One model for points, boxes, masks, and alpha mattes. WeVisOne-VL-4B is open-weight, a visual perception model from Tencent's WeChat Vision, built on Qwen3-VL. 🤖 🎨 Try the demo: 🏆 Leading across 30+ perception benchmarks: The team reports strong results from object-level tasks to pixel-level understanding, including 71.3 on Dense200, 89.6 on PixMo-Count, and 71.2 on ReasonSeg. 🧩 One model, seven perception tasks: Detect, localize, count, point to, read, segment, and matte objects through structured next-token prediction. ✂️ From boxes to clean alpha mattes: A lightweight 9.6M pixel decoder produces masks and full-resolution continuous alpha mattes without relying on SAM. 🚫 Rejects targets that are not there: Reaches a 96.4% rejection rate on WeRef-Bench, helping avoid confident boxes for absent queried objects. 📦 Structured outputs for downstream workflows: Returns normalized boxes, points, masks, alpha mattes, and None when no matching target is found. 📜 Apache-2.0 License.