HUST Vision Lab's Projects
[CVPR 2022] SparseInst: Sparse Instance Activation for Real-Time Instance Segmentation
Official PyTorch implementation of SparseTrack (the new version of code will come soon)
Stabilized Activation Scale Estimation for Precise Post-Training Quantization
checkpoints storage of hustvl
[CVPR 2024] Symphonies (Scene-from-Insts): Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral
TiNeuVox: Fast Dynamic Radiance Fields with Time-Aware Neural Voxels (SIGGRAPH Asia 2022)
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation, CVPR2022
[ICCV 2023] VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
[Information Fusion] Boosting Image Matting with Pretrained Plain Vision Transformers
A general map auto annotation framework based on MapTR, with high flexibility in terms of spatial scale and element type
WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
You Only Look Once for Panopitic Driving Perception.(MIR2022)
[NeurIPS 2021] You Only Look at One Sequence