https://github.com/wondervictor/wondervictor

https://github.com/wondervictor/wondervictor

Last synced: about 13 hours ago
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• Host: GitHub
• URL: https://github.com/wondervictor/wondervictor
• Owner: wondervictor
• Created: 2020-12-25T11:26:38.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-08-22T02:57:02.000Z (3 months ago)
• Last Synced: 2024-08-22T03:53:19.325Z (3 months ago)
• Size: 13.7 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### Hi there 👋

I'm Tianheng Cheng, pursuing my Ph.D. now and working on Computer Vision and Machine Intelligence.

My research goal is to enable machines/robots to **see** and **understand** the world.

Previous works/publications are listed at [Google Scholar 📚](https://scholar.google.com/citations?user=PH8rJHYAAAAJ).

Currently, I'm devoted to research on **large multimodal models**, **foundational visual-language modeling**, and **image generation**. Before that, I mainly focused on fundamental tasks such as **object detection** and **instance segmentation**, as well as visual perception for autonomous driving.

**Highlighted Works of those pinned works:**

* 🔥 [ControlAR (arXiv)](https://github.com/hustvl/ControlAR) explores controllable image generation with autoregressive models and empowers autoregressive models with arbitrary-resolution generation.

* 🔥 [EVF-SAM (arXiv)](https://github.com/hustvl/EVF-SAM) empowers segment-anything (SAM, SAM-2) with the strong text-prompting ability. Try our [demo](https://github.com/hustvl/EVF-SAM) on HuggingFace.

* [OSP (ECCV 2024)](https://github.com/hustvl/osp) explores sparse set of points to predict 3D semantic occupancy for autonomous vehicles, which is a brand new formulation!

* 🔥 [YOLO-World (CVPR 2024)](https://github.com/AILab-CVC/YOLO-World) for real-time open-vocabulary object detection; [Symphonies (CVPR 2024)](https://github.com/hustvl/Symphonies) for camera-based 3D scene completion.

* [SparseInst (CVPR 2022)](https://github.com/hustvl/SparseInst) aims for real-time instance segmentation with a simple fully convolutional framework! [MobileInst (AAAI 2024)](https://ojs.aaai.org/index.php/AAAI/article/view/28555) further explores temporal consistency and kernel reuse for efficient mobile video instance segmentation.

* [BoxTeacher (CVPR 2023)](https://github.com/hustvl/BoxTeacher) bridges the gap between fully supervised and box-supervised instance segmentation. With ~1/10 annotation cost, BoxTeacher can achieve 93% performance versus fully supervised methods.