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

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.How does WeVisOne-VL work?

Every task is cast as structured next-token prediction.The model adds coordinate tokens <0> to <1000> for normalized boxes and points.An absent target produces None instead of a guessed box.For pixel tasks, the model emits an 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 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.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.window.addEventListener("message",function(e){if(e.data&&e.data.

wvoHeight){var f=document.getElementById("wvo-frame");if(f)f.style.height=e.data.wvoHeight+"px";}}); WeVisOne-VL vs Qwen3-VL, Rex-Omni and LocateAnything FeatureWeVisOne-VL-4BQwen3-VL-4B-InstructRex-OmniLocateAnything-3BDeveloperTencent WeChat VisionAlibaba QwenIDEA ResearchNVIDIASize4.

83B (HF listing)4B3B (card)3B (card)Base modelQwen3-VLNot applicableQwen2.5-VL-3B-InstructQwen2.5-3B-Instruct + MoonViT-SO-400MLicenseApache 2.0Apache 2.0IDEA License 1.0NVIDIA non-commercialNative masks / mattesYes (9.

6M decoder)Not in model cardNot in model cardNot in model cardContextNot disclosed (8,192 in training)256K nativeNot disclosedNot disclosedLVIS F1 Mean55.344.046.950.7Dense200 F1 Mean71.315.258.358.7RefCOCOg test F1 Mean81.874.374.377.6RefCOCOg-Rej rejection67.4%18.8%0.5%0.6%PointBench71.957.169.

956.9TotalText F1 Mean52.033.040.643.3 How do you run WeVisOne-VL?Weights for the 2B and 4B models are on Hugging Face.The wevisonevl package wraps Transformers and vLLM backends.Tasks include detection, referring, point, countbox, 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 Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter.

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