289 citations · 682 across the 9 of their papers we have counts for
5 papers · 1 filter
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception
Yibo Wang, Ruiyuan Gao, Kai Chen +8
Current perceptive models heavily depend on resource-intensive datasets, prompting the need for innovative solutions. Leveraging recent advances in diffusion models, synthetic data…
TransformMix: Learning Transformation and Mixing Strategies from Data
Tsz-Him Cheung, Dit-Yan Yeung
Data augmentation improves the generalization power of deep learning models by synthesizing more training samples. Sample-mixing is a popular data augmentation approach that create…
SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation
Xuechao Chen, Shuangjie Xu, Xiaoyi Zou +3
LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving. Due to the motion of objects and static/dynamic occlusion, temporal information plays an…
CLIP: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data
Yihan Zeng, Chenhan Jiang, Jiageng Mao +7
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks. Howeve…
Transferring Rich Feature Hierarchies for Robust Visual Tracking
Naiyan Wang, Siyi Li, Abhinav Gupta +1
Convolutional neural network (CNN) models have demonstrated great success in various computer vision tasks including image classification and object detection. However, some equall…