I'm Tianheng Cheng, pursuing my Ph.D. now and working on Vision.
Previous works/publications are listed at Google Scholar 📚.
Currently, I'm devoted in the research on visual-language modeling and multimodal models. 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:
- The latest works 🔥: YOLO-World (CVPR 2024) for real-time open-vocabulary object detection; Symphonies (CVPR 2024) for camera-based 3D scene completion.
- SparseInst (CVPR 2022) aims for real-time instance segmentation with a simple fully convolutional framework! MobileInst (AAAI 2024) further explores temporal consistency and kernel reuse for efficient mobile video instance segmentation.
- BoxTeacher (CVPR 2023) 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.
- BMask R-CNN (ECCV 2020) is the first work to introduce boundary modeling for objects and aims for high-performance instance segmentation. It leads the research about object boundaries for instance segmentation.
- GKT (arXiv) addresses the ill-posed 2D-to-3D (Surrounding views to Bird-Eye views) transformation with the concern about accuracy and speed, especially for practical implementation for autonomous systems.