Search Results for author:
Found 3 papers, 2 papers with code
LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies
To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets.
Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
We develop fixed-angle, long-duration video datasets across several domains, and we show that the dataset size can be culled by a factor of 300x to reduce the total training time by 47x with no accuracy loss or even with slight improvement.
Training Domain Specific Models for Energy-Efficient Object Detection
For the limited domain, we observed that compact DSMs significantly surpass the accuracy of COCO trained models of the same size.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
