriptions and spatial regions. This elegant architecture establishes an efficient foundation for multimodal tasks through precise vision-language alignment.