43 citations · 66 across the 13 of their papers we have counts for
7 papers · 1 filter
Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?
Yifan Zhang, Junhui Hou
Cross-modal contrastive distillation has recently been explored for learning effective 3D representations. However, existing methods focus primarily on modality-shared features, ne…
E-Motion: Future Motion Simulation via Event Sequence Diffusion
Song Wu, Zhiyu Zhu, Junhui Hou +2
Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capt…
Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework
Hao Jing, Anhong Wang, Lijun Zhao +5
In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often…
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models
Yifan Zhang, Junhui Hou
Contrastive image-to-LiDAR knowledge transfer, commonly used for learning 3D representations with synchronized images and point clouds, often faces a self-conflict dilemma. This is…
Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking
Jiading Li, Zhiyu Zhu, Jinhui Hou +2
This paper tackles the problem of passive gaze estimation using both event and frame data. Considering the inherently different physiological structures, it is intractable to accur…
Hybrid Pooling and Convolutional Network for Improving Accuracy and Training Convergence Speed in Object Detection
Shiwen Zhao, Wei Wang, Junhui Hou +1
This paper introduces HPC-Net, a high-precision and rapidly convergent object detection network.
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Yifan Zhang, Junhui Hou, Siyu Ren +3
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D…