zlsfe
Hi there 👋 I'm a computer science student at @UCAS 🎓 I'm interested in artificial intelligence, web development, and game design 🤖
Beijing
Pinned Repositories
ATLAS
ATLAS: A Sequence-based Learning Approach for Attack Investigation
NewsSpider
爬取今日头条，网易，腾讯等新闻,并建立简单的搜索引擎
PV-SSD
The proposed approach enhances the CenterPoint baseline with a multimodal fusion mechanism. First, inspired by PointPainting, an off-the-shelf Mask-RCNN model trained from nuImages is employed to generate 2D object mask information based on the camera images. Furthermore, the Cylinder3D is also adopted to produce the 3D semantic information of the input LiDAR point cloud. Then, an improved version of CenterPoint takes the painted points(with 2D instance segmentation and 3D semantic segmentation) as inputs for accurate object detection. Specifically, we replace the RPN module in CenterPoint with modified Spatial-Semantic Feature Aggregation(SSFA) to well address multi-class detection. A simple pseudo labeling technique is also integrated in a semi-supervised learning manner. In addition, the Test Time Augmentation(TTA) strategy including multiple flip and rotation operations is applied during the inference time. Finally, the detections generated from multiple voxel resolutions (0.05m to 0.125m) are assembled with 3D Weighted Bounding Box Fusion(WBF) technique to produce the final results.
zlsfe.github.io
hexo博客
zlsfe's Repositories
zlsfe/ATLAS
ATLAS: A Sequence-based Learning Approach for Attack Investigation
zlsfe/NewsSpider
爬取今日头条，网易，腾讯等新闻,并建立简单的搜索引擎
zlsfe/PV-SSD
The proposed approach enhances the CenterPoint baseline with a multimodal fusion mechanism. First, inspired by PointPainting, an off-the-shelf Mask-RCNN model trained from nuImages is employed to generate 2D object mask information based on the camera images. Furthermore, the Cylinder3D is also adopted to produce the 3D semantic information of the input LiDAR point cloud. Then, an improved version of CenterPoint takes the painted points(with 2D instance segmentation and 3D semantic segmentation) as inputs for accurate object detection. Specifically, we replace the RPN module in CenterPoint with modified Spatial-Semantic Feature Aggregation(SSFA) to well address multi-class detection. A simple pseudo labeling technique is also integrated in a semi-supervised learning manner. In addition, the Test Time Augmentation(TTA) strategy including multiple flip and rotation operations is applied during the inference time. Finally, the detections generated from multiple voxel resolutions (0.05m to 0.125m) are assembled with 3D Weighted Bounding Box Fusion(WBF) technique to produce the final results.
zlsfe/zlsfe.github.io
hexo博客