Search Results for author:
Found 12 papers, 7 papers with code
MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving
In recent years, data-driven techniques have greatly advanced autonomous driving systems, but the need for rare and diverse training data remains a challenge, requiring significant investment in equipment and labor.
Learning A Zero-shot Occupancy Network from Vision Foundation Models via Self-supervised Adaptation
Thus, we adapt the relative depth derived from VFMs into metric depth by optimising the scale and offset using temporal consistency, also known as novel view synthesis, without access to ground-truth metric depth.
Self-Supervised Multi-Frame Neural Scene Flow
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field
Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems.
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision
3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels.
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR.
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks.
Knowledge Distillation via the Target-aware Transformer
To this end, we propose a novel one-to-all spatial matching knowledge distillation approach.
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation
Extensive experiments on two vision tasks, includ-ing ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consis-tently outperforms many existing methods, advancing thestate-of-the-art in the fields of Knowledge Distillation.
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge Distillation
Extensive experiments on two vision tasks, including ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consistently outperforms many existing methods, advancing the state-of-the-art in the fields of Knowledge Distillation.
Ranked #32 on
Knowledge Distillation
on ImageNet
Accelerating Neural Network Inference by Overflow Aware Quantization
The inherent heavy computation of deep neural networks prevents their widespread applications.
LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices
Finally, we provide a variety of experimental results to show that the proposed framework is able to achieve state-of-the-art accuracy with significantly reduced computational cost, which are the key properties for enabling real-time applications in low-cost commercial devices such as mobile devices and AR/VR headsets.
