腾讯微信视觉团队开源 WeVisOne-VL 视觉语言模型

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Home Editors Pick Agentic AITencent WeVisOne-VL Unifies Detection, OCR, Segmentation and Matting

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Tencent WeVisOne-VL Unifies Detection, OCR, Segmentation and Matting

Michal Sutter

November 3, 2026

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Tencent’s WeChat Vision team has released WeVisOne-VL, an open vision language model in 2B and 4B sizes. The WeVisOne-VL vision language model handles detection, referring, counting, pointing, OCR, segmentation and alpha matting in one model. Pixel outputs come from a 9.6M-parameter decoder, so no separate SAM is needed. It also answers “None” when a queried object is absent. For developers, that can replace a detector, an OCR engine and a segmenter with one model.

TL;DR

Size: 2B (2.44B params) and 4B (4.83B params) on Hugging Face, and a 9.6M-parameter pixel decoder. Trained with up to 8,192 tokens of context; deployment context length is not disclosed.
Runs on: A single GPU through Transformers or vLLM in BF16. The package also lists NPU and CPU devices. VRAM needs are not disclosed. A community GGUF ran object-level tasks on a 6 GB RTX 2060.
Performance: Beats the same-scale VLM baselines in Tencent’s tables on most of 30+ benchmarks (self-reported).
Best: 71.3 F1 Mean on Dense200 dense detection, versus 58.7 for LocateAnything-3B.
Worst: 52.6% rejection rate on PR-Bench, so many absent-target queries still get a box.

What is WeVisOne-VL?

WeVisOne-VL (“WeChat Vision All-in-One”) is a perception-focused model from Tencent built on Qwen3-VL. It targets fine-grained localization rather than open-ended chat. Users choose the output granularity: points, boxes, binary masks or continuous alpha mattes.

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How does WeVisOne-VL work?

Every task is cast as structured next-token prediction. The model adds coordinate tokens

<0>

<1000>

for normalized boxes and points. An absent target produces

None

instead of a guessed box.

For pixel tasks, the model emits an

<OBJ>

token after each predicted box. Its hidden state becomes a query for the pixel decoder, according to the model card. The decoder has 3 parts:

Visual Feature Adapter: reuses multi-level Qwen3-VL ViT features instead of a second image encoder.
Box-Guided Query Decoder: 6 deformable cross-attention blocks seeded by the

<OBJ>

state and the box.

Dense Prediction Head: refines output at S/4, S/2 and S/1 into a full-resolution mask or matte.

How was it trained?

A knowledge-graph-driven data engine annotated over 100M images with about 3.6B boxes. SAM3, Rex-Omni and an in-house detector proposed regions, and Qwen3-VL verified them.

Training used 3 SFT stages. Stage 1 aligned output formats on 90M examples. Stage 2 raised input to 1,960 visual tokens and up to 500 boxes per response. Stage 3 trained the pixel decoder, then specialized it for matting on 130M samples. A final GRPO stage used 178K uncertainty-filtered examples, as described in the research paper.

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How does WeVisOne-VL perform on benchmarks?

All numbers below are self-reported, using greedy decoding and at most 1,960 visual tokens per image. Detection, referring and OCR use F1 averaged over IoU 0.5 to 0.95.

Detection: 55.3 F1 on LVIS and 71.3 on Dense200 for the 4B model.
Referring: 81.8 F1 on RefCOCOg test, versus 77.6 for LocateAnything-3B.
Rejection: 96.4% on Tencent’s in-house WeRef-Bench (2,880 queries), versus 73.0%.
Counting: 89.6% exact-match on PixMo-Count test, versus 82.8%.
OCR: 52.0 F1 on TotalText, ahead of the PP-OCRv5 specialist at 25.7.
Segmentation: 71.2 gIoU on ReasonSeg val, versus 65.0 for LISA-13B.
Matting: 7.3 MAD on P3M-500-NP box prompts, versus 15.4 for SAM2-Matting (SAM3). Lower is better.

The 2B model trails the 4B model on most tasks. It still scores better box-prompted matting on P3M-500-NP, at 6.6 MAD.

WeVisOne-VL vs Qwen3-VL, Rex-Omni and LocateAnything

Feature	WeVisOne-VL-4B	Qwen3-VL-4B-Instruct	Rex-Omni	LocateAnything-3B
Developer	Tencent WeChat Vision	Alibaba Qwen	IDEA Research	NVIDIA
Size	4.83B (HF listing)	4B	3B (card)	3B (card)
Base model	Qwen3-VL	Not applicable	Qwen2.5-VL-3B-Instruct	Qwen2.5-3B-Instruct + MoonViT-SO-400M
License	Apache 2.0	Apache 2.0	IDEA License 1.0	NVIDIA non-commercial
Native masks / mattes	Yes (9.6M decoder)	Not in model card	Not in model card	Not in model card
Context	Not disclosed (8,192 in training)	256K native	Not disclosed	Not disclosed
LVIS F1 Mean	55.3	44.0	46.9	50.7
Dense200 F1 Mean	71.3	15.2	58.3	58.7
RefCOCOg test F1 Mean	81.8	74.3	74.3	77.6
RefCOCOg-Rej rejection	67.4%	18.8%	0.5%	0.6%
PointBench	71.9	57.1	69.9	56.9
TotalText F1 Mean	52.0	33.0	40.6	43.3

How do you run WeVisOne-VL?

Weights for the 2B and 4B models are on Hugging Face. The

wevisone_vl

package wraps Transformers and vLLM backends. Tasks include

detection

referring

point

count_box

ocr

segmentation

and

matting

from wevisone_vl import WeVisOneVL
model = WeVisOneVL("tencent/WeVisOne-VL-4B", backend="transformers")  # or "vllm"
result = model.predict(image, task="matting", query="person")

Hosted demos are live for 2B and 4B. Tencent also open-sourced Mask2Alpha, the matting annotator from its data engine. Fine-tuning code is still on the TODO list. GGUF builds cannot produce masks, since the decoder needs native visual features.

Key Takeaways

1 model covers detection, OCR, counting, pointing, segmentation and matting.
A 9.6M-parameter decoder replaces SAM for masks and alpha mattes.
4B scores 71.3 F1 on Dense200, 12.6 points above LocateAnything-3B.
Apache 2.0 weights, unlike non-commercial LocateAnything-3B.

Check out the Paper, GitHub repo, Model weights and Project page. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

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Michal Sutter

+ posts Bio ⮌

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.