160 citations · 364 across the 8 of their papers we have counts for
9 papers · 1 filter
Geometry-Aware Spatio-Temporal Context Modeling for 4D Occupancy Forecasting
Sitao Chen, Zhuangwei Zhuang, Hui Luo +2
4D occupancy forecasting models the spatio-temporal evolution of 3D scenes and is crucial for autonomous driving, especially for corner-case simulation. Existing methods often rely…
Semantic Occupancy Prediction with Dual Range-Voxel Representation
Sitao Chen, Zhuangwei Zhuang, Hui Luo +3
LiDAR-based 3D semantic occupancy prediction, which aims to provide accurate and comprehensive scene representation, is crucial for autonomous driving systems. As point clouds suff…
Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation
Zhuangwei Zhuang, Ziyin Wang, Sitao Chen +3
3D semantic occupancy prediction, which seeks to provide accurate and comprehensive representations of environment scenes, is important to autonomous driving systems. For autonomou…
Contrastive Vision-Language Alignment Makes Efficient Instruction Learner
Lizhao Liu, Xinyu Sun, Tianhang Xiang +3
We study the task of extending the large language model (LLM) into a vision-language instruction-following model. This task is crucial but challenging since the LLM is trained on t…
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation
Lizhao Liu, Zhuangwei Zhuang, Shangxin Huang +5
We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of d…
DAS: Densely-Anchored Sampling for Deep Metric Learning
Lizhao Liu, Shangxin Huang, Zhuangwei Zhuang +3
Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such…
EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation
Mingkui Tan, Zhuangwei Zhuang, Sitao Chen +4
We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-b…
Discrimination-aware Network Pruning for Deep Model Compression
Jing Liu, Bohan Zhuang, Zhuangwei Zhuang +4
We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with…
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang +5
Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimi…