WeVisOne-VL-2B

WeVisOne-VL: Unified Visual Perception from Objects to Pixels#

Paper: WeVisOne-VL: Unified Visual Perception from Objects to Pixels | Project Page: WeVisOne-VL

WeVisOne-VL (WeChat Vision All-in-One) is a unified vision-language model developed by WeChat Vision, Tencent Inc. Built upon Qwen3-VL, it unifies object-level perception (open-vocabulary object detection, referring localization, object counting, object pointing, and OCR) and pixel-level perception (referring segmentation and promptable alpha matting) through structured next-token prediction and a lightweight 9.6M-parameter pixel decoder.

Quick Start#

Install the inference package from GitHub, then:

from PIL import Image
from wevisone_vl import WeVisOneVL

model = WeVisOneVL("tencent/WeVisOne-VL-2B", device="auto", dtype="bfloat16")
image = Image.open("example.jpg").convert("RGB")
result = model.predict(image, task="matting", query="person", temperature=0.0)
print(result.response, len(result.masks))

Architecture & Released Revision#

The model uses dedicated coordinate tokens (<0> ... <1000>) to represent normalized bounding boxes and points, returns None when a queried target is absent, and emits a special instance token (<OBJ>, token ID 152678) after each predicted box for dense tasks.

The attached mask_decoder.pt (9,609,764 parameters) is a required core component that combines:

• A Visual Feature Adapter over multi-level Qwen3-VL pre-merging ViT features;
• A Box-Guided Query Decoder with 6 deformable cross-attention blocks initialized from the <OBJ> hidden state and Fourier-encoded box prior;
• An Instance-Conditioned Dense Prediction Head with FullCanvasAlphaCascadeHead (refining predictions across S/4, S/2, and S/1 to output full-resolution continuous alpha mattes at stride 1).

Intended Use#

The release is intended for research in visual grounding, referring localization, pointing, counting, OCR localization, referring segmentation, and image matting. It is not intended to make autonomous high-impact decisions or to identify individuals.

Limitations#

• Fine alpha boundaries remain sensitive to image resolution, severe occlusion, complex transparency, and prompt phrasing.
• For inference efficiency, the model directly generates structured outputs without explicit chain-of-thought (CoT) reasoning, and currently targets single-turn image perception (video grids are rejected by the pixel decoder).

License#

Unless otherwise noted, Tencent-authored code in this repository is released under the Apache License 2.0.

Third-party components retain their original licenses and copyright notices. Please review the corresponding source files before use.

Citation#

If WeVisOne-VL is helpful to your work, please consider giving us a ⭐ and citing:

@article{liang2026wevisone,
  title={WeVisOne-VL: Unified Visual Perception from Objects to Pixels},
  author={Liang, Tianming and Pan, Junyi and Li, Yuxi and Fu, Canmiao and Huang, Zhipeng and Sun, Chong and Li, Chen and Lyu, Jing},
  journal={arXiv preprint arXiv:2610.31329},
  year={2026}
}